This article presents two networks that improve upon the behavior and performance of previously published sliding discrete Fourier transform (SDFT) algorithms. The proposed networks are structurally simple, computationally efficient, guaranteed stable networks used for real-time sliding spectrum analysis. The first real-time network computes one spectral output sample, equal to a single-bin output of an N-point DFT, for each input signal sample. The second real-time network is frequency flexible, in that its analysis frequency can be any scalar value in the range of zero to one-half the input data sample rate measured in cycles per second.

中文翻译：

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滑动离散傅立叶变换算法的改进 [提示与技巧]


本文介绍了两个网络，它们改进了先前发布的滑动离散傅立叶变换 (SDFT) 算法的行为和性能。所提出的网络结构简单、计算高效、保证网络稳定，用于实时滑动谱分析。第一个实时网络为每个输入信号样本计算一个频谱输出样本，等于 N 点 DFT 的单箱输出。第二个实时网络是频率灵活的，因为它的分析频率可以是以每秒周期测量的输入数据采样率的零到二分之一范围内的任何标量值。