Abstract:

Focusing on the dynamic tracking problem in resource constrained sensor networks, a new maneuvering target collaborative tracking algorithm with selecting tracking sensors adaptively is proposed. Firstly, the particle swarm optimization algorithm is us to trade off the sensor networks’ energy consumption and the effective coverage of the target area, and obtain the optimized sensors location. Then, the tracking sensors are selected according to the maximal Rényi information gain and the minimal energy consumption by a binary particle swarm optimization algorithm. Finally, the kinematic state of the maneuvering target is estimated by the interacting multiple model particle filtering algorithm, and the estimate states of the selected tracking sensors are fused. Simulation results show that the proposed algorithm can adaptively select tracking sensors, achieve the desired tracking accuracy and reduce network energy consumption compared with traditional methods in a nonlinear non-Gaussian system.