The problem of data association for tracking multiple targets based on using the ship-borne radar is addressed in this study. A robust fuzzy density clustering algorithm is proposed, that contains three steps. At first, a customized form of adaptive density clustering is used to determine valid measurements for each target’s state. In the second step, the degree of fuzzy membership for each valid measurement is determined based on the maximum entropy approach. At the final step, the measurements with a maximum degree of membership are used for updating the position of the targets. The proposed approach does not require gating techniques and led to the reduction of steps in comparison with other data association methods. In addition, the effect of ship movement in the performance of the tracking filter, based on the adaptive extended Kalman filter (AEKF) was studied. The efficiency and effectiveness of the proposed algorithm are compared with the nearest neighbor (NN) with Mahalanobis distance and Fuzzy nearest neighbor (FNN) methods. The results demonstrate the main advantages of the proposed algorithm, including its simplicity and suitability for real-time target tracking in cluttered environments.

中文翻译：

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多目标数据关联的实时自适应模糊密度聚类

这项研究解决了基于使用舰载雷达跟踪多个目标的数据关联问题。提出了一种鲁棒的模糊密度聚类算法，该算法包括三个步骤。首先，使用定制形式的自适应密度聚类来确定每个目标状态的有效度量。在第二步中，基于最大熵方法确定每个有效度量的模糊隶属度。在最后一步，使用具有最大隶属度的测量来更新目标的位置。所提出的方法不需要选通技术，并且与其他数据关联方法相比，减少了步骤。此外，船舶运动对跟踪过滤器性能的影响，基于自适应扩展卡尔曼滤波器（AEKF）的研究。将该算法的效率和有效性与马氏距离和模糊最近邻居（FNN）方法进行了比较。结果证明了该算法的主要优势，包括其简单性和适用于在杂乱环境中进行实时目标跟踪的能力。