An Efficient Sparse Recovery STAP Algorithm for Airborne Bistatic Radars Based on Atomic Selection under the Bayesian Framework
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| Publicado en: | Remote Sensing vol. 16, no. 14 (2024), p. 2534 |
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| Autor Principal: | |
| Outros autores: | , |
| Publicado: |
MDPI AG
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| Acceso en liña: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs16142534 |2 doi | |
| 035 | |a 3085010068 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Liu, Kun | |
| 245 | 1 | |a An Efficient Sparse Recovery STAP Algorithm for Airborne Bistatic Radars Based on Atomic Selection under the Bayesian Framework | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The traditional sparse recovery (SR) space-time adaptive processing (STAP) algorithms are greatly affected by grid mismatch, leading to poor performance in airborne bistatic radar clutter suppression. In order to address this issue, this paper proposes an SR STAP algorithm for airborne bistatic radars based on atomic selection under the Bayesian framework. This method adopts the idea of atomic selection for the process of Bayesian inference, continuously evaluating the contribution of atoms to the likelihood function to add or remove atoms, and then using the selected atoms to estimate the clutter support subspace and perform sparse recovery in the clutter support subspace. Due to the inherent sparsity of clutter signals, performing sparse recovery in the clutter support subspace avoids using a massive number of atoms from an overcomplete space-time dictionary, thereby greatly improving computational efficiency. In airborne bistatic radar scenarios where significant grid mismatch exists, this method can mitigate the performance degradation caused by grid mismatch by encrypting grid points. Since the sparse recovery is performed in the clutter support subspace, encrypting grid points does not lead to excessive computational burden. Additionally, this method integrates out the noise term under a new hierarchical Bayesian model, preventing the adverse effects caused by inaccurate noise power estimation during iterations in the traditional SR STAP algorithms, further enhancing its performance. Our simulation results demonstrate the high efficiency and superior clutter suppression performance and target detection performance of this method. | |
| 653 | |a Dictionaries | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Algorithms | ||
| 653 | |a Radar | ||
| 653 | |a Atomic properties | ||
| 653 | |a Target detection | ||
| 653 | |a Recovery | ||
| 653 | |a Spacetime | ||
| 653 | |a Multistatic radar | ||
| 653 | |a Computer applications | ||
| 653 | |a Methods | ||
| 653 | |a Performance degradation | ||
| 653 | |a Clutter | ||
| 653 | |a Subspaces | ||
| 653 | |a Airborne radar | ||
| 653 | |a Statistical inference | ||
| 653 | |a Space-time adaptive processing | ||
| 653 | |a Efficiency | ||
| 653 | |a Adaptive algorithms | ||
| 700 | 1 | |a Wang, Tong | |
| 700 | 1 | |a Huang, Weijun | |
| 773 | 0 | |t Remote Sensing |g vol. 16, no. 14 (2024), p. 2534 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3085010068/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3085010068/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3085010068/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |