  This paper presents an adaptive learning method for data fusion in autonomous
driving vehicles. The localization is based on the integration of Inertial
Measurement Unit (IMU) with two Real-Time Kinematic (RTK) Global Positioning
System (GPS) units in an adaptive Kalman filter (KF). The observability
analysis reveals that $i$) integration of a single GPS with IMU does not
constitute an observable system; $ii$) integration of two GPS units with IMU
results in a locally observable system provided that the line connecting two
GPS antennas is not parallel with the vector of the measured acceleration,
i.e., the sum of inertial and gravitational accelerations. The latter case
makes it possible to compensate for the error in the estimated orientation due
to gyro drift and its bias without needing additional instruments for absolute
orientation measurements, e.g., a magnetic compass. Moreover, in order to cope
with the fact that GPS systems sometimes lose their signal and receive
inaccurate position data, the self-tuning filter estimates the covariance
matrix associated with the GPS measurement noise. This allows the KF to
incorporate GPS measurements in the data fusion process heavily only when the
information received by GPS becomes reliably available.
