Wireless sensing technologies have shown significant promise in various applications, but their spatial coverage is confined to the vicinity of the transmitters, limiting their applicability in broader environments. In this paper, we introduce an innovative wireless sensing approach based on the globally covered Global Navigation Satellite System (GNSS) signals. While GNSS signals have been widely used in remote sensing to monitor slow changes in the Earth’s surface, like sea level and snow depth, their ability to accurately detect highly dynamic target motions, such as human respiration, gestures, and intrusions, remains unclear. The main challenge arises from the interference brought by the large-scale satellite movement and severe GNSS signal errors. In this study, we present a novel GNSS signal enhancement system named FineSat to address these interference. Specifically, we first utilize polynomial representations to cancel satellite movement interference. Then, based on the analysis of GNSS signal errors, we propose a signal differential processing module to mitigate the errors. We implement our system on commercial devices and validate its performance in three sensing applications: respiration monitoring, gesture recognition, and intrusion detection. Results show that we achieve 0.42 bpm mean absolute error in respiration monitoring, 96.5% average accuracy in gesture recognition, and 98.6% accuracy in intrusion detection.

The Global Navigation Satellite System (GNSS) provides continuous high-precision positioning, enabling many applications such as vehicle navigation and pedestrian monitoring. However, in challenging environments such as urban canyons, positioning accuracy is significantly degraded due to multi-path and non-line-of-sight (NLOS) issues. To tackle this issue, we introduce a single point positioning (SPP) framework based on pseudorange correction, including three modules: feature extraction, pseudorange correction, and positioning model. Heavy pseudorange error is the primary cause of inaccurate localization, and in particular, we introduce GNSSFormer, a transformer-based model to obtain the pseudorange correction values. After preprocessing raw observations of GNSS, we designed a pseudorange correction model GNSSFormer containing a temporal transformer block and a multi-satellite joint spatial transformer block. GNSSFormer extracts multiple features related to both satellites and receivers, learning the complex global relationships between these features and the pseudorange errors to derive correction values. Subsequently, an extended Kalman filter (EKF)-based rauch tung striebel (RTS) smoothing SPP algorithm is employed to determine the location. Validation demonstrates that GNSSFormer significantly enhances SPP performance compared to state-of-the-art algorithms, improving positioning accuracy at least by 29.42% and 28.40% on two open source datasets and 18.91% on real-world dataset.