The classical Kalman filter provides optimal least-squares estimates of all of the states of a linear time-varying system under process and measurement noise. In many applications, however, optimal estimates are desired for a specified subset of the system states, rather than all of the system states. For example, for systems arising from discretized partial differential equations, the chosen s...

Many methods using ensemble integrations of prediction models as integral parts of data assimilation have appeared in the atmospheric and oceanic literature. In general, these methods have been derived from the Kalman filter and have been known as ensemble Kalman filters. A more general class of methods including these ensemble Kalman filter methods is derived starting from the nonlinear filter...

Using a non-uniformly distributed aperture radar system for forming a SAR image will result in data correlations between the SAR image resolution cells. Thus, this requires that a more robust filter than the Matched Filter, i.e. the MMSE or Wiener Filter to be used in the receiver processing. As the Wiener Filter involves a computationally expensive matrix inverse operation, it can be avoided b...

This paper describes an effective method for dynamic location estimation by Kalman Filter for range-based wireless network. The problem of locating a mobile terminal has received significant attention in the field of wireless communications. In this paper, Kalman Filter with TDOA technique describes the ranging measurement tracking approach. Kalman filter is used for smoothing range data and mi...

We want to discuss a proposal on an implementation of Robust Kalman filtering based on S4 classes. To do so, we are geared to the existing implementations of the Kalman filter from the basic R distribution (cf. [5] and [1]) as well as from the bundle dse (cf. [2]). By means of the setOldClass mechanism (cf. [5]), we register existing S3 classes from these implementations as S4 classes and exten...

— This paper presents review of techniques and algorithms used for filtering and data association in visual tracking. Kalman filter is an optimal Bayesian filter for linear dynamic models with Gaussian noise. Most of the processes and systems in real world are nonlinear, and in these situations there is extension of Kalman filter named the Extended Kalman filter (EKF). In case when the noise is...

A new observer based fault detection and diagnosis scheme for predicting induction motors’ faults is proposed in this paper. Prediction of incipient faults, using different variants of Kalman filter and their relative performance are evaluated. Only soft faults are considered for this work. The data generation, filter convergence issues, hypothesis testing and residue estimates are addressed. S...

In this paper we propose a novel training algorithm for RBF networks that is based on extended kalman filter and fuzzy logic.After the user choose how many prototypes to include in the network, the extended kalman filter simultancously solves for the prototype vectors and the weight matrix.The fuzzy logic is used to cope with the devergence problem caused by the insufficiently known a priori fi...

Compared with loose and tight integration, ultra-tight GPS/INS integration offers improved performance under exposure to high dynamics, jamming and interference by employing the advanced Kalman filter design methods. The ultra-tight configuration delivers unique advantages by aiding the GPS tracking loops with velocity estimates derived from the Kalman Filter. In this manner, only the errors in...

Abstract: In this study a sensor fusion technique was developed for indoor localization of omnidirectional mobile robots. The proposed sensor fusion method combines the measurements made by an indoor localization system (e.g. ultrasound based localization) with the measurements that comes from an IMU (Inertial Measurement Unit). It was taken into consideration that the measurements made by the ...