LSTM Based Data Association for Radar Target Tracking

In complex tracking scenarios, traditional target tracking methods cannot solve the problem of data association. This article proposes a data association method based on LSTM to address this issue. This method is an end-to-end approach that can automatically learn association criteria through annotated samples, resulting in higher accuracy in data association in complex tracking scenarios. Two simulation experiments are carried out and they can show the better performance of our method.