Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction
This work addresses the challenge of fine-grained activity recognition in wireless sensing for real-time systems, offering a solution that balances sensing accuracy and communication overhead, though it appears incremental as it builds on existing unrolled iterative methods with an attention mechanism.
The paper tackles the problem of reconstructing micro-Doppler signatures from incomplete channel measurements in Joint Communication and Sensing systems, proposing STAR, a neural network that achieves substantial improvements in reconstruction quality and enables human activity recognition with satisfactory accuracy even with 90% missing measurements.
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.