Radar Sea Clutter Feature Classification Based on Machine Learning

Abstract

The Marine environment is complex and changeable. The classification of sea clutter and target detection are the most important parts in the signal processing of sea detection radar. A large number of researches have been carried out at home and abroad to identify radar clutter by extracting different features. Machine learning is a common method. In this paper, SVM algorithm and DNN algorithm were respectively used to classify the short-range and medium-range clutter. By taking the measured sea clutter data as the training input, three power spectral features were extracted, which achieved high detection accuracy. Considering the training sample number, operation efficiency and detection accuracy, the application scope of the two machine learning methods is compared, and it is pointed out that the machine learning is of great significance for advancing the intelligent classification and identification of sea clutter.