GA-JPDA and fuzzy data association algorithms coupled with IMM-PF estimator for highly maneuvering multiple-target tracking

• Autorzy: Djouadi M. S. , Morsly Y.
• Języki publikacji: EN

Abstrakty

EN

In this paper, we present filtering algorithm to perform accurate estimation in jump Markov nonlinear systems, in case of multi-target tracking. With this paper, we aim to contribute in solving the problem of model-based body motion estimation by using data coming from visual sensors. The Interacting Multiple Model (IMM) algorithm is specially designed to track accurately targets whose state and/or measurement (assumed to be linear) models change during motion transition. However, when these models are nonlinear, the IMM algorithm must be modified in order to guarantee an accurate tracking. In order to deal with this problem, the IMM algorithm was combined with the Unscented Kalman Filter (UKF). Even if the later algorithm proved its efficacy in nonlinear model case, it presents a serious drawback in the case of non Gaussian noise. To deal with this problem we propose to substitute the UKF with the Particle Filter (PF). To overcome the problem of data association, we propose the use the JPDA approach. To reduce the computational burden of this technique, we choose firstly the most likely feasible events by applying a Genetic Algorithm; finally the derived algorithm from the combination of the IMM-PF algorithm and the GA-JPDA approach is noted GA-JPDA-IMM-PF. To insure a more reduction of the computation complexity of the latter data association approach, we propose a fuzzy data association approach which we combine with the IMM-PF estimator, the derived algorithm is noted fuzzy IMM-PF. Finally the two algorithms are compared according to the target loss rate inferred by each of them.

Słowa kluczowe

EN

estimation; particle filtering JPDA; genetic algorithm; multi-target tracking; fuzzy logic; visual servoing; data association

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2007
• Tom: Vol. 17, no. 3
• Strony: 311--327
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Djouadi M. S.

autor

Morsly Y.

• Robotic Laboratory, Ecole Militaire Polytechnique, Algiers, Algeria

Bibliografia

• [1] Y. BAR-SHALOM and T.E. FORTMAN: Tracking and data association. Mathematics in Science and Engineering, 179 Academic Press, 1988.
• [2] Y. BAR-SHALOM and X. R. LI: Estimation and tracking. Principles, techniques and software. Artech House, Boston, MA (USA), 1993.
• [3] B. ESPIAU, F. CHAUMETTE and P. RIVES: A new approach to visual servoing in robotics. IEEE Trans. on Robotics and Automation, 8(3) (1992), 313-326.
• [4] P. DANES, M. S. DJOUADI and D. BELLOT: A 2-D point-wise motion estimation scheme for visual-based robotic tasks. 7th Int. Symp. on Intelligent Robotic Systems, Coimbra, Portugal, (1999), 119-128.
• [5] Y. BAR-SHALOM and X. R. LI: Multi-target multi-sensor tracking. Principles and techniques. Storrs, CT, YBS Publishing, 1995.
• [6] M. S. DJOUADI, A. SEBBAGH and D. BERKANI: IMM-UKF and IMM-EKF algorithms for tracking highly maneuvering target. Archive of Control Sciences, 15(1), (2005).
• [7] M. HADZAGIC, H. MICHALSKA and A. JOUAN: IMM-JVC and IMM-JPDA for closely maneuvering targets. 35th Asilomar Conf. on Signals, Systems and Computers, 2001.
• [8] Y. BOERS and J. N. DRIESSEN: Interactin g. multiple model particle filter. IEE Proc. Radar Sonar Navig, 150(5), (2003).
• [9] S. MCGINNITY and G. W. IRWIN: Multiple model bootstrap filter for maneuvering target tracking. IEEE Trans. Aerosp. Electron. Syst. 36(3), (2000), 1006-1012.
• [10] S. MCGINNITY and G. W. Manoevering, target tracking using a multiplemodel bootstrap filter. In A. Doucet, N. de Freitas and N. Gordon (Eds.): Sequential Monte Carlo methods in practice, Springer, New York, 2001, 247-271.
• [11] N. J. GORDON: A hybrid bootstrap filter for target tracking in clutter. IEEE Trans. Aerosp. Electron Syst., 33(1), (1997), 353-358.
• [12] B. ZHOU and N. K. BOSE: Multitarget tracking in clutter. Fast algorithm for data association. IEEE Trans. Aero. Elect., 29(2), (1993).
• [13] E. M. REINGOLD, J. NEIVERGELT and N. DEO: Combinational algorithms. Theory and practice. Prentice-Hall, 1977.
• [14] T. E. FORTMANN, Y. BAR-SHALOM and M. SCHEFFE: Sonar tracking of multiple targets using joint probabilistic data association. IEEE J. of Oceanic Engineering, OE-8 (1983).
• [15] D. E. GOLDBERG: Genetic algorithms in search, optimization, and machine learning,. Addison-Wesley Publication, 1989.
• [16] K. S. TANG, K. F. MAN, S. KWONG and Q. HE: Genetic algorithms and their applications. IEEE Signal Processing Magazine, 13 (1996), 22-37.
• [17] Y. M. CHEN: A new data association algorithm for multi-target tracking in a cluttered environment. Proc. 6th Int. Conf. on Information Fusion, Queensland, Australia, (2003).
• [18] B. P. SINGH and W. H. BAILEY: Fuzzy logic applications to multisensormultitarget correlation. IEEE Trans. on Aerosp. Electron. Syst., 33(3), (1997), 752-768.