Large-range maritime vessel targets can be detected continuously by High-Frequency Surface Wave Radar (HFSWR), but the tracking trajectory of the target is easily broken in the presence of disturbing factors such as sea clutter. In current studies on HFSWR track association, the case of broken tracks is usually ignored and the track association is considered as a bipartite graph matching problem, which can lead to the possibility of judging broken tracks of a single target as multiple targets, and thus wrong target association results are obtained. For the above situation, fuzzy integrated evaluation and iterative search algorithms are considered in this paper. The Gale-Shapley (GS) algorithm is introduced into the field of track association for the first time, and it is improved to satisfy the many-to-many track association case when the track is broken , the Improved Gale-Shapley (IGS) algorithm is proposed. In this algorithm, the tendency sequences between the tracks can be obtained by calculating the fuzzy composite judgment values between the tracks. Then, the tracks are clustered by an iterative search method to obtain the track clusters. Finally, the track clusters and the propensity sequences are fed into the Gale-Shapley algorithm to perform several rounds of games to give the association results. The measured data and simulation data of dual-frequency HFSWR and Automatic Identification System (AIS) are used for experimental tests. Experimental tests are conducted using simulated and measured data from dual-frequency HFSWR and AIS. The experimental results show that the multi-sensor track association problem in the case of track break can be solved by the proposed algorithm, and the track association effect in dense areas is better than that of the conventional algorithm.
