🤖 AI Summary
This work addresses the challenge of achieving high-sensitivity motion detection in low-multipath outdoor environments, where conventional Wi-Fi sensing methods suffer from device-induced phase offsets and noise. The authors propose a model-driven, low-complexity approach that uniquely leverages the structural properties of channel state information (CSI) phase combined with a phase averaging mechanism to effectively suppress phase errors and enhance signal-to-noise ratio. By transforming the typically detrimental low-multipath condition into an advantage, the method enables robust detection of subtle movements at range. Using compressed beamforming frames collected from off-the-shelf 802.11ac devices, the system successfully detects crows flying several meters away in a real orchard environment and remains largely unaffected by vegetation clutter when wind speeds are below 3 m/s, significantly improving both sensitivity and robustness for distant micro-motion targets.
📝 Abstract
This paper presents a low-complexity motion detection method for outdoor Wi-Fi sensing based on a model-driven approach. The method exploits the structural characteristics of the phase components in channel state information (CSI) for low-multipath propagation environments, which are generally considered disadvantageous for Wi-Fi sensing, to mitigate the phase offset errors originating from wireless devices. In addition, phase averaging provides a processing gain that reduces the random noise components, including quantization and thermal noise. The theoretical basis of the method is described and its effectiveness is experimentally evaluated using Compressed Beamforming frames obtained from commercial IEEE 802.11ac devices. The experiments primarily focus wild crows flying in an outdoor orchard environment. The experimental results demonstrate that the method can detect birds even when they fly several meters away from the direct line-of-sight path between the transmitter and receiver antennas. Furthermore, the results indicated that fluctuations caused by vegetation movement were negligible when the wind speed was less than 3~m/s. The proposed approach is expected to be applicable not only to orchard monitoring but also to other outdoor Wi-Fi sensing applications in low-multipath environments.