Cross-Attention Augmented End-to-End Architecture for SISO FMCW Radar Activity Recognition With Swin-Transformer

Frequency-modulated continuous-wave (FMCW) radar is extensively used in human activity recognition (HAR) due to its nonintrusive operation, all-weather performance, and anti-interference ability. However, FMCW signals predominantly relies on the discrete Fourier transform (DFT) method, which is limited by its fixed basis functions and restricted time–frequency resolution (TFR). Therefore, this article proposes a cross-attention augmented end-to-end architecture for FMCW radar HAR called CASA-SWIN. The preprocessing module of CASA-SWIN learns the potential Range-Time (R-T), Range-Doppler (R-D), Dopper-Time (D-T) features of rawdata through multiple complex fully connected layers with preset parameters. Furthermore, it utilizes a three-branch cross-attention mechanism (TBCAM) module to extract intrinsic related information for R-T, D-T, R-D branches. For the D-T branch, proposed multiwindow filtering module (MWFM) comprises multiple 1-D complex convolutions with varying kernel sizes, emulating different window lengths in the short-time Fourier transform (STFT). Finally, a three-branch swin-transformer is used as the HAR classifier. Moreover, proposed CASA-SWIN is evaluated on self-collected HAR, Gait recognition datasets, and open-source gesture recognition datasets. Compared to 12 advanced HAR algorithms, a series of experiments, including comparative experiments, cross-environment experiments, open-source datasets experiments, and ablation experiments verify the effectiveness of the proposed module and CASA-SWIN.