Constructive neural-networks-based MEMS/GPS integration scheme

Kai Wei Chiang, Aboelmagd Noureldin, Naser El-Sheimy

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)582-594
Number of pages13
JournalIEEE Transactions on Aerospace and Electronic Systems
Issue number2
Publication statusPublished - 2008 Apr 1

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Electrical and Electronic Engineering

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