Variance Analysis of Linear SIMO Models with Spatially Correlated Noise

TL;DR

This paper analyzes how spatially correlated noise affects the variance of parameter estimates in SIMO models, revealing dependencies on model structure and noise correlation, and identifying optimal noise correlation structures for minimal variance.

Contribution

It introduces an orthogonal representation to analyze variance dependencies in SIMO models with correlated noise, providing explicit variance-error quantification and optimal noise correlation conditions.

Findings

Variance of parameter estimates depends on other modules' structure and noise correlation.

Quantified variance-error for finite model orders considering noise, structure, and spectra.

Derived conditions for noise correlation that minimize variance in parameter estimation.

Abstract

Substantial improvement in accuracy of identified linear time-invariant single-input multi-output (SIMO) dynamical models is possible when the disturbances affecting the output measurements are spatially correlated. Using an orthogonal representation for the modules composing the SIMO structure, in this paper we show that the variance of a parameter estimate of a module is dependent on the model structure of the other modules, and the correlation structure of the disturbances. In addition, we quantify the variance-error for the parameter estimates for finite model orders, where the effect of noise correlation structure, model structure and signal spectra are visible. From these results, we derive the noise correlation structure under which the mentioned model parameterization gives the lowest variance, when one module is identified using less parameters than the other modules.