Nonlinear multiple model particle filters algorithm for tracking multiple targets

• Autorzy: Sebbagh A. , Tebbikh H.
• Języki publikacji: EN

Abstrakty

EN

The paper addresses multiple targets tracking problem encountered in number of situations in signal and image processing. In this paper, we present an efficient filtering algorithm to perform accurate estimation in jump Markov nonlinear systems, which we aim to contribute in solving the problem of multiple targets tracking using bearings-only measurements. The idea of this algorithm consists of the combination between the multiple model approach and particle filtering methods, which give a nonlinear multiple model particle filters algorithm. This algorithm is used to estimate the trajectories of multiple targets assumed to be nonlinear, from their noisy bearings.

Słowa kluczowe

EN

estimation; particle filter; multiple targets tracking; multiple model approach

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2011
• Tom: Vol. 21, no. 1
• Strony: 37--60
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Sebbagh A.

autor

Tebbikh H.

• Laboratoire d'Automatique and Informatique de Guelma (LAIG), Université 08 Mai 1945, Guelma, BP: 401, 24000 Guelma, Algeria

Bibliografia

• [1] A. SEBBAGH and H. TEBBIKH: Particle filtering for air craft tracking with bearings-only measurement. Int. Conf on Systems and Information Processing, ICSIP'09, (2009), Algeria.
• [2] C. HUE, J.P. LE CADRE and P. PEREZ: Tracking multiple objects with particle filtering. IEEE Trans. on Aerospace and Electronic Systems, 38(3), (2002), 791- 810.
• [3] C. HUE, J-P. LE CADRE and P. PEREz: Sequential Monte Carlo methods for multiple target tracking and data fusion. IEEE Trans. on Signal Processing, 50(2), (2002), 309-325.
• [4] D. ARNAUD: On sequential simulation-based methods for Bayesian filtering. Technical report, CUED/F-INFENG/TR 310, Signal Processing Group, Department of Engineering, University of Cambridge, 1998.
• [5] D. ARNAUD: Convergence of sequential Monte Carlo methods. Technical report, CUED/F-INFENG/TR 381, Signal processing group, Department of Engineering, University of Cambridge, 2000.
• [6] D. ARNAUD, N. DE FREITAS and N. GORDON: Sequential Monte Carlo Methods in Practice. New York: Springer, 2001, 525-532.
• [7] N. GORDON: A hybrid bootstrap filter for target tracking in clutter. IEEE Trans. on Aerospace and Electronic Systems, 33(1), (1997), 353-358.
• [8] N. GORDON, D. SALMOND and A. SMITH: Novel approach to nonlinear/nonGaussian Bayesian state estimation. IEE Proceedings, Pt. F, Radar and Signal Processing, 140(2), (1993), 107-113.
• [9] H. J. KUSHNER: Approximations to optimal nonlinear filtering. IEEE Trans. on Automatic Control, AC-12(5), (1967), 546-556.
• [10] H. J. KUSHNER: Dynamical equations for optimum nonlinear filtering. J. of Differential Equations, 3 (1967), 179-190.
• [11] H. WANG, T. KIRUBARAJAN and Y. BAR-SHALOM: Precision large scale air traffic surveillance using an IMM estimator with assignment. IEEE Trans. on Aerospace and Electronic Systems, AES-35(1), (1999), 255-266.
• [12] M. ISARD and A. BLAKE: CONDENSATION-Conditional density propagation for visual tracking. Int. J. of Computer Vision, 29(1), (1998), 5-28.
• [13] J. K. UFIHMANN: Algorithms for multiple target tracking. American Scientist, 80(2), (1992), 128-141.
• [14] A. KONG, J.S. Ltu and H.W. WONG: Sequential imputation method and Bayesian missing data problems. J. of American Statistical Association, 89 (1994), 278-288.
• [15] J. S. Liu: Metropolized independent sampling with comparison to rejection sampling and importance sampling. Statistics and Computing, 6(1996), 113-119.
• [l6] J. MACCORMICK: Probabilistic modelling and stochastic algorithms for visual localisation and tracking. Ph.D. dissertation, University of Oxford. Jan., 2000.
• [17] M. S. DJOUADI, A. SEBBAGH and D. BERKANI: IMM based UKF and IMM base EKF algorithms for tracking highly manoeuverable target. Archives of Control Sciences, 15(1), (2005), 19-51.
• [18] M. S. DJOUADI, A. SEBBAGH and D. BERKANI: A nonlinear algorithm for maneuvering target visual-based tracking. IEEE Proc. of the Second Int. Conf. on Intelligent Sensing and Information Proceeding, Chennai, India, (2005), 61-66.
• [19] R. VAN DER MERWE, A. DOUCET, N. DE FREITAS and E. WAN: The unscented particle filter. Technical report, CUED/F-INFENG/TR 380, Cambridge University Engineering Department, 2000.
• [20] D. J. SALMOND: Mixture reduction algorithms for target tracking in clutter. SPIE Signal and Data Processing of Small Targets, 1305 (1990), 434-445.
• [21] Y. BAR-SHALOM, X. R. LI and T. KIRUBARAJAN: Estimation with applications to tracking and navigation. USA, 2001.