With the rapid development of WLAN technology, wireless device-free passive human detection becomes a newly-developing technique and holds more potential to worldwide and ubiquitous smart applications. Recently, indoor fine-grained device-free passive human motion detection based on the PHY layer information is rapidly developed. Previous wireless device-free passive human detection systems either rely on deploying specialized systems with dense transmitter-receiver links or elaborate off-line training process, which blocks rapid deployment and weakens system robustness. In the paper, we explore to research a novel fine-grained real-time calibration-free device-free passive human motion via physical layer information, which is independent of indoor scenarios and needs no prior-calibration and normal profile. We investigate sensitivities of amplitude and phase to human motion, and discover that phase feature is more sensitive to human motion, especially to slow human motion. Aiming at lightweight and robust device-free passive human motion detection, we develop two novel and practical schemes: short-term averaged variance ratio (SVR) and long-term averaged variance ratio (LVR). We realize system design with commercial WiFi devices and evaluate it in typical multipath-rich indoor scenarios. As demonstrated in the experiments, our approach can achieve a high detection rate and low false positive rate.

Device-free passive localization (DFPL) has been an emerging application with fast increasing development. Channel State Information- (CSI-) based DFPL is recently paid more attention to for fine-granularity and stability of CSI. However, lots of dead spots exist in the area of interest. And the accuracy of localization is not still completely satisfactory, especially for outside of the first Fresnel zone. In our paper, we put forward a new metric to estimate the sensitivity of a receiver to changes in the detecting area. In our experiment, we observe that the performance of DFPL can be raised when the receiver is placed at the location with high receiver sensitivity. Hence, we develop a new high-performance indoor device-free passive localization (HiDFPL), which employs a Bayesian a posteriori approach and possesses high receiver sensitivity. The experiment results demonstrate the outstanding performance of the proposed scheme.

Passive indoor localisation acts as a key enabler for various emerging applications such as secured region monitoring, smart homes, intelligent nursing, etc. Despite of years of research, their accuracy of localisation still remains unsatisfactory for practical uses. The main hurdle lies in the coarse measurement of wireless channels, e.g., received signal strength indicator (RSSI), employed in most existing schemes. In this work, we explore the potential of using channel state information (CSI) for fine-grained passive indoor localisation on a single communication link. To achieve high accuracy, we propose a solution based CSI fingerprint and devise two novel localisation estimator approaches suited to different conditions: weighted Bayesian (WBayes) and the maximum similarity metric (MSM). Compared with RSSI, CSI has demonstrated itself with a high accuracy of location distinction. Experimental results show that our schemes can achieve a higher accuracy.