Tracking and data association
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
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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.