Target Tracking Algorithm Based on Feature Association

To address the issue of degraded target tracking performance with Range Gate Pull Off (RGPO) jamming, we propose a target tracking algorithm based on radar multi-dimensional feature track association to counter RGPO jamming. Relying on the stepped-frequency radar system, we develop a target tracking algorithm that replaces kinematic features with multi-dimensional feature association of the target. After tracking filtering, the radar can adaptively select target features with jamming conditions through two rounds of track screening and two rounds of feature screening. The algorithm calculates feature similarity and discrimination , finally uses the Hungarian algorithm to determine the minimum cost matching result as the final association result. Simulation and measured data processing results show that this algorithm can mitigate the impact of jamming and significantly improve the radar’s target tracking success rate.