Improved estimation performance using known linear constraints

TL;DR: It is shown, without using optimality, that the constrained estimator outperforms the other filters for estimating the constrained system state.

TL;DR: It is demonstrated that inclusion of differential constraints in the least squares optimization scheme can prevent polynomial divergence close to edges of the model domain where local data coverage may be insufficient, a situation often encountered with global recursive data approximation.

TL;DR: In this paper, the state estimation problem for linear stochastic systems with state equality constraints was studied and a projected system representation was proposed. But the constrained estimator was not considered.

TL;DR: In this article, the authors considered the design of KF for systems subject to norm constraints on the state and unknown inputs, whose models or statistical properties are not assumed to be available.

TL;DR: This book will be most useful to applied mathematicians, communication engineers, signal processors, statisticians, and time series researchers, both applied and theoretical.

TL;DR: A comparison of Numerical Data with Test Results and Iterative Methods Using Modal Data for Model Updating shows that the former is more accurate than the latter.

TL;DR: In this article, the authors present a book that is most useful to applied mathematicians, communication engineers, signal processors, statisticians, and time series researchers, both applied and theoretical.