基于果蝇优化算法改进的粒子滤波及其在目标跟踪中的应用

针对粒子滤波算法重采样导致的样本贫化问题,提出一种基于果蝇优化思想的粒子滤波算法.该方法视粒子权值为个体适应度值,并将果蝇不断从低浓度的地方飞向高浓度的地方的觅食寻优过程引入到粒子滤波当中,驱使粒子不断向高似然区域移动,提高了粒子群的整体质量.为了解决标准果蝇优化算法易陷入早熟的问题,将遗传算法中的交叉、变异操作自适应地应用到果蝇优化算法寻优过程当中.首先通过交叉操作改善粒子分布,当果蝇优化算法陷入局部最优时,再采用柯西变异扰动,促使算法快速跳出局部极值并继续搜索全局极值.通过非线性模型仿真以及目标跟踪实验表明该算法有效提高了非线性系统状态估计精度,具有较好的稳定性,同时降低了状态估计所需的粒子数量.
A particle filter method based on fruit fly optimization algorithm is proposed to alleviate the sample impoverishment caused by resampling. When fruit flies forage, they usually fly from low concentration areas to high concentration areas efficiently and constantly. This optimum process is introduced into the particle filter to drive particles towards the high likelihood areas ceaselessly, and thus improves the overall quality of the particle swarm. Considering that the premature convergence is always associated with the fruit fly optimization algorithm, crossover and mutation operations of genetic algorithms are applied herein adaptively to keep the diversity of samples. Firstly, the particle distribution is improved by cross operation. When the algorithm falls into the local optimum, the Cauchy mutation perturbation is then used to help the fruit fly optimization algorithm jump out of the local optimal point effectively and continue searching for global extremum. The nonlinear simulations and target tracking experiments show that the proposed algorithm improves the estimation accuracy of the nolinear systems state, and it has better stability and reduces the number of particles required for state estimation at the same time.