This paper presents a new generalisation of simultaneous localisation and mapping (SLAM). SLAM implementations based on extended Kalman filter (EKF) data fusion have traditionally relied on simple geometric models for defining landmarks. This limits EKF-SLAM to environments suited to such models and tends to discard much potentially useful data. The approach presented in this paper is a marriag...

Extended Kalman filter (EKF) is widely used for speed estimation in sensorless vector control of induction motor. The major and unsolved issue in the practical implementation of the EKF is the choice of the process and measurement noise covariance matrices. In this paper, a speed estimation method using EKF optimized by improved particle swarm optimization (IPSO) is proposed. By combining the a...

This paper elaborates a new version of extended Kalman filtering (EKF) for state estimation in chemical nonlinear continuous-discrete stochastic systems. Such a state estimation always compounds real measurements of some system's variables (depending on the utilized technology) with computation of remaining (not measurable) parameters by means of appropriate filtering algorithms. Here, we consi...

The Extended Kalman Filter (EKF) is a well known tool for the recursive parameter estimation of static and dynamic nonlinear models. In particular, the EKF has been applied to the estimation of the weights of feedforward and recurrent neural network models, i.e. to their training, and shown to be more efficient than recursive and non recursive first-order training algorithms; nevertheless, thes...

The Extended Kalman Filter (EKF) is the most widely used state estimation technique for non-linear systems in the field of process engineering. In this contribution, we investigate the performance of the EKF for continuous polymerization of acrylic acid in a tubular reactor with multiple side feeds of monomer. The EKF usually yields satisfactory estimations if the nonlinearities of the underlyi...

The extended Kalman lter (EKF) can be used as an on-line algorithm to determine the weights in a recurrent network given target outputs as it runs. This involves forming an augmented network state vector consisting of all unit activities and weights. This report notes some relationships between the EKF as applied to recurrent net learning and some simpler techniques that are more widely used. I...

The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the radiation treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting respiratory motion in 3D space and realizing a gating function without pre-specifying a particular phase of the patient's brea...

In this paper we address the inconsistency of the EKF-based SLAM algorithm that stems from non-observability of the origin and orientation of the global reference frame. We prove on the non-linear two-dimensional problem with point landmarks observed that this type of inconsistency is remedied using the Invariant EKF, a recently introduced variant of the EKF meant to account for the symmetries ...

Model assimilation of data strives to determine optimally the state of an evolving physical system from a limited number of observations. The present study represents the first attempt of applying the extended Kalman filter (EKF) method of data assimilation to shock-wave dynamics induced by a high-speed impact. EKF solves the full nonlinear state evolution and estimates its associated error-cov...

Due to both the physiological and morphological differences in the vascularization between healthy and diseased tissues, pharmacokinetic diffuse fluorescence tomography (DFT) can provide contrast-enhanced and comprehensive information for tumor diagnosis and staging. In this regime, the extended Kalman filtering (EKF) based method shows numerous advantages including accurate modeling, online es...