Abstract
This article investigates the use of a multilayer feedforward artificial neural network into a GPS integrated low cost inertial navigation system based on MEMS sensors. The neural network is applied as an alternative of integration technique, with the purpose of providing better navigation solutions, during the lack of information in GPS outages portions of time. An input-output neural network signals model is proposed, based on a set of simplified terrestrial vehicle navigation equations. Also an adaptive Kalman filter training methodology is tested with real navigation data. Preliminary simulated numerical results are presented, based on urban vehicular positioning application data trials, acquired from low cost Crossbow CD400-200 IMU and an Ashtech Z12 GPS receiver.
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