中文摘要：

针对多假设跟踪（MHT）算法在跟踪多目标时出现的关联矩阵随目标及量测数急剧增长的情况,从降低聚矩阵行、列向量维数角度,提出了两种改进的m-最优MHT关联算法。仿真结果表明,所提出的改进方法不仅大大减少了由高维聚矩阵拆分所引起的庞大计算量,而且实现了对多个目标的有效量测-航迹关联,具有一定的实用性。

英文摘要：

In Multiple Hypothesis Tracking（MHT） algorithm,one challenge is the number of feasible hypothesis matrices increase rapidly with the number of targets and measurements.To handle with this problem,two improved m-best MHT algorithms are proposed by reducing the dimension of the row vectors and column vectors of the cluster matrix.The simulation is carried out based on a multiple targets tracking scenario,and the results show that the methods significantly reduce the amount of calculation by avoiding split of high-dimension cluster matrix. Moreover,the methods simultaneously implement the measurement-track association of multiple targets,extending application scope.