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Passive Radar Detection and Imaging Using Low-rank Matrix Recovery

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개인저자Mason, Eric.
단체저자명Rensselaer Polytechnic Institute. Electrical Engineering.
서명/저자사항Passive Radar Detection and Imaging Using Low-rank Matrix Recovery.
발행사항[S.l.] : Rensselaer Polytechnic Institute., 2017
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2017
형태사항202 p.
소장본 주기School code: 0185.
ISBN9780438206212
일반주기 Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Birsen Yazici.
요약The objective of this thesis is to develop passive radar imaging methods in an optimization framework that utilize prior information. Passive radar relies on transmitters of opportunity such as commercial television, radio, and cell phone base s
요약First, this thesis presents a non-linear optimization based reconstruction method for passive radar that overcomes the drawbacks of currently used Fourier based methods, such as passive coherent localization (PCL) and time difference of arrival
요약Next, we study the performance of the convex LRMR based approach. We show that at sufficiently high center frequencies and commonly used imaging configurations the convex LRMR method recovers the scene reflectivity exactly. Furthermore, we deriv
요약We then use non-convex optimization methods to reduce computational complexity and enforce the rank-one structure directly. We derive a descent algorithm using the majorization-minimization framework and prove convergence to an optimal solution
요약Then we study the structure of orthogonal frequency division multiplexed (OFDM) waveforms used by common television and cellular illuminators of opportunity. Using this waveform model, we pose joint estimation as maximum a posteriori (MAP) estim
일반주제명Electrical engineering.
Applied mathematics.
언어영어
기본자료 저록Dissertation Abstracts International79-12B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T14996720

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