Windowing Design and Performance Assessment for Mitigation of Spectrum Leakage

Abstract

This paper investigates the windowing design and performance assessment for mitigation of spectral leakage. A pretreatment method to reduce the spectral leakage is developed. In addition to selecting appropriate window functions, the Welch method is introduced. Windowing is implemented by multiplying the input signal with a windowing function. The periodogram technique based on Welch method is capable of providing good resolution if data length samples are selected optimally. Windowing amplitude modulates the input signal so that the spectral leakage is evened out. Thus, windowing reduces the amplitude of the samples at the beginning and end of the window, altering leakage. The influence of various window functions on the Fourier transform spectrum of the signals was discussed, and the characteristics and functions of various window functions were explained. In addition, we compared the differences in the influence of different data lengths on spectral resolution and noise levels caused by the traditional power spectrum estimation and various window-function-based Welch power spectrum estimations.

Not All Clear Air Turbulence Is Kolmogorov—The Fine‐Scale Nature of Atmospheric Turbulence

“…The shortcoming is the loss of resolution due to smearing of peaks (see Prabhu, 2014 and references therein). At the resolutions attained, spectral leakage and signal-to-noise ratio are expected to be similarly mitigated by both Hamming and Hann windowing methods (Jwo et al, 2019(Jwo et al, 2021, in good agreement with the fact that the Hamming window is 92% a Hann window and 8% a rectangular window. A Tukey window is also a combination of rectangular and Hann windows, depending on the taper length, which in our case is 50%.…”

“…On each of these segments, data are linearly detrended, and a windowing procedure is applied before calculating the periodograms. Whereas window selection and overlapping strategies are still widely discussed in the context of spectral methods for data analysis, reported optimal overlap for Welch method lies between 30% and 66%, also applicable for cosine based windowing functions, such as Hann, Hamming, and Tukey windows (see, for instance, Antoni & Schoukens, 2007;Jwo et al, 2019Jwo et al, , 2021Prabhu, 2014). Our final choice after a sensitivity test (see below) was the use of a Tukey window with 50% overlap, that is, periodograms are calculated in subsegments of N/2 data points overlapped by 50%.…”

Comparison of FIR Window Filter Variation Results on Pink Noise Audio

“…When compared with other methods, the Blackman method has a better improvement in damping the stopband. The Blackman method has good characteristics for filtering an audio [18]. The equation of the function of this method is as follows:…”

Influence of PSD Estimation Parameters on Fatigue Life Prediction in Spectral Method

“…The disadvantage of fast Fourier transform is the spectral leakage that occurs when the signal being measured is not periodic in the sample interval. This effect cannot be entirely eliminated; however, it has been proven that this effect can be reduced by using weighting windows, e.g., Hanning, Hamming and others [ 15 , 16 ]. The periodogram has many variants, such as Bartlett’s method, Welch’s method, multitaper method and more.…”