Recognition and Classification of Radar Targets Base on Multiple Features of High-Resolution Range Profiles

High-resolution range profiles contain important scattering and structural features of targets and are often used for radar target recognition and classification. Based on the characteristics of the high-resolution range profiles, neural networks and feature-based machine learning could be used as classifiers for radar target classification. The accuracy of feature-based machine learning depends on the quality of the extracted features, while the recognition accuracy based on neural networks depends on the structure of neural networks. This article proposes a set of features suitable for target identification, which has low dimensionality, real-time performance, and without translation sensitivity and amplitude sensitivity. Using features proposed, an intelligent target recognition classifier based on ground target simulation is analyzed.