2017
DOI: 10.1049/iet-rsn.2017.0007
|View full text |Cite
|
Sign up to set email alerts
|

Non‐linear shrinkage‐based precision matrix estimation for space–time adaptive processing

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2019
2019
2019
2019

Publication Types

Select...
1

Relationship

0
1

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 23 publications
0
1
0
Order By: Relevance
“…To tackle this problem, a multitarget shrinkage estimator is proposed, where the compromise between the sample covariance matrix and a well‐conditioned target matrix is considered . In addition, the nonlinear shrinkage‐based precision matrix estimation algorithm is investigated, in which the eigenvalues of the samples covariance matrix are shrunken nonlinearly …”
Section: Introductionmentioning
confidence: 99%
“…To tackle this problem, a multitarget shrinkage estimator is proposed, where the compromise between the sample covariance matrix and a well‐conditioned target matrix is considered . In addition, the nonlinear shrinkage‐based precision matrix estimation algorithm is investigated, in which the eigenvalues of the samples covariance matrix are shrunken nonlinearly …”
Section: Introductionmentioning
confidence: 99%