improving the reliability of gps and glonass navigation solution in urban canyons using a tuned kalman filter

urban canyon is categorized as hard environment for positioning of a dynamic vehicle due to low number and also bad configuration of inview satellites. in this paper, a tuning procedure is proposed to adjust the important factors in kalman filter (kf) using genetic algorithm (ga). the authors tested the algorithm on a dynamic vehicle in an urban canyon with hard condition and compared the results with traditional kf and weighted least square (wls) methods. the outputs showed that this algorithm could be more reliable more than 114% and 61% against wls and traditional kf. abstract: urban canyon is categorized as hard environment for positioning of a dynamic vehicle due to low number and also bad configuration of inview satellites. in this paper, a tuning procedure is proposed to adjust the important factors in kalman filter (kf) using genetic algorithm (ga). the authors tested the algorithm on a dynamic vehicle in an urban canyon with hard condition and compared the results with traditional kf and weighted least square (wls) methods. the outputs showed that this algorithm could be more reliable more than 114% and 61% against wls and traditional kf.