Overview

This project will develop passive radio frequency (RF) tags and UAS-mounted radar sensing technologies to more accurately characterize the under-canopy biomass fuels that are the primary drivers of wildfire initiation and spread. While deploying wide-area sensor networks on forest terrains presents many difficulties—from the lack of power sources to maintaining the network infrastructure—a maintenance-free sensing infrastructure on the surface layer could significantly boost forest-sensing capabilities by offering ground references.

Science Area

Accurately assessing the risk and predicting the spread of wildfires relies on accurate characterization of the understory biomass available as combustible fuel to wildfires. Current remote sensing approaches offer limited vertical resolution to characterize understory fuels obscured by the canopy layer, resulting in errors sometimes even more than 100% in predicting fire perimeter and burned areas. Optical sensors only see the uppermost layer of the forest and are not able to penetrate into the understory, leaving a sensing gap throughout an important medium of forest fire initiation and spread.

Technology

In this application, RF tags will be distributed sparsely (e.g., every 50 – 200 meters) in areas of interest, such as wildland urban interfaces, with no need for power or communication infrastructure. The tags will then be remotely interrogated from custom-built C-band radar mounted on an Unmanned Aerial System (UAS) flying above the canopy layer. The reflected signal from the tag will carry nuances associated with dielectric permittivity, wavelength, and frequency responses of vegetation moisture and volume between the tag and radar.

Advancements

• A long-range, UAS-based RFID framework will be developed to reflect radar signals directly back at their sources, boosting the radar’s operating range despite the high attenuation of GHz frequencies.
• A lightweight C-band Frequency Modulated Continuous Wave (FMCW) radar front end will be built to seamlessly pair with ground-based RFID tags.
• Physics-informed biomass character-ization will be modeled to correlate the backscatter signal from the tags with fuel load and moisture.

Principal Investigator

Elahe Soltanaghai is an Assistant Professor in the Department of Computer Science and Electrical and Computer Engineering at the University of Illinois Urbana-Champaign. Previously, she was a postdoctoral researcher at Carnegie Mellon University in CyLab working with Anthony Rowe. She received her PhD from the University of Virginia in 2019 and her M.S. in computer engineering from Sharif University of Technology in 2013. Elahe’s research interests span the areas of wireless sensing and networking with applications in Cyber-Physical Systems, and Internet of Things. She creates the next generation of intelligent wireless systems that are faster, lower power, pervasive, and can even sense the physical environment. Her recent work seeks to build a foundation for joint communication and sensing in wireless embedded systems of the future by applying signal processing and machine learning techniques to low-level RF signals.