基于萤火虫群优化的空间非合作目标相对导航粒子滤波算法

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。

Particle filter based on firefly algorithm optimization for relative navigation of non-cooperative target

The particle-filter resampling technology in the relative navigation of the non-cooperative target may cause the particle impoverishment phenomenon and decrease the relative navigation precision.To solve this problem,the operation mechanism of the firefly optimization algorithm is analyzed,and then an improved particle filter algorithm based on the firefly intelligent optimization algorithm is proposed.With the purpose of ensuring the diversity of particles and improving the integral quality of particle swarm,the improved algorithm enables the particle swarm to move towards the high likelihood region by optimizing the re-sampling process of particle filter,and reasonably remains some particles in low likelihood region at the same time.The simulation results show that,compared with the standard algorithm,the improved algorithm improves the navigation accuracy by about 39.35％,requires less quantity of the particles to achieve stable precision,and effectively restrain the problem of particle impoverishment.