Improvement of autoregressive spectral estimates in the presence of noise

The autoregressive (maximum entropy, linear prediction) power spectral density estimator has been shown to possess excellent resolution properties. However, the addition of noise to the time series under analysis may drastically alter the spectral estimate. To reduce the effects of noise, an adaptive filtering algorithm is proposed that is directly applicable to sinusoidal signals in white noise. Its use for more general signals is discussed.