K-nearest neighbor particle filters for dynamic hybrid Bayesian networks

IEEE Transactions on Aerospace and Electronic Systems

Volumn 44, Issue 3, 2008, Pages 1091-1101

  Chen, Hongda ^a   Chang, K C ^b  

^a SCIENCE SYSTEMS AND APPLICATIONS INC   (United States)

^b George Mason University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AIR FILTERS; CONTROL THEORY; DISTRIBUTED PARAMETER NETWORKS; ELECTRIC NETWORK ANALYSIS; EVOLUTIONARY ALGORITHMS; HIDDEN MARKOV MODELS; HYBRID SENSORS; IMAGE RETRIEVAL; INFERENCE ENGINES; INTELLIGENT NETWORKS; LEARNING SYSTEMS; MONTE CARLO METHODS; NETWORK MANAGEMENT; NONLINEAR FILTERING; PROBABILITY DISTRIBUTIONS; SIGNAL FILTERING AND PREDICTION; SPEECH ANALYSIS; STATE ESTIMATION; TRELLIS CODES; WAVE FILTERS;

AND MODELS; DYNAMIC BAYESIAN NETWORKS; DYNAMIC SYSTEMS; DYNAMIC VARIABLES; EVOLUTION ALGORITHMS; EXTENSIVE SIMULATIONS; FINITE STATE SPACES; GAUSSIAN MODELS; HMM MODELS; HYBRID BAYESIAN NETWORKS; K-NEAREST NEIGHBORS; MAJORITY VOTING; NEW APPROACHES; NON LINEARITIES; NONLINEAR AND NON-GAUSSIAN; NONSTATIONARITY; NOVEL METHODS; NUMERICAL RESULTS; PARTICLE FILTERING; PARTICLE FILTERS; PERFORMANCE ANALYSES; PERFORMANCE PREDICTIONS; PRACTICAL PROBLEMS; RE SAMPLES; RE SAMPLINGS; SAMPLE DEGENERACIES; SEQUENTIAL MONTE CARLO METHODS; STATE SPACES; THEORETICAL ANALYSES;

BAYESIAN NETWORKS;

EID: 55349085795     PISSN: 00189251     EISSN: None     Source Type: Journal    
DOI: 10.1109/TAES.2008.4655366     Document Type: Article

References (22)

1. Heckerman, D., Mamdani, A., and Wellman, M. P. Real-world applications of Bayesian networks. Communications of the ACM, 38, 3 (Mar. 1995), 24-68.
2. Pitt, M. K., and Shephard, N. Filtering via simulation: Auxiliary particle filters. Journal of the Americian Statistical Association 94, 446 (1999), 590-599.
3. Kozlov, A. V., and Koller, D. Nonuniform dynamic discretization in hybrid networks. Proceedings of the 13th Uncertainty in AI Conference 1997, 314-325.
4. Kjaerulff, U. A computational scheme for reasoning in dynamic probabilistic networks. Proceedings of the 8th UAI Conference, 1992.
5. Boyen, X., and Koller, D. Tractable inference for complex stochastic processes. Proceedings of the 14th UAI Conference, Seattle, WA, 1998.
6. Olesen, K. G. Causal probabilistic networks with both discrete and continuous variables. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15 (1993), 275-279.
7. Poland, W. B., and Shachter, R. D. Mixtures of Gaussians and minimum relative entropy techniques for modeling continuous uncertainties. Proceedings of the 9th Uncertainty in AI Conference, 1993.
8. Anderson, B. D., and Moore, J. B. Optimal Filtering. Upper Saddle River, NJ: Prentice-Hall, 1979.
9. Lerner, U. N., and Parr, R. Inference in hybrid networks: Theoretical limits and practical algorithms. Proceedings of the 17th UAI Conference, Seattle, WA, 2001.
10. Takikawa, M., D'Ambrosio, B., and Wright, E. Real-time inference with large-scale temporal Bayes nets. Proceedings of the 18th UAI Conference, 2002.
11. Bayes, A. J. A minimum variance sampling technique for simulation models. Journal of the ACM Archive, 19, 4 (Oct. 1972), 734-741.
12. Gordon, N., Salmond, D., and Smith, A. F. Novel approach to nonlinear and non-Gaussian Bayesian state estimation. IEE Proceedings, Part F, 140 (1993), 107-113.
13. Hong, S., Miodrag, M. B., and Djuric', P. M. An efficient fixed-point implementation of residual resampling scheme for high-speed particle filter. IEEE Signal Processing Letters, 11, 5 (May 2004), 482-485.
14. Fung, R., and Chang, K. C. Weighing and integrating evidence for stochastic simulation in Bayesian networks. In Uncertainty in Artificial Intelligence 5, New York: Elsevier, 1989, 209-219.
15. Zhang, J. L., and Liua, J. S. A new sequential importance sampling method and its application to the two-dimensional hydrophobic-hydrophilic model. Journal of Chemical Physics, 117 7 (Aug. 2002), 3492-3498.
16. Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T. A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50 (2002), 174-188.
17. Proakis, J. G. Digital Communications (3rd ed.) (Electrical and Computer Engineering Series). New York: McGraw-Hill, 1995, 44.
18. Rose, C., and Smith, M. D. Mathematical Statistics with Mathematics New York: Springer-Verlag, 2002, 311-322.
19. Ross, S. A First Course in Probability. New York: Macmillan College Publishing Company, 1994, 279.
20. van der Heijden, F. Consistency checks for particle filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 1 (Jan. 2006).
21. Khan, Z., Balch, T., and Dellaert, F. MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 11 (Nov. 2005).
22. Isaac, A., Zhang, X., Willett, P., and Bar-Shalom, Y. A particle filter for tracking two closely spaced objects using monopulse radar channel signals. IEEE Signal Processing Letters, 13, 6 (June 2006).