Novel algorithms for motion detection and imaging in complex scenes
Abstract/Contents
- Abstract
- This thesis focuses on data structures and algorithms used in motion detection and imaging in complex settings. The work consists of four projects, addressing different aspects of imaging with a synthetic aperture radar (SAR) system or in an inverse synthetic aperture radar (iSAR) setting. In the first project, I analyzed an algorithm for the detection of moving targets in SAR using robust principal component analysis (RPCA). In the second project, I introduced an extension of the SAR data structure to tensors and a modified tensor RPCA algorithm, to improve detection of slowly moving targets. In the third project, I introduced a cross correlation data structure for iSAR imaging of low earth orbit (LEO) fast moving satellites, as well as novel imaging algorithms adapted to the cross correlation data structure. In the fourth project, I extended the problem to rotating satellites, analyzed the effect rotation has on performance, and showed how the rotation parameters can be extracted from the data. In a broad sense, all of these projects explore the effect data representation can have in imaging algorithms. The RPCA problems show that specific features in the raw data can be be exploited to detect motion. Moreover, the specific model of the data and different ways in which they are represented can significantly improve the performance of linear algebra and optimization based tools when applied to this problem. In the correlation based imaging problems, the choice of an appropriate data representation can provide insight into both improved imaging algorithms and their analysis. While these projects are distinct, they all demonstrate the importance of the choice of data structures and representations in imaging problems. The specific data structure may not only improve the applicability of previously used algorithms, but can also provide insight into extensions and modifications, as well as a rigorous mathematical analysis of the imaging algorithms
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Leibovich, Matan |
|---|---|
| Degree supervisor | Papanicolaou, George |
| Thesis advisor | Papanicolaou, George |
| Thesis advisor | Montanari, Andrea |
| Thesis advisor | Tsogka, Chrysoula |
| Degree committee member | Montanari, Andrea |
| Degree committee member | Tsogka, Chrysoula |
| Associated with | Stanford University, Institute for Computational and Mathematical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Matan Leibovich |
|---|---|
| Note | Submitted to the Institute for Computational & Mathematical Engineering |
| Thesis | Thesis Ph.D. Stanford University 2020 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Matan Leibovich
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