Improvement of positioning accuracy of integrated GNSS by using fuzzy Kalman filter based on carrier phase data

In this paper, after reviewing the positioning based on the combination of GPS and GLONASS, a solution is provided for increasing the accuracy of positioning with integrated systems. In order to taking advantage of different GNSS systems, some researches carried out in the field of combination of GPS and GLONASS measurements. The investigations show that addition of GLONASS system measurements to GPS, have a slight improvement in positioning accuracy. The main known reasons for this little effect are limited number of available GLONASS satellites and GLONASS generations error. In this study, in the process of calculating integrated positioning, Kalman filter estimator method is used to estimate the position. Since state estimation of GPS and GLONASS update individually in this method, it is provided weighting satellites observations independently. In order to improve the positioning accuracy, in the process of calculation Kalman filter gain, a fuzzy system is used based on input data of carrier phase in L1 or L2 band for GLONASS satellites. This input can provide weighting the observations of GLONASS satellites. Increasing the accuracy of positioning by using fuzzy Kalman filter is investigeted on several data sets of RINEX format in GPS noise conditions. Assessment, simulation and test results are presented in this paper. According to the results of changing the structure of the Kalman filter by adding fuzzy system, average positioning error is reduced from 16 to 10 meters.