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.

中文翻译：

---

低信噪比下转子目标微动信号时频结果反演

提出了一种信号时频结果反演方法，用于提取低信噪比（SNR）下转子目标的微运动特征。在低信噪比的情况下，转子目标的回波能量主要集中在闪光灯的回波分量中。传统的转子目标微动特征提取主要利用时频结果中的正弦调制特征，其能量远低于闪光。在低信噪比下，回波时频结果中的正弦调制将淹没在噪声中，使得特征提取具有挑战性。根据闪光灯在时频结果中的特征，使用深度学习网络对包含正弦调制的时频结果进行反演。基于时频反演结果，采用GS-IRadon算法提取微运动特征，可以显着减少IRadon变换次数，提高特征提取速度和精度。通过仿真分析，采用UNet等深度学习网络的新方法可以在低信噪比下有效地反演时频结果，为微运动特征提取提供了一种新的技术途径。时频结果反演是一种用于实现转子目标微运动特征提取的新颖方法。