The utilization of artificial neural networks for multisensor system integration in navigation and positioning instruments

Naser El-Sheimy, Kai Wei Chiang, Aboelmagd Noureldin

研究成果: Article同行評審

103 引文 斯高帕斯(Scopus)

摘要

Inertial-navigation system (INS) and global position system (GPS) technologies have been widely applied in many positioning and navigation applications. INS determines the position and the attitude of a moving vehicle in real time by processing the measurements of three-axis gyroscopes and three-axis accelerometers mounted along three mutually orthogonal directions. GPS, on the other hand, provides the position and the velocity through the processing of the code and the carrier signals of at least four satellites. Each system has its own unique characteristics and limitations. Therefore, the integration of the two systems offers several advantages and overcomes each of their drawbacks. The integration of INS and GPS is usually implemented utilizing the Kalman filter, which represents one of the best solutions for INS/GPS integration. However, the Kalman filter performs adequately only under certain predefined dynamic models. Alternatively, this paper suggests an INS/GPS integration method based on artificial neural networks (ANN) to fuse uncompensated INS measurements and differential GPS (DGPS) measurements. The proposed method suggests two different architectures: the position update architecture (PUA) and the position and velocity PUA (PVUA). Both architectures were developed utilizing multilayer feed-forward neural networks with a conjugate gradient training algorithm.

原文English
頁(從 - 到)1606-1615
頁數10
期刊IEEE Transactions on Instrumentation and Measurement
55
發行號5
DOIs
出版狀態Published - 2006 十月

All Science Journal Classification (ASJC) codes

  • 儀器
  • 電氣與電子工程

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