基于Laplace先验的复贝叶斯压缩感知ISAR高分辨成像算法
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摘要
针对传统方法相位校正后存在残余相位误差导致图像散焦的问题,提出基于Laplace先验的复贝叶斯压缩感知(complex Bayesian compressed sensing,CBCS)逆合成孔径雷达高分辨成像算法。首先,假设目标图像各像元服从Laplace先验,建立稀疏先验模型;然后,把相位误差作为模型误差,利用BCS理论通过迭代交替求得目标图像并实现相位误差更新。该算法直接在复数域进行贝叶斯推理求解,避免了传统方法中将复数转换为实数处理所带来的运算复杂度高、自聚焦效果不强的问题。另外,在求解过程中采用分布式计算方法,与传统的矩阵矢量化求解方法相比,进一步提高了运算效率,仿真实验验证了算法的有效性。
In order to solve the problem of image defocus caused by the space-variant errors after traditional phase correction methods,a high-resolution algorithm for inverse synthetic aperture radar imaging based on complex Bayesian compressed sensing(CBCS)using Laplace priors is proposed.Assume that each pixel of the target image follows a Laplace prior to establish the prior models,then take the phase errors as the model errors.Target images and phase errors are solved using alternating iteration based on BCS theory.The algorithm is directly solved in the complex domain by Bayesian inference so as to avoid increasing the computational complexity of converting the complex to the real.In addition,the distributed computing method improves the computational efficiency compared with the traditional matrix vectorization method.Simulation experiments verify the effectiveness of the algorithm.
In order to solve the problem of image defocus caused by the space-variant errors after traditional phase correction methods,a high-resolution algorithm for inverse synthetic aperture radar imaging based on complex Bayesian compressed sensing(CBCS)using Laplace priors is proposed.Assume that each pixel of the target image follows a Laplace prior to establish the prior models,then take the phase errors as the model errors.Target images and phase errors are solved using alternating iteration based on BCS theory.The algorithm is directly solved in the complex domain by Bayesian inference so as to avoid increasing the computational complexity of converting the complex to the real.In addition,the distributed computing method improves the computational efficiency compared with the traditional matrix vectorization method.Simulation experiments verify the effectiveness of the algorithm.
引文
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[8]王天云,陆新飞,孙麟,等.基于贝叶斯压缩感知的ISAR自聚焦成像[J].电子与信息学报,2015,37(11):2719-2726.WANG T Y,LU X F,SUN L,et al.An autofocus imaging method for ISAR based on Bayesian compressive sensing[J].Journal of Electronics and Information Technology,2015,37(11):2719-2726.
[9]BABACAN S D,MOLINA R,KATSAGGELOS A K.Bayesian compressive sensing using Laplace priors[J].IEEE Trans.on Image Processing,2010,19(1):53-63.
[10]CHEN C C,ANDREW H C.Target-motion-induced radar imaging[J].IEEE Trans.on Aerospace and Electronics Systems,1980,16(1):2-14.
[11]CARLIN M,ROCCA P,OLIVERI G,et al.Directions-of-arrival estimation through Bayesian compressive sensing strategies[J].IEEE Trans.on Antennas and Propagation,2013,61(7):3828-3838.
[12]WU Q,ZHANG Y D,AMIN M G,et al.Complex multitask Bayesian compressive sensing[C]∥Proc.of the IEEE International Conference on Acoustics,Speech and Signal Processing,2014:3375-3379.
[13]ANDREWS D F,MALLOWS C L.Scale mixtures of normal distributions[J].Journal of the Royal Statistical Society,Series B,1974,36(1):99-102.
[14]TIPPING M E,SMOLA A.Sparse Bayesian learning and the relevance vector machine[J].Journal of Machine Learning Research,2001,1(3):211-244.
[15]ZHANG L,WANG H,QIAO Z J.Resolution enhancement for ISAR imaging via improved statistical compressive sensing[J].EURASIP Journal on Advances in Signal Processing,2016,2016(1):80.
[2]邢孟道,保铮,李真芳,等.雷达成像算法进展[M].北京:电子工业出版社,2014.XING M D,BAO Z,LI Z F,et al.Advances in radar imaging algorithms[M].Beijing:Electronics Industry Press,2014.
[3]章鹏飞,李潺,周洪喜,等.基于贝叶斯估计的ISAR自聚焦算法[J].宇航动力学学报,2016,6(2):55-60.ZHANG P F,LI C,ZHOU H X,et al.ISAR autofocus algorithm based on Bayesian estimation[J].Journal of Astrodynamics,2016,6(2):55-60.
[4]DONOHO D L.Compressed sensing[J].IEEE Trans.on Information Theory,2006,52(4):1289-1306.
[5]徐刚,包敏,李亚超,等.基于贝叶斯估计的高精度ISAR成像[J].系统工程与电子技术,2011,33(11):2382-2388.XU G,BAO M,LI Y C,et al.High precision ISAR imaging via Bayesian statistics[J].Systems Engineering and Electronics,2011,33(11):2382-2388.
[6]ZHANG L,QIAO Z J,XING M D,et al.High-resolution ISAR imaging by exploiting sparse apertures[J].IEEE Trans.on Antennas and Propagation,2012,60(2):997-1008.
[7]徐刚,张磊,陈倩倩,等.基于稀疏约束最优化的ISAR相位自聚焦成像算法[J].电子学报,2013,41(9):1772-1777.XU G,ZHANG L,CHEN Q Q,et al.Novel autofocusing algorithm for ISAR imaging based on sparse constraint[J].Acta Electronica Sinica,2013,41(9):1772-1777.
[8]王天云,陆新飞,孙麟,等.基于贝叶斯压缩感知的ISAR自聚焦成像[J].电子与信息学报,2015,37(11):2719-2726.WANG T Y,LU X F,SUN L,et al.An autofocus imaging method for ISAR based on Bayesian compressive sensing[J].Journal of Electronics and Information Technology,2015,37(11):2719-2726.
[9]BABACAN S D,MOLINA R,KATSAGGELOS A K.Bayesian compressive sensing using Laplace priors[J].IEEE Trans.on Image Processing,2010,19(1):53-63.
[10]CHEN C C,ANDREW H C.Target-motion-induced radar imaging[J].IEEE Trans.on Aerospace and Electronics Systems,1980,16(1):2-14.
[11]CARLIN M,ROCCA P,OLIVERI G,et al.Directions-of-arrival estimation through Bayesian compressive sensing strategies[J].IEEE Trans.on Antennas and Propagation,2013,61(7):3828-3838.
[12]WU Q,ZHANG Y D,AMIN M G,et al.Complex multitask Bayesian compressive sensing[C]∥Proc.of the IEEE International Conference on Acoustics,Speech and Signal Processing,2014:3375-3379.
[13]ANDREWS D F,MALLOWS C L.Scale mixtures of normal distributions[J].Journal of the Royal Statistical Society,Series B,1974,36(1):99-102.
[14]TIPPING M E,SMOLA A.Sparse Bayesian learning and the relevance vector machine[J].Journal of Machine Learning Research,2001,1(3):211-244.
[15]ZHANG L,WANG H,QIAO Z J.Resolution enhancement for ISAR imaging via improved statistical compressive sensing[J].EURASIP Journal on Advances in Signal Processing,2016,2016(1):80.