Maximum-Likelihood Autoregressive Estimation on Incomplete Spectra

摘要

Frequency-selective autoregressive (AR) estimation is arousing increasing interest. We propose herein a new method to estimate the AR model from a reduced set of spectral samples. The proposed method is founded on the maximum likelihood criterion over the logarithmic spectral residue, and it is implemented efficiently with a multivariate Newton-Raphson algorithm. Results over deterministic and stochastic scenarios show its excellent performance