自适应抑制噪声的压缩感知ISAR成像方法
|
摘要
基于压缩感知(Compressed Sensing,CS)的逆合成孔径雷达(Inverse Synthetic Aperture Radar,ISAR)在高信噪比情况下由有限脉冲成像表现良好,但在不可避免的强噪声情况下,基于压缩感知的成像方法受到挑战。针对这一挑战,提供一种自适应抑制噪声的CS-ISAR成像方法。该方法用能量门限分离不含目标的噪声单元,根据分离出的噪声单元估计噪声水平,再根据估计的噪声水平和设定的恒虚警率自适应地调整用于正交匹配追踪(Orthogonal Matched Pursuit,OMP)算法的残差门限,利用残差门限OMP算法在减少脉冲数情况下成像。实测数据验证了所提方法能够利用有限数据脉冲在不同信噪比情况下自适应地抑制噪声,得到高质量ISAR图像。
Compressed sensing( CS)-based inverse synthetic aperture radar( ISAR) imaging with limited pulses performs well in the case of high signal-to-noise ratios. However,strong noises are usually inevitable in radar imaging,which challenges the CS-based approach. In this paper,we present an adaptive noise depression CS-ISAR imaging method,which can sustain strong noise and provide more scattering centers extracted with limited number of pulses. The ISAR images are reconstructed via orthogonal matched pursuit( OMP),in which the iteration is terminated by a preseted residual thresholding( RT).The RT is yielded by combining the noise level with a preseted constant false alarm rate and the number of measurements. The noise level is estimated from the noise range cells which are discriminated by Gaussianity test. Experiments show that the method is capable of precise estimation of scattering centers and adaptive suppression of noise with limited measurements.
Compressed sensing( CS)-based inverse synthetic aperture radar( ISAR) imaging with limited pulses performs well in the case of high signal-to-noise ratios. However,strong noises are usually inevitable in radar imaging,which challenges the CS-based approach. In this paper,we present an adaptive noise depression CS-ISAR imaging method,which can sustain strong noise and provide more scattering centers extracted with limited number of pulses. The ISAR images are reconstructed via orthogonal matched pursuit( OMP),in which the iteration is terminated by a preseted residual thresholding( RT).The RT is yielded by combining the noise level with a preseted constant false alarm rate and the number of measurements. The noise level is estimated from the noise range cells which are discriminated by Gaussianity test. Experiments show that the method is capable of precise estimation of scattering centers and adaptive suppression of noise with limited measurements.
引文
[1]保铮,邢孟道,王彤.雷达成像技术[M].北京:电子工业出版社,2005:5-9.
[2]DONOHO D L. Compressed Sensing[J]. IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[3]Candès E J and Wakin M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine,2008,25(2):21-30.
[4]HOU Q K,LIU Y,and CHEN Z P. Reducing microDoppler effect in compressed sensing ISAR imaging for aircraft using limited pulses[J]. Electronics Letters,2015,51(12):937-939.
[5]刘向阳,杨君刚,孟进等.低信噪比下基于Hough变换的前视阵列SAR稀疏三维成像[J].雷达学报,2017,6(3):316-323.
[6]WANG L,ZHAO L F,BI G A,and WANG C R. Sparse Representation-Based ISAR Imaging Using Markov Random Fields[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(8):3941-3953.
[7]LI S Y,ZHAO G Q,ZHANG W,et al. ISAR imaging by two-dimensional convex optimization-based compressive sensing[J]. IEEE Sensors Journal,2016,16(19):7088-7093.
[8]Tropp J and Gilbert A. C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory,2007,53(12):4655-4666.
[9]JACK L,WALKER. Range-Doppler imaging of rotating objects[J]. IEEE Transactions on Aerospace and Electronic Systems,1980,AES-16(1):23-52.
[10]王勇,黄鑫. FMCW-ISAR对舰船目标成像脉内补偿方法研究[J].雷达学报.
[11]屈高龙,王文光,翟宇霄.一种连续多脉冲相参海杂波的仿真方法[J].中国电子科学研究院学报,2018,13(3):272-278.
[12]CHEN X L,GUAN J,HUANG Y,et al.. Radon-linear canonical ambiguity function-based detection and estimation method for marine target with micromotion. IEEE Transactions on Geoscience and Remote Sensing,2015,53(4):2225-2240.
[13]CAI T T and WANG L. Orthogonal matching pursuit for sparse signal recovery with noise[J]. IEEE Transactions on Information Theory,2011,57(7):4680-4688.
[14]张龙,张磊,邢孟道.一种基于改进压缩感知的低信噪比ISAR高分辨成像方法[J].电子与信息学报,2010,32(9):2263-2267.
[15]DONOHO D L,TSAIG Y,DRORI I,et al.. Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit[J]. IEEE Transactions on Information Theory,2012,58(2):1094-1121.
[2]DONOHO D L. Compressed Sensing[J]. IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[3]Candès E J and Wakin M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine,2008,25(2):21-30.
[4]HOU Q K,LIU Y,and CHEN Z P. Reducing microDoppler effect in compressed sensing ISAR imaging for aircraft using limited pulses[J]. Electronics Letters,2015,51(12):937-939.
[5]刘向阳,杨君刚,孟进等.低信噪比下基于Hough变换的前视阵列SAR稀疏三维成像[J].雷达学报,2017,6(3):316-323.
[6]WANG L,ZHAO L F,BI G A,and WANG C R. Sparse Representation-Based ISAR Imaging Using Markov Random Fields[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(8):3941-3953.
[7]LI S Y,ZHAO G Q,ZHANG W,et al. ISAR imaging by two-dimensional convex optimization-based compressive sensing[J]. IEEE Sensors Journal,2016,16(19):7088-7093.
[8]Tropp J and Gilbert A. C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory,2007,53(12):4655-4666.
[9]JACK L,WALKER. Range-Doppler imaging of rotating objects[J]. IEEE Transactions on Aerospace and Electronic Systems,1980,AES-16(1):23-52.
[10]王勇,黄鑫. FMCW-ISAR对舰船目标成像脉内补偿方法研究[J].雷达学报.
[11]屈高龙,王文光,翟宇霄.一种连续多脉冲相参海杂波的仿真方法[J].中国电子科学研究院学报,2018,13(3):272-278.
[12]CHEN X L,GUAN J,HUANG Y,et al.. Radon-linear canonical ambiguity function-based detection and estimation method for marine target with micromotion. IEEE Transactions on Geoscience and Remote Sensing,2015,53(4):2225-2240.
[13]CAI T T and WANG L. Orthogonal matching pursuit for sparse signal recovery with noise[J]. IEEE Transactions on Information Theory,2011,57(7):4680-4688.
[14]张龙,张磊,邢孟道.一种基于改进压缩感知的低信噪比ISAR高分辨成像方法[J].电子与信息学报,2010,32(9):2263-2267.
[15]DONOHO D L,TSAIG Y,DRORI I,et al.. Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit[J]. IEEE Transactions on Information Theory,2012,58(2):1094-1121.