Adaptive Kalman Filtering for Low Cost INS/GPS
C. Hide, Terry Moore, Martin Smith · 2002
GPS and low cost INS sensors are widely used for positioning and attitude determination applications. Low cost inertial sen- sors exhibit large errors which are compensated using position and velocity updates from GPS. Combining both sensors using a Kalman filter provides high accuracy real time navigation. The Kalman filter relies on the definition of the correct mea- surement and process noise matrices which are generally de- fined a priori and remain fixed throughout the processing run. Adaptive Kalman filtering techniques use the residual sequences to adapt the stochastic properties of the filter on- line to correspond to the temporal dependence of the errors involved. This paper examines the use of three adaptive fil- tering techniques. These are artificially scaling the predicted Kalman filter covariance, the Adaptive Kalman Filter and Mul- tiple Model Adaptive Estimation. The algorithms are tested with the IESSG's GPS and inertial data simulation software. A trajectory taken from a real marine trial is used to test the dynamic alignment of the inertial sensor errors. Results show that on-line estimation of the stochastic properties of the inertial system can significantly improve the speed of the dynamic alignment and potentially improve the overall navigation accuracy and integrity.