For maneuvering target tracking, we propose a novel grey prediction based particle filter (GP-PF), which incorporates the grey prediction algorithm into the standard particle filter (SPF). The basic idea of the GP-PF is that new particles are sampled by both the state transition prior and the grey prediction algorithm. Since the grey prediction algorithm is a kind of model-free method and is able to predict the system state based on historical measurements other than establishing a priori dynamic model, the GP-PF can significantly alleviate the sample degeneracy problem which is common in SPF, especially when it is used for maneuvering target tracking. Simulations are conducted in the context of two typical maneuvering motion scenarios and the results indicate that the overall performance of the proposed GP-PF is better than the SPF and the multiple model particle filter (MMPF) when the tracking accuracy, computational complexity and tracking lost probability are considered. The performance improvements can be attributed to that the GP-PF has both model-based and model-free features.
2. Bar-Shalom, Y. and T. E. Fortmann, Tracking and Data Association, Academic Press, Orlando, 1988.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. Tanizaki, H., Nonlinear Filters: Estimation and Application, Springer, Berlin, 1996.
9. Bar-Shalom, Y. and X. R. Li, Multitarget Multisensor Tracking: Principles and Techniques, YBS Publishing, Storrs, CT, 1995.
10. 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.
11. Gordon, N. J., D. J. Salmond, and A. F. M. Smith, "Novel approach to nonlinear/non-Gaussian Bayesian state estimation," IEE Proceeding---F, Vol. 140, No. 2, 107-113, 1993.
12. Doucet, A., N. D. Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice, Springer, New York, 2001.
13. Du, S. C., Z. G. Shi, W. Zang, and K. S. Chen, "Using interacting multiple model particle filter to track airborne targets hidden in blind Doppler," Journal of Zhejiang University-Science A, Vol. 8, No. 8, 1277-1282, 2007.
14. Shi, Z. G., S. H. Hong, and K. S. Chen, "Experimental study on tracking the state of analog Chua's circuit with particle filter for chaos synchronization," Physics Letters A, Vol. 372, 5575-5580, 2008.
15. 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, 411-422, 2008.
16. 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.
17. 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.
18. Wang, X., X. Guan, X. Ma, D. Wang, and Y. Su, "Calculating the poles of complex radar targets," Journal of Electromagnetic Waves and Applications, Vol. 20, No. 14, 2065-2076, 2006.
19. Stratakos, Y., G. Geroulis, and N. Uzunoglu, "Analysis of glint phenomenon in a monopulse radar in the presence of skin echo and non-ideal interferometer echo signals," Journal of Electromagnetic Waves and Applications, Vol. 19, No. 5, 697-711, 2005.
20. Abdelaziz, A. A., "Improving the performance of an antenna array by using radar absorbing cover," Progress In Electromagnetics Research Letters, Vol. 1, 129-138, 2008.
21. Chan, Y. K. and S. Y. Lim, "Synthetic aperture radar (SAR) signal generation," Progress In Electromagnetics Research B, Vol. 1, 269-290, 2008.
22. Li, X. R. and V. P. Jilkov, "Survey of maneuvering target tracking --- Part I: Dynamic models," IEEE Aerospace and Electronic Systems Magazine, Vol. 39, No. 4, 1333-1364, 2003.
23. Blom, H. A. P. and Y. Bar-Shalom, "The interacting multiple model algorithm for systems with Markovian switching coefficients," IEEE Transactions on Automatic Control, Vol. 33, No. 8, 780-783, 1988.
24. McGinnity, S. and G. W. Irwin, "Multiple model bootstrap filter for maneuvering target tracking," IEEE Aerospace and Electronic Systems Magazine, Vol. 36, No. 3, 1006-1012, 2000.
25. Boers, Y. and J. N. Driessen, Interacting multiple model particle filter, IEE Proceedings on Radar, Sonar and Navigation, Vol. 150, No. 5, 344-349, October 2003.
26. Ristic, B., S. Arulampalam, and N. Gordon, Beyond the Kalman Filter: Particle Filter for Tracking Applications, Artech House, Boston, 2004.
27. Li, X. R. and V. P. Jilkov, "A survey of maneuvering target tracking---Part V: Multiple-model methods," IEEE Aerospace and Electronic Systems Magazine, Vol. 41, No. 4, 1255-1321, 2003.
28. Arulampalam, S., N. Gordon, M. Orton, and B. Ristic, A variable structure multiple model particle filter for GMTI tracking, Proceedings of 5th International Conference on Information Fusion, 927-934, Annapolis, USA, July 2002.
29. Jilkov, V. P., D. S. Angelova, and T. A. Semerdijev, "Design and comparison of mode-set adaptive IMM for maneuvering target tracking," IEEE Trans. on Aerosp. Electron. Syst., Vol. 35, No. 1, 343-350, 1999.
30. Luo, R. C., T. M. Chen, and K. L. Su, "Target tracking using hierarchical grey-fuzzy motion decision-making method," IEEE Transactions on Systems, Man, and Cybernetics---Part A, Vol. 31, No. 3, 179-186, 2001.
31. Luo, R. C. and T. M. Chen, Target tracking by grey prediction theory and look-ahead fuzzy logic control, IEEE International Conference on Robotics and Automation, 1176-1181, Detroit, America, May 1999.
32. Deng, J. L., "Control problems of grey system," Systems and Control Letters, Vol. 5, 288-294, 1982.
33. Wong, C. C., B. C. Lin, and C. T. Cheng, Fuzzy tracking method with a switching grey prediction for mobile robot, IEEE International Conference on Fuzzy Systems, 103-106, Melbourne, Australia, December 2001.
34. Arulampalam, M. S., S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Transaction on Signal Processing, Vol. 50, No. 2, 174-188, 2002.
35. Li, X. R. and Y. Bar-Shalom, "Multiple-model estimation with variable structure," IEEE Transactions on Automatic Control, Vol. 41, No. 4, 478-493, 1996.