Ga based data fusion approach in an intelligent integrated gps/ins system

A new concept regarding to the GPS/INS integration, based on artificial intelligence here is presented. Most integrated inertial navigation systems (INS) and global positioning systems (GPS) have been implemented using the Kalman filtering technique with its drawbacks related to the need for predefined INS error model and observability of at least four satellites. Most recently, an INS/GPS integration method using a hybridadaptive network based fuzzy inference system (ANFIS) has been proposed in literature. During the availability of GPS signal, the ANFIS is trained to map the error between the GPS and the INS. Then it will be used to predict the error of the INS position components during GPS signal blockage. As ANFIS will be employed in real time applications, the change in the system parameters (e.g., the number of membership functions, the step size, and step increase and decrease rates) to achieve the minimum training error during each time period is automated. This paper introduces a genetic optimization algorithm that is used to update the ANFIS parameters with the INS/GPS error function used as the objective function to be minimized. The results demonstrate the advantages of the genetically optimized ANFIS for INS/GPS Integration in comparison with conventional ANFIS specially in the cases when facing satellites’ outages. Coping with this problem plays an important role in assessment of the fusion approach in land navigation.