PCRLB-Based Cluster Selection for Target Tracking in Wireless Sensor Networks

Abstract

To deal with the problem of tracking a maneuvering target in range-based sensor networks, we firstly derive the expression that describes the relation of the PCRLB and the distance from the sensor to the target, and on the basis of this expression, choose the sensor subset that may attend the incoming tracking event. Secondly, we design the cluster head selection strategy under communication constraint, and further pick up the optimal sensor or the cluster head. Thirdly, we can estimate the state of the maneuvering target and the model index by making use of the interacting multiple model (IMM) algorithm. Finally, the simulation results show the effectiveness of the proposed scheme.