基于压缩感知的合成孔径雷达二维成像算法
|
摘要
通过对合成孔径雷达回波信号的分析,利用压缩感知理论基于信号稀疏性或可压缩性的基本原理,提出了方位稀疏表示的一种新方法,在此基础上给出了基于压缩感知的SAR回波信号处理方法和二维成像算法,实现了压缩感知对信号的全新采集和编解码,以较少的数据量实现成像,有效地抑制旁瓣,在一定程度上提高了成像中目标的分辨率,为有效降低高分辨合成孔径雷达的数据率提供了一种有效途径。通过对仿真数据和实测数据的处理验证了所提方法的可行性和有效性。
By the use of the principle of compressed sensing(CS)theory which is based on the sparsity or compressibility of signals,a new sparse representation for azimuth is proposed through the analysis of synthetic aperture radar echo signal.On this basis,a SAR echo signal processing method is obtained and a 2D SAR imaging algorithm which achieve a fire-new data collection and coding of signal is established.The use of this algorithm not only suppresses the side lobe effectively but also enhances the resolution of imaging target to a certain extent,and simultaneously needs less amounts of data for imaging,which provides an efficient path of decreasing the data rate of high resolution synthetic aperture radar.The feasibility and validity of the method proposed in this paper are tested through processing both simulated data and real radar data.
By the use of the principle of compressed sensing(CS)theory which is based on the sparsity or compressibility of signals,a new sparse representation for azimuth is proposed through the analysis of synthetic aperture radar echo signal.On this basis,a SAR echo signal processing method is obtained and a 2D SAR imaging algorithm which achieve a fire-new data collection and coding of signal is established.The use of this algorithm not only suppresses the side lobe effectively but also enhances the resolution of imaging target to a certain extent,and simultaneously needs less amounts of data for imaging,which provides an efficient path of decreasing the data rate of high resolution synthetic aperture radar.The feasibility and validity of the method proposed in this paper are tested through processing both simulated data and real radar data.
引文
[1]Donoho D L.Compressed sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[2]Yun Lin,Wen Hong,Weixian Tan.Compressed Sensing Technique for Circular SAR Imaging[C]//IET International Radar Conference.Guilin,2009:1123-1134.
[3]侯颖妮,李道京,洪文.基于稀疏阵列和压缩感知理论的艇载雷达运动目标成像研究[J].自然科学进展,2009(10):1110-1116.HOU Yingni,LI Daojing,HONG Wen.Study of Moving Target Imaging of Shipload Radar Based on The Theory of Compressed Sensing and Sparse Array[J].Progress in Natural Science,2009(10):1110-1116.(in Chinese)
[4]李炳杰,马青海,闫龙,等.基于位置参数二分法控制的信号稀疏分解[J].空军工程大学学报:自然科学版,2013,14(5):89-91.LI Bingjie,MA Qinghai,YAN Long,et al.Dichotomy Control of Positional parameter Based Signal Sparse Decomposition[J].Journal of Air Force Engineering University:Natural Science Edition,2013,14(5):89-91.(in Chinese)
[5]Si Xiaoyun,Jiao Licheng,Yu Hang,et al.SAR Images Reconstruction Based on Compressive Sensing[C]//2nd Asian-Pacific Conference on Synthetic Aperture Radar.Xian,2009:1056-1059.
[6]谢晓春,张云华.基于压缩感知的二维雷达成像算法[J].电子与信息学报,2010,32(5):1234-1238.XIE Xiaochun,ZHANG Yunhua.2D Radar Imaging Scheme Based on Compressive Sensing Technique[J].Journal of Electronics&Information Technology,2010,32(5):1234-1238.(in Chinese)
[7]ZHANG Lei,XING Mengdao,QIU Chengwei,et al.Achieving Higher Resolution ISAR Imaging With Limited Pulses via Compressed Sampling[J].IEEE Geoscience and Remote Sensing Letters,2009,6(3):567-571.(in Chinese)
[8]李炳杰,吕园,叶萌,等.基于非相干准则的压缩感知观测矩阵设计的极大极小方法[J].空军工程大学学报:自然科学版,2011,12(5):81-84.LI Bingjie,LYuan,YE Meng,et al.The Minimax Method of Design of Measurement Matrices for Compressed Sensing Based on Incoherence Criterion[J].Journal of Air Force Engineering University:Natural Science Edition,2011,12(5):81-84.(in Chinese)
[9]赵瑞珍,林婉娟,李浩,等.基于光滑l0范数和修正牛顿法的压缩感知重建算法[J].计算机辅助设计与图形学学报,2012,24(4):478-484.ZHAO Ruizhen,LIN Wanjuan,LI Hao,et al.Reconstruction Algorithm for Compressive Sensing Based on Smoothed l0 Norm and Revised Newton Method[J].Journal of Computer-Aided Design&Computer Graphics,2012,24(4):478-484.(in Chinese)
[10]洪文,胡东辉.合成孔径雷达成像——算法与实现[M].北京:电子工业出版社,2012.HONG Wen,HU Donghui.Digital Processing of Synthetic Aperture Radar Date:Algorithms and Implementation[M].Beijing:Publishing House of Electronics Industry,2012.(in Chinese)
[2]Yun Lin,Wen Hong,Weixian Tan.Compressed Sensing Technique for Circular SAR Imaging[C]//IET International Radar Conference.Guilin,2009:1123-1134.
[3]侯颖妮,李道京,洪文.基于稀疏阵列和压缩感知理论的艇载雷达运动目标成像研究[J].自然科学进展,2009(10):1110-1116.HOU Yingni,LI Daojing,HONG Wen.Study of Moving Target Imaging of Shipload Radar Based on The Theory of Compressed Sensing and Sparse Array[J].Progress in Natural Science,2009(10):1110-1116.(in Chinese)
[4]李炳杰,马青海,闫龙,等.基于位置参数二分法控制的信号稀疏分解[J].空军工程大学学报:自然科学版,2013,14(5):89-91.LI Bingjie,MA Qinghai,YAN Long,et al.Dichotomy Control of Positional parameter Based Signal Sparse Decomposition[J].Journal of Air Force Engineering University:Natural Science Edition,2013,14(5):89-91.(in Chinese)
[5]Si Xiaoyun,Jiao Licheng,Yu Hang,et al.SAR Images Reconstruction Based on Compressive Sensing[C]//2nd Asian-Pacific Conference on Synthetic Aperture Radar.Xian,2009:1056-1059.
[6]谢晓春,张云华.基于压缩感知的二维雷达成像算法[J].电子与信息学报,2010,32(5):1234-1238.XIE Xiaochun,ZHANG Yunhua.2D Radar Imaging Scheme Based on Compressive Sensing Technique[J].Journal of Electronics&Information Technology,2010,32(5):1234-1238.(in Chinese)
[7]ZHANG Lei,XING Mengdao,QIU Chengwei,et al.Achieving Higher Resolution ISAR Imaging With Limited Pulses via Compressed Sampling[J].IEEE Geoscience and Remote Sensing Letters,2009,6(3):567-571.(in Chinese)
[8]李炳杰,吕园,叶萌,等.基于非相干准则的压缩感知观测矩阵设计的极大极小方法[J].空军工程大学学报:自然科学版,2011,12(5):81-84.LI Bingjie,LYuan,YE Meng,et al.The Minimax Method of Design of Measurement Matrices for Compressed Sensing Based on Incoherence Criterion[J].Journal of Air Force Engineering University:Natural Science Edition,2011,12(5):81-84.(in Chinese)
[9]赵瑞珍,林婉娟,李浩,等.基于光滑l0范数和修正牛顿法的压缩感知重建算法[J].计算机辅助设计与图形学学报,2012,24(4):478-484.ZHAO Ruizhen,LIN Wanjuan,LI Hao,et al.Reconstruction Algorithm for Compressive Sensing Based on Smoothed l0 Norm and Revised Newton Method[J].Journal of Computer-Aided Design&Computer Graphics,2012,24(4):478-484.(in Chinese)
[10]洪文,胡东辉.合成孔径雷达成像——算法与实现[M].北京:电子工业出版社,2012.HONG Wen,HU Donghui.Digital Processing of Synthetic Aperture Radar Date:Algorithms and Implementation[M].Beijing:Publishing House of Electronics Industry,2012.(in Chinese)