Research on Multi-Target Motion Estimation Method Based on Generalized Probability Hypothesis Density

Abstract

Aiming at the development needs of air-to-air operations, this paper proposes a multi-target motion estimation method based on generalized probability hypothesis density. Multi-scale analysis is introduced into based on the Faster-RCNN algorithm, and the improved K-means method is used to perform coarse clustering on the observed targets. With this as a pre-input, a probability hypothesis density filter based on generalized poisson distribution is proposed, and the clustering information is included in the weight update of the filter estimate to enhance the tracking timeliness of targets for the variable group. The simulation results show that the proposed method can still complete the recognition and classification of multiple targets without prior knowledge of the initial clustering information, and is superior to present swarm target motion estimation method in precision.

Keywords

• |multi-target motion estimation|k-means clustering|target recognition|probability hypothesis density|situational awareness