Spatial subtractive clustering-based particle filter

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 sub-cluster 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.