Plot-to-Track Association Using IGMM Course Modeling for Target Tracking With Compact HFSWR

Due to the high false alarm rate and low positioning accuracy of compact high-frequency surface wave radar (HFSWR), plot-to-track association methods using kinematic parameters alone may not achieve satisfactory performance. In this letter, the course consistency of vessels is analyzed, and a plot-to-track association method using statistical course modeling is proposed. First, vessel course data sequences are obtained by applying a course estimation method to each track, and each course data sequence is modeled using the incremental Gaussian mixture model (IGMM) by considering the measurement uncertainty. Next, the measurements within the association gate of each track are connected with the last plot, respectively, to determine instantaneous courses, and the membership probability set that the instantaneous courses belong to the established IGMM is obtained using Bayes’ rule. Subsequently, the obtained membership probability values and kinematic parameters of each measurement are incorporated to calculate the association cost, and a cost matrix is obtained for tracks with shared candidate plots in the overlapped region of their association gates. Finally, the Hungarian algorithm is applied to the cost matrix to obtain plot-to-track association results. Plot-to-track association experiments using both simulated and field data were conducted, and experiment results demonstrate that the optimal subpattern assignment (OSPA) distance in latitudes and longitudes obtained by the proposed method is 0.001° lower than that of the nearest neighbor data association (NNDA) method on average and achieves a competitive performance over the joint probability data association (JPDA) method but with running time being reduced by 0.48 s for each frame.