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Ku-Band Ultra-High Resolution Radar Tomography of an Alpine Snowpack

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Ku-Band Ultra-High Resolution Radar Tomography of an Alpine Snowpack Brigham Young University BYU ScholarsArchive Theses and Dissertations 2020-04-07 Ku-Band Ultra-High Resolution Radar Tomography of an Alpine Snowpack Ryan Natale Bartley Brigham Young University Follow this and additional works at: https://scholarsarchive.byu.edu/etd Part of the Engineering Commons BYU ScholarsArchive Citation Bartley, Ryan Natale, "Ku-Band Ultra-High Resolution Radar Tomography of an Alpine Snowpack" (2020). Theses and Dissertations. 8401. https://scholarsarchive.byu.edu/etd/8401 This Thesis is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact [email protected], [email protected]. Ku-Band Ultra-High Resolution Radar Tomography of an Alpine Snowpack Ryan Natale Bartley A thesis submitted to the faculty of Brigham Young University in partial fulfillment of the requirements for the degree of Master of Science David G. Long, Chair Wood Chiang Cameron Peterson Department of Electrical Engineering Brigham Young University Copyright © 2020 Ryan Natale Bartley All Rights Reserved ABSTRACT Ku-Band Ultra-High Resolution Radar Tomography of an Alpine Snowpack Ryan Natale Bartley Department of Electrical Engineering, BYU Master of Science A commercial-off-the-shelf Ku-band Frequency Modulated Continuous Wave (FMCW) synthetic aperture radar (SAR) system is coupled with a custom built two-dimensional scanning system. This system is installed in an alpine environment and pointed at a snow-unstable moun- tain slope for the duration of a Utah winter. The radar scanning system, designed to be capable of mapping a snowpack and its layers, is employed to create a series of three-dimensional images from a remote location. Individual images demonstrate the ability to directly detect snow layers, Furthermore, successive images are compared to track volume magnitude and phase values over the course of winter, including many snow deposition and melt events. The digital signal process- ing techniques used to create a high-resolution voxel (a three-dimensional pixel) map describing these snow layers is discussed. Results are discussed and further work is suggested for improving upon the results of this work. Keywords: radar, ku-band, remote sensing, high-resolution, penetrating radar, snow, stratigraphy, tomography, backprojection, change detection ACKNOWLEDGMENTS This thesis has been a Long time in the making. That said, I would like to thank Dr. David Long for his immense amount of support, encouragement, instruction and humor. Perhaps most importantly, I would like to thank my devoted wife, Rebekah, for stepping up to the challenge of essentially being a single mother while I completed coursework and this thesis. During this time, she reared a child, while caring for two young children, a sleep-deprived spouse, two dogs, two cats and too many chickens - all without much, if any, complaint. A “thank you” is in order to the brain trust at IMSAR LLC who assisted me throughout this effort. This includes Luke Kunz, Ivan Ashcraft, Brett Young, Rhett Phillips and many more. Thank you to Ryan Smith, IMSAR’s Founder and CEO, for lending BYU “Betsy”, the trusty NanoSAR B Ku-Band radar which made these data collections possible. Finally, I would like to thank Andre Brummer, for initiating and making possible this very cool effort. TABLE OF CONTENTS LIST OF TABLES ....................................... v LIST OF FIGURES ...................................... vi Chapter 1 Introduction ................................... 1 1.1 Purpose . .1 1.2 Contribution . .2 1.3 Outline . .3 Chapter 2 Background ................................... 4 2.1 Linear Frequency Modulated Continuous Wave Radar . .4 2.1.1 Resolution . .7 2.2 Backprojection Algorithm . 10 2.3 Prior Art . 12 2.3.1 Snow Structure and Radar Relationship . 15 2.3.2 Volume Scattering . 18 2.4 Conclusion . 21 Chapter 3 System Design .................................. 23 3.1 Motivation . 23 3.2 Radar Requirements . 24 3.3 Scanner Requirements and Design . 27 3.4 Site Selection . 32 3.5 Conclusion . 35 Chapter 4 Results ...................................... 38 4.1 Data Overview . 38 4.2 Results . 41 4.2.1 Base Imagery . 41 4.2.2 Difference Imagery . 59 4.3 Conclusion . 61 Chapter 5 Conclusion ................................... 64 5.1 Contributions . 65 5.2 Future Work . 65 REFERENCES ......................................... 67 iv LIST OF TABLES 4.1 Overview of collections taken during the Winter of 2016-2017. In total 12 different collections were made, although only a select few are analyzed (green rows). The number of snow events and the depths of each event between each collection are provided by [1]. The temperatures between each collection are provided by [2]. 39 4.2 List of change detection images with days elapsed between passes, snowfall events and average temperatures. 60 v LIST OF FIGURES 2.1 Example LFMCW radar timing. The frequency sweep from f1 to f2, centered at f0 for a single sweep which lasts tswp seconds. The received signal from an ideal point scatter occurs t seconds after transmission . .6 2.2 Transformation of a rect in Frequency Domain is a sinc with peak-to-null width of 1=T (le ft). Illustration of Rayleigh Resolution Criterion (right)..........8 2.3 Transiting a full aperture, DSAR, results in the target being viewed across the full beamwidth , q, and provides the maximum azimuth resolution . .9 2.4 Diagram of a two dimensional synthetic aperture scan. A synthetic pencil beam is created by dragging the radar in space along the azimuth and elevation directions. Range resolution is a function of bandwidth as discussed in the text. 10 2.5 Visualization of voxel image cube and demonstration of complexity for three- dimensional image formation. Image cube on left has 5 voxels per side, while image on the right has 10 voxels per side. 12 2.6 Two dimension profile of snowpack provided by [3] (le ft). Three dimensional radar image, known as tomography, provided by [4] (right). 15 2.7 Dielectric constant and dielectric loss factor of snow measured at 18 GHz by [5] at varying liquid water contents. 16 2.8 Simplification of snow stratigraphy represented by various thicknesses and refrac- tive indexes, n. Incident waves (solid lines) enter normal to snowpack, where faint returns (dotted lines) are mixed in with strong returns (dashed lines) (le ft). In- cident waves enter from left at approx 45°, faint returns return back to the radar, while strong returns forward scatter (right). ..................... 19 2.9 Snell’s Laws for a specular surface. Incident wave travels through the first medium with refractive index, n1, at q1 and enters the second medium with refractive index n2 at a new angle of q2 (le ft). Snell’s Laws for a rough surface such as snow. In addition to reflection and refraction, backscatter is created, shown in purple, due to rough surface (right)................................. 20 3.1 IMSAR’s NanoSAR B displayed next to a ruler for scale (le ft). Summary of NanoSAR B operational parameters used in this study (right). 24 3.2 Absorption coefficient by frequency and snow water content using Debye-like model. Assumed snow density of 0.25 g=cm3. Chart courtesy of Ulaby and Long [6]. 26 3.3 Volume backscattering coefficient, sv, for a range of particles sizes and multiple discrete densities. 27 3.4 Output of the RRE, Eq. 2.1, using the terms discussed in Section 3.2. Values used assume that snow water equivalent is < 1%, minimizing the loss factor. Assuming a minimum of 15 dB SNR for high quality imaging, the system provides enough transmit power for most particle sizes as indicated by the dashed line. 28 3.5 Diagram of the two-dimension mechanical system. The scanners external dimen- sions are 2 meters wide by 3 meters high. The radar scans within the structure and is restricted to 182 cm high by 283 cm wide. A radar sled resides on the gantry allowing for freedom in two dimensions. 30 vi 3.6 Image of antennas and radar on the gantry mounted sled, all residing on the scanner gantry. Assorted RF, Ethernet, and power cables also shown. 30 3.7 The Raspberry Pi SBC acts as the central nervous system for the scanner. It con- trols the radar, encoders, GPS and stepper motors. The Rapsberry Pi also accepts inputs from the GPS and encoders and fuses that information with the raw radar data which it transfers from the radar. 31 3.8 Front-view illustration of the scanner with auxiliary items such as full motion video cameras (to observe scanning and the mountainside remotely), tool balances, WiFi and Ethernet back-haul datalink. 33 3.9 Profile view of scanner at Aspen Grove, Utah (le ft). Rear view of scanner with observation site in the background (right)....................... 33 3.10 The scanner and observation locations. The radars viewshed is highlighted in bright green wile the white trapezoid highlights the coverage area within the beamwidth. The intersection of these two areas represents the observable area. 34 3.11 A profile view of the Aspen Grove site. The latitude, longitude and altitude of the scanner and observation center are provided on the left and right of the image. The approximate range between the two points is 650 meters. 35 3.12 Scanner orientation at Aspen Grove. 36 4.1 Observation site graphics provided for situational awareness. The white lines sig- nify the beamwidth and center line. The black square represents imaging location. The red contour line represents approximate center swath distance. Observation site profile illustrating where to expect voxel locations (le ft). The image is formed 20 meters above to 20 meters below the Digital Elevation Map (DEM) (middle). One-way slant range relative to radar location (right). 40 4.2 An example image cube. The x axis aligns with the east/west directions, the y axis aligns with the north/south directions and the z axis aligns with the up/down directions.
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