A novel three-dimensional(3-D)imaging algorithm of interferometric inverse synthetic aperture radar(InISAR)using multichannel joint sparse recovery(MCJSR)
named MCJSR-InISAR
is proposed to improve the problem that the super-resolution images recovered by the single channel super-resolution imaging algorithm do not necessarily share the same positions of the scattering centers. The combined range alignment approach is used to correct the envelope delay caused by the target's translational motion
and then the combined phase adjustment approach is employed to complete the phase compensation of translational motion. The range differences between two echo signals received by different antennas are then compensa
ted. Then
MCJSR is used for super-resolution imaging
and the computational efficiency of the proposed algorithm is improved by using the fast Fourier transform(FFT). Finally
a 3-D InISAR imagery of the maneuvering target is obtained by using both the horizontal and the pitching interferometric phase information. The MCJSR-InISAR algorithm can improve the recovery precision of the strong centers in the target scattering field
and preserve effectively the relative phase information between different channels. Experimental results on real-measured data and comparisons with the single channel super-resolution imaging algorithm show that the proposed algorithm decreases the entropy of the resulting image by about 0.17
and reduces the computational complexity by about O(10
5
).
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references
WANG G, XIA X, CHEN V C. Three-dimensional ISAR imaging of maneuvering target using three receivers[J]. IEEE Transactions on Image Processing, 2001, 10(3): 436-447.
XU X J, NARAYANAN R M. Three-dimensional interferometric ISAR imaging for target scattering diagnosis and modeling[J]. IEEE Transactions on Image Processing, 2001, 10(7): 1094-1102.
LIU Y B, SONG M C, WU K, et al. High-quality 3-D InISAR imaging of maneuvering target based on a combined processing approach[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(5): 1036-1040.
ZHANG Q, TAT S Y, DU G, et al. Estimation of three-dimensional motion parameters in interferometric ISAR imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(2): 292-300.
MOORE T G, ZUERNDORFER B W, BURT E C. Enhanced imagery using spectral-estimation-based techniques[J]. Lincoln Laboratory Journal, 1997, 10(2): 171-186.
ZHANG L, XING M D, QIU C W, et al. Resolution enhancement for inversed synthetic aperture radar imaging under low SNR via improved compressed sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(10): 3824-3838.
CANDES E J, ROMBERG J, TAO T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509.
CHEN Q Q, XU G, LI Y C, et al. Cross-range scaling for ISAR with short aperture data[J]. Journal of Electronics Information Technology, 2013, 35(8): 1854-1861.
LIU Y B, LI N, WANG R, et al. Achieving high-quality three-dimensional InISAR imageries of maneuvering target via super-resolution ISAR imaging by exploiting sparseness[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(4): 828-832.
XING M D, WU R, LAN J, et al. Migration through resolution cell compensation in ISAR imaging[J]. IEEE Geoscience and Remote Sensing Letters, 2004, 1(2): 141-144.
XU G, XING M D, ZHANG L, et al. Bayesian inverse synthetic aperture radar imaging[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(6): 1150-1154.
HYDER M M, MAHATA K. A robust algorithm for joint-sparse recovery[J]. IEEE Signal Processing Letters, 2009, 16(12): 1091-1094.
RICHARD G, BARANIUK V C, MARCO F D, et al. Model-based compressive sensing[J]. IEEE Transactions on Information Theory, 2010, 56(4): 1982-2001.
BOYD S, VANDENBERGHE L. Convex optimization[M]. Cambridge, England: Cambridge University Press, 2004.