Multiobjective Optimization of SAR Reconstruction on Hybrid Multicore Systems

Wijayasiri, Adeesha; Banerjee, Tania; Ranka, Sanjay; Sahni, Sartaj; Schmalz, Mark S.

doi:10.1109/jstars.2020.3014531

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…However, these fast BP algorithms increase model complexity, and the imaging process involves approximations, which somewhat compromises the image quality [13,14]. Another type of method maintains the structure of the classical BP algorithm model, with primary enhancements focused on the core steps that impact the efficiency of the BP algorithm, notably the performance of interpolation, and deeply analyzes the characteristics of the BP algorithm to fully combine the computational benefits of advanced processing devices such as graphics processing units (GPUs) to achieve parallel acceleration [15][16][17][18][19]. At present, the interpolation methods commonly used in engineering that are suitable for the BP algorithm include linear or neighbor interpolation after FFT upsampling, sinc interpolation, and weighted sinc interpolation, etc.…”

Section: Introductionmentioning

confidence: 99%

An Efficient BP Algorithm Based on TSU-ICSI Combined with GPU Parallel Computing

Li,

Qiu,

Yang

et al. 2023

Remote Sensing

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High resolution remains a primary goal in the advancement of synthetic aperture radar (SAR) technology. The backprojection (BP) algorithm, which does not introduce any approximation throughout the imaging process, is broadly applicable and effectively meets the demands for high-resolution imaging. Nonetheless, the BP algorithm necessitates substantial interpolation during point-by-point processing, and the precision and effectiveness of current interpolation methods limit the imaging performance of the BP algorithm. This paper proposes a TSU-ICSI (Time-shift Upsampling-Improved Cubic Spline Interpolation) interpolation method that integrates time-shift upsampling with improved cubic spline interpolation. This method is applied to the BP algorithm and presents an efficient implementation method in conjunction with the GPU architecture. TSU-ICSI not only maintains the accuracy of BP imaging processing but also significantly boosts performance. The effectiveness of the BP algorithm based on TSU-ICSI is confirmed through simulation experiments and by processing measured data collected from both airborne SAR and spaceborne SAR.

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Section: Introductionmentioning

confidence: 99%

An Efficient BP Algorithm Based on TSU-ICSI Combined with GPU Parallel Computing

Li,

Qiu,

Yang

et al. 2023

Remote Sensing

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show abstract

“…Originating from synthetic aperture radar (SAR) [1][2][3][4][5][6][7][8][9][10], synthetic aperture sonar (SAS) [11][12][13][14][15][16] attracts investigators' interests due to its high resolution in the underwater field. Nowadays, it is widely applied to underwater mapping [14,15,17,18], target recognition [19][20][21][22], and so on.…”

Section: Introductionmentioning

confidence: 99%

An Improved Imaging Algorithm for Multi-Receiver SAS System with Wide-Bandwidth Signal

Zhang

Yang²

2021

Remote Sensing

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When the multi-receiver synthetic aperture sonar (SAS) works with a wide-bandwidth signal, the performance of the range-Doppler (R-D) algorithm is seriously affected by two approximation errors, i.e., point target reference spectrum (PTRS) error and residual quadratic coupling error. The former is generated by approximating the PTRS with the second-order term in terms of the instantaneous frequency. The latter is caused by neglecting the cross-track variance of secondary range compression (SRC). In order to improve the imaging performance in the case of wide-bandwidth signals, an improved R-D algorithm is proposed in this paper. With our method, the multi-receiver SAS data is first preprocessed based on the phase center approximation (PCA) method, and the monostatic equivalent data are obtained. Then several sub-blocks are generated in the cross-track dimension. Within each sub-block, the PTRS error and residual quadratic coupling error based on the center range of each sub-block are compensated. After this operation, all sub-blocks are coerced into a new signal, which is free of both approximation errors. Consequently, this new data is used as the input of the traditional R-D algorithm. The processing results of simulated data and real data show that the traditional R-D algorithm is just suitable for an SAS system with a narrow-bandwidth signal. The imaging performance would be seriously distorted when it is applied to an SAS system with a wide-bandwidth signal. Based on the presented method, the SAS data in both cases can be well processed. The imaging performance of the presented method is nearly identical to that of the back-projection (BP) algorithm.

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Design and Implementation of a Real-Time Imaging Processor for Spaceborne Synthetic Aperture Radar With Configurability

Lin,

Chen,

Chin

et al. 2024

IEEE Trans. VLSI Syst.

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Multiobjective Optimization of SAR Reconstruction on Hybrid Multicore Systems

Cited by 4 publications

References 22 publications

An Efficient BP Algorithm Based on TSU-ICSI Combined with GPU Parallel Computing

An Efficient BP Algorithm Based on TSU-ICSI Combined with GPU Parallel Computing

An Improved Imaging Algorithm for Multi-Receiver SAS System with Wide-Bandwidth Signal

Design and Implementation of a Real-Time Imaging Processor for Spaceborne Synthetic Aperture Radar With Configurability

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