Multistep Knowledge-Aided Iterative ESPRIT: Design and Analysis

In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA) estimation that iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the mean squared error of the re...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2018-10, Vol.54 (5), p.2189-2201
Main Authors: Pinto, Silvio F. B., de Lamare, Rodrigo C.
Format: Article
Language:English
Subjects:
Algorithms
Arrays
Computational complexity
Computer simulation
Covariance matrices
Covariance matrix
direction finding
Direction of arrival
Direction-of-arrival estimation
Economic models
Error analysis
Estimation
high-resolution parameter estimation
Iterative methods
knowledge-aided techniques
Large sensor arrays
Signal processing algorithms
Signal to noise ratio
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this work, we propose a subspace-based algorithm for direction-of-arrival (DOA) estimation that iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the mean squared error of the reshaped data covariance matrix is carried out along with comparisons between computational complexities of the proposed and existing algorithms. Simulations focusing on closely-spaced sources, where they are uncorrelated and correlated, illustrate the improvements achieved.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2018.2811098