Phase-space and related methods in sensor imaging
- This thesis focuses on computations, algorithm development and the analysis of sensor imaging. The work consists of three projects, each of which are inverse problems for the wave equation. In the first project, I introduced and analyzed an inverse filter for improved imaging by small arrays in both homogeneous and random media. In the second project, I introduced and implemented with real data statistical methods for passive sensor localization in a complex scattering medium. In the third project, I developed and analyzed a framework for motion estimation and autofocus within synthetic aperture radar (SAR) imaging. In a broad sense, these three projects address three important issues that could arise in a coherent sensor imaging application. The radio localization problem addresses sensor network awareness that would be a necessary first step in a larger effort to image moving targets within the network. The inverse filter work addresses the fundamental problem of understanding how to maximize the image resolution (or spatial focusing in a time-reversal context) that can be achieved given an array configuration in a homogeneous medium. The SAR project involves both compensating for error in knowledge of the position of the array used for imaging and also tracking of an target when the array is synthetic. While each of these problems is distinct, they all require dealing with noise in a careful and systematic way. In the inverse filter problem, we explicitly quantify the trade-off between the resolution enhancement and the noise amplification when using the inverse filter. For the radio localization problem, standard theory and methods using correlations are not sufficient in a medium that has very strong scattering that results in multiple peaks in the correlation functions. Statistical methods were developed to try to deal with this noisy correlation information. Finally, noise enters the SAR problem through measurement error of the flight platform that must be corrected to reach the image's resolution potential. Phase-space methods are important to the approaches taken to solve each of these problems.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Callaghan, Thomas Shields
|Stanford University, Institute for Computational and Mathematical Engineering.
|Statement of responsibility
|Thomas Shields Callaghan.
|Submitted to the Institute for Computational and Mathematical Engineering.
|Thesis (Ph.D.)--Stanford University, 2010.
- © 2010 by Thomas Shields Callaghan
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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