Next Article in Journal
A Neural Network Compiler for Efficient Data Storage Optimization in ReRAM-Based DNN Accelerators
Previous Article in Journal
Track-Before-Detect Algorithm Based on Particle Filter with Sub-Band Adaptive Weighting
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Electronics
Volume 14
Issue 12
10.3390/electronics14122350
electronics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Self-Calibrating STAP Algorithm for Dictionary Dimensionality Reduction Based on Sparse Bayesian Learning

by
Zhiqi Gao
1,2,
Na Yang
1,2,*,
Pingping Huang
1,2,
Wei Xu
1,2,
Weixian Tan
1,2 and
Zhixia Wu
1,2
1
College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China
2
Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China
*
Author to whom correspondence should be addressed.
Electronics 2025, 14(12), 2350; https://doi.org/10.3390/electronics14122350
Submission received: 11 April 2025 / Revised: 2 June 2025 / Accepted: 5 June 2025 / Published: 8 June 2025
Versions Notes

Abstract

Sparse recovery space–time adaptive processing (STAP) has an off-grid feature and high computational complexity. To address these shortcomings, this study proposes a self-calibrating STAP algorithm based on sparse Bayesian learning (SBL). The proposed algorithm constructs a dimensionality reduction dictionary by selecting the steering vectors corresponding to atoms with high power values. Then, a small-scale auxiliary dictionary is constructed with a stepwise search approach to calibrate the uniformly discretized dictionary. In this way, the atoms of the auxiliary dictionary can converge to the clutter ridge adaptively when off-grid occurs. The clutter plus noise covariance matrix is estimated via SBL combined with the updated dictionary. The experimental results demonstrate that the proposed algorithm can effectively suppress the clutter ridge expansion caused by the off-grid problem while reducing the computation burden significantly compared with the existing methods.
Keywords: space–time adaptive processing; sparse recovery; moving target detection; sparse Bayesian learning; dictionary correction space–time adaptive processing; sparse recovery; moving target detection; sparse Bayesian learning; dictionary correction

Share and Cite

MDPI and ACS Style

Gao, Z.; Yang, N.; Huang, P.; Xu, W.; Tan, W.; Wu, Z. Self-Calibrating STAP Algorithm for Dictionary Dimensionality Reduction Based on Sparse Bayesian Learning. Electronics 2025, 14, 2350. https://doi.org/10.3390/electronics14122350

AMA Style

Gao Z, Yang N, Huang P, Xu W, Tan W, Wu Z. Self-Calibrating STAP Algorithm for Dictionary Dimensionality Reduction Based on Sparse Bayesian Learning. Electronics. 2025; 14(12):2350. https://doi.org/10.3390/electronics14122350

Chicago/Turabian Style

Gao, Zhiqi, Na Yang, Pingping Huang, Wei Xu, Weixian Tan, and Zhixia Wu. 2025. "Self-Calibrating STAP Algorithm for Dictionary Dimensionality Reduction Based on Sparse Bayesian Learning" Electronics 14, no. 12: 2350. https://doi.org/10.3390/electronics14122350

APA Style

Gao, Z., Yang, N., Huang, P., Xu, W., Tan, W., & Wu, Z. (2025). Self-Calibrating STAP Algorithm for Dictionary Dimensionality Reduction Based on Sparse Bayesian Learning. Electronics, 14(12), 2350. https://doi.org/10.3390/electronics14122350

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop