A method for feature extraction based on SVD and machine learning
Wei Mao, Shuxian Huang, Xin Liu, Hongyan Liu, Jiaqi Liu, Yi Shu · IOP Conference Series Materials Science and Engineering · 2019
By studying the shortcomings of feature, which extracted from Radar-Cross Section(RCS),using mathematical and statistical method, using the idea of extracting abstract features in image recognition and speech recognition by artificial intelligence for reference [2][3] . This paper explores the possibility of extracting abstract features of target's RCS sequence, and proposes an abstract feature extraction method of RCS sequence based on singular value decomposition(SVD) feature decomposition. Because of the poor interpretability of abstract features, four different machine learning algorithms are used to classify the extracted Abstract features. The experimental results show that the machine learning algorithm can classify different types of spatial objects with high accuracy, which shows that the RCS features of different spatial objects can be characterized by abstract features.