Signal structure information-based data association for maneuvering targets with a convolutional Siamese network

Traditional data association methods usually suffer performance degradation caused by model mismatch when target maneuvers occur. In addition, the signal structure information (SSI) available from the employed signal processing techniques is rarely used in data association due to the lack of appropriate tools. To overcome the model mismatch problem brought by the target maneuvers, we resort to a data driven method to design the data association algorithm. To make full use of the SSI, a convolutional neural network is used to transform the SSI into a feature space, where the similarity of the SSI between two observations can be calculated using the Euclidean distance. The resulting structure is the convolutional Siamese network (CSN). By comparing the similarity with a predetermined margin parameter, the decision about association can be made. Simulation results are presented to show that the use of SSI in the designed CSN can improve the association probability of maneuvering targets.