Estimating unknown clutter intensity for PHD filter

IEEE Transactions on Aerospace and Electronic Systems

Volumn 46, Issue 4, 2010, Pages 2066-2078

  Lian, Feng ^a   Han, Chongzhao ^a   Liu, Weifeng ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

AVERAGE NUMBERS; CLUTTER DISTRIBUTIONS; CLUTTER MODELS; FALSE ALARMS; FINITE MIXTURE MODELS; GROUND TRUTH; MARKOV CHAIN MONTE CARLO ALGORITHMS; MONTE CARLO SIMULATIONS; MULTI-TARGET TRACKING; MULTITARGET; PHD FILTERS; PROBABILITY DENSITIES; PROBABILITY HYPOTHESIS DENSITY FILTER; RANDOM FINITE SETS; SPATIAL DISTRIBUTION;

COMPUTER SIMULATION; ESTIMATION; MARKOV PROCESSES; MONTE CARLO METHODS; PROBABILITY DENSITY FUNCTION; PROBABILITY DISTRIBUTIONS; TARGET TRACKING;

CLUTTER (INFORMATION THEORY);

EID: 78651063428     PISSN: 00189251     EISSN: None     Source Type: Journal    
DOI: 10.1109/TAES.2010.5595616     Document Type: Article

References (59)

1. Bar-Shalom, Y. and Fortman, T. E. Tracking and Data Association. Burlington, MA: Academic Press, 1988.
2. Blackman, S. Multiple Target Tracking with Radar Applications. Norwood, MA: Artech House, 1986.
3. Blackman, S. and Popoli, R. Design and Analysis of Modern Tracking Systems. Norwood, MA: Artech House, 1999.
4. Singer, R. A. and Stein, J. J. An optimal tracking filter for processing sensor data of imprecisely determined origin in surveillance system. In Proceedings of the Tenth IEEE Conference on Decision and Control, 1971, 171-175.
5. Fortmann, T. E., Bar-Shalom, Y., and Scheffe, M. Sonar tracking of multiple targets using joint probabilistic data association. IEEE Journal of Oceanic Engineering, OE-8 (1983), 173-184.
6. Blackman, S. Multiple hypothesis tracking for multitarget tracking. IEEE Transactions on Aerospace and Electronic Systems, 40, 1 (2004), 5-18.
7. Goodman, I., Mahler, R., and Nguyen, H. Mathematics of Data Fusion. Norwell, MA: Kluwer Academic Publishing Co., 1997.
8. Mahler, R. An Introduction to Multisource-Multitarget Statistics and Applications. Lockheed Martin Technical Monograph, 2000.
9. Mahler, R. Random set theory for tracking and identification. In M. E. Liggins, D. Hall, and J. Llinas (Eds.), The Handbook of Sensor Data Fusion: Theory and Practice (2nd ed.), Boca Raton, FL: CRC Press, 2008, ch. 16.
10. Mahler, R. Multi-target Bayes filtering via first-order multi-target moments. IEEE Transactions on Aerospace and Electronic Systems, 39, 4 (2003), 1152-1178.
11. Mahler, R. Statistical Multisource Multitarget Information Fusion. Norwood, MA: Artech House 2007.
12. Zajic, T. and Mahler, R. A particle-systems implementation of the PHD multitarget tracking filter. In I. Kadar (Ed.), Proceedings of SPIE, Signal Processing, Sensor Fusion, and Target Recognition XII, vol. 5096, 2003, 291-299.
13. Sidenbladh, H. Multi-target particle filtering for the probability hypothesis density. In Proceedings of the 6th International Conference on Information Fusion, Cairns, Australia, 2003, 800-806.
14. Vo, B.-N., Singh, S., and Doucet, A. Sequential Monte Carlo methods for multi-target filtering with random finite sets. IEEE Transactions on Aerospace and Electronic Systems, 41, 4 (2005), 1224-1245.
15. Clark, D. E. and Bell, J. Multi-target state estimation and track continuity for the particle PHD filter. IEEE Transactions on Aerospace and Electronic Systems, 43, 4 (2007), 1441-1453.
16. Liu, W. F., Han, C. Z., Lian, F., and Zhu, H. Y. Multi-target state extraction for the probability hypotheses density using Markov chain Monte Carlo. IEEE Transactions on Aerospace and Electronic Systems, 46 (Apr. 2010), 864-883.
17. Vo, B.-N. and Ma, W-K. The Gaussian mixture probability hypothesis density filter. IEEE Transactions on Signal Processing, 54, 11 (2006), 4091-4104.
18. Johansen, A., Singh, S., and Vo, B.-N. Convergence of the SMC-PHD filter. Methodology and Computing in Applied Probability, 8, 2 (2006), 265-291.
19. Clark, D. E. and Bell, J. Convergence results for the particles PHD filter. IEEE Transactions on Signal Processing, 54, 7 (2006), 2652-2661.
20. Clark, D. E. and Vo, B.-N. Convergence analysis of the Gaussian mixture PHD filter. IEEE Transactions on Signal Processing, 55, 4 (2007), 1204-1212.
21. Clark, D. E., Panta, K., and Vo, B.-N. The GM-PHD filter multiple target tracker. In Proceedings of the 9th International Conference on Information Fusion, Florence, Italy, 2006, 1-8.
22. Panta, K., Vo, B.-N., and Singh, S. Novel data association schemes for the probability hypothesis density filter. IEEE Transactions on Aerospace and Electronic Systems, 43, 2 (2007), 556-570.
23. Panta, K., Vo, B.-N., and Clark, D. E. An efficient track management scheme for the Gaussian-mixture probability hypothesis density tracker. In Proceeding of the Fourth International Conference on Intelligent Sensing and Information Processing, 2006, 230-235.
24. Panta, K., Clark, D. E., and Vo, B.-N. Data association and track management for the Gaussian mixture probability hypothesis density filter. IEEE Transactions on Aerospace and Electronic Systems, 45, 3 (July 2009), 1003-1016.
25. Lin, L. Bar-Shalom, Y., and Kirubarajan, T. Track labelling and PHD filter for multi-target tracking. IEEE Transactions on Aerospace and Electronic Systems, 42, 3 (2006), 778-793.
26. Punithakumar, K., Kirubarajan, T., and Sinha, A. Multiple-model probability hypothesis density filter for tracking maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, 44, 1 (2008), 87-98.
27. Vo, B.-N., Pasha, A., and Tuan, H. D. A Gaussian mixture PHD filter for nonlinear jump Markov models. In Proceeding of the 45th IEEE Conference on Decision and Control, San Diego, CA, 2006, 3162-3167.
28. Erdinc, O., Willett, P., and Bar-Shalom, Y. A physical-space approach for the probability hypothesis density and cardinalized probability hypothesis density filters. In O. E. Drummond (Ed.), Proceedings of Signal and Data Processing of Small Targets, vol. 6236, Apr. 2006.
29. Nandakumaran, N., Punithakumar, K., and Kirubarajan, T. Improved multi-target tracking using probability hypothesis density smoothing. In Proceedings of Signal and Data Processing of Small Targets, vol. 6699, 2007.
30. Nandakumaran, N., Tharmarasa, R., Lang, T., and Kirubarajan, T. Gaussian mixture probability hypothesis density smoothing with multistatic sonar. In Proceedings of Signal and Data Processing of Small Targets, vol. 6968, 2008.
31. Tobias, M. and Lanterman, A. D. Probability hypothesis density-based multi-target tracking with bistatic range and Doppler observations. IEE Proceedings-Radar, Sonar and Navigation, 152, 3 (2005), 195-205.
32. Sidenbladh, H. and Wirkander, S-L. Tracking random sets of vehicles in terrain. In Proceedings of the IEEE Workshop Multi-Object Tracking, Madison, WI, 2003.
33. Maehlisch, M., Schweiger, R., Ritter, W., and Dietmayer, K. Multisensor vehicle tracking with the probability hypothesis density filter. In Proceedings of the 9th International Conference on Information Fusion, Florence, Italy, 1-8, 2006.
34. Clark, D. E., Ruiz, I. T., Petillot, Y., and Bell, J. Particle PHD filter multiple target tracking in sonar image. IEEE Transactions on Aerospace and Electronic Systems, 43, 1 (Jan. 2007), 409-416.
35. Maggio, E., Taj, M., and Cavallaro, A. Efficient multitarget visual tracking using random finite sets. IEEE Transactions on Circuits and Systems for Video Technology, 18, 8 (2008), 1016-1027.
36. Wang, Y. D., Wu, J. K., Kassim, A., and Huang, W. Data-driven probability hypothesis density filter for visual tracking. IEEE Transactions on Circuits and Systems for Video Technology, 18, 8 (2008), 1085-1095.
37. Mullane, J., Vo, B.-N., Adams, M. D., and Wijesoma, W. S. Random set formulation for Bayesian SLAM. In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems Acropolis Convention Center, Nice, France, Sept. 22-26, 2008, 1043-1049.
38. Musicki, D. and Evans, R. Clutter map information for data association and track initialization. IEEE Transactions on Aerospace and Electronic Systems, 40, 2 (2004), 387-398.
39. Musicki, D., Morelande, M., and Scala, B. L. Non parametric target tracking in non uniform clutter. In Proceeding of the 8th International Conference on Information Fusion, Philadelphia, PA, 2005, 48-53. (Pubitemid 46276189)
40. Rozovsky, B. L. Stochastic Evolution Systems. Linear Theory with Applications to Non-Linear Filtering (Mathematics and its Applications (Soviet Series)) 35. Norwell, MA: Kluwer Academic Publishing Co., 1990.
41. Kligys, S., Rozovsky, B. L., and Tartakovsky, A. G. Detection algorithms and track before detect architecture based on nonlinear filtering for infrared search and track systems. Center for Applied Mathematical Sciences, University of Southern California, Technical Report CAMS-98.9.1, 1998. Available at http://www.usc.edu/dept/LAS/CAMS/ usr/facmemb/tartakov/preprints.html.
42. McLachlan, G. J. and Peel, D. Finite Mixture Models. Hoboken, NJ: Wiley, 2000.
43. Dempster, A., Laird, N., and Rubin, D. Maximum likelihood estimation from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39 (1977), 1-38.
44. Robert, C. P. and Casella, G. Monte Carlo Statistical Methods. New York: Springer, 1999.
45. Vo, B-T., Vo, B.-N., and Cantoni, A. Analytic implementations of the cardinalized probability hypothesis density filter. IEEE Transactions on Signal Processing, Pt. 2, 55, 7 (2007), 3553-3567. (Pubitemid 47018838)
46. Mahler, R. PHD filters of higher order in target number. IEEE Transactions on Aerospace and Electronic Systems, 43, 3 (2007), 1523-1543.
47. Bartfai, P. and Tomko, J. Point Processes and Queuing Problems. Amsterdam, Holland: North-Holland Publishing Co., 1981.
48. Schroeter, P., Vesin, J. M., Langenberger, T., and Meuli, R. Robust parameter estimation of intensity distribution for brain magnetic resonance images. IEEE Transactions on Medical Imaging, 17, 2 (1998), 172-186.
49. Wallance, C. and Dowe, D. Minimum message length and Kolmogorov complexity. The Computer Journal, 42, 4 (1999), 270-283.
50. Dasgupta, A. and Raftery, A. E. Detecting features in spatial point processes with clutter via model-based clustering. Journal of the American Statistical Association, 93 (1999), 294-302.
51. Akaike, H. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 6 (1974), 716-723.
52. Seidel, W., Mosler, K., and Alker, M. Likelihood ratio tests based on subglobal optimization: A power comparison in exponential mixture models. Statistical Papers, 41, 85-98.
53. Figueiredo, M. A. F. and Jain, A. K. Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 3 (2002), 381-396.
54. Green, P. J. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 4, 82 (1997), 711-732.
55. Matthew, S. Bayesian analysis of mixture models with an unknown number of components-An alternative to reversible jump methods. The Annals of Statistics, 28, 1 (2000), 40-74.
56. Oh, S., Russell, S., and Sastry, S. Markov chain Monte Carlo data association for multi-target tracking. IEEE Transactions on Automatic Control, 54, 3 (2009), 481-497.
57. Li, X. R. and Jilkov, V. P. A survey of maneuvering target tracking: Dynamic models. In Proceedings of SPIE International Conference on Signal and Data Processing of Small Targets, 2000, 212-235.
58. Hoffman, J. and Mahler, R. Multitarget miss distance via optimal assignment. IEEE Transactions on Systems, Man, and Cybernetics, Pt. A, 34, 3 (2004), 327-336.
59. Ruan, Y. and Willett, P. The turbo PMHT. IEEE Transactions on Aerospace and Electronic Systems, 40, 4 (2004), 1388-1398.