Variance Analysis of Linear SIMO Models with Spatially Correlated Noise
(English)Manuscript (preprint) (Other academic)
Substantial improvement in accuracy of identied linear time-invariant single-input multi-output (SIMO) dynamical models ispossible when the disturbances aecting 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 identied using less parameters than the other modules.
IdentifiersURN: urn:nbn:se:kth:diva-159094OAI: oai:DiVA.org:kth-159094DiVA: diva2:782494
FunderSwedish Research Council, 621-2009-4017EU, European Research Council, 267381
QS 20152015-01-212015-01-212015-02-19Bibliographically approved