A Time-Efficient and Robust Indoor Stationary Human Localization Method Using a Through-wall MIMO Radar

Advancements in Internet of Things (IoT) technology are fueling a surge in demand for radar-based through-wall indoor human localization (IHL) solutions. However, radar-based IHL is challenged by stationary targets, as they are difficult to extract from static clutter and are susceptible to moving target interference. In this paper, we present a time-efficient and robust method for indoor stationary human localization to address challenges faced in stationary target detection. Then, a sub-10 GHz multiple-input multiple-output (MIMO) throughwall radar system is designed to validate the proposed algorithm. The proposed imaging-free approach extracts human micromovements features directly from signal domain to identify and focus on regions of interest (RoIs) where the target is likely to be present. Subsequently, RoI-based robust Capon beamforming (RoI-RCB) algorithm is devised to generate RoIs' two-dimensional heatmaps with reduced computational overhead. Comparative experiments with state-of-the-art methods demonstrate the superior performance of the proposed method, generating stationary human heatmaps with higher signal-to-noiseand-clutter ratios (SNCR) under various interference scenarios, particularly in the presence of moving targets. Furthermore, rigorous analyses validate the method's robustness against moving target interference and its time-efficiency. Overall evaluations of the prototype system demonstrate high accuracy, achieving a median location accuracy of 9.5 cm in the free-space scenario. In through-wall scenarios, the prototype system achieved a median localization accuracy of 18.4 cm and 95.6% accuracy in people counting (error ≤ 1) within a ±45° detection range, tested across five rooms with three wall thicknesses.