Distributed Estimation and Sensor Selection in Wireless Sensor Network in the Presence of State-Dependent Noise

In this paper, the problem of distributed state estimation of a nonlinear dynamical system in a decentralized Wireless Sensor Network (WSN) in the presence of state-dependent observation noise is considered. Some bearings or ranging devices, such as ultrasonic sensors, have distance-dependent measurement error and their measurement noise variance grows as their relative distance to the target increases. This state-dependent measurement error leads to poor performance of estimation algorithm. To solve this problem, a consensus-based distributed state estimation methodology is presented in this paper by reaching a consensus on likelihood functions in the presence of state-dependent observation noise of bearings sensors. To reduce energy consumption in WSN, a distributed sensor selection algorithm is proposed. Unlike centralized networks, no fusion center is deployed in decentralized networks to gather and process the collected data, globally. Moreover, there is no global knowledge of the network topology in decentralized networks. Therefore, the Posterior Cramér-Rao Lower Bound (PCRLB) is derived in a distributed fashion in the presence of state-dependent noise of bearings sensors, to perform an adaptive sensor selection algorithm. Simulation results demonstrate the effectiveness of the proposed state estimation and sensor selection algorithms for a target tracking problem.