Neural Network Aided Kalman Filtering for Integrated Gps/ins Geo-referencing Platform

Kalman filtering theory plays an important role in integrated GPS/INS georeference system design. A Kalman filter (KF) uses measurement updates to correct system states error and to limit the errors in navigation solutions. However, only when the system dynamic and measurement models are correctly defined, and the noise statistics for the process are completely known, a KF can optimally estimate a system’s states. Without measurement updates, a Kalman filter’s prediction diverges; therefore the performance of an integrated GPS/INS georeference system may degrade rapidly when GPS signals are unavailable. It is a challenge to deal with this problem in real time though it can be handled in post processing by smoothing methods. This paper presents a neural network (NN) aided Kalman filtering method to improve navigation solutions of integrated GPS/INS georeference system. It is known that the errors inherent to strapdown inertial sensors are affected by the platform manoeuvre and environment conditions etc., which are hard to be modelled precisely. On the other hand, NNs have the capability to map inputoutput relationships of a system without apriori knowledge about them. A properly designed NN is able to learn and extract complex relationships given enough training. Furthermore, it is able to adapt to the change of sensors and dynamic platforms. In the proposed loosely coupled GPS/INS georeference system, an extended KF (EKF) estimates the INS measurement errors, plus position, velocity and attitude errors, and provides precise navigation solutions while GPS signals are available. At the same time, a multi-layer NN is trained to map the vehicle manoeuvre with INS prediction errors during each GPS epoch, which is the input of the EKF. During GPS signal blockages, the NN can be used to predict the INS errors for EKF measurement updates, and in this way to improve navigation solutions. The principle of this hybrid method and the NN design are presented. Land vehicle based field test data are processed to evaluate the performance of the proposed method. * Corresponding author.