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Multiple Target Tracking With a 2-D Radar Using the JPDAF Algorithm and Combined Motion Model | ||
| AUT Journal of Modeling and Simulation | ||
| مقاله 13، دوره 41، شماره 2، بهمن 2009، صفحه 43-51 اصل مقاله (168.23 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/miscj.2009.239 | ||
| نویسندگان | ||
| Aliakbar Gorji Daronkolaeii؛ Mohammad Bagher Menhaj؛ Ali Doostmohammadi | ||
| چکیده | ||
| Multiple target tracking (MTT) is taken into account as one of the most important topics in tracking targets with radars. In this paper, the MTT problem is used for estimating the position of multiple targets when a 2-D radar is employed to gather measurements. To do so, the Joint Probabilistic Data Association Filter (JPDAF) approach is applied to tracking the position of multiple targets. To characterize the motion of each target, two models are used. First, a simple near constant velocity model is considered and then to enhance the tracking performance, specially, when targets make maneuvering movements a variable velocity model is proposed. In addition, a combined model is also proposed to mitigate the maneuvering movements better. This new model gives an advantage to explore the movement of the maneuvering objects which is common in many tracking problems. Simulation results show the superiority of the new motion model and its effect in the tracking performance of multiple targets. | ||
| کلیدواژه‌ها | ||
| Multiple target tracking؛ JPDAF algorithm؛ data association؛ maneuvering movement | ||
| عنوان مقاله [English] | ||
| Multiple Target Tracking With a 2-D Radar Using the JPDAF Algorithm and Combined Motion Model | ||
| چکیده [English] | ||
| Multiple target tracking (MTT) is taken into account as one of the most important topics in tracking targets with radars. In this paper, the MTT problem is used for estimating the position of multiple targets when a 2-D radar is employed to gather measurements. To do so, the Joint Probabilistic Data Association Filter (JPDAF) approach is applied to tracking the position of multiple targets. To characterize the motion of each target, two models are used. First, a simple near constant velocity model is considered and then to enhance the tracking performance, specially, when targets make maneuvering movements a variable velocity model is proposed. In addition, a combined model is also proposed to mitigate the maneuvering movements better. This new model gives an advantage to explore the movement of the maneuvering objects which is common in many tracking problems. Simulation results show the superiority of the new motion model and its effect in the tracking performance of multiple targets. | ||
| کلیدواژه‌ها [English] | ||
| Multiple target tracking, JPDAF algorithm, data association, maneuvering movement | ||
| مراجع | ||
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[1] R. E. Kalman, and R. S. Bucy, New Results in Linear Filtering and Prediction, Trans. American Society of Mechanical Engineers, Series D, Journal of Basic Engineering, Vol. 83D, pp. 95–108, 1961. [2] Branko Ristic, Sanjeev Arulampalam, and Neil Gordon, Beyond the Kalman Filter: Particle Filters for Tracking Applications, Artech House, Boston, London, 2004. [3] R. Siegwart and I. R. Nourbakhsh, Intoduction to Autonomous Mobile Robots, MIT Press, 2004. [4] A. Howard, Multi- robot Simultaneous Localization and Mapping using Particle Filters, Robotics: Science and Systems I, pp. 201–208, 2005. [5] N.J. Gordon, D.J. Salmond, and A.F.M. Smith, A Novel Approach to Nonlinear/non-Gaussian Bayesian State Estimation, IEE Proceedings F, 140(2):107–113, 1993. [6] M. Isard, and A. Blake, Contour Tracking by Stochastic Propagation of Conditional Density, In Proc. of the European Conference of Computer Vision, 1996. [7] D. Fox, S. Thrun, F. Dellaert, andW. Burgard, Particle Filters for Mobile Robot Localization, Sequential Monte Carlo Methods in Practice. Springer Verlag, New York, 2000. [8] C. Hue, J. P. L. Cadre, and P. Perez, Sequential Monte Carlo Methods for Multiple Target Tracking and Data Fusion, IEEE Transactions on Signal Processing, Vol. 50, NO. 2, February 2002. [9] B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter, Artech House, 2004. [10] K. Kanazawa, D. Koller, and S.J. Russell, Stochastic Simulation Algorithms for Dynamic Probabilistic Networks, In Proc. of the 11th Annual Conference on Uncertainty in AI (UAI), Montreal, Canada, 1995. [11] F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P. J. Nordlund, Particle Filters for Positioning, Navigation, and Tracking, IEEE Transactions on Signal Processing, Vol. 50, No. 2, February 2002. [12] D. Schulz, W. Burgard, D. Fox, and A. B. Cremers, People Tracking with a Mobile Robot Using Sample-based Joint Probabilistic Data Association Filters, International Journal of Robotics Research (IJRR), 22(2), 2003. [13] M. S. Arulampalam, 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, February 2002. [14] N. Ikoma, N. Ichimura, T. Higuchi, and H. Maeda, Particle Filter Based Method for Maneuvering Target Tracking, IEEE International Workshop on Intelligent Signal Processing, Budapest, Hungary, May 24-25, pp.3– 8, 2001. [15] R. R. Pitre, V. P. Jilkov, and X. R. Li, A comparative study of multiple model algorithms for maneuvering target tracking, Proc. 2005 SPIE Conf. Signal Processing, Sensor Fusion, and Target Recognition XIV, Orlando, FL, March 2005. [16] T. E. Fortmann, Y. Bar-Shalom, and M. Scheffe, Sonar Tracking of Multiple Targets Using Joint Probabilistic Data Association, IEEE Journal of Oceanic Engineering, Vol. 8, pp. 173–184, 1983. [17] J. Vermaak, S. J. Godsill, and P. Perez, Monte Carlo Filtering for Multi-Target Tracking and Data Association, IEEE Transactions on Aerospace and Electronic Systems, Vol. 41, No. 1, pp. 309–332, January 2005. [18] O. Frank, J. Nieto, J. Guivant, and S. Scheding, Multiple Target Tracking Using Sequential Monte Carlo Methods and Statistical Data Association, Proceedings of the 2003 IEEE/IRSJ, International Conference on Intelligent Robots and Systems, Las Vegas, Nevada, October 2003. [19] Jao F. G. de Freitas, Bayesian Methods for Neural Networks, PhD thesis, Trinity College, University of Cambridge, 1999. [20] P. D. Moral, A. Doucet, and A. Jasra, Sequential Monte Carlo Samplers, J. R. Statist. Soc. B, 68, Part 3, pp. 411–436, 2006. | ||
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