Minimum bias multiple taper spectral estimation
Kurt S. Riedel, Alexander Sidorenko

TL;DR
This paper introduces two new families of orthonormal tapers, minimum bias and sinusoidal, for multi-taper spectral analysis, offering analytic expressions and adjustable bandwidth with performance close to optimal Slepian tapers.
Contribution
The paper proposes sinusoidal tapers with analytic forms, eliminating the need for eigenvalue decomposition, and compares their spectral concentration and bias properties to Slepian tapers.
Findings
Sinusoidal tapers converge to minimum bias tapers as N increases.
Both taper types have near-optimal spectral concentration.
Bandwidth can be adjusted locally by adding or removing tapers.
Abstract
Two families of orthonormal tapers are proposed for multi-taper spectral analysis: minimum bias tapers, and sinusoidal tapers , where , and is the number of points. The resulting sinusoidal multitaper spectral estimate is , where is the Fourier transform of the stationary time series, is the spectral density, and is the number of tapers. For fixed , the sinusoidal tapers converge to the minimum bias tapers like . Since the sinusoidal tapers have analytic expressions, no numerical eigenvalue decomposition is necessary. Both the minimum bias and sinusoidal tapers have no additional parameter for the spectral bandwidth. The bandwidth of the th taper is simply centered about the…
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