in the several past years, extended kalman filter (ekf) and unscented kalman filter (ukf) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.the ukf has consistently outperformed for estimation. sometimes least estimation error doesn't yieldwith ukf for the most nonlinear systems. in this paper, we use a new approach for a two variablesta...

In the several past years, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.The UKF has consistently outperformed for estimation. Sometimes least estimation error doesn't yieldwith UKF for the most nonlinear systems. In this paper, we use a new approach for a two variablestate no...

this paper presents a new multi-sensor data fusion method based on the combination of wavelettransform (wt) and extended kalman filter (ekf). input data are first filtered by a wavelettransform via daubechies wavelet “db4” functions and the filtered data are then fused based onvariance weights in terms of minimum mean square error. the fused data are finally treated byextended kalman filter for...

This paper presents a new multi-sensor data fusion method based on the combination of wavelet transform (WT) and extended Kalman filter (EKF). Input data are first filtered by a wavelet transform via Daubechies wavelet “db4” functions and the filtered data are then fused based on variance weights in terms of minimum mean square error. The fused data are finally treated by extended Kalman filter...

Minimizing forecast error requires accurately specifying the initial state from which the forecast is made by optimally using available observing resources to obtain the most accurate possible analysis. The Kalman filter accomplishes this for linear systems and experience shows that the extended Kalman filter also performs well in nonlinear systems. Unfortunately, the Kalman filter and the exte...

In this paper, a homogenous multi-sensor fusion method is used to estimate the trueangular rate and acceleration with a combination of four low cost (< 10$) MEMS Inertial MeasurementUnits (IMU). An information form of steady state Kalman filter is designed to fuse the output of four lowaccuracy sensors to reduce the noise effect by the square root of the number of sensors. A hardware isimplemen...

In this paper a new method is introduced for path planning of an autonomous vehicle. In this method, the environment is considered cluttered and with some uncertainty sources. Thus, the state of detected object should be estimated using an optimal filter. To do so, the state distribution is assumed Gaussian. Thus the state vector is estimated by a Kalman filter at each time step. The estimation...

accuracy of inertial navigation system (ins) is limited by inertial sensors imperfections. before using inertial sensors signals in the data fusion algorithm, noise removal method should be performed, in which, wavelet decomposition method is used. in this method the raw data is decomposed into high and low frequency data sets. in this study, wavelet multi-level resolution analysis (wmra) techn...

obviously navigation is one of the most complicated issues in mobile robots.intelligent algorithms are often used for error handling in robot navigation. thispaper deals with the problem of inertial measurement unit (imu) error handling byusing extended kalman filter (ekf) as an expert algorithms. our focus is put onthe field of mobile robot navigation in the 2d environments. the main challenge...