An Information-Driven Sensor Selection Algorithm for Target Estimation in Sensor Networks
Lin Zhang · Beijing Youdian Xueyuan xuebao · 2006
An information-driven sensor selection algorithm is proposed to select sensors to participate in Kalman filtering for target state estimation in sensor networks.The mutual information between the measurements of sensors and the estimated distribution of the target state is considered as the information utility function to evaluate the information contributions of sensors.Only those sensors with larger mutual information are selected to participate in Kalman filtering iterations.Then the geographic rou-(ting) mechanism is utilized to visit these selected sensors sequentially and to set up a path to transport the state estimation information to the sink node.Simulation results show that the information-driven sensor selection algorithm has excellent estimation performance.