Electronics and Control

Track distribution optimization method based on TomoSAR via RIPless theory

  • BI Hui ,
  • ZHANG Bingchen ,
  • HONG Wen
Expand
  • 1. Science and Technology on Microwave Imaging Laboratory, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;
    2. University of Chinese Academy of Sciences, Beijing 100190, China

Received date: 2015-01-21

  Revised date: 2015-05-10

  Online published: 2015-05-25

Supported by

CAS/SAFEA International Partnership Program for Creative Research Team

Abstract

Synthetic aperture radar tomography(TomoSAR) applies measured repeat-pass SAR images to synthetize an aperture in the elevation direction, so as to achieve three-dimensional imaging. In recent years, compressive sensing(CS) has been used for elevation reconstruction for the sparse elevation distribution. The imaging quality of elevation of CS-based TomoSAR depends on the recovery property of measurement matrix, which is affected by the track distribution. Compared to other restrictions of recovery property for measurement matrix, RIPless theory is intuitionistic, effective and simple to calculate. In this paper, we propose a track distribution optimal criterion for CS-based TomoSAR via RIPless theory to optimize the distribution of flight tracks and achieve optimal reconstruction of elevation when the number of tracks is fixed. Simulation and experimental results validate the validity of the proposed optimization criterion.

Cite this article

BI Hui , ZHANG Bingchen , HONG Wen . Track distribution optimization method based on TomoSAR via RIPless theory[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016 , 37(2) : 680 -687 . DOI: 10.7527/S1000-6893.2015.0131

References

[1] REIGBER A, MOREIRA A. First demonstration of airborne SAR tomography using multibaseline L-band data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5):2142-2152.
[2] FORNARO G, LOMBARDINI F, SERAFINO F. Three-dimensional focusing multipass data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(3):507-517.
[3] FORNARO G, LOMBARDINI F, SERAFINO F. Three-dimensional multipass SAR focusing:Experiments with long-term spaceborne data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4):702-714.
[4] LOMBARDINI F, MONTANARI M, GINI F. Reflecitivity estimation for multibaseline interferometric radar imaging of layover extended sources[J]. IEEE Transactions on Signal Processing, 2003, 51(6):1508-1519.
[5] BAMLER R, HARTL P. Synthetic aperture radar interferoetry[J]. Inverse Problem, 1998, 14(4):1-54.
[6] GABRIEL A, GOLDSTEIN R. Crossed orbit interferometry:Theory and experimental results from SIR-B[J]. International Journal of Remote Sensing, 1988, 9(5):857-872.
[7] 胡庆东, 毛士艺. 干涉合成孔径雷达基线的估计[J]. 航空学报, 1998, 19(S1):20-24. HU Q D, MAO S Y. Estimation of interferometric SAR baseline[J]. Acta Aeronoutica et Astronautica Sinica, 1998, 19(S1):20-24(in Chinese).
[8] DONOHO D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
[9] CANDES E J, TAO T. Near-optimal signal recovery from random projections:Universal encoding strategies[J]. IEEE Transactions on Information Theory, 2006, 52(12):5406-5425.
[10] CANDES E, ROMBERG J, TAO T. Stable signal recovery from incomplete and inaccurate measurements[J]. Communications on Pure and Applied Mathematics, 2006, 59(8):1207-1223.
[11] BUDILLON A, EVANGELISTA A, SCHIRINZI G. SAR tomography from sparse samples[C]//International Geoscience and Remote Sensing Symposium, 2009:865-868.
[12] ZHU X, BAMLER R. Very high resolution spaceborne SAR tomography in urban environment[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(12):4296-4308.
[13] ZHU X, BAMLER R. Tomographic SAR inversion L1-norm regularization-the compressive sensing approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(10):3939-3846.
[14] CANDES E, TAO T. Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005, 51(12):4203-4215.
[15] CANDES E, PLAN Y. A probabilistic and RIPless theory of compressed sensing[J]. IEEE Transactions on Information Theory, 2011, 57(11):7235-7254.
[16] KUENG R, GROSS D. RIPless compressed sensing from anisotropic measurements[J]. Linear Algebra and its Applications, 2014, 441:110-123.
[17] ZHANG B C, HONG W, WU Y R. Sparse microwave imaging:principles and applications[J]. Science China Information Sciences, 2012, 55(8):1722-1754.
[18] 赵曜, 张冰尘, 洪文, 等. 基于RIPless理论的稀疏微波成像波形分析方法[J]. 雷达学报, 2013, 2(3):265-270. ZHAO Y, ZHANG B C, HONG W, et al. RIPless based radar waveform analysis in sparse microwave imaging[J]. Journal of Radars, 2013, 2(3):265-270(in Chinese).
[19] HORN R. The DLR airborne SAR project E-SAR[C]//International Geoscience and Remote Sensing Symposium, 1996:1624-1628.
[20] HAJNSEK, I, SCHEIBER R, ULANDER L, et al. BioSAR 2007:Technical assistance for the development of airborne SAR and geophysical measurements during the BioSAR 2007 experiment[R]. Oberpfaffenhofen:DLR, 2008.

Outlines

/