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This book introduces advanced sparsity-driven models and methods and their applications in radar tasks such as detection, imaging and classification. It is based on research from the last decade, with a particular focus on applying compressed-sensing-based models and algorithms to solve practical problems in radar.
Inhaltsverzeichnis
- Chapter 1: Introduction
- Chapter 2: Hybrid greedy pursuit algorithms for enhancing radar imaging quality
- Chapter 3: Two-level block sparsity model for multichannel radar signals
- Chapter 4: Parametric sparse representation for radar imaging with model uncertainty
- Chapter 5: Poisson disk sampling for high-resolution and wide-swath SAR imaging
- Chapter 6: When advanced sparse signal models meet coarsely quantized radar data
- Chapter 7: Sparsity aware micro-Doppler analysis for radar target classification
- Chapter 8: Distributed detection of sparse signals in radar networks via locally most powerful test
- Chapter 9: Summary and perspectives
Über den Autor / die Autorin
Gang Li is a Professor at the Department of Electronic Engineering, Tsinghua University, China. His research interests include radar signal processing, remote sensing, distributed signal processing, and information fusion. He has published over 150 papers on these subjects. He is a recipient of the National Science Fund for Distinguished Young Scholars of China and the Royal Society Newton Advanced Fellowship of United Kingdom. He is a Senior Member of the IEEE.
Zusammenfassung
This book introduces advanced sparsity-driven models and methods and their applications in radar tasks such as detection, imaging and classification. It is based on research from the last decade, with a particular focus on applying compressed-sensing-based models and algorithms to solve practical problems in radar.
