Radio frequency (RF) propagation modeling poses unique electromagnetic simulation challenges. While recent neural representations have shown success in visible spectrum rendering, the fundamentally different scales and physics of RF signals require novel modeling paradigms. In this paper, we introduce RFScape, a novel framework that bridges the gap between neural scene representation and RF propagation modeling. Our key insight is that complex RF-object interactions can be captured through object-centric neural representations while preserving the composability of traditional ray tracing. Unlike previous approaches that either rely on crude geometric approximations or require dense spatial sampling of entire scenes, RFScape learns perobject electromagnetic properties and enables flexible scene composition. Through extensive evaluation on real-world RF testbeds, we demonstrate that our approach achieves 13 dB improvement over conventional ray tracing and 5 dB over state-of-the-art neural baselines in modeling accuracy, while requiring only sparse training samples.

MmWave FMCW radars are integrated into many sensing systems for robust sensing. However, their sensing functions are vulnerable to spoofing attacks and interfered with by backscatter communications, both of which can cause sensor malfunction and system failure. Noticing that radar spoofing and communication share similar signal modulation mechanisms, in this paper, we present SCR, a new Spoofing-resilient and Communication-integrated Radar sensing scheme. SCR is based on the rigorous analysis of the radar sensing model that highlights the differences between modulated spoofing and communication signals and normal sensing signals reflected by natural objects. The key designs of SCR are a novel chirp configuration scheme and signal processing pipeline, which signify different patterns between modulated and normal signals in radar spectra, for reliable detection of spoofing and communication. We have developed SCR and tested it with actual 77 GHz mmWave radar sensors and backscatter prototypes. Our field tests show that SCR can reliably detect fake objects created by modulated signals in both velocity and distance radar sensing domains.

Accurate and responsive simulation of radio frequency (RF) signal propagation is crucial for designing wireless systems operating in dynamic environments. Conventional ray tracing approaches struggle to accurately model the intricate geometries and material properties of objects that impact propagation. Recently proposed neural scene representations can learn such intricacies from RF data, but they treat the entire scene as implicit neural networks, necessitating retraining with a massive amount of RF data upon any environmental changes. In this paper, we propose RFCanvas, which fuses visual priors and RF measurements to achieve high accuracy for realistic scenes and be responsive to environmental changes. To ensure compatibility between visual priors and RF measurements, we introduce RFCanvas scene representations that model shapes and materials of substantial objects with tensorial fields and signed distance fields. We further extract motion information from visual priors to adapt RFCanvas scene representations to scene dynamics. RFCanvas is built upon an end-to-end optimization framework with differentiable RF simulation. Extensive evaluations across real-world wireless communication and sensing environments demonstrate RFCanvas's superiority over both existing methods.

Millimeter-wave backscatter can simultaneously support high-precision sensing and massive communication and represent one prominent technical evolution in next-generation wireless systems. The backscatter tags should ideally work across a wide mmWave spectrum range with consistent signal strength and angular coverage to accommodate highly diverse application scenarios. However, existing tags made of resonant antennas and RFICs only achieve a few GHz of bandwidth and hardly meet these requirements. In this paper, we present UniScatter, a new backscatter tag structure based on metamaterials. The key design of UniScatter is a graphene-based modulator and a lens-based retroreflector, which have consistent electromagnetic responses across an extensive frequency range and wide angular field-of-view. We have developed a robust fabrication process for UniScatter, and tested it on various mmWave sensing and communication devices. Our field tests show that UniScatter can backscatter signals across a wide frequency band from 24 GHz to 77 GHz with consistently high signal strength and wide angular coverage in 3D space.

Radar sensors have recently been explored in the industrial and consumer Internet of Things (IoT). However, such applications often require self-sustainable or untethered operations, which are at odds with the high power consumption of radar. This paper proposes NeuroRadar, a neuromorphic radar sensor, to achieve low-power wireless sensing. NeuroRadar jointly optimizes the analog hardware and the computation model, in order to mimic the highly efficient biological sensing and neural processing system. NeuroRadar features a highly simplified radar front end, which eliminates the power-hungry components in conventional radars. It directly "encodes" ambient motion into spiking signals, which can be processed using spiking neural networks running on energy-efficient neuromorphic computing platforms. We have prototyped NeuroRadar and evaluated its performance in two use cases: gesture sensing and localization. Our experiments demonstrate that NeuroRadar can achieve high sensing accuracy, at orders of magnitude lower power consumption compared with traditional radar.

A large number of traffic collisions occur as a result of non-line-of-sight (NLoS) obstructions. Recent work has explored NLoS automotive radar sensing systems to detect objects in occluded regions. However, current NLoS radars require substantial ambient reflectors, whose size needs to scale with the desired angular resolution and coverage, impeding their deployment in real-world scenarios. In this paper, we propose Metasight, which leverages carefully designed passive millimeter-wave metasurface reflectors and a novel angular encoding scheme to dramatically reduce the reflector size. The Metasight metasurfaces are fully passive, low cost, and can be fabricated by simply using a 3D printer and copper tape. By processing the reflected signals with a robust angle decoding algorithm on the radar, Metasight achieves high NLoS sensing resolution and wide coverage, with an asymptotically higher space-efficiency than conventional natural or artificial reflectors.

Next generation wireless networks embrace mmWave technology for its high capacity. Yet, mmWave radios bear a fundamental coverage limitation due to the high directionality and propagation artifacts. In this paper, we explore an economical paradigm based on 3D printing technology for mmWave coverage expansion. We propose MilliMirror, a fully passive metasurface, which can reshape and resteer mmWave beams to anomalous directions to illuminate the coverage blind spots. We develop a closed-form model to efficiently synthesize the MilliMirror design with thousands of unit elements and across a wide frequency band. We further develop an economical process based on 3D printing and metal deposition to fabricate MilliMirror. Our field test results show that MilliMirror can effectively fill the coverage holes and operate transparently to the standard mmWave beam management protocols.

This demonstration presents a working prototype of MilliMirror. This fully passive metasurface expands coverage blind spots of mmWave radios by reshaping and re-steering mmWave signals to any anomalous directions. The MilliMirror prototype consists of thousands of unit elements. A closed-form model is developed for efficient beam pattern synthesis. MilliMirror further explores 3D printing technology and metal deposition to achieve economical fabrication. MilliMirror prototype successfully establishes an indirect link between the WiGig transceivers, with the maximum gain over 10 dB.

Millimeter-wave (mmWave) technologies represent a cornerstone for emerging wireless network infrastructure, and for RF sensing systems in security, health, and automotive domains. Through a MIMO array of phased arrays with hundreds of antenna elements, mmWave can boost wireless bit-rates to 100+ Gbps, and potentially achieve near-vision sensing resolution. However, the lack of an experimental platform has been impeding research in this field. We propose to fill the gap with M3 (M-Cube), the first mmWave massive MIMO software radio [1]. M3 features a fully reconfigurable array of phased arrays, with up to 8 RF chains and 256 antenna elements. Despite the orders of magnitude larger antenna arrays, its cost is orders of magnitude lower, even when compared with state-of-the-art single RF chain mmWave software radios. In this demo, we will show M3's hardware modules, and demonstrate its usage in mmWave MIMO communication and sensing.

With the development of signal processing technology, the ubiquitous Wi-Fi devices open an unprecedented opportunity to solve the challenging human gesture recognition problem by learning motion representations from wireless signals. Wi-Fi-based gesture recognition systems, although yield good performance on specific data domains, are still practically difficult to be used without explicit adaptation efforts to new domains. Various pioneering approaches have been proposed to resolve this contradiction but extra training efforts are still necessary for either data collection or model re-training when new data domains appear. To advance cross-domain recognition and achieve fully zero-effort recognition, we propose Widar3.0, a Wi-Fi-based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and extract domain-independent features of human gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all general model that requires only one-time training but can adapt to different data domains. Experiments on various domain factors (i.e. environments, locations, and orientations of persons) demonstrate the accuracy of 92.7% for in-domain recognition and 82.6%-92.4% for cross-domain recognition without model re-training, outperforming the state-of-the-art solutions.

Accurate forecast of citywide passenger demand helps online car-hailing service providers to better schedule driver supplies. Previous research either uses only passenger order history and fails to capture the deep dependency of passenger demand, or is restricted on grid region partition that loses physical context. Recent advance in mobile traffic analysis has fostered understanding of city functions. In this article, we propose FlowFlexDP, a demand prediction model that integrates regional crowd flow and applies to flexible region partition. Analysis on a cellular dataset covering 1.5 million users in a major city in China reveals strong correlation between passenger demand and crowd flow. FlowFlexDP extracts both order history and crowd flow from cellular data, and adopts Graph Convolutional Neural Network to adapt prediction for regions of arbitrary shapes and sizes in a city. Evaluation on a large scale data set of 6 online car-hailing applications from cellular data shows that FlowFlexDP accurately predicts passenger demand and outperforms the state-of-the-art demand prediction methods.

Gait, the walking manner of a person, has been perceived as a physical and behavioral trait for human identification. Compared with cameras and wearable sensors, Wi-Fi-based gait recognition is more attractive because Wi-Fi infrastructure is almost available everywhere and is able to sense passively without the requirement of on-body devices. However, existing Wi-Fi sensing approaches impose strong assumptions of fixed user walking trajectories, sufficient training data, and identification of already known users. In this article, we present GaitSense , a Wi-Fi-based human identification system, to overcome the above unrealistic assumptions. To deal with various walking trajectories and speeds, GaitSense first extracts target specific features that best characterize gait patterns and applies novel normalization algorithms to eliminate gait irrelevant perturbation in signals. On this basis, GaitSense reduces the training efforts in new deployment scenarios by transfer learning and data augmentation techniques. GaitSense also enables a distinct feature of illegal user identification by anomaly detection, making the system readily available for real-world deployment. Our implementation and evaluation with commodity Wi-Fi devices demonstrate a consistent identification accuracy across various deployment scenarios with little training samples, pushing the limit of gait recognition with Wi-Fi signals.

Vehicle detection with visual sensors like lidar and camera is one of the critical functions enabling autonomous driving. While they generate fine-grained point clouds or high-resolution images with rich information in good weather conditions, they fail in adverse weather (e.g., fog) where opaque particles distort lights and significantly reduce visibility. Thus, existing methods relying on lidar or camera experience significant performance degradation in rare but critical adverse weather conditions. To remedy this, we resort to exploiting complementary radar, which is less impacted by adverse weather and becomes prevalent on vehicles. In this paper, we present Multimodal Vehicle Detection Network (MVDNet), a two-stage deep fusion detector, which first generates proposals from two sensors and then fuses region-wise features between multimodal sensor streams to improve final detection results. To evaluate MVDNet, we create a procedurally generated training dataset based on the collected raw lidar and radar signals from the open-source Oxford Radar Robotcar. We show that the proposed MVDNet surpasses other state-of-the-art methods, notably in terms of Average Precision (AP), especially in adverse weather conditions. The code and data are available at https://github.com/qiank10/MVDNet.

Modern autonomous vehicles are commonly instrumented with radars for all-weather perception. Yet the radar functionality is limited to identifying the positions of reflectors in the environment. In this paper, we investigate the feasibility of smartening transportation infrastructure for the purpose of conveying richer information to automotive radars. We propose RoS, a passive PCB-fabricated smart surface which can be reconfigured to embed digital bits, and inform the radar much like visual road signs do to cameras. We design the RoS signage to act as a retrodirective reflector which can reflect signals back to the radar from wide viewing angles. We further introduce a spatial encoding scheme, which piggybacks information in the reflected analog signals based on the geometrical layout of the retroreflective elements. Our prototype fabrication and experimentation verifies the effectiveness of RoS as an RF ''barcode'' which is readable by radar in practical transportation environment.

Millimeter-wave (mmWave) technologies represent a cornerstone for emerging wireless network infrastructure, and for RF sensing systems in security, health, and automotive domains. Through a MIMO array of phased arrays with hundreds of antenna elements, mmWave can boost wireless bit-rates to 100+ Gbps, and potentially achieve near-vision sensing resolution. However, the lack of an experimental platform has been impeding research in this field. This paper fills the gap with M3 (M-Cube), the first mmWave massive MIMO software radio. M3 features a fully reconfigurable array of phased arrays, with up to 8 RF chains and 288 antenna elements. Despite the orders of magnitude larger antenna arrays, its cost is orders of magnitude lower, even when compared with state-of-the-art single RF chain mmWave software radios. The key design principle behind M3 is to hijack a low-cost commodity 802.11ad radio, separate the control path and data path inside, regenerate the phased array control signals, and recreate the data signals using a programmable baseband. Extensive experiments have demonstrated the effectiveness of the M3 design, and its usefulness for research in mmWave massive MIMO communication and sensing.

Millimeter-wave (mmWave) technologies represent a cornerstone for emerging wireless network infrastructure, and for RF sensing systems in security, health, and automotive domains. Through a MIMO array of phased arrays with hundreds of antenna elements, mmWave can boost wireless bit-rates to 100+ Gbps, and potentially achieve near-vision sensing resolution. However, the lack of an experimental platform has been impeding research in this field. We propose to fill the gap with M3 (M-Cube), the first mmWave massive MIMO software radio. M3 features a fully reconfigurable array of phased arrays, with up to 8 RF chains and 256 antenna elements. Despite the orders of magnitude larger antenna arrays, its cost is orders of magnitude lower, even when compared with state-of-the-art single RF chain mmWave software radios. In this demo, we will show M3's hardware modules, and demonstrate its usage in mmWave MIMO communication and sensing.

Wi-Fi based sensing systems, although sound as being deployed almost everywhere there is Wi-Fi, are still practically difficult to be used without explicit adaptation efforts to new data domains. Various pioneering approaches have been proposed to resolve this contradiction by either translating features between domains or generating domain-independent features at a higher learning level. Still, extra training efforts are necessary in either data collection or model re-training when new data domains appear, limiting their practical usability. To advance cross-domain sensing and achieve fully zero-effort sensing, a domain-independent feature at the lower signal level acts as a key enabler. In this paper, we propose Widar3.0, a Wi-Fi based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and estimate velocity profiles of gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all model that requires only one-time training but can adapt to different data domains. We implement this design and conduct comprehensive experiments. The evaluation results show that without re-training and across various domain factors (i.e. environments, locations and orientations of persons), Widar3.0 achieves 92.7% in-domain recognition accuracy and 82.6%-92.4% cross-domain recognition accuracy, outperforming the state-of-the-art solutions. To the best of our knowledge, Widar3.0 is the first zero-effort cross-domain gesture recognition work via Wi-Fi, a fundamental step towards ubiquitous sensing.

The past years have witnessed the rapid conceptualization and development of wireless sensing based on Channel State Information (CSI) with commodity WiFi devices.Many research efforts have been devoted to promote WiFi sensing by innovating applications, refining models and optimizing algorithms. A critical issue of Cross-Technology Interference (CTI), however, is surprisingly unnoticed and largely unexplored in the existing literature. In this paper, we demonstrate that CTI poses severe impacts on CSI measurements and further degrades the performance of CSI-based sensing. Based on in-depth understanding of such impacts, we present PERFIC to deal with CTI for CSI on commercial WiFi. We first exploit the inherent cyclostationarity property of different signals to detect CTI and further identify the specific distorted subcarriers on CSI. For each interfered CSI, we then propose to mitigate the impacts of CTI by amending the abnormal subcarriers. We conduct experiments on typical wireless sensing applications, including human detection and activity classification, using off-the-shelf WiFi devices. The results demonstrate that PERFIC yields a remarkable performance gain of >30% with high efficiency and outperforms existing robust classifiers.By providing interference-free CSI that is amendable to existing and emerging CSI-based sensing applications, PERFIC underpins new insights for improving the sensitivity and reliability of wireless sensing.

Vital signs such as heart rate and heartbeat interval are currently measured by electrocardiograms (ECG) or wearable physiological monitors. These techniques either require contact with the patient's skin or are usually uncomfortable to wear, rendering them too expensive and user-unfriendly for daily monitoring. In this paper, we propose a new noninvasive technology to generate an Acousticcardiogram (ACG) that precisely monitors heartbeats using inaudible acoustic signals. ACG uses only commodity microphones and speakers commonly equipped on ubiquitous off-the-shelf devices, such as smartphones and laptops. By transmitting an acoustic signal and analyzing its reflections off human body, ACG is capable of recognizing the heart rate as well as heartbeat rhythm. We employ frequency-modulated sound signals to separate reflection of heart from that of background motions and breath, and continuously track the phase changes of the acoustic data. To translate these acoustic data into heart and breath rates, we leverage the dual microphone design on COTS mobile devices to suppress direct echo from speaker to microphones, identify heart rate in frequency domain, and adopt an advanced algorithm to extract individual heartbeats. We implement ACG on commercial devices and validate its performance in real environments. Experimental results demonstrate ACG monitors user's heartbeat accurately, with median heart rate estimation error of 0.6 beat per minute (bpm), and median heartbeat interval estimation error of 19 ms.

This paper presents Widar2.0, the first WiFi-based system that enables passive human localization and tracking using a single link on commodity off-the-shelf devices. Previous works based on either specialized or commercial hardware all require multiple links, preventing their wide adoption in scenarios like homes where typically only one single AP is installed. The key insight underlying Widar2.0 to circumvent the use of multiple links is to leverage multi-dimensional signal parameters from one single link. To this end, we build a unified model accounting for Angle-of-Arrival, Time-of-Flight, and Doppler shifts together and devise an efficient algorithm for their joint estimation. We then design a pipeline to translate the erroneous raw parameters into precise locations, which first finds parameters corresponding to the reflections of interests, then refines range estimates, and ultimately outputs target locations. Our implementation and evaluation on commodity WiFi devices demonstrate that Widar2.0 achieves better or comparable performance to state-of-the-art localization systems, which either use specialized hardwares or require 2 to 40 Wi-Fi links.

Accurate forecast of citywide passenger demand helps online car-hailing service providers to better schedule driver supplies. Previous research either uses only passenger order history and fails to capture the deep dependency of passenger demand, or is restricted on grid region partition that loses physical context. Recent advance in mobile traffic analysis has fostered understanding of city functions. In this paper, we propose FlowFlexDP, a demand prediction model that integrates regional crowd flow and applies to flexible region partition. Analysis on a cellular dataset covering 1.5 million users in a major city in China reveals strong correlation between passenger demand and crowd flow. FlowFlexDP extracts both order history and crowd flow from cellular data, and adopts Graph Convolutional Neural Network to adapt prediction for regions of arbitrary shapes and sizes in a city. Evaluation on a large scale data set of DiDi Chuxing from cellular data shows that FlowFlexDP accurately predicts passenger demand and outperforms the state-of-the-art demand prediction methods.

Device-free passive detection is an emerging technology to detect whether there exist any moving entities in the areas of interest without attaching any device to them. It is an essential primitive for a broad range of applications including intrusion detection for safety precautions, patient monitoring in hospitals, child and elder care at home, and so forth. Despite the prevalent signal feature Received Signal Strength (RSS), most robust and reliable solutions resort to a finer-grained channel descriptor at the physical layer, e.g., the Channel State Information (CSI) in the 802.11n standard. Among a large body of emerging techniques, however, few of them have explored the full potential of CSI for human detection. Moreover, space diversity supported by nowadays popular multiantenna systems are not investigated to a comparable extent as frequency diversity. In this article, we propose a novel scheme for device-free PAssive Detection of moving humans with dynamic Speed (PADS). Both full information (amplitude and phase) of CSI and space diversity across multiantennas in MIMO systems are exploited to extract and shape sensitive metrics for accuracy and robust target detection. We prototype PADS on commercial WiFi devices, and experiment results in different scenarios demonstrate that PADS achieves great performance improvement in spite of dynamic human movements.

Modern mobile devices are equipped with multiple antennas, which brings various wireless sensing applications such as accurate localization, contactless human detection, and wireless human-device interaction. A key enabler for these applications is phased array signal processing, especially Angle of Arrival (AoA) estimation. However, accurate AoA estimation on commodity devices is non-trivial due to limited number of antennas and uncertain phase offsets. Previous works either rely on elaborate calibration or involve contrived human interactions. In this paper, we aim to enable practical AoA measurements on commodity off-the-shelf (COTS) mobile devices. The key insight is to involve users' natural rotation to formulate a virtual spatial-temporal antenna array and conduce a relative incident signal of measurements at two orientations. Then by taking the differential phase, it is feasible to remove the phase offsets and derive the accurate AoA of the equivalent incoming signal, while the rotation angle can also be captured by built-in inertial sensors. On this basis, we propose Differential MUSIC (D-MUSIC), a relative form of the standard MUSIC algorithm that eliminates the unknown phase offsets and achieves accurate AoA estimation on COTS mobile devices with only one rotation. We further extend DMUSIC to 3-D space, integrate extra measurements during rotations for higher estimation accuracy, and fortify it in multipath-rich scenarios. We prototype D-MUSIC on commodity WiFi infrastructure and evaluate it in typical indoor environments. Experimental results demonstrate a superior performance with average AoA estimation errors of 130 with only three measurements and 50 with at most 10 measurements. Requiring no modifications or calibration, D-MUSIC is envisioned as a promising scheme for practical AoA estimation on COTS mobile devices.

In-air interaction acts as a key enabler for ambient intelligence and augmented reality. As an increasing popular example, exergames, and the alike gesture recognition applications, have attracted extensive research in designing accurate, pervasive and low-cost user interfaces. Recent advances in wireless sensing show promise for a ubiquitous gesture-based interaction interface with Wi-Fi. In this work, we extract complete information of motion-induced Doppler shifts with only commodity Wi-Fi. The key insight is to harness antenna diversity to carefully eliminate random phase shifts while retaining relevant Doppler shifts. We further correlate Doppler shifts with motion directions, and propose a light-weight pipeline to detect, segment, and recognize motions without training. On this basis, we present WiDance, a Wi-Fi-based user interface, which we utilize to design and prototype a contactless dance-pad exergame. Experimental results in typical indoor environment demonstrate a superior performance with an accuracy of 92%, remarkably outperforming prior approaches.

In recent years, WiFi-based sensing applications have been proliferated due to growing capacities of the physical layer. Channel State Information (CSI), which depicts the characteristics of propagation environment and reflects different human behaviors, can be easily obtained on commodity WiFi devices with slight driver modification. For the sake of higher accuracy and robustness of CSI-based sensing, a variety of research efforts have been devoted to model refinement, algorithm optimization and data sanitization. Radio frequency interference (RFI) is a crucial problem, which, however, is surprisingly overlooked and largely unexplored. The sensing performance can be significantly boosted by identifying and properly handling the interfered CSI measurements. In this paper, we demonstrate that it is feasible to identify the interfered CSI measurements due to the unique properties induced by RFI. We propose two RFI detection algorithms by utilizing cyclostationary analysis from different angles. Experimental results on off-the-shelf WiFi devices show that both algorithms are robustly stable for different scenarios and can achieve a remarkable overall accuracy of > 90%.

Sensorless sensing using wireless signals has been rapidly conceptualized and developed recently. Among numerous applications of WiFi-based sensing, human presence detection acts as a primary and fundamental function to boost applications in practice. Many complicated approaches have been proposed to achieve high detection accuracy, which, however, frequently omit various practical constraints like real-time capability, computation efficiency, sampling rates, deployment efforts, etc. A practical detection system that works in real world lacks. In this paper, we design and implement WiSH, a real-time system for contactless human detection that is applicable for whole-day usage. WiSH employs lightweight yet effective methods and thus enables detection under practical conditions even on resource-limited devices with very low signal sampling rates. We deploy WiSH on commodity desktops and customized tiny nodes in different everyday scenarios. The experimental results demonstrate superior performance of WiSH, achieving a detection accuracy of >98% using a sampling rate of 20Hz with an average detection delay of merely 1.5s, which renders it a promising system for real-world deployment.

Various pioneering approaches have been proposed for Wi-Fi-based sensing, which usually employ learning-based techniques to seek appropriate statistical features, yet do not support precise tracking without prior training. Thus to advance passive sensing, the ability to track fine-grained human mobility information acts as a key enabler. In this paper, we propose Widar, a Wi-Fi-based tracking system that simultaneously estimates a human's moving velocity (both speed and direction) and location at a decimeter level. Instead of applying statistical learning techniques, Widar builds a theoretical model that geometrically quantifies the relationships between CSI dynamics and the user's location and velocity. On this basis, we propose novel techniques to identify frequency components related to human motion from noisy CSI readings and then derive a user's location in addition to velocity. We implement Widar on commercial Wi-Fi devices and validate its performance in real environments. Our results show that Widar achieves decimeter-level accuracy, with a median location error of 25 cm given initial positions and 38 cm without them and a median relative velocity error of 13%.

Modern mobile devices are equipped with multiple antennas, which brings various wireless sensing applications such as accurate localization, contactless human detection and wireless human-device interaction. A key enabler for these applications is phased array signal processing, especially Angle of Arrival (AoA) estimation. However, accurate AoA estimation on commodity devices is non-trivial due to limited number of antennas and uncertain phase offsets. Previous works either rely on elaborate calibration or involve contrived human interactions. In this paper, we aim to enable practical AoA measurements on commodity off-the-shelf (COTS) mobile devices. The key insight is to involve users' natural rotation to formulate a virtual spatial-temporal antenna array and conduce a relative incident signal of measurements at two orientations. Then by taking the differential phase, it is feasible to remove the phase offsets and derive the accurate AoA of the equivalent incoming signal, while the rotation angle can also be captured by built-in inertial sensors. On this basis, we propose Differential MUSIC (D-MUSIC), a relative form of the standard MUSIC algorithm that eliminates the unknown phase offsets and achieves accurate AoA estimation on COTS mobile devices with only one rotation. We further extend D-MUSIC to 3-D space and fortify it in multipath-rich scenarios. We prototype D-MUSIC on commodity WiFi infrastructure and evaluate it in typical indoor environments. Experimental results demonstrate a superior performance with an average AoA estimation error of 13°. Requiring no modifications or calibration, D-MUSIC is envisioned as a promising scheme for practical AoA estimation on COTS mobile devices.

WiFi technology has fostered numerous mobile computing applications, such as adaptive communication, finegrained localization, gesture recognition, etc., which often achieve better performance or rely on the availability of Line-Of-Sight (LOS) signal propagation. Thus the awareness of LOS and Non-Line-Of-Sight (NLOS) plays as a key enabler for them. Realtime LOS identification on commodity WiFi devices, however, is challenging due to limited bandwidth of WiFi and resulting coarse multipath resolution. In this work, we explore and exploit the phase feature of PHY layer information, harnessing both space diversity with antenna elements and frequency diversity with OFDM subcarriers. On this basis, we propose PhaseU, a real-time LOS identification scheme that works in both static and mobile scenarios on commodity WiFi infrastructure. Experimental results in various indoor scenarios demonstrate that PhaseU consistently outperforms previous approaches, achieving overall LOS and NLOS detection rates of 94.35% and 94.19% in static cases and both higher than 80% in mobile contexts. Furthermore, PhaseU achieves real-time capability with millisecond-level delay for a connected AP and 1-second delay for unconnected APs, which is far beyond existing approaches.

Device-free passive detection is an emerging technology to detect whether there exists any moving entities in the area of interests without attaching any device to them. It is an essential primitive for a broad range of applications including intrusion detection for safety precautions, patient monitoring in hospitals, child and elder care at home, etc. Despite of the prevalent signal feature Received Signal Strength (RSS), most robust and reliable solutions resort to finer-grained channel descriptor at physical layer, e.g., the Channel State Information (CSI) in the 802.11n standard. Among a large body of emerging techniques, however, few of them have explored full potentials of CSI for human detection. Moreover, space diversity supported by nowadays popular multi-antenna systems are not investigated to the comparable extent as frequency diversity. In this paper, we propose a novel scheme for device-free PAssive Detection of moving humans with dynamic Speed (PADS). Both amplitude and phase information of CSI are extracted and shaped into sensitive metrics for target detection; and CSI across multi-antennas in MIMO systems are further exploited to improve the detection accuracy and robustness. We prototype PADS on commercial WiFi devices and experiment results in different scenarios demonstrate that PADS achieves great performance improvement in spite of dynamic human movements.