Velocity-ISAR: On the application of ISAR techniques to multichannel SAR imaging

The U.S. Naval Research Laboratory (NRL) Multichannel Synthetic Aperture Radar (MSAR) consists of multiple receive channels arranged along the flight direction and is unique in its ability to measure and correct for radial motion at each pixel in the scene. A well-known algorithm for performing MSAR imaging, and which have we applied for the first time to data captured by an airborne system, is the Velocity Synthetic Aperture Radar (VSAR) algorithm. VSAR calculates the distribution of Doppler radial velocities associated with each pixel and subsequently compensates for the velocities in order to combat motion blur. However, as we demonstrate in this paper, the VSAR algorithm does not fully exploit the special structure associated with the motion dynamics of rigid bodies (including translational and roll-pitch-yaw motions) in maritime conditions. To this end we propose the use of Inverse Synthetic Aperture Radar (ISAR) based motion compensation techniques-in conjunction with velocity filtering-as a means of accomplishing this objective for MSAR imaging. After describing the rudiments of the NRL MSAR system and the basics of ISAR processing, we subsequently proceed to describe our proposed Velocity-ISAR (VISAR) imaging algorithm. We demonstrate the performance of our VISAR algorithm by imaging boats captured by our airborne NRL MSAR system; and highlight its relative advantages over VSAR in imaging maritime targets.

[1]  B. Friedlander,et al.  VSAR: a high resolution radar system for ocean imaging , 1998 .

[2]  Raghu G. Raj,et al.  A hierarchical Bayesian-map approach to computational imaging , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[3]  Mark A. Sletten,et al.  Demonstration of SAR Distortion Correction Using a Ground-Based Multichannel SAR Test Bed , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Julius O. Smith,et al.  PARSHL: An Analysis/Synthesis Program for Non-Harmonic Sounds Based on a Sinusoidal Representation , 1987, ICMC.

[5]  Mark A. Richards,et al.  Fundamentals of Radar Signal Processing , 2005 .

[6]  Raghu G. Raj,et al.  SAR Automatic Target Recognition Using Discriminative Graphical Models , 2014, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Thomas L. Ainsworth,et al.  Motion Analysis in SAR Images of Unfocused Objects Using Time–Frequency Methods , 2007, IEEE Geoscience and Remote Sensing Letters.

[8]  S. Deans The Radon Transform and Some of Its Applications , 1983 .

[9]  Luke Rosenberg,et al.  The NRL Multi Aperture SAR system , 2015, 2015 IEEE Radar Conference (RadarCon).

[10]  D. Munson,et al.  A tomographic formulation of spotlight-mode synthetic aperture radar , 1983, Proceedings of the IEEE.

[11]  M. Soumekh,et al.  Synthetic aperture radar-moving target indicator processing of multi-channel airborne radar measurement data , 2006 .

[12]  Raghu G. Raj,et al.  Performance studies of emulated multichannel SAR for motion characterization , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Ralph L. Fiedler,et al.  Adventures in SAR processing , 2000, SPIE Defense + Commercial Sensing.

[14]  Hao Ling,et al.  Time-Frequency Transforms for Radar Imaging and Signal Analysis , 2002 .

[15]  Joachim H. G. Ender,et al.  Multi-channel SAR/MTI system development at FGAN: from AER to PAMIR , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[16]  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).

[17]  Marco Martorella,et al.  Inverse Synthetic Aperture Radar Imaging: Principles, algorithms and applications , 2014 .