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。

Recognition of Radar Target Based on Improved Optimal Cluster Centers

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 orocess, 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.