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International Conference on Instrumentation, Measurement, Circuits and Systems (ICIMCS 2011)
Chen Ming
Chen Ming
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ASME Press
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Recently, the newest methods based on Artificial Intelligence (AI) are used in order to provide reliable positioning information for various navigation applications for ground vehicles with the help of (GNSS) technology, with integrated inertial navigation system (INS).

All existing methods based on Artificial Intelligence (AI) rest upon the INS system error regarding the correct INS operation, at certain times, and do not take into account the error relation for the past INS values. This study presented, therefore, suggests the use of entry-Delayed Networks Neural (IDNN) to model both the INS position and velocity errors based on past samples of the current INS position and speed of travel respectively.

This aspect leads to a more reliable positioning solution during interruption longer GNSS signals. The proposed method is evaluated using different data paths for both roads and INS navigation test are mounted inside the ground vehicles and integrated with GNSS receivers. IDNN performance — on which the model is based is also compared with both conventional (mainly based on Kaiman filtering) and recent techniques based on Artificial Intelligence (AI). The results showed a significant improvement in positioning accuracy in particular cases using INS and long interruptions of receiving GNSS signals.

Dynamic Neural Networks Used by the INS and IDNN
Road Test Experiment
Results and Discussions
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