Joint Tracking and Recognition Method for Ballistic Targets and False Targets Based on Fine Difference Feature Estimation of Motion Pattern Set

Abstract

Aiming at the difficulty of tracking and recognizing ballistic targets and active multi-false targets in the presence of countermeasures, a joint tracking and recognition method for ballistic targets and false targets based on the robust interacting multiple model (RIMM) strategy is proposed. This method develops the interacting multiple model (IMM) strategy based on the deduced true target and false target motion pattern set and the fine difference features within the set, using the extended Kalman filter (EKF) as sub filters. Additionally, this method introduces probability adjustment factors and time-varying factors into the IMM strategy to update the probability transition matrix in real time and amplify the fine feature difference of the motion pattern set effectively, which not only achieves stable tracking of ballistic targets and false targets, improves the tracking accuracy, but also identifies them online in real time, achieving integrated tracking and identification. Simulation results show that the proposed method has better performance than traditional single model EKF algorithm and classical IMM+EKF algorithm, and it can track and recognize ballistic targets and false targets in real time, which is conducive to improving the efficiency of radar resource scheduling.

Keywords

• |ballistic target|active false target|target tracking|target recognition|interacting multiple model