Probabilistic data association algorithm based on adaptive robust Kalman filtering

Abstract

The data association problem in cluttered environments is one of the difficult problems in the field of object tracking. When probabilistic data association algorithms are used in motion model deviation scenarios, incorrect tracking occurs. Combining the adaptive robust Kalman filter (ARKF) with the probabilistic data association (PDA), this paper presents the adaptive robust probabilistic data association (ARPDA) algorithm for estimating the target state in cluttered environments. The results of the experiment indicate that the proposed algorithm has higher tracking accuracy under the condition of motion model deviation compared with the traditional probabilistic data association algorithm.