Multi-target tracking optimization sensor control strategy based on particle swarm optimization algorithm

Abstract

Abstract: This paper presents a sensor control strategy based on particle swarm optimization for multi-target tracking optimization. Among the multi-target tracking methods, the Poisson multi Bernoulli mixture (PMBM) filter is widely used for its effective representation of undetected (existing but not detected) target information and more efficient recursive structure. First, the multi-target prediction state is obtained through the prediction process of the PMBM filter. Then, taking this as a priori information, the particle swarm optimization algorithm is employed to solve the optimal observation position of the sensor based on the criterion of maximizing the proximity to each target. The sensor then captures the optimal quality measurement information. Finally, the optimized multi-objective posterior state is obtained by the update process of the PMBM filter. Simulation experiments were conducted to compare the effectiveness of multi-target tracking optimization, and the results show that the proposed sensor control strategy has better multi-target tracking accuracy.

Key words: sensor control, PSO, multi-target tracking, PMBM, optimal observation

CLC Number:

TP274

CHEN Hui, WEI Feng-qi, ZHAO Yong-hong, PENG Tian-shu. Multi-target tracking optimization sensor control strategy based on particle swarm optimization algorithm[J]. Journal of Lanzhou University of Technology, 2025, 51(2): 88-93.

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