Browse > Article
http://dx.doi.org/10.5302/J.ICROS.2003.9.3.210

A MAP Estimate of Optimal Data Association in Multi-Target Tracking  

이양원 (호남대학교 정보통신공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.9, no.3, 2003 , pp. 210-217 More about this Journal
Abstract
We introduced a scheme for finding an optimal data association matrix that represents the relationships between the measurements and tracks in multi-target tracking (MIT). We considered the relationships between targets and measurements as Markov Random Field and assumed a priori of the associations as a Gibbs distribution. Based on these assumptions, it was possible to reduce the MAP estimate of the association matrix to the energy minimization problem. After then, we defined an energy function over the measurement space that may incorporate most of the important natural constraints. To find the minimizer of the energy function, we derived a new equation in closed form. By introducing Lagrange multiplier, we derived a compact equation for parameters updating. In this manner, a pair of equations that consist of tracking and parameters updating can track the targets adaptively in a very variable environments. For measurements and targets, this algorithm needs only multiplications for each radar scan. Through the experiments, we analyzed and compared this algorithm with other representative algorithm. The result shows that the proposed method is stable, robust, fast enough for real time computation, as well as more accurate than other method.
Keywords
multi-target tracking; estimation; data association;
Citations & Related Records
연도 인용수 순위
  • Reference
1 D. L. Alspach, 'A Gaussian sum approach to multi-target identification tracking problem,' Automatica, vol. 11, pp. 285-296, May 1975   DOI   ScienceOn
2 D. B. Reid, 'An algorithm for tracking multiple targets,' IEEE Trans. on Automat. Contr., vol. 24, pp. 843-854, Dec. 1979   DOI
3 Y. Bar-Shalom, 'Extension of probabilistic data associatiation filter in multitarget tracking,' in Proc. 5th Symp. Nonlinear Estimation Theory and its Application, pp. 16-21, Sept. 1974
4 D. Sengupta and R. A. Iltis, 'Neural solution to the multitarget tracking data association problem,' IEEE Trans. on AES-25, pp. 96-108, Jan. 1989   DOI   ScienceOn
5 R. Kuczewski, 'Neural network approaches to multitarget tracking,' in proceedings of the IEEE ICNN conference, 1987
6 T. E. Fortmann, Y. Bar-Shalom, Tracking and Data Association. Orland Acdemic Press, Inc. p. 224, 1988
7 J. B. Hiriart-Urruty and C. Lemarrecchal, Convex Analysis and Minimization Algorithms I, Springer-Verlag, 1993
8 D. G. Luenberger, Linear and Nonlinear Programming, Addition-wesley Publishing Co., 1984
9 Y. W. Lee and H. Jeong, 'A Neural Network Approach to the Optimal Data Association in Multi-Target Tracking,' Proc. of WCNN'95, INNS Press
10 R. A. Singer, 'Estimating optimal tracking filter performance for manned maneuvering targets,' IEEE Transactions on Aerospace and Electronic Systems, vol. 6, pp. 473-483, July 1970   DOI   ScienceOn
11 C. S. Won and H. Derin, 'Unsupervised segmentation of noisy and textured images using Markov random fields,' CVGIP: Graphical Models and Image Processing, vol. 54, No. 4, pp. 308-328, July, 1992   DOI
12 R. E. Kalman, 'A new approach to linear filtering and prediction problems,' Trans. ASME, (J. Basic Eng.), vol.82, pp. 34-45, Mar. 1960
13 J. Moussouris, 'Gibbs and Markov systems with con straints,' Journal of statistical physics, vol. 10, pp. 11-33, 1974   DOI
14 D. Griffeath, Introduction to random fields, In J. G. Kemeny, J. L. Snell and A. W. Knapp, editors, Denumerable Markov Chains, chapter 12, ppl. 425-458, Springer-Verlag, New York, 2nd edition, 1976
15 T. E. Fortmann, Y. Bar-Shalom and M. Scheffe, 'Sonar Tracking of Multiple Targets Using Joint Probabilistic Data Association,' IEEE J. Oceanic Engineering, vol. OE-8, pp.173-184, July 1983   DOI