A joint model and data driven track segment association algorithm for manoeuvring target tracking

Abstract

In the field of manoeuvring target tracking, track breakage is a common issue due to strong manoeuvrability, missed detection, large measurement error, and long sampling interval, etc. In order to improve the track continuity of manoeuvring targets, a joint model and data‐driven track segment association (JMDD‐TSA) algorithm is proposed. First, the interacting multiple model (IMM) smoother is invoked to pre‐process the track segments to obtain a smoothed state sequence. And the mode switching time (MST) is estimated by the IMM when a motion mode changing occurs in a track segment. Second, the estimated MST is used to select the partial smoothed states as the training data and the local Gaussian process (LGP) is proposed to predict and backtrack the track segments. Finally, a two‐step association strategy is designed to assign and mend the candidate track segment pairs. Compared with the model‐driven TSA and the data‐driven TSA, the proposed algorithm can effectively improve the average track life and tracking accuracy of strong manoeuvring targets.