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Constructive neural-networks-based MEMS/GPS integration scheme

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3 Author(s)
Kai-wei Chiang ; Dept. of Geomatics, Nat. Cheng Kung Univ., Tainan ; Noureldin, A. ; El-Sheimy, N.

This article exploits the idea of developing an alternative data fusion scheme that integrates the outputs of low-cost micro-electro-mechanical systems (MEMS) inertial measurements units (IMUs) and receivers of the global positioning system (GPS). The proposed scheme is implemented using a constructive neural network (cascade-correlation network (CCNs)) to overcome the limitations of conventional techniques that are predominantly based on the Kalman filter (KF). The CNN applied in this research has the advantage of having a flexible topology if compared with the recently utilized multi-layer feed-forward neural networks (MFNNs) for inertial navigation system (INS)/GPS integration. The preliminary results presented in this article illustrate the effectiveness of proposed CCNs over both MFNN-based and Kalman filtering techniques for INS/GPS integration.

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Aerospace and Electronic Systems, IEEE Transactions on  (Volume:44 ,  Issue: 2 )