EngineeringSpectrum EstimationCoherenceStochastic AnalysisProbabilistic Wave ModellingStatistical Signal ProcessingFiltering TechniqueStochastic ProcessesTex XmlnsMsc EstimatesPublic HealthEstimation TheoryWiener Filter ApproachStatisticsInformation TheoryInverse ProblemsFunctional Data AnalysisSignal ProcessingGaussian ProcessSpeech ProcessingFinite Impulse Filter
This paper presents a new method of estimating the magnitude-squared-coherence (MSC) between two random processes <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</tex> . An unrealizable Wiener filter transfer function is first computed in the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</tex> channel, then another one in the y channel. The product of these transfer functions then gives the MSC. The unrealizable Wiener filter is approximated by a finite impulse filter whose coefficients can be computed from any standard parameter estimation algorithm. Theoretical upper bounds on the variances of the MSC estimates, together with experimental results, are given.
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Probability, Random Variables, and Stochastic Processes
Irwin Miller · Technometrics · 1966 · 2K citations