| LDR | | 02681nam u200445 4500 |
| 001 | | 000000418225 |
| 005 | | 20190215162703 |
| 008 | | 181129s2017 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438206212 |
| 035 | |
▼a (MiAaPQ)AAI10681319 |
| 035 | |
▼a (MiAaPQ)rpi:11199 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Mason, Eric. |
| 245 | 10 |
▼a Passive Radar Detection and Imaging Using Low-rank Matrix Recovery. |
| 260 | |
▼a [S.l.]:
▼b Rensselaer Polytechnic Institute.,
▼c 2017. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2017. |
| 300 | |
▼a 202 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Birsen Yazici. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2017. |
| 520 | |
▼a The 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 |
| 520 | |
▼a First, 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 |
| 520 | |
▼a Next, 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 |
| 520 | |
▼a We 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 |
| 520 | |
▼a Then 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 |
| 590 | |
▼a School code: 0185. |
| 650 | 4 |
▼a Electrical engineering. |
| 650 | 4 |
▼a Applied mathematics. |
| 690 | |
▼a 0544 |
| 690 | |
▼a 0364 |
| 710 | 20 |
▼a Rensselaer Polytechnic Institute.
▼b Electrical Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0185 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996720
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |