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Comparison of time domain maximum likelihood method and sample maximum likelihood method in errors-in-variables identification

The time domain maximum likelihood (TML) method and the Sample Maximum Likelihood (SML) method are two general approaches for identifying errors-in-variables models. In the TML method, an important assumption is that the noise-free input signal must be a stationary process with rational spectrum. For SML, the noise-free input needs to be periodic. In this report, numerical comparisons of these two methods are done under different situations. The results suggest that TML and SML have similar estimation accuracy at moderate or high signal-to-noise ratio (SNR).