Radar Signal Recognition Based on the Local Binary Pattern Feature of Time-Frequency Image.

To correctly classify advanced radar emitter signals in the condition of low signal-to-noise ratio, a novel approach is proposed by using image feature of time-frequency distribution for radar emitter signal recognition, which transforms the classification of emitter signals into image processing and image recognition. Time-frequency images of radar emitter signals are obtained by using modified B distribution, and then these images are transformed into grayscale images. In addition, the time-frequency images are enhanced and denoised by image processing methods. Finally, the texture features of time-frequency image are extracted for signal recognition based on the local binary patterns, and the support vector machine is applied to identify radar emitter signals automatically. Simulation results show that the proposed approach can achieve satisfactory accurate recognition in low signal-to-noise rate ( SNR). Even for SNR = OdB, the overall correct classification rate of twelve typical radar emitter signals is 95. 35% . The proposed approach can reduce the impact of noise effectively, and also distinguish the approximate time-frequency images well. The validity of the approach is demonstrated by experimental results. [ABSTRACT FROM AUTHOR]