in this article, the discrete time state space model with first-order autoregressive dependent process noise is considered and the recursive method for filtering, prediction and smoothing of the hidden state from the noisy observation is designed. the explicit solution is obtained for the hidden state estimation problem. finally, in a simulation study, the performance of the designed method ...

A decentralized method for estimating the interior states of a synchronous machine using analogue measurements from instrument transformers (that is, current transformer and potential transformer) has been proposed in this paper. The is robust to instrumentation chain anomalies, which have not considered existing dynamic state estimation literature. works two-step manner, wherein adaptive detec...

A state observer is designed for a class of generalized Persidskii systems with nonlinear measurements, disturbances, and output noise. We assume that all nonlinearities are diagonal belong to sector. The robust stability convergence conditions the estimation error obtained by applying theory input-to-output stability. These established in form linear matrix inequalities. provide numerical exam...

One of the major challenges for state estimation algorithms, such as the Kalman lter, is the impact of outliers that do not match the assumed Gaussian process and measurement noise. When these errors occur they can induce large state estimate errors and even lter divergence. This paper presents a robust recursive ltering algorithm, the l1-norm lter, that can provide reliable state estimates in ...

We consider the problem of robust state estimation in the presence of integrity attacks. There are m sensors monitoring a dynamical process. Subject to the integrity attacks, p out of m measurements can be arbitrarily manipulated. The classical approach such as the MMSE estimation in the literature may not provide a reliable estimate under this so-called (p,m)-sparse attack. In this work, we pr...

Virtually all robotic and autonomous systems rely on navigation and mapping algorithms (e.g. the Kalman filter or simultaneous localization and mapping (SLAM)) to determine their location in the world. Unfortunately, these algorithms are not robust to outliers and even a single faulty measurement can cause a catastrophic failure of the navigation system. This thesis proposes several novel robus...