Maneuvering Multi-target Tracking Algorithm Based on Modified Generalized Probabilistic Data Association
Zhentao Hu, Chunling Fu, Xianxing Liu
.
DOI: 10.4236/eng.2011.312144   PDF    HTML     3,907 Downloads   6,773 Views   Citations

Abstract

Aiming at the problem of strong nonlinear and effective echo confirm of multi-target tracking system in clutters environment, a novel maneuvering multitarget tracking algorithm based on modified generalized probabilistic data association is proposed in this paper. In view of the advantage of particle filter which can deal with the nonlinear and non-Gaussian system, it is introduced into the framework of generalized probabilistic data association to calculate the residual and residual covariance matrices, and the interconnection probability is further optimized. On that basis, the dynamic combination of particle filter and generalized probabilistic data association method is realized in the new algorithm. The theoretical analysis and experimental results show the filtering precision is obviously improved with respect to the tradition method using suboptimal filter.

Share and Cite:

Z. Hu, C. Fu and X. Liu, "Maneuvering Multi-target Tracking Algorithm Based on Modified Generalized Probabilistic Data Association," Engineering, Vol. 3 No. 12, 2011, pp. 1155-1160. doi: 10.4236/eng.2011.312144.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] O. Cappe, S. J. Godsill and E. Moulines, “An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo,” Proceedings of the IEEE, Vol. 95, No. 5, 2007, pp. 899-924. doi:10.1109/JPROC.2007.893250
[2] M. S. Arulampalam, S. Maskell, N. Gordon, et al., “A Tutorial on Particle Filters for Online Nonlinear/Non- Gaussian Bayesian Tracking,” IEEE Transactions on Sig- nal Processing, Vol. 50, No. 2, 2002, pp. 174-188. doi:10.1109/78.978374
[3] H. A. P. Blom and E. A. Bloem, “Exact Bayesian and Particle Filtering of Stochastic Hybrid Systems,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 43, No. 1, 2007, pp. 55-70. doi:10.1109/TAES.2007.357154
[4] S. Puranik and J. K. Tugnait, “Tracking of Multiple Maneuvering Targets Using Multiscan JPDA and IMM Filtering,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 43, No. 1, 2007, pp. 23-35. doi:10.1109/TAES.2007.357152
[5] H. X. Liu, Y. Liang, Q. Pan, et al., “A Multi-Path Viterbi Data Association Algorithm,” Acta Electronica Sinica, Vol. 34, No. 3, 2006, pp. 1640-1644.
[6] R. L. Popp, K. R. Pattipati and Y. Bar-Shalom, “M-Best S-D Assignment Algorithm with Application to Multi-Target Tracking,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 37, No. 1, 2001, pp. 22-39. doi:10.1109/7.913665
[7] H. L. Kennedy, “Comparison of MHT and PDA Track Initiation Performance,” International Conference on Radar, Adelaide, 2-5 September 2008, pp. 508-512. doi:10.1109/RADAR.2008.4653977
[8] M. Ekman, “Particle Filters and Data Association for Multi-Target Tracking,” The 11th International Conference on Information Fusion, Cologne, 30 June-3 July 2008, pp. 1-8.
[9] Z. T. Hu, Q. Pan and F. Yang, “A Novel Maneuvering Multi-Target Tracking Algorithm Based on Multiple Model Particle Filter in Clutters,” High Technology Letters, Vol. 17, No. 1, 2011, pp. 19-24.
[10] X. N. Ye, Q. Pan and Y. M. Cheng, “A New and Better Algorithm for Multi-Target Tracking in Dense Clutter,” Journal of Northwestern Polytechnical University, Vol. 22, No. 3, 2004, pp. 388-391.
[11] Q. Pan, X. N. Ye and H. C. Zhang, “Generalized Probability Data Association Algorithm,” Acta Electronica Sinica, Vol. 33, No. 3, 2005, pp. 467-472.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.