Consensus-Based Distributed Batch Estimation in Asynchronous Wireless Sensor Networks

Abstract

In this paper, to address the distributed estimation problem over an asynchronous wireless sensor network (aWSN), an average consensus-based distributed batch estimation (DBE) method is proposed. The DBE seeks to update the global posterior with a predefined global update period (GUP) and is implemented with a local filter (LF) and a fusion filter (FF). For LF, we develop two different asynchronous batch estimation approaches to align and compute the asynchronous local posteriors of multiple nodes in an aWSN. At FF, an average consensus filter is adopted to compute the global posterior via a proposed DBE fusion rule. Numerical results show that the proposed DBE method has high target-tracking accuracy and is robust to strong asynchronism. Besides, the optimality of DBE fusion can be approximately achieved with a sufficiently large number of particles and consensus iterations.