Synthetic aperture radar imaging with motion estimation and autofocus

We introduce from first principles a synthetic aperture radar (SAR) imaging and target motion estimation method that is combined with compensation for radar platform trajectory perturbations. The main steps of the method are (a) segmentation of the data into properly calibrated small apertures, (b) motion or platform trajectory perturbation estimation using the Wigner transform and the ambiguity function of the data in a complementary way and (c) combination of small aperture estimates and construction of high-resolution images over wide apertures. The analysis provides quantitative criteria for implementing the aperture segmentation and the parameter estimation process. X-band persistent surveillance SAR is a specific application that is covered by our analysis. Detailed numerical simulations illustrate the robust applicability of the theory and validate the theoretical resolution analysis.

[1]  Yu Ding,et al.  Time-frequency methods in SAR imaging of moving targets , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[2]  M. Abramowitz,et al.  Handbook of Mathematical Functions With Formulas, Graphs and Mathematical Tables (National Bureau of Standards Applied Mathematics Series No. 55) , 1965 .

[3]  Sergio Barbarossa.  New autofocusing technique for SAR images based on the Wigner-ville distribution , 1990 .

[4]  W. Brown.  Synthetic Aperture Radar , 1967, IEEE Transactions on Aerospace and Electronic Systems.

[5]  S. Barbarossa.  Detection and imaging of moving objects with synthetic aperture radar , 1992 .

[6]  Charles V. Jakowatz,et al.  Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach , 1996 .

[7]  Sergio Barbarossa,et al.  Detection and imaging of moving objects with synthetic aperture radar. Part 2: Joint time-frequency analysis by Wigner-Ville distribution , 1992 .

[8]  John C. Curlander,et al.  Synthetic Aperture Radar: Systems and Signal Processing , 1991 .

[9]  P. Zweifel.  Advanced Mathematical Methods for Scientists and Engineers , 1980 .

[10]  C. V. Jakowatz,et al.  Eigenvector method for maximum-likelihood estimation of phase errors in synthetic-aperture-radar imagery , 1993 .

[11]  Margaret Cheney,et al.  A Mathematical Tutorial on Synthetic Aperture Radar , 2001, SIAM Rev..

[12]  Ning Xue,et al.  An analysis of time-frequency methods in SAR imaging of moving targets , 2000, Proceedings of the 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop. SAM 2000 (Cat. No.00EX410).

[13]  Minh N. Do,et al.  SAR Image Autofocus By Sharpness Optimization: A Theoretical Study , 2007, IEEE Transactions on Image Processing.

[14]  Gregory Beylkin,et al.  Imaging of discontinuities in the inverse scattering problem by inversion of a causal generalized Radon transform , 1985 .

[15]  Anna Scaglione,et al.  Autofocusing of SAR images based on the product high-order ambiguity function , 1998 .

[16]  LeRoy A. Gorham,et al.  A challenge problem for 2D/3D imaging of targets from a volumetric data set in an urban environment , 2007, SPIE Defense + Commercial Sensing.

[17]  Frank Natterer,et al.  Mathematical methods in image reconstruction , 2001, SIAM monographs on mathematical modeling and computation.

[18]  Trygve Sparr.  Comparison of quadratic time-frequency methods applied to ISAR imaging of aircraft , 2002, SPIE Defense + Commercial Sensing.

[19]  Trygve Sparr.  Time-Frequency Signatures of a Moving Target in SAR Images , 2005 .