Vol. 30
Latest Volume
All Volumes
PIERB 105 [2024] PIERB 104 [2024] PIERB 103 [2023] PIERB 102 [2023] PIERB 101 [2023] PIERB 100 [2023] PIERB 99 [2023] PIERB 98 [2023] PIERB 97 [2022] PIERB 96 [2022] PIERB 95 [2022] PIERB 94 [2021] PIERB 93 [2021] PIERB 92 [2021] PIERB 91 [2021] PIERB 90 [2021] PIERB 89 [2020] PIERB 88 [2020] PIERB 87 [2020] PIERB 86 [2020] PIERB 85 [2019] PIERB 84 [2019] PIERB 83 [2019] PIERB 82 [2018] PIERB 81 [2018] PIERB 80 [2018] PIERB 79 [2017] PIERB 78 [2017] PIERB 77 [2017] PIERB 76 [2017] PIERB 75 [2017] PIERB 74 [2017] PIERB 73 [2017] PIERB 72 [2017] PIERB 71 [2016] PIERB 70 [2016] PIERB 69 [2016] PIERB 68 [2016] PIERB 67 [2016] PIERB 66 [2016] PIERB 65 [2016] PIERB 64 [2015] PIERB 63 [2015] PIERB 62 [2015] PIERB 61 [2014] PIERB 60 [2014] PIERB 59 [2014] PIERB 58 [2014] PIERB 57 [2014] PIERB 56 [2013] PIERB 55 [2013] PIERB 54 [2013] PIERB 53 [2013] PIERB 52 [2013] PIERB 51 [2013] PIERB 50 [2013] PIERB 49 [2013] PIERB 48 [2013] PIERB 47 [2013] PIERB 46 [2013] PIERB 45 [2012] PIERB 44 [2012] PIERB 43 [2012] PIERB 42 [2012] PIERB 41 [2012] PIERB 40 [2012] PIERB 39 [2012] PIERB 38 [2012] PIERB 37 [2012] PIERB 36 [2012] PIERB 35 [2011] PIERB 34 [2011] PIERB 33 [2011] PIERB 32 [2011] PIERB 31 [2011] PIERB 30 [2011] PIERB 29 [2011] PIERB 28 [2011] PIERB 27 [2011] PIERB 26 [2010] PIERB 25 [2010] PIERB 24 [2010] PIERB 23 [2010] PIERB 22 [2010] PIERB 21 [2010] PIERB 20 [2010] PIERB 19 [2010] PIERB 18 [2009] PIERB 17 [2009] PIERB 16 [2009] PIERB 15 [2009] PIERB 14 [2009] PIERB 13 [2009] PIERB 12 [2009] PIERB 11 [2009] PIERB 10 [2008] PIERB 9 [2008] PIERB 8 [2008] PIERB 7 [2008] PIERB 6 [2008] PIERB 5 [2008] PIERB 4 [2008] PIERB 3 [2008] PIERB 2 [2008] PIERB 1 [2008]
2011-05-06
H -Infinity Filter Based Particle Filter for Maneuvering Target Tracking
By
Progress In Electromagnetics Research B, Vol. 30, 103-116, 2011
Abstract
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.
Citation
Qicong Wang, Jing Li, Meixiang Zhang, and Chenhui Yang, "H -Infinity Filter Based Particle Filter for Maneuvering Target Tracking," Progress In Electromagnetics Research B, Vol. 30, 103-116, 2011.
doi:10.2528/PIERB11031504
References

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