改进最优聚类中心雷达目标识别法

最优聚类中心法是一种有效的雷达目标一维距离像识别方法，但当训练数据较少时，该方法的识别性能急剧下降。其原因是该算法在利用少量数据进行训练时易产生“病态”矩阵，“病态”矩阵直接参与运算，导致错误识别结果。因此，该文提出了一种改进最优聚类中心法，主要思想是把“病态”矩阵进行“良态”化处理后再参与运算，以得到正确的识别结果，从而使该算法在训练数据较少时仍能保持较高识别率。仿真实验结果表明该方法的有效性。Approach based on optimal cluster centers is an effective approach to radar target recognition. But its performance degrades rapidly when only a few training data are available, because badly-conditioned matrixes are generated with a few training data during the training process, and mistake appears when badly-conditioned matrixes directly participate in calculation. An improved approach based on optimal cluster centers is proposed in this paper. It solves the above problem by transforming badly-conditioned matrixes to well-conditioned matrixes, so accurate recognition results are obtained. It ensures high recognition rate when just a few training data are available. The simulation results show the efficiency of the proposed approach.