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Inference Methods for Synthetic Aperture Radar and Array Processing With Lattices

Tucker, David
http://orcid.org/0000-0001-7769-3130
http://rave.ohiolink.edu/etdc/view?acc_num=osu1714875020032109

Abstract Details

2024, Doctor of Philosophy, Ohio State University, Electrical and Computer Engineering.
This dissertation addresses two distinct topics: inference from synthetic aperture radar (SAR) data and the application of lattice theory to array signal processing. First, we propose a series of methods that mitigate the effect of speckle in inference tasks involving multichannel SAR data. Coherent imaging methods like SAR are subject to speckle, and the suppression of this noise-like quality is often a prerequisite to image interpretation. We propose a technique that recovers the per-pixel multichannel SAR covariance matrix and incorporates a statistical model of speckle and a priori knowledge of the varieties of clutter present in the scene. In this approach, an expectation-maximization algorithm is made computationally tractable by a graph-coloring probing technique. We next address the problem of coherent change detection in repeat-pass SAR data. A Bayesian change detection approach is given that assigns prior distributions to the unobserved model variables to exploit spatial structure both in the geophysical scattering qualities of the scene and among the scene disturbances that take place between the passes. We also give a polarimetric SAR (PolSAR) despeckling method based on convolutional neural networks (CNNs). An invertible transformation involving a matrix logarithm is used to facilitate CNN processing of the PolSAR data. A residual learning strategy is adopted, in which the CNN is trained to identify the speckle component which is then removed from the corrupted image. The latter portion of this dissertation is concerned with the application of lattice theory to array signal processing. We consider the problem of maximum likelihood parameter estimation in mixed integer linear models and provide two polynomial-time solution methods for special cases of this problem. These approaches extend the prior art by allowing for multivariate real-valued unknowns and more general linear models. We then provide a generally applicable alternative solution method that uses sphere decoding. We next consider the design and analysis of nonuniform arrays in one, two, and three dimensions. We give simply tested necessary and sufficient conditions for an array of sensors to unambiguously determine the direction of arrival for a specified set of possible directions of arrival. The new results facilitate the design of nonuniform arrays, allowing for configurations with widely separated sensors or increased apertures without an increase in the number of sensors. The unambiguous region is shown to be a parallelotope, a property which admits simple geometric interpretation and facilitates array design.
Lee Potter (Advisor)
Kiryung Lee (Committee Member)
Emre Ertin (Committee Member)
Electrical Engineering; Engineering
Synthetic aperture radar; Chinese remainder theorem; phase unwrapping; lattices; mixed integer quadratic programming; array processing

Recommended Citations

Citations

  • Tucker, D. (2024). Inference Methods for Synthetic Aperture Radar and Array Processing With Lattices [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1714875020032109

    APA Style (7th edition)

  • Tucker, David. Inference Methods for Synthetic Aperture Radar and Array Processing With Lattices. 2024. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1714875020032109.

    MLA Style (8th edition)

  • Tucker, David. "Inference Methods for Synthetic Aperture Radar and Array Processing With Lattices." Doctoral dissertation, Ohio State University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=osu1714875020032109

    Chicago Manual of Style (17th edition)

osu1714875020032109
130
© 2024, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.
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