This paper elaborates how the time update of the continuous-discrete extended Kalman-Filter (EKF) can be computed in the most efficient way. The specific structure of the EKF-moment differential equations leads to a new Taylor-Heun-approximation of the nonlinear vector field. Furthermore, the order of consistency and stability behavior of the outlined procedure is investigated. The results are ...

Pose estimation is one of the most fundamental tasks in unmanned vehicles because many high level tasks depend directly on the vehicle localization. In this paper, an embedded pose estimation module based on the extended Kalman filter (EKF) is implemented in an autonomous vehicle with Network Controlled System (NCS) architecture. The implemented EKF fuses inexpensive sensor’s data in order to p...

This paper develops a monocular vision-aided inertial navigation system based on the factored extended Kalman filter (EKF) proposed by Bierman and Thornton. The simultaneous localization and mapping (SLAM) algorithm measurement update and propagation steps are formulated in terms of the factored covariance matrix P = UDU, and a novel method for efficiently adding and removing features from the ...

In this paper, a novel application of state estimation in environmental engineering is presented. The objective is to use on-line estimation techniques including moving horizon estimator (MHE) and extended Kalman filer (EKF) for an early concentration estimation of toxic agent presented in water supply. These estimation techniques (MHE and EKF) can be integrated in an early warning system of se...

A deterministic method for sequential estimation of 3-D rotations is presented. The Bingham distribution is used to represent uncertainty directly on the unit quaternion hypersphere. Quaternions avoid the degeneracies of other 3-D orientation representations, while the Bingham distribution allows tracking of large-error (high-entropy) rotational distributions. Experimental comparison to a leadi...

In this paper, a novel method based on a combination of Extended Kalman Filter (EKF) with Self-adaptive Differential Evolution (SaDE) algorithm to estimate rotor position, speed and machine states for a Permanent Magnet Synchronous Motor (PMSM) is proposed. In the proposed method, as a first step SaDE algorithm is used to tune the noise covariance matrices of state noise and measurement noise i...

Todays, use of combination of two or more methods was considered to control of systems. In this paper ispresented how to design of a nonlinear H∞ (NL-H∞) controller for flexible joint robot (FJR) based on boundedUKF state estimator. The UKF has more advantages to standard EKF such as low bios and no need toderivations. In this research, based on spong primary model for FJRs, same as rigid robot...

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, ...

It is known that the Kalman filter estimation performance heavily depends on the statistical parameters to both the dynamic and observation models, especially the covariance matrix Q. This paper presents a fast genetic algorithm (GA) to adapt extended Kalman filter (EKF) for real time tracking. In the proposed method, the covariance matrix Q can be real-time adjusted by GA to meet some specific...

In this note, we illustrate the effect of nonlinear state propagation in the unscented Kalman filter (UKF). We consider a simple nonlinear system, consisting of a two-axis inertial measurement unit. Our intent is to show that the propagation of a set of sigma points through a nonlinear process model in the UKF can produce a counterintuitive (but correct) updated state estimate. We compare the r...