Kalman filtering has been widely considered for dynamic state estimation in smart grids. Despite its unique merits, the Kalman Filter (KF)-based dynamic state estimation can be undesirably influenced by cyber adversarial attacks that can potentially be launched against the communication links in the Cyber-Physical System (CPS). To enhance the security of KF-based state estimation, in this paper...

This article presents a new nonlinear joint (state and parameter) estimation algorithm based on fusion of Kalman filter and randomized unscented Kalman filter (UKF), called Kalman randomized joint UKF (KR-JUKF). It is assumed that the measurement equation is linear. The KRJUKF is suitable for time varying and severe nonlinear dynamics and does not have any systematic error. Finally, joint-EKF, ...

The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications in...

There is a growing interest in using Kalman-filter models in brain modelling. In turn, it is of considerable importance to make Kalman-filters amenable for reinforcement learning. In the usual formulation of optimal control it is computed off-line by solving a backward recursion. In this technical note we show that slight modification of the linear-quadratic-Gaussian Kalman-filter model allows ...

This paper details the stability analysis of the continuous-time Kalman filter dynamics for linear timevarying systems subject to exponentially decaying perturbations. It is assumed that estimates of the input, output, and matrices of the system are available, but subject to unknown perturbations which decay exponentially with time. It is shown that if the nominal system is uniformly completely...

—Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously. To improve the robustness of KF against impulsive noises, we propose in ...

The maneuver characteristic of the most commonly used AUV integrated navigation systems was investigated in this paper. After analyzing the error cause of conversional used Kalman filter of SINS/DVL integrated navigation systems in maneuver state, a novel method was proposed which is to use the output of complex navigation systems to revise the SINS in real-time, and an improved adaptive Kalman...

In this paper a new approach for the steady state Kalman Filter implementation is proposed. The method is faster than the classical one; this is very important due to the fact that, in most real-time applications, it is essential to obtain the estimate in the shortest possible time.

In this paper, an observer design is proposed for linear time delay systems. An easy way to compute least square estimation error of an observer for time delay systems is derived, where the time delay terms exist in the state and output of the system. Based on the least square estimation error an optimization algorithm to compute a Kalman filter for time delay systems is proposed. By employing ...

In this paper, we investigate one of the possibilities to adapt an unbiased moving average (MA) filter cfinite impulse response [FIRIjZter) to the slope of time error function. The linear regression coefficient is used as a statistical estimator of sample slope. We evaluate the error of the slope estimate and present two options for the adapting coefficient determination. To examine them, we ge...