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Journal of Applied Remote Sensing • Open Access

General purpose graphic processing unit implementation of adaptive pulse compression algorithms

Paper 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.

Paper Details

Date Published: 17 August 2017
PDF: 17 pages
J. Appl. Rem. Sens. 11(3) 035009 doi: 10.1117/1.JRS.11.035009
Published in: Journal of Applied Remote Sensing Volume 11, Issue 3
Show Author Affiliations
Jingxiao Cai, Advanced Radar Research Ctr. (United States)
The Univ. of Oklahoma (United States)
Yan R. Zhang, Advanced Radar Research Ctr. (United States)
The Univ. of Oklahoma (United States)


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