Determination of uncertainty measure for estimate of noise power spectral density

摘要

Systems/methods for computing a power spectral density estimate for a noise signal. Where the noise signal appears in two channels (a single channel), n successive data acquisitions from the two channels (the single channel) are used to compute n respective cross (power) spectral densities, which are then averaged. The averaged cross (power) spectral density may then be smoothed in the spectral domain. The magnitude of the smoothed cross (power) spectral density comprises an estimate for the noise power spectral density. An effective number of independent averages may be computed based on the number n, the time-domain window applied to the acquired sample sets, the amount of overlap between successive sample sets, and the shape of the frequency-domain smoothing function. A statistical error bound (or uncertainty measure) may be determined for the power spectral density estimate based on the effective number of averages and the averaged single-channel and cross-channel spectral estimates.

主权项

1. A method comprising:
determining at a computational device a measure of uncertainty for an estimate of a power spectral density of a noise signal, wherein the estimate is determined by computing an average of n power spectral densities derived from n respective sets of samples of the noise signal, wherein each of the n power spectral densities is computed based on a corresponding one of the sample sets, wherein said determining the uncertainty measure includes computing the uncertainty measure based on the number n, wherein said determining the uncertainty measure includes computing an effective number of independent averages corresponding to said power spectral density estimate based on data including the number n and a relative amount of overlap between successive ones of the n sample sets, wherein the uncertainty measure is computed based on the effective number of independent averages; and storing the uncertainty measure in a memory.