Adaptive parameter selection of SAR sparse imaging model

Dou SUN; Shiqi XING; Yongzhen LI; Dahai DAI

doi:10.11834/jrs.20176207

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Adaptive parameter selection of SAR sparse imaging model
Vol. 21, Issue 4, Pages: 579-587(2017)
Received：08 July 2016，

Accepted：20 February 2017，

Published：2017-07
DOI： 10.11834/jrs.20176207
稿件说明：

移动端阅览

孙豆, 邢世其, 李永祯, 代大海. 2017. SAR稀疏成像模型参数自适应选择. 遥感学报, 21(4): 579–587 DOI： 10.11834/jrs.20176207.

Sun D, Xing S Q, Li Y Z and Dai D H. 2017. Adaptive parameter selection of SAR sparse imaging model. Journal of Remote Sensing, 21(4): 579–587 DOI： 10.11834/jrs.20176207.

摘要

合成孔径雷达(SAR)稀疏成像模型中的参数选择对于SAR稀疏成像的性能有重要影响，也是当前SAR稀疏成像研究中的难点问题。已有参数选择方法普遍存在适用于个别模型或者运算量大的缺点。基于最大后验概率估计和贝叶斯推理，提出了一种无需额外先验信息的自适应参数选择方法，所有需要的参数都可从已知的数据中获取。通过推导得到模型参数与信号、噪声方差的关系，避免了对数据进行一系列的训练处理，因此极大地减小了计算量。仿真数据和实测数据处理表明，本文方法在实现了较为精确的参数优化选择的前提下，其计算量远低于贝叶斯信息论准则、L-曲线等已有参数选择方法。

Abstract

High resolution is an important direction for the development of Synthetic Aperture Radar (SAR). Conventional SAR imaging algorithms based on matched filtering is limited by the signal bandwidth and the synthetic aperture length. Compressive Sensing (CS) theory has recently attracted the attention of domestic and foreign scholars. Electromagnetic scattering signals of man-made objects

such as vehicles and buildings

have obvious sparse characteristics

and the total electromagnetic scattering of the target can be approximated by the synthesis of the center scattering of the local scattering. Studies have shown that CS has considerable potential in improving the quality of radar imaging and high precision imaging of man-made targets. However

the application of CS theory in radar imaging is novel; therefore

it has some problems that require further study. Studies have shown that the regularization and shrinkage parameter have an obvious impact on the performance of radar sparse imaging. Traditional parameter selection methods have serious disadvantages in computation cost and accuracy or are limited to a few specific models. Therefore

proposing a parameter selection method is of great importance. A fully automated algorithm is proposed in this paper to improve the performance of radar sparse imaging in the case of parameter optimization and to overcome the preceding disadvantages mentioned. First

combined with the Maximum A Posteriori (MAP) estimation and Bayesian inference

we obtain the relationship between regularization and shrinkage parameter. The value of regularization parameter is determined by the choices of shrinkage parameter

noise

and signal variance

thereby reducing the dimension of the parameter selection problem. Second

we propose a radar sparse imaging algorithm which can solve the joint optimization problem to simultaneously achieve model parameters estimation and SAR imaging. In each iterative of the approach

all required parameters are updated with training data without the necessary prior information

and the image is reconstructed by regularization technique with the updated observation model constructed by new parameters. Extensive comparisons are carried out between the proposed method and several other competing methods based on simulations and real-data processes. The experimental results demonstrate that the proposed method can achieve accurate parameter estimation and imaging performance with low computational complexity. Compared with the traditional imaging method

the imaging results of this method are sparse

thereby reducing the sidelobes and maintaining cleanliness without noise. This paper presents a fully automated parameter selection method based on maximum a posteriori estimation and Bayesian inference without extra prior information. All required parameters can be obtained through known data. We deduce the relationship between model parameters and signal and noise variance; thus

a series of training processes has been avoided

and the computational cost has been considerably reduced. Simulations and realdata experiment demonstrate that the proposed method can achieve accurate parameter selection with lower computational cost than other parameter selection methods

such as Bayesian information criteria and L-curve.

关键词

Keywords

references

Austin C D, Moses R L, Ash J N and Ertin E. 2010. On the relation between sparse reconstruction and parameter estimation with model order selection. IEEE Journal of Selected Topics in Signal Processing, 4(3): 560–570

Austin C D, Ertin E and Moses R L. 2011. Sparse signal methods for 3-D radar imaging. IEEE Journal of Selected Topics in Signal Processing, 5(3): 408–423

Candès E J, Romberg J and Tao T. 2006. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2): 489–509

Cetin M, Karl W C and Castanon D A. 2003. Feature enhancement and ATR performance using nonquadratic optimization-based SAR imaging. IEEE Transactions on Aerospace and Electronic Systems, 39(4): 1375–1395

Chaari L, Tourneret J Y and Batatia H. 2013. Sparse Bayesian regularization using Bernoulli-Laplacian priors//Proceedings of the 21st European Signal Processing Conference. Marrakech: IEEE: 1–5.

陈国新, 陈生昌. 2014. 位场数据重构的 l P 范数稀疏约束正则化方法. 浙江大学学报(工学版), 48(4): 748–756

Chen G X and Chen S C. 2014. Regularization method with l p -norm sparsity constraints for potential field data reconstruction. Journal of Zhejiang University (Engineering Science), 48(4): 748–756

Chen S. 2002. Locally regularised orthogonal least squares algorithm for the construction of sparse kernel regression models//Proceedings of the 6th International Conference on Signal Processing. Beijing: IEEE: 1229–1232 [DOI: 10.1109/ICOSP.2002.1180013]

Chien J T. 2015. Laplace group sensing for acoustic models. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(5): 909–922

Cotter S F, Rao B D, Engan K and Kreutz-Delgado K. 2005. Sparse solutions to linear inverse problems with multiple measurement vectors. IEEE Transactions on Signal Processing, 53(7): 2477–2488

Keller J B. 1962. Geometrical theory of diffraction. Journal of the Optical Society of America, 52(2): 116–130

Lu H P, Eng H L, Guan C T, Plataniotis K N and Venetsanopoulos A N. 2010. Regularized common spatial pattern with aggregation for EEG classification in small-sample setting. IEEE Transactions on Biomedical Engineering, 57(12): 2936–2946

Seeger M W. 2008. Bayesian inference and optimal design for the sparse linear model. The Journal of Machine Learning Research, 9: 759–813

Tan X, Roberts W, Li J and Stoica P. 2011. Sparse learning via iterative minimization with application to MIMO radar imaging. IEEE Transactions on Signal Processing, 59(3): 1088–1101

Wang Z Y, Hu R M, Wang S Z and Jiang J J. 2014. Face hallucination via weighted adaptive sparse regularization. IEEE Transactions on Circuits and Systems for Video Technology, 24(5): 802–813

Xu Z B, Chang X Y, Xu F M and Zhang H. 2012. L 1/2 regularization: a thresholding representation theory and a fast solver. IEEE Transactions on Neural Networks and Learning Systems, 23(7): 1013–1027

Zhang Y, Zhou G X, Jin J, Zhao Q B, Wang X Y and Cichocki A. 2014. Aggregation of sparse linear discriminant analyses for event-related potential classification in brain-computer interface. International Journal of Neural Systems, 24(1): 1450003

Zhao Q B, Zhang L Q and Cichocki A. 2015. Bayesian CP factorization of incomplete tensors with automatic rank determination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9): 1751–1763

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