Pulse Shape-Aided Multipath Delay Estimation for Fine-Grained WiFi Sensing

Due to the finite bandwidth of practical wireless systems, one multipath component can manifest itself as a discrete pulse consisting of multiple taps in the digital delay domain. This effect is called channel leakage, which complicates the multipath delay estimation problem. In this paper, we develop a new algorithm to estimate multipath delays of leaked channels by leveraging the knowledge of pulse-shaping functions, which can be used to support fine-grained WiFi sensing applications. Specifically, we express the channel impulse response (CIR) as a linear combination of overcomplete basis vectors corresponding to different delays. Considering the limited number of paths in physical environments, we formulate the multipath delay estimation as a sparse recovery problem. We then propose a sparse Bayesian learning (SBL) method to estimate the sparse vector and determine the number of physical paths and their associated delay parameters from the positions of the nonzero entries in the sparse vector. Simulation results show that our algorithm can accurately determine the number of paths, and achieve superior accuracy in path delay estimation and channel reconstruction compared to two benchmarking schemes

Sensorless sensing with WiFi

Abstract: Can WiFi signals be used for sensing purpose? The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing without wearable sensors, simultaneous perception and data transmission without extra communication infrastructure, and contactless sensing in privacy-preserving mode. Due to the popularity of WiFi devices and the ubiquitous deployment of WiFi networks, WiFi-based sensing networks, if fully connected, would potentially rank as one of the world’s largest wireless sensor networks. Yet the concept of wireless and sensorless sensing is not the simple combination of WiFi and radar. It seeks breakthroughs from dedicated radar systems, and aims to balance between low cost and high accuracy, to meet the rising demand for pervasive environment perception in everyday life. Despite increasing research interest, wireless sensing is still in its infancy. Through introductions on basic principles and working prototypes, we review the feasibilities and limitations of wireless, sensorless, and contactless sensing via WiFi. We envision this article as a brief primer on wireless sensing for interested readers to explore this open and largely unexplored field and create next-generation wireless and mobile computing applications. Key words: Channel State Information (CSI); sensorless sensing; WiFi; indoor localization; device-free human detection; activity recognition; wireless networks; ubiquitous computing

Non-invasive detection of moving and stationary human with WiFi

Non-invasive human sensing based on radio signals has attracted a great deal of research interest and fostered a broad range of innovative applications of localization, gesture recognition, smart health-care, etc., for which a primary primitive is to detect human presence. Previous works have studied the detection of moving humans via signal variations caused by human movements. For stationary people, however, existing approaches often employ a prerequisite scenario-tailored calibration of channel profile in human-free environments. Based on in-depth understanding of human motion induced signal attenuation reflected by PHY layer channel state information (CSI), we propose DeMan, a unified scheme for non-invasive detection of moving and stationary human on commodity WiFi devices. DeMan takes advantage of both amplitude and phase information of CSI to detect moving targets. In addition, DeMan considers human breathing as an intrinsic indicator of stationary human presence and adopts sophisticated mechanisms to detect particular signal patterns caused by minute chest motions, which could be destroyed by significant whole-body motion or hidden by environmental noises. By doing this, DeMan is capable of simultaneously detecting moving and stationary people with only a small number of prior measurements for model parameter determination, yet without the cumbersome scenario-specific calibration. Extensive experimental evaluation in typical indoor environments validates the great performance of DeMan in various human poses and locations and diverse channel conditions. Particularly, DeMan provides a detection rate of around 95% for both moving and stationary people, while identifies human-free scenarios by 96%, all of which outperforms existing methods by about 30%.Department of Computin

On multipath link characterization and adaptation for device-free human detection

Abstract—Wireless-based device-free human sensing has raised increasing research interest and stimulated a range of novel location-based services and human-computer interaction appli-cations for recreation, asset security and elderly care. A primary functionality of these applications is to first detect the presence of humans before extracting higher-level contexts such as physical coordinates, body gestures, or even daily activities. In the presence of dense multipath propagation, however, it is non-trivial to even reliably identify the presence of humans. The multipath effect can invalidate simplified propagation models and distort received signal signatures, thus deteriorating detection rates and shrinking detection range. In this paper, we characterize the impact of human presence on wireless signals via ray-bouncing models, and propose a measurable metric on commodity WiFi infrastructure as a proxy for detection sensitivity. To achieve higher detection rate and wider sensing coverage in multipath-dense indoor scenarios, we design a lightweight subcarrier and path configuration scheme harnessing frequency diversity and spatial diversity. We prototype our scheme with standard WiFi devices. Evaluations conducted in two typical office environments demonstrate a detection rate of 92.0 % with a false positive of 4.5%, and almost 1x gain in detection range given a minimal detection rate of 90%. I