2022
DOI: 10.1049/rsn2.12358
|View full text |Cite
|
Sign up to set email alerts
|

Compressive detection of multiple targets in passive bistatic radar

Abstract: The problem of compressive detection of multiple targets in a passive bistatic radar system consisting of one illuminator of opportunity and one receiver is considered. Specifically, a measurement matrix is designed to remove the direct-path and multi-path interference and obtain the compressive observations simultaneously. Considering that the degree of sparsity, that is, the number of targets, is unknown, an orthogonal matching pursuit (OMP)-based detector, referred to as the ‖r k ‖ ∞ -based OMP detector, is… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(3 citation statements)
references
References 33 publications
0
3
0
Order By: Relevance
“…Next, in order to solve the challenge of obtaining D and X variables in unconstrained optimization, we apply the overcomplete technique of the discrete cosine transform (DCT) [34] to construct the initial dictionary D 0 . The orthogonal matching pursuit (OMP) algorithm [35] is used by the associated starting sparse matrix X 0 to carry out sparse encoding on the image blocks. The two optimization variables, d k and X k T , are solved for the use of singular value decomposition (SVD) algorithms.…”
Section: Sparse Dictionary Learningmentioning
confidence: 99%
“…Next, in order to solve the challenge of obtaining D and X variables in unconstrained optimization, we apply the overcomplete technique of the discrete cosine transform (DCT) [34] to construct the initial dictionary D 0 . The orthogonal matching pursuit (OMP) algorithm [35] is used by the associated starting sparse matrix X 0 to carry out sparse encoding on the image blocks. The two optimization variables, d k and X k T , are solved for the use of singular value decomposition (SVD) algorithms.…”
Section: Sparse Dictionary Learningmentioning
confidence: 99%
“…However, most of the work focuses on scenarios where the detection capabilities of the system focus on searching for a single target. A more challenging scenario arises when multiple targets are present, especially when the number of targets is not available [ 63 , 64 ]. Smart employment of the scenario information and target attributes is the key to obtaining reasonable performance detection even without reference signals [ 65 , 66 ].…”
Section: Open Issues and Future Research Directionsmentioning
confidence: 99%
“…The advantages of radar technology are due to its ability to perform multiple detections while not violating privacy and ensuring user convenience [1]. Two radar topologies commonly used for movement detection are the monostatic [2] and bistatic topologies [3]. The former holds the advantage of relatively lower cost as both the transmitter and receiver components are embedded in the same sensing module.…”
Section: Introductionmentioning
confidence: 99%