Track segment association based on Gaussian regression analysis

In harsh environments, the detected target track is easily interrupted to form fragmented track segments, which creates a great challenge for track management and tracking. The traditional track segment association (TSA) method is based on a hypothetical target motion model and utilizes a large amount of a priori information to complete the association task. When the hypothetical a priori model does not match the actual motion pattern, the inference time is too long, and the performance decreases significantly. In this article, we propose a track segment association algorithm based on Gaussian regression analysis, in which the new and old segments of the track are back predicted to the associated moment for pairwise discrimination respectively. The Hungarian algorithm is introduced to solve the correlation problem of the track, and the track correlation problem is transformed into an allocation problem by establishing the correlation matrix, and the Hungarian algorithm is used to find the optimal solution. The results show that the proposed method can effectively improve the accuracy of the discrimination.