Abstract
An underwater sensor network (UWSN) has sparse and dynamic characteristics. In sparse and dynamic UWSNs, the traditional particle filter based on multi-rate consensus/fusion (CF/DPF) has the problems of a slow convergence rate and low filtering accuracy. To solve these problems, a tracking algorithm for sparse and dynamic UWSNs based on particle filter (TASD) is proposed. Firstly, the estimation results of a local particle filter are processed by a weighted average consensus filter (WACF). In this way, the reliability difference of state estimation between nodes in sparse and dynamic UWSN is reasonably eliminated. Secondly, a delayed update mechanism (DUM) is added to WACF, which effectively solves the problem of time synchronization between the two particle filters. Thirdly, under the condition of limited communication energy consumption, an alternating random scheme (ARS) is designed, which optimizes the mean square convergence rate of the fusion particle filter. Simulation results show that the proposed algorithm can be applied to maneuvering target tracking in sparse and dynamic UWSN effectively. Compared with the traditional method, it has higher tracking accuracy and faster convergence speed. The average estimation error of TASD is 91.3% lower than that of CF/DPF, and the weighted consensus tracking error of TASD is reduced by 85.6% compared with CF/DPF.
Highlights
underwater sensor network (UWSN) are currently used in a variety of fields
According to the different adaptabilities of UWSN communication topology, the distributed method can be divided into two forms: message passing form represented by channel filter, and message diffusion form represented by distributed filter based on consensus algorithm [10–12]
The distributed filter based on the consensus algorithm only needs single hop communication between neighbor nodes, so it can be used in various communication topologies
Summary
UWSNs are currently used in a variety of fields. For example, environment monitoring and modeling, target detection and tracking, collaborative multi-agent system navigation [1], and so on. The distributed filter based on the consensus algorithm only needs single hop communication between neighbor nodes, so it can be used in various communication topologies It becomes the main research direction of distributed methods. References [15–28] studied the distributed filter based on the consensus algorithm under sparse and dynamic communication conditions, respectively. References [23–25] designed the distributed H∞ filtering algorithm, which could be applied in random dynamic UWSN to fuse multi-source information. Multi-rate consensus/fusion filtering algorithm [28] (CF/DPF) is suitable for a nonlinear system model and non-Gaussian noise. It is mainly used in distributed multi-sensor navigation and tracking, and it primarily solves the problem of data fusion in UWSNs. In essence, the algorithm is an extended application of a particle filter. If the CF/DPF can be improved to make it suitable for sparse and dynamic networks, its application fields will be broadened
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