内容説明
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 modeling
Featuring 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.
目次
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.
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