Track-to-Track Association Based on Maximum Likelihood Estimation for T/R-R Composite Compact HFSWR

Due to its low transmit power and reduced aperture size of a receiving antenna array, compact high-frequency surface wave radar (HFSWR) suffers from low detection probability, low positioning accuracy, and high false alarm rate. In a multitarget tracking scenario, similar kinematic parameters of adjacent targets raise challenges to the track-to-track association procedure. Taking the measurement uncertainty of compact HFSWR into consideration, a track-to-track association method based on maximum likelihood estimation (MLE) for T/R-R composite compact HFSWR is proposed. First, a multitarget tracking algorithm is applied to plot data sequences acquired by both T/R monostatic and T-R bistatic radars to produce two track sets. Then, the measurement errors of range, azimuth, and Doppler velocity are calculated using the obtained radar track and corresponding automatic identification system (AIS) track data, and a Gaussian distribution model is derived through probability distribution fitting. Subsequently, likelihood functions are established using the obtained Gaussian distribution model to calculate the association cost of tracks for T/R monostatic and T-R bistatic radars, and a cost matrix is obtained. Finally, the Jonker–Volgenant–Castanon (JVC) assignment algorithm is applied to the cost matrix to determine associated track-to-track pairs. Track-to-track association experiments using both simulated and field data were conducted, and the association performance of the proposed method is compared with that of Mahalanobis distance-based nearest neighbor (NN) method. Experimental results demonstrate that the proposed method can effectively resolve association ambiguity and achieve correct track-to-track association in track crossing and adjacent multitarget scenarios.