This chapter presents the minimum mean square error (MMSE) estimation of Gaussian random vectors and the linear MMSE estimator for arbitrarily distributed random vectors. The estimation of unknown constant vectors according to the least squares (LS) criterion is discussed. The batch and recursive versions are derived and the results are applied to polynomial fitting. The statistical tools for deciding what is the appropriate order of the polynomial when fitting a set of data points is detailed, followed by a realistic example of the localization of a target. A problem solving section appears at the end of the chapter.
Linear Estimation in Static Systems
2002-01-04
57 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
static systems , bearings‐only target motion analysis , linear minimum mean square error estimation , polynomial fitting , use of LS for a nonlinear problem , linear estimation , least squares estimation , project , estimation of Gaussian random vectors , goodness‐of‐fit of parameter estimates , statistical significance of parameter estimates , problems
Static Set‐membership State Estimation
Wiley | 2019
|Network-Based Stabilization of Linear Systems via Static Output Feedback
Springer Verlag | 2014
|Network-Based Stabilization of Linear Systems via Static Output Feedback
British Library Conference Proceedings | 2014
|Improvement of propeller static thrust estimation
NTRS | 1975
|