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DOWNWARD LOOKING SPARSE LINEAR ARRAY 3D SAR IMAGING ALGORITHM BASED ON BACK-PROJECTION AND CONVEX OPTIMIZATION 被引量:1

DOWNWARD LOOKING SPARSE LINEAR ARRAY 3D SAR IMAGING ALGORITHM BASED ON BACK-PROJECTION AND CONVEX OPTIMIZATION
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摘要 Downward Looking Sparse Linear Array Three Dimensional SAR(DLSLA 3D SAR) is an important form of 3D SAR imaging, which has a widespread application field. Since its practical equivalent phase centers are usually distributed sparsely and nonuniformly, traditional 3D SAR algorithms suffer from low resolution and high sidelobes in cross-track dimension. To deal with this problem, this paper introduces a method based on back-projection and convex optimization to achieve 3D high accuracy imaging reconstruction. Compared with traditional SAR algorithms, the proposed method sufficiently utilizes the sparsity of the 3D SAR imaging scene and can achieve lower sidelobes and higher resolution in cross-track dimension. In the simulated experiments, the reconstructed results of both simple and complex imaging scene verify that the proposed method outperforms 3D back-projection algorithm and shows satisfying cross-track dimensional resolution and good robustness to noise. Downward Looking Sparse Linear Array Three Dimensional SAR (DLSLA 3D SAR) is an important form of 3D SAR imaging,which has a widespread application field.Since its practical equivalent phase centers are usually distributed sparsely and nonuniformly,traditional 3D SAR algorithms suffer from low resolution and high sidelobes in cross-track dimension.To deal with this problem,this paper introduces a method based on back-projection and convex optimization to achieve 3D high accuracy imaging reconstruction.Compared with traditional SAR algorithms,the proposed method sufficiently utilizes the sparsity of the 3D SAR imaging scene and can achieve lower sidelobes and higher resolution in cross-track dimension.In the simulated experiments,the reconstructed results of both simple and complex imaging scene verify that the proposed method outperforms 3D back-projection algorithm and shows satisfying cross-track dimensional resolution and good robustness to noise.
出处 《Journal of Electronics(China)》 2014年第4期298-309,共12页 电子科学学刊(英文版)
基金 Supported by the National Natural Science Foundation of China General Programs(Nos.61072112,61372186) the National Natural Science Foundation of China Key Program(No.60890071)
关键词 Three Dimensional SAR (3D SAR) Downward looking Sparse linear array Convex optimizationCLC number:TN957 Three Dimensional SAR(3D SAR) Downward looking Sparse linear array Convex optimization
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  • 1Donoho D L, Tsaig Y, Drori I, et al. Sparse solution of underdetermined linear equations by stage wise orthogonal matching pursuit[ R]. 2006.
  • 2Klare J.A new airborne radar for 3D imaging simulationstudy of ARTINO[C].European Conference on SyntheticAperture Radar,Germany,May 16-18,2006:1-4.
  • 3Fery O and Meier E.3-D Time-domain SAR imaging of aforest using airborne multibaseline data at L-and P-bands[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(10):3660-3664.
  • 4Potter L C,Ertin E,Parker J T,et al..Sparsity andcompressed sensing in radar imaging[J].Proceedings of theIEEE,2010,98(6):1006-1020.
  • 5Zhang Y X,Sun J P,Tian J H,et al..Compressed sensingSAR imaging with real data[C].International Congress onImage and Signal Processing,Yantai,China,Oct.16-18,2010:2026-2029.
  • 6Ji S H,Xue Y,and Carin L.Bayesian compressive sensing[J].IEEE Transactions on Signal Processing,2008,56(6):2346-2356.
  • 7Candes E J and Tao T.Near-optimal signal recovery fromrandom projections:universal encoding strategies?[J].IEEETransactions on Information Theory,2006,52(12):5406-5425.
  • 8Candes E J and Tao T.Decoding by linear programming[J].IEEE Transactions on Information Theory,2005,51(12):4203-4215.
  • 9Li Jun,Xing Meng-dao,and Wu Shun-jun.Application ofcompressed sensing in sparse aperture imaging of radar[C].Asian Pacific Conference on Synthetic Aperture RadarProceedings,Xi,an,China,Oct.26-30,2009:651-655.
  • 10Zhu X X and Bamler R.Tomographic SAR inversion byL1-norm regularization-the compressive sensing approach[J].IEEE Transactions on Geoscience and Remote Sensing,2010,48(10):3839-3846.

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