Aiming at the problems of low feature stability in emitter signal recognition and poor adaptability to low signal-to-noise (SNR) environment

a recognition method based on secondary time-frequency distribution

kernel collaborative representation and discriminative projection (KCRDP) was proposed. First

the pre-processing algorithms of time-frequency transform

sparse domain noise reduction

and secondary feature extraction are used to reduce noise interference and feature redundancy

and secondary time-frequency distribution features with high stability were obtained. Then

the kernel collaborative representation and discriminative projection ideas are used to complete the dimensionality reduction learning and dictionary learning to improve the low-dimensional representation and inter-class discrimination capabilities of the data. Finally

the system is optimized through offline training and used for classification verification. Simulation results show that the secondary time-frequency distribution feature has high stability

and the recognition method has strong robustness

timeliness and adaptability. When the SNR is -10dB

the overall average recognition rate of the eight signals reaches 96.88%.