About

Elahe Soltanaghai is an Assistant Professor at the University of Illinois Urbana-Champaign in the Department of Computer Science and a Faculty Affiliate in the Electrical and Computer Engineering department. She earned her PhD in Computer Science from the University of Virginia in 2019, her MS in Computer Engineering from Sharif University of Technology in 2014, and her BS in Computer Engineering and Information Technology Engineering from Amirkabir University of Technology in Tehran, Iran. Her research interests include wireless sensing and communication, wireless embedded systems, and cyber-physical systems, with a focus on systems and networking. She has contributed to the development of innovative sensing technologies, such as radar-based passive sensing, in-vehicle child detection, biomass characterization using radars, and long-range localization for autonomous driving. She has also worked on enabling long-range micro-displacement sensing with passive tags and WiFi-based localization systems. Prior to her current position, she was a Postdoctoral Researcher at Carnegie Mellon University from 2019 to 2021. She has received several honors, including the ACM SIGMOBILE Dissertation Award and being named one of the 10 N2Women Rising Star Women Worldwide in 2021.

Research topics

• Computer Science
• Artificial Intelligence
• Telecommunications
• Remote sensing
• Geology
• Optics
• Mathematics
• Systems engineering
• Electrical engineering
• Human–computer interaction
• Physics
• Acoustics
• Computer vision
• Engineering

Selected publications

• Work in Progress: Assessing AI Integration in School Education

  2026-02-23

  articleOpen accessSenior author

  Artificial Intelligence (AI) is increasingly influencing the educational landscape, offering new opportunities for personalized learning and administrative efficiency.This study investigates AI integration in secondary education by examining teachers' familiarity, willingness to adopt AIdriven tools, and perceived challenges.A survey was conducted among nine teachers from two high schools, covering subjects such as mathematics, history, and art.Preliminary findings indicate that while teachers acknowledge AI's potential benefits, barriers such as lack of training, privacy concerns, and institutional resistance hinder adoption.Future work includes pilot studies and codesign efforts with educators to develop an AI-based teaching assistant system (ATAS).This study also explores how lessons from secondary education may inform AI adoption in post-secondary settings, particularly in engineering and physics departments, where AI applications are becoming increasingly relevant.

  PublisherOA PDFDOI

• GreenScatter: Through-Canopy Soil Moisture Sensing with UAV-Mounted Radar

  arXiv (Cornell University) · 2026-04-10

  articleOpen accessSenior author

  Soil moisture is a critical variable for managing irrigation, improving crop yield, and understanding field-scale hydrology. Radars mounted on unmanned aerial vehicles (UAVs) offer a promising means to monitor soil moisture over large fields with flexible, high-resolution coverage. However, during the growing season, canopy scattering and soil reflections become strongly coupled in the radar measurement. These coupled effects vary with crop structure or flight altitude, complicating the retrieval of soil moisture. To overcome this challenge, we present GreenScatter, a physics-based soil moisture retrieval framework for nadir-looking wideband UAV radars. GreenScatter introduces a microwave radiative transfer model that explicitly captures the dominant electromagnetic interactions between vegetation and soil, enabling accurate modeling of coherent ground backscatter through canopy. In parallel, it develops a radar cross-section (RCS) estimation method that transforms time-domain radar signals into calibrated wideband RCS spectra, isolating soil reflections while compensating for hardware and waveform effects. Together, these components enable robust soil moisture estimation through vegetation across varying canopy conditions and UAV configurations. Field experiments across multiple corn and soybean sites demonstrate consistent retrieval with an average volumetric water content (VWC) error of 4.49%.

  PublisherOA PDF

• GreenScatter: Through-Canopy Soil Moisture Sensing with UAV-Mounted Radar

  2026-05-08

  articleOpen accessSenior author

  Soil moisture is a critical variable for managing irrigation, improving crop yield, and understanding field-scale hydrology. Radars mounted on unmanned aerial vehicles (UAVs) offer a promising means to monitor soil moisture over large fields with flexible, high-resolution coverage. However, during the growing season, canopy scattering and soil reflections become strongly coupled in the radar measurement. These coupled effects vary with crop structure or flight altitude, complicating the retrieval of soil moisture. To overcome this challenge, we present GreenScatter, a physics-based soil moisture retrieval framework for nadir-looking wideband UAV radars. GreenScatter introduces a microwave radiative transfer model that explicitly captures the dominant electromagnetic interactions between vegetation and soil, enabling accurate modeling of coherent ground backscatter through canopy. In parallel, it develops a radar cross-section (RCS) estimation method that transforms time-domain radar signals into calibrated wideband RCS spectra, isolating soil reflections while compensating for hardware and waveform effects. Together, these components enable robust soil moisture estimation through vegetation across varying canopy conditions and UAV configurations. Field experiments across multiple corn and soybean sites demonstrate consistent retrieval with an average volumetric water content (VWC) error of 4.49%.

  PublisherDOI

• GreenScatter: Through-Canopy Soil Moisture Sensing with UAV-Mounted Radar

  arXiv (Cornell University) · 2026-04-10

  preprintOpen accessSenior author

  Soil moisture is a critical variable for managing irrigation, improving crop yield, and understanding field-scale hydrology. Radars mounted on unmanned aerial vehicles (UAVs) offer a promising means to monitor soil moisture over large fields with flexible, high-resolution coverage. However, during the growing season, canopy scattering and soil reflections become strongly coupled in the radar measurement. These coupled effects vary with crop structure or flight altitude, complicating the retrieval of soil moisture. To overcome this challenge, we present GreenScatter, a physics-based soil moisture retrieval framework for nadir-looking wideband UAV radars. GreenScatter introduces a microwave radiative transfer model that explicitly captures the dominant electromagnetic interactions between vegetation and soil, enabling accurate modeling of coherent ground backscatter through canopy. In parallel, it develops a radar cross-section (RCS) estimation method that transforms time-domain radar signals into calibrated wideband RCS spectra, isolating soil reflections while compensating for hardware and waveform effects. Together, these components enable robust soil moisture estimation through vegetation across varying canopy conditions and UAV configurations. Field experiments across multiple corn and soybean sites demonstrate consistent retrieval with an average volumetric water content (VWC) error of 4.49%.

  PublisherDOI

• Poster: Feasibility of Bistatic Millimeter-Wave Radar Sensing under Loose Synchronization

  2025-11-03

  articleOpen accessSenior author

  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.

  PublisherOA PDFDOI

• ChirpEye: Passive Sensing and Profiling of FMCW Radars with a Resource-constrained Tag

  2025-11-03

  articleOpen accessSenior author

  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.

  PublisherDOI

• Evaluation of Soil Moisture Retrievals from a Portable L-Band Microwave Radiometer

  Remote Sensing · 2024-12-06 · 4 citations

  articleOpen access

  A novel Portable L-band radiometer (PoLRa), compatible with tower-, vehicle- and drone-based platforms, can provide gridded soil moisture estimations from a few meters to several hundred meters yet its retrieval accuracy has rarely been examined. This study aims to provide an initial assessment of the performance of PoLRa-derived soil moisture at a spatial resolution of approximately 0.7 m × 0.7 m at a set of sampling pixels in central Illinois, USA. This preliminary evaluation focuses on (1) the consistency of PoLRa-measured brightness temperatures from different viewing directions over the same area and (2) whether PoLRa-derived soil moisture retrievals are within an acceptable accuracy range. As PoLRa shares many aspects of the L-band radiometer onboard NASA’s Soil Moisture Active Passive (SMAP) mission, two SMAP operational algorithms and the conventional dual-channel algorithm (DCA) were applied to calculate volumetric soil moisture from the measured brightness temperatures. The vertically polarized brightness temperatures from the PoLRa are typically more stable than their horizontally polarized counterparts across all four directions. In each test period, the standard deviations of observed dual-polarization brightness temperatures are generally less than 5 K. By comparing PoLRa-based soil moisture retrievals against the simultaneous moisture values obtained by a handheld capacitance probe, the unbiased root mean square error (ubRMSE) and the Pearson correlation coefficient (R) are mostly below 0.05 m3/m3 and above 0.7 for various algorithms adopted here. While SMAP models and the DCA algorithm can derive soil moisture from PoLRa observations, no single algorithm consistently outperforms the others. These findings highlight the significant potential of ground- or drone-based PoLRa measurements as a standalone reference for the calibration and validation of spaceborne L-band synthetic aperture radars and radiometers. The accuracy of PoLRa-yielded high-resolution soil moisture can be further improved via standardized operational procedures and appropriate tau-omega parameters.

  PublisherOA PDFDOI

• Integrated Two-way Radar Backscatter Communication and Sensing with Low-power IoT Tags

  2024-07-31 · 11 citations

  articleSenior author

  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.

  PublisherDOI

• Joint Soil and Above-Ground Biomass Characterization Using Radars

  arXiv (Cornell University) · 2024-04-23

  preprintOpen accessSenior author

  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.

  PublisherOA PDFDOI

• Dual-Frequency Radar Wave-Inversion for Sub-Surface Material Characterization

  2024-07-07 · 1 citations

  article

  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.

  PublisherDOI

Frequent coauthors

• Gerhard Fettweis Keynote

  Vodafone (United Kingdom)

  25 shared

• Jennifer Chen

  Yale University

  25 shared

• Fredrik Gunnarsson

  Ericsson (Sweden)

  25 shared

• Gerhard Fettweis

  Vodafone (Germany)

  25 shared

• Mohamad Alipour

  Tongji University

  13 shared

• Anthony Rowe

  11 shared

• Adam C. Watts

  11 shared

• Chenyang Zhang

  Tibet University

  7 shared

Labs

• Siebel School of Computing and Data SciencePI

Awards & honors

• Google Research Scholar Award (2022)
• Named one of the 10 N2Women Rising Star Women Worldwide (202…
• Recipient of ACM SIGMOBILE Dissertation Award (2020)
• EECS Rising Stars (2019)