合成孔径雷达(简称SAR)自动目标识别(简称ATR)算法是一个标准的目标检测算法，该算法分为3级：Prescreener、Discriminator和Classifier，处于Prescreener级和Discriminator级之间的聚类算法对于检测算法的整体性能有重要的影响。为此首先介绍了常规聚类算法的聚类步骤，然后针对实际应用情况分析了常规算法在聚类过程中存在的问题，并从图像数据读取顺序和准聚类中心计算方法两个方面对聚类算法进行了改进，基于两幅实际SAR图像得到的聚类结果验证了聚类算法改进的有效性。

The synthetic aperture radar (SAR) auto target recognition (ATR) algorithm developed at Lincoln Laboratory is a standard algorithm for target detection/recognition. It has three main stages: a prescreener, a discriminator and a classifier. The clustering algorithm between the prescreener stage and the discriminator stage is significant for the performance of the detection algorithm. This letter introduces the steps of the common clustering algorithm and analyzes its disadvantages. We improve the common clustering algorithm from two aspects of the read sequence of image data and the calculation means of clustering quasicenter coordinates. The clustering results based on two actual images testify the efficiency of improved clustering algorithm.