Improving INS/GPS Navigation Accuracy through Compensation of Kalman Filter Errors

The Kalman filter is often used to integrate satellite navigation systems with inertial navigation systems. Such integrated systems are especially useful for navigation of vehicles in urban environments where satellite signals are frequently blocked by tall buildings. The filter weights the measurements of both navigation systems to provide an overall optimal solution. Unfortunately, an optimal solution is only achieved when the filter has been supplied with ideal a priori information such as proper measurement noise characteristics and system dynamics. If such parameters are not perfect they can be detected and compensated for using an intelligent navigation scheme which is adaptable to different sensors. As dynamics are encountered, satellite signal blockages are simulated to test the optimality of the filter. A neural network is then trained to learn any residual deterministic errors which are then removed from future system drifts during signal blockages.