Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
445741 | Ad Hoc Networks | 2014 | 11 Pages |
Abstract
This paper addresses the selection of sensors for target localization and tracking under nonlinear and nonGaussian dynamic conditions. We have used the Posterior Cramér-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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Networks and Communications
Authors
Sara Pino-Povedano, Francisco-Javier González-Serrano,