ANFIS-Based Model for Real-time INS/GPS Data Fusion for Vehicular Navigation System

Presently, Kalman filter (KF) is used to fuse data from both inertial navigation systems (INS) and Global Positioning Systems (GPS) to provide position, velocity and attitude information. However, several drawbacks associated with KF like its immunity to noise, its dependency on predefined errors models, has encouraged research activates towards investigation of other integration techniques. This study proposes and discusses the real-time implementation of adaptive neuro-fuzzy inference system (ANFIS) to fuse GPS and INS data for vehicular navigation applications. The proposed method was examined and compared to KF when applied to Ashtech Z12 GPS receiver and a navigation-grade INS (Honeywell LRF-III) that have been utilized inside a land vehicle. The system is evaluated while considering several intentionally introduced GPS outages for periods of 20 seconds. The ANFIS-based navigation system was able to provide the vehicle position with errors, which were mostly below 2 m. The experimental results demonstrated the advantages of the proposed AI-based INS/GPS integration techniques in regards of robustness while ensuring system position accuracy in real-time.