Miniaturized s-UAS-Based Ultra-Wideband Radar for Snow Water Equivalent Map Synthesis

This paper presents a miniaturized, small unmanned aerial system (sUAS)-based ultra-wideband (UWB) radar system and results from measurements on snow over Grand Mesa, Colorado, during Spring 2024. The system was developed to estimate snow water equivalent (SWE) directly from radar data without extensive in-situ snow density measurements. The system can operate both as a monostatic and bistatic pulsed-radar over the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.7-2.1 \text{GHz}$</tex> frequency range. The bistatic operation utilized two drones, with their radars synchronized via GPS-disciplined oscillators. The transmit signal is generated through direct synthesis of a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1-\mu$</tex> s chirp using an RF-SoC platform. Received signals are directly digitized and pulse-compressed. A Synthetic Aperture Radar (SAR) algorithm was applied to obtain fine resolution in the along-track direction. The radar successfully measured snow depths ranging from 0.5 to 2.5 meters, even in forested areas with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${1 5}-{2 5}$</tex> meter tall trees, showing strong agreement with in-situ measurements, with errors within a few centimeters. Using the bistatic radar mode, we measured snow permittivity and estimated snow density. We generated high-resolution SWE and snow depth maps and compared these with in-situ measurements.

Authors

Institutions

Publication Details

Published
2025-05-03
DOI
https://doi.org/10.1109/radar52380.2025.11031795
Primary Topic
Cryospheric studies and observations
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Miniaturized s-UAS-Based Ultra-Wideband Radar for Snow Water Equivalent Map Synthesis

Prasad Gogineni, Drew Taylor, Omid Reyhanigalangashi, Jordan D. Larson et al.
Cryospheric studies and observations
article

Miniaturized s-UAS-Based Ultra-Wideband Radar for Snow Water Equivalent Map Synthesis

Prasad Gogineni, Drew Taylor, Omid Reyhanigalangashi, Jordan D. Larson, Allain Rapadas, Zachary Herring, Adam Hallmark
article en

Abstract

This paper presents a miniaturized, small unmanned aerial system (sUAS)-based ultra-wideband (UWB) radar system and results from measurements on snow over Grand Mesa, Colorado, during Spring 2024. The system was developed to estimate snow water equivalent (SWE) directly from radar data without extensive in-situ snow density measurements. The system can operate both as a monostatic and bistatic pulsed-radar over the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.7-2.1 \text{GHz}$</tex> frequency range. The bistatic operation utilized two drones, with their radars synchronized via GPS-disciplined oscillators. The transmit signal is generated through direct synthesis of a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1-\mu$</tex> s chirp using an RF-SoC platform. Received signals are directly digitized and pulse-compressed. A Synthetic Aperture Radar (SAR) algorithm was applied to obtain fine resolution in the along-track direction. The radar successfully measured snow depths ranging from 0.5 to 2.5 meters, even in forested areas with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${1 5}-{2 5}$</tex> meter tall trees, showing strong agreement with in-situ measurements, with errors within a few centimeters. Using the bistatic radar mode, we measured snow permittivity and estimated snow density. We generated high-resolution SWE and snow depth maps and compared these with in-situ measurements.

University of Alabama (US)
Clean water and sanitation
Openalex Percentile: Top 81%
Cryospheric studies and observations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.