Since the joint probabilistic data association (JPDA) algorithm results in calculation explosion with the increasing number of targets, a multi-target tracking algorithm based on Gaussian mixture model (GMM) clustering is proposed. The algorithm is used to cluster the measurements, and the association matrix between measurements and tracks is constructed by the posterior probability. Compared with the traditional data association algorithm, this algorithm has better tracking performance and less computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm.

中文翻译：

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一种基于高斯混合模型的多目标跟踪算法

针对联合概率数据关联(JPDA)算法随着目标数量的增加导致计算量激增的问题，提出了一种基于高斯混合模型(GMM)聚类的多目标跟踪算法。该算法用于对测量进行聚类，并通过后验概率构建测量与轨迹之间的关联矩阵。与传统的数据关联算法相比，该算法具有更好的跟踪性能和更小的计算复杂度。仿真结果证明了该算法的有效性。