Manifold Separation-Based DOA Estimation for Nonlinear Arrays via Compressed Super-Resolution of Positive Sources

Pan, Jie

doi:10.1007/s00034-022-02044-0

Circuits Syst Signal Process

2022

DOI: 10.1007/s00034-022-02044-0

|View full text |Cite

Manifold Separation-Based DOA Estimation for Nonlinear Arrays via Compressed Super-Resolution of Positive Sources

Jie Pan

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2023

Publication Types

Select...

Article2

Relationship

Self Cite1

Independent1

Authors

Journals

Cited by 2 publications

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Low complexity beamspace super‐resolution for direction‐of‐arrival estimation via prior information encoding

Jie

2023

IET Radar Sonar & Navi

Self Cite

View full text Add to dashboard Cite

Beamspace processing is widely applied in Direction‐of‐Arrival (DOA) estimation thanks to dimensional reduction and super‐resolution characterisations. However, the conventional atomic norm minimisation (ANM) based methods for beamspace DOA estimation are of high computational complexity for large arrays. To deal with this issue, the proposed method focuses on locating the sources in the mainlobe of the beamspace and encodes such prior information into the ANM problem without frequency‐selective constraints. The proposed method approximates the beamspace array manifold in the mainlobe sector with the truncated sector Fourier series. The theoretical analysis shows that such approximation with the properly designed fitting error relaxation on the boundary yields the low dimensional semidefinite programming (SDP) approximate implementation of the proposed ANM method and guarantees the support recovery inside the mainlobe. Furthermore, the low complexity Burer‐Monteiro factorisation based alternating direction method of multipliers method is proposed to solve the SDP problem. The complexity analysis and simulations show that the proposed method results in significant computational complexity reduction and slightly better performance compared with state‐of‐art benchmarks.

show abstract

Low complexity beamspace super‐resolution for direction‐of‐arrival estimation via prior information encoding

Jie

2023

IET Radar Sonar & Navi

Self Cite

View full text Add to dashboard Cite

show abstract

Underdetermined DOA Estimation Using Arbitrary Planar Arrays via Coarray Manifold Separation

Yadav

George

2022

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Manifold Separation-Based DOA Estimation for Nonlinear Arrays via Compressed Super-Resolution of Positive Sources

Cited by 2 publications

References 42 publications

Low complexity beamspace super‐resolution for direction‐of‐arrival estimation via prior information encoding

Low complexity beamspace super‐resolution for direction‐of‐arrival estimation via prior information encoding

Underdetermined DOA Estimation Using Arbitrary Planar Arrays via Coarray Manifold Separation

Contact Info

Product

Resources

About