SVM-Based Target Recognition Algorithm for Radar High-Resolution Range Profiles

This article presents a radar target recognition algorithm utilizing Support Vector Machines (SVM) for High-Resolution Range Profiles (HRRP). The algorithm demonstrates superior resolution capabilities when processing HRRP data, enabling accurate target identification. Implementation typically involves preprocessing steps like range alignment and amplitude normalization, followed by feature extraction techniques such as Principal Component Analysis (PCA) to reduce dimensionality before SVM classification. Beyond explaining the algorithmic principles and workflow, we discuss its practical advantages including strong generalization performance and effectiveness in high-dimensional spaces, while addressing limitations like sensitivity to kernel function selection and computational complexity with large datasets. The discussion includes practical examples illustrating feature engineering and SVM parameter tuning (e.g., kernel functions like RBF, penalty parameter C optimization) to enhance reader comprehension. Through this exposition, readers will develop a comprehensive understanding of SVM-based HRRP target recognition algorithms, including key implementation considerations for real-world radar systems.