Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction

• URL: http://arxiv.org/abs/2306.14233v2
• Date: Mon, 5 Feb 2024 14:25:03 GMT
• Title: Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction
• Authors: Riccardo Mazzieri, Jacopo Pegoraro and Michele Rossi
• Abstract summary: STAR is a neural network that reconstructs micro-Doppler sequences of human movement even from highly incomplete channel measurements. It substantially outperforms state-of-the-art techniques in terms of the reconstructed micro-Doppler quality.
• Score: 1.1356542363919058
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The reconstruction of micro-Doppler signatures of human movements is a key enabler for fine-grained activity recognition wireless sensing. In Joint Communication and Sensing (JCS) systems, unlike in dedicated radar sensing systems, a suitable trade-off between sensing accuracy and communication overhead has to be attained. It follows that the micro-Doppler has to be reconstructed from incomplete windows of channel estimates obtained from communication packets. Existing approaches exploit compressed sensing, but produce very poor reconstructions when only a few channel measurements are available, which is often the case with real communication patterns. In addition, the large number of iterations they need to converge hinders their use in real-time systems. In this work, we propose and validate STAR, a neural network that reconstructs micro-Doppler sequences of human movement even from highly incomplete channel measurements. STAR is based upon a new architectural design that combines a single unrolled iterative hard-thresholding layer with an attention mechanism, used at its output. This results in an interpretable and lightweight architecture that reaps the benefits of both model-based and data driven solutions. STAR is evaluated on a public JCS dataset of 60 GHz channel measurements of human activity traces. Experimental results show that it substantially outperforms state-of-the-art techniques in terms of the reconstructed micro-Doppler quality. Remarkably, STAR enables human activity recognition with satisfactory accuracy even with 90% of missing channel measurements, for which existing techniques fail.

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