利用多个无源观测站对多个目标进行测向交叉定位时，会产生大量的虚假交叉点。针对这个问题，提出一种新的基于改进K-means的聚类融合定位算法。算法对每条测向线上的交叉定位点进行聚类，获得每条测向上的目标位置估计，从而可以剔除大部分虚假交叉点，然后对各个观测站的聚类结果进行分步融合，充分利用各个观测站的聚类结果，进一步消除残余虚假交叉点的影响，最终获得目标的估计位置。仿真结果证明了该算法在进行多站交叉定位时具有更好的定位效果和鲁棒性。

There are a large number of false intersection points when using multiple passive observation stations for making bearing-crossing localization to multiple targets.To solve the problem, a new algorithm is proposed, which is based on improved K-means cluster and data fusion.The intersection points on the same direction line are clustered, so that the targets in the direction line can be estimated.In this way, the most of false intersection points are eliminated.And then the results of clustering obtained from different stations are fused step by step.In the process of fusion, the algorithm makes full use of the clustering result of each observation station to reduce the influence of the residual false intersection points.Therefore,more accurate results of target location are obtained.The result of simulation show that the algorithm has better locating performance and robustness in multi-station cross positioning.