Analysis and implementation of a neural extended kalman filter for target tracking

International Journal of Neural Systems

Volumn 16, Issue 1, 2006, Pages 1-13

  Kramer, Kathleen A ^a   Stubberud, Stephen C ^b  

^a UNIVERSITY OF SAN DIEGO   (United States)

^b BOEING COMPANY   (United States)

Author keywords

Adaptive Kalman filtering; Parametric analysis; State estimation; Target tracking

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; COMPARATIVE STUDY; COMPUTER SIMULATION; FILTRATION; HUMAN; INSTRUMENTATION; MOTION; NONLINEAR SYSTEM; THEORETICAL MODEL; TIME;

COMPUTER SIMULATION; FILTRATION; HUMANS; MODELS, BIOLOGICAL; MODELS, THEORETICAL; MOTION; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; TIME FACTORS;

EID: 33646824402     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065706000457     Document Type: Article

References (18)

1. R. A. Best and J. P. Norton, A new model and efficient tracker for a target with curvilinear motion, Transactions on Aerospace Electronics Systems 34(2) (1997) 1030-1037.
2. J. C. Wang and P. K. Varshney, A tracking algorithm for maneuvering targets, IEEE Transactions on Aerospace Electronics Systems 29(3) (1993) 910-925.
3. S. Blackman and R. Popoli, Design and Analysis of Modern Tracking Systems (Artech House, Norwood, MA, 1999).
4. T. Kirubarajan and Y. Bar-Shalom, Tracking evasive move-stop-move targets with a GMTI radar using a VS-IMM estimator, Transactions on Aerospace Electronics Systems 39(3) (2003) 1098-1103.
5. M. Mallick, S. Maskell, T. Kirubarajan and N. Gordon, Littoral tracking using particle filter, in Proc. Fifth Int. Conf. Information Fusion (2002), (Annapolis, MD) pp. 935-942.
6. L. Jetto, S. Longhi and G. Venturini Development and experimental validation of an adaptive extended Kalman filter for the localization of mobile robots, IEEE Transactions on Robotics and Automation 15(2) (1999) 219-229.
7. S. C. Stubberud, R. N. Lobbia and M. Owen, An adaptive extended Kalman filter using artificial neural networks, Int. J. Smart System Design 1 (1998) 207-221.
8. M. W. Owen and A. R. Stubberud, NEKF IMM tracking algorithm, in Proc. SPIE: Signal and Data Processing of Small Targets (2003), Vol. 5204, ed. Oliver Drummond (San Diego, CA).
9. M. W. Owen and A. R. Stubberud, A neural extended Kalman filter multiple model tracker, in Proc. OCEANS (2003) (San Diego, CA), pp. 2111-2119.
10. S. C. Stubberud and K. A. Kramer, A 2-D intercept problem using a neural extended Kalman filter and controller modification, in Proc. Int. Conf. Computing, Communications and Control Technologies: CCCT'04 (2004) (Austin, TX), pp. 228-233.
11. S. Singhal and L. Wu, Training multilayer perceptrons with the extended Kalman algorithm (Chapter), Advances in Neural Processing Systems I, ed. D. S. Touretsky (Morgan Kaufmann, San Francisco), pp. 133-140.
12. A. R. Stubberud and H. M. Wabgaonkar, Neural network techniques and applications to robust control, (Chapter), Advances in Controls and Dynamics, Vol. 53, ed. C. T. Leondes (Academic Press, New York), pp. 427-522.
13. D. L. Alspach, A bayesian approximation technique for estimation and control of time discrete stochastic systems, Ph.D. Dissertation, University of California San Diego.
14. S. C. Stubberud and M. Owen, Artificial neural network feedback loop, in Proc. 11th Int. Symp. Intelligent Control (1996) (Dearborn, Ml), pp. 514-519.
15. S. Geman, E. Bienenstock and R. Doursat, Neural networks and the bias/variance dilemma, Neural Computation 4 (1992) 1-58.
16. A. Weigend, On overfitting and the effective number of hidden units, in Proc. 1993 Connectionist Models Summer School (1994) (Hillsdale, NJ), pp. 335-342.
17. R. G. Brown, Introduction to Random Signal Analysis and Kalman Filtering (John Wiley and Sons, New York, 1983).
18. I. B. Rhodes, A tutorial introduction to estimation and filtering, IEEE Transactions on Automatic Control. AC-16(6) (1971) 688-706.