A vehicular positioning with GPS/IMU using adaptive control of filter noise covariance

Vehicular positioning with GPS/IMU has been studied a lot to increase positioning accuracy. The positioning algorithms mainly use DR (Dead Reckoning) which uses EKF (Extended Kalman Filter). It is basic and very important core technology in positioning section. However, EKF has a major drawback in that it is impossible to make very accurate system and measurement models for a real environment. In this work, we propose an algorithm to estimate vehicle’s position as distribution form, and to control the system and measurement noise covariance to compensate for this major disadvantage. The proposed method to control noise covariance is independently processed, using fading factor and sensor error while considering the driving condition.

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