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FPGA-based Calculation of Eigenvalues for Autocorrelation Matrix in DLMS Filter Suppressing RFI in Radio Detection of Cosmic Rays

机译:基于FPGA的DLMS滤波器中的自相关矩阵特征值计算抑制了宇宙射线无线电检测中的RFI

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The LMS filter is parametrized by the learning factor. Its value is crucial for stability. Low values provide stable operations, but rather slow converge, high values improve a converge speed, but may be a cause of instability. The learning factor should be smaller than an inverse maximal eigenvalue of the autocorrelation matrix. The paper presents the system calculating the eigenvalues of the auto-correlation matrix in the radio detector of in the Auger Engineering Radio Array in Argentinean real pampas conditions. The virtual NIOS® processor embedded in the FPGA allows optimizing the learning factors for a perfect stability. The calculation speed is at the level of tens of seconds; however, this factor is not critical for relatively slow changing environmental conditions. Most important is an accuracy of calculations exactly the same as provided by Visual C++ or Mathematica.
机译:LMS滤波器由学习因素设置参数。它的值对于稳定性至关重要。较低的值可提供稳定的操作,但收敛速度较慢,较高的值可提高收敛速度,但可能会导致不稳定。学习因子应小于自相关矩阵的逆最大特征值。本文提出了一种系统,用于计算阿根廷实际潘帕斯条件下俄歇工程无线电阵列中无线电探测器的自相关矩阵的特征值。虚拟NIOS ® 嵌入在FPGA中的处理器允许优化学习因素,以实现完美的稳定性。计算速度为数十秒;但是,对于相对缓慢变化的环境条件而言,此因素并不重要。最重要的是计算的准确性与Visual C ++或Mathematica提供的准确性完全相同。

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