Deep Learning-based High-Resolution Radar Micro-Doppler Signature Reconstruction for Enhanced Activity Recognition

Micro-Doppler signatures (mu-DS) play a crucial role in activity classification using radar. However, conventional methods for mu-DS generation, such as the Short-Time Fourier Transform (STFT), suffer from several limitations, such as the resolution limit, sensitivity to noise, and the need for parameter tuning. To overcome these challenges, we introduce a novel deep learning (DL) based approach that directly reconstructs high-resolution mu-DS from 1D complex time-domain signals. Our deep learning architecture comprises three key components: an autoencoder block to enhance the signal-to-noise ratio (SNR), a Convolutional STFT block to acquire the knowledge of frequency transformations necessary for generating pseudo-spectrograms, and a UNET block for the reconstruction of high-resolution spectrogram images. We conducted evaluations of the proposed method using both synthetic and real-world datasets. In the case of synthetic data, we generated 1D complex time-domain signals with multiple time-varying frequencies and assessed the network's performance in generating high-resolution mu-DS under different SNR levels. For real-world data, A radar-based American Sign Language (ASL) dataset, consisting of 20 ASL signs are used, to assess the classification performance achieved with mu-DS generated by the proposed approach. Our results demonstrated a 3.34% increase in classification accuracy compared to traditional STFT-based mu-DS. Both synthetic and experimental mu-DS revealed that our approach effectively learns to reconstruct higherresolution and sparser spectrograms, showcasing its potential for improving radar-based activity recognition applications.