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Open AccessArticle
A Data and Model-Driven Clutter Suppression Method for Airborne Bistatic Radar Based on Deep Unfolding
by
Weijun Huang
Weijun Huang ,
Tong Wang
Tong Wang * and
Kun Liu
Kun Liu
National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2024, 16(14), 2516; https://doi.org/10.3390/rs16142516 (registering DOI)
Submission received: 25 May 2024
/
Revised: 7 July 2024
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Accepted: 8 July 2024
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Published: 9 July 2024
Abstract
Space–time adaptive processing (STAP) based on sparse recovery achieves excellent clutter suppression and target detection performance, even with a limited number of available training samples. However, most of these methods face performance degradation due to grid mismatch, which impedes their application in bistatic clutter suppression. Some gridless methods, such as atomic norm minimization (ANM), can effectively address grid mismatch issues, yet they are sensitive to parameter settings and array errors. In this article, the authors propose a data and model-driven algorithm that unfolds the iterative process of atomic norm minimization into a deep network. This approach establishes a concrete and systematic link between iterative algorithms, extensively utilized in signal processing, and deep neural networks. This methodology not only addresses the challenges associated with parameter settings in traditional optimization algorithms, but also mitigates the lack of interpretability issues commonly found in deep neural networks. Moreover, due to more rational parameter settings, the proposed algorithm achieves effective clutter suppression with fewer iterations, thereby reducing computational time. Finally, extensive simulation experiments demonstrate the effectiveness of the proposed algorithm in clutter suppression for airborne bistatic radar.
Share and Cite
MDPI and ACS Style
Huang, W.; Wang, T.; Liu, K.
A Data and Model-Driven Clutter Suppression Method for Airborne Bistatic Radar Based on Deep Unfolding. Remote Sens. 2024, 16, 2516.
https://doi.org/10.3390/rs16142516
AMA Style
Huang W, Wang T, Liu K.
A Data and Model-Driven Clutter Suppression Method for Airborne Bistatic Radar Based on Deep Unfolding. Remote Sensing. 2024; 16(14):2516.
https://doi.org/10.3390/rs16142516
Chicago/Turabian Style
Huang, Weijun, Tong Wang, and Kun Liu.
2024. "A Data and Model-Driven Clutter Suppression Method for Airborne Bistatic Radar Based on Deep Unfolding" Remote Sensing 16, no. 14: 2516.
https://doi.org/10.3390/rs16142516
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