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An SDR-Based Real-Time Testbed for GNSS Adaptive Array Anti-Jamming Algorithms Accelerated by GPU

机译:GPU加速的基于SDR的GNSS自适应阵列抗干扰算法的实时测试平台

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摘要

Nowadays, software-defined radio (SDR) has become a common approach to evaluate new algorithms. However, in the field of Global Navigation Satellite System (GNSS) adaptive array anti-jamming, previous work has been limited due to the high computational power demanded by adaptive algorithms, and often lack flexibility and configurability. In this paper, the design and implementation of an SDR-based real-time testbed for GNSS adaptive array anti-jamming accelerated by a Graphics Processing Unit (GPU) are documented. This testbed highlights itself as a feature-rich and extendible platform with great flexibility and configurability, as well as high computational performance. Both Space-Time Adaptive Processing (STAP) and Space-Frequency Adaptive Processing (SFAP) are implemented with a wide range of parameters. Raw data from as many as eight antenna elements can be processed in real-time in either an adaptive nulling or beamforming mode. To fully take advantage of the parallelism resource provided by the GPU, a batched method in programming is proposed. Tests and experiments are conducted to evaluate both the computational and anti-jamming performance. This platform can be used for research and prototyping, as well as a real product in certain applications.
机译:如今,软件定义无线电(SDR)已成为评估新算法的常用方法。然而,在全球导航卫星系统(GNSS)自适应阵列抗干扰领域中,由于自适应算法需要很高的计算能力,因此先前的工作受到了限制,并且通常缺乏灵活性和可配置性。在本文中,记录了基于SDR的实时测试平台的设计和实现,该平台用于由图形处理单元(GPU)加速的GNSS自适应阵列抗干扰。该测试平台突出显示了自己是一个功能丰富且可扩展的平台,具有极大的灵活性和可配置性,以及很高的计算性能。空时自适应处理(STAP)和空频自适应处理(SFAP)都可以通过多种参数来实现。来自多达八个天线元件的原始数据可以采用自适应调零或波束成形模式进行实时处理。为了充分利用GPU提供的并行资源,提出了一种分批编程的方法。进行测试和实验以评估计算和抗干扰性能。该平台可用于研究和原型设计,以及在某些应用中的真实产品。

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