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The total variance of a periodogram-based spectral estimate of a stochastic process with spectral uncertainty and its application to classifier design

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3 Author(s)
Yanwu Zhang ; Monterey Bay Aquarium Res. Inst., Moss Landing, CA, USA ; Baggeroer, A.B. ; Bellingham, J.G.

The variance of a spectral estimate of a stochastic process is essential to the formulation and performance of a spectral classifier. The overall variance of a spectral estimate originates from two sources: the within-class spectral uncertainty and the variance introduced in the spectral estimation procedure. In this paper, we derive the total variance of a periodogram-based spectral estimate under some assumptions. Using this result, we formulate a linear spectral classifier based on Fisher's separability metric. The classifier is used to classify two oceanographic processes: ocean convection versus internal waves.

Published in:

Signal Processing, IEEE Transactions on  (Volume:53 ,  Issue: 12 )

Date of Publication:

Dec. 2005

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