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PCRLB-Based Optimal Sensor Selection for Maneuvering Target Tracking

To deal with the problem of tracking a maneuvering target in range-based sensor networks, we firstly derive the multiple model posterior Cram\'{e}r-Rao lower bound(PCRLB), and on the basis of this bound, choose the sensor subset that may attend the incoming tracking event. Secondly, we design the sensor selection strategy under communication constraint, and further pick up the optimal sensor. Thirdly, we can estimate the state of the maneuvering target by making use of the interacting multiple model(IMM)algorithm. Finally, the simulation results show the effectiveness of the proposed scheme.