| Citation: | ZHANG Y P，FU Q，SHAN G L，et al. Scheduling method for multi-sensor cooperative area search and target tracking[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（3）：850-860 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0277
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A multi-sensor scheduling method is proposed to address the competition of sensor resources during simultaneous execution of area search tasks and target tracking tasks. Firstly, for the dual-task cooperation, sensor scheduling is transformed into a multi-objective optimization problem, and the search performance and tracking performance are taken as the optimization objectives. Secondly, the area search model is established, in which the process of updated undetected targets is built as a non-homogeneous Poisson process, considering the rebirth, extinction, and transfer of targets. The missed alarm loss is proposed to quantify the search performance. Then, the target tracking model is established, and the posterior Carmér-Rao lower bound (PCRLB) is introduced to quantify the tracking performance in the future. Finally, to search the best scheduling scheme, a chaotic map multi-objective cooperative differential evolution algorithm (CM-MOCDEA) is proposed, utilizing the chaotic mapping theory and dual population cooperative method based on the traditional multi-objective differential evolution algorithm. Simulation results show that the proposed algorithm can consider both convergence and diversity, and has a strong global search ability. The proposed scheduling method can also effectively allocate sensor resources to complete area search and target tracking tasks, thus achieving higher operational gains.
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