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here to go to the bottom.
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AtAGlance 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 Web Site.)
 Click
here to view our recent short comment submitted to the Institute of
Navigation for publication in their Journal. Kerr,
T. H., “Comment on
'LowNoise Linear Combination of TripleFrequency Carrier Phase Measurements',”
Navigation: Journal of the Institute of Navigation,
Vol.57, No. 2, pp. 161,162, Summer 2010. Also
see Item 719 below. 
 Click
here to view our abstract for GNC Challenges for Miniature Autonomous Systems
Workshop, 2628 October 2009 to occur at Fort Walton Beach,.FL Click
here to obtain the corresponding 1.40 MByte PowerPoint presentation.
Also see: http://www.ion.org/meetings/session.cfm?meetingID=26&t=E&s=1 
 Kerr, T. H., “Comment on
‘Precision FreeInertial Navigation with Gravity Compensation by an Onboard
Gradiometer’,”
AIAA Journal of Guidance, Control, and Dynamics,
JulyAug. 2007. 
 Kerr, T. H., “Integral Evaluation Enabling Performance
Tradeoffs for Two Confidence RegionBased Failure Detection,”
AIAA Journal of Guidance, Control, and Dynamics,
MayJun. 2006. 
 Kerr, T. H., “Comments on ‘Determining if Two Solid Ellipsoids
Intersect’,” AIAA Journal of Guidance, Control, and Dynamics,
JanuaryFebruary 2005. 
 Kerr, T. H., “Further
Critical Perspectives on Certain Aspects of GPS Development and Use,” Proceedings
of 57th Annual Meeting of the Institute of Navigation, Session A4:
Testing of Advanced Navigation Systems, 913 June 2001. 
 Kerr, T. H., “Sensor Scheduling in Kalman Filters: Evaluating a Procedure for
Varying Submarine Navaids,” Proceedings of 57th Annual Meeting
of the Institute of Navigation, 913
June 2001. 
 Satz, H. S., Kerr, T. H., “Comparison of Batch and Kalman Filtering for Radar
Tracking,”
Proceedings
of 10th Annual AIAA\BMDO Conference, 25 July 2001. 
 Kerr, T. H., “Sensor Scheduling in Kalman Filters:
varying navaid fixes for tradingoff submarine NAV accuracy vs. ASW exposure,”
Proceedings of The Workshop on Estimation, Tracking, and Fusion: A Tribute
to Yaakov BarShalom (on the occasion of his 60th Birthday)
following the Fourth ONR/GTRI Workshop on
Target Tracking and Sensor Fusion, 17 May 2001. 
 Kerr, T. H., “Verification of Linear System Software
SubModules using Analytic ClosedForm Results,”
Proceedings of The Workshop on Estimation, Tracking, and Fusion: A Tribute
to Yaakov BarShalom (on the occasion of his 60th Birthday)
following the Fourth ONR/GTRI Workshop on
Target Tracking and Sensor Fusion, 17 May 2001. 
 Kerr, T. H., “Vulnerability of Recent GPS Adaptive Antenna Processing (and all
STAP\SLC)
to Statistically NonStationary Jammer Threats,”
Proceedings of
SPIE, Session 4473: Tracking Small Targets, 29 July3 August 2001.
For another perspective on this topic, please click here. 
 Kerr, T. H., “Exact Methodology for Testing Linear System Software Using Idempotent
Matrices and Other ClosedForm Analytic Results,” Proceedings of
SPIE, Session 4473:
Tracking Small Targets, 29 July3 August 2001. 
 Kerr, T. H., “New Lamps for Old: a shell game for generalized likelihood
ratio use in
radar? Or this isn’t your father’s GLR!,” Proceedings of
SPIE, Session 4473: Tracking Small Targets, 29 July3 August 2001. 
 Kerr, T. H., and Satz, H. S., “Evaluation of Batch Filter Behavior in comparison to
EKF,” TeK
Associates, Lexington, MA, (for Raytheon, Sudbury, MA), 22 Nov. 1999. 
 Kerr, T. H., “TeK Associates’ view in comparing use of a recursive Extended Kalman Filter (EKF) versus use of Batch Least Squares (BLS) algorithm for
UEWR,” TeK
Associates, Lexington, MA, (for Raytheon, Sudbury, MA), 12 Sep. 2000. 
 Kerr, T. H., “Considerations in whether to use Marquardt Nonlinear Least Squares vs. Lambert Algorithm for NMD Cue Track Initiation (TI)
Calculations,”
TeK Associates Technical Report No. 2000101, Lexington, MA, (for Raytheon, Sudbury, MA), 27 Sep. 2000. 
 Participated in the writing of the Software Requirements Specification
(SRS) for Raytheon’s Batch Least Squares (BLS) Algorithm, RVCC EKF,
RUV EKF, Interactive Multiple Model (IMM) Filter, and Track Initiation
(TI). 
 Kerr, T. H., “UEWR Design NotebookSection 2.3: Track
Analysis,”
TeK Associates, Lexington, MA, (for XonTech, Hartwell Rd, Lexington, MA), XonTech Report No. D744 10300, 29 Mar. 1999. 
 Kerr, T. H., “Developing CramerRao Lower Bounds to Gauge the Effectiveness of UEWR
Target Tracking Filters,” Proceedings of AIAA\BMDO Technology
Readiness Conference and Exhibit,
37 August 1998. 
 Kerr, T. H., “NMD White Paper on Designated Action
Item,” MITRE, Bedford, MA, Jan.
1998. 
 Kerr, T. H., “"CramerRao Lower Bound Implementation and Analysis: CRLB Target Tracking Evaluation Methodology for NMD
Radars,” MITRE Technical
Report, Contract No. F1962894C0001, Project No. 03984000N0, Bedford, MA, Feb. 1998. 
 Kerr, T. H., “Critique of Some Neural Network Architectures and Claims for Control
and Estimation,” IEEE
Transactions on Aerospace and Electronic Systems, April 1998. 
 Kerr, T. H., “Comments on ‘An Algorithm for RealTime Failure Detection in Kalman
Filters’,” IEEE Trans. on Automatic Control, May
1998. 
 Kerr, T . H., “CramerRao Lower Bound Implementation and Analysis for NMD Radar Target
Tracking,”
TeK Associates Technical Report No. 97101 (for MITRE), Lexington, MA, 2630 Oct. 1997. 
 Kerr, T. H., “A Critical Perspective on Some Aspects of GPS Development
and Use,” Proceedings of 16th Digital Avionics System Conference,
Vol. II, pp. 9.49 to 9.420, Irvine, CA, 2630 Oct. 1997. [All
worries expressed here by the author regarding GPS vulnerabilities were
confirmed in experience in Three W. Mark Barnes, "Artillery Surveyors:
Nomads of the Battlefield," Field Artillery, A Professional Bulletin
for Redlegs, HQDA P B6011, pp.4347, Jan.Feb. 2001] 
 Kerr, T. H., “Extending Decentralized Kalman Filtering
(KF) to 2D for RealTime
Multisensor Image Fusion and\or Restoration: Optimality of Some
Decentralized KF Architectures,” Proceedings of the
International Conference on Signal Processing Applications & Technology
(ICSPAT96), 710 October 1996. 
 Kerr, T. H., “Assessing and Improving the Status of Existing AngleOnly Tracking (AOT)
Results,” Proceedings of the International Conference on Signal
Processing Applications & Technology (ICSPAT), 2426 October 1995. 
 Kerr, T. H., “Use of GPS\INS in the Design of Airborne Multisensor Data Collection
Missions (for Tuning NNbased ATR algorithms),”
Institute of
Navigation Proceedings of GPS94, 2023 Sept. 1994. 
 Kerr, T. H., “Emulating Random Process Target Statistics (using
MSF),” IEEE Transactions on Aerospace and Electronic Systems,
April 1994. 
 Kerr, T. H., “Numerical Approximations and Other Structural Issues in Practical
Implementations of Kalman Filtering,”
a chapter in Approximate
Kalman Filtering, 1993. 
 Kerr, T. H., “Streamlining Measurement Iteration for EKF Target
Tracking,”
IEEE
Transactions on Aerospace and Electronic Systems, March
1991. Click this link here to see a more
comprehensive
list of our publications. 
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/thomashkerriii
https://blogs.mathworks.com/headlines/2016/09/08/this56yearoldalgorithmiskeytospacetravelgpsvrandmore/
(Please see list at bottom of the prior linked screen)
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 online “Web
of Science” at any good research library. For confirmation, please search under “Kerr,
T. H.”
Our preview and informed prediction of things to come in the Kalman Filtering
arena:
Besides the exciting areas of multisensor\multitarget 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 (JVC)
Algorithm or Murty’s
Algorithm (1968) or TrackBeforeDetect 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
“BankofKalmanFilters”
(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 online 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
multitarget Sonar
applications (since hardware has now caughtup to make these approaches
practicable). More flexible new variations from the Univ. of Connecticut (from
Prof. Yaakov BarShalom 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
1dimensional (1D) random process Kalman filter scenario to 2dimensional (2D)
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:

Online “Failure Detection, Identification, and
Reconfiguration”
(FDIR) in analytic (virtual) and actual redundancy management of complementary and supplementary sensors within coherently focused NAV Systems;


“Sensor Fusion” of framesynchronized scenes for multiple sensordedicated Kalman Filters using OPTIMAL and SUBoptimal combining
schemes (many more routine sensor fusion applications do not require use of
decentralized Kalman filters); 

Decentralized detection [combining decentralized Kalman filtering and hierarchical decision processes]; 

Generalization of Kalman filterbased Schweppe likelihood ratio to multiple filter situation, and nonlinear system situation
(nonGaussian noise statistics). 
4. Generalization of Kalman filterbased Schweppe likelihood ratio
from 1D to 2D 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 Hilbertspace 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
continuoustime and in discretetime, the nonlinear 1D in time
ndimensional 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 2D and other Partial Differential Equation (PDE) Riccati equations as
well. Go to Top
TeK Associates’
primary software product for the PC is TKMIP,
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 TKMIP™ intuitively responsive and easy to use
and less error prone than alternative approaches that do not yet crosscheck 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
bookworms, 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:

Chung, P.J., Roberts, W. J. J., Bohme, J. F., “Recursive KDistribution Parameter
Estimation,” IEEE Trans. on Signal
Processing, Vol. 53, No. 2, pp. 397402, Feb. 2005.

Korn, P., “Some Uncertainty Principles for TimeFrequency Transforms of the Cohen
Class,” IEEE Trans. on Signal
Processing, Vol. 53, No. 2, pp. 523527, Feb. 2005.

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. 589598, Feb. 2005.

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. 696704, Feb. 2005.

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.

Schlosser, M. S., Kronshel, K., “Limits in Tracking with Extended Kalman
Filters,” IEEE Trans. on Aerospace and Electronic
Systems, Vol. 40, No. 4, pp. 13511359, Oct. 2004.

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.

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. 13661374, Oct. 2004.

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. 13741375,
Oct. 2004.

Rhee, I., AbdelHafez, 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. 14211422,
Oct. 2004.

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

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

Sun, B.Y., Huang, D.S., Fang, H.T., “Lidar Signal Denoising Using LeastSquares Support
Vector Machine,”
IEEE Signal Processing Letters, Vol. 12, No. 2, pp.101104, Feb. 2005.

Shorrock, T., “U.S. Deploys Missile Defense
System,” IEEE
Spectrum, Vol. 42, No. 2, pp. 1013, Feb. 2005.

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

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. 19291933, Oct. 1999.

Aihara, S. I., Bagchi, A., “On the Mortensen Equation for Maximum Likelihood State
Estimation,” IEEE Trans. on Automatic
Control, Vol. 44, No. 10, pp. 19551960, Oct. 1999.

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

Guerci, J. R., SpaceTime Adaptive Processing for Radar, Artech House, Boston, MA, 2003.

Ibrahim, S., Rajagopalan, A. N., “Image Estimation in FilmGrain
Noise,”
IEEE Signal Processing Letters, Vol. 12, No. 3, pp. 238241, Mar. 2005.

Chang, C., Ansari, R., “Kernel Particle Filtering for Visual
Tracking,”
IEEE Signal Processing Letters, Vol. 12, No. 3, pp. 242246, Mar. 2005.

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. 312, Feb.
2005.

Gini, F., Regggianini,
R., Mengali, U., “The
Modified CramerRao Lower Bound in Vector Parameter Estimation,”
IEEE Trans.
on Signal Processing, Vol. 46, No. 1, pp. 5260, Jan. 1998.

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

Prasad, M. K., “Stack Filter Design
Using Selection Probabilities,”
IEEE Trans.
on Signal Processing, Vol. 53, No. 3, pp. 10251037, Mar. 2005.

Brcich, R. F., Iskander, D. R., Zoubir, A. M., “The
Stability Test for Symmetric AlphaStable Distributions,”
IEEE Trans.
on Signal Processing, Vol. 53, No. 3, pp. 977986, Mar. 2005.

Spriet, A., Moonen, M., Wouters, J., “Stochastic
GradientBased 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. 911925, Mar. 2005.

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.
185195, Mar. 2005.

Chen, Y.C., Closkey, R. T. M., Tran, T. A., Blaes, B., “A
Control and Signal Processing Integrated Circuit for the JPLBoeing
Micromachined Gyroscopes,”
IEEE Trans. on Control Systems Technology, Vol. 13, No. 2, pp.
286300, Mar. 2005.

Zhang, X., Xu, W., Nair, S. S., Chellaboina, V., “PDE
Modeling and Control of a Flexible TwoLink Manipulator,”
IEEE Trans. on Control Systems Technology, Vol. 13, No. 2, pp.
301312, Mar. 2005.

Seife, C., “Flaw Found in
DataProtection Method [viz., SHA1],”
Science, Vol. 307, No. 5714, p. 1389, 4 Mar. 2005.

Callegari, S., Rovatti, R., Setti, G., “Embeddable
ADCBased True Random Number Generator for Cryptographic Applications
Exploiting Nonlinear Signal Processing and Chaos,”
IEEE Trans. on Signal Processing, Vol. 53, No. 2, pp. 793805, 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 crosscorrelations in the random number generators
contaminating or degrading output results.]

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

Xiong, Z., Liveris, A. D., Cheng, S., “Distributed
Source Coding for Sensor Networks,”
IEEE Signal Processing Magazine, Vol. 21, No. 5, pp. 8094, Sep.
2004.

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.1523, Mar. 2005.

Zheng, Z. W., Zhu, Y.S., “New
LeastSquares Registration Algorithm for Data Fusion,”
IEEE Trans. on Aerospace and Electronic Systems, Vol. 40, No. 4, pp.
14101416,
Oct. 2004.

Barroca, L., Hall, J., Hall, P. (Eds.), Software
Architectures: Advances and Applications, 2^{nd} Printing,
SpringerVerlag, London, UK, 2000.

Lawrence, B. F., “Anechoic Chambers,
Past and Present,”
Conformity, Vol. 10, No. 2, pp. 4145, Feb. 2005.

Wall, A., “A Historical Perspective on
the FCC Rules for Digital Devices and a Look to the Future,”
Conformity, Vol. 10, No. 2, pp. 1218, Feb. 2005.

Samadi, S., Ahmad, M. O., Swamy, M. N. S., “Ramanujan
Sums and Discrete Fourier Transforms,”
IEEE Signal Processing Letters, Vol. 12, No. 4, pp. 293296, April
2005.

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. 297300, April
2005.

Michiels, W., Van Assche, V., Niculescu, S.I., “Stabilization
of TimeDelay Systems with a Controlled TimeVarying Delay and Applications,”
IEEE Trans. on Automatic Control, Vol. 50, No. 4, pp. 493504, Apr.
2005.

Farrell, J. L., “Strapdown at the
Crossroads,”
Navigation, Journal of the Institute of Navigation, Vol. 51, No. 4,
pp. 249257, 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.)

Nassar, S., Schwartz, K.P., ElSheimy, N., “Modeling
Inertial Sensor Errors Using Autoregressive (AR) Models,”
Navigation, Journal of the Institute of Navigation,
Vol. 51, No. 4, pp. 259268, Winter 2004.

Liu, Z., Gao, Y., “Development and
Evaluation of a new 3D Ionospheric Modeling Method,”
Navigation, Journal of the Institute of Navigation,
Vol. 51, No. 4, pp. 311329, Winter 2004.

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.
110132, Jan. 2005. (Also see references [355] below
and [43] above.)

BarShalom, Y., Chen, H., “IMM Estimator
with OutofSequence Measurements,”
IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 1, pp.
9098, Jan. 2005.

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.
153174, Jan. 2005.

Kay, S., “Exponentially Embedded
FamiliesNew Approaches to Model Order Estimation,”
IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 1, pp.
333345, Jan. 2005.

Kaplan, L. M., Sinha, A., BarShalom, Y., “Comments
on ‘Maximum Likelihood
Angle Extraction for Two Closely Spaced Targets’,”
IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 1, pp.
383384, Jan. 2005.

Vermaak, J., Godsill, S. J., Perez, P., “MonteCarlo
Filtering for MultiTarget Tracking and Data Association,”
IEEE Trans. on Aerospace and Electronic Systems, Vol. 41, No. 1, pp.
309331, Jan. 2005.

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. 357360, May
2005.

Leshem, A., Tong, L., “Estimating Sensor
Population via Probabilistic Sequential Polling,”
IEEE Signal Processing Letters, Vol. 12, No. 5, pp. 395398, May
2005.

Achard, S., Jutten, C., “Identifiability
of PostNonlinear Mixtures,”
IEEE Signal Processing Letters, Vol. 12, No. 5, pp. 423426, May
2005.

Weisman, R., “Under the Radar,”
The Boston Globe, p. C1, p. C4, 25 April 2005.

Smith, S. T., “Statistical Resolution
Limits and the Complexified CramerRao Bound,”
IEEE Trans. on Signal Processing, Vol.
53, No. 5, pp. 15971609, May
2005.

Smith, S. T., “Covariance, Subspace, and
Intrinsic CramerRao Bounds,”
IEEE Trans. on Signal Processing, Vol.
53, No. 5, pp. 16101630, May
2005.

Jahromi, O. S., Aarabi, P., “Theory and
Design of Multirate Sensor Arrays,”
IEEE Trans. on Signal Processing, Vol.
53, No. 5, pp. 17391753, May
2005.

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. 16311639, May
2005.

Lorentz, R. G., Boyd, S. P., “Robust
Minimum Variance Beamforming,”
IEEE Trans. on Signal Processing, Vol.
53, No. 5, pp. 16841696, May
2005.

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.
401411, May 2005.

Stein, G., Gorinevsky, D., “Design of
Surface Shape Control for Large TwoDimensional Arrays,”
IEEE Trans. on Control Systems Technology, Vol. 13, No. 3, pp.
422433, May 2005.

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.
452458, May 2005.

Anderson, E. P., Beard, R. W., McLain, T. W., “RealTime
Dynamic Trajectory Smoothing for Unmanned Air Vehicles,”
IEEE Trans. on Control Systems Technology, Vol. 13, No. 3, pp.
471477, May 2005.

Jiang, B., Chowdhury, F. N., “Fault
Estimation and Accommodation for Linear MIMO DiscreteTime Systems,”
IEEE Trans. on Control Systems Technology, Vol. 13, No. 3, pp.
493499, May 2005.

Allen, R., “Compiling HighLevel
Languages to DSPs,”
IEEE Signal Processing Magazine, Vol. 22, No. 3, pp. 4756, May 2005.

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

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

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

Seibel, P. Practical
Common Lisp, Gary Cornell (Lead Editor), Apress, SpringerVerlag New
York, Inc., NY, 2005.

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

Wang, Z., Yang, F., Ho,
D. W. C., Lin, X., “Robust
FiniteHorizon Filtering for Stochastic Systems With Missing Measurements,”
IEEE Signal Processing Letters, Vol. 12, No. 6, pp.
437440, June
2005.

Samuelsson,J., Plasberg, J. H., “Multiple
Description Coding Based on Gaussian Mixture Models,”
IEEE Signal Processing Letters, Vol. 12, No. 6, pp. 449452, June
2005.

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.
19791991, 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.)]

Ang, K. H., Chong, G., Li, Y., “PID
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Lin, Y., Chen, B., Varsheney, P. K.,, “Decision
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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. 549564, 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.)

Ristic, B., Smets, P., “Target
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Ruan, Y., Willett, P., “A Quantization
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Wang, R., Horan, S., “Impact of Van
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Orton, M., Marrs, A., “Particle Filters
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Alouani, A. T., Gray, J. E., McCabe, D. H., “Theory
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Schon, T., Gustafsson,
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Godavarti, M., Hero
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Villares, J., Vazquez.,
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Bolic, M., Djuric, P.
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Cotter, S. F., Rao, B.
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Bronstein, A. M.,
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Weng, B., Barner, K.
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Astrom, K. J., Klein, R. E., Lennartsson, A., “Bicycle
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Hodgkinson, J., “History
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Psiaki, M. L., “Estimation
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Picket, W. C., “Estimation
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Barkana, I., “Classical
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Shore, D., Bodson, M., “Flight
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Tahk, M.J., Park,
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Wolfe, J. D., Speyer,
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Segal, S., BenAsher,
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Pittelkau, M. E., “Calibration
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Gurfil, P., “Relative
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Seywald, H., Roithmayr,
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Prussing, J. E.,
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Ulybyshev, Y., “Terminal
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Lin, A. E. B., Zhou, X. Y., “A New
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Hashttudi Zad, S., Kwong, R. H., Wonham, W. M., “Fault
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Moreno, J. A., “Simultaneous Observation
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Seo, J., Chung, D., Park, C. G., Lee, J. G., “The
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Khosrowjerdi, M. J., Nikoukhah, R., SafariShad, N., “Fault
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Piccoli, B., Kulkarni, J., “Pumping a
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Malioutov, D., Cetin, M., Willsky, A. S., “A
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ElKeyi, A., Kirubarajan, T., Gershman, A. B., “Robust
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Leung, S. H., So, C. F., “GradientBased
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BenHaim, Z., Eldar, Y. C., “Maximum Set
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Gao, H., Lam, J., Xie, L., Wang, C., “New
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Wu, Y., Hu, X., Hu, D., Wu, M., “Comments
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Xiao, J.J., Luo, Z.Q., “Universal
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Payne, S. J., “A Bayesian Approach for
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Smyshlyaev, A.,
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Lechevin, N., Rabbath,
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Daum, F., “Nonlinear
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Rapoport, I, Oshman,
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Li, X.R., Zhao, Z.,
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Fragoso, M. D., Costa,
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MacMynowski, D. G., “Hierachic Estimation
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Rao, G. N., Bhat, M. S., Alex, T. K., “Fast
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Gurfil, P., “Euler Parameters as
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Liu, S., Tian, Z., “A KalmanPDA Approach
to SoftDecision Equalization for FrequencySelective MIMO Channels,”
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Sun, W., Amin, M. G., “A SelfCoherence
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Shui, P.L., “Image Denoising Algorithm
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Ramirez, J., Segura, J. C., Benitez, C., Garcia, L., Rubio,
A., “Statistical Voice Activity Detection Using a
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Yoma, N. B., Carrasco, J., Molina, C., “BayesBased
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Sayed, A. H., Younis, W. M., Tarighat, A., “An
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Jou, Y.D., “LeastSquares Design of
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Xia, X.G., Liu, K., “A Generalized
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Chappalli, M. B., Bose, N. K., “Simultaneous
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Duan, H., Ng, B. P., See, C. M., “A New
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Han, B., Lin, X., “Adapt the
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Nguyen, X., Wainwright, M. J., Jordan, M. I., “Nonparametric
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Eidinger, E., Yeredor, A., “Blind MIMO
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Sigut, J., Demetrio, J., Moreno, L., Estevez, J., Aguilar,
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Gabriel, J. R., Kay, S. M., “On the
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Collier, S. L., “Fisher Information for a
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Feuer, A., Goodwin, G. C., “Reconstruction
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Besson, O., Varfolomeev, G., Vincent, F., “Amplitude
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Karlsson, R., Schon, T., Gustafsson, F., “Complexity
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Zaidi, A., “Positive Definite Combination
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Gurfil, P., Kasdin, N. J., “TwoStep
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Niu, R., Varshney, P. K., Mehrotra, K., Mohan, C., “Temporally
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Zhang, X., BarShalom, Y., Willet, P., Segall, I., “Application
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Li, X.R., Jilkov, V. P., Ru, J., “MultipleModel
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BarShalom, Y., Kirubarajan, T., Gokberg, C., “Tracking
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Buzzi, S., Laps, M., Venturino, L., “TrackBeforeDetect
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BarShalom, Y., Challa, S., Blom, H. A. P., “IMM
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986991, July 2005. [If
M(k) were in fact known beforehand, then {x(k), M(k)} would, in fact, be a
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the system will switch is unknown to the user or engineer a priori or even
immediately after the evolution in time of the system of interest having
this potential structure. Notice that the authors need to assume that the
underlying multiple model systems have a linear structure before a Gaussian
sums mixture model can be invoked. When models are nonlinear, probability
density function (pdf)
calculations are infinite dimensional in general (i. e., the calculations
for the associated probabilities are intractable). You can write expressions
that involve integral evaluations but you are SOL for realtime
computations. This is essentially Bucy’s representation result
as a ratio of integrals that need to be evaluated in order to have
what one needs in a solution for the general nonlinear estimation problem.]

Cai, J., Sinha, A., Kirubarajan, T., “EMML
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Barton, D. K., “Universal
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De Mario, A., Farina, A, Gini, F., “Performance
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Hmam, H., “SNR
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Ristic, B., Smets, P., “Target
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Bruno, M. G. S., Pavlov, A., “Improved
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Besada, J. A., Garcia, J., De Miguel, G., Berlanga, A.,
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Jeong, S., Tugnait, J. K., “Multisensor
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Fowler, M. L., Chen, M., “FisherInformationBased
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Coraluppi, S., Carthel, C., “Distributed
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Milanese, M., Novara,
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Bhat, S. P., “Controllability
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Alessandri, A.,
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Yan, Z., Duan, G., “Time
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Immonen, E.,
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Baten, Md. A.,
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Chen, Z., Huang, J., “A
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Markovsky, I., Willems,
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Sokolov, V. F., “ControlOriented
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Juloski, A. L., Weiland,
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Saligrama, V., “A
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Gibson, S., Wills, A.,
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Raich, R., Zhou, G. T.,
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Yin, G., Special Section on Signal Processing Aspects of
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Besson, O., Scharf, L. L., Vincent, F., “Matched
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also offered an approximate solution using CADET®, as a software embodiment
and implementation of the multiinput timevarying describing function
techniques espoused in Gelb, A. and Vander Velde, W. E., MultipleInput
Describing Functions and Nonlinear System Design, McGrawHill Book
Company, NY, 1968. TeK Associates’
TKMIP also discusses describing function techniques and provides
simple illustrative numerical examples demonstrating their use in
system analysis and design. TKMIP also provides a covariance
analysis capability and includes a stateoftheart technique for
generating accuracy error budgets.]

Peng, J.K., Chmielewski, D. J., “CovarianceBased
Hardware SelectionPart II: Equivalence Results for the Sensor, Actuator,
and Simultaneous Selection Problem,”
IEEE Trans. on Control Systems Technology, Vol. 14, No. 2, pp.
362368, 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 wellknown 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 multiinput timevarying describing function
techniques espoused in Gelb, A. and Vander Velde, W. E., MultipleInput
Describing Functions and Nonlinear System Design, McGrawHill Book
Company, NY, 1968. TeK Associates’
TKMIP also discusses describing function techniques and provides
simple illustrative numerical examples demonstrating their use in
system analysis and design. TKMIP also provides a covariance
analysis capability and includes a stateoftheart technique for
generating accuracy error budgets.]

Candy, J. V., ModelBased Signal Processing, Simon Haykin
(Editor), IEEE Press and WileyInterscience, 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’
TKMIP
also offers but our software costs only $499. This new book also mentions their
associated TechniSoft SSPACK and SSPACKPC software made commercially
available for sale in 1992 and 1993 (see their Web Site at http://www.technisoft.net).
However, please be aware of the following perceived drawbacks: (1) Numerous typos
appear in
their Web Site 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 SSPCKPC requires user to have his own copy of MatLab
(itself, being rather
expensive) in order to run SSPACKPC; (4) That their TechniSoft Web Site
specifies use of MSDOS 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 SSPACKPC, at a cost of $1495 for a
SingleUser for SSPACkPC Supervisor, Pre and PostProcessor (along with
and additional $995 for other options like DiscreteTimeVarying 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
SSPACKPC depicted in Appendix of the above book showns merely push button controls for SSPACKPC,
where pushbutton controls are notoriously limited but are merely what is available for Graphical User Interface (GUI) development from
The MathWorks for MatLabcompatible 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
SSPACKPC, 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) SSPACKPC
being a software program that normally runs interpretively as MatLab
itself does
(rather than as compiled software, as TKMIP does) and so SSPACK will also
be notoriously slow in comparison as a consequence of this structure; (7c) SSPACKPC
needs other expensive
MatLab
Toolboxes in order to run properly (as already identified); (7d) the
alternative TechniSoft 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 SingleUser 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 SSPACKPC
includes what is neccessary for running MatLab and its associated
Toolboxes. TKMIP 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 SSPACKPC is evidently not
equipped with an outofthebox 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 TKMIP.
(TKMIP is
selfprompting and doesn’t need a training course in order to use it. TKMIP
is intuitive to use and tracks the user’s
processing actions as well.)
TKMIP 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 spinoffs in 1967 and
1969, respectively, from navigation specialists at Charles Stark Draper Laboratory
and Dynamics Research Corporation {DRC}, DRC itself being a
spinoff 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 multisensor 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.]

Dawber,
B., Branson, J., “Improved
CFAR Performance in the Littoral,”
IEEE A&E Systems Magazine, Vol. 21, No. 3, pp. 2630, March
2006.

Kulatunga, H., Kadirkamanathan, V., “Multiple
H_{infinity} FilterBased Deterministic Sequence Estimation in NonGaussian
Channels,”
IEEE Signal Processing Letters, Vol. 13, No. 4, pp. 185188, April
2006.

Bae, J., Park, J., “An Efficient
Algorithm for Bandpass Sampling of Multiple RF Signal,”
IEEE Signal Processing Letters, Vol. 13, No. 4, pp. 193196, April
2006.

Gunturk, B. K., Gevrekei, M., “HighResolution
ImageReconstruction From Multiple Differently Exposed Images,”
IEEE Signal Processing Letters, Vol. 13, No. 4, pp. 197200, April
2006.

Deng, H., Himed, B., Wicks, M. C., “Image
FeatureBased SpaceTime Processing for Ground Moving Target Detection,”
IEEE Signal Processing Letters, Vol. 13, No. 4, pp. 216219, April
2006.

Ni, Y., Krichen, E., Hizen, W., GarciaSalicetti, S.,
Dorizzi, B., “Active Differential CMOS Imaging Device for
Human Face Recognition,”
IEEE Signal Processing Letters, Vol. 13, No. 4, pp. 220223, April
2006.

Diene, O., Bhaya, A., “Adaptive Filtering
Algorithms Designed Using Control Liapunov Functions,”
IEEE Signal Processing Letters, Vol. 13, No. 4, pp. 224227, April
2006.

Lim, C. H., Mulgrew, B., “Prediction of
Inverse Covariance Matrix (PICM) Sequences for STAP,”
IEEE Signal Processing Letters, Vol. 13, No. 4, pp. 236239, April
2006.

ElSheimy, N., Shin, E.H., Niu, X., “Kalman
Filter FaceOff: 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. 4854, March 2006. [This
study
stacks up benefits and drawbacks to conclude that both EKF and UKF performed
well for integration of MEMSbased 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 inmotion alignment
activities. TeK Associates’
observation here is that this postprocessing 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 realtime 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.]

Maithripala, D. H. S., Berg, J. M., Dayawansa, W. P., “AlmostGlobal
Tracking of Simple Mechanical Systems on a General Class of Lie Groups,”
IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 216225, Feb.
2006.

Aguiar, A. P., Hespanha, J. P., “MinimumEnergy
State Estimation for Systems with Perspective Outputs,”
IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 226241, Feb.
2006.

Malisoff, M., De Leenheer, P.,“A Small
Gain Theorem for Monotone Systems with Multivalued InputState
Characteristics,”
IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 287292, Feb.
2006.

Delvenne, J.C., “An Optimal Quantized
Feedback Strategy for Scalar Linear Systems,”
IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 298303, Feb.
2006.

Wu, C.H., Lewis, M. E., Veatch, M., “Dynamic
Allocation of Refigurable Resources in a TwoStage Tandem Queueing System
With Reliability Considerations,”
IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 309314, Feb.
2006.

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. 320324, Feb.
2006.

Lee, C.H., “New Upper Solution Bounds
of the Continuous Algebraic Riccati Matrix Equation,”
IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 330334, Feb.
2006.

Griffin, C., “A Note on Deciding
Controllability in Pushdown Systems,”
IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 334337, Feb.
2006.

Kang, C., “Moving Horizon Numerical
Observers of Nonlinear Control Systems,”
IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 344350, Feb.
2006.

Zhang, J., Xia, X., Moog, C. H., “Parameter
Identifiability of Nonlinear Systems with TimeDelay,”
IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 371375, Feb.
2006.

Ionrdache, M. V., Antsaklis, P.J., “Decentralized
Supervision of Petrie Nets,”
IEEE Trans. on Automatic Control, Vol. 51, No. 2, pp. 376381, Feb.
2006.

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. 460464, Mar.
2006.

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. 465469, Mar.
2006.

Theiler, J., Alper, J., “On the Choice
of Random Directions for Stochastic Approximation Algorithms,”
IEEE Trans. on Automatic Control, Vol. 51, No. 3, pp. 476481, Mar.
2006.

Hagen, G., “Absolute Stability via
Boundary Control of a Semilinear Parabolic PDE,”
IEEE Trans. on Automatic Control, Vol. 51, No. 3, pp. 489493, Mar.
2006.

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. 511518, Mar.
2006.

Pereina, R., Vettori, P., “Stability of
Quaternionic Linear Systems,”
IEEE Trans. on Automatic Control, Vol. 51, No. 3, pp. 518523, Mar.
2006.

Hue, C., Le Cadre, J.P., Perez, P., “Posterior
CramerRao Bounds for MultiTarget Tracking,”
IEEE Trans. on Aerospace and Electronic Systems, Vol.
42, No. 1, pp. 3749, January 2006.

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. 7083, January 2006.

LopezRisuelio,G., Grajal, J., “Multiple
Signal Detection and Estimation using Atomic Decomposition and EM,”
IEEE Trans. on Aerospace and Electronic Systems, Vol.
42, No. 1, pp. 84102, January 2006.

Kaplan, I. M., “Global Node Selection for
Localization in a Distributed Sensor Network,”
IEEE Trans. on Aerospace and Electronic Systems, Vol.
42, No. 1, pp. 113135, January 2006.

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. 136146, January 2006.

Choukroun, D., Weiss, H., BarItzhack, I. Y., Oshman, Y., “Kalman Filtering
for Matrix Estimation,”
IEEE Trans. on Aerospace and Electronic Systems, Vol.
42, No. 1, pp. 147159, January 2006.

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. 160173, January 2006.

Choukroun, D., BarItzhack, I. Y., Oshman, Y., “Novel
Quaternion Kalman Filter,”
IEEE Trans. on Aerospace and Electronic Systems, Vol.
42, No. 1, pp. 174190, January 2006.

Soumekh, M., “SARECCM using
PhasePerturbed LFM Chirp Signals and DRFM Repeat Jammer Penalization,”
IEEE Trans. on Aerospace and Electronic Systems, Vol.
42, No. 1, pp. 191205, January 2006.

Kajenski, P. J., “Comparison of Two
Theories of AngleGlint: Polarization Considerations,”
IEEE Trans. on Aerospace and Electronic Systems, Vol.
42, No. 1, pp. 191205, January 2006.

Levanon, N., Levanon, U., “TwoValued
Frequency Coded Waveforms with Favorable Periodic Autocorrelations,”
IEEE Trans. on Aerospace and Electronic Systems, Vol.
42, No. 1, pp. 237248, January 2006.

Wu, Y., Hu, X., Wu, M., Hu, D., “Strapdown
Inertial Navigation using Dual Quaternion Algebra: Error Analysis,”
IEEE Trans. on Aerospace and Electronic Systems, Vol.
42, No. 1, pp. 259266, January 2006.

Sinha, A., Kirubarajan, T., BarShalom, Y., “Tracker
and Signal Processing for the Benchmark Problem with Unresolved Targets,”
IEEE Trans. on Aerospace and Electronic Systems, Vol.
42, No. 1, pp. 279300, January 2006.

Oshman, Y., Arad, D., “DifferentialGameBased
Guidance Law using Target Orientation Observations,”
IEEE Trans. on Aerospace and Electronic Systems, Vol.
42, No. 1, pp. 316326, January 2006.

Moges, M., Robertazzi, T. G., “Wireless
Sensor Networks: Scheduling for Measurement and Data Reporting,”
IEEE Trans. on Aerospace and Electronic Systems, Vol.
42, No. 1, pp. 327340, January 2006.

Jablon, A., Agnawal, A., “Optimal Number
of Array Faces for Active Phased Array Radars,”
IEEE Trans. on Aerospace and Electronic Systems, Vol.
42, No. 1, pp. 351360, January 2006.

Chang, H., Zhang, J., “New Metrics for
Clutter Affecting Human Target Acquisition,”
IEEE Trans. on Aerospace and Electronic Systems, Vol.
42, No. 1, pp. 361368, January 2006.

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. 379384, January 2006.

Savage, P. G., “A Unified Mathematical
Framework for Strapdown Algorithm Design,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
237249, MarchApril 2006. (Also see References [43]
and [46] for perspective.)

Kabamba, P. T., Meerkov, S. M., Zeitz III, F. H. “Optimal
Path Planning for Unmanned Combat Aerial Vehicles to Defeat Radar Tracking,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
279288, MarchApril 2006.

Dever, C., Mettler, B., Feron, E., Popovic, J., McConley,
M., “Nonlinear Trajectory Generation for Autonomous
Vehicles via Parameterized Maneuver Classes,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
289302, MarchApril 2006.

Manchester, I. R., Savkin, A. V., “CircularNavigationGuidance
Law for Precision Missile/Target Engagements,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
314320, MarchApril 2006.

Park, C., Guibout, V., Scheeres, D. J., “Solving
Optimal Continuous Thrust Rendezvous Problems with Generating Functions,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
321331, MarchApril 2006.

Russell, R. P., Ocampo, C. A., “Optimization
of a Broad Class of Ephemeris ModelMars Cyclers,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
354367, MarchApril 2006.

Williams, P., Sgarioto, D., Trivailo, P., “Optimal
Control of an AircraftTowed Flexible Cable System,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
401410, MarchApril 2006.

Sahani, N. A., Horn, J. F., “Adaptive
Model Inversion Control of a Helicopter with Structural Load Limiting,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
411421, MarchApril 2006.

Richardson, T., Lowenberg, M., diBernardo, M., Charles, G., “Design
of a GainScheduled Flight Control System Using Bifurcation Analysis,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
444453, MarchApril 2006.

Dionne, D., Michaiska, H., Oshman, Y., Shinar, J., “Novel
Adapptive Generalized Likelihood Ratio Detector with Application to
Maneuvering Target Tracking,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
465474, MarchApril 2006.

Looye, G., Joos, H.D., “Design of
Autoland Controller Functions with Multiobjective Optimization,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
475484, MarchApril 2006.

Karlgaard, C. D., “Robust Rendezvous
Navigation in Elliptical Orbit,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
495499, MarchApril 2006.

Ramanan, R. V., Adimurty, V., “Precise
Lunar Gravity Assist Transfers to Geostationary Orbits,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
500502, MarchApril 2006.

Qiao, D., Cui, H., Cui, P., “Evaluating
Accessibility of NearEarth Asteroids via Earth Gravity Assists,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
502505, MarchApril 2006.

Yanushevsky, R. T., “Concerning LyapunovBased
Guidance,”
AIAA Journal of Guidance, Control, and Dynamics, Vol. 29, No. 2, pp.
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Fitzgerald, R. J., “Random Tropospheric
Effects in Command Guidance by Multilateration and Interferometry,”
IEEE Trans. on Aerospace and Electronic Systems, Vol. 30, No, 1, pp.
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Fitzgerald, R. J., “Analytic Track
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BarShalom, Y., Blackman, S. S., Fitzgerald, R. J., “The
Dimensionless Score Function for Multiple Hypothesis Decision in Tracking,”
2005 IEEE International Conference on Systems, Man, and Cybernetics,
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Eren, T., Whiteley, W.,
Morse, A. S., Belhumer, P. N., Anderson, B. D. O., “Sensor
and Network Topologies of Formations with Direction, Bearing, and Angle
Information,”
42^{nd} IEEE Conf. on
Decision and Control, Vol. 3, pp. 30643069, 912 Dec. 2003.

Eren, T., Whiteley,
W.,Morse, A. S., Belhumer, P. N., Anderson, “Sensor
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Information,” 42^{nd}
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2003.

Sinopoli, B., Schenato,
L., Franceschetti, M., Poolla, K., Jordan, M., Sastry, S., “Kalman
Filtering with Intermittent Observations,” IEEE Trans. on Automatic Control, Vol. 49, No. 9, pp.
14531464, Sept.
2006. [Use of automatic time tags or time stamp upon
receipt, as commonly routinely used to accompany the sensor measurement
data, makes most of this paper moot since one explicitly knows the time that
corresponds to the data being received. Even if a significant amount of time has
elapsed since the measurement was generated before the measurement is ready and available for incorporation, there are
simple formulas (in the recent literature by Y. BarShalom, immediately
below as Ref. [376]) for properly
incorporating past data to properly account for the senescence of the data
in performing a Kalman filter update. Predicted covariance is claimed to
sometimes go unbounded but this paper claims to provide expressions for
both the upper and lower
bounds of the predicted covariance. Having upper bounds would seemingly defy
claim of covariance going unbounded as an obvious contradiction. Material on
random sampling is a repeat of what was in vogue and published 30 years ago.
For a confirmation, please see the work of Selvin.]

BarShalom, Y., Chen,
H., Mallick, M., “One
Step Solution for the Multistep OutofSequenceMeasurement Problem,”
IEEE Trans. on Aerospace and Electronic Systems, Vol. 40, No, 1, pp.
2737, Jan. 2004.

Belita, J., Gross, A., Grosset, G., “New
Generation of Inertial Clusters for Standby Instruments Based on Vibrating
Sensors,”
IEEE A&E Systems Magazine, Vol. 21, No. 4, pp. 1926, April
2006.

Hennenfent, G.,
Herrmann, F. J., “Seismic
Denoising with Nonuniformly Sampled Curvelets,”
IEEE Computing in Science & Engineering, Vol. 8, No. 3, pp.
1625, May/June 2006.

Kreuzer, E., Sichermann,
W., “The
Effect of Sea Irregularities on Ship Rolling,”
IEEE Computing in Science & Engineering, Vol. 8, No. 3, pp.
2634, May/June 2006.

Kuske, R., “MultipleScales
Approximation of a Coherence Resonance Route to Chatter,”
IEEE Computing in Science & Engineering, Vol. 8, No. 3, pp.
3545, May/June 2006.

O’leary,
D. P., “Updating
and Downdating Matrix Factorizations: A Change in Plans,”
IEEE Computing in Science & Engineering, Vol. 8, No. 3, pp.
5859, May/June 2006.

Zhang, H., Zhu, C.,
Peng, Q., Chen, J. X., “Using
Geometric Algebra for 3D Linear Transformations,”
IEEE Computing in Science & Engineering, Vol. 8, No. 3, pp.
6875, May/June 2006.

Saltzman, M., White Collar Slackers Handbook: tech tricks
to fool your boss, Que, Indianapolis, IN, 2005. [This
book is hilarious.]

Rakovic, S. V.,
Kerrigan, E. C., Mayne, D. Q., Lygeros, J., “Reachability
Analysis of DiscreteTime Systems with Disturbances,” IEEE Trans. on Automatic Control, Vol. 51, No. 4, pp.
546561, Apr. 2006.

Niethammer, M.,
Tannenbaum, A., Angenent, S.,“Dynamic
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562579, Apr. 2006.

Gu, G., Cao, X.R.,
Badr, H., “Generalized LQR Control and Kalman
Filtering with Relations to Computations of InnerOuter and Spectral
Factorizations,” IEEE Trans. on Automatic Control, Vol. 51, No. 4, pp.
595605, Apr.
2006.

Deb, S., Srikant, R., “RateBased
Versus QueueBased Models of Congestion Control,” IEEE Trans. on Automatic Control, Vol. 51, No. 4, pp.
606619, Apr.
2006.

Liu, X., Shen, J., “Stability
Theory of Hybrid Dynamical Systems with Time Delay,” IEEE Trans. on Automatic Control, Vol. 51, No. 4, pp.
620625, Apr.
2006.

Wu, N. E., Fowler, M.
L., “An Error Bound for Sensor Fusion with
Application to Doppler Frequency Emitter Location,” IEEE Trans. on Automatic Control, Vol. 51, No. 4, pp.
631635, Apr.
2006.

Pavon, M., Ferrante,
A., “On the GeorgiouLindquist Approach to
Constrained KullbackLeibler Approximation of Spectral Densities,” IEEE Trans. on Automatic Control, Vol. 51, No. 4, pp.
639644, Apr.
2006. [The above title reminds me of the heated plagiarism
dispute between Anders Lindquist and Prof. Tom Kailath (Standford) in the
late 1970’s concerning results that Kailath allegedly later published after
refereeing and reviewing an earlier paper by Lindquist that was ultimately
rejected for publication. Later, Lindquist, as an IEEE Fellow, joked that “just
because one is paranoid doesn’t mean that they are NOT out to get
you!”]

Tan, A. H., “WienerHammerstein
Modeling of Nonlinear Effects in Bilinear Systems,” IEEE Trans. on Automatic Control, Vol. 51, No. 4, pp.
648652, Apr.
2006.

Goebel, R., Teel, A.
R., Hu, T., Lin, Z., “Conjugate Convex Lyapunov
Functions for Dual Linear Differential Inclusions,” IEEE Trans. on Automatic Control, Vol. 51, No. 4, pp.
661666, Apr.
2006.

Elliot, R. J., van der
Hoek, J., “Optimal Linear Estimation and Data
Fusion,” IEEE Trans. on Automatic Control, Vol. 51, No. 4, pp.
686690, Apr.
2006.

Su, S. W., Anderson, B.
D. O., Brinsmead, T. S., “Minimal Multirealization
of MIMO Linear Systems,” IEEE Trans. on Automatic Control, Vol. 51, No. 4, pp.
690695, Apr.
2006.

Guo, L., Wang, H., “Minimum
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Werner, K., Jansson, M., “Reduced
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Guo, D., Wang, X., “QuasiMote Carlo
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Averbuch, A. Z., Zheludev, V. A., Cohen, T., “Interpolatory
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Jovanovic, I., BeferullLozano, B., “Oversampled
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Eldar, Y. C., Dvorkind, T. G., “A minimum
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Santamaria, I, Pokharel, P., Principe, J. C., “Generalized
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Du, L., Lin, H., Bao, Z., Zhang, J., “A
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Gemelos, G. M., Sigurjonsson, S., Weissman, T., “Algorithms
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Bonestry, J., Gansler, T., “Computation
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Triggs, B. Sdika, M., “Boundary
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Vikalo, H., Hassibi, B., Hassibi, A., “A
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Bristow, D. A., Tharyil, M., Alleyne, A. G., “A
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Heidergott, B.,. Olsder,
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Levitin, A., Introduction to the Design & Analysis of
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Farrell, J. A., Polycarpou, M. M., Adaptive
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Yu, C.C., Autotuning of PID Controllers, 2nd
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Cooke, D., Fun with GPS, ESRI Press, Redlands, CA,
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Wang, L., Tan, K. C., Modern Industrial Automation
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Tran, L. C., Wysock, T. A., Mertins, A., Seberry, J., Complex
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Zbu, Y., Cangellaris, A., Multigrid Finite Element
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Yalamanchili, S., VHDL: A Starters Guide, 2nd
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Krivts, I. L., Krejnin, G. V., Pneumatic Actuating
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Anderson, S. B., Hristu,
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Cheah, C. C., Liu, C.,
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Koening, D.,“Observer Design for Unknown Input Nonlinear
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Mirkin, L., “On
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Padhi, R., Balakrishnan, S. N., “Optimal
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Tang, P. L., de Silva, C. W., “Compensation
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Shima, T., Golan, O. M., “Bounded
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Wang, Z., Yang, F., Ho, D. W. C., Liu, X., “Robust
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Clark, D. FE., Bell, J., “Convergence
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Ribeino, A., Giannakis, G. B., “Bandwith
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Song, T. L., Lee, D. G., “A Probabilistic
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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 OfftheShelf (COTS) cards and
processors in Line Replaceable Units (LRU’s) and in ARINC 3/4 ATRs
to match prior Form, Fit, and Function (F^{3}). 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 25Year 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 Go
to Top Our TKMIP™
software is a masterpiece of engineering implementation by properly anticipating our
users’
needs. 