Neural network aided adaptive Kalman filtering for GPS applications

The Kalman filtering theory plays an important role in the fields of navigation system and receiver tracking loop designs. For obtaining optimal (in the viewpoint of minimum mean square error) estimate of the system state vector, the designers are required to have exact knowledge on both dynamic process and measurement models, in addition to the assumption that both the process and measurement are corrupted by zero mean Gaussian white noises. The neural network can be incorporated into the filtering mechanism as a dynamic model corrector for identifying the real-time nonlinear dynamics modeling error when the modeling uncertainty is considered. The partially unknown part of the dynamics is identified by the neural network and the modeling error is compensated. Applications of the neural network aided adaptive Kalman filter is introduced to the GPS navigation and receiver tracking loop design.