Compressive Sensing for Urban Radar
Editat de Moeness Aminen Limba Engleză Paperback – 29 mar 2017
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 modeling
Preț: 456.63 lei
Preț vechi: 590.26 lei
-23% Nou
Puncte Express: 685
Preț estimativ în valută:
87.38€ • 90.68$ • 73.04£
87.38€ • 90.68$ • 73.04£
Carte tipărită la comandă
Livrare economică 17-31 martie
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9781138073401
ISBN-10: 1138073407
Pagini: 508
Ilustrații: 196
Dimensiuni: 156 x 234 mm
Greutate: 0.45 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
ISBN-10: 1138073407
Pagini: 508
Ilustrații: 196
Dimensiuni: 156 x 234 mm
Greutate: 0.45 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Cuprins
Compressive Sensing Fundamentals. Overcomplete Dictionary Design for Sparse Reconstruction of Building Layout Mapping. Compressive Sensing for Radar Imaging of Underground Targets. Wall Clutter Mitigations for Compressive Imaging of Building Interiors. Compressive Sensing for Urban Multipath Exploitation. Compressive Sensing Kernel Design for Imaging of Urban Objects. Compressive Sensing for Multi-Polarization Through-Wall Radar Imaging. Sparseness-Aware Human Motion Indication. Time-Frequency Analysis of Micro-Doppler Signals based on Compressive Sensing. Urban Target Tracking using Sparse Representations. 3D Imaging of Vehicles from Sparse Apertures in Urban Environment. Compressive Sensing for MIMO Urban Radar. Compressive Sensing Meets Noise Radar.
Notă biografică
Dr. Moeness G. Amin has been a faculty member of the Department of Electrical and Computer Engineering at Villanova University, Pennsylvania, USA for nearly 30 years. In 2002, he became the director of the Center for Advanced Communications, College of Engineering. Currently he is the chair of the Electrical Cluster of the Franklin Institute Committee on Science and the Arts, as well as an IEEE, SPIE, and IET fellow. The recipient of many prestigious awards, he has conducted extensive research in radar signal processing, authored over 650 journal and conference papers, and served as an editor for numerous publications.
Recenzii
"The essential feature of this book is that it brings together the areas of compressive sensing and radar imaging for urban sensing. These areas of attributes are highly relevant to promote sustainability and for a range of civil and military applications, such as search and rescue missions, hostage rescue situations, urban design, and surveillance and reconnaissance in urban environments."
—Fulvio Gini, University of Pisa, Italy
—Fulvio Gini, University of Pisa, Italy
Descriere
This is the first book to focus on a hybrid of compressive 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 includes successful applications of compressive sensing for target detection and revealing building interiors, sparse reconstruction techniques for urban environments, and supporting examples using real data and computational electromagnetic modeling.