Sensor Scheduling for Target Tracking in a Wireless Sensor Network Using Modified Particle Swarm Optimization

Abstract

Sensor scheduling for target tracking in a sensor network is a research hotspot for its effectiveness in improving performance of the network. If numbers of targets and sensors are very large, the scale of the problem may be too large to solve using traditional methods. A method based on modified particle swarm optimization algorithm (MPSO) is proposed to solve the problem. Firstly, extended Kalman filter (EKF) is adopted for target tracking, and based on the tracking model, a mathematical model is founded to formulate the problem. Then MPSO is designed based on operator redefinition that modifies standard PSO to suit with this problem. Finally, feasibility and efficiency of the method presented are verified through numerical experiments by comparing it with a genetic algorithm (GA) based method and a rule-based method.