An adaptive modified covariance algorithm for spectral analysis

An optimum block modified covariance algorithm is developed for computing time-varying autoregressive (AR) parameters. The method presented differs from those presented previously in that it uses optimally selected time-varying convergence factors such that the block mean square error is minimized from one iteration to the next. In particular, the algorithm developed here, called block modified covariance algorithm with individual adaptation of parameters (BMCAI), uses individual time-varying convergence factors computed using modified covariance matrix approximations along with the Gauss-Seidel method. Even though the BMCAI is gradient based it retains the attractive spectral matching properties of fixed-window least squares modified covariance algorithms while at the same time providing capabilities for time-varying spectral estimation