Adaptive neuro-fuzzy module for inertial navigation system/global positioning system integration utilising position and velocity updates with real-time cross-validation

Recently, methods based on artificial intelligence (AI) have been suggested to provide reliable positioning information for different land vehicle navigation applications. The majority of these applications utilise both the global positioning system (GPS) and the inertial navigation system (INS). These AI modules were trained to mimic the latest vehicle dynamics so that, in case of GPS outages, the system relies on INS and the recently updated AI module to provide the vehicle position. Several neural networks and neuro-fuzzy techniques were implemented in real-time in a de-centralised fashion and provided acceptable accuracy for short GPS outages. It was reported that these methods provided poor positioning accuracy during relatively long GPS outages. In order to prevail over this limitation, this study optimises the Al-based INS/GPS integration schemes utilising adaptive neuro-fuzzy inference system with performing, in real-time, both GPS position and velocity updates. In addition, a holdout cross validation method during the update procedure was utilised in order to ensure generalisation of the model. The proposed system is tested using differential GPS and both navigational and tactical grades INS field test data obtained from a land vehicle experiment. The results showed that the effectiveness of the proposed system over both the existing Al-based and the conventional INS/GPS integration techniques, especially during long GPS outages. This method may have one limitation related to the unusual significant changes of the vehicle dynamics between the update and the prediction stages of operation which may influence the overall positioning accuracy.