空间域减法聚类粒子滤波算法

Spatial subtractive clustering-based particle filter

摘要:

针对粒子滤波计算复杂度高的问题,为降低滤波中所需的样本数目,提出了一种基于减法聚类的粒子滤波算法,算法将样本及对应权重进行映射构成聚类向量,在设定的聚类半径下,采用改进的减法聚类算法对向量进行分类,得到若干在空间中分离的子类中心,然后用子类中心代替整个向量集,并利用产生的新向量集重构样本集和权重.仿真实验表明该算法在保持了粒子滤波估计精度的同时,有效降低了样本数目,提高了计算效率.

Abstract:

Aiming at the high computational complexity of particle filters,in order to reduce the number of samples,this paper proposes an improved particle filter based on subtractive clustering.Cluster vectors,composed of particles and their corresponding weights,are classified at a given radius through the improved subcluster algorithm presented by this paper,and then all the cluster vectors are replaced by the central vectors obtained from the classifying processing.Finally the central vectors are decomposed and the new particles and their weights are restructured.The simulation results show that the proposed algorithm maintains the performance of the general particle filters,and meanwhile keeps less number of samples and higher computational efficiency.