Micro-motion signal time-frequency results inversion of rotor targets under low signal-to-noise ratios

Abstract

A signal time-frequency results inversion method is proposed for extracting micro-motion features of rotor targets under low signal-to-noise ratios (SNRs). In the case of low SNRs, the echo's energy of rotor targets is mainly concentrated in the flash's echo component. Conventional micro-motion feature extraction of rotor targets primarily utilises the sinusoidal modulation feature in time-frequency results, whose energy is much lower than the flash. Under low SNRs, the sinusoidal modulation in the echo's time-frequency results will be submerged in the noise, making feature extraction challenging. A deep learning network is used to inverse the time-frequency results containing sinusoidal modulation based on the flash's features in the time-frequency results. Based on the inversion time-frequency results, the GS-IRadon algorithm is used to extract micro-motion features, which can significantly reduce the times of IRadon transformations and improve feature extraction speed and accuracy. Through simulation and analysis, a novel method using a deep learning network like UNet can effectively inverse time-frequency results under low SNRs, providing a new technical approach for micro-motion feature extraction. Time-frequency results inversion is a novelty method used to achieve micro-motion feature extraction of rotor targets.