Attitude Determination from GNSS Using Adaptive Kalman Filtering

Abstract

An adaptive Kalman filtering approach is proposed for attitude determination to replace the fixed (conventional) Kalman filtering approach. The filter is used to adaptively reflect system dynamics changes or rapid changes in vehicle trajectory. The estimation procedure is carried out through the use of a measurement residual sequence. The sequence is used as a piece-wise stationary process inside an estimation window. The measurement noise covariance matrix and the system noise matrix are adaptively estimated. An extended Kalman filter approach with iteration of the states within-an-epoch was performed to overcome the non-linearity of the observation equations. A test was performed to evaluate the proposed technique. Different trajectory scenarios are presented to show the difference in performance between the adaptive Kalman filter and the conventional one. Results show that the proposed adoptive filtering approach has a better performance than the conventional filter.