Frequency-selective autoregressive estimation in noise

This paper proposes a novel method for autoregressive estimation on additive noise. The method is founded on the maximum-likelihood criterion in the spectral domain. This problem statement yields a non-linear optimization problem that can be revamped as a re-weighted least square problem. The resulting spectral weighting function turns out to be an integer power of the Wiener filter, this meaning that spectral regions with higher signal-to-noise ratio are more relevant in the estimation. Furthermore, this frequency-selective scenario allows to interpret this problem as one with missing samples. Simulated experiments prove the validity of the problem statement, showing as well the excellent performance of the proposed algorithm.