In this paper, we propose a novel H-infinity filter based particle filter (H∞PF), which incorporates the H-infinity filter (H∞F) algorithm into the particle filter (PF). The basic idea of the H∞PF is that new particles are sampled by the H∞F algorithm. Since the H∞F algorithm can fully take into account the current measurements, when the new algorithm calculates the proposed probability density distribution, the sampling particles can take advantage of the system current measurements to predict the system state. The particles distribution we obtained approaches nearer to the state posterior probability distribution and the H∞PF alleviates the sample degeneracy problem which is common in the PF, especially when the maneuvers of the target tracking are large. Furthermore, the H∞F algorithm can adjust gain imbalance factor by adjusting disturbance decay factor, from that the new algorithm can get the compromise between the accuracy and robustness and we can obtain satisfied accuracy and robustness. Some simulations and experimental results show that the proposed particle filter performed better than the PF and the Kalman particle filter (KPF) in tracking maneuvering target.
-Infinity Filter Based Particle Filter for Maneuvering Target Tracking," Progress In Electromagnetics Research B,
Vol. 30, 103-116, 2011. doi:10.2528/PIERB11031504
1. Bi, S. Z. and X. Y. Ren, "Maneuvering target doppler-bearing tracking with signal time delay using interacting multiple model algorithms ," Progress In Electromagnetics Research, Vol. 87, 15-41, 2008. doi:10.2528/PIER08091501
2. Turkmen, I. and K. Guney, "Tabu search tracker with adaptive neuro-fuzzy inference system for multiple target tracking," Progress In Electromagnetics Research, Vol. 65, 169-185, 2006. doi:10.2528/PIER06090601
3. Chen, J. M., X. Cao, Y. Xiao, and Y. Sun, "Simulated annealing for optimisation with wireless sensor and actuator networks," Electronics Letters, Vol. 44, No. 20, 1208-1209, 2008. doi:10.1049/el:20081574
2004. Ristic, B., A. Arulampalam, and N. Gordon, Beyond the Kalman Filter-particle Filters for Tracking Applications, Artech House, Boston, 2004.
5. Vander Merwe, R., A. Doucet, N. D. Freitas, and E. Wan, The unscented particle filter, Vol. 13, Advances in Neural Information Processing Systems, 2000.
6. Li, A., L. J. Zhong, and S. Q. Hu, "Robust observation model for visual tracking in particle filter," International Journal of Electronics and Communications, Vol. 61, No. 3, 186-194, 2007. doi:10.1016/j.aeue.2006.03.009
7. Gordon, N. J., A. Doucet, and N. D. Freitas, "On sequential monte carlo sampling methods for bayesian filtering," Statistics and Computing, Vol. 10, 197-208, 2000. doi:10.1023/A:1008935410038
8. Li, Y., Y. J. Gu, Z. G. Shi, and K. S. Chen, "Robust adaptive beamforming based on particle filter with noise unknown," Progress In Electromagnetics Research, Vol. 90, 151-169, 2009. doi:10.2528/PIER09010302
9. Hong, S. H., Z. G. Shi, and K. S. Chen, "Novel roughening algorithm and hardware architecture for bearings-only tracking using particle filter," Journal of Electromagnetic Waves and Applications, Vol. 22, No. 2--3, 411-422, 2008. doi:10.1163/156939308784160857
10. Chen, J. F., Z. G. Shi, S. H. Hong, and K. S. Chen, "Grey prediction based particle filter for maneuvering target tracking," Progress In Electromagnetics Research , Vol. 93, 237-254, 2009. doi:10.2528/PIER09042204
11. Zang, W., Z. G. Shi, S. C. Du, and K. S. Chen, "Novel roughening method for reentry vehicle tracking using particle filter," Journal of Electromagnetic Waves and Applications, Vol. 21, No. 14, 1969-1981, 2007. doi:10.1163/156939307783152975
12. Shi, Z. G., S. H. Hong, and K. S. Chen, "Tracking airborne targets hidden in blind doppler using current statistical model particle filter ," Progress In Electromagnetics Research, Vol. 82, 227-240, 2008. doi:10.2528/PIER08012407
13. Singh, A. K., P. Kumar, T. Chakravarty, G. Singh, and S. Bhooshan, "A novel digital beamformer with low angle resolution for vehicle tracking radar," Progress In Electromagnetics Research, Vol. 66, 229-237, 2006. doi:10.2528/PIER06112102
14. Shi, Z. G., S.Qiao, K. S. Chen, W. Z. Cui, W. Ma, T. Jiang, and L. X. Ran, "Ambiguity functions of direct chaotic radar employing microwave chaotic Colpitts oscillator," Progress In Electromagnetics Research, Vol. 77, 1-14, 2007. doi:10.2528/PIER07072001
15. Bugallo, M. F., J. Miguez, and P. M. Djuric, "Positioning by cost reference particle filters: Study of various implementations," The International Conference on Computer as a Tool, EUROCON , 1610-1613, 2005. doi:10.1109/EURCON.2005.1630277
16. Miodrag, B., S. J. Hong, and M. D. Petar, "Finite precision effect on performance and complexity of particle filters for bearing only tracking," Proceedings of the Conference on Signals, Systems and Computers, Asilomar, 838-842, 2002.
17. Blom, H. A. P. and E. A. Bloem, "Particle filtering for stochastic hybrid systems," Nation Aerospace Laboratory, 43rd IEEE Conference on Decision and Control, 3221-3226, 2004.
18. Arulampalam, M. S., S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Transactions on Signal Processing, Vol. 50, No. 2, 174-188, 2002. doi:10.1109/78.978374
19. Liuand, J. S. and R. Chen, "Sequential monte carlo methods for dynamic systems," Journal of the American Statistical Association, Vol. 443, No. 93, 1032-1044, 1998.
20. Zaugg, D. A., A. A. Samuel, D. E. Waagen, and H. A. Schmitt, "A combined particle/Kalman filter for improved tracking of beam aspect targets," IEEE Workshop on Statistical Signal Processing, 535-538, 2003. doi:10.1109/SSP.2003.1289512
21. Turkmen, I. and K. Guney, "Tabu search tracker with adaptive neuro-fuzzy inference system for multiple target tracking," Progress In Electromagnetics Research, Vol. 65, 169-185, 2006. doi:10.2528/PIER06090601
22. Xu, S. S., F. M. Bugallo, and M. D. Petar, "Performance comparison of EKF and particle filtering methods for maneuvering targets," Digital Signal Process, Vol. 10, No. 001, 1-13, 2006.
23. Wang, J. J. and Q. Chatym, "Object tracking by multi-degrees of freedom mean shift procedure combined with the Kalman particle filter algorithm," Proceedings of the 2006 International Conference on Machine Learning and Cybernatics, 3793-3797, 2006.
24. Xie, L., L. Lu, D. Zhang, and H. Zhang, "Improved robust H2 and H filtering for uncertain discrete-time systems," Automatica, Vol. 40, No. 5, 873-880, 2004. doi:10.1016/j.automatica.2004.01.003
25. Babak, H., "H optimality of the LMS algorithm," IEEE Transactions on Signal Processing, Vol. 44, No. 2, 267-280, 1996. doi:10.1109/78.485923
26. Jin, S. H., J. B. Park, and K. K. Kim, "Krein space approach to decentralised H state estimation," IEE Proceedings --- Control Theory and Applications, 502-508, 2001. doi:10.1049/ip-cta:20010693
27. Rami, S. M., D. A. Brent, and C. George, "Stochastic interpretation of H and robust estimation," Proceedings of the 33rd Conference on Decision and Control, 3943-3948, 1994.
28. Xu, B., J. Wei, and P. Q. Yan, "Application of robust H ¯ltering in TV tracking system for maneuvering targets," IEEE International Conference on Control and Automation Guangzhou, 756-760, 2007.
29. Djuric, P., J. Koteeha, J. Zhang, Y. Huang, T. Ghinnai, M. Bugallo, and J. Miguez, "Particle filtering," IEEE Signal Processing Magazine, Vol. 20, No. 5, 19-38, 2003. doi:10.1109/MSP.2003.1236770
. Gustafsson, G., F. Gunnarsson, N. Bergman, U. Forssell, J. jansson, R. Karlsson, and P. J. Nordlund, "Particle filters for positioning, navigation, and tracking," IEEE Transations on Signal Processing, Vol. 50, No. 2, 425-436, 2002. doi:10.1109/78.978396