HRSpecNET: A Deep Learning-Based High-Resolution Radar Micro-Doppler Signature Reconstruction for Improved HAR Classification

Micro-Doppler signatures ($\mu$-DS) are widely used for human activity recognition (HAR) using radar. However, traditional methods for generating $\mu$-DS, such as the Short-Time Fourier Transform (STFT), suffer from limitations, such as the trade-off between time and frequency resolution, noise sensitivity, and parameter calibration. To address these limitations, we propose a novel deep learning-based approach to reconstruct high-resolution $\mu$-DS directly from 1D complex time-domain signal. Our deep learning architecture consists of an autoencoder block to improve signal-to-noise ratio (SNR), an STFT block to learn frequency transformations to generate pseudo spectrograms, and finally, a UNET block to reconstruct high-resolution spectrogram images. We evaluated our proposed architecture on both synthetic and real-world data. For synthetic data, we generated 1D complex time domain signals with multiple time-varying frequencies and evaluated and compared the ability of our network to generate high-resolution $\mu$-DS and perform in different SNR levels. For real-world data, a challenging radar-based American Sign Language (ASL) dataset consisting of 100 words was used to evaluate the classification performance achieved using the $\mu$-DS generated by the proposed approach. The results showed that the proposed approach outperforms the classification accuracy of traditional STFT-based $\mu$-DS by 3.48\%. Both synthetic and experimental $\mu$-DS show that the proposed approach learns to reconstruct higher-resolution and sparser spectrograms.

Recommended citation: S. Biswas, A. M. Alam, and A. C. Gurbuz, "HRSpecNET: A Deep Learning-Based High-Resolution Radar Micro-Doppler Signature Reconstruction for Improved HAR Classification," in IEEE Transactions on Radar Systems, vol. 2, pp. 484-497, 2024, doi: 10.1109/TRS.2024.3396172.
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