Accelerated information weighted consensus-based DPF algorithm for target tracking in sparse wireless sensor networks

To improve convergence rate of the information weighted consensus-based distributed particle filter (IDPF) which applies to sparse wireless sensor networks (WSNs), an accelerated IDPF (AIDPF) algorithm is proposed. In the AIDPF algorithm, the top filter of IDPF, i.e., the weighted-average consensus filter (WACF) is replaced by the accelerated WACF (AWACF), which has improved the implementation algorithm of the WACF by reconfiguring the edge weights of the undirected gragh of the sparse WSNs. Initially, the edge weights are set by solving the fastest distributed linear averaging (FDLA) problem. For any node, then the localized node one-step predicted state acquired by a linear prediction model is introduced into the current state, thereby getting a new form of weights. And then the convergence rate is improved by determining the optimal mixing parameter of the new weights. Finally, the convergence analysis of the ADUIF algorithms and the simulation experiments are carried on, which have verified that the convergence rate of the AIDPF algorithms is faster than the IDPF algorithm when applying to the sparse WSNs.