Optimization of Planar Subarray Structure Based on Random Search Method for Large Active Electronically Scanned Array Antenna

Kim, Doo-Soo; Han, InSoo; Kim, Tae‐Wan; Kwon, Ho‐Sang; Kim, Kyung‐Tae

doi:10.1587/transele.2022ecs6009

IEICE Trans. Electron.

2023

DOI: 10.1587/transele.2022ecs6009

|View full text |Cite

Optimization of Planar Subarray Structure Based on Random Search Method for Large Active Electronically Scanned Array Antenna

Doo-Soo Kim

InSoo Han

Tae‐Wan Kim

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Subarray partition based on sparse array weighted K‐means clustering

Zhao,

Huang,

Cui

et al. 2024

Electronics Letters

View full text Add to dashboard Cite

This paper introduces the subarray partition based on the sparse array weighted K‐means clustering method, which extends the conventional K‐means clustering method through the inclusion of a weight matrix approach. This matrix is derived by recording the frequency of each element's occurrence across multiple independent sparse arrays, thereby generating a frequency matrix. The performance of SWKCM is demonstrated through simulations and comparisons with four similar methods. To assess the effectiveness and superiority of the SWKCM, it is applied to the subarray partition of a 40×40 uniform planar phased array and compared with the other four methods. The simulation results show that the proposed SWKCM method maintains comparable sidelobe suppression capabilities to those of KCM, achieving a normalized peak sidelobe level of ‐43.1076 dB. Furthermore, compared to the K‐means clustering method, the sparse array weighted K‐means clustering method significantly enhances the stability of subarray partition outcomes, as evidenced by a reduction in the peak sidelobe level standard deviation from 1.0991 to 0.8104, resulting in a 26.3% decrease in variability.

show abstract

Subarray partition based on sparse array weighted K‐means clustering

Zhao,

Huang,

Cui

et al. 2024

Electronics Letters

View full text Add to dashboard Cite

show abstract

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.

Optimization of Planar Subarray Structure Based on Random Search Method for Large Active Electronically Scanned Array Antenna

Cited by 1 publication

References 16 publications

Subarray partition based on sparse array weighted K‐means clustering

Subarray partition based on sparse array weighted K‐means clustering

Contact Info

Product

Resources

About