Adaptive weighted norm linear prediction for sinusoids incorporating a priori frequency information

A new adaptive autoregressive spectral estimator which incorporates prior frequency location information in frequency estimation from noisy data is developed. This method is formulated to exploit some of the same structural features of the problems as the principal component modified covariance (PCMC) technique. When the given samples are corrupted by noise and the extra information is available, the minimum norm least squares solution given by the PCMC is not chosen; instead, a frequency weight function is defined from prior information and then used to find the minimum weighted norm least squares solution for the autoregressive parameters. Adaptive modification of the frequency weight further enhances the performance of the estimator. Examples are included to illustrate the improvement achieved with the use of prior information