Online Method for Spectral Flatness Estimation Based on the Gradient Adaptive Lattice Predictor

抄録

In this paper, we present an online method for estimating the spectral flatness of a stochastic process, in which a flatness measure is computed as a function of reflection coefficients obtained by linear prediction. Its implementation is straight-forward as the task of linear prediction is performed using a well-established algorithm known as the gradient adaptive lattice predictor. Several simulation results show that the algorithm can discriminate the magnitude of flatness, particularly the deviation from the ideal flatness in real time. This capability seems to be suitable for detecting anomalies in a nearly white stochastic process, including the innovation process in Kalman filtering as a typical example.