A Novel Fast Sparse Bayesian Learning STAP Algorithm for Conformal Array Radar
Space-time adaptive processing (STAP) is an important method of clutter suppression that requires adequate training samples. For an airborne conformal array radar, conventional STAP methods do not have enough training samples to acquire good performance due to the range dependent clutter caused by g...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/11/2824 |
| _version_ | 1827739248440115200 |
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| author | Bing Ren Tong Wang |
| author_facet | Bing Ren Tong Wang |
| author_sort | Bing Ren |
| collection | DOAJ |
| description | Space-time adaptive processing (STAP) is an important method of clutter suppression that requires adequate training samples. For an airborne conformal array radar, conventional STAP methods do not have enough training samples to acquire good performance due to the range dependent clutter caused by geometry and the problem of polarization. Sparse-recovery-based STAP (SR-STAP) methods have garnered significant attention in the past few decades because they only require a small number of training samples. Sparse Bayesian Learning (SBL) methods have seen increasing amounts of development due to its robust, self-regularizing nature and because it is not sensitive to user parameters, but it converges slowly. In this paper, a novel fast SBL (NFSBL) method is put forward to increase the rate of convergence. To minimize the SBL penalty function, the proposed method introduces the conjugate function to construct a surrogate function. Additional solution sparsity will be achieved through iteratively minimizing the surrogate function. Then, from the proposed method, we could obtain a more accurate clutter plus noise covariance matrix. Numerical simulation results express that this method could acquire better performance of STAP and improvement in convergence and computational complexity for a conformal array. |
| first_indexed | 2024-03-11T02:59:04Z |
| format | Article |
| id | doaj.art-e52b96c9e6b24ef1ab33aafc5eace5ec |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T02:59:04Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e52b96c9e6b24ef1ab33aafc5eace5ec2023-11-18T08:29:13ZengMDPI AGRemote Sensing2072-42922023-05-011511282410.3390/rs15112824A Novel Fast Sparse Bayesian Learning STAP Algorithm for Conformal Array RadarBing Ren0Tong Wang1National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaSpace-time adaptive processing (STAP) is an important method of clutter suppression that requires adequate training samples. For an airborne conformal array radar, conventional STAP methods do not have enough training samples to acquire good performance due to the range dependent clutter caused by geometry and the problem of polarization. Sparse-recovery-based STAP (SR-STAP) methods have garnered significant attention in the past few decades because they only require a small number of training samples. Sparse Bayesian Learning (SBL) methods have seen increasing amounts of development due to its robust, self-regularizing nature and because it is not sensitive to user parameters, but it converges slowly. In this paper, a novel fast SBL (NFSBL) method is put forward to increase the rate of convergence. To minimize the SBL penalty function, the proposed method introduces the conjugate function to construct a surrogate function. Additional solution sparsity will be achieved through iteratively minimizing the surrogate function. Then, from the proposed method, we could obtain a more accurate clutter plus noise covariance matrix. Numerical simulation results express that this method could acquire better performance of STAP and improvement in convergence and computational complexity for a conformal array.https://www.mdpi.com/2072-4292/15/11/2824sparse Bayesian learningspace-time adaptive processingairborne radarconformal array |
| spellingShingle | Bing Ren Tong Wang A Novel Fast Sparse Bayesian Learning STAP Algorithm for Conformal Array Radar Remote Sensing sparse Bayesian learning space-time adaptive processing airborne radar conformal array |
| title | A Novel Fast Sparse Bayesian Learning STAP Algorithm for Conformal Array Radar |
| title_full | A Novel Fast Sparse Bayesian Learning STAP Algorithm for Conformal Array Radar |
| title_fullStr | A Novel Fast Sparse Bayesian Learning STAP Algorithm for Conformal Array Radar |
| title_full_unstemmed | A Novel Fast Sparse Bayesian Learning STAP Algorithm for Conformal Array Radar |
| title_short | A Novel Fast Sparse Bayesian Learning STAP Algorithm for Conformal Array Radar |
| title_sort | novel fast sparse bayesian learning stap algorithm for conformal array radar |
| topic | sparse Bayesian learning space-time adaptive processing airborne radar conformal array |
| url | https://www.mdpi.com/2072-4292/15/11/2824 |
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