Sensor Scheme for Target Tracking in Mobile Sensor Networks

Abstract

Wireless sensor networks is an important component of Internet of everything, and can be deployed in many applications, such as search and rescue, border patrols, environmental monitoring, and combat scenarios. In these applications, target tracking is a crucial difficulty. Compared with the traditional static wireless sensor networks (WSN), the mobile sensor networks (MSN) has the advantages of strong robustness, flexibility, energy saving, etc., and has been widely deployed. For target tracking applications in mobile wireless sensor networks, this paper investigates an extended Kalman filter (EKF) algorithm in a dynamic scenario, and proposes a low-power, high-accuracy sensor scheduling strategy based on the extend kalman filter algorithm. The properly sensors selection and path planning at each sample time of target tracking can make the EKF algorithm in dynamic scenarios complete target trajectory prediction more efficiently. Simulation results show that the proposed sensor scheduling strategies have better performances in power consumption and tracking accuracy, compared with the static network extend Kalman filter algorithm.