Improved Variational Bayes for Space-Time Adaptive Processing
To tackle the challenge of enhancing moving target detection performance in environments characterized by small sample sizes and non-uniformity, methods rooted in sparse signal reconstruction have been incorporated into Space-Time Adaptive Processing (STAP) algorithms. Given the prominent sparse nat...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/1099-4300/27/3/242 |
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| author | Kun Li Jinyang Luo Peng Li Guisheng Liao Zhixiang Huang Lixia Yang |
| author_facet | Kun Li Jinyang Luo Peng Li Guisheng Liao Zhixiang Huang Lixia Yang |
| author_sort | Kun Li |
| collection | DOAJ |
| description | To tackle the challenge of enhancing moving target detection performance in environments characterized by small sample sizes and non-uniformity, methods rooted in sparse signal reconstruction have been incorporated into Space-Time Adaptive Processing (STAP) algorithms. Given the prominent sparse nature of clutter spectra in the angle-Doppler domain, adopting sparse recovery algorithms has proven to be a feasible approach for accurately estimating high-resolution spatio-temporal two-dimensional clutter spectra. Sparse Bayesian Learning (SBL) is a pivotal tool in sparse signal reconstruction and has been previously utilized, yet it has demonstrated limited success in enhancing sparsity, resulting in insufficient robustness in local fitting. To significantly improve sparsity, this paper introduces a hierarchical Bayesian prior framework and derives iterative parameter update formulas through variational inference techniques. However, this algorithm encounters significant computational hurdles during the parameter update process. To overcome this obstacle, the paper proposes an enhanced Variational Bayesian Inference (VBI) method that leverages prior information on the rank of the temporal clutter covariance matrix to refine the parameter update formulas, thereby significantly reducing computational complexity. Furthermore, this method fully exploits the joint sparsity of the Multiple Measurement Vector (MMV) model to achieve greater sparsity without compromising accuracy, and employs a first-order Taylor expansion to eliminate grid mismatch in the dictionary. The research presented in this paper enhances the moving target detection capabilities of STAP algorithms in complex environments and provides new perspectives and methodologies for the application of sparse signal reconstruction in related fields. |
| format | Article |
| id | doaj-art-a8fcd3b566994a8cbb5d1f5674a03f3c |
| institution | DOAJ |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-a8fcd3b566994a8cbb5d1f5674a03f3c2025-08-20T02:42:48ZengMDPI AGEntropy1099-43002025-02-0127324210.3390/e27030242Improved Variational Bayes for Space-Time Adaptive ProcessingKun Li0Jinyang Luo1Peng Li2Guisheng Liao3Zhixiang Huang4Lixia Yang5School of Electronic Information Engineering, Anhui University, Hefei 230601, ChinaSchool of Electronic Information Engineering, Anhui University, Hefei 230601, ChinaSun Create Electronics Co., Ltd., Hefei 230088, ChinaSchool of Electronic Information Engineering, Anhui University, Hefei 230601, ChinaSchool of Electronic Information Engineering, Anhui University, Hefei 230601, ChinaSchool of Electronic Information Engineering, Anhui University, Hefei 230601, ChinaTo tackle the challenge of enhancing moving target detection performance in environments characterized by small sample sizes and non-uniformity, methods rooted in sparse signal reconstruction have been incorporated into Space-Time Adaptive Processing (STAP) algorithms. Given the prominent sparse nature of clutter spectra in the angle-Doppler domain, adopting sparse recovery algorithms has proven to be a feasible approach for accurately estimating high-resolution spatio-temporal two-dimensional clutter spectra. Sparse Bayesian Learning (SBL) is a pivotal tool in sparse signal reconstruction and has been previously utilized, yet it has demonstrated limited success in enhancing sparsity, resulting in insufficient robustness in local fitting. To significantly improve sparsity, this paper introduces a hierarchical Bayesian prior framework and derives iterative parameter update formulas through variational inference techniques. However, this algorithm encounters significant computational hurdles during the parameter update process. To overcome this obstacle, the paper proposes an enhanced Variational Bayesian Inference (VBI) method that leverages prior information on the rank of the temporal clutter covariance matrix to refine the parameter update formulas, thereby significantly reducing computational complexity. Furthermore, this method fully exploits the joint sparsity of the Multiple Measurement Vector (MMV) model to achieve greater sparsity without compromising accuracy, and employs a first-order Taylor expansion to eliminate grid mismatch in the dictionary. The research presented in this paper enhances the moving target detection capabilities of STAP algorithms in complex environments and provides new perspectives and methodologies for the application of sparse signal reconstruction in related fields.https://www.mdpi.com/1099-4300/27/3/242space-time adaptive processingsparse Bayesian learningvariational Bayesian inferencesparse recovery |
| spellingShingle | Kun Li Jinyang Luo Peng Li Guisheng Liao Zhixiang Huang Lixia Yang Improved Variational Bayes for Space-Time Adaptive Processing Entropy space-time adaptive processing sparse Bayesian learning variational Bayesian inference sparse recovery |
| title | Improved Variational Bayes for Space-Time Adaptive Processing |
| title_full | Improved Variational Bayes for Space-Time Adaptive Processing |
| title_fullStr | Improved Variational Bayes for Space-Time Adaptive Processing |
| title_full_unstemmed | Improved Variational Bayes for Space-Time Adaptive Processing |
| title_short | Improved Variational Bayes for Space-Time Adaptive Processing |
| title_sort | improved variational bayes for space time adaptive processing |
| topic | space-time adaptive processing sparse Bayesian learning variational Bayesian inference sparse recovery |
| url | https://www.mdpi.com/1099-4300/27/3/242 |
| work_keys_str_mv | AT kunli improvedvariationalbayesforspacetimeadaptiveprocessing AT jinyangluo improvedvariationalbayesforspacetimeadaptiveprocessing AT pengli improvedvariationalbayesforspacetimeadaptiveprocessing AT guishengliao improvedvariationalbayesforspacetimeadaptiveprocessing AT zhixianghuang improvedvariationalbayesforspacetimeadaptiveprocessing AT lixiayang improvedvariationalbayesforspacetimeadaptiveprocessing |