Distributed Sequential State Estimation in Wireless Sensor Networks With Random Link Failures

Abstract

Wireless sensor networks (WSNs) are widely used in applications such as environmental monitoring, target tracking, and industrial automation. However, achieving accurate distributed state estimation in these networks is often challenged by the substantial communication and computational complexity inherent in conventional consensus-based methods. To address these limitations, this paper proposes a distributed state estimation algorithm that sequentially fuses local measurements with information exchanged among neighboring nodes. By eliminating batch fusion over fixed time intervals, the proposed approach significantly reduces communication and computational overhead while maintaining estimation accuracy in the presence of random link failures. A rigorous theoretical analysis derives sufficient conditions to ensure bounded estimation error covariance. Extensive numerical simulations further demonstrate the effectiveness of the proposed algorithm.