最優(yōu)聚類中心法是一種有效的雷達目標(biāo)一維距離像識別方法，但當(dāng)訓(xùn)練數(shù)據(jù)較少時，該方法的識別性能急劇下降。其原因是該算法在利用少量數(shù)據(jù)進行訓(xùn)練時易產(chǎn)生“病態(tài)”矩陣，“病態(tài)”矩陣直接參與運算，導(dǎo)致錯誤識別結(jié)果。因此，該文提出了一種改進最優(yōu)聚類中心法，主要思想是把“病態(tài)”矩陣進行“良態(tài)”化處理后再參與運算，以得到正確的識別結(jié)果，從而使該算法在訓(xùn)練數(shù)據(jù)較少時仍能保持較高識別率。仿真實驗結(jié)果表明該方法的有效性。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.