NDLI: Rank-Constrained Maximum Likelihood Estimation of Structured Covariance Matrices

Content Provider	IEEE Xplore Digital Library
Author	Bosung Kang Monga, V. Rangaswamy, M.
Copyright Year	1965
Abstract	This paper develops and analyzes the performance of a structured covariance matrix estimate for the important practical problem of radar space-time adaptive processing in the face of severely limited training data. Traditional maximum likelihood (ML) estimators are effective when training data are abundant, but they lead to poor estimates, degraded false alarm rates, and detection loss in the realistic regime of limited training. The problem is exacerbated by recent advances, which have led to high-dimensional N of the observations arising from increased antenna elements, as well as higher temporal resolution (P time epochs and finally N = JP). This work addresses the problem by incorporating constraints in the ML estimation problem obtained from the geometry and physics of the airborne phased array radar scenario. In particular, we exploit the structure of the disturbance covariance and, importantly, knowledge of the clutter rank to derive a new rank-constrained maximum likelihood (RCML) estimator of clutter and disturbance covariance. We demonstrate that despite the presence of the challenging rank constraint, the estimation can be transformed to a convex problem and derive closed-form expressions for the estimated covariance matrix. Performance analysis using the knowledge-aided sensor signal processing and expert reasoning data set (where ground truth covariance is made available) shows that the proposed estimator outperforms state-of-the-art alternatives in the sense of a higher normalized signal-to-interference and noise ratio. Crucially, the RCML estimator excels for low training, including the notoriously difficult regime of K ≤ N training samples.
Starting Page	501
Ending Page	515
Page Count	15
File Size	1065011
File Format	PDF
ISSN	00189251
Volume Number	50
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Covariance matrices Maximum likelihood estimation Radar signal processing Eigenvalues and eigenfunctions Adaptive signal processing Phased arrays
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Aerospace Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Application of Maximum Likelihood Estimation of Persymmetric Covariance Matrices to Adaptive Processing

On the practical merits of rank constrained ML estimator of structured covariance matrices

NAR estimators of spatial covariance matrices for adaptive array detection

Maximum Likelihood Estimation of a Structured Covariance Matrix With a Condition Number Constraint

Multiple target tracking using maximum likelihood principle

Application of the Normalized LMS Algorithm to MSLC

Fast converging adaptive processor or a structured covariance matrix

Constrained ML estimation of structured covariance matrices with applications in radar STAP

On Kronecker and Linearly Structured Covariance Matrix Estimation

Rank-Constrained Maximum Likelihood Estimation of Structured Covariance Matrices

Similar Documents

Application of Maximum Likelihood Estimation of Persymmetric Covariance Matrices to Adaptive Processing

On the practical merits of rank constrained ML estimator of structured covariance matrices

NAR estimators of spatial covariance matrices for adaptive array detection

Maximum Likelihood Estimation of a Structured Covariance Matrix With a Condition Number Constraint

Multiple target tracking using maximum likelihood principle

Application of the Normalized LMS Algorithm to MSLC

Fast converging adaptive processor or a structured covariance matrix

Constrained ML estimation of structured covariance matrices with applications in radar STAP

On Kronecker and Linearly Structured Covariance Matrix Estimation

Rank-Constrained Maximum Likelihood Estimation of Structured Covariance Matrices