Sensor Network Resource Management for Target Tracking under Decentralized Architecture

초록

In target tracking with sensor network under decentralized architecture, the sensor network resources including sensing and communication resources are always constrained, meanwhile, the quality of measurements acquired by different sensor nodes toward the same target usually differs from each other. Both the selected sensor nodes and communication topology among them significantly affect not only the tracking accuracy but also network resource consumption. Thus, to improve resource utilization efficiency, this paper proposes a sensor network resource management method for target tracking under decentralized architecture, focusing on the selection of sensor nodes acquiring high-quality measurements and the optimization of their communication topology. First, this paper derives an iteration-adaptive decentralized PCRLB(IA-DPCRLB) as a tracking accuracy metric, and a tracking accuracy constraint function is established based on the IA-DPCRLB. Additionally, an objective function is constructed by modeling both the sensor scheduling cost and the communication cost. Finally, to solve the non-convex optimization problem, an enumeration-based genetic algorithm is employed. Simulation results demonstrate that the proposed algorithm can adaptively select sensor nodes and dynamically optimize the communication topology according to the target's motion state, achieving the minimized system resource consumption while ensuring the predefined tracking accuracy, thereby significantly improving resource utilization efficiency.

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