基于混沌免疫遗传优化的粒子滤波重采样

针对粒子滤波算法中粒子多样性退化问题，提出一种利用混沌免疫遗传算法进行重采样的粒子滤波改进方法。该算法利用混沌的局部寻优加快搜索速度；通过免疫原理的浓度计算及加入新的混沌序列来增加种群的多样性，提高全局搜索能力，避免早熟收敛。实验结果表明该方法与基于免疫遗传算法的重采样相比较，具有更好的全局寻优能力和更快的收敛速度。

Particle filter resampling based on chaos immunity genetic optimization

In view of the particle filtering algorithm in the particle multiplicity degeneration question, this paper proposes a corrective method using chaos immunity genetic algorithm to carry on particle resampling. This algorithm speeds up the search speed using the chaos partial optimization and joins the new chaos sequence through the density computation of immunity principle to supplement the population multiplicity. It sharpens the overall situation search ability, avoids restraining immaturely. The experimental result shows that it has the better overall situation optimization ability and the quicker convergence rate, compared with the particle resampling based on the immunity genetic algorithm.