Airborne Radar Space–Time Adaptive Processing Algorithm Based on Dictionary and Clutter Power Spectrum Correction

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Опубликовано в::Electronics vol. 13, no. 11 (2024), p. 2187
Главный автор: Gao, Zhiqi
Другие авторы: Deng, Wei, Huang, Pingping, Xu, Wei, Tan, Weixian
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MDPI AG
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100 1 |a Gao, Zhiqi  |u College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China; <email>gzqngd@imut.edu.cn</email> (Z.G.); <email>hpp@imut.edu.cn</email> (P.H.); <email>xuwei1983@imut.edu.cn</email> (W.X.); <email>wxtan@imut.edu.cn</email> (W.T.); Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China 
245 1 |a Airborne Radar Space–Time Adaptive Processing Algorithm Based on Dictionary and Clutter Power Spectrum Correction 
260 |b MDPI AG  |c 2024 
513 |a Journal Article 
520 3 |a Sparse recovery space–time adaptive processing (SR-STAP) technology improves the moving target detection performance of airborne radar. However, the sparse recovery method with a fixed dictionary usually leads to an off-grid effect. This paper proposes a STAP algorithm for airborne radar based on dictionary and clutter power spectrum joint correction (DCPSJC-STAP). The algorithm first performs nonlinear regression in a non-stationary clutter environment with unknown yaw angles, and it corrects the corresponding dictionary for each snapshot by updating the clutter ridge parameters. Then, the corrected dictionary is combined with the sparse Bayesian learning algorithm to iteratively update the required hyperparameters, which are used to correct the clutter power spectrum and estimate the clutter covariance matrix. The proposed algorithm can effectively overcome the off-grid effect and improve the moving target detection performance of airborne radar in actual complex clutter environments. Simulation experiments verified the effectiveness of this algorithm in improving clutter estimation accuracy and moving target detection performance. 
653 |a Sparsity 
653 |a Covariance matrix 
653 |a Dictionaries 
653 |a Accuracy 
653 |a Yaw 
653 |a Radar detection 
653 |a Signal to noise ratio 
653 |a Moving targets 
653 |a Target detection 
653 |a Recovery 
653 |a Clutter 
653 |a Arrays 
653 |a Algorithms 
653 |a Machine learning 
653 |a Airborne radar 
653 |a Space-time adaptive processing 
653 |a Adaptive algorithms 
700 1 |a Deng, Wei  |u College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China; <email>gzqngd@imut.edu.cn</email> (Z.G.); <email>hpp@imut.edu.cn</email> (P.H.); <email>xuwei1983@imut.edu.cn</email> (W.X.); <email>wxtan@imut.edu.cn</email> (W.T.); Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China 
700 1 |a Huang, Pingping  |u College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China; <email>gzqngd@imut.edu.cn</email> (Z.G.); <email>hpp@imut.edu.cn</email> (P.H.); <email>xuwei1983@imut.edu.cn</email> (W.X.); <email>wxtan@imut.edu.cn</email> (W.T.); Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China 
700 1 |a Xu, Wei  |u College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China; <email>gzqngd@imut.edu.cn</email> (Z.G.); <email>hpp@imut.edu.cn</email> (P.H.); <email>xuwei1983@imut.edu.cn</email> (W.X.); <email>wxtan@imut.edu.cn</email> (W.T.); Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China 
700 1 |a Tan, Weixian  |u College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China; <email>gzqngd@imut.edu.cn</email> (Z.G.); <email>hpp@imut.edu.cn</email> (P.H.); <email>xuwei1983@imut.edu.cn</email> (W.X.); <email>wxtan@imut.edu.cn</email> (W.T.); Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China 
773 0 |t Electronics  |g vol. 13, no. 11 (2024), p. 2187 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
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