Space-Time Adaptive Processing Based on Modified Sparse Learning via Iterative Minimization for Conformal Array Radar
Space-time adaptive processing (STAP) is a well-known technique for slow-moving target detection in the clutter spreading environment. For an airborne conformal array radar, conventional STAP methods are unable to provide good performance in suppressing clutter because of the geometry-induced range-...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/18/6917 |
| _version_ | 1827656848734420992 |
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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 a well-known technique for slow-moving target detection in the clutter spreading environment. For an airborne conformal array radar, conventional STAP methods are unable to provide good performance in suppressing clutter because of the geometry-induced range-dependent clutter, non-uniform spatial steering vector, and polarization sensitivity. In this paper, a knowledge aided STAP method based on sparse learning via iterative minimization (SLIM) combined with Laplace distribution is proposed to improve the STAP performance for a conformal array. The proposed method can avoid selecting the user parameter. the proposed method constructs a dictionary matrix that is composed of the space-time steering vector by using the prior knowledge of the range cell under test (CUT) distributed in clutter ridge. Then, the estimated sparse parameters and noise power can be used to calculate a relatively accurate clutter plus noise covariance matrix (CNCM). This method could achieve superior performance of clutter suppression for a conformal array. Simulation results demonstrate the effectiveness of this method. |
| first_indexed | 2024-03-09T22:34:05Z |
| format | Article |
| id | doaj.art-ad056eec9f01457fa34a35b9203c66da |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:34:05Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ad056eec9f01457fa34a35b9203c66da2023-11-23T18:51:22ZengMDPI AGSensors1424-82202022-09-012218691710.3390/s22186917Space-Time Adaptive Processing Based on Modified Sparse Learning via Iterative Minimization for Conformal Array RadarBing Ren0Tong Wang1National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaSpace-time adaptive processing (STAP) is a well-known technique for slow-moving target detection in the clutter spreading environment. For an airborne conformal array radar, conventional STAP methods are unable to provide good performance in suppressing clutter because of the geometry-induced range-dependent clutter, non-uniform spatial steering vector, and polarization sensitivity. In this paper, a knowledge aided STAP method based on sparse learning via iterative minimization (SLIM) combined with Laplace distribution is proposed to improve the STAP performance for a conformal array. The proposed method can avoid selecting the user parameter. the proposed method constructs a dictionary matrix that is composed of the space-time steering vector by using the prior knowledge of the range cell under test (CUT) distributed in clutter ridge. Then, the estimated sparse parameters and noise power can be used to calculate a relatively accurate clutter plus noise covariance matrix (CNCM). This method could achieve superior performance of clutter suppression for a conformal array. Simulation results demonstrate the effectiveness of this method.https://www.mdpi.com/1424-8220/22/18/6917space-time adaptive processingsparse learning via iterative minimizationLaplace priorclutter suppressionconformal array |
| spellingShingle | Bing Ren Tong Wang Space-Time Adaptive Processing Based on Modified Sparse Learning via Iterative Minimization for Conformal Array Radar Sensors space-time adaptive processing sparse learning via iterative minimization Laplace prior clutter suppression conformal array |
| title | Space-Time Adaptive Processing Based on Modified Sparse Learning via Iterative Minimization for Conformal Array Radar |
| title_full | Space-Time Adaptive Processing Based on Modified Sparse Learning via Iterative Minimization for Conformal Array Radar |
| title_fullStr | Space-Time Adaptive Processing Based on Modified Sparse Learning via Iterative Minimization for Conformal Array Radar |
| title_full_unstemmed | Space-Time Adaptive Processing Based on Modified Sparse Learning via Iterative Minimization for Conformal Array Radar |
| title_short | Space-Time Adaptive Processing Based on Modified Sparse Learning via Iterative Minimization for Conformal Array Radar |
| title_sort | space time adaptive processing based on modified sparse learning via iterative minimization for conformal array radar |
| topic | space-time adaptive processing sparse learning via iterative minimization Laplace prior clutter suppression conformal array |
| url | https://www.mdpi.com/1424-8220/22/18/6917 |
| work_keys_str_mv | AT bingren spacetimeadaptiveprocessingbasedonmodifiedsparselearningviaiterativeminimizationforconformalarrayradar AT tongwang spacetimeadaptiveprocessingbasedonmodifiedsparselearningviaiterativeminimizationforconformalarrayradar |