Multi-sensor scheduling for target tracking based on constrained ADP in energy harvesting WSN

With the development of energy harvesting technologies, the building of wireless sensor networks (WSN) based on energy harvesting has become possible, and helps to weaken the limitation of battery energy in WSN. The main objective of target tracking is to improve the tracking accuracy and to optimize the resource utilization, hence sensor scheduling is essential. Based on an artificial neural network energy acquisition model and extended Kalman filter (EKF) estimation for sensor data fusion, this paper will propose a novel constrained adaptive dynamic programming (ADP) algorithm for multi-sensor scheduling of energy harvesting WSN to optimize the tracking accuracy and resource utilization. Simulation results show that the proposed algorithm is feasible and efficient.

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