Abstract
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.
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