改进最近邻算法及其在雷达目标识别中的应用

最近邻（Nearest Neighbor,简记NN）算法是一种广泛采用的非参量模式识别方法,其渐近分类错误概率不超过Bayes错误概率的两倍。但NN算法需要存贮的训练样本较多。剪辑最近邻算法通过对训练样本进行预处理,改善了NN分类器的渐近性能,也略减少了设计NN分类器所需要存贮的训练样本,但需要存贮的样本仍然较多。

A IMPROVED NEAREST NEIGHBOR ALGORITHM AND ITS APPLICATION TO RADAR TARGET RECOGNITION

In this paper a improved nearest neighbor（INN）algorithm is proposed, it consists of three steps:1.editing the primary training sample set;2.finding the minimal consistent subset of the edited subset with the help of the dirichlet tesselation （DT） of the edited subset;3.designing the nearest neighbor（NN） classifier using the minimal consistent subset.The storage reguirement for INN algorithm is much less than the storage requirement for NN algorithm and its performance is batter than the NN algorithm. An algorithm of computing the DT of point set in euclidean space is presented and proved. The INN algorithm is extended to some of non-euclidean metric space. In table 1., the results of Bayes classifier are compared with those of application INN algorithm to maha-lanobis distance, City-Block distance, Chebyshev distance and Euclidean distance As an example of application, the INN algorithm is applied, to recognize two categories of radar targets-tank and jeep, by means of computer simulation, the results are shown in Table2.