Synthetic aperture radar processing is a complex task that involves advanced signal processing techniques and intense computational effort. While the first issue has now reached a mature stage, the question of how to produce accurately focused images in real-time, without mainframe facilities, is still under debate. The recent introduction of general-purpose graphic processing units seems to be quite promising in this view, especially for the decreased per-core cost barrier and for the affordable programming complexity. The authors explain, in this work, the main computational features of a range-Doppler Synthetic Aperture Radar (SAR) processor, trying to disclose the degree of parallelism in the operations at the light of the CUDA programming model. Given the extremely flexible structure of the Single Instruction Multiple Threads (SIMT) model, the authors show that the optimization of a SAR processing unit cannot reduce to an FFT optimization, although this is a quite extensively used kernel. Actually, it is noticeable that the most significant advantage is obtained in the range cell migration correction kernel where a complex interpolation stage is performed very efficiently exploiting the SIMT model. Performance show that, using a single Nvidia Tesla-C1060 GPU board, the obtained processing time is more than fifteen time better than our test workstation.

Processing of synthetic Aperture Radar data with GPGPU

This paper describes a general-purpose parallel scheme for efficiently focusing synthetic aperture radar (SAR) data on multicore-based shared-memory architectures. The rationale of the proposed tiling-based parallel focusing model is first discussed, and then, its implementation structure is illustrated. The adopted parallel solution, which is based on a canonical processing pattern, exploits a segmented-block-based approach and works successfully on data acquired by different spaceborne SAR platforms. Insofar as a significant portion of the focusing algorithm is amenable to tiling, our approach decomposes the problem into simpler subproblems of the same type, also providing a suitable mechanism to explicitly control the granularity of computation through the proper specification of the tiling at the different stages of the algorithm itself. Relevant implementation makes use of multithreading and high-performance libraries. Achievable performances are then experimentally investigated by quantifying the benefit of the parallelism incorporated into the prototype solution, thus demonstrating the validity of our approach. Accordingly, canonical performance metrics have been evaluated, and the pertinent scalability has been examined on different multicore architectures. Furthermore, in order to emphasize the practical ability of the proposed parallel model implementation to efficiently deal with data of different SAR sensors, a performance analysis has been carried out in different realistic scenarios including data acquired by the Envisat/ASAR, RADARSAT-1, and COSMO-SkyMed platforms.

Spaceborne Synthetic Aperture Radar Data Focusing on Multicore-Based Architectures

Multiprocessor Architectures For SAR Data Processing

X-sar Precision Processing

The Synthetic Aperture Radar (SAR) is an active sensor widely used in remote sensing to obtain high resolution ground images from the back-scattered echo signals. When a digital processing is performed, a high computational load is involved and hw/sw solutions based on both specialized and general pupose machines can be exploited to speedup the image focusing algorithm. A more economical solution based on cluster of workstations is presented. The performance results of the version 1.0 of our distributed SAR processor achieved on a homogeneous cluster of IBM RISC System 6000/360 under PVM are reported.

The use of PVM with workstation clusters for distributed SAR data processing

A modular digital SAR exploiting the range-doppler been developed .and tested both simulated SAR raw-data. processor, approach, has on real and The processor includes modules for range migration correction and Doppler parameters estimation, as key points for high quality image generation. The output from the processor consists of a complex data matrix, at full geometrical resolution; when more radiometric quality is required, a multi-look technique allows to produce detected images at user defined resolutions. . The results of two experiments are reported in the paper: the first regards the "Doppler centroid" estimation based on a non standard use of the "Clutterlock Algorithm", directly applied on sensor's raw-data; the second on the improvement obtained in the processor throughtput, combining the performance of the Prime Factor Algorithm (PFA) with the "overlap & save" optimization technique for faster convolutions. An example of image focalized by this processor on DEC-VAX and the related CPU time are reported in the paper, referring to SIR-B data processing. Potential applications of the processor are also considered, referring to the operative conditions of future spaceborne SAR missions. Key-words: SAR-processor, fast convolution, Doppler parameters estimation.

Fmt Digital Signal Processing Techniques For Spaceborne Synthetic Aperture Radars

http://ieeexplore.ieee.org/iel3/5006/13730/00633345.pdf

Performance Of Mulitprocessor Structures For Fast Digital Sar Processing

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Multiprocessor Architectures For SAR Data Processing

Fmt Digital Signal Processing Techniques For Spaceborne Synthetic Aperture Radars

Performance Of Mulitprocessor Structures For Fast Digital Sar Processing