Sparse methods for direction-of-arrival estimation
TLDR
An overview of these sparse methods for DOA estimation is provided, with a particular highlight on the recently developed gridless sparse methods, e.g., those based on covariance fitting and the atomic norm.Abstract:
Direction-of-arrival (DOA) estimation refers to the process of retrieving the direction information of several electromagnetic waves/sources from the outputs of a number of receiving antennas that form a sensor array. DOA estimation is a major problem in array signal processing and has wide applications in radar, sonar, wireless communications, etc. With the development of sparse representation and compressed sensing, the last decade has witnessed a tremendous advance in this research topic. The purpose of this article is to provide an overview of these sparse methods for DOA estimation, with a particular highlight on the recently developed gridless sparse methods, e.g., those based on covariance fitting and the atomic norm. Several future research directions are also discussed.read more
Citations
More filters
Journal ArticleDOI
Adaptive Virtual Waveform Design for Millimeter-Wave Joint Communication–Radar
TL;DR: Simulations show that an optimal virtual (non-uniform) waveform achieves a significant performance improvement as compared to a uniform waveform, and develops three different MMSE-based optimization problems for the adaptive JCR waveform design.
Journal ArticleDOI
A survey on 5G massive MIMO Localization
TL;DR: An overview of the emerging field of massive MIMO localization is provided, which can be used to meet the requirements of 5G, by exploiting different spatial signatures of users.
Journal ArticleDOI
Deep Networks for Direction-of-Arrival Estimation in Low SNR
TL;DR: This work introduces a Convolutional Neural Network that predicts angular directions using the sample covariance matrix estimate and presents a training method, where the CNN learns to infer their number and predict the DoAs with high confidence.
Journal ArticleDOI
Atomic Norm Minimization for Modal Analysis From Random and Compressed Samples
TL;DR: A bound is established on the sample complexity of modal analysis with random temporal compression, and in this scenario it is proved that the required number of samples per sensor can actually decrease as the number of sensors increases.
References
More filters
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book
Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
Book
Matrix Analysis
Roger A. Horn,Charles R. Johnson +1 more
TL;DR: In this article, the authors present results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrate their importance in a variety of applications, such as linear algebra and matrix theory.
Book
Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
Book
Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.