Advanced Sparsity-Driven Models and Methods for Radar Applications /

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Bibliographic Details
Author / Creator:Li, Gang.
Imprint:Stevenage : SciTech Publishing Inc., 2020.
Description:1 online resource (272 p.).
Language:English
Series:Radar, Sonar & Navigation
Radar, Sonar & Navigation.
Subject:Doppler radar.
Imaging.
Radar.
Signal processing.
Synthetic aperture radar.
Target acquisition.
Testing.
Doppler radar.
Radar.
Signal processing.
Synthetic aperture radar.
Target acquisition.
Testing.
compressed sensing.
Doppler radar.
greedy algorithms.
image classification.
image coding.
image enhancement.
image representation.
image resolution.
image sampling.
iterative methods.
radar detection.
radar imaging.
radar resolution.
radar target recognition.
synthetic aperture radar.
testing.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12873654
Hidden Bibliographic Details
ISBN:1839530766
9781839530760
Summary:The book has 9 chapters. The following topics are dealt with: Introduction; Hybrid greedy pursuit algorithms for enhancing radar imaging quality; Two-level block sparsity model for multichannel radar signals; Parametric sparse representation for radar imaging with model uncertainty; Poisson disk sampling for high-resolution and wide-swath SAR imaging; When advanced sparse signal models meet coarsely quantized radar data; Sparsity aware micro-Doppler analysis for radar target classification; Distributed detection of sparse signals in radar networks via locally most powerful test; and Summary and perspectives.
Standard no.:10.1049/SBRA535E
Description
Summary:This book introduces advanced sparsity-driven models and methods and their applications in radar tasks such as detection, imaging and classification. Compressed sensing (CS) is one of the most active topics in the signal processing area. By exploiting and promoting the sparsity of the signals of interest, CS offers a new framework for reducing data without compromising the performance of signal recovery, or for enhancing resolution without increasing measurements.
Physical Description:1 online resource (272 p.).
ISBN:1839530766
9781839530760