基于RMDLPP的雷达空中目标分类

Classification of radar air targets based on RMDLPP

针对鉴别局部保持投影(discriminant locality preserving projections, DLPP)在窄带雷达目标数据降维时出现的类内离散度矩阵奇异和对孤立点敏感进而导致类别之间可分性弱的问题,提出了基于鲁棒性边界DLPP(robust margin DLPP, RMDLPP)的窄带雷达空中目标分类方法。首先,在计算样本之间距离时将两样本点的欧氏距离与同类样本均值相关联。然后,挑选一定数量的边界样本点进行处理并对优化DLPP目标函数进行降维。最后,使用高性能分类器对降维后的数据进行区分,实现对空中目标的分类。通过对X波段对空警戒雷达实测数据的对比实验表明,所提方法具有更好的分类准确率和对孤立点的鲁棒性。

Aiming at the problem of exoticism of the intraclass dispersion matrix and sensitivity to isolated points in narrowband radar target data reduction of discriminant locality preserving projections(DLPP)in narrow-band radar target data,a narrow-band radar air targets classification method based on robust margin DLPP(RMDLPP)is proposed.Firstly,the Euclidean distance of the two sample points is correlated with the homogeneous sample mean value when calculating the distance between samples.Then,a certain number of boundary sample points are selected for processing and the DLPP objective function is optimized for dimensionality reduction.Finally,a high-performance classifier is used to distinguish the dimensionality reduction data and achieve the classification of aerial targets.Comparative experiments on X-band air-to-air alert radar measurements show that the proposed method has better classification accuracy and robustness to isolated points.