Simulation of Fast Threshold CFAR Processing Algorithm with CUDA

Xue, Wenhu; Jiang, Yanyang

doi:10.1109/cdciem.2012.153

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…There are many existing applications of GPU super-computing in radar algorithms, such as Synthetic Aperture Radars (SAR) [15] and Constant False Alarm Rate (CFAR) [16] processing algorithms. Many of the applications show potential of acceleration using GPUs collaborating with CPUs, such as one thousand times acceleration of image formation than using CPU alone [17].…”

Section: Introductionmentioning

confidence: 99%

Acceleration of generalized adaptive pulse compression with parallel GPUs

Cai

Zhang

2015

Radar Sensor Technology XIX; And Active and Passive Signatures VI

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Super-computing based on Graphic Processing Unit (GPU) has become a booming field both in research and industry. In this paper, GPU is applied as the main computing device on traditional RADAR super resolution algorithms. Comparison is provided between GPU and CPU as computing architecture and MATLAB, as a widely used scientific implementation, is also included as well as C++ implementation in demonstrations of CPU part in the comparison. Fundamental RADAR algorithms as matched filter and least square estimation (LSE) are used as standard procedure to measure the efficiency of each implementation. Based on the result in this paper, GPU shows an enormous potential to expedite the traditional process of RADAR super-resolution applications.

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Section: Introductionmentioning

confidence: 99%

Acceleration of generalized adaptive pulse compression with parallel GPUs

Cai

Zhang

2015

Radar Sensor Technology XIX; And Active and Passive Signatures VI

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“…To further increase the computational efficiency of the CFARbased method, the NVIDIA CUDA is used for parallel implementations in [13]. In [14], the research shows that the operation performance of CFAR based on GPU with CUDA is better than that based on CPU. A Parzen-window-kernel-based CFAR algorithm for SAR ship detection is proposed in [15] and implemented via CUDA.…”

mentioning

confidence: 99%

SAR Target CFAR Detection Via GPU Parallel Operation

Cui

Quan

Cao

et al. 2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The constant false alarm rate with convolution and pooling (CP-CFAR) method, which can improve the detection efficiency via GPU parallel acceleration in the airborne synthetic aperture radar (SAR) images, is proposed in this paper. The constant false alarm rate (CFAR) method is one of the most widely used methods for target detection in airborne SAR images. However, since the CFAR is performed on a pixel-by-pixel basis, the time consumption will increase rapidly with the expansion of image scene. Even if the GPU is used for acceleration, the efficiency improvement is still limited, which cannot meet the real-time processing requirements. Therefore, the CP-CFAR method for the target detection of SAR images is proposed in this paper. The convolution layer uses the horizontal and vertical Sobel operators to improve the contrast between targets and background, and the pooling layer can reduce the processing dimension of the images. The convolution and pooling layers are added before the twoparameter CFAR, which can reduce the computational elements but without losing the main feature of the original image. More importantly, compared to the traditional CFAR, the proposed CP-CFAR is more suitable for GPU acceleration, which can improve the detection efficiency significantly. Experiments on the moving and stationary target acquisition and recognition SAR images with a size of 1478 × 1784 show that, compared with the traditional cell-averaging CFAR, two-parameter CFAR and their CPU, multithread CPU, and GPU acceleration modes, the proposed CP-CFAR with GPU acceleration can obtain the best detection performance with the highest acceleration ratio, and the operation time is less than 192 ms.

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“…3,4 Many of the applications show the potential of acceleration using GPUs collaborating with CPUs, 5,6 such as one thousand times acceleration of image formation over using CPUs alone. 7 The performance of actual processing implementations has variations, and a well-organized approach to achieve optimal performance for surveillance radar processing has not been available.…”

Section: Introductionmentioning

confidence: 99%

General purpose graphic processing unit implementation of adaptive pulse compression algorithms

Cai

Zhang

2017

J. Appl. Remote Sens

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, "General purpose graphic processing unit implementation of adaptive pulse compression algorithms," J. Appl. Remote Sens. 11(3), 035009 (2017), doi: 10.1117/1.JRS.11.035009. Abstract. This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.

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Simulation of Fast Threshold CFAR Processing Algorithm with CUDA

Cited by 5 publications

References 7 publications

Acceleration of generalized adaptive pulse compression with parallel GPUs

Acceleration of generalized adaptive pulse compression with parallel GPUs

SAR Target CFAR Detection Via GPU Parallel Operation

General purpose graphic processing unit implementation of adaptive pulse compression algorithms

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