Comparison of optimization algorithms in the sensor selection for predictive target tracking

摘要

This paper addresses the selection of sensors for target localization and tracking under nonlinear and nonGaussian dynamic conditions. We have used the Posterior Cramer-Rao lower Bound (PCRB) as the performance-based optimization criteria because of its built-in capability to produce online estimation performance predictions, a "must" for high maneuverable targets or when slow-response sensors are used. In this paper, we analyze, and compare, three optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and a new discrete-variant of the cuckoo search algorithm (CS). Finally, we propose local-search versions of the previous optimization algorithms that provide a significant reduction of the computation time.