Abstract
The unscented Kalman filter (UKF) became very attractive for the navigation sensors data fusion, because of algorithm significant accuracy and implementation advantages. The unscented Kalman filter is based on the unscented transform (UT) to perform the estimation of the system states. The main idea of the unscented transformation is following. It’s more effective to approximate probability distribution function than arbitrary transformation or nonlinear function. The developed sensors data fusion algorithm using the UKF is considered in this work. This algorithm was applied for the state estimation of the loosely coupled GPS/INS integrated navigation system. GPS/INS integrated navigation system contains low cost inertial sensors and low cost GPS receiver. To demonstrate the estimation performance, the processing of sensors data was done using linear Kalman filter (KF), extended Kalman filter (EKF) and UKF. As a result, UKF has lower velocity estimation error than EKF during simulated GPS signal outage. DOI: http://dx.doi.org/10.5755/j01.eee.19.10.5886
Highlights
Nowadays, the data fusion from different sensors for the performance improving of the low cost integrated navigation system becomes necessary in the land vehicle navigation application
Algorithm was implemented using Matlab® software. These data processing algorithms were adjusted in order to guarantee minimal velocity estimation error (RMSE) in the presence of the gyroscope and one position sensor (GPS) signal
The rather high value of the velocity estimation error (RMSE) by the unscented Kalman filter (UKF) for GPS signal outage during 120 s...180 s can be explained by the fact that the GPS outage period starts quite quickly after high dynamic mode of the car motion
Summary
The data fusion from different sensors for the performance improving of the low cost integrated navigation system becomes necessary in the land vehicle navigation application. Core to the integrated navigation systems is the concept of fusing noisy measurements from GPS sensor, inertial measurement unit using nonlinear estimation techniques [1]. The UKF has performance equivalent to the standard Kalman filter for linear systems and can be used for the state estimation of nonlinear systems without the linearization procedure required for the EKF. The results of performance analysis for KF, EKF and UKF are discussed
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