Information weighted consensus‐based distributed particle filter for large‐scale sparse wireless sensor networks

Abstract

To the problem of information fusion estimation for large-scale sparse wireless sensor networks (WSNs), a novel algorithm, named the information weighted consensus-based distributed particle filter, is presented. The proposed filter can avoid the divergence of the consensus error introduced by the naive nodes in the large-scale sparse WSNs. This is achieved by embedding an information weighted local particle filter (LPF) and a weighted-average consensus filter as the underlying filter and the top filter, respectively in each sensor node. The information weighted LPF will enable the weighted-average consensus filter to be used in the information space to communicate the information matrix and information state in a distributed fashion. And the weighted-average consensus filter will guarantee that a weighted average consensus for all initial states can be reached with some consensus error, which will not be divergent. Moreover, at the same time, the cross correlation between each pair of networked nodes can be approximately computed, which will further inhibit the divergence among the local estimated states of the filters embedded in each node. Finally, some examples are presented to illustrate the reasonability of the theoretical derivation.