Low-cost FMCW millimeter-wave (mmWave) radars are increasingly used beyond automotive applications due to their robustness to occlusion and lighting changes. However, the monostatic configuration of off-the-shelf radars limits spatial coverage and often misses reflections that scatter away from the receiver. Bistatic radars, with spatially separated transmitter and receiver, can capture these additional scattering paths, providing bistatic radar cross-section (RCS) information. However, a key challenge is the need for continuous phase synchronization between transceivers. Traditional solutions rely on costly ultra-stable oscillators, fiber links, or shared external clocks. In this paper, we investigate the feasibility of bistatic FMCW sensing with only frame-level synchronization. We demonstrate the feasibility of bistatic tracking of a single object movement and range estimation using two TI AWR1843 radars at 77 GHz with frame-level synchronization through a shared hardware trigger.

As radars become more prevalent as a sensing solution, detecting and profiling unknown radars become crucial for privacy or interference avoidance purposes. ChirpEye [3] is a radar detection system capable of identifying the parameters and angle of arrival (AoA) of Frequency-Modulated Continuous-Wave (FMCW) radars without prior knowledge of radar configuration or location. In this paper, we provide and extended analysis on robustness of ChirpEye under varying multipath conditions or highly mobile scenarios. First, we prove that Chirpeye can preserve its accurate radar detection and characterization even under high mobility due to the unique tag structure that cancels out Doppler shifts. Furthermore, through controlled simulations, we show that multipath interference could cause chirp slope and AoA estimation errors only under low SINR conditions and within a specific multipath delay range.

As Frequency-Modulated Continuous Wave (FMCW) radar systems become increasingly prevalent across various sensing applications, detecting their presence is crucial to mitigate interference and address potential security risks. Existing methods for spectrum sensing or detecting unintended Radio Frequency (RF) transmissions rely on expensive specialized hardware because these radars typically operate in the GHz frequency range and utilize large bandwidths. To overcome these limitations, we present ChirpEye, a simple but effective tag design that is capable of identifying FMCW radar waveforms without requiring prior knowledge of radar parameters such as chirp slope, operating frequency, or bandwidth. In addition, ChirpEye can identify the direction of incident signal, hinting at the potential location of the radar. The key innovation of ChirpEye lies in its novel tag design, which uses multiple antennas and delay lines to process GHz-level radar signals with only kHz sampling rates. The tag structure generates unique baseband frequencies that are proportional to FMCW waveform parameters. We also propose a new super-resolution algorithm, called Spectra-MUSIC, which can accurately estimate these beat frequencies from noisy data. Our extensive evaluations demonstrate that ChirpEye achieves 99% accuracy in detecting FMCW radars at distances up to 15 meters with less than 5% median error in estimating the radar chirp slope and less than 15 degrees median error in estimating the direction of the radar.

Gaze interaction presents a promising avenue in Virtual Reality (VR) due to its intuitive and efficient user experience. Yet, the depth control inherent in our visual system remains underutilized in current methods. In this study, we introduce FocusFlow, a hands-free interaction method that capitalizes on human visual depth perception within the 3D scenes of Virtual Reality. We first develop a binocular visual depth detection algorithm to understand eye input characteristics. We then propose a layer-based user interface and introduce the concept of “Virtual Window” that offers an intuitive and robust gaze-depth VR interaction, despite the constraints of visual depth accuracy and precision spatially at further distances. Finally, to help novice users actively manipulate their visual depth, we propose two learning strategies that use different visual cues to help users master visual depth control. Our user studies on 24 participants demonstrate the usability of our proposed virtual window concept as a gaze-depth interaction method. In addition, our findings reveal that the user experience can be enhanced through an effective learning process with adaptive visual cues, helping users to develop muscle memory for this brand-new input mechanism. We conclude the paper by discussing potential future research topics of gaze-depth interaction.

Moisture estimation of sub-surface soil and the overlaying biomass layer is pivotal in precision agriculture and wildfire risk assessment. However, the characterization of layered material is nontrivial due to the radar penetration-resolution tradeoff. Here, a waveform inversion-based method was proposed to predict the dielectric permittivity (as a moisture proxy) of the bottom soil layer and the top biomass layer from radar signals. Specifically, the use of a combination of a higher and a lower frequency radar compared to a single frequency in predicting the permittivity of both the soil and the overlaying layer was investigated in this study. The results show that each layer was best characterized via one of the frequencies. However, for the simultaneous prediction of both layers’ permittivity, the most consistent results were achieved by inversion of data from a combination of both frequencies, showing better correlation with in situ permittivity and reduced prediction errors.

Soil moisture sensing through biomass or vegetation canopy has challenged researchers, even those who use SAR sensors with penetration capabilities. This is mainly due to the imposed extra time and phase offsets on Radio Frequency (RF) signals as they travel through the canopy. These offsets depend on the vegetation canopy moisture and height, both of which are typically unknown in agricultural and forest fields. In this paper, we leverage the mobility of an unmanned aerial system (UAS) to collect spatially-diverse radar measurements, enabling the joint estimation of soil moisture, above-ground biomass moisture, and biomass height, all without assuming any calibration steps. We leverage the changes in time-of-flight (ToF) and angle-of-arrival (AoA) measurements of reflected radar signals as the UAS flies above a reflector buried under the soil. We demonstrate the effectiveness of our algorithm by simulating its performance under realistic measurement noises as well as conducting lab experiments with different types of above-ground biomass. Our simulation results conclude that our algorithm is capable of estimating volumetric soil moisture to less than 1% median absolute error (MAE), vegetation height to 11.1cm MAE, and vegetation relative permittivity to 0.32 MAE. Our experimental results demonstrate the effectiveness of the proposed method in practical scenarios for varying biomass moistures and heights.

Coordination across multiple disciplines is necessary for current and future generations of modeling systems and the decision-support tools used for understanding and managing wildland fires. This coordination manifests in active-fire campaigns involving practitioners, modelers, and data-collection and management groups to produce datasets for training and evaluating models and underlying theories upon which they are built. New approaches and technology often are involved in these efforts, and in many cases their development is specified or driven by requirements identified by data-collection or model-evaluation activities. The Fire and Smoke Model Evaluation Experiment (FASMEE) is an effort to 1) conduct large-scale, active-fire data collection in order to produce a library of wildland fire model inputs 2) to advance wildland fire and smoke models, decision tools, and underlying science, and 3) to encourage disciplinary cross-training, diversity of backgrounds, and the development of new partnerships and technology in wildland fire science.

The use of coarse scale and temporally static effective roughness and single scattering albedo parameters in the Soil Moisture Active Passive (SMAP) operational algorithms falls short in achieving high spatial resolution in soil moisture (SM) retrieval. To address this issue, this study leverages in-situ SM measurements from 2016 to 2018 at the SMAP core validation sites to derive monthly parameters. These parameters were then applied to estimate SM for subsequent periods. Our findings suggest that SM retrievals from these monthly-adjusted parameters exhibit a marginal improvement in mean absolute error relative to the standard SMAP retrievals. This scheme will be applied to develop high spatiotemporal resolution parameters for passive microwave SM retrieval from instruments aboard uncrewed aerial systems (UAS). The proposed method also allows for these parameters to be tailored to targeted agricultural and forested areas for applications ranging from precision agriculture to intelligent wildfire management. This adaptation promises to refine our ability to retrieve SM with greater accuracy and resolution.

This paper introduces BiScatter2 as an extension of BiScatter [3], an integrated radar backscatter communication and sensing system. BiScatter2 provides simultaneous uplink and downlink communications, and precise radar-based sensing and localization. By refining the signal processing techniques and tag architecture design, BiScatter2 extends the operational range, setting a new baseline for two-way radar backscatter systems. This functionality is enabled through the use of chirp-slope-shift-keying modulation applied to Frequency Modulated Continuous Wave (FMCW) radars. BiScatter2 incorporates passive differential circuitry on backscatter tags for efficient, low-power decoding and extends its coverage range by combining the tag decoder and retro-reflective structure. Our evaluation results show an increase of 46% maximum range under the same throughput in downlink communication, which increases the original work's capability for commercial radars.

Backscatter communication is well-suited for agriculture, forest sensing, and environmental monitoring due to its low power and maintenance requirements. It can support a range of applications, including reading sensor values such as soil moisture or nutrient levels, monitoring plant health, tracking animal movements, or detecting forest fires. In these settings, sensors and backscatter nodes are often placed deep in the forest or under the plant canopy, while the reader is positioned on a mobile node such as a drone flying above the canopy. Consequently, signal attenuation caused by canopy blockage remains a significant challenge. This paper explores the feasibility of using backscatter communication in such environments and argues that signal-to-noise ratio (SNR) is not the most reliable metric for evaluating link quality. Instead, we propose a new metric, called template correlation, which provides a more accurate assessment in low-SNR conditions. Our findings demonstrate that backscatter communication, with its low power consumption and minimalist hardware design at the tags, is effective for environmental monitoring, even in the presence of canopy attenuators like grasses or crops.

Recent regulations on monitoring infants and children in vehicle cabins have spurred interest in using Millimeter-wave (mmWave) radars due to their reliability in various lighting conditions and privacy benefits. However, existing radar-based vital sign detection solutions fail in car settings with abundant occlusions or closely-seated multi-person scenarios. To resolve these limitations, we introduce BSENSE, a joint occupancy and vital sign monitoring system using a single radar that is robust to occlusion and varying seating arrangements and number of occupants in vehicle cabins. BSENSE incorporates synthetic wireless reflectors positioned in car corners to redirect radar signals toward blind spots, enabling Non-Line-of-Sight (NLoS) vital sign detection while maintaining sensing performance in Line-of-Sight (LoS) areas. The proposed system employs a hybrid architecture combining signal processing and a deep learning pipeline that can detect the car seating layout and jointly learn occupied seats and signatures of breathing to distinguish adults from children and infants, and monitor their vital signs over time. Our extensive evaluations with 120,000 radar data points, 400 different experimental scenarios, a mix of 10 adults, 5 children of age 1--11, and two programmable infant and child simulators demonstrate BSENSE's capability in child detection with over 97% accuracy and estimating their breathing rate within 6 BPM error, even in multi-person and NLoS scenarios, and across different car models.

Integrated Sensing and Communication (ISAC) represents an innovative paradigm for enhancing spectrum and hardware utilization for both sensing and communication. A specific type of ISAC, radar backscatter communication, involves low-power nodes embedding data onto radar signal reflections rather than generating new signals. However, existing radar backscatter techniques only facilitate uplink communication from the tag to the radar, neglecting downlink communication. This paper introduces BiScatter, an integrated radar backscatter communication and sensing system that enables simultaneous uplink and downlink backscatter communication, radar sensing, and backscatter localization. This is achieved through the design of chirp-slope-shift-keying modulation on top of Frequency Modulated Continuous Wave (FMCW) radars, complemented by passive differential circuitry at the backscatter tags for low-power decoding. BiScatter also presents a packet structure compatible with off-the-shelf radars that offer accurate data processing and synchronization between radar and tag. We prototype this backscatter network in both 9GHz and 24GHz, demonstrating its capability to extend across different frequency bands. Our evaluations demonstrate that BiScatter supports two-way backscatter communication with BER lower than 10-3 up to 7m range and centimeter-level tag localization accuracy on top of off-the-shelf FMCW radars. The presented approach significantly augments the versatility and efficiency of ISAC for low-power devices.

This paper explores a machine learning approach on data from a single-chip mmWave radar for generating high resolution point clouds – a key sensing primitive for robotic applications such as mapping, odometry and localization. Unlike lidar and vision-based systems, mmWave radar can operate in harsh environments and see through occlusions like smoke, fog, and dust. Unfortunately, current mmWave processing techniques offer poor spatial resolution compared to lidar point clouds. This paper presents RadarHD, an end-to-end neural network that constructs lidar-like point clouds from low resolution radar input. Enhancing radar images is challenging due to the presence of specular and spurious reflections. Radar data also doesn't map well to traditional image processing techniques due to the signal's sinc-like spreading pattern. We overcome these challenges by training RadarHD on a large volume of raw I/Q radar data paired with lidar point clouds across diverse indoor settings. Our experiments show the ability to generate rich point clouds even in scenes unobserved during training and in the presence of heavy smoke occlusion. Further, RadarHD's point clouds are high-quality enough to work with existing lidar odometry and mapping workflows.

Accurate forest biomass estimation is crucial for effective wildfire risk assessment and management as well as in agricultural applications. However, existing remote sensing methods cannot estimate under-canopy biomass. This paper investigates the feasibility of a novel approach that leverages ultra-wideband radars mounted on uncrewed aerial systems in conjunction with reference ground reflectors to estimate the under-canopy biomass profile. Through extensive electromagnetic wave propagation simulations encompassing diverse material configurations, we investigate the sensing capabilities of our proposed technique. We leverage deep learning to effectively extract biomass information from radar signals, enabling the prediction of material properties and geometries. The developed deep learning system was successfully trained with an R2metric of 98% in testing and produced dielectric permittivity distributions that visually match the target configurations. The results underscore the potential of our approach in advancing wildfire management strategies, bolstering carbon sequestration efforts, preserving forests, and aiding agricultural applications by facilitating accurate biomass characterization and estimations of crop yield and soil moisture.

We demonstrate Platypus, a sub-millimeter micro-displacement sensing system presented in [3]. Micro-displacement measurement is a crucial task in industrial systems such as structural health monitoring, where millimeter-level displacement of specific points on the structure or machinery parts can jeopardize the integrity of the structure and potentially leading to catastrophic damage or collapse. Platypus enables sub-millimeter level sensing accuracy by using mmWave backscatter tags and their reflection as phase carriers to shift the phase changes due to tiny displacements to clean frequency bins for precise tracking. It then reconstructs the tag phase changes with sub-millimeter level accuracy even from extended ranges (over 100m) or in non-line-of-sight (NLoS) situations where the tag is blocked by other objects. Here, we demonstrate Platypus’s performance by attaching a Platypus tag to a stepper motor-driven motion-stage and demonstrating the micro-displacement detection in real time, and the system robustness against multipath and occlusions.

Micro-displacement measurement is a crucial task in industrial systems such as structural health monitoring, where millimeter-level displacement of specific points on the structure or machinery displace can jeopardize the integrity of the structure and potentially leading to catastrophic damage or collapse. Traditionally, such displacements on large structures are measured using visual sensing platforms or advanced surveying equipment. However, they either fall short in varying weather and lighting conditions or require installation and maintenance of high-power sensing platforms that are expensive to deploy at scale, especially if continuous measurements are desired.

Millimeter wave radars can perceive through occlusions like dust, fog, smoke and clothes. But compared to cameras and lidars, their perception quality is orders of magnitude poorer. RadarHD [3] tackles this problem of poor quality by creating a machine learning super resolution pipeline trained against high quality lidar scans to mimic lidar. RadarHD ingests low resolution radar and generates high quality lidar-like point clouds even in occluded settings. RadarHD can also make use of the high quality output for typical robotics tasks like odometry, mapping and classification using conventional lidar workflows. Here, we demonstrate the effectiveness of RadarHD's point clouds against lidar in occluded settings.

Current collaborative augmented reality (AR) systems establish a common localization coordinate frame among users by exchanging and comparing maps comprised of feature points. However, relative positioning through map sharing struggles in dynamic or feature-sparse environments. It also requires that users exchange identical regions of the map, which may not be possible if they are separated by walls or facing different directions. In this paper, we present Cappella11Like its musical inspiration, Cappella utilizes collaboration among agents to forgo the need for instrumentation, an infrastructure-free 6-degrees-of-freedom (6DOF) positioning system for multi-user AR applications that uses motion estimates and range measurements between users to establish an accurate relative coordinate system. Cappella uses visual-inertial odometry (VIO) in conjunction with ultra-wideband (UWB) ranging radios to estimate the relative position of each device in an ad hoc manner. The system leverages a collaborative particle filtering formulation that operates on sporadic messages exchanged between nearby users. Unlike visual landmark sharing approaches, this allows for collaborative AR sessions even if users do not share the same field of view, or if the environment is too dynamic for feature matching to be reliable. We show that not only is it possible to perform collaborative positioning without infrastructure or global coordinates, but that our approach provides nearly the same level of accuracy as fixed infrastructure approaches for AR teaming applications. Cappella consists of an open source UWB firmware and reference mobile phone application that can display the location of team members in real time using mobile AR. We evaluate Cappella across mul-tiple buildings under a wide variety of conditions, including a contiguous 30,000 square foot region spanning multiple floors, and find that it achieves median geometric error in 3D of less than 1 meter.

In this paper, we demonstrate Adaptive Millimetro as an extension of Millimetro, an ultra-low power millimeter-wave (mmWave) retro-reflector presented in [1], for high mobility scenarios. Adaptive Millimetro makes use of automotive radars and enables communication with and accurate localization of roadside infrastructure overextended distances (i.e. >100m). Millimetro achieves this by designing ultra-low-power retro-reflective tags that operate in the mmWave frequency band and can be embedded in road signs, pavements, bi-cycles, or even the clothing of pedestrians. Millimetro addresses the severe path loss problem of mmWave signals by combining coding gain and retro-reflective antenna front-end to achieve long-range operation. However, highly mobile scenarios may still experience unreliable performance due to the Doppler effect changing the received signals. In this paper, we demonstrate a simple solution for robust localization in high mobility by implementing a Moving Target Indication (MTI) filter and an adaptive Kalman filter. We also present an augmented reality app, as an in-car AR platform, that uses Adaptive Millimetro’s algorithms to estimate the tag positions and overlay a virtual box at the estimated locations.

This paper presents Millimetro, an ultra-low-power tag that can be localized at high accuracy over extended distances. We develop Millimetro in the context of autonomous driving to efficiently localize roadside infrastructure such as lane markers and road signs, even if obscured from view, where visual sensing fails. While RF-based localization offers a natural solution, current ultra-low-power localization systems struggle to operate accurately at extended ranges under strict latency requirements. Millimetro addresses this challenge by re-using existing automotive radars that operate at mmWave frequency where plentiful bandwidth is available to ensure high accuracy and low latency. We address the crucial free space path loss problem experienced by signals from the tag at mmWave bands by building upon Van Atta Arrays that retro-reflect incident energy back towards the transmitting radar with minimal loss and low power consumption. Our experimental results indoors and outdoors demonstrate a scalable system that operates at a desirable range (over 100 m), accuracy (centimeter-level), and ultra-low-power (< 3 uW).

The Wireless of the Students, by the Students, and for the Students (S3) Workshop provides a unique venue for graduate students around the world to present, discuss, and exchange ideas on cross-cutting research on mobile wireless networks. As its name suggests, the workshop is organized by students, and the technical sessions are given by student presenters. The workshop aims at fostering early-career development among students and exposing them to the workings of academic life. It provides a venue for students to learn about each other's' work and discover opportunities for collaboration. The workshop invites students to submit papers, posters, and demos. All submissions are peer-reviewed by the student program committee.

The performance of autonomous cars can be greatly enhanced through wireless coordination. However, mobility has traditionally been a challenge for wireless networks due to rapid fluctuation of the signal quality. Current control systems handle this challenge by slowing down the vehicle to preserve safety. However, in this research, we demonstrate that we can robustly characterize the channel quality by mapping the multipath signals to the dynamics of the physical environment, thus controlling the trajectory of the mobile agent to a safe efficient motion path. This allows mobile systems to realize the performance benefits of wireless coordination while providing safety.

Many homes today are logically or physically "zoned" based on properties such as HVACs, activities, or physical layouts. Accurately sensing the occupancy of these zones can yield energy savings, aid in automatic heating and lighting control, energy disaggregation, etc. Existing systems that attempt to sense occupancy are power consuming, non real-time, pet unfriendly and/or sensitive to ambient heat, light and air flow. In this paper, we address these by building Doorpler, a time, space and power-aware radar-based system that detects occupancy at zone transition points by sensing crossings and their direction. It detects a crossing via the Doppler Principle, and infers the direction of crossing by measuring the angle-of-arrival of the human reflection. We evaluate Doorpler by conducting a scripted study and two in-situ studies for 200 hours, collecting over 1600 doorway crossings. We obtain a precision, recall and direction accuracy of over 99% in the scripted studies, and over 95% in the in-situ studies. Our results estimate that Doorpler can fall in the energy-harvestable range of indoor environments with an average power consumption of 6.1mW. With an execution time of 13.8ms, Doorpler has the potential to enable several real-time smart home applications like smart-lighting and HVAC control.

Decimeter-level localization has become a reality, in part due to the ability to eliminate the effects of multipath interference. In this paper, we demonstrate the ability to use multipath reflections to enhance localization rather than throwing them away. We present Multipath Triangulation, a new localization technique that uses multipath reflections to localize a target device with a single receiver that does not require any form of coordination with any other devices. In this paper, we leverage multipath triangulation to build the first decimeter-level WiFi localization system, called MonoLoco, that requires only a single access point (AP) and a single channel, and does not impose any overhead, data sharing, or coordination protocols beyond standard WiFi communication. As a bonus, it also determines the orientation of the target relative to the AP. We implemented MonoLoco using Intel 5300 commodity WiFi cards and deploy it in four environments with different multipath propagation. Results indicate median localization error of 0.5m and median orientation error of 6.6 degrees, which are comparable to the best performing prior systems, all of which require multiple APs and/or multiple frequency channels. High accuracy can be achieved with only a handful of packets.

Super-resolution subspace methods are popular in estimating multipath parameters such as angle of arrival and time of flight. However, they require decorrelation techniques to resolve coherent multipath components. The conventional decorrelation techniques reduce the effective aperture of the MIMO array, thus reducing the resolution and number of resolved paths. In this paper, we introduce MIMO smoothing as a new technique to bring decorrelation effect by leveraging the spacial and frequential diversity in MIMO transmitters and receivers. Via extensive experiments on WiFi links, we show that MIMO smoothing can increase the accuracy of multipath resolution.

A large amount of energy could be saved by detecting home occupancy and automatically controlling the lights, HVAC, water heating, and other mechanical systems. Existing systems rely on motion information, which usually fail to detect occupied rooms with stationary people. In this project, we study the possibility of converting commodity WiFi access points to occupancy sensors by exploiting multipath reflections as individual spatial sensors. The proposed method measures fine-grained distortions caused by human body on phase and amplitude of WiFi signals. Our initial results suggest that formulating WiFi parameters into angle of arrival provides a more sensitive metric to measure occupancy.

This work advances the state-of-art in path scheduling and route selection by considering multiple call classes and allowing users to provide multiple start-time options in their requests for bandwidth in advance-reservation systems. Our system model supports two call classes: User-Specified Start Times (USST) and Earliest-Start Time (EST). The USST class is suitable for applications such as remote visualization or 4K immersive video-conferencing, while the EST class is suitable for high-speed large file transfers. Two path-selection algorithms, Earliest Available Path (EAP) and Shortest Available Path (SAP), are considered. Given that most core network links are operated at low utilization to support failure-recovery, we focus our simulation study on low-load operation. At low loads, there is no significant difference in call-blocking rates between EAP and SAP, and therefore, we recommend choosing EAP to lower mean waiting time. Since file-transfers, unlike audio-video calls, do not have an intrinsic rate requirement, we studied the question of what rate to choose for EST calls, and found that if EST calls were assigned the full link capacity, call blocking rates and mean waiting times were higher than if EST calls were assigned half the link capacity. Finally, we studied inter-class effects, and recommend design choices that mitigate these effects.