This paper proposes an Lp (0<p<1) quasi norm state estimator for power system static estimation. Compared with the existing L, and L2 estimators, proposed can suppress bad data more effectively. The robustness of is discussed, analysis shows that its ability to increases as $p$ decreases. Moreover, algorithm suggested solve non-convex estimation pr...

When making estimates of a quantum state, point estimators express one’s best guess of the true state, but estimator regions are important to express the uncertainty associated with the estimate. Employing the Bayesian approach, we discuss the use of smallest credible regions (SCRs), the smallest possible regions for given credibilities, as optimal estimator regions. However, constructing the S...

This paper proposes an interval-based method for estimating the state of a linear continuous-time dynamical system. In this work, we assume that measurements are provided at discrete times and all errors bounded. Interval analysis is used to propagate interval uncertainties continuously over time. The resulting guaranteed never lose any feasible solution provides optimal polygonal enclosure tra...

A novel design of a state estimator is presented using second-order dynamic equations of mechanical systems. The eigenvalues and eigenvectors of the state estimator are assigned by solving the second-order eigenvalue problem of the structural system. Three design methods for the state estimator are given in this paper. The first design method uses collocated sensors to measure the desired signa...

Consider a first order linear time-invariant discrete time system driven by process noise, a preprocessor that accepts causal measurements of the state of the system, and a state estimator. The preprocessor and the state estimator are not co-located, and, at every time-step, the pre-processor transmits either a real number or an erasure symbol to the estimator. We seek the pre-processor and the...

This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm....

In this paper, the state estimation problem for continuous-time linear systems with two types of sampling is considered. First, the optimal state estimator under periodic sampling is presented. Then the state estimator with event-based updates is designed, i.e., when an event occurs the estimator is updated linearly by using the measurement of output, while between the consecutive event times t...

Systems containing uncertainty are traditionally analyzed with probabilistic methods. However, for non-linear, non-Gaussian systems solutions can sometimes be very di4cult to obtain. The focus of this work is to determine if in such cases fuzzy dynamic system models may provide an alternative approach that more easily leads us to a good solution. In this paper, we present a fuzzy estimator whos...