A battery’s state-of-charge (SOC) can be used to estimate the mileage an electric vehicle (EV) can travel. It is desirable to make such an estimation not only accurate, but also economical in computation, so that the battery management system (BMS) can be cost-effective in its implementation. Existing computationally-efficient SOC estimation algorithms, such as the Luenberger observer, suffer f...

As a form of optimal estimator characterized by recursive evaluation, the Kalman filter (KF) (Bar-Shalom, et al, 2001; Brown and Hwang, 1997, Gelb, 1974; Grewal & Andrews, 2001) has been shown to be the filter that minimizes the variance of the estimation mean square error (MSE) and has been widely applied to the navigation sensor fusion. Nevertheless, in Kalman filter designs, the divergence d...

The Kalman filter provides an efficient means to estimate the state of a linear process, so that it minimizes the mean of the squared estimation error. However, for naturally distributed applications, the construction and tuning of a centralized observer may present difficulties. Therefore, we propose the decomposition of a linear process model into a cascade of simpler subsystems and the use o...

Ensemble Kalman Filtering is a sequential Monte Carlo method commonly used in meteorology to track atmospheric states and make numerical weather predictions (NWP). With the intent to introduce statisticians to this important area of application we address some of the practical aspects of the ensemble Kalman Filter in dynamic systems. We focus on three topics related to NWP: extending the ensemb...

This paper proposes a sampling based kinodynamic planning technique for planning persistent monitoring trajectories for a sensing robot in a spatiotemporal environmental field. The robot uses a Kalman-Bucy filter to estimate the spatiotemporal field. Since the error covariance matrix of the Kalman-Bucy filter evolves according to the nonlinear Riccati differential equation, this requires planni...

This paper describes the development of a modified Kalman filter to integrate a multi-camera vision system and strapdown inertial navigation system (SDINS) for tracking a hand-held moving device for slow or nearly static applications over extended periods of time. In this algorithm, the magnitude of the changes in position and velocity are estimated and then added to the previous estimation of ...

The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stoch...

Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence, although this algorithm is more complex and sensitive to the nature of noises. In this paper, extended Kalman filter is applied to train state-space recurrent neural networks for nonlinear system identification. In order to improve robustness...

In this paper, the optimal filtering problem for linear systems with state and observation delays is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate, error variance, and various error covariances. As a result, the optimal estimate equation similar to the traditional Kalman-Bucy one is derived; however, the resulting system of equations ...

The problem of tracking a vessel modelled at a distance by an equivalent magnetic dipole is investigated. Tracking a magnetic dipole from magnetic field measurements is a complex non-linear problem. The determination of target position, velocity and magnetic moment is formulated as an optimal stochastic estimation problem, which could be solved using the non-linear Kalman filtering methods. The...