دانلود کتاب Advanced Sparsity-Driven Models and Methods for Radar Applications

Compressed sensing (CS) has been one of the most active topics in signal processing
area. By exploiting the sparsity of the signals, CS offers a prospective way for
reducing data amount without compromising the performance of signal recovery or
enhancing resolution without increasing the number of measurements. The signals
in many radar applications are sparse or compressible, so the radar systems may
benefit from the sparsity-driven models and methods in terms of reducing observation
duration, simplifying hardware, and enhancing performance. However, in
practical radar applications, it is found that directly applying the basic CS models
and algorithms to radar data may be less than optimal and even unsatisfactory.
Thus, it is necessary to develop advanced sparsity-based models and algorithms to
fit various radar tasks, which has become a fast-growing branch of radar signal
processing in recent years.
The objective of this book is to introduce more recent developments on
advanced sparsity-driven models and methods that are designed for radar tasks
including clutter suppression, signal detection, radar imaging, target parameter
estimation, and target recognition, mainly based on my publications in the last
decade. Besides the theoretical analysis, numerous simulation examples and
experiments on real radar data are presented throughout the book. The material
presented in this book can be understood by readers who have a fundamental
knowledge of radar signal processing. The book can serve as a reference book for
academic researchers, practicing engineers, and graduate students.
The outline of this book is as follows. Before introducing the advanced sparsitydriven
models and methods designed for radar tasks, the fundamentals of CS are
briefly reviewed in Chapter 1. In Chapter 2, the hybrid greedy pursuit algorithms are
presented for enhancing radar imaging quality. In Chapter 3, the two-level block
sparsity model is introduced to promoting the sparsity of signals of multichannel
radar systems. In Chapter 4, the parametric sparse representation is studied to deal
with model uncertainty during the radar data collection. Chapter 5 investigates how
to simultaneously achieve high-resolution and wide-swath in single-channel synthetic
aperture radar (SAR) imaging by utilizing the Poisson disk sampling.
Chapter 6 concentrates on the sparsity-driven algorithms of radar image formation
from coarsely quantized data. Chapter 7 is concerned with sparsity aware radar
micro-Doppler analysis for micromotion parameter estimation and target recognition.
Chapter 8 is devoted to the distributed detection of sparse signals with
radar networks. Chapter 9 summarizes the book and discusses some perspectives.