Multi-sensor data fusion for land vehicle attitude estimation using a fuzzy expert system

Abstract

In Inertial Navigation Systems (INS), the attitude estimated from gyro measurements by the Kalman filter is subject to an unbound error growth during the stand-alone mode, especially for land vehicle applications using low-cost sensors. To improve the attitude estimation of a land vehicle, this paper applies a fuzzy expert system to assist in multi-sensor data fusion from MEMS accelerometers, MEMS gyroscopes and a digital compass based on their complementary motion detection characteristics. Field test results have shown that drift-free and smooth attitude estimation can be achieved and will lead to a significant performance improvement for velocity and position estimation.