基于改进K-means++聚类的多扩展目标跟踪算法

Multi-extended target tracking algorithm based on improved K-means++ clustering

针对多扩展目标跟踪过程中量测集划分准确度低和计算量大的问题,提出一种基于改进K-means++聚类划分的高斯混合假设密度强度多扩展目标跟踪算法。首先,根据下一时刻目标可能变化的情况缩小K值的遍历范围;其次,利用目标预测状态选择初始聚类中心点,为正确划分量测集提供依据,从而提高聚类算法的精度;最后,将所提改进K-means++聚类划分方法应用到高斯混合概率假设滤波器中,联合估计多目标的个数和状态。仿真实验结果表明:与基于距离划分和基于K-means++的多扩展目标跟踪算法相比,该算法在平均跟踪时间上分别减小了59.16%和53.25%,同时其最优子模式指派度量(OSPA)远小于以上两种算法。综上,该算法能在大幅度降低计算复杂度的同时取得比现有量测集划分方法更为优异的跟踪性能。

In order to solve the problem of low partition accuracy of measurement set and high computational complexity, a Gaussian-mixture hypothesis density intensity multi-extended target tracking algorithm based on improved K-means++ clustering algorithm was proposed. Firstly, the traversal range of K value was narrowed according to the situations that the targets may change at the next moment. Secondly, the predicted states of targets were used to select the initial clustering centers, providing a basis for the correct partition of measurement set to improve the accuracy of clustering algorithm. Finally, the proposed improved K-means++ clustering algorithm was applied to the Gaussian-mixture probability hypothesis filter to jointly estimate the number and states of multiple targets. The simulation results show that the average tracking time of the proposed algorithm is reduced by 59.16% and 53.25% respectively, compared with that of multi-extended target tracking algorithms based on distance partition and K-means++. Meanwhile, the Optimal Sub-Pattern Assignment(OSPA) of the proposed algorithm is much lower than that of above two algorithms. In summary, the algorithm can greatly reduce the computational complexity and achieve better tracking performance than existing measurement set partition methods.