Multisensor integration using neuron computing for land-vehicle navigation

Kai-Wei Chiang, Aboelmagd Noureldin, Naser El-Sheimy

Research output: Contribution to journalArticlepeer-review

66 Citations (Scopus)


Most of the present navigation sensor integration techniques are based on Kalman-filtering estimation procedures. Although Kalman filtering represents one of the best solutions for multisensor integration, it still has some drawbacks in terms of stability, computation load, immunity to noise effects and observability. Furthermore, Kalman filters perform adequately only under certain predefined dynamic models. Neuron computing, a technology of artificial neural network (ANN), is a powerful tool for solving nonlinear problems that involve mapping input data to output data without having any prior knowledge about the mathematical process involved. This article suggests a multisensor integration approach for fusing data from an inertial navigation system (INS) and differential global positioning system (DGPS) hardware utilizing multilayer feed-forward neural networks with a back propagation learning algorithm. In addition, it addresses the impact of neural network (NN) parameters and random noise on positioning accuracy.

Original languageEnglish
Pages (from-to)209-218
Number of pages10
JournalGPS Solutions
Issue number4
Publication statusPublished - 2003 Mar 1

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)


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