Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction
CoRR(2023)
摘要
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
measurements, for which existing techniques fail.
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关键词
attention-based,micro-doppler
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