The Total Variance of a Periodogram-Based Spectral Estimate of a Stochastic Process With Spectral Uncertainty and Its Application to Classifier Design

摘要

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.