修正最近特征分类器及其在雷达目标识别中的应用

Modified nearest feature classifiers and their application in radar target recognition

针对最近特征线(NFL)与最近特征平面(NFP)分类器在大数据样本量与高维数时计算复杂度大的问题,依据局部最近邻准则,提出了一种新的搜索策略,使这两种分类器在保持较高识别率的同时,提高了算法的实时性能。对三类不同飞机实测数据的分类结果表明了所提方法的有效性。

The classifiers of Nearest Feature Line （NFL） and Nearest Feature Plane （NFP） share the same drawback in terms of the computation complexity under large data sample size and high dimensionality. Therefore, a new search strategy based on locally nearest neighborhood ride was proposed to modify the two classifiers. Compared to the traditional NFL and NFP, the modified ones can not only improve the real-time performance significantly, but also achieve competitive recognition rate. Experimental results on three measured airplanes data have confirmed the effectiveness of the proposed methods.