We consider the numerical stability of discretisation schemes for continuous-time state estimation filters. The dynamical systems we consider model the indirect observation of a continuous-time Markov chain. Two candidate observation models are studied. These models are (a) the observation of the state through a Brownian motion, and (b) the observation of the state through a Poisson process. It...

One of the advantages for the varying-coeecient model is to allow the coeecients to vary as smooth functions of other variables and the model can be estimated easily through a simple local maximum likelihood method. This leads a simple one-step estimation procedure. We show that such a one-step method can not be optimal when some coeecient functions possess diierent degrees of smoothness. This ...

The event-triggered state estimation problem for linear time-invariant systems is considered in the framework of Maximum Likelihood (ML) estimation in this paper. We show that the optimal estimate is parameterized by a special time-varying Riccati equation, and the computational complexity increases exponentially with respect to the time horizon. For ease in implementation, a one-step event-bas...

[1] This article investigates the performance of Monte Carlo-based estimation methods for estimation of flow state in large-scale open channel networks. After constructing a state space model of the flow based on the Saint-Venant equations, we implement the optimal sampling importance resampling filter to perform state estimation in a case in which measurements are available at every time step....

The real-time monitoring of electric distribution grids via state estimation is a fundamental requirement to deploy smart automation and control in the system. Due large size networks poor coverage measurement instrumentation on field, designing fast algorithms achieving accurate results are two major challenges associated system estimation. In this paper, an efficient solution for performing m...

The aim of this contribution is to analyze a class of state-space subspace system identiication (4SID) methods. In particular, the eeect of diierent weighting matrices is studied. By a linear regression formulation, diierent cost-functions, which are rather implicit in the ordinary framework of 4SID, are compared. Expressions for asymptotic variances of pole estimation error are analyzed and fr...