基于时变阈值的单比特压缩感知SAR成像
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摘要
近年来,单比特压缩感知已经被应用于合成孔径雷达(SAR)成像中。现有的单比特压缩感知SAR成像一般是与零阈值比较进行单比特量化,无法保留场景中目标反射系数的幅度信息。因此,时变阈值已经受到关注。文中提出了一种新的基于时变阈值的单比特压缩感知SAR成像模型,将单比特量化视为一个线性分类的过程,采用L_1范数正则化的逻辑回归算法重构稀疏目标原始的反射系数。仿真结果表明,该方法可以在远低于Nyquist采样率的前提下准确地恢复出目标原始的反射系数,并且降低了雷达系统硬件的成本和能耗,还有利于SAR图像的特征提取。
In recent years, one-bit compressed sensing has been applied in the field of synthetic aperture radar(SAR) imaging. Previous one-bit compressed sensing SAR imaging algorithms with zero thresholds will lose the amplitude information about the reflectivity coefficient of the targets in scenes. Therefore, time-varying thresholds have received attentions recently. In this study, we propose a novel one-bit compressed sensing SAR imaging method with time-varying thresholds. The one-bit quantization is considered as a linear classification procedure, and the logistic regression algorithm with norm regularization is used to recover the original reflectivity coefficient of the sparse targets in scenes. Simulation results show that the proposed method can accurately recover the original reflectivity coefficient of the targets in scenes with much less data than that required by the Nyquist rate. The cost of hardware and energy consumption are reduced for radar system, and the algorithm is also beneficial for feature extraction of SAR images.
In recent years, one-bit compressed sensing has been applied in the field of synthetic aperture radar(SAR) imaging. Previous one-bit compressed sensing SAR imaging algorithms with zero thresholds will lose the amplitude information about the reflectivity coefficient of the targets in scenes. Therefore, time-varying thresholds have received attentions recently. In this study, we propose a novel one-bit compressed sensing SAR imaging method with time-varying thresholds. The one-bit quantization is considered as a linear classification procedure, and the logistic regression algorithm with norm regularization is used to recover the original reflectivity coefficient of the sparse targets in scenes. Simulation results show that the proposed method can accurately recover the original reflectivity coefficient of the targets in scenes with much less data than that required by the Nyquist rate. The cost of hardware and energy consumption are reduced for radar system, and the algorithm is also beneficial for feature extraction of SAR images.
引文
[1] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306
[2] Candès E J, Wakin M B. An introduction to compressive sampling[J].IEEE Signal Processing Magazine, 2008, 25(2):21-30
[3] Yang J, Thompson J, Huang X, et al. Random-frequency SAR imaging based on compressed sensing[J]. IEEE Transactions on Geoscience & Remote Sensing, 2013, 51(2):983-994
[4] Li B, Liu F, Zhou C, et al. Phase error correction for approximated observation based compressed sensing radar imaging[J]. Sensors, 2017, 17(3):613-635
[5] 周崇彬,刘发林,李博. 基于压缩感知的单比特合成孔径雷达成像算法[J]. 微波学报, 2015, 31(6):71-77 Zhou C B, Liu F L, Li B. A compressive sensing method for one bit coded synthetic aperture radar imaging[J]. Journal of Microwaves, 2015, 31(6):71-77
[6] Boufounos P T, Baraniuk R G. 1-bit compressive sensing [A]. Conference on Information Sciences and Systems[C], 2008.16-21
[7] 高玉龙,胡德顺,陈艳萍,等. 一种基于逻辑回归的单比特空间谱估计方法[P]. 中国,201611109952.2,2016.12.06 Gao Y L, Hu D S, Chen Y P, et al. A spectrum estimation method with one bit sampling based on logistic regression[P]. China, 201611109952.2. 2016.12.06
[8] Zhou C, Liu F, Li B, et al. A 1-bit compressive sensing approach for SAR imaging based on approximated observation[A]. Eighth International Conference on Digital Image Processing[C], 2016.1-5
[9] Dong X, Zhang Y. A MAP approach for 1-bit compressive sensing in synthetic aperture radar imaging[J]. IEEE Geoscience & Remote Sensing Letters, 2015,12(6): 1237-1241
[10] 吕元浩,刘发林,李博,等.基于单比特的分段SAR成像重建算法[J].中国科学技术大学学报,2017,47(10):848-853 Lv Y H, Liu F L, Li B, et al. Segmented reconstruction for SAR imaging based on 1-bit compressed sensing[J]. Journal of University of Science and Technology of China, 2017, 47(10):848-853
[11] Wang X Q, Li G, Liu Y, et al. Enhanced 1-bit radar imaging by exploiting two-level block sparsity[J]. IEEE Transactions on Geoscience & Remote Sensing, 2018, 1-11
[12] Knudson K, Saab R, Ward R. One-bit compressive sensing with norm estimation[J]. IEEE Transactions on Information Theory, 2016, 62(5):2748-2758
[13] 赵博, 黄磊, 周汉飞, 等. 基于单频时变阈值的1-bit SAR成像方法研究[J]. 雷达学报, 2018, 7(4):446-454 Zhao B, Huang L, Zhou H F, et al. 1-bit SAR imaging method based on single frequency time-varying threshold[J]. Journal of Radars, 2018, 7(4): 446-454
[14] Li J, Naghsh M M, Zahabi S J, et al. Compressive radar sensing via one-bit sampling with time-varying thresholds[A]. 2016 50th Asilomar Conference on Signals, Systems and Computers[C], 2016.1164-1168
[15] Zahabi S J, Naghsh M M, Modarres Hashemi M, et al. Compressive pulse-Doppler radar sensing via 1-bit sampling with time-varying threshold[A]. IEEE International Conference on Acoustics, Speech and Signal Processing[C], 2017. 3419-3423韩浩 男, 1994 年生, 硕士生。 主要研究方向:雷达成像、压缩感知。E-mail: haohan@ustc.edu.cn刘发林(通信作者) 男, 1963 年生,教授,博士生导师。主要研究方向: 微波通信、计算电磁学。E-mail: liufl@ustc.edu.cn李博 男, 1990 年生,博士生。主要研究方向:雷达成像、压缩感知。王峥 男, 1990 年生,博士生。 主要研究方向:雷达成像、压缩感知。
[2] Candès E J, Wakin M B. An introduction to compressive sampling[J].IEEE Signal Processing Magazine, 2008, 25(2):21-30
[3] Yang J, Thompson J, Huang X, et al. Random-frequency SAR imaging based on compressed sensing[J]. IEEE Transactions on Geoscience & Remote Sensing, 2013, 51(2):983-994
[4] Li B, Liu F, Zhou C, et al. Phase error correction for approximated observation based compressed sensing radar imaging[J]. Sensors, 2017, 17(3):613-635
[5] 周崇彬,刘发林,李博. 基于压缩感知的单比特合成孔径雷达成像算法[J]. 微波学报, 2015, 31(6):71-77 Zhou C B, Liu F L, Li B. A compressive sensing method for one bit coded synthetic aperture radar imaging[J]. Journal of Microwaves, 2015, 31(6):71-77
[6] Boufounos P T, Baraniuk R G. 1-bit compressive sensing [A]. Conference on Information Sciences and Systems[C], 2008.16-21
[7] 高玉龙,胡德顺,陈艳萍,等. 一种基于逻辑回归的单比特空间谱估计方法[P]. 中国,201611109952.2,2016.12.06 Gao Y L, Hu D S, Chen Y P, et al. A spectrum estimation method with one bit sampling based on logistic regression[P]. China, 201611109952.2. 2016.12.06
[8] Zhou C, Liu F, Li B, et al. A 1-bit compressive sensing approach for SAR imaging based on approximated observation[A]. Eighth International Conference on Digital Image Processing[C], 2016.1-5
[9] Dong X, Zhang Y. A MAP approach for 1-bit compressive sensing in synthetic aperture radar imaging[J]. IEEE Geoscience & Remote Sensing Letters, 2015,12(6): 1237-1241
[10] 吕元浩,刘发林,李博,等.基于单比特的分段SAR成像重建算法[J].中国科学技术大学学报,2017,47(10):848-853 Lv Y H, Liu F L, Li B, et al. Segmented reconstruction for SAR imaging based on 1-bit compressed sensing[J]. Journal of University of Science and Technology of China, 2017, 47(10):848-853
[11] Wang X Q, Li G, Liu Y, et al. Enhanced 1-bit radar imaging by exploiting two-level block sparsity[J]. IEEE Transactions on Geoscience & Remote Sensing, 2018, 1-11
[12] Knudson K, Saab R, Ward R. One-bit compressive sensing with norm estimation[J]. IEEE Transactions on Information Theory, 2016, 62(5):2748-2758
[13] 赵博, 黄磊, 周汉飞, 等. 基于单频时变阈值的1-bit SAR成像方法研究[J]. 雷达学报, 2018, 7(4):446-454 Zhao B, Huang L, Zhou H F, et al. 1-bit SAR imaging method based on single frequency time-varying threshold[J]. Journal of Radars, 2018, 7(4): 446-454
[14] Li J, Naghsh M M, Zahabi S J, et al. Compressive radar sensing via one-bit sampling with time-varying thresholds[A]. 2016 50th Asilomar Conference on Signals, Systems and Computers[C], 2016.1164-1168
[15] Zahabi S J, Naghsh M M, Modarres Hashemi M, et al. Compressive pulse-Doppler radar sensing via 1-bit sampling with time-varying threshold[A]. IEEE International Conference on Acoustics, Speech and Signal Processing[C], 2017. 3419-3423韩浩 男, 1994 年生, 硕士生。 主要研究方向:雷达成像、压缩感知。E-mail: haohan@ustc.edu.cn刘发林(通信作者) 男, 1963 年生,教授,博士生导师。主要研究方向: 微波通信、计算电磁学。E-mail: liufl@ustc.edu.cn李博 男, 1990 年生,博士生。主要研究方向:雷达成像、压缩感知。王峥 男, 1990 年生,博士生。 主要研究方向:雷达成像、压缩感知。
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