Passive Millimeter-wave Metal Target Recognition Based on Manifold Learning

摘要：

The existence and characteristics of law dimensional embedded manifold of the short-time Fourier spectrum of metal target echo signal are explored using manifold learning algorithm, Laplacian eigenmaps, aiming at the disadvantages of feature extraction and selection of the traditional methods in passive millimeter-wave (MMW) metal target recognizing process. Target classification is performed through comparing the similarity of the test samples and the positive class in terms of the embedded 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.

关键词： manifold learning Laplacian eigenmaps nonlinear dimensionality reduction MMW target recognition

作者: Lei Luo Yuehua Li Yinghong Luan

作者单位: School of Electronic Engineering and Optoelectronic Technology,Nanjing University of Science and Technology Xiaolingwei 200,Nanjing,210094,China

会议类型: 国际会议

会议名称: 2009 9th International Conference on Electronic Measurement & Instruments(第九届电子测量与仪器国际会议 ICEMI2009)

会议地点: 北京

会议语种:英文

页码: 3499-3502

在线出版日期: 2009-08-16（万方平台首次上网日期，不代表论文的发表时间）