A centralized multi-sensor particle filter algorithm of formation targets

Aiming to solve the track refined tracking problem of the formation targets with the multi-sensor detections, intensive analysis on target echo characteristics of the formation targets, the centralized multi-sensor particle filter algorithm of formation targets are advanced. The algorithm set up a shape vector, which can represent the graph of each target in formation using shape and azimuth descriptor, then based on the measurement set to fall within the formation each target door of the waves, that associated to all graphics within each target formation might be associated measurement configuration for the object, through establish similarity model through space and shape similarity measurement step with the target state prediction value distance, at the same time, using the idea of selecting the master station to remove redundant images. Finally, achieve each formation target status updating, which based on particle filter using measurement set and the corresponding weight set. The analysis results of the simulation data show, this algorithm has obvious advantages in tracking accuracy, real-time performance and effective tracking rate three aspects, which can meet the engineering requirement of the track refined tracking of the formation targets with multi-sensor detections very well, is better than the algorithm based on data compression centralized multi-sensor multi-algorithm hypothesis which has superior performance in the traditional multi-sensor multi-target tracking algorithm.