期刊
AUTOMATICA
卷 46, 期 11, 页码 1812-1818出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2010.06.045
关键词
Sensor management; Bayesian estimation; Random finite sets; Particle filter; Sequential Monte Carlo estimation; Information measure
资金
- Australian Research Council [FT0991854]
- Australian Research Council [FT0991854] Funding Source: Australian Research Council
The problem addressed in this paper is information theoretic sensor control for recursive Bayesian multi-object state-space estimation using random finite sets. The proposed algorithm is formulated in the framework of partially observed Markov decision processes where the reward function associated with different sensor actions is computed via the Renyi or alpha divergence between the multi-object prior and the multi-object posterior densities. The proposed algorithm in implemented via the sequential Monte Carlo method. The paper then presents a case study where the problem is to localise an unknown number of sources using a controllable moving sensor which provides range-only detections. Four sensor control reward functions are compared in the study and the proposed scheme is found to perform the best. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.
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