Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
Iterative sparse asymptotic minimum variance based approaches for array processing
IEEE Transactions on Signal Processing, Volume 61, No. 4, Article 6373742, Year 2013
Notification
URL copied to clipboard!
Description
This paper presents a series of user parameter-free iterative Sparse Asymptotic Minimum Variance (SAMV) approaches for array processing applications based on the asymptotically minimum variance (AMV) criterion. With the assumption of abundant snapshots in the direction-of-arrival (DOA) estimation problem, the signal powers and noise variance are jointly estimated by the proposed iterative AMV approach, which is later proved to coincide with the Maximum Likelihood (ML) estimator. We then propose a series of power-based iterative SAMV approaches, which are robust against insufficient snapshots, coherent sources and arbitrary array geometries. Moreover, to overcome the direction grid limitation on the estimation accuracy, the SAMV-Stochastic ML (SAMV-SML) approaches are derived by explicitly minimizing a closed form stochastic ML cost function with respect to one scalar paramter, eliminating the need of any additional grid refinement techniques. To assist the performance evaluation, approximate solutions to the SAMV approaches are also provided for high signal-to-noise ratio (SNR) and low SNR scenarios. Finally, numerical examples are generated to compare the performances of the proposed approaches with those of the existing ones. © 1991-2012 IEEE.
Authors & Co-Authors
Abeida, Habti
Saudi Arabia, Taif
Taif University
Zhang, Qilin
United States, Hoboken
Stevens Institute of Technology
Li, Jian N.
United States, Gainesville
University of Florida
Merabtine, Nadjim
Saudi Arabia, Taif
Taif University
Statistics
Citations: 54
Authors: 4
Affiliations: 3
Identifiers
Doi:
10.1109/TSP.2012.2231676
ISSN:
1053587X