Finite Sample MIMO System Identification with Multisine Excitation: Nonparametric, Direct, and Two-step Parametric Estimators

TL;DR

This paper develops a finite-sample statistical framework for MIMO system identification using multisine excitation, providing exact distributional properties, conditions for unbiasedness, and equivalence between frequency and time-domain estimators.

Contribution

It introduces a comprehensive finite-sample analysis for frequency-domain estimators under multisine excitation, linking nonparametric and parametric methods with explicit statistical properties.

Findings

Exact distributional and covariance properties of FRF estimators derived.

Conditions for unbiasedness, uncorrelatedness, and consistency established.

Finite-sample bounds and closed-form estimators provided for practical use.

Abstract

Multisine excitations are widely used for identifying multi-input multi-output systems due to their periodicity, data compression properties, and control over the input spectrum. Despite their popularity, the finite sample statistical properties of frequency-domain estimators under multisine excitation, for both nonparametric and parametric settings, remain insufficiently understood. This paper develops a finite-sample statistical framework for least-squares estimation of the frequency response function (FRF) and its implications for parametric modeling. First, we derive exact distributional and covariance properties of the FRF estimator, explicitly accounting for aliasing effects under slow sampling regimes, and establish conditions for unbiasedness, uncorrelatedness, and consistency across multiple experiments. Second, we show that the FRF estimate is a sufficient statistic for any parametric model under Gaussian noise, leading to an exact equivalence between optimal two stage frequency-domain methods and time-domain prediction error and maximum likelihood estimation. This equivalence is shown to yield finite-sample concentration bounds for parametric maximum likelihood estimators, enabling rigorous uncertainty quantification, and closed-form prediction error method estimators without iterative optimization. The theoretical results are demonstrated in a representative case study.