Radar Signal Automatic Classification Based on PCA
Zhibin Yu, Chunxia Chen, Weidong Jin · 2009
This paper introduces an efficient approach to radar signal automatic classification by extracted fusion feature entropy. In this approach, wavelet packet reconstruct coefficient features are extracted from given radar signals in frequency domain based on wavelet packet decomposition. Then, these features are fused with the principal component analysis and a single characteristic feature vector which can effectively represent difference radar signals is obtained. Aiming at the single fusion feature, its energy entropy and symbolization probability entropy are extracted and extracted fusion feature entropy are used to classify emitter radar signal with fuzzy c-mean clustering algorithm. Simulation experiment show that the proposed approach is verified to be highly accurate and robust even in the low SNR, and the classification algorithm needs only very small memory space to store the reference information and can fast implement radar signal classification.