SDR-Sense: Enabling Robust Wi-Fi Sensing Under Strong Secondary Dynamic Reflection Interference

Highly reflective surfaces such as TV, whiteboard, and granite counters are common indoors. When human motion occurs near these surfaces, the transmitted Wi-Fi signal can undergo multiple specular reflections that involve both the moving target and the surrounding reflectors. These interactions give rise to secondary dynamic reflections (SDRs), additional time-varying signal components beyond the primary target reflection. SDRs can be comparable in strength to the desired sensing path, inducing non-negligible interference and significantly degrading Wi-Fi sensing performance. This paper presents SDR-Sense, a novel approach to recover target reflections in presence of strong SDRs. We analyze the spatial characteristics of SDRs and reveal their localized distribution within a sensing area. Two complementary metrics, Curvature Standard Deviation (CSD) and Interference to Sensing Path Loss Ratio (ISPLR), are proposed to detect periods of strong-reflection interference. SDR-Sense further identifies optimal differential intervals and applies CSI differencing to successively recover target reflections during interfered periods, thereby enhancing the sensing signal-to-interference ratio. Implemented on commodity Wi-Fi devices, SDR-Sense is evaluated through two representative sensing applications, namely, human trajectory tracking and gesture recognition, across diverse participants and environments. Experimental results show substantial performance gains over existing methods and demonstrate its potential to benefit a broad range of downstream Wi-Fi sensing tasks.