TeK Associates has new results in each of our research topics mentioned below.

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At-A-Glance Overview of TeK Associates’ recent documented research:

(Our most recent manuscripts are currently still under review. More detailed information on all these citations may be found elsewhere on our Website.)

Also see:  

-https://scholar.google.com/citations?user=UjaYY4EAAAAJ&hl=en 

-https://www.researchgate.net/profile/Thomas_Kerr_Iii 

-https://academic.microsoft.com/#/detail/2424976702 

-https://scicomp.stackexchange.com/users/27180/thomas-h-kerr-iii?tab=profile    

-https://blogs.mathworks.com/headlines/2016/09/08/this-56-year-old-algorithm-is-key-to-space-travel-gps-vr-and-more/, Our pertinent comments were removed from this particular MathWork's Blog on 11 May 2021 and did not appear there for another week. I suspect that I had probably overstepped my welcome. Sorry! It now appears there again but it can always be found by clicking here.

Please notice the high ratings of relevance and importance ascribed to the bulleted publications listed above, when they are obtained from the Citation Index associated with the on-line “Web of Science” at any good research library. [Ostensibly, my Web of Science Researcher ID: K-4879-2019] (or from the PC-based relatively new  “Publons”) by searching under “Kerr, T. H.”.

Our preview and informed prediction of things to come in the Kalman Filtering arena:

Besides the exciting areas of multi-sensor\multi-target tracking (sometimes pursued even in clutter) [mathematically related to the problem of “optimal resource allocation” and solved by invoking either the Munkres Algorithm or the Hungarian Algorithm or Jonker- Volgenant- Castanon’s (J-V-C) Algorithm or Murty’s Algorithm (1968) or Track-Before-Detect in conjunction with use of Generalized Likelihood Ratio (GLR) or Multi- Hypothesis Testing (MHT) algorithms from the Operations Research area (or the Auction Algorithm for a decentralized, distributed implementation)], TeK Associates perceives some of the cutting edges of Kalman filter\estimation theory and technology to be along the following six application fronts enumerated next:

1. Application of parallel “Bank-of-Kalman-Filters” (each having a different system model matched to or representing a different hypothesized underlying situation) with GLOBAL probability assessments of each filter possibly coinciding exactly with the TRUE situation [CURRENTLY prevailing and from which the ONLY measurements are availed throughout] being automatically calculated on-line as an integral part of this totally rigorous methodology for LINEAR systems. As originally conceived of in 1965 by Mcgill but only relatively recently pursued for actual use within the last three decades in IR, GPS, Radar, and multi-target Sonar applications (since hardware has now caught-up to make these approaches practicable). More flexible new variations from the Univ. of Connecticut (from Prof. Yaakov Bar-Shalom and his many students) are known as Interactive Multiple Models (IMM), with transition probabilities that now obey a Markov chain law, with adjustable “sojourn” times to tweak along with initial prior probabilities to potentially improve performance in a better match to some particular applications.

2. Extension beyond standard 1-dimensional (1-D) random process Kalman filter scenario to 2-dimensional (2-D) Markov Random Fields (MRF) for image processing situations used in motion blur reduction and noise abatement.

3. “Decentralized Kalman filter formulations” tailored to applications where there is a natural fit and an obvious advantage to be exploited, such as in: 

4. Generalization of Kalman filter-based Schweppe likelihood ratio from 1-D to 2-D situations.

5. A fully rigorous extension of the standard Kalman filter beyond those systems routinely described by ordinary differential equations (ODE’s) to systems described by partial differential equations (PDE’s) [using infinite dimensional Banach space derivation techniques rather than the Hilbert-space techniques that Rudolph Kalman had originally used in uncovering the appropriate Kalman Filter structure for optimal estimation] has materialized and matured in the last 50 years. (These PDE’s are appropriate to use in describing the monitoring of lakes, rivers, and streams for impurities & contaminating effluents [corresponding to  thermal & chemical fluxes], where the conduction type linear PDE is encountered as a more appropriate mathematical description of these systems than are ODE’s.) As with Maxwell's equations, there are equivalent differential and integral formulations.

6. In both continuous-time and in discrete-time, the nonlinear 1-D in time n-dimensional Riccati Equation solution possesses two factors, both of which propagate forward in time according to a (2n x 2n) linear matrix equation. One factor must first be inverted before being multiplied by the other factor to jointly constitute a solution to the Riccati equation of interest. This structural simplification likely generalizes to both 2-D and other Partial Differential Equation (PDE) Riccati equations as well.   Go to Top

TeK Associates’ primary software product  for the PC is TK-MIP, which encapsulates both historical and recent statistical estimation and Kalman filtering developments and performs the necessary signal processing of implementation conveniently, understandably, and accessibly. We take human factors into account as we seek to make TK-MIP™ intuitively responsive and easy to use and less error prone than alternative approaches that do not yet cross-check the user all along the way, as we do. We also include concise tutorials that lay out and elucidate the entire historical panorama of significant technical contributions that lead to the technology’s current status. To that end, we include a 1000+ entry searchable database listing of supporting applications papers and textbook citations (different from the one below). We seek to let the user have a pleasant experience (even fun) while using our software.

     Now for book-worms, we have the list below of likely high interest...

It is important to be cognizant of new developments (by others) that we view as being germane to our business interests:

1. Chung, P.-J., Roberts, W. J. J., Bohme, J. F., “Recursive K-Distribution Parameter Estimation,” IEEE Trans. on Signal Processing, Vol. 53, No. 2, pp. 397-402, Feb. 2005.

2. Korn, P., “Some Uncertainty Principles for Time-Frequency Transforms of the Cohen Class,” IEEE Trans. on Signal Processing, Vol. 53, No. 2, pp. 523-527, Feb. 2005.

3. Zhang, W., Chen, B.-S., Tseng, C.-S., “Robust H_infinity Filtering for Nonlinear Stochastic Systems,” IEEE Trans. on Signal Processing, Vol. 53, No. 2, pp. 589-598, Feb. 2005.

4. Lazaro, M., Santarmaria, I., Erdogmus, D., Hild, K. E., Pantaleon, C., Principe, J. C., “Stochastic Blind Equalization Based on PDF Fitting Using Parzen Estimator,” IEEE Trans. on Signal Processing, Vol. 53, No. 2, pp. 696-704, Feb. 2005.

5. Zhao, Z., Li, X.-R., Jilkov, V. P., “Best Linear Unbiased Filtering with Nonlinear Measurements for Target Tracking,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 40, No. 4, pp. 1324- 1336, Oct. 2004.

6. Schlosser, M. S., Kronshel, K., “Limits in Tracking with Extended Kalman Filters,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 40, No. 4, pp. 1351-1359, Oct. 2004.

7. Ruam, Y., Willet, P., “Multiple Model PMHT and its Application in the Second Benchmark Radar Tracking Problem,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 40, No. 4, pp. 1337- 1350, Oct. 2004.

8. Wettergren, T. A., Streit, R. L., Short, J. R., “Tracking with Distributed Sets of Proximity Sensors,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 40, No. 4, pp. 1366-1374, Oct. 2004.

9. Duan, Z., Han, C., and Li, X.-R., “Comments on ‘Unbiased Converted Measurements for Tracking’” IEEE Trans. on Aerospace and Electronic Systems, Vol. 40, No. 4, pp. 1374-1375, Oct. 2004.

10. Rhee, I., Abdel-Hafez, M. F., Speyer, J. L., “Errata: Observability of an Integrated GPS/INS During Maneuvers,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 40, No. 4, pp. 1421-1422, Oct. 2004.

11. Ibraggimov, N. H. (ed.), CRC Handbook of Lie Group Analysis of Differential Equations, Vol. 1, CRC Press, Ann Arbor, MI, 1994.

12. “Special Issue on Program Generation, Optimization, and Platform Adaptation,” Proceedings of the IEEE, Vol. 93, No. 2, Feb. 2005.

13. Sun, B.-Y., Huang, D.-S., Fang, H.-T., “Lidar Signal Denoising Using Least-Squares Support Vector Machine,” IEEE Signal Processing Letters, Vol. 12, No. 2, pp.101-104, Feb. 2005.

14. Shorrock, T., “U.S. Deploys Missile Defense System,” IEEE Spectrum, Vol. 42, No. 2, pp. 10-13, Feb. 2005.

15. Son, J. S., Thomas, G., Flores, B. C., Range-Doppler Radar Imaging and Motion Compensation, Artech House, Boston, MA, 2001.

16. Elliot, R. J., Krishnamurthy, V., Poor, H. V., “Exact Filters for Certain Moments and Stochastic Integrals of the State of Systems with Benes Nonlinearity,” IEEE Trans. on Automatic Control, Vol. 44, No. 10, pp. 1929-1933, Oct. 1999.

17. Aihara, S. I., Bagchi, A., “On the Mortensen Equation for Maximum Likelihood State Estimation,” IEEE Trans. on Automatic Control, Vol. 44, No. 10, pp. 1955-1960, Oct. 1999.

18. Barton, D. K., Radar System Analysis and Modeling, Artech House, Boston, MA, 2005.

19. Guerci, J. R., Space-Time Adaptive Processing for Radar, Artech House, Boston, MA, 2003.

20. Ibrahim, S., Rajagopalan, A. N., “Image Estimation in Film-Grain Noise,” IEEE Signal Processing Letters, Vol. 12, No. 3, pp. 238-241, Mar. 2005.

21. Chang, C., Ansari, R., “Kernel Particle Filtering for Visual Tracking,” IEEE Signal Processing Letters, Vol. 12, No. 3, pp. 242-246, Mar. 2005.

22. Johnson, G., Shalaev, R., Hartnett, R., Swaszek, P., Naris, M., “Can LORAN Meet GPS Backup Requirements?,” IEEE A&E Systems Magazine, Vol. 20, No. 2, pp. 3-12, Feb. 2005.

23. Gini, F., Regggianini, R., Mengali, U., “The Modified Cramer-Rao Lower Bound in Vector Parameter Estimation,” IEEE Trans. on Signal Processing, Vol. 46, No. 1, pp. 52-60, Jan. 1998.   

24. Buzzi, S., Lops, M., Sardellitti, S., “Further Results on Cramer-Rao Bounds for Parameter Estimation in Long-Code DS/CDMA Systems,” IEEE Trans. on Signal Processing, Vol. 53, No. 3, pp. 1216- 1221, Mar. 2005.

25. Prasad, M. K., “Stack Filter Design Using Selection Probabilities,” IEEE Trans. on Signal Processing, Vol. 53, No. 3, pp. 1025-1037, Mar. 2005.

26. Brcich, R. F., Iskander, D. R., Zoubir, A. M., “The Stability Test for Symmetric Alpha-Stable Distributions,” IEEE Trans. on Signal Processing, Vol. 53, No. 3, pp. 977-986, Mar. 2005.

27. Spriet, A., Moonen, M., Wouters, J., “Stochastic Gradient-Based Implementation of Spatially Preprocessed Speech Distortion Weighted Multichannel Wiener Filtering for Noise Reduction in Hearing Aids,” IEEE Trans. on Signal Processing, Vol. 53, No. 3, pp. 911-925, Mar. 2005.

28. Piyabonkarn, D., Rajamani, R., Greminger, M., “The Development of a MEMS Gyroscope for Absolute Angle Measurement,” IEEE Trans. on Control Systems Technology, Vol. 13, No. 2, pp. 185-195, Mar. 2005.

29. Chen, Y.-C., Closkey, R. T. M., Tran, T. A., Blaes, B., “A Control and Signal Processing Integrated Circuit for the JPL-Boeing Micromachined Gyroscopes,” IEEE Trans. on Control Systems Technology, Vol. 13, No. 2, pp. 286-300, Mar. 2005.

30. Zhang, X., Xu, W., Nair, S. S., Chellaboina, V., “PDE Modeling and Control of a Flexible Two-Link Manipulator,” IEEE Trans. on Control Systems Technology, Vol. 13, No. 2, pp. 301-312, Mar. 2005.

31. Seife, C., “Flaw Found in Data-Protection Method [viz., SHA-1],” Science, Vol. 307, No. 5714, p. 1389, 4 Mar. 2005.

32. Callegari, S., Rovatti, R., Setti, G., “Embeddable ADC-Based True Random Number Generator for Cryptographic Applications Exploiting Nonlinear Signal Processing and Chaos,” IEEE Trans. on Signal Processing, Vol. 53, No. 2, pp. 793-805, Feb. 2005 [TeK Comment: this approach to generation may be too strong for  easy decryption but results still germane to excellent computational simulation of systems without subtle cross-correlations in the random number generators contaminating or degrading output results.]

33. “Special Issue on Verification and Validation,” Computing in Science & Engineering, a peer-reviewed joint publication of the IEEE Computer Society and the American Institute of Physics, Vol. 6, No. 5, Sep./Oct. 2004.

34. Xiong, Z., Liveris, A. D., Cheng, S., “Distributed Source Coding for Sensor Networks,” IEEE Signal Processing Magazine, Vol. 21, No. 5, pp. 80-94, Sep. 2004.

35. de Morais, T. N., Oliveira, A. B. V., Walter, F., “Global Behavior of the Equatorial Anomaly Since 1999 and Effects on GPS,” IEEE A&E Systems Magazine, Vol. 20, No. 3, pp.15-23, Mar. 2005.

36. Zheng, Z. W., Zhu, Y.-S., “New Least-Squares Registration Algorithm for Data Fusion,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 40, No. 4, pp. 1410-1416, Oct. 2004.

37. Barroca, L., Hall, J., Hall, P. (Eds.), Software Architectures: Advances and Applications, 2^nd Printing, Springer-Verlag, London, UK, 2000.

38. Lawrence, B. F., “Anechoic Chambers, Past and Present,” Conformity, Vol. 10, No. 2, pp. 41-45, Feb. 2005.

39. Wall, A., “A Historical Perspective on the FCC Rules for Digital Devices and a Look to the Future,”  Conformity, Vol. 10, No. 2, pp. 12-18, Feb. 2005.

40. Samadi, S., Ahmad, M. O., Swamy, M. N. S., “Ramanujan Sums and Discrete Fourier Transforms,” IEEE Signal Processing Letters, Vol. 12, No. 4, pp. 293-296, April 2005.

41. Scharf, L. L., Schreier, P. J., Hanssen, A., “The Hilbert Space Geometry of the Rihaczek Distribution for Stochastic Analytic Signals,” IEEE Signal Processing Letters, Vol. 12, No. 4, pp. 297-300, April 2005.

42. Michiels, W., Van Assche, V., Niculescu, S.-I., “Stabilization of Time-Delay Systems with a Controlled Time-Varying Delay and Applications,” IEEE Trans. on Automatic Control, Vol. 50, No. 4, pp. 493-504, Apr. 2005.

43. Farrell, J. L., “Strapdown at the Crossroads,” Navigation, Journal of the Institute of Navigation, Vol. 51, No. 4, pp. 249-257, Winter 2004. (Good modern perspective! Correction in Vol. 52, No. 1, page iii, Spring 2005. Conclusions in this paper may be viewed as being very controversial. Also see References [46] and [355] for another perspective.)

44. Nassar, S., Schwartz, K.-P., El-Sheimy, N., “Modeling Inertial Sensor Errors Using Autoregressive (AR) Models,” Navigation, Journal of the Institute of Navigation, Vol. 51, No. 4, pp. 259-268, Winter 2004.

45. Liu, Z., Gao, Y., “Development and Evaluation of a new 3-D Ionospheric Modeling Method,” Navigation, Journal of the Institute of Navigation, Vol. 51, No. 4, pp. 311-329, Winter 2004.

46. Wu, Y., Hu, X., Hu, D., Li, T., and Liam, J., “Strapdown Inertial Navigation System Algorithms Based on Dual Quaternions,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 1, pp. 110-132, Jan. 2005. (Also see references [355] below and [43] above.)

47. Bar-Shalom, Y., Chen, H., “IMM Estimator with Out-of-Sequence Measurements,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 1, pp. 90-98, Jan. 2005.

48. Kalandros, M., Pao, L. Y., “Multisensor Covariance Control Strategies for Reducing Bias Effects in Interacting Target Scenarios,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 1, pp. 153-174, Jan. 2005.

49. Kay, S., “Exponentially Embedded Families-New Approaches to Model Order Estimation,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 1, pp. 333-345, Jan. 2005.

50. Kaplan, L. M., Sinha, A., Bar-Shalom, Y., “Comments on ‘Maximum Likelihood Angle Extraction for Two Closely Spaced Targets’,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 1, pp. 383-384, Jan. 2005.

51. Vermaak, J., Godsill, S. J., Perez, P., “Monte-Carlo Filtering for Multi-Target Tracking and Data Association,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 1, pp. 309-331, Jan. 2005.

52. Wu, Y., Hu, D., Wu, M., and Hu, X., “Unscented Kalman Filtering for Additive noise Case: Augmented versus Nonaugmented,” IEEE Signal Processing Letters, Vol. 12, No. 5, pp. 357-360, May 2005.

53. Leshem, A., Tong, L., “Estimating Sensor Population via Probabilistic Sequential Polling,” IEEE Signal Processing Letters, Vol. 12, No. 5, pp. 395-398, May 2005.

54. Achard, S., Jutten, C., “Identifiability of Post-Nonlinear Mixtures,” IEEE Signal Processing Letters, Vol. 12, No. 5, pp. 423-426, May 2005.

55. Weisman, R., “Under the Radar,” The Boston Globe, p. C-1, p. C-4, 25 April 2005.

56. Smith, S. T., “Statistical Resolution Limits and the Complexified Cramer-Rao Bound,” IEEE Trans. on Signal Processing, Vol. 53, No. 5, pp. 1597-1609, May 2005.

57. Smith, S. T., “Covariance, Subspace, and Intrinsic Cramer-Rao Bounds,” IEEE Trans. on Signal Processing, Vol. 53, No. 5, pp. 1610-1630, May 2005.

58. Jahromi, O. S., Aarabi, P., “Theory and Design of Multirate Sensor Arrays,” IEEE Trans. on Signal Processing, Vol. 53, No. 5, pp. 1739-1753, May 2005.

59. Zhu, Y., Song, E., Zhou, J., You, Z., “Optimal Dimensionality Reduction of Sensor Data in Multisensor Estimation Fusion,” IEEE Trans. on Signal Processing, Vol. 53, No. 5, pp. 1631-1639, May 2005.

60. Lorentz, R. G., Boyd, S. P., “Robust Minimum Variance Beamforming,” IEEE Trans. on Signal Processing, Vol. 53, No. 5, pp. 1684-1696, May 2005.

61. Chen, S., Hong, X., Harris, C. J., Wang, X., “Identification of Nonlinear Systems Using Generalized Kernel Models,” IEEE Trans. on Control Systems Technology, Vol. 13, No. 3, pp. 401-411, May 2005.

62. Stein, G., Gorinevsky, D., “Design of Surface Shape Control for Large Two-Dimensional Arrays,” IEEE Trans. on Control Systems Technology, Vol. 13, No. 3, pp. 422-433, May 2005.

63. Leuschen, M. L., Walker, I. D., Cavallaro, J. R., “Fault Residual Generation via Nonlinear Analytical Redundancy,” IEEE Trans. on Control Systems Technology, Vol. 13, No. 3, pp. 452-458, May 2005.

64. Anderson, E. P., Beard, R. W., McLain, T. W., “Real-Time Dynamic Trajectory Smoothing for Unmanned Air Vehicles,” IEEE Trans. on Control Systems Technology, Vol. 13, No. 3, pp. 471-477, May 2005.

65. Jiang, B., Chowdhury, F. N., “Fault Estimation and Accommodation for Linear MIMO Discrete-Time Systems,” IEEE Trans. on Control Systems Technology, Vol. 13, No. 3, pp. 493-499, May 2005.

66. Allen, R., “Compiling High-Level Languages to DSPs,” IEEE Signal Processing Magazine, Vol. 22, No. 3, pp. 47-56, May 2005.

67. Allen, R., Kennedy, K.,  Optimizing Compilers for Modern Architectures, Morgan Kaufmann, San Mateo, CA, 2002.

68. Heller, B., MACSYMA for Statisticians, Wiley Series in Probability and mathematical Statistics, John Wiley & Sons, Inc., NY, 1991.

69. Norvig, P., Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp, Morgan Kaufmann, San Francisco, CA, 1992.

70. Seibel, P. Practical Common Lisp, Gary Cornell (Lead Editor), Apress, Springer-Verlag New York, Inc., NY, 2005.

71. Roberts, P. F., “MIT research and grid hacks reveal SSH holes,” eWeek, Vol. 22, No. 20, pp.7, 8, 16 May 2005.

72. Wang, Z., Yang, F., Ho, D. W. C., Lin, X., “Robust Finite-Horizon Filtering for Stochastic Systems With Missing Measurements,” IEEE Signal Processing Letters, Vol. 12, No. 6, pp. 437-440, June 2005.

73. Samuelsson,J., Plasberg, J. H., “Multiple Description Coding Based on Gaussian Mixture Models,” IEEE Signal Processing Letters, Vol. 12, No. 6, pp. 449-452, June 2005.

74. Evans, R., Krishnamurthy, V., Nair, G., Sciacca, L., “Networked Sensor Management and Data Rate Control for Tracking Maneuvering Targets,” IEEE Transactions on Signal Processing, Vol. 53, No. 6, pp. 1979-1991, June 2005. [Interactive Multiple Model (IMM) Filtering approach is again implemented only for targets described by linear systems, even under maneuvers. Strategic Reentry Vehicles are targets that are well known to be adequately modeled only as being nonlinear in both the dynamics and in the algebraic measurement model. This article also considers Particle Filtering (PF) and Probabilistic Data Association (PDA) approaches as well. As with almost all PF examples to date, the state space target dynamics model used within the calculations is planar and consists of only 4 states. The complexity of PF implementation as a CPU computational burden increases drastically as the state dimension increases. Practical Early Warning Radar target models must be at least six states, and must be nonlinear to realistically reflect the action of an inverse square gravity and the oblateness of the earth (effects that cannot be ignored in a realistic EWR application). Article doesn’t reflect Bit Error Rates (BER) or possible requests for retransmission within the communications channels as a consequence of BER. Article does not consider having adequate processor capability at each sensor site, as would be the case for EWR with two way communication links present to Colorado Spring’s Cheyenne Mountain, and which is more compatible with methodology of decentralized filtering, reported on herein within Consulting Section. See “Sensor Fusion”. Also see Ref. 335 below for how to handle the resulting effects of BER in simulations as a random delay in the communications channel. (However, we do not endorse use of the robust H_infinity estimator approach in Ref. 335.)]

75. Ang, K. H., Chong, G., Li, Y., “PID Control System Analysis, Design, and Technology,” IEEE Trans. on Control Systems Technology, Vol. 13, No. 4, pp. 559-576, July 2005.

76. Pervan, B., Gratton, L., “Orbit Ephemeris Monitors for Local Area Differential GPS,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 2, pp. 449-460, April 2005. 

77. Lin, Y., Chen, B., Varsheney, P. K.,, “Decision Fusion Rules in Multi-Hop Wireless Sensor Networks,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 2, pp. 475-488, April 2005. 

78. Kastella, K., Kreucher, C., “Multiple Model Nonlinear Filtering for Low Signal Ground Target Applications,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 2, pp. 549-564, April 2005. (Whether IMM is actually being used for targets with nonlinear system dynamics is somewhat obscurred. Target dynamics model for ground targets is usually purely linear but model is not discussed explicitly in the paper. However, Ito correction term is mentioned in connection with a representation of target motion. A purely linear dynamics model should have Ito correction term that is identically zero. Measurement model is definitely nonlinear but that has already frequently been the case within earlier IMM results.)

79. Ristic, B., Smets, P., “Target Classification Approach Based on the Belief Function Theory,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 2, pp. 574-584, April 2005. 

80. Mookerjee, P., Reifler, F., “Reduced State Estimators for Consistent Tracking of Maneuvering Targets,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 2, pp. 608-619, April 2005. 

81. Erickson, J. W., Maybeck, P. S., Raquet, J. F., “Multipath-Adaptive GPS/INS Receiver,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 2, pp. 645-657, April 2005. 

82. Ruan, Y., Willett, P., “A Quantization Architecture for Track Fusion,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 2, pp. 671-681, April 2005. 

83. Wang, R., Horan, S., “Impact of Van Jacobson Header Compression on TCP/IP Throughput Performance over Lossy Space Channels,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 2, pp. 681-693, April 2005. 

84. Orton, M., Marrs, A., “Particle Filters for Tracking with Out-of-Sequence Measurements,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 2, pp. 693-702, April 2005. 

85. Alouani, A. T., Gray, J. E., McCabe, D. H., “Theory of Distributed Estimation using Multiple Asynchronous Sensors,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 2, pp. 717-723, April 2005.

86. Schon, T., Gustafsson, F., Nordlund, P.-J., “Marginalized Particle Filters for Mixed Linear/Nonlinear State-Space Models,” IEEE Trans. on Signal Processing, Vol. 53, No. 7, pp. 2279-2289, July 2005.

87. Godavarti, M., Hero III, A. O., “Partial Update LMS Algorithms,” IEEE Trans. on Signal Processing, Vol. 53, No. 7, pp. 2382-2399, July 2005.

88. Villares, J., Vazquez., G., “Second Order Parameter Estimation,” IEEE Trans. on Signal Processing, Vol. 53, No. 7, pp. 2408-2420, July 2005.

89. Bolic, M., Djuric, P. M., Hong, S., “Resampling Algorithms and Architectures for Distributed Particle Filters,” IEEE Trans. on Signal Processing, Vol. 53, No. 7, pp. 2442-2450, July 2005.

90. Cotter, S. F., Rao, B. D., Engan, K., Kreutz-Delegato, K., “Sparse Solutions to Linear Inverse Problems With Multiple Measurement Vectors,” IEEE Trans. on Signal Processing, Vol. 53, No. 7, pp. 2477-2488, July 2005.

91. Bronstein, A. M., Bronstein, M. M.,, Zibulevsky, M., “Quasi Maximum Likelihood MIMO Blind Deconvolution: Super- and Sub-Gaussianity vs. Consistency,” IEEE Trans. on Signal Processing, Vol. 53, No. 7, pp. 2576-2579, July 2005.

92. Weng, B., Barner, K. E., “Nonlinear System Identification in Impulsive Environments,” IEEE Trans. on Signal Processing, Vol. 53, No. 7, pp. 2588-2595, July 2005.

93. Astrom, K. J., Klein, R. E., Lennartsson, A., “Bicycle Dynamics and Control,” IEEE Control Systems Magazine, Vol. 25, No. 4, pp. 26-47, August 2005.

94. Hodgkinson, J., “History of Low-Order Equivalent Systems for Aircraft Flying Qualities,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 4, pp. 577-583, July-August 2005.

95. Psiaki, M. L., “Estimation of Spacecraft Attitude Dynamics Parameters by Using Flight Data,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 4, pp. 594-603, July-August 2005. 

96. Picket, W. C., “Estimation of Postlaunch Angular Motion for Kinetic Energy Projectiles,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 4, pp. 604-610, July-August 2005. 

97. Barkana, I., “Classical and Simple Adaptive Control for Nonminimum Phase Autopilot Design,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 4, pp. 631-638, July-August 2005. 

98. Ross, I. M., D’Souza, C. N., “Hybrid Optimal Control Framework for Mission Planning,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 4, pp. 686-697, July-August 2005. 

99. Shore, D., Bodson, M., “Flight Testing of a Reconfigurable Control System on an Unmanned Aircraft,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 4, pp. 698-707, July-August 2005.

100. Tahk, M.-J., Park, C.-S., Ryoo, C.-K., “Line-of-Sight Guidance Laws for Formation Flight,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 4, pp. 708-716, July-August 2005.

101. Wolfe, J. D., Speyer, J. L., “Improved Integer Ambiguity Resolution Technique for Fixed Arrays,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 4, pp. 717-723, July-August 2005.

102. Segal, S., Ben-Asher, J. Z., Weiss, H., “Derivation of Formation-Flight Guidance Laws for Unmanned Air Vehicles,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 4, pp. 733-743, July-August 2005.

103. Pittelkau, M. E., “Calibration and Attitude Determination with Redundant Inertial Measurement Units,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 4, pp. 743-753, July-August 2005.

104. Gurfil, P., “Relative Motion Between Elliptic Orbits: Generalized Boundedness Conditions and Optimal Formationkeeping,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 4, pp. 761-768, July-August 2005.

105. Seywald, H., Roithmayr, C. M., Troutman, P. A., Park, S.-Y., “Fuel-Optimal Transfers Between Coplanar Circular Orbits Using Variable-Specific-Impulse Engines,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 4, pp. 795-801, July-August 2005.

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109. Hashttudi Zad, S., Kwong, R. H., Wonham, W. M., “Fault Diagnosis in Discrete-Event Systems: Incorporating Timing Information,” IEEE Trans. on Automatic Control, Vol. 50, No. 7, pp. 1010-1015, July 2005.

110. Moreno, J. A., “Simultaneous Observation of Linear Systems: A State Space Interpretation,” IEEE Trans. on Automatic Control, Vol. 50, No. 7, pp. 1021-1025, July 2005.

111. Seo, J., Chung, D., Park, C. G., Lee, J. G., “The Robustness of Controllability and Observability for Discrete Linear Time-Varying Systems with Norm-Bounded Uncertainty,” IEEE Trans. on Automatic Control, Vol. 50, No. 7, pp. 1039-1043, July 2005.

112. Khosrowjerdi, M. J., Nikoukhah, R., Safari-Shad, N., “Fault Detection in a Mixed H₂/H_infinity Setting,” IEEE Trans. on Automatic Control, Vol. 50, No. 7, pp. 1063-1068, July 2005.

113. Piccoli, B., Kulkarni, J., “Pumping a Swing by Standing and Squatting: do children pump time optimally?,” IEEE Control Systems Magazine, Vol. 25, No. 4, pp. 48-56, August 2005.

114. Malioutov, D., Cetin, M., Willsky, A. S., “A Sparse Signal Reconstruction Perspective for Source Localization With Sensor Arrays,” IEEE Trans. on Signal Processing, Vol. 53, No. 8, pp. 3010-3022, August 2005.

115. El-Keyi, A., Kirubarajan, T., Gershman, A. B., “Robust Adaptive Beamforming Based on the Kalman Filter,” IEEE Trans. on Signal Processing, Vol. 53, No. 8, pp. 3032-3041, August 2005.

116. Leung, S. H., So, C. F., “Gradient-Based Variable Forgetting Factor RLS Algorithm in Time-Varying Environment,” IEEE Trans. on Signal Processing, Vol. 53, No. 8, pp. 3141-3151, August 2005.

117. Ben-Haim, Z., Eldar, Y. C., “Maximum Set Estimators with Bounded Estimation Error,” IEEE Trans. on Signal Processing, Vol. 53, No. 8, pp. 3172-3183, August 2005.

118. Gao, H., Lam, J., Xie, L., Wang, C., “New Approach to Mixed H₂/H_infinity Filtering for Polytopic Discrete-Time Systems,” IEEE Trans. on Signal Processing, Vol. 53, No. 8, pp. 3010-3022, August 2005.

119. Wu, Y., Hu, X., Hu, D., Wu, M., “Comments on ‘Gaussian Particle Filtering’,” IEEE Trans. on Signal Processing, Vol. 53, No. 8, pp. 3350-3351, August 2005.

120. Xiao, J.-J., Luo, Z.-Q., “Universal Decentralized Detection in a Bandwidth-Constrained Sensor Network,” IEEE Trans. on Signal Processing, Vol. 53, No. 8, Part 1, pp. 2617-2624, August 2005.

121. Payne, S. J., “A Bayesian Approach for the Estimation of Model Parameters from Noisy Data Sets,” IEEE Trans. on Signal Processing, Vol. 12, No. 8, pp. 553-556, August 2005.

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123. Paschalidis, J. C., Sa, C., Caramanis, M. C., “Target-Pursuing Scheduling and Routing Policies for Multiclass Queueing Networks,” IEEE Trans. on Automatic Control, Vol. 49, No. 10, pp. 1709-1722, October 2004.

124. Smyshlyaev, A., Khorrami, F., “Closed Form Boundary State Feedbacks for a Class of 1-D Partial Integro-Differential Equations,” IEEE Trans. on Automatic Control, Vol. 49, No. 12, pp. 2185-2202, December 2004.

125. Lechevin, N., Rabbath, C. A., Sicand, P., “A Passivity Perspective for the Synthesis of Robust Terminal Guidance,” IEEE Trans. on Control Systems Technology, Vol. 13, No. 5, pp. 760-765, September 2005.

126. Daum, F., “Nonlinear Filters: Beyond the Kalman Filter,” IEEE Aerospace and Electronic Systems Magazine, Tutorials II, Vol. 20, No. 8, Part 2 of 2, pp. 57-69, August 2005.

127. Rapoport, I, Oshman, Y., “A Cramer-Rao-Type Estimation Lower Bound for Systems with Measurement Faults,” IEEE Trans. on Automatic Control, Vol. 50, No. 9, pp. 1234-1245, September 2005.

128. Li, X.-R., Zhao, Z., Li, X.-B., “General Model-Set Design Methods for Multiple-Model Approach,” IEEE Trans. on Automatic Control, Vol. 50, No. 9, pp. 1260-1276, September 2005.

129. Fragoso, M. D., Costa, O. L. V., Baczynski, J., Rocha, N., “Optimal Linear Mean Square Filter for Continuous-Time Jump Linear Systems,” IEEE Trans. on Automatic Control, Vol. 50, No. 9, pp. 1364-1369, September 2005.

130. MacMynowski, D. G., “Hierachic Estimation for Control of Segmented-Mirror Telescopes,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 5, pp. 1072-1076, September-October 2005.

131. Rao, G. N., Bhat, M. S., Alex, T. K., “Fast Access Method for Onboard Star Catalog,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 28, No. 5, pp. 1032-1038, September-October 2005.

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133. Liu, S., Tian, Z., “A Kalman-PDA Approach to Soft-Decision Equalization for Frequency-Selective MIMO Channels,” IEEE Trans. on Signal Processing, Vol. 53, No. 10, pp. 3819-3830, October 2005.

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230. Chen, S.-F., Fong, I.-K., “Robust Filtering for 2-D State-Delayed Systems with NFT Uncertainties,” IEEE Trans. on Signal Processing, Vol. 54, No. 1, pp. 274-285, January 2006.

231. Ramanujam, J., Hong, J., Kandemir, M., Narayan, A., Agarwal, A., “Estimating and Reducing the Memory Requirements of Signal Processing Codes for Embedded Systems,” IEEE Trans. on Signal Processing, Vol. 54, No. 1, pp. 286-294, January 2006.

232. Feng, G., “Nonsynchronized State Estimation of Discrete Time Piecewise Linear Systems,” IEEE Trans. on Signal Processing, Vol. 54, No. 1, pp. 295-303, January 2006.

233. Niedzeiecki, M., Kaczmarek, P., “Tracking Analysis of a Generalized Adaptive Notch Filter,” IEEE Trans. on Signal Processing, Vol. 54, No. 1, pp. 304-314, January 2006.

234. Gazzah, H., Marcos, S., “Cramer-Rao Bounds for Antenna Array Design,” IEEE Trans. on Signal Processing, Vol. 54, No. 1, pp. 336-345, January 2006.

235. Lott, C., Teneketzis, D., “Stochastic Routing in Ad-Hoc Networks,” IEEE Trans. on Automatic Control, Vol. 51, No. 1, pp. 52-70, January 2006.

236. Fan, X., Arcak, M., “Delay Robustness of a Class of Nonlinear Systems and Applications to Communication Networks,” IEEE Trans. on Automatic Control, Vol. 51, No. 1, pp. 139-144, January 2006.

237. Denis-Vidal, L., Jauberthie, C., Joly-Blanchard, G., “Identifiability of a Nonlinear Delayed-Differential Aerospace Model,” IEEE Trans. on Automatic Control, Vol. 51, No. 1, pp. 154-158, January 2006.

238. Hough, M. E., “Nonlinear Recursive Estimation of Boost Trajectories, Including Batch Initialization and Burnout Estimation,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 1, pp. 72-81, January-February 2006.

239. Oshman, Y., Koifan, M., “Robust Navigation Using the Global Positioning System in the Presence of Spoofing,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 1, pp. 95-104, January-February 2006.

240. Horie, K., Conway, B. A., “Opotimal Fighter Pursuit-Evasion Maneuvers Found via Two-Sided Oprimization,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 1, pp. 105-112, January-February 2006.

241. Belanger, G. M., Ananyev, S., Speyer, J. L., Chichka, D. F., Carpenter, J. R., “Decentralized Control of Satellite Clusters Under Limited Communication,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 1, pp. 134-145, January-February 2006.

242. Lane, C., Axelrad, P., “Formation Design in Eccentric Orbits Using Linearized Equations of Relative Motion,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 1, pp. 146-160, January-February 2006.

243. Bordner, R. E., Wiesel, W. E., “Trajectory Estimation for Satellite Clusters,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 1, pp. 172-178, January-February 2006.

244. Alfriend, K. T., Lee, D.-J., Creamer, N. G., “Optimal Servicing of Geosynchronous Satellites,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 1, pp. 203-205, January-February 2006.

245. Shuster, M. D., Markley, F. L., “General Formula for Extracting the Euler Angles,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 1, pp. 215-216, January-February 2006.

246. Wang, Y., Papageorgiou, M., Messmer, A., “A Real-Time Freeway Network Traffic Surveillance Tool,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 1, pp. 18-32, January 2006.

247. Kim, D. J., M’Closky, R. T., “A Systematic Method for Tuning the Dynamics of Electrostatically Actuated Vibratory Gyros,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 1, pp. 69-81, January 2006.

248. Peterson, J. A. M., Bodson, M., “Constrained Quadratic Programming Techniques for Control Allocation,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 1, pp. 91-98, January 2006.

249. Chwa, D., Choi, J. Y., Anavatti, S. G., “Observer-Based Adaptive Guidance Law Considering Target Uncertainties and Contol Loop Dynamics,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 1, pp. 112-123, January 2006.

250. Tao, G., Tang, X., Chen, S., Fei, J., Joshi, S. M., “Adaptive Failure Compensation of Two-State Aircraft Morphing Actuators,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 1, pp. 157-164, January 2006.

251. Ding, J., Marcassa, F., Wu, S.-C., Tomizuka, M., “Multirate Control for Computation Saving,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 1, pp. 165-169, January 2006.

252. Shahapurkar, N., Ramalingam, C. S., “Threshold Performance of MUSIC When Using the Forward-Backward Data Matrix,” IEEE Signal Processing Letters, Vol. 13, No. 2, pp. 80-83, Feb. 2006.

253. Falk, T. H., Chan, W.-Y., “Nonintrusive Speech Quality Estimation Using Gaussian Mixture Models,” IEEE Signal Processing Letters, Vol. 13, No. 2, pp. 108-111, Feb. 2006.

254. Gebre-Egziabher, D., Razavi, A., Enge, P. K., Gautier, J., Pullen, S., Pervan, B. S., Akos, D. M., “Sensitivity and Performance Analysis of Doppler-Aided GPS Carrier-Tracking Loops,” Navigation, Journal of the Institute of Navigation, Vol. 52, No. 2, pp. 49-60, Summer 2005.

255. Gaylor, D. E., Lightsey, E. G., Key, K. W., “Effects of Multipath and Signal Blockage on GPS Navigation in the Vicinity of the International Space Station,” Navigation, Journal of the Institute of Navigation, Vol. 52, No. 2, pp. 61-70, Summer 2005.

256. Psiaki, M. L., Akos, D. M., Thor, J., “A Comparison of Direct Radio Frequency Sampling and Conventional GNSS Receiver Architectures,” Navigation, Journal of the Institute of Navigation, Vol. 52, No. 2, pp. 71-82, Summer 2005.

257. Groves, P. D., “GPS Signal-to-Noise Measurement in Weak Signal and High-Interference Environments,” Navigation, Journal of the Institute of Navigation, Vol. 52, No. 2, pp. 83-94, Summer 2005.

258. Won, J.-H., Lee, J.-S., “A Note on the Group Delay and Phase Advance Phenomenon Associated with GPS Signal Propagation Through the Atmosphere,” Navigation, Journal of the Institute of Navigation, Vol. 52, No. 2, pp. 95-98, Summer 2005.

259. Verhagen, S., “On the Reliability of Integer Ambiguity Resolution,” Navigation, Journal of the Institute of Navigation, Vol. 52, No. 2, pp. 49-60, Summer 2005.

260. Li, Y., Zhang, K., Grenfell, R., “Improved Knight Method Based on Narrowed Search Space for Instantaneous GPS Attitude Determination,” Navigation, Journal of the Institute of Navigation, Vol. 52, No. 2, pp. 111-119, Summer 2005.

261. Ho, T.-J., Chen, B.-S., “Novel Extended Viterbi-Based Multiple-Model Algorithms for State Estimation of Discrete-Time Systems with Markov Jump Parameters,” IEEE Trans. on Signal Processing, Vol. 54, No. 2, pp. 393-404, February 2006.

262. Simon, D., “A Game Theory Approach to Constrained Minimax State Estimation,” IEEE Trans. on Signal Processing, Vol. 54, No. 2, pp. 405-412, February 2006.

263. Xiao, J.-J., Cui, S., Luo, Z.-Q., Goldsmith, A.-J., “Power Scheduling of Universal Decentralized Estimation in Sensor Networks,” IEEE Trans. on Signal Processing, Vol. 54, No. 2, pp. 413-422, February 2006.

264. Barner, K. E., Aysal, T. C., “Polynomial Weighted Median Filtering,” IEEE Trans. on Signal Processing, Vol. 54, No. 2, pp. 636-650, February 2006.

265. Joachim, D., Deller, Jr., J. R., “Multiweight Optimization in Optimal Bounding Ellipsoid Algorithms,” IEEE Trans. on Signal Processing, Vol. 54, No. 2, pp. 679-690, February 2006.

266. Li, Y., Er, M. H., “Theoretical Analyses of Gain and Phase Error Calibration with Optimal Implementation for Linear Equispaced Array,” IEEE Trans. on Signal Processing, Vol. 54, No. 2, pp. 712-723, February 2006.

267. Tugnait, J. K., Meng, X., “On Superimposing Training for Channel Estimation: Performance Analysis, Training Power Allocation, and Frame Synchronization,” IEEE Trans. on Signal Processing, Vol. 54, No. 2, pp. 752-765, February 2006.

268. Lombardi, M. J., Godsill, S. J., “On-Line Bayesian Estimation of Signals in Symmetric alpha-Stable Noise,” IEEE Trans. on Signal Processing, Vol. 54, No. 2, pp. 775-779, February 2006.

269. Li, Y., Ho, K. C., Kwan, C., “3-D Array Pattern Synthesis with Frequency Invariant Property for Concentric Ring Array,” IEEE Trans. on Signal Processing, Vol. 54, No. 2, pp. 780-784, February 2006.

270. Zhao, Z., Li, G., “Roundoff Noise Analysis of Two Efficient Digital Filter Structures,” IEEE Trans. on Signal Processing, Vol. 54, No. 2, pp. 790-795, February 2006.

271. Capraro, G. T., Farina, A., Griffiths, H., Wicks, M. C., “Knowledge-Based Radar Signal and Data Processing,” IEEE Signal Processing Magazine, Vol. 23, No. 1, pp. 18-29, Jan. 2006.

272. Haykin, S., “Cognitive Radar,” IEEE Signal Processing Magazine, Vol. 23, No. 1, pp. 18-29, Jan. 2006.

273. Guerci, J. R., Baranosk, E. J., “Knowledge-Aided Adaptive Radar at DARPA,” IEEE Signal Processing Magazine, Vol. 23, No. 1, pp. 41-50, Jan. 2006.

274. Wicks, M. C., Rangaswamy, M., Adve, R., Hale, T. B., “Space-Time Adaptive Processing,” IEEE Signal Processing Magazine, Vol. 23, No. 1, pp. 51-65, Jan. 2006.

275. Burdic, W. S., Radar Signal Analysis, Prentice Hall EE Series, Englewood Cliffs, NJ, 1968. [Section 7.5 on Radar Clutter Process and Section 7.6 on Radar Target Scintillation and Section 8.5 on Target Discrimination in Clutter are first rate by the author William Burdic from the Autonetics Division of North American Rockwell (previously North American Aircraft).] 

276. Cui, T., Tellambura, C., “Polynomial-Constrained Detection Using a Penalty Function and a Differential-Equation Algorithm for MIMO Systems,” IEEE Signal Processing Letters, Vol. 13, No. 3, pp. 133-136, Mar. 2006.

277. Wang, Z., Fang, J., “Robust H_infinity Filter Design With Variance Constraints and Parabolic Pole Assignment,” IEEE Signal Processing Letters, Vol. 13, No. 3, pp. 137-140, Mar. 2006.

278. Maione, G., “A Rational Discrete Approximation to the Operator s^0.5,” IEEE Signal Processing Letters, Vol. 13, No. 3, pp. 141-144, Mar. 2006.

279. Mielikainen, J., “Lossless Compression of Hyperspectral Images Using Lookup Tables,” IEEE Signal Processing Letters, Vol. 13, No. 3, pp. 157-160, Mar. 2006.

280. Gong, Y., Cowan, C. F. N., “A Self-Structured Adaptive Decision Feedback Equalizer,” IEEE Signal Processing Letters, Vol. 13, No. 3, pp. 169-172, Mar. 2006.

281. Li, X.-L., “A New Gradient Search Interpretation of Super-Exponential Algorithm,” IEEE Signal Processing Letters, Vol. 13, No. 3, pp. 173-176, Mar. 2006.

282. Miranda, S., Baker, C., Woodbridge, K., Griffiths, H., “Knowledge-Based Resource Management for Multifunction Radar,” IEEE Signal Processing Magazine, Vol. 23, No. 1, pp. 66-76, Jan. 2006.

283. McNames, J., “An Effective Color Scale for Simultaneous Color and Gray-Scale Publications,” IEEE Signal Processing Magazine, Vol. 23, No. 1, pp. 82-87, Jan. 2006.

284. Cheng, C.-H., “Signal Processing for Optical Communication,” IEEE Signal Processing Magazine, Vol. 23, No. 1, pp. 88-96, Jan. 2006.

285. Ho, K. C., Chan, Y. T., “An Asymptotically Unbiased Estimator for Bearings-Only and Doppler-Bearing Target Motion Analysis,” IEEE Trans. on Signal Processing, Vol. 54, No. 3, pp. 809-822, Mar. 2006.

286. Fishler, E., Haimovich, A., Blum, R. S., Cimini, Jr., L. J., Chizhik, D., Valenzuela, R. A., “Spacial Diversity in Radars--Models and Detection Performance,” IEEE Trans. on Signal Processing, Vol. 54, No. 3, pp. 823-838, Mar. 2006.

287. Varadarajan, V., Krolik, J. L., “Joint Space-Time Interpolation for Distorted Linear and Bistatic Array Geometries,” IEEE Trans. on Signal Processing, Vol. 54, No. 3, pp. 848-860, Mar. 2006.

288. Ferreol, A., Larzabal, P., Viberg, M., “On the Asymptotic Performance Analysis of Subspace DOA Estimation in the Presence of Modeling Errors: Case of MUSIC,” IEEE Trans. on Signal Processing, Vol. 54, No. 3, pp. 907-920, Mar. 2006.

289. Abu-Al-Saud, W. A., Stuber, G. L., “Efficient Sample Rate Conversion for Software Radio Systems,” IEEE Trans. on Signal Processing, Vol. 54, No. 3, pp. 932-939, Mar. 2006.

290. Vosoughi, A., Scaglione, A., “Everything You Always Wanted to Know About Training: Guidelines Derived Using the Affine Precoding Framework and the CRB,” IEEE Trans. on Signal Processing, Vol. 54, No. 3, pp. 940-954, Mar. 2006.

291. Ding, F., Shi, Y., Chen, T., “APerformance Analysis of Estimation Algorithms of Nonstationary ARMA Processes,” IEEE Trans. on Signal Processing, Vol. 54, No. 3, pp. 1041-1053, Mar. 2006.

292. Einicke, G. A., “Optimal and Robust Noncausal Filter Formulations,” IEEE Trans. on Signal Processing, Vol. 54, No. 3, pp. 1069-1077, Mar. 2006.

293. Arenas-Garcia, J., Figueiras-Vidal, A. R., Sayed, A. H., “Mean-Square Performance of a Convex Combination of Two Adaptive Filters,” IEEE Trans. on Signal Processing, Vol. 54, No. 3, pp. 1078-1090, Mar. 2006.

294. Lueng, H., Shanmugam, S., Xie, N., Wang, S., “An Ergodic Approach for Chaotic Signal Estimation at Low SNR with Application to Ultra-Wide-Band Communications,” IEEE Trans. on Signal Processing, Vol. 54, No. 3, pp. 1091-1103, Mar. 2006.

295. Ribeiro, A., Giannakis, G. B., “Bandwidth-Constrained Distributed Estimation for Wireless Sensor Networks-Part I: Gaussian Case,” IEEE Trans. on Signal Processing, Vol. 54, No. 3, pp. 1131-1143, Mar. 2006.

296. Hong, S., Djuric, P. M., “High-Throughput Scalable Parallel Resampling Mechanism for Effective Redistribution of Particles,” IEEE Trans. on Signal Processing, Vol. 54, No. 3, pp. 1144-1155, Mar. 2006.

297. Milbert, D., “Correction to ‘Influence of Pseudorange Accuracy of Phase Ambiguity Resolution in Various GPS Modernization Scenarios’,” Navigation, Journal of the Institute of Navigation, Vol. 52, No. 3, pp. 121-122, Fall 2005.

298. Bian, S.,Jim, J., Fang, Z., “The Beidou Satellite Positioning System and Its Positioning Accuracy,” Navigation, Journal of the Institute of Navigation, Vol. 52, No. 3, pp. 123-130, Fall 2005.

299. Adhya, S., Ziebart, M., Sibthorpe, A., Arrowsmith, P., Cross, P., “Thermal Force Modeling For Precise Prediction and Determination of Spacecraft Orbits,” Navigation, Journal of the Institute of Navigation, Vol. 52, No. 3, pp. 131-144, Fall 2005.

300. Weiss, J. P., Anderson, S., Axelrad, P., “Development of Multipath Error Budgets for JPALS Ground Station Receivers,” Navigation, Journal of the Institute of Navigation, Vol. 52, No. 3, pp. 145-154, Fall 2005.

301. Shively, C. A., Hsiao, T. T., “Error and Availability Analysis of CAT IIIb LAAS Augmented by Radar Altimeter,” Navigation, Journal of the Institute of Navigation, Vol. 52, No. 3, pp. 155-162, Fall 2005.

302. Mason, J., Romero, L., “TOA/FOA Geolacation Solutions Using Multivariate Resultants,” Navigation, Journal of the Institute of Navigation, Vol. 52, No. 3, pp. 163-178, Fall 2005.

303. Zheng, Y., Feng, Y., “Interpolating Residual Zenith Troppospheric Delays for Improved Regional Area Differential GPS Positioning,” Navigation, Journal of the Institute of Navigation, Vol. 52, No. 3, pp. 179-187, Fall 2005.

304. Kallesøe, C. S., Cocquempot, V., Izadi-Zamanabadi, R., “Model-Based Fault Detection in a Centrifugal Pump Application,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 2, pp. 204-215, Mar. 2006.

305. Jeon, I.-S., Lee, J.-I., Tahk, M.-J., “Impact-Time-Control Guidance Law for Anti-Ship Missiles,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 2, pp. 260-266, Mar. 2006.

306. Leland, R. P., “Adaptive Control of a MEMS Gyroscope Using Lyapunov Methods,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 2, pp. 2278-283, Mar. 2006.

307. Jiang, J., Zhang, Y., “Accepting Performance Degradation in Fault-Tolerant Control System Design,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 2, pp. 284-292, Mar. 2006.

308. Chmielewski, D. J., Peng, J.-K., “Covariance-Based Hardware Selection-Part I: Globally Optimal Actuator Selection,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 2, pp. 355-361, Mar. 2006. [This reference again pursues the approach made famous in the text book by Arthur Gelb (Editor) entitled Applied Optimal Estimation, MIT Press, Cambridge, MA, 1974. Evidently, if one waits long enough, then history will repeat itself, as it has here. A well-known limitation in seeking to use covariance analysis to set error budgets is that simulated covariances, using the underlying Kalman filter theory, are truly representative of accuracy accrued only when the system dynamics model and sensor measurement models are both purely linear and when the noises are strictly Gaussian and white. Arthur Gelb, then at TASC, also offered an approximate solution using CADET®, as a software embodiment and implementation of the multi-input time-varying describing function techniques espoused in  Gelb, A. and Vander Velde, W. E., Multiple-Input Describing Functions and Nonlinear System Design, McGraw-Hill Book Company, NY, 1968. TeK Associates’ TK-MIP also discusses describing function techniques and provides simple  illustrative numerical examples demonstrating their use in system analysis and design. TK-MIP also provides a covariance analysis capability and includes a state-of-the-art technique for generating accuracy error budgets.]

309. Peng, J.-K., Chmielewski, D. J., “Covariance-Based Hardware Selection-Part II: Equivalence Results for the Sensor, Actuator, and Simultaneous Selection Problem,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 2, pp. 362-368, Mar. 2006. [This reference again pursues the approach made famous in the text book by Arthur Gelb (Editor) entitled Applied Optimal Estimation, MIT Press, Cambridge, MA, 1974. Evidently, if one waits long enough, then history will repeat itself, as it has here. A well-known limitation in seeking to use covariance analysis to set error budgets is that simulated covariances, using the underlying Kalman filter theory, are truly representative of accuracy accrued only when the system dynamics model and sensor measurement models are both purely linear and when all the noises are strictly Gaussian and reducible to white noise stimuli. Arthur Gelb, then at TASC, also offered an approximate solution using CADET®, as a software embodiment and implementation of the multi-input time-varying describing function techniques espoused in  Gelb, A. and Vander Velde, W. E., Multiple-Input Describing Functions and Nonlinear System Design, McGraw-Hill Book Company, NY, 1968. TeK Associates’ TK-MIP also discusses describing function techniques and provides simple  illustrative numerical examples demonstrating their use in system analysis and design. TK-MIP also provides a covariance analysis capability and includes a state-of-the-art technique for generating accuracy error budgets.]

310. Candy, J. V., Model-Based Signal Processing, Simon Haykin (Editor), IEEE Press and Wiley-Interscience, A John Wiley & Sons, Inc. Publication, Hoboken, NJ, 2006. [This new book, by an author with a Lawrence Livermore National Laboratory/University of California, Santa Barbara affiliation, offers a reasonably wide modern coverage and good pointers to precedents just like our own TeK Associates’ TK-MIP also offers but our software costs only $499. This new book also mentions their associated Techni-Soft SSPACK and SSPACK-PC software made commercially available for sale in 1992 and 1993 (see their Website at http://www.techni-soft.net). However, please be aware of the following perceived drawbacks: (1) Numerous typos appear in their Website descriptions of what their software is and what it does; (2) Lack of real specificity in stating exactly what the software is capable of doing; (3) That SSPCK-PC requires user to have their own copy of MatLab (itself, being rather expensive) in order to run SSPACK-PC; (4) That their Techni-Soft Website specifies use of MS-DOS Operating System for PC version (among other Operating Systems offered but not specifically calling out any version of MS Windows, or MS VISTA, or even Longhorn as merely a suspected oversight I hope); (5) That SSPACK-PC, at a cost of $1495 for a Single-User for SSPACk-PC Supervisor, Pre- and Post-Processor (along with and additional $995 for other options like Discrete-Time-Varying Simulator and Estimator and Discrete Time Nonlinear Simulation and Estimation), plus $1,000/year for annual License renewal/Maintenance Contract (for Revisions, Updates, Compatible OS Upgrades but for Workstation only), is rather expensive after User is "nickel and dimed" to death; (6) That view of SSPACK-PC depicted in Appendix of the above book showns merely push button controls for SSPACK-PC, where push-button controls are notoriously limited but are merely what is available for Graphical User Interface (GUI) development from The MathWorks for MatLab-compatible programs such as this. Also notice that the limited low resolution GUI picture in the Appendix only allowed the reader to confirm the presence of pushbuttons but that the captions on the pushbuttons, that could potentially confirm GUI utility for the reader, are indecipherable at such a coarse scale; (7) That SSPACK-PC, by being linked with MatLab as its main computing engine, inherits all MatLab’s limitations such as: (7a) needing MatLab’s System Identification Toolbox in order to perform Recursive Least Squares (RLS), as reminded on page 167; (7b) SSPACK-PC being a software program that normally runs interpretively as MatLab itself does (rather than as compiled software, as TK-MIP does) and so SSPACK will also be notoriously slow in comparison as a consequence of this structure; (7c) SSPACK-PC needs other expensive MatLab Toolboxes in order to run properly (as already identified); (7d) the alternative Techni-Soft SSPACK software (without the hyphenated “-PC” suffix) is in 1992 FORTRAN (prior to benefits of FORTRAN 1995) source code that costs $3,500 for a Single-User Workstation plus $1,000/year for annual License renewal/Maintenance Contract (for Revisions, Updates, Compatible OS Upgrades); (8) That the required memory size for SSPACK is 4 Megabytes and 25 Megabytes of Disk Space and that of SSPACK-PC includes what is neccessary for running MatLab and its associated Toolboxes. TK-MIP has a much smaller footprint and can run in just 16 Megabytes of RAM and is independent of MatLab,  while applications requiring 256 Megabytes of RAM are not unheard of for running realistic MatLab simulations; (9) That SSPACK-PC is evidently not equipped with an out-of-the-box capability of processing sensor measurements availed to the program through PC DMA or Serial Ports, or wireless, or USB, using the vast array of modern serial protocol options, as is a capability of TK-MIP. (TK-MIP is self-prompting and doesn’t need a training course in order to use it. TK-MIP is intuitive to use and tracks the user’s processing actions as well.) TK-MIP evidently costs much less at $499 and can do so much more and is easier to use by possessing a much more flexible and compliant GUI. One final note: Unlike studies and pursuits undertaken at Sandia or at Johns Hopkins University Applied Physics Laboratory (JHU/APL), Lawrence Livermore is better known for handling energy and nuclear studies and not for handling navigation or target tracking, and so, any estimation and state space simulation software developed at Lawrence Livermore evidently harkens back to mere earlier experience during educational pursuits of those key employees as an apparent academic “me too!” endeavor. The prior experience of our critical personnel at TeK Associates is in mainstream Department of Defense R&D and DT&E navigation applications for terrestrial, naval, and airborne applications, done while at The Analytic Sciences Corporation {TASC}, while at Intermetrics, Inc. (both the preceding two businesses being spin-offs in 1967 and 1969, respectively, from navigation specialists at Charles Stark Draper Laboratory and Dynamics Research Corporation {DRC}, DRC itself being a spin-off from Draper Laboratory), and while at MIT Lincoln Laboratory, where radar and IR target tracking are routinely performed along with airborne INS/GPS investigations associated with their multi-sensor test aircraft. Such realistic pursuits are also within the goals of TeK Associates’s prior clients: MITRE, XonTech, Raytheon, and Arête. I strongly suspect that TeK Associates is more aware of and attuned to “what engineers want”. To J. V. Candy’s credit, within his nice book, he didn’t fall for any of the robust control and/or robust estimation “malarkey” that permeates most of the current research literature, nor do we at TeK Associates fall for such techniques that can not yet be used in realistic situations. This is always our touchstone for determining true utility. We routinely apply the “smell test” too.]

311. Dawber, B., Branson, J., “Improved CFAR Performance in the Littoral,” IEEE A&E Systems Magazine, Vol. 21, No. 3, pp. 26-30, March 2006.

312. Kulatunga, H., Kadirkamanathan, V., “Multiple H_infinity Filter-Based Deterministic Sequence Estimation in Non-Gaussian Channels,” IEEE Signal Processing Letters, Vol. 13, No. 4, pp. 185-188, April 2006.

313. Bae, J., Park, J., “An Efficient Algorithm for Bandpass Sampling of Multiple RF Signal,” IEEE Signal Processing Letters, Vol. 13, No. 4, pp. 193-196, April 2006.

314. Gunturk, B. K., Gevrekei, M., “High-Resolution Image-Reconstruction From Multiple Differently Exposed Images,” IEEE Signal Processing Letters, Vol. 13, No. 4, pp. 197-200, April 2006.

315. Deng, H., Himed, B., Wicks, M. C., “Image Feature-Based Space-Time Processing for Ground Moving Target Detection,” IEEE Signal Processing Letters, Vol. 13, No. 4, pp. 216-219, April 2006.

316. Ni, Y., Krichen, E., Hizen, W., Garcia-Salicetti, S., Dorizzi, B., “Active Differential CMOS Imaging Device for Human Face Recognition,” IEEE Signal Processing Letters, Vol. 13, No. 4, pp. 220-223, April 2006.

317. Diene, O., Bhaya, A., “Adaptive Filtering Algorithms Designed Using Control Liapunov Functions,” IEEE Signal Processing Letters, Vol. 13, No. 4, pp. 224-227, April 2006.

318. Lim, C. H., Mulgrew, B., “Prediction of Inverse Covariance Matrix (PICM) Sequences for STAP,” IEEE Signal Processing Letters, Vol. 13, No. 4, pp. 236-239, April 2006.

319. El-Sheimy, N., Shin, E.-H., Niu, X., “Kalman Filter Face-Off: Extended vs. Unscented Kalman Filters for Integrated GPS and MEMS Inertial,” Inside GNSS: Engineering Solutions for the Global Navigation Satellite System Community, Vol. 1, No. 2, pp. 48-54, March 2006. [This study stacks up benefits and drawbacks to conclude that both EKF and UKF performed well for integration of MEMS-based IMU and GPS, with and without GPS blockage. However, Unscented Kalman Filter (UKF) was deemed superior in seamlessly handling situations where large initial attitude errors are present without needing to initiate special in-motion alignment activities. TeK Associates’ observation here is that this post-processing analysis study neglected to include a consideration of total operations counts incurred or a particular algorithm’s ability to keep up with processing demands in real-time navigation applications, where timeliness of computed results should be a primary consideration. This is where use of the Unscented Kalman Filter is less satisfying and where use of an Extended Kalman Filter (EKF) would win the contest hands down. This is TeK Associates’ assessment of the situation based on our experience in both INS/GPS and in EKF.]

320. Maithripala, D. H. S., Berg, J. M., Dayawansa, W. P., “Almost-Global Tracking of Simple Mechanical Systems on a General Class of Lie Groups,” IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 216-225, Feb. 2006.

321. Aguiar, A. P., Hespanha, J. P., “Minimum-Energy State Estimation for Systems with Perspective Outputs,” IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 226-241, Feb. 2006.

322. Malisoff, M., De Leenheer, P.,“A Small Gain Theorem for Monotone Systems with Multivalued Input-State Characteristics,” IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 287-292, Feb. 2006.

323. Delvenne, J.-C., “An Optimal Quantized Feedback Strategy for Scalar Linear Systems,” IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 298-303, Feb. 2006.

324. Wu, C.-H., Lewis, M. E., Veatch, M., “Dynamic Allocation of Refigurable Resources in a Two-Stage Tandem Queueing System With Reliability Considerations,” IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 309-314, Feb. 2006.

325. Chu, T., Zhang, C., Wang, L., “A Solvable Lie Algebra Condition for Stability of Linear Multidimensional Systems,” IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 320-324, Feb. 2006.

326. Lee, C.-H., “New Upper Solution Bounds of the Continuous Algebraic Riccati Matrix Equation,” IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 330-334, Feb. 2006.

327. Griffin, C., “A Note on Deciding Controllability in Pushdown Systems,” IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 334-337, Feb. 2006.

328. Kang, C., “Moving Horizon Numerical Observers of Nonlinear Control Systems,” IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 344-350, Feb. 2006.

329. Zhang, J., Xia, X., Moog, C. H., “Parameter Identifiability of Nonlinear Systems with Time-Delay,” IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 371-375, Feb. 2006.

330. Ionrdache, M. V., Antsaklis, P.J., “Decentralized Supervision of Petrie Nets,” IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 376-381, Feb. 2006.

331. Guo, X., Yint, G., “The Wonham Filter With Random Parameters: Rate of Convergence and Error Bounds,” IEEE Trans. on Automatic Control, Vol. 51, No. 3, pp. 460-464, Mar. 2006.

332. Chandra, R. S., D’Andrea, R., “A Scaled Small Gain Theorem With Applications to Spacially Interconnected Systems,” IEEE Trans. on Automatic Control, Vol. 51, No. 3, pp. 465-469, Mar. 2006.

333. Theiler, J., Alper, J., “On the Choice of Random Directions for Stochastic Approximation Algorithms,” IEEE Trans. on Automatic Control, Vol. 51, No. 3, pp. 476-481, Mar. 2006.

334. Hagen, G., “Absolute Stability via Boundary Control of a Semilinear Parabolic PDE,” IEEE Trans. on Automatic Control, Vol. 51, No. 3, pp. 489-493, Mar. 2006.

335. Yang, F., Wang, Z., Hung, Y. S., Gani, M., “HJ Control for Networked Systems With Random Communications Delays,” IEEE Trans. on Automatic Control, Vol. 51, No. 3, pp. 511-518, Mar. 2006.

336. Pereina, R., Vettori, P., “Stability of Quaternionic Linear Systems,” IEEE Trans. on Automatic Control, Vol. 51, No. 3, pp. 518-523, Mar. 2006.

337. Hue, C., Le Cadre, J.-P., Perez, P., “Posterior Cramer-Rao Bounds for Multi-Target Tracking,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 42, No. 1, pp. 37-49, January 2006.

338. Ahn, H.-S., Won, C.-H., “Fast Alignment using Rotation Vector and Adaptive Kalman Filter,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 42, No. 1, pp. 70-83, January 2006.

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341. Kaplan, I. M., “Local Node Selection for Localization in a Distributed Sensor Network,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 42, No. 1, pp. 136-146, January 2006.

342. Choukroun, D., Weiss, H., Bar-Itzhack, I. Y., Oshman, Y., “Kalman Filtering for Matrix Estimation,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 42, No. 1, pp. 147-159, January 2006.

343. Zhang, Y., Nanayanan, R. M., “Monopulse Radar Based on Spatiotemporal Correlation of Stochastic Signals,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 42, No. 1, pp. 160-173, January 2006.

344. Choukroun, D., Bar-Itzhack, I. Y., Oshman, Y., “Novel Quaternion Kalman Filter,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 42, No. 1, pp. 174-190, January 2006.

345. Soumekh, M., “SAR-ECCM using Phase-Perturbed LFM Chirp Signals and DRFM Repeat Jammer Penalization,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 42, No. 1, pp. 191-205, January 2006.

346. Kajenski, P. J., “Comparison of Two Theories of Angle-Glint: Polarization Considerations,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 42, No. 1, pp. 191-205, January 2006.

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349. Sinha, A., Kirubarajan, T., Bar-Shalom, Y., “Tracker and Signal Processing for the Benchmark Problem with Unresolved Targets,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 42, No. 1, pp. 279-300, January 2006.

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353. Chang, H., Zhang, J., “New Metrics for Clutter Affecting Human Target Acquisition,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 42, No. 1, pp. 361-368, January 2006.

354. Boers, Y., Driessen, H., “Results on the Modified Riccati Equation: Target Tracking Applications,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 42, No. 1, pp. 379-384, January 2006.

355. Savage, P. G., “A Unified Mathematical Framework for Strapdown Algorithm Design,” AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp. 237-249, March-April 2006. (Also see References [43] and [46] for perspective.)

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454. Perhinschi, M. G., Napolitano, M. R., Campa, G., Seanor, B., Burken, J., Larson, R., “An Adaptive Threshold Approach for the Design of an Actuator Failure Detection and Identification Scheme,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 3, pp. 519-525, May 2006. [Although coauthor M. R. Napolitano has previously referenced both Kerr, T. H., “Real-Time Failure Detection: A Static Nonlinear Optimization Problem that Yields a Two Ellipsoid Overlap Test,” Journal of Optimization Theory and Applications, Vol. 22, No. 4, pp. 509-535, August 1977 and  Kerr, T. H., “Decentralized Filtering and Redundancy Management for Multisensor Navigation,” IEEE Trans. on Aerospace and Electronic Systems, Vol. AES-23, No. 1, pp. 83-119, Jan. 1987, he appears to have evidently forgotten that an adaptive threshold scheme was originally developed and offered in the latter in 1987 for an airborne application. However, he doesn’t reference them in this current article. Please see his earlier papers...,  where he does reference them.]

455. Rosenqvist, F., Tan. A. H., Godfrey, K., Karlstrom, A., “Direction-Dependent System Modeling Approaches Exemplified Through an Electronic Nose System,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 3, pp. 526-531, May 2006.

456. Odelson, B. J., Lutz, A., Rawlings, J. B., “The Autocovariance Least-Squares Method for Estimating Covariances: Application to Model-Based Control of Chemical Reactors,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 3, pp. 532-540, May 2006.

457. Tayebi, A., McGilvray, S., “Attitude Stabilization of a VTOL Quadrotor Aircraft,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 3, pp. 562-571, May 2006.

458. Kulkarni, J. E., Campbell, M. E., Dullerud, G. E., “Stabilization of Spacecraft Flight in Halo Orbits: An H_infinity Approach,” IEEE Trans. on Control Systems Technology, Vol. 14, No. 3, pp. 572-578, May 2006.

459. Seatzu, C., Corona, D., Giua, A., Bemporad, A., “Optimal Control of Continuous-Time Switched Affine Systems,” IEEE Trans. on Automatic Control, Vol. 51, No. 5, pp. 726-742, May 2006. [Although Affine  is essentially what most people think of as merely  linear, these authors failed to reference David Sworder’s 1968 Academic Press Research Monograph on this same subject as a precedent.]

460. Lin, X., Shroff, N. B., “Utility Maximization for Communication Networks with Multipath Routing,” IEEE Trans. on Automatic Control, Vol. 51, No. 5, pp. 766-781, May 2006.

461. Ma, C., Wonham, W. M.,  “Nonblocking Supervisory Control of State Tree Structures,” IEEE Trans. on Automatic Control, Vol. 51, No. 5, pp. 782-793, May 2006.

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486. Boche, H., Schubert, M., “Resource Allocation in Multiantenna Systems-Achieving Max-Min Fairness by Optimizing a Sum of Inverse SIR,” IEEE Trans. on Signal Processing, Vol. 54, No. 6, Part  1 of Two Parts, pp. 1990-1997, June 2006.

487. Fang, J., Leyman, A. R., Chew, Y. H., “Blind Equalization of SIMO FIR Channels Driven by Colored Signals with Unknown Statistics,” IEEE Trans. on Signal Processing, Vol. 54, No. 6, Part  1 of Two Parts, pp. 1998-2008, June 2006.

488. Wu, X., Tian, Z., Davidson, T. N., Giannakis, G. B., “Optimal Waveform Design for UWB Radios,” IEEE Trans. on Signal Processing, Vol. 54, No. 6, Part  1 of Two Parts, pp. 2009-2021, June 2006.

489. Liang, Y.-C., Sun, S., Ho, C. K., “Block-Iterative Generalized Decision Feedback Equalizers for Large MIMO Systems: Algorithm Design and Asymptotic Performance,” IEEE Trans. on Signal Processing, Vol. 54, No. 6, Part  1 of Two Parts, pp. 2035-2048, June 2006.

490. Werner, K., Jansson, M.,  “Reduced Rank Linear Regression and Weighted Low Rank Approximations,” IEEE Trans. on Signal Processing, Vol. 54, No. 6, Part  1 of Two Parts, pp. 2063-2075, June 2006.

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492. deSousa, C. E., Barbosa, K. A., Trofino,  Neto, A., “Robust H_infinity Filtering for Discrete-Time Linear Systems with Uncertain Time-Varying Parameters,” IEEE Trans. on Signal Processing, Vol. 54, No. 6, Part  1 of Two Parts, pp. 2110-2118, June 2006.

493. Averbuch, A. Z., Zheludev, V. A., Cohen, T., “Interpolatory Frames in Signal Space,” IEEE Trans. on Signal Processing, Vol. 54, No. 6, Part  1 of Two Parts, pp. 2126-2139, June 2006.

494. Jovanovic, I., Beferull-Lozano, B., “Oversampled A/D Conversion and Error-Rate Dependence of Bandlimited Signals With Finite Rate of Innovation,” IEEE Trans. on Signal Processing, Vol. 54, No. 6, Part  1 of Two Parts, pp. 2140-2154, June 2006.

495. Eldar, Y. C., Dvorkind, T. G., “A minimum Squared Error Framework for Generalized Sampling,” IEEE Trans. on Signal Processing, Vol. 54, No. 6, Part  1 of Two Parts, pp. 2155-2167, June 2006.

496. Santamaria, I, Pokharel, P., Principe, J. C., “Generalized Correlation Function Definition, Properties, and Application to Blind Equalization,” IEEE Trans. on Signal Processing, Vol. 54, No. 6, Part  1 of Two Parts, pp. 2187-2197, June 2006.

497. Du, L., Lin, H., Bao, Z., Zhang, J., “A Two-Distribution Compounded Statistical Model for Radar HRRP Target Recognition,” IEEE Trans. on Signal Processing, Vol. 54, No. 6, Part  1 of Two Parts, pp. 2226-2238, June 2006.

498. Gemelos, G. M., Sigurjonsson, S., Weissman, T., “Algorithms for Discrete Denoising Under Channel Uncertainty,” IEEE Trans. on Signal Processing, Vol. 54, No. 6, Part  1 of Two Parts, pp. 2263-2276, June 2006.

499. Bonestry, J., Gansler, T., “Computation of the Condition Number of a Nonsingular Symmetric Toplitz Matrix With the Levinson-Durbin Algorithm,” IEEE Trans. on Signal Processing, Vol. 54, No. 6, Part  1 of Two Parts, pp. 2362-2364, June 2006.

500. Triggs, B. Sdika, M., “Boundary Conditions for Young-van Vliet Recursive Filtering,” IEEE Trans. on Signal Processing, Vol. 54, No. 6, Part  1 of Two Parts, pp. 2365-2367, June 2006.

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714. Junichiro Toriwaki, Hiroyuki Yoshida, Fundamentals of Three-Dimensional Digital Processing, Springer-Verlag, London, 2009.

715. Julien Bourgeois, Wolfgang Minker, Time-Domain Beamforming and Blind Source Separation: Speech Input in the Car Environment, Springer Science + Business Media, NY, 2009.

716. http://www.geomag.bgs.ac.uk/research/modelling/KalmanFiltering.html (enssemble Kalman filtering to improve magnetic field geo-maps)

717. Japkowicz, N., Shah, M., Evaluating Learning Algorithms: a classification perspective, Cambridge University Press, UK, 2011.

718. Gottlieb, S., Ketcheson, D., Shu, C.-Wang, Strong Stability Preserving Runge-Kutta and Multistep Time Discretization, World Scientific, NY, 2011. 

719. Richert, T., El-Sheimy, N., “Low-Noise Linear Combinations of Triple-Frequency Carrier Phase Measurements,” Navigation: Journal of the Institute of Navigation, Vol. 53, No. 1, pp. 61-67, Spring 2006. [While there is considerable fanfare over the  simple mathematics encountered within the minimization of a 3-dimensional ellipsoidal paraboloid subject to a linear planar constraint, this particular optimization problem is well known to be tractable and to have a similarly well-known closed-form solution. Despite how easy the solution is, the two authors mannage to botch the solution in going from Equations 21, 22, 23 to Eq. 24 by forgetting to square alpha, beta, and gamma appearing in Eq. 24. This minor error in algebraic manipulation makes all of their consequential numerical results invalid!]

720. Bar-Shalom, Y., Chen, H.,  “Removal of Out-Of-Sequence Measurements from Tracks,” IEEE Trans. on Aerospace and Electronic Systems, Vol. 45, No. 2, pp. 612-619, Apr. 2009Haering, Niels, da Vitoria Lobo, Niels, Visual Event Detection, Kluwer Academic Publication, Boston, MA, 2

721. Owen, A. B., Empirical Likelihood, Chapman and Hall, London, UK, 2000

722. Rotating Machinery Vibrations: From Analysis to Trouble Shooting, 2^nd Ed., CRC Press, Taylor & Francis Group, Boca Raton, FL, 20

723. Thrishantha Nanayakkara, Ferat Sahin, Mo Jamshidi (Eds.), Intelligent Control Systems with an Introduction to System of Systems Engineering, CRC Press, Taylor & Francis Group, Boca Raton, FL, 2010.

724. Joseph Berk, Systems Failure Analysis, ASM International, Materials Park, OH, 20

725. Jerry Wong, & Michael D. Miller, Eds., Guidelines for Electrical Transmission Line Structural Loading, 3^rd Ed., ASCE Manuals and Reports on Engineering Practice, No. 74, ASCE, Reston, VA, 2010.

726. Ambrosio, J. A. C., Eberhard, P., Eds., Advanced Design of Mechanical Systems: From Analysis to Optimization, Wien, Springer-Verlag, NY, 2009.

727. William Yming Huang, Beamforming Using the Nuttal Algorithm, M.S. in the Department of electrical Engineering and computer Science, Supervised by Prof. Arthur B. Baggeroer, MIT, 1987. 

728. Yilmaz, L., Oren, T., Agent-Directed Simulation and Systems Engineering, Wiley-VCH Verlag Gmblt & Co., KGaA, FRG, 2009.

729. Alacnamara, T. M., Introduction to Antenna Placement and Installation, Wiley Aerospace Series, NY, 2010.

730. Sanchez, J., Canton, M. P., Software Solutions for Engineers and Scientists, CRC Press, Taylor & Francis Group, Boca Raton, FL, 2008.

731. Ching, S.-N., Eun, Y., Gokcek, C., Kabamba, P. T., Meerkov, S. M., Quasilinear Control: Performance analysis and design of feedback systems with nonlinear sensors and actuators, Cambridge University Press, Cambridge, UK, 2011.

732. Bouwman, R., Fundamentals of Ground Radar for Air Traffic Control Engineers and Technicians, Scitech Publishing, Inc., Raleigh, N.C., 2009.Rosenfeld, P., “To COM or Not to COM-That is the Question,” RTC Magazine, pp. 40-45, June 2005. [Cautions about using PCI Express with Computer-on-Module (COM) technology. This COM is different from the Microsoft “COM”.]

733. Gutterman, L., “Integrating VISI, IVI, and ATEasy to Migrate Legacy Test Systems,” IEEE A&E Systems Magazine, Vol. 20, No. 6, pp. 36-38, June 2005. [Excellent Discussion of history and current status while providing cautions, where appropriate.]

734. Mielke, J. A., “VXI or PXI Test System Improvement Using DSP,” IEEE A&E Systems Magazine, Vol. 20, No. 7, pp. 7-10, July 2005. [Tells what but not how. Specifics of the implementations are neglected or perhaps too concisely abbreviated.]

735. Drenkow, G., “Future Test System Architectures,” IEEE A&E Systems Magazine, Vol. 20, No. 5, pp. 27-32, May 2005. [Contains a high level overview of benefits and drawbacks of the the following I/O device protocols and hardware listed below]

TeK Associates is also following recent serial gigabit per second rate developments in VXS (Switched Fabric for 6U VMEbus such as Infiniband, Serial RapidIO, PCI Express & PCI Express AS, Gigabit Ethernet, StarFabric and associated specifications as, respectively, VITA 41.1, 41.2,.41.3, 41.4, 41.5), XMC (Switched Fabric Mezzanine Module), and VITA 49 Digital IF Forum. TeK Associates has also become aware that the best of both worlds may now be achieved by ruggedized packaging of Commercial Off-the-Shelf (COTS) cards and processors in Line Replaceable Units (LRU’s) and in ARINC 3/4 ATRs to match prior Form, Fit, and Function (F³). TeK Associates is especially enthusiastic about the emerging PCI Express standard that is already shipping on 200 Million Laptops in 2006 and more in 2007 and PCI Express’s capability for handling multiple CPU’s and cables up to 7 meters long and with data rates of 10 GigaHertz/Second each way and to be low cost as well ( $30-$40) with high end multiple CPU versions at ~ $100 (and also 3U version  already approved by the PXI Consortium)!  To aid in making the transition as gentle as possible over the next few years, new computers will have both the older PCI ports as well as the new PCI Express (PCIe) ports. (As of early May 2006, the IEEE Draft 802.11n, constituting emerging Wireless standards, failed to pass but did pass in September 2009.) Meanwhile a new standard is evolving for the Small Computer System Interface (SCSI) as a serial version aptly named Serial Attached SCSI (SAS). According to page 7 of the November 2006 issue of Military & Aerospace Electronicss, Vol. 17, No. 11, “VITA and VME Technology Mark 25-Year Milestone,” it is stated that “VME technology is in more than 25 million products”. In the past, nearly 60 VME technology specifications have been defined to meet the evolving needs of critical embedded systems. Most recently, VITA introduced VPX VITA 48 specifications to enable networked fabrics in 6U rugged systems, VITA 51 methods to determine system reliability, VITA 56 mezzanine module options, and VITA 58 packaging schemes to make systems more robust and interchangeable.  Go to Top

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