한국항해항만학회:학술대회논문집 (Proceedings of the Korean Institute of Navigation and Port Research Conference)
- 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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- Pages.491-494
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- 2006
A Neural Network Aided Kalman Filtering Approach for SINS/RDSS Integrated Navigation
- Xiao-Feng, He (College of Mechatronics Engineering and Automation, National Univ. of Defense Technology) ;
- Xiao-Ping, Hu (College of Mechatronics Engineering and Automation, National Univ. of Defense Technology) ;
- Liang-Qing, Lu (College of Mechatronics Engineering and Automation, National Univ. of Defense Technology) ;
- Kang-Hua, Tang (College of Mechatronics Engineering and Automation, National Univ. of Defense Technology)
- 발행 : 2006.10.18
초록
Kalman filtering (KF) is hard to be applied to the SINS (Strap-down Inertial Navigation System)/RDSS (Radio Determination Satellite Service) integrated navigation system directly because the time delay of RDSS positioning in active mode is random. BP (Back-Propagation) Neuron computing as a powerful technology of Artificial Neural Network (ANN), is appropriate to solve nonlinear problems such as the random time delay of RDSS without prior knowledge about the mathematical process involved. The new algorithm betakes a BP neural network (BPNN) and velocity feedback to aid KF in order to overcome the time delay of RDSS positioning. Once the BP neural network was trained and converged, the new approach will work well for SINS/RDSS integrated navigation. Dynamic vehicle experiments were performed to evaluate the performance of the system. The experiment results demonstrate that the horizontal positioning accuracy of the new approach is 40.62 m (1