CAJ | 학술논문

　　粒子群优化粒子滤波算法能有效改善粒子退化问题，但其适应度函数受量测噪声方差影响较大，限制了滤波精度的提高。为此，提出了一种基于粒子群优化的粒子滤波改进算法。该算法给出一种新的适应度函数，用当前状态估计值与各粒子状态的差值大小作为评价标准，使得最终优化粒子受噪声方差影响减小，在量测模型精度高的场合中提高了滤波精度。理论分析及仿真结果表明，本文所提算法的滤波性能优于标准粒子滤波与粒子群优化粒子滤波算法。
　　입자군우화입자려파산법능유효개선입자퇴화문제，단기괄응도함수수량측조성방차영향교대，한제료려파정도적제고。위차，제출료일충기우입자군우화적입자려파개진산법。해산법급출일충신적괄응도함수，용당전상태고계치여각입자상태적차치대소작위평개표준，사득최종우화입자수조성방차영향감소，재량측모형정도고적장합중제고료려파정도。이론분석급방진결과표명，본문소제산법적려파성능우우표준입자려파여입자군우화입자려파산법。
Although the particle filtering algorithm of particle swarm optimization (PSO) can effectively lower the particle degeneracy, the fitness function is affected greatly by measured noise variance, which bounds the improvement of filtering precision. Therefore, an improved algorithm for particle filtering based on particle swarm optimization (PSO-PF) is proposed in this paper. As for this algorithm, a new fitness function is put forward, and the size of difference between the current state estimation value and the state of each particle is taken as the evaluation standard, thus, allowing finally the optimized particles is less affected by noise invariance and increasing the filtering precision on the occasion of measured model with higher precision. Theoretical analysis and simulation findings show that the filtering performance of this proposed algorithm is superior to those of standard particle filtering and particle swarm optimization algorthms.