Abstract
To enhance the modulation recognition performance of emitter signals under low signal-to-noise ratio (SNR), a recognition system based on secondary time-frequency distribution, discriminative projection, and collaborative representation is proposed. Firstly, a novel time-frequency processing method, including sparse-domain noise reduction and secondary feature extraction, is proposed to reduce noise interference and information redundancy in time-frequency images. In this way, secondary time-frequency distribution with high stability and detailed representation is obtained. Then, the classifier based on discriminative projection and collaborative representation was designed to enhance the ability of low-dimensional representation and between-class discrimination, which optimised using the mini-batch random gradient descent method. As shown in the simulation, the overall average recognition success rate of this system aiming at eight types of emitter signals reaches 95.6% at the SNR of -8 dB. Results of simulation and analysis indicate the superiority of the proposed classification system in terms of robustness, timeliness, and adaptability.
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