Distributed Moving Horizon Estimation Over Energy Harvesting Wireless Sensor Networks: A Switching Topology Approach
Chaoyang Liang, Defeng He, Chenhui Xu, Yun Ruo Chen · IEEE Transactions on Circuits and Systems I Regular Papers · 2025
Energy harvesting wireless sensor networks (EHWSNs) face significant challenges, including unpredictable energy availability, communication disruptions, and nonlinear state estimation. This work addresses the distributed moving horizon estimation problem over EHWSNs. First, we establish models for the energy harvesting process, dynamic evolution of energy level, and information transmission, complemented by a priority-based energy allocation mechanism to manage inter-sensor communication. Unlike existing approaches that typically assume known statistical properties of the energy harvesting process, this work treats communication intermittency, resulting from the unpredictability of energy availability and energy allocation strategy, as a switching network topology, thereby eliminating the need to calculate the probability of successful information transmission. Subsequently, based on switched system theory that contains both stable and unstable subsystems, a novel distributed moving horizon estimator (DMHE) framework suitable for nonlinear systems under bounded disturbances is designed to achieve accurate state estimation. A case study on vehicle localization demonstrates that the proposed method maintains high estimation accuracy even in complex scenarios with disconnected network topologies; specifically, if each sensor’s energy harvesting rate is 0.8, the root mean square error (RMSE) is less than 0.06.