A Space-Time Adaptive Processing Method Based on Sparse Bayesian Learning for Maneuvering Airborne Radar
Space-time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. Because airborne radar moves at a constant acceleration, and there is a lack of independent and identically distributed (IID) training samples caused by the heterog...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/15/5479 |
| _version_ | 1827601688866848768 |
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| author | Shuguang Zhang Tong Wang Cheng Liu Degen Wang |
| author_facet | Shuguang Zhang Tong Wang Cheng Liu Degen Wang |
| author_sort | Shuguang Zhang |
| collection | DOAJ |
| description | Space-time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. Because airborne radar moves at a constant acceleration, and there is a lack of independent and identically distributed (IID) training samples caused by the heterogeneous environment, using the conventional STAP methods directly cannot ensure a good performance. To eliminate these effects and improve the performance of clutter suppression, a STAP method based on a sparse Bayesian learning (SBL) framework for uniform acceleration radar is proposed here. This paper introduces the signal model of the uniform acceleration radar. To promote the sparsity, a generalized double Pareto (GDP) prior is introduced into our method, and the estimation of hyper parameters via expectation maximization (EM) is given. The effectiveness of the proposed method is demonstrated by simulations. |
| first_indexed | 2024-03-09T05:01:04Z |
| format | Article |
| id | doaj.art-614743f01a034776b89c910b57398e47 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:01:04Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-614743f01a034776b89c910b57398e472023-12-03T12:59:35ZengMDPI AGSensors1424-82202022-07-012215547910.3390/s22155479A Space-Time Adaptive Processing Method Based on Sparse Bayesian Learning for Maneuvering Airborne RadarShuguang Zhang0Tong Wang1Cheng Liu2Degen Wang3National Lab of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Lab of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Lab of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaNational Lab of Radar Signal Processing, Xidian University, Xi’an 710071, ChinaSpace-time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. Because airborne radar moves at a constant acceleration, and there is a lack of independent and identically distributed (IID) training samples caused by the heterogeneous environment, using the conventional STAP methods directly cannot ensure a good performance. To eliminate these effects and improve the performance of clutter suppression, a STAP method based on a sparse Bayesian learning (SBL) framework for uniform acceleration radar is proposed here. This paper introduces the signal model of the uniform acceleration radar. To promote the sparsity, a generalized double Pareto (GDP) prior is introduced into our method, and the estimation of hyper parameters via expectation maximization (EM) is given. The effectiveness of the proposed method is demonstrated by simulations.https://www.mdpi.com/1424-8220/22/15/5479space-time adaptive processingsparse Bayesian learninguniform acceleration radar |
| spellingShingle | Shuguang Zhang Tong Wang Cheng Liu Degen Wang A Space-Time Adaptive Processing Method Based on Sparse Bayesian Learning for Maneuvering Airborne Radar Sensors space-time adaptive processing sparse Bayesian learning uniform acceleration radar |
| title | A Space-Time Adaptive Processing Method Based on Sparse Bayesian Learning for Maneuvering Airborne Radar |
| title_full | A Space-Time Adaptive Processing Method Based on Sparse Bayesian Learning for Maneuvering Airborne Radar |
| title_fullStr | A Space-Time Adaptive Processing Method Based on Sparse Bayesian Learning for Maneuvering Airborne Radar |
| title_full_unstemmed | A Space-Time Adaptive Processing Method Based on Sparse Bayesian Learning for Maneuvering Airborne Radar |
| title_short | A Space-Time Adaptive Processing Method Based on Sparse Bayesian Learning for Maneuvering Airborne Radar |
| title_sort | space time adaptive processing method based on sparse bayesian learning for maneuvering airborne radar |
| topic | space-time adaptive processing sparse Bayesian learning uniform acceleration radar |
| url | https://www.mdpi.com/1424-8220/22/15/5479 |
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