【摘要】 针对粒子滤波中重采样所引起的粒子贫化问题,在将鸡群优化算法融入到粒子滤波采样阶段的基础上,提出了一种鸡群优化粒子滤波算法。该算法将粒子权值作为适应度以确定粒子的类型及相互关系,通过不同类型粒子的运动机制完成相应的位置更新,并利用动态变化的粒子群体结构来克服陷入局部最优的不足和加快寻优速度,使粒子向后验概率的高似然区域运动,既保证了样本多样性又提高了粒子质量。仿真实验结果表明该方法提高了滤波的估计精度并保持了滤波过程中粒子的多样性,同时减少了状态估计所需的粒子数量。更多还原

【Abstract】 To solve the particle impoverishment caused by resampling in particle fileter(PF), the Chicken Swarm Optimization(CSO) was integrated into the sampling phase of generic particle filter and an intelligent optimized particle filter of CSO was proposed. According to the weight of focused particles as the fitness, the type of each particle in the population and interrelation between each one was determined. Various designed mechanisms about individual movement were introduced to update the location. Moreover, the dynamical structure of particle population was utilized to overcome weakness of local optimum and improve the optimization. On the basis, particles moved towards to the high likelihood region of posterior probability density. As a result, the diversity of samples was kept and quality of particles was ameliorated. The result of simulation experiment shows that this algorithm has higher estimation accuracy and keeps the diversity of particles, and reduces the quantity of particles required by the state estimation.更多还原