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首页> 外文期刊>Nuclear Instruments & Methods in Physics Research. Section A, Accelerators, Spectrometers, Detectors and Associated Equipment >Stable convergence of the algorithm computing the coefficients of the LMS filter suppressing RFI in radio detection of cosmic rays in the Auger Engineering Radio Array
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Stable convergence of the algorithm computing the coefficients of the LMS filter suppressing RFI in radio detection of cosmic rays in the Auger Engineering Radio Array

机译：计算螺旋工程无线电阵列中的宇宙射线无线电检测中LMS滤波器抑制RFI系数的算法稳定的汇聚

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摘要

Radio stations of the Auger Engineering Radio Array (AERA) investigates radio signals from coherent emissions due to geomagnetic radiation and charge excess processes. The frequency band is limited to 30-80 MHz. This range is highly contaminated by human-made RFI. In order to improve the signal to noise ratio, RFI filters have to be used to suppress this contamination. The LMS filter proposed for the RFI suppression should use correct learning factor, crucial for a stability of the filter, which could be determined by eigenvalues of the auto-correlation matrix built for ADC samples. A detailed analysis of almost one million of AERA events confirms that eigenvalues of real cosmic events taken from the AERA database fully allow their use in the LMS filters, with learning factors optimal for the most efficient RFI suppression. This type of the filter can be used in other radio cosmic ray experiments e.g. the Giant Radio Array for Neutrino Detection (GRAND).
机译：螺旋钻工程无线电阵列（AERA）的无线电台调查由于地磁辐射和充电过量过程引起的相干排放的无线电信号。频带限制为30-80MHz。该范围受人造RFI的高度污染。为了提高信噪比，必须使用RFI过滤器来抑制这种污染。用于RFI抑制所提出的LMS滤波器应该使用正确的学习因子，这对于滤波器的稳定性至关重要，这可以由为ADC样本构建的自动相关矩阵的特征值来确定。详细分析了近一百万的Aera事件证实，从AEA数据库中取出的真实宇宙事件的特征值完全允许它们在LMS过滤器中使用，并且学习因素对于最有效的RFI抑制而最佳。这种类型的过滤器可用于其他无线电宇宙射线实验中。用于中微子检测的巨型无线电阵列（Grand）。

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