Closed form PHD filtering for linear jump Markov models

2006 9th International Conference on Information Fusion, FUSION

Volumn , Issue , 2006, Pages

  Pasha, A ^a   Vo, B ^b   Tuan, H D ^a   Ma, W K ^c  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

^b UNIVERSITY OF MELBOURNE   (Australia)

^c NATIONAL TSING HUA UNIVERSITY   (Taiwan)

Author keywords

Linear jump Markov models; Multi target tracking; Optimal filtering; Random sets

Indexed keywords

DATA FUSION; FUSION REACTIONS; INFORMATION FUSION; MARKOV PROCESSES; NUCLEAR PHYSICS; SENSOR DATA FUSION; WAVE FILTERS;

CLOSED FORM; CLOSED-FORM SOLUTIONS; DATA ASSOCIATIONS; FILTERING ALGORITHMS; GAUSSIAN ASSUMPTIONS; INTERNATIONAL CONFERENCES; JUMP MARKOV MODELING; JUMP MARKOV SYSTEMS; LINEAR JUMP MARKOV MODELS; MANEUVERING TARGETS; MISS-DETECTION; MULTI-TARGET TRACKING; OPTIMAL FILTERING; PHD FILTER; PROBABILITY HYPOTHESIS DENSITY; RANDOM SETS; TARGET DYNAMICS;

TARGETS;

EID: 50149094477     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIF.2006.301593     Document Type: Conference Paper

References (25)

1. Y. Bar-Shalom and T. E. Fortmann. Tracking and Data Association. Academic Press, San Diego, 1988.
2. Y. Bar-Shalom, X.-R. Li, and T. Kirubarajan. Estimation with Application to Tracking and Navigation. Wiley, 2001.
3. X.-R. Li and V. Jilkov. Survey of maneuvering target tracking. Part 1: Dynamic models. IEEE Trans. AES, 39(4):1333-1364, 2003.
4. X.-R. Li and V. Jilkov. Survey of maneuvering target tracking. Part V: Multiple-model methods. IEEE Trans. AES, 41(4): 1255-1321, 2005.
5. H. Blom and Y. Bar-Shalom. The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans. AC, AC-33:780-783, 1988.
6. Y. Bar-Shalom, K. C. Chang, and H. Blom. Tracking splitting targets in clutter by using an Interactive Multiple Model Joint Probabilistic Data Association filter. Multitarget Multisensor Tracking: Applications and Advances, Artech House, 11:93-110, 1992.
7. H. Blom and E. Bloem. Combining IMM and JPDA for tracking multiple maneuvering targetsin clutter, in Proc. Int'l Conf. on Information Fusion, Cairns, Australia, 1:705-712, 2002.
8. J.K. Tugnait. Tracking of multiple maneuvering targets in clutter using multiple sensors, IMM, and JPDA coupled filtering. IEEE 'Trans. AES, 40(1):320-330, 2004.
9. S. Jeong and J. K. Tugnait. Tracking of multiple maneuvering targets in clutter with possibly unresolved measurements using IMM and JPDAM coupled filtering, in Proc. of the American Control Conference, pages 1257-1262, 2005.
10. S.S. Blackman R.J. Dempster and T.S. Nichols. Combining IMM filtering and MHT data association for multitarget tracking, in Proc. of the Twenty-Ninth Southeastern Symposium, 1997.
11. W. Koch. Fixed-interval retrodiction approach to Bayesian IMM-MHT for maneuvering multiple targets. IEEE Trans. AES, 36(1):2-14, 2000.
12. D. Reid. An algorithm for tracking multiple targets. IEEE Trans. AC, AC-24(6):843-854, 1979.
13. S. Blackman. Multiple Target Tracking with Radar Applications. Artech House, Norwood, 1986.
14. S. Blackman. Multiple hypothesis tracking for multiple target tracking. IEEE A & E Systems Magazine, 19(1, part 2):5-18, 2004.
15. R. Mahler. Multi-target Bayes filtering via firstorder multi-target moments. IEEE Trans. AES, 39(4):1152-1178, 2003.
16. B. Vo and W. K. Ma. Joint detection and tracking of multiple maneuvering targets using random finite sets, in Proc. ICARCV, Kunming, China, 2004.
17. K. Punithakumar, T. Kirubarajan, and A. Sinha. A multiple model Probability Hypothesis Density filter for tracking maneuvering targets, in O. E. Drummond (ed.) Signal and Data Processing of Small Targets, Proc. SPIE, 5428:113-121, 2004.
18. B. Vo, S. Singh, and A. Doucet. Sequential Monte Carlo implementation of the PHD filter for multitarget tracking, in Proc. Int'l Conf. on Information Fusion, Cairns, Australia, pages 792-799, 2003.
19. B. Vo, S. Singh, and A. Doucet. Sequential Monte Carlo methods for multi-target filtering with random finite sets. in IEEE Trans. AES, 41(4):1224-1245, 2005. also: http://www.ee.unimelb.edu.au/staff/bv/index.html.
20. B. Vo and W. K. Ma. A closed-form solution to the Probability Hypothesis Density filter. in Proc. Int'l Conf. on Information Fusion, Philadelphia, 2005.
21. B. Vo and W. K. Ma. The Gaussian mixture Probability Hypothesis Density filter. to appear in IEEE Trans. Signal Processing, 2006. also: http://www.ee.unimelb.edu.au/staff/bv/index.html.
22. D. Daley and D. Vere-Jones. An Introduction to the Theory of Point Processes. Springer-Verlag, 1988.
23. A. Johansen, S. Singh, A. Doucet, and B. Vo. Convergence of the sequential Monte Carlo implementation of the PHD filter. to appear in Methodology and Computing in Applied Probability, 2006.
24. D. Clark and J. Bell. Bayesian multiple target tracking in forward scan sonar images using the PHD filter. in IEE Radar, Sonar and Navigation, 152(5):327-334, October 2005.
25. Y. Bar-Shalom and X.-R. Li. MultitargetMultisensor Tracking: Principles and Techniques. Storrs, CT: YBS Publishing, 1995.