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在电力系统中的负荷电流谐波检测环节，若采用DFT则需要在一个基波周期后，才能给出负荷电流中的谐波成分；若用SDFT则可在每个采样时刻更新谐波成分分析结果。 为此，滑动离散傅里叶变换（SDFT）作为一种改进算法，逐渐受到关注。 本文将深入探讨基于SDFT修正算法的Python信号处理优化实践，展示其在提升计算效率和实时性方面的显著优势。 文章浏览阅读5.7k次，点赞10次，收藏37次。本博客分享电子设计技巧，聚焦滑动DFT算法。在频域信号处理中，滑动DFT比FFT性能更优。文中给出滑动DFT的数学推导，其与DFT等价且无信息丢失。算法实现上，它能递归计算，降低复杂度和功耗，还介绍了两种初始化方法。
滑动离散傅里叶变换（SDFT）是一种基于滑动窗口机制的信号处理算法，通过在固定长度滑动窗口内递归更新离散傅里叶变换（DFT），实现实时频谱追踪分析。 下面主要讲一下，滑动DFT变换（SDFT）如何实现（目前网上还没有现成的，本来想自己推一遍的，后来发现有本书上有写，就稍作了一下总结。 为了解决这个问题，滑动DFT（SDFT）算法应运而生。 本文将深入探讨滑动DFT算法的原理、实现方法以及在实际应用中的优势。
本文回顾了滑动离散傅里叶变换 ( SDFT ) 算法的不同递归结构。
滑动离散傅里叶变换 (SDFT) 是一种逐个样本计算傅里叶变换的递归方法。 在这种特殊情况下，它比基于 FFT 的 短时傅里叶变换 (STFT) 方法更有效，只有一个样本跳。 本文介绍了一种实时频谱分析方法——滑动DFT (SDFT)，该方法通过对新增数据点进行快速计算来更新频谱，适用于需要实时频谱分析的应用场景。 与传统FFT相比，SDFT能显著降低计算复杂度并减少延迟。 The sliding discrete fourier transform (sdft) is a recursive approach to compute the fourier transform sample by sample In this particular case it's more efficient than the fft based short time fourier transform (stft) approach with one sample hops.
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