Micro-motion Signal Enhancement via Convolutional Autoencoder Equipped with Multi-scale Feature Pyramid

Due to the time-varying and non-stationary characteristics of micro-motion signals, micro-Doppler spectrograms have been widely used for micro-motion signature analysis, which plays an important role in civil and military fields. However, the background noise in the spectrogram severely restricts subsequent applications such as feature extraction and parameter estimation. In this paper, we propose a novel micro-motion signal enhancement method based on the multi-scale feature pyramid structure and convolutional autoencoder. Compared to the traditional micro-Doppler spectrogram enhancement, the proposed method can restore micro-motion components and suppress background noise even under low signal-to-noise (SNR) conditions by integrating high-level semantic features and low-level detail information. Experimental results demonstrate the effectiveness and robustness of our method.