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ANFIS-Based Model with Hybrid Evolutionary Algorithm for Optimized INS/GPS Data Fusion

M. Malleswaran, Dr.V. Vaidehi, J. Mary Anita

Abstract


The GPS/INS integration is the adequate solution to provide a navigation system that has superior performance in comparison with either a GPS or an INS stand-alone system. The GPS/INS integration is typically carried out through Kalman filter (KF). However, the fact that KF highly depends on a predefined dynamics model forms a major drawback. Most recently, Adaptive neuro fuzzy inference system (ANFIS) has been proposed which is trained during the availability of GPS signal 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. This paper introduces a hybrid evolutionary algorithm of cooperative particle swarm optimization (CPSO) and cultural algorithm that is used to update the ANFIS parameters. The results demonstrate the comparison of the optimized ANFIS with PSO, CPSO and with hybrid evolutionary algorithm of cultural cooperative particle swarm optimization (CPSO) and cultural algorithm (CA), for INS/GPS integration.

Keywords


INS/GPS, ANFIS, Data Fusion, CPSO, CA

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References


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