Journal ArticleDOI
SPICE: A Sparse Covariance-Based Estimation Method for Array Processing
Petre Stoica,Prabhu Babu,Jian Li +2 more
Reads0
Chats0
TLDR
This paper presents a novel SParse Iterative Covariance-based Estimation approach, abbreviated as SPICE, to array processing, obtained by the minimization of a covariance matrix fitting criterion and is particularly useful in many- snapshot cases but can be used even in single-snapshot situations.Abstract:
This paper presents a novel SParse Iterative Covariance-based Estimation approach, abbreviated as SPICE, to array processing. The proposed approach is obtained by the minimization of a covariance matrix fitting criterion and is particularly useful in many-snapshot cases but can be used even in single-snapshot situations. SPICE has several unique features not shared by other sparse estimation methods: it has a simple and sound statistical foundation, it takes account of the noise in the data in a natural manner, it does not require the user to make any difficult selection of hyperparameters, and yet it has global convergence properties.read more
Citations
More filters
Proceedings ArticleDOI
Online Sparse reconstruction for scanning radar based on Generalized SParse Iterative Covariance-based Estimation
TL;DR: The implementation could update and refine the super-resolution result for each obtained data sample along beam scanning, offering a significant reduction in the required computational complexity, as compared to forming the batch implementation of the generalized SPICE method.
Journal ArticleDOI
Demixing Sines and Spikes Using Multiple Measurement Vectors
TL;DR: In this article , a semi-definite program (SDP) was proposed to demix the two components and for that, a convex problem whose objective function promotes both of the structures was designed.
Proceedings ArticleDOI
Direction-of-Arrival Estimation Based on Enhanced Sparse Representation
TL;DR: An algorithm based on an enhanced sparse representation for direction-of-arrival (DOA) estimation is proposed, which first design a weight vector by making use of the spatial spectrum and is incorporated into the sparse representation framework for DOA estimation.
Journal ArticleDOI
Structured channel covariance estimation from limited samples for large antenna arrays
TL;DR: In this paper , a parametric representation of the channel angular scattering function is proposed, which includes a discrete specular component which is addressed using the well-known MUltiple SIgnal Classification (MUSIC) method, and a diffuse scattering component modeled as the superposition of suitable dictionary functions.
Journal ArticleDOI
Intelligent Signal Detection Under Spatially Correlated Noise
TL;DR: This paper proposes a new method called PCASE, which can estimate the number of signals and the direction of arrival at the same time and combines the SPA and the Principal Component Analysis method (PCA) in machine learning.
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.
Journal ArticleDOI
Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones
TL;DR: This paper describes how to work with SeDuMi, an add-on for MATLAB, which lets you solve optimization problems with linear, quadratic and semidefiniteness constraints by exploiting sparsity.
Book
Interior-Point Polynomial Algorithms in Convex Programming
TL;DR: This book describes the first unified theory of polynomial-time interior-point methods, and describes several of the new algorithms described, e.g., the projective method, which have been implemented, tested on "real world" problems, and found to be extremely efficient in practice.
Book
Spectral analysis of signals
Petre Stoica,Randolph L. Moses +1 more
TL;DR: 1. Basic Concepts. 2. Nonparametric Methods. 3. Parametric Methods for Rational Spectra.
Related Papers (5)
Two decades of array signal processing research: the parametric approach
Hamid Krim,Mats Viberg +1 more