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Inference Methods for Synthetic Aper...

Tucker, David,

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Inference Methods for Synthetic Aperture Radar and Array Processing With Lattices /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Inference Methods for Synthetic Aperture Radar and Array Processing With Lattices // David Tucker.
Author:	Tucker, David,
Description:	1 electronic resource (221 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
Contained By:	Dissertations Abstracts International86-04B.
Subject:	Engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31693951
ISBN:	9798384462729

Inference Methods for Synthetic Aperture Radar and Array Processing With Lattices /
Tucker, David,

Inference Methods for Synthetic Aperture Radar and Array Processing With Lattices /David Tucker. - 1 electronic resource (221 pages)

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

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.

English

ISBN: 9798384462729Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Synthetic aperture radar

Inference Methods for Synthetic Aperture Radar and Array Processing With Lattices /
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520 $a 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.
546 $a English
590 $a School code: 0168
650 4 $a Engineering. $3 586835
650 4 $a Electrical engineering. $3 649834
650 4 $a Geophysics. $3 535228
650 4 $a Remote sensing. $3 535394
650 4 $a Information technology. $3 532993
653 $a Synthetic aperture radar
653 $a Chinese remainder theorem
653 $a Phase unwrapping
653 $a Lattices
653 $a Mixed integer quadratic programming
653 $a Array processing
690 $a 0544
690 $a 0537
690 $a 0489
690 $a 0799
690 $a 0373
710 2 $a The Ohio State University. $b Electrical and Computer Engineering. $e degree granting institution. $3 3765725
720 1 $a Potter, Lee C. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-04B.
790 $a 0168
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31693951