A comparison of least squares algorithms for estimating Markov parameters

The purpose of this work is to compare model structures and identification algorithms for estimating Markov parameters in the presence of uncorrelated and correlated input, process, and output noise. We consider several least-squares variants with ARX and μ-Markov model structures, which are compared with white noise identification signals.