the error of inertial navigation systems increase versus time, therefore for achieving higher accuracy specially in long time navigations we have to use an aiding system. global positioning system is the best aiding system in this case. in this paper we first simulate a gps and ins; then simulate tightly integration and finally review adaptation method of kalman filtering a fuzzy adaptive kalma...

In this paper we have applied radial basis function neural networks for equalization of wireless channel in the presence of additive noise. We have been tested the adaptive filter algorithms of simple Kalman filter, extended Kalman filter, rbfn Kalman filter, rbfn extended Kalman filter, fuzzy Kalman filter and fuzzy extended Kalman filter which might be considered applicable for wireless chann...

A fuzzy Kalman filter algorithm is developed for target tracking applications and its performance evaluated using several numerical examples. The approach is relatively novel. A comparison with Kalman filter and an adaptive tuning algorithm is carried out. The applicability and usefulness of fuzzy logic in data fusion is also demonstrated. The performance of both the extended Kalman filter and ...

− In this paper a development of an adaptive Kalman filter through a fuzzy inference system (FIS) is outlined. The adaptation is concerned with the imposition of conditions under which the filter measurement noise covariance matrix R or the process noise covariance matrix Q are estimated. The adaptive adjustment is carried out using a FIS based on the whiteness of the filter innovation sequence...

This paper presents the m e w of sensor fusion based on the Adaptive Fuzzy Kalman Filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles. The presented method has been validated in 3-0 environment and is of particular importance for guidance, navigation, and control of flyi...

This paper is an attempt to generalize the results obtained earlier and presents the method of sensor fusion based on the Adaptive Fuzzy Kalman Filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles. The presented method has been validated in 3-D environment and is of parti...

The topic of Doppler and Bearing Tracking (DBT) problem is to achieve a target trajectory using the Doppler and Bearing measurements. The difficulty of DBT problem comes from the nonlinearity terms exposed in the measurement equations. Several techniques were studied to deal with this topic, such as the unscented Kalman filter. Nevertheless, the performance of the filter depends directly on the...

Kalman filtering is a method for estimating state variables of a dynamic systems recursively from noise-contaminated measurements. For systems with nonlinear dynamics, a natural extension of the Linear Kalman Filter (LKF), called Extended Kalman filter (EKF) is used. The Kalman filter represents one of the most popular estimation techniques for integrating signals from navigation systems, like ...

This is a survey paper.The performance of the Kalman filter (KF), which is Algoritstandard as an outstanding implementation for dynamic system state estimation, greatly depends on its parameter R, called the measurement noise covariance matrix. . However, it’s difficult to obtain the accurate value of R before the filter starts, and the value of R is possible to change with the measurement envi...

against the range-dependent accuracy of the tracking radar measurements including range, elevation and bearing angles, a new hybrid adaptive kalman filter is proposed to enhance the performance of the radar aided strapdown inertial navigation system (ins/radar). this filter involves the concept of residual-based adaptive estimation and adaptive fading kalman filter and tunes dynamically the fil...