Document Type


Date of Degree

Summer 2015

Degree Name

PhD (Doctor of Philosophy)

Degree In

Electrical and Computer Engineering

First Advisor

Anton Kruger

Second Advisor

Witold F. Krajewski


Over the last two decades, dual-polarimetric weather radar has proven to be a valuable instrument providing critical precipitation information through remote sensing of the atmosphere. Modern weather radar systems operate with high sampling rates and long dwell times on targets. Often only limited target information is desired, leading to a pertinent question: could lesser samples have been acquired in the first place? Recently, a revolutionary sampling paradigm – compressed sensing (CS) – has emerged, which asserts that it is possible to recover signals from fewer samples or measurements than traditional methods require without degrading the accuracy of target information. CS methods have recently been applied to point target radars and imaging radars, resulting in hardware simplification advantages, enhanced resolution, and reduction in data processing overheads. But CS applications for volumetric radar targets such as precipitation remain relatively unexamined. This research investigates the potential applications of CS to radar remote sensing of precipitation. In general, weather echoes may not be sparse in space-time or frequency domain. Therefore, CS techniques developed for point targets, such as in aircraft surveillance radar, are not directly applicable to weather radars. However, precipitation samples are highly correlated both spatially and temporally. We, therefore, adopt latest advances in matrix completion algorithms to demonstrate the sparse sensing of weather echoes. Several extensions of this approach are then considered to develop a more general CS-based weather radar processing algorithms in presence of noise, ground clutter and dual-polarimetric data. Finally, a super-resolution approach is presented for the spectral recovery of an undersampled signal when certain frequency information is known.

Public Abstract

A scanning radar beams a signal in multiple directions, and extracts the information about the targets from the signal that comes back after interaction with a target. A fast scanning radar would be able to update the changing target scenario quickly but, at the same time, it would also hit a target less frequently leading to inaccurate interpretation of target information. Recent research on the application of a novel technique called compressed sensing (CS) to synthetic aperture radars and point target radars suggests that the radar scan rate can be increased without compromising the information accuracy. However, these research efforts have not investigated application of CS to weather radars. In this research, we propose a CS framework for weather radars where the target-of-interest is volumetric. Our approach is based on the recent advances in low-rank matrix completion. We use Iowa X-band Polarimetric (XPOL) radar data to test our algorithms.


publicabstract, compressed sensing, dual-polarization, Iowa XPOLs, matrix completion, sparsity, weather radar


xiv, 94 pages


Includes bibliographical references (pages 85-94).


Copyright 2015 Kumar Vijay Mishra