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研究了空间非合作目标相对导航算法,针对标准粒子滤波的重采样过程导致的粒子贫化现象及其造成的相对导航精度下降问题,分析了萤火虫优化算法的运行机制,提出一种基于萤火虫智能优化算法的改进粒子滤波算法。改进算法通过优化粒子滤波的重采样过程,使粒子群智能的向高似然区域移动,同时在低似然区域也合理保留了部分粒子,保证了粒子的多样性,提高了样本的整体质量。仿真结果表明,改进算法导航精度较标准算法提高了39.35%,达到稳定精度所需粒子数较少,有效抑制了粒子贫化问题。
In this paper, we study the relative navigation algorithm of space non-cooperative target. In view of the problem of particle depletion caused by the resampling process of standard particle filter and the relative navigation precision declining problem, the operating mechanism of firefly optimization algorithm is analyzed and a firefly intelligent optimization Improved Particle Filtering Algorithms for Algorithms. By optimizing the resampling process of particle filter, the improved algorithm can move the particle swarm to the high likelihood region intelligently and retain some particles reasonably in low likelihood region, which ensures the diversity of particles and improves the overall quality of the sample. The simulation results show that the accuracy of the improved algorithm is 39.35% higher than that of the standard algorithm, and the number of particles needed to achieve the stable accuracy is less, which effectively suppresses the particle depletion problem.