基于频率选择表面的毫米波压缩感知成像
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摘要
基于频率选择表面结构,提出了一种毫米波压缩感知成像方法,为压缩感知成像的硬件实现提供了研究途径。通过在频率选择表面单元中加载开关二极管,并随机控制它们处于开/关状态,仿真设计了一种可随机切换的新型毫米波成像掩膜板.把设计的随机掩膜板放置于毫米波天线上,构造出相应的随机测量矩阵并获取足够多的有效测量次数.结合压缩感知理论,利用恢复重构算法进行成像仿真验证,结果证实了所提方法的可行性,并能在较低采样率的情况下实现对原始图像的恢复重构.
Based on frequency selective surface(FSS) structure,a millimeter wave compressed sensing imaging method is proposed,which provides the research approach for hardware realization of compressed sensing imaging.By loading switching diodes into FSS and making them on/off randomly,a new randomswitching millimeter wave imaging mask is simulated and designed.The mask designed is put on a millimeter wave antenna.The corresponding random measurement matrix can be constructed and enough effective measurement times can be obtained.Combined with compressed sensing theory,the imaging simulation experiments are completed by using reconstruction algorithm.Results confirm feasibility of the method proposed,and reconstruction of the original images can be achieved in the case of lower sampling rate.
Based on frequency selective surface(FSS) structure,a millimeter wave compressed sensing imaging method is proposed,which provides the research approach for hardware realization of compressed sensing imaging.By loading switching diodes into FSS and making them on/off randomly,a new randomswitching millimeter wave imaging mask is simulated and designed.The mask designed is put on a millimeter wave antenna.The corresponding random measurement matrix can be constructed and enough effective measurement times can be obtained.Combined with compressed sensing theory,the imaging simulation experiments are completed by using reconstruction algorithm.Results confirm feasibility of the method proposed,and reconstruction of the original images can be achieved in the case of lower sampling rate.
引文
[1]Donoho D L.Compressed sensing[J].IEEE Trans.on Information Theory,2006,52(4):1289-1306.
[2]Donoho D L,Tsaig Y.Extensions of compressed sensing[J].Signal Processing,2006,86(3):549-571.
[3]Candes E.Compressive sampling[C].Proc.of the International Congress of Mathematicians,Madrid,Spain,2006,3:1433-1452.
[4]Candes E,Romberg J.Quantitative robust uncertainty principles and optimally sparse decompositions[J].Foundations of Computational Mathematics,2006,6(2):227-254.
[5]Candes E,Romberg J,Tao T.Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information[J].IEEE Trans.on Information Theory,2006,52(3):489-509.
[6]Candes E,Tao T.Near optimal signal recovery fr6m random projections:Universal encoding strategies[J].IEEE Trans.on Information Theory,2006,52(12):5406-5425.
[7]Qiao Rui,Zhang Jinhua.Compressed sensing imaging of moving objects[J].Chinese Journal of Quantum Electronics(量子电子学报),2015,32(1):17-23(in Chinese).
[8]Duarte M F,Davenport M A,Takhar D,et al.Single-pixel imaging via compressive sampling[J].IEEE Signal Processing Magazine,2008,25(2):83-91.
[9]Pang Shaozheng,Deng Jing.Scene monitoring algorithm based on single-pixel imaging system[J].Chinese Journal of Quantum Electronics(量子电子学报),2015,32(1):24-29(in Chinese).
[10]Alonso M T,Dekker P L,Mallorqui J J.A novel strategy for radar imaging based on compressive sensing[J].IEEE Trans.on Geoscience and Remote Sensing,2010,48(12):4285-4295.
[11]Baraniuk R.A lecture on compressive sensing[J].IEEE Signal Processing Magazine,2007,24(4):118-121.
[12]Munk B A.Frequency Selective Surface Theory and Design[M].New York:Wiley,2000.
[13]Tropp J A,Gilbert A C.Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Trans.on Information Theory,2007,53(12):4655-4666.
[2]Donoho D L,Tsaig Y.Extensions of compressed sensing[J].Signal Processing,2006,86(3):549-571.
[3]Candes E.Compressive sampling[C].Proc.of the International Congress of Mathematicians,Madrid,Spain,2006,3:1433-1452.
[4]Candes E,Romberg J.Quantitative robust uncertainty principles and optimally sparse decompositions[J].Foundations of Computational Mathematics,2006,6(2):227-254.
[5]Candes E,Romberg J,Tao T.Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information[J].IEEE Trans.on Information Theory,2006,52(3):489-509.
[6]Candes E,Tao T.Near optimal signal recovery fr6m random projections:Universal encoding strategies[J].IEEE Trans.on Information Theory,2006,52(12):5406-5425.
[7]Qiao Rui,Zhang Jinhua.Compressed sensing imaging of moving objects[J].Chinese Journal of Quantum Electronics(量子电子学报),2015,32(1):17-23(in Chinese).
[8]Duarte M F,Davenport M A,Takhar D,et al.Single-pixel imaging via compressive sampling[J].IEEE Signal Processing Magazine,2008,25(2):83-91.
[9]Pang Shaozheng,Deng Jing.Scene monitoring algorithm based on single-pixel imaging system[J].Chinese Journal of Quantum Electronics(量子电子学报),2015,32(1):24-29(in Chinese).
[10]Alonso M T,Dekker P L,Mallorqui J J.A novel strategy for radar imaging based on compressive sensing[J].IEEE Trans.on Geoscience and Remote Sensing,2010,48(12):4285-4295.
[11]Baraniuk R.A lecture on compressive sensing[J].IEEE Signal Processing Magazine,2007,24(4):118-121.
[12]Munk B A.Frequency Selective Surface Theory and Design[M].New York:Wiley,2000.
[13]Tropp J A,Gilbert A C.Signal recovery from random measurements via orthogonal matching pursuit[J].IEEE Trans.on Information Theory,2007,53(12):4655-4666.