2020 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) 2020
DOI: 10.1109/isspit51521.2020.9408790
|View full text |Cite
|
Sign up to set email alerts
|

A Derivative-Based MUSIC Algorithm for Two-Dimensional Angle Estimation Employing an L-Shaped Array

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2022
2022
2022
2022

Publication Types

Select...
2

Relationship

1
1

Authors

Journals

citations
Cited by 2 publications
(1 citation statement)
references
References 29 publications
0
1
0
Order By: Relevance
“…By considering the covariance matrix and pseudo covariance matrix of the signals, the number of signals that can be estimated by the proposed algorithm is much higher than the number of sensors. As a special case, for the situation with circular signals only, the DM-MUSIC algorithm is developed as in our earlier published conference paper [26]. As the Cramer-Rao bound (CRB) provides an important benchmark for assessing the performance of various 2D DOA estimation algorithms [27], [28], the CRB for 2D DOA estimation for a mixture of circular and noncircular signals is derived following the approach in [29], which can handle the general underdetermined problem.…”
Section: Introductionmentioning
confidence: 99%
“…By considering the covariance matrix and pseudo covariance matrix of the signals, the number of signals that can be estimated by the proposed algorithm is much higher than the number of sensors. As a special case, for the situation with circular signals only, the DM-MUSIC algorithm is developed as in our earlier published conference paper [26]. As the Cramer-Rao bound (CRB) provides an important benchmark for assessing the performance of various 2D DOA estimation algorithms [27], [28], the CRB for 2D DOA estimation for a mixture of circular and noncircular signals is derived following the approach in [29], which can handle the general underdetermined problem.…”
Section: Introductionmentioning
confidence: 99%