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2024 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Study on Signal Detection and Recovery Methods with Joint Sparsity

verfasst von: Xueqian Wang

Verlag: Springer Nature Singapore

Buchreihe : Springer Theses

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik"

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SUCHEN

Über dieses Buch

The task of signal detection is deciding whether signals of interest exist by using their observed data. Furthermore, signals are reconstructed or their key parameters are estimated from the observations in the task of signal recovery. Sparsity is a natural characteristic of most of signals in practice. The fact that multiple sparse signals share the common locations of dominant coefficients is called by joint sparsity. In the context of signal processing, joint sparsity model results in higher performance of signal detection and recovery. This book focuses on the task of detecting and reconstructing signals with joint sparsity. The main contents include key methods for detection of joint sparse signals and their corresponding theoretical performance analysis, and methods for joint sparse signal recovery and their application in the context of radar imaging.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
Traditional signal processing methods are based on Nyquist sampling theory, i.e., the sampling frequency of a real signal should be larger than twice its bandwidth, while for a complex signal (e.g., radar signal), the sampling frequency is supposed to be larger than its bandwidth.
Xueqian Wang
Chapter 2. Detection of Jointly Sparse Signals via Locally Most Powerful Tests with Gaussian Noise
Abstract
In recent years, compressive sensing (CS) has emerged as a new paradigm for sparse signal processing, which aims at obtaining valuable information of sparse signals from a small number of measurements.
Xueqian Wang
Chapter 3. Detection of Jointly Sparse Signals via Locally Most Powerful Tests with Generalized Gaussian Model
Abstract
In Chap. 2, we consider the problem of detection of jointly sparse signals with Gaussian signals and noise.
Xueqian Wang
Chapter 4. Jointly Sparse Signal Recovery Method Based on Look-Ahead-Atom-Selection
Abstract
Different from the signal detection algorithms in Chaps. 2 and 3 with the output H0 or H1, signal recovery approaches aim to solve an inverse problem, i.e., accurately reconstruct sparse signals with an overcomplete sensing matrix and a small number of measurements. The task of jointly sparse signal recovery plays an important role in distributed sensor networks, multi-channel radar imaging, spectrum sensing, etc.
Xueqian Wang
Chapter 5. Signal Recovery Methods Based on Two-Level Block Sparsity
Abstract
The clustering property of sparse signals is that the non-zero elements occur in clusters instead of a few isolated points. Existing literature shows that the exploiting of the clustering structure of sparse signals is helpful to enhance the performance of signal recovery.
Xueqian Wang
Chapter 6. Summary and Perspectives
Abstract
Joint sparsity is a natural feature of signals and implies the dependence of multiple sparse signals. Compared with the independent model and processing of multiple sparse signals, joint sparsity is helpful to improve the performance of signal detection and recovery. Focusing on the problems of detection and recovery of jointly sparse signals, this book mainly introduces the jointly sparse signal detection and recovery methods and the corresponding performance analysis with analog and coarsely quantized measurements.
Xueqian Wang
Backmatter
Metadaten
Titel
Study on Signal Detection and Recovery Methods with Joint Sparsity
verfasst von
Xueqian Wang
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Electronic ISBN
978-981-9941-17-9
Print ISBN
978-981-9941-16-2
DOI
https://doi.org/10.1007/978-981-99-4117-9

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