基于Laplacian特征映射的被动毫米波目标识别

Passive millimeter-wave target recognition based on Laplacian eigenmaps

针对传统被动毫米波金属目标识别方法中特征提取、选择的缺点,采用Laplacian特征映射流形学习算法发现了金属目标回波信号短时傅立叶谱中低维流形的存在,并研究了其特性。通过比较测试样本与正类样本低流形的匹配程度进行分类识别,与其他性降维及基于核的非线性降维算法相比,识别率更高,且对数据混叠分布鲁棒性好。

Aiming at the disadvantages of feature extraction and selection in the traditional method for passive millimeter-wave（MMW） metal target recognition,the existence and characteristics of low dimensional manifold of the short-time Fourier spectrum of metal target echo signal are explored using manifold learning algorithm,Laplacian eigenmaps.Target classification is performed through comparing the similarity of the test samples and the positive class in terms of the low dimensional manifold.The experiments show that the method gets higher recognition rate than other linear and kernel-based nonlinear dimensionality reduction algorithm,and is robust to data aliasing.