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A likelihood-based distributed particle filter for asynchronous sensor networks

This paper focuses on addressing the data fusion problems in asynchronous sensor networks using distribute particle filter (DPF). Generally, the type of the local information communicated between sensors and the time synchronization of the local information are two major issues for DPF algorithms, which have significant influence on fusion accuracy and communication requirements. To address these issues, in this paper, a likelihood-based asynchronous batch estimation (ABE) scheme is developed, wherein local likelihood function is regarded as the local information to ensure a high fusion accuracy, and the asynchronous likelihood functions of the multiple sensors during a predefined update period are fused to jointly estimate the target states. Then, to implement this framework distributively using particle filter, a likelihood-based ABE DPF (LB-ABE-DPF) algorithm is proposed. In addition, to achieve low communication requirements, the likelihood function is parametrically represented by polynomial approximation and least square (LS) approximation strategies. Numerical results show the efficiency of the proposed algorithm.