Nearest Neighbor Data Association Algorithm Based on Robust Kalman Filtering
Rongqing Su, Jun Tang, Jiangnan Yuan, Yuewen Bi · 2021
The radar data association algorithm is one of the most difficult problems in the field of target tracking. Among them, it is easy to cause bug tracking when using the nearest neighbor data association (NNDA) algorithm. Combined with robust Kalman filtering and nearest neighbor ideas, this paper proposes the robust nearest neighbor data association (RNNDA) algorithm. This paper introduces the process of RNNDA. The simulation results show that the target tracking accuracy of RNNDA is high than NNDA. Moreover, the times of target bug tracking are reduced significantly.