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Compressive Sensing for Urban Radar Moeness Amin 1º edição
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Compressive Sensing for Urban Radar
Moeness Amin
With the emergence of compressive sensing and sparse signal reconstruction, approaches to urban radar have shifted toward relaxed constraints on signal sampling schemes in time and space, and to effectively address logistic difficulties in data acquisition. Traditionally, these challenges have hindered high resolution imaging by restricting both bandwidth and aperture, and by imposing uniformity and bounds on sampling rates.
Compressive Sensing for Urban Radar is the first book to focus on a hybrid of two key areas: compressive sensing and urban sensing. It explains how reliable imaging, tracking, and localization of indoor targets can be achieved using compressed observations that amount to a tiny percentage of the entire data volume. Capturing the latest and most important advances in the field, this state-of-the-art text:
Covers both ground-based and airborne synthetic aperture radar (SAR) and uses different signal waveforms Demonstrates successful applications of compressive sensing for target detection and revealing building interiors Describes problems facing urban radar and highlights sparse reconstruction techniques applicable to urban environments Deals with both stationary and moving indoor targets in the presence of wall clutter and multipath exploitation Provides numerous supporting examples using real data and computational electromagnetic modelingFeaturing 13 chapters written by leading researchers and experts, Compressive Sensing for Urban Radar is a useful and authoritative reference for radar engineers and defense contractors, as well as a seminal work for graduate students and academia.
508 pages, 196 black & white illustrations, 9 black & white tables
| Mídia | Livros Paperback Book (Livro de capa flexível e brochura) |
| Lançado | 29 de março de 2017 |
| ISBN13 | 9781138073401 |
| Editoras | Taylor & Francis Ltd |
| Páginas | 508 |
| Dimensões | 150 × 220 × 10 mm · 453 g |
| Idioma | Inglês |
| Editor | Amin, Moeness (Villanova University, Pennsylvania, USA) |
