State estimation of a flexible industrial manipulator is presented using experimental data. The problem is formulated in a Bayesian framework where the extended Kalman filter and particle filter are used. The filters use the joint positions on the motor side of the gearboxes as well as the acceleration at the end-effector as measurements and estimates the corresponding joint angles on the arm s...

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 ...

In this work, three-dimensional (3-D) motion estimation is applied to the problem of motion compensation for video coding. We suppose that the video sequence consists of the perspective projections of a collection of rigid bodies which undergo a rototranslational motion. Motion compensation can be performed on the sequence once the shape of the objects and the motion parameters are determined. ...

The (Extended) Kalman filter has been established as a standard method for object tracking. While a constraining motion model stabilizes the tracking results given noisy measurements, it limits the ability to follow an object in non-modeled maneuvers. In the context of a stereo-vision based vehicle tracking approach, we propose and compare three different strategies to automatically adapt the d...

We investigate the problem of bearings-only tracking of manoeuvring targets using particle filters (PFs). Three different (PFs) are proposed for this problem which is formulated as a multiple model tracking problem in a jumpMarkov system (JMS) framework. The proposed filters are (i) multiple model PF (MMPF), (ii) auxiliary MMPF (AUX-MMPF), and (iii) jump Markov system PF (JMS-PF). The performan...

Vehicle mass is an important parameter when developing features which improve drivability and performance feel for passenger cars. A vehicle’s mass naturally depends on the load and the number of passengers. Therefore it is desired to have a fast, accurate and robust mass estimation algorithm. In this thesis an extended Kalman filter is used to estimate the mass of a passenger car. The filter u...

An application of the extended Kalman filter for a nonlinear model of an activated sludge process (ASP), working in an alternating aerobic-anoxic phase medium, is proposed. The filter is used to estimate both the states and non-stationary disturbances, to better evaluate changes in operating conditions. It is to be pointed out that, according to the structure of the reduced nonlinear system, th...

Many recent approaches to Simultaneous Localisation and Mapping (SLAM) use an Extended Kalman Filter (EKF) to update and maintain a map of vehicle location and multiple feature positions as a sensor moves through a scene. Although it is a highly powerful and well-used tool, it suffers from a well-known complexity problem, that the amount of computation at each recursion step is proportional to ...

Over the last 20-30 years, the extended Kalman filter (EKF) has become the algorithm of choice in numerous nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system as well estimating parameters for nonlinear system identification (e.g., learning the weights of a neural network). The EKF applies the standard linear Kalman filter met...

Kalman filters and observers are two main classes of dynamic state estimation (DSE) routines. Power system DSE has been implemented by various Kalman filters, such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). In this paper, we discuss two challenges for an effective power system DSE: (a) model uncertainty and (b) potential cyber attacks. To address this, the cubatu...