Wi-Fi Sensing Based on Deep Supervised Dictionary Learning for Robust Device-Free Localization

Device-free localization (DFL) represents an emerging technology in autonomous driving assistance systems, sensing targets around a vehicle without requiring them carrying any devices. Current machine learning methodologies for DFL struggle with adaptively extracting discriminative features, resulting in weak robustness. Although dictionary learning techniques have shown potentiality in robust feature extraction for images and signals, their direct application to improving the robustness of DFL presents significant challenges. Specifically, the unsupervised mechanism of dictionary learning complicates the calibration of target location bases. The complex optimization processes also lead to inefficient parameter updating. To address these issues, we propose a deep-supervised dictionary learning (DSDL) approach for enhancing the robustness of DFL. The proposed DSDL method synergizes the advantages of sparse representation and deep learning, incorporating the robustness and interpretability of dictionary learning with the efficient parameter updating characteristic of deep learning. The supervised mechanism enables collaborative labeling of the learned signal bases. Experimental results, derived from an established DFL system, demonstrate that our DSDL outperforms existing techniques in both robustness and accuracy. DSDL maintains strong robustness and achieves high localization accuracy, surpassing 99% on clean data and sustaining over 97% accuracy under 30 dB signal-to-noise ratio (SNR) conditions. This work highlights the potential of existing WiFi infrastructure to provide cost-efficiency solutions for target localization, paving the way for future applications of wireless sensing.