Time-frequency image and high-order spectrum characteristics based radar signal recognition.

Aiming at improving the accuracy of radar signal recognition under a low signal-to-noise ratio, a radar signal recognition algorithm based both on time-frequency image and high-order spectrum feature was proposed. Firstly, the time-frequency image was obtained by Choi-Williams distribution (CWD) transform, based on which the time-frequency image was preprocessed and the texture features were extracted by gray level co-occurrence matrix (GLCM) in sequence. Meanwhile, the symmetrical holder coefficient was used to extract the high-order spectral features of the signal. Then, the texture features and high-order spectrum features were form a new set of joint feature vectors. Finally, with the proposed feature vector the classification and recognition of radar signals were implemented by a support vector machine. The algorithm was verified on the data set with eight typical radar signals. Experimental results show that the recognition accuracy of different radar signals can achieve higher than 90% when the signal-to-noise ratio is -8 dB. [ABSTRACT FROM AUTHOR]