| dc.description.abstract | With the progress of radar devices and signal receiver, radio frequency has been used widely for target recognition applications such as military and civilian ship identification for coastal warnings, sea rescue, navigation management and naval warfare. Generally, satellite imagery, infrared image, optical image, Synthetic aperture radar (SAR) and Inverse synthetic aperture radar (ISAR) are usually used to extract signal features. Alternatively, the high-resolution range profile emphasizes the geometric feature of the target, and it is considered a one-dimensional signature of an object, in this paper, the real high-resolution range profiles are used as input data. Furthermore, Convolution Neural Network (CNN) model is adopted as the primary method for ship recognition.
Besides the original input one-dimensional high-resolution range profiles, two-dimensional Grayscale images are also used in this paper. Additionally, the high-resolution range profile can be roughly recognized by eyes. Hence, the high-resolution range profiles are also converted into a histogram in order to enhance the contrast of different signal and fed to the established CNN for classification as well. Experimental results exhibit that the CNN model achieves high performance in radar recognition. Furthermore, the CNN model can effectively reduce noise and aspect sensitivity of high-resolution range profiles, and also improves the ability of radio frequency recognition to achieve the goal of classification successfully. In all the experiments, accuracy for test case can reach more than 97%.
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