Robust track-to-track association in the presence of sensor biases and missed detections

• We model the structural difference between two sets by a non-rigid transformation.
• The robust track-to-track association is turned into the TPS-RPM framework.
• The SA and DA techniques are used to solve the MINLP model.
• The structural similarity of tracks is introduced to improve the accuracy.

The paper addresses the problem of robust track-to-track association in the presence of sensor biases and missed detections. Under the condition of large range biases with sensors, it is validated that the structural difference between two sets of local tracks from different sensors can be described by a non-rigid transformation. After that, we turn the robust track-to-track association problem into the non-rigid point matching problem in the framework of TPS-RPM (Thin Plate Spline-Robust Point Matching). Further, to improve the performance of the track-to-track association, the structural feature is introduced for each local track, and the structural similarity is incorporated by regularizing the energy function of the TPS-RPM algorithm. Simulation results demonstrate the effectiveness of the proposed approaches compared with competing algorithms.