Automatic Target Recognition Based on HRRP Using SKO-KPCA

摘要：

In this paper, an adaptive and data-dependent single kernel Optimization (SKO) algorithm is developed to improve the performance of radar target feature extraction and recognition by optimizing the kernel function of iterative Kernel principal component analysis (KPCA). Based on SKO-KPCA and support vector machine (SVM), a radar target high resolution range profile (HRRP) feature extraction and recognition approach is proposed, and ensures, while comparing with other approaches, the satisfactory performances which are illustrated through automatic target recognition (ATR) experiments of Su-27, F-16 and M2000.

作者: Zhengwei Zhu Jianjiang Zhou

作者单位: College of Information Science and Technology Nanjing University of Aeronautics and Astronautics, Na College of Information Science and Technology Nanjing University of Aeronautics and Astronautics, Na

会议类型: 国际会议

会议名称: The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

会议地点: 三亚

会议语种:英文

页码: 874-877

在线出版日期: 2009-04-24（万方平台首次上网日期，不代表论文的发表时间）