Cross-entropy based data association for multi target tracking

Multiple-target tracking (MTT) in the presence of spurious measurements poses difficult computational challenges related to the measurement-to-track data association problem. Different approaches have been proposed to tackle this problem, including various approximations and heuristic optimization tools. The Cross Entropy (CE) and the related Parametric MinxEnt (PME) methods are recent optimization heuristics that have proved useful in many combinatorial optimization problems. They are akin to evolutionary algorithms in that a population of solutions is evolved, however the solution improvement mechanism is based on statistical methods of sampling and parameter estimation. In this work we apply the Cross-Entropy method and its recent MinxEnt variants to solve approximately the multiscan version of the data association problem in the presence of misdetections, false alarms, and unknown number of targets. We formulate the algorithms, and explore via simulation their efficiency and performance compared to other recently proposed algorithms.

[1]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[2]  Reuven Y. Rubinstein,et al.  Semi-Iterative Minimum Cross-Entropy Algorithms for Rare-Events, Counting, Combinatorial and Integer Programming , 2008 .

[3]  Dirk P. Kroese,et al.  The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning , 2004 .

[4]  J. Vermaak,et al.  A review of recent results in multiple target tracking , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..

[5]  J. Hammersley.  SIMULATION AND THE MONTE CARLO METHOD , 1982 .

[6]  Nahum Shimkin,et al.  Data Association in Multi Target Tracking Using Cross Entropy Based Algorithms , 2008 .

[7]  Dirk P. Kroese,et al.  The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics) , 2004 .

[8]  James C. Spall,et al.  Introduction to stochastic search and optimization - estimation, simulation, and control , 2003, Wiley-Interscience series in discrete mathematics and optimization.

[9]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[10]  Songhwai Oh,et al.  Markov chain Monte Carlo data association for general multiple-target tracking problems , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[11]  R. Rubinstein.  A Stochastic Minimum Cross-Entropy Method for Combinatorial Optimization and Rare-event Estimation* , 2005 .

[12]  J. B. Collins,et al.  Efficient gating in data association with multivariate Gaussian distributed states , 1992 .

[13]  C. Morefield.  Application of 0-1 integer programming to multitarget tracking problems , 1977 .

[14]  Y. Bar-Shalom,et al.  A generalized S-D assignment algorithm for multisensor-multitarget state estimation , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[15]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[16]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .