Synthetic Aperture Radar Processing with GPGPU

Bisceglie, M. Di; Santo, M. Di; Galdi, C.; Lanari, R.; Ranaldo, N.

doi:10.1109/msp.2009.935383

Cited by 26 publications

(13 citation statements)

References 16 publications

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“…In this sense, each stage of the factorization can be executed in parallel. Moreover, after factorization, the conventional BP algorithm can also be executed in parallel, such as with a GPU [29,30] or a multi-thread technical CPU. This will further accelerate the proposed algorithm.…”

Section: Parallelizationmentioning

confidence: 99%

An Accelerated Backprojection Algorithm for Monostatic and Bistatic SAR Processing

et al. 2018

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Abstract:The backprojection (BP) algorithm has been applied to every SAR mode due to its great focusing quality and adaptability. However, the BP algorithm suffers from immense computational complexity. To improve the efficiency of the conventional BP algorithm, several fast BP (FBP) algorithms, such as the fast factorization BP (FFBP) and Block_FFBP, have been developed in recent studies. In the derivation of Block_FFBP, range data are divided into blocks, and the upsampling process is performed using an interpolation kernel instead of a fast Fourier transform (FFT), which reduces the processing efficiency. To circumvent these limitations, an accelerated BP algorithm based on Block_FFBP is proposed. In this algorithm, a fixed number of pivots rather than the beam centers is applied to construct the relationship of the propagation time delay between the "new" and "old" subapertures. Partition in the range dimension is avoided, and the range data are processed as a bulk. This accelerated BP algorithm benefits from the integrated range processing scheme and is extended to bistatic SAR processing. In this sense, the proposed algorithm can be referred to simply as MoBulk_FFBP for the monostatic SAR case and BiBulk_FFBP for the bistatic SAR case. Furthermore, for monostatic and azimuth-invariant bistatic SAR cases where the platform runs along a straight trajectory, the slant range mapping can be expressed in a continuous and analytical form. Real data from the spaceborne/stationary bistatic SAR experiment with TerraSAR-X operating in the staring spotlight mode and from the airborne spotlight SAR experiment acquired in 2016 are used to validate the performances of BiBulk_FFBP and MoBulk_FFBP, respectively.

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Section: Parallelizationmentioning

confidence: 99%

An Accelerated Backprojection Algorithm for Monostatic and Bistatic SAR Processing

et al. 2018

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“…The reason that all the computation time cannot be hided is the overhead of streams management. Table V shows the performance of this work and a recently published work [10]. First, let's limit the scope within this work.…”

Section: B Speed Of Imagingmentioning

confidence: 99%

GPU-based Parallel Implementation of SAR Imaging

Jin

2012

2012 International Symposium on Electronic System Design (ISED)

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Synthetic Aperture Radar (SAR) is an all-weather remote sensing technology and occupies a great position in disaster observation and geological mapping. The main challenge for SAR processing is the huge volume of raw data, which demands tremendous computation. This limits the utilization of SAR, especially for real-time applications. On the other hand, recent developments in Graphics Processing Unit (GPU) technology, which obtain general processing capability, high parallel computation performance, and ultra wide memory bandwidth, offer a novel method for computationally intensive applications. This work proposes a parallel implementation of SAR imaging on GPU via Compute Unified Device Architecture (CUDA), and provides a potential solution for SAR real-time processing. The results show that the proposed method obtained a speedup of 31.72, compared to a CPU platform.

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“…Graphics Processing Units (GPUs), which provide platforms for parallel computing with very competitive "flops per dollar" ratios [23][24][25][26][27][28], represent an excellent opportunity in this framework. The use of GPUs is spreading over the SAR community [10,11,[29][30][31][32][33][34] whose first attempts in this context date even back to the times when extensions of the ANSI C simplifying the programming of graphic cards (as CUDA -Compute Unified Device Architecture -or OpenCL) [35] were not yet available [36].…”

Section: Introductionmentioning

confidence: 99%

Fast Gpu-Based Interpolation for Sar Backprojection

Capozzoli¹,

Curcio²,

Liseno³

2013

PIER

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Abstract-We introduce and discuss a parallel SAR backprojection algorithm using a Non-Uniform FFT (NUFFT) routine implemented on a GPU in CUDA language.The details of a convenient GPU implementation of the NUFFT-based SAR backprojection algorithm, amenable to further generalizations to a multi-GPU architecture, are also given.The performance of the approach is analyzed in terms of accuracy and computational speed by comparisons to a "standard", parallel version of the backprojection algorithm exploiting FFT + interpolation instead of the NUFFT. Different interpolators have been considered for the latter processing scheme. The NUFFT-based backprojection has proven significantly more accurate than all the compared approach, with a computing time of the same order. An analysis of the computational burden of all the different steps involved in both the considered approaches (i.e., standard and NUFFT backprojections) has been also reported.Experimental results against the Air Force Research Laboratory (AFRL) airborne data delivered under the "challenge problem for SAR-based Ground Moving Target Identification (GMTI) in urban environments" and collected over circular flight paths are also shown.

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Synthetic Aperture Radar Processing with GPGPU

Cited by 26 publications

References 16 publications

An Accelerated Backprojection Algorithm for Monostatic and Bistatic SAR Processing

An Accelerated Backprojection Algorithm for Monostatic and Bistatic SAR Processing

GPU-based Parallel Implementation of SAR Imaging

Fast Gpu-Based Interpolation for Sar Backprojection

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