Author:
Moafipoor S.,Grejner-Brzezinska D. A.,Toth C. K.
Abstract
The basic idea of a dead reckoning personal navigator is to integrate incremental motion information in the forms of step length and step direction over time. Considering that the displacement components are estimated for each step-cycle, it is essential to monitor the integrity of these parameters; otherwise, the error accumulation may render the system unstable. In this paper, a two-stage Kalman Filter (KF) augmented by a neural network is developed to facilitate integrity monitoring. The preliminary results, obtained from several tests performed on simulated and real-word data, indicate an 80% success rate in integrity monitoring.
Publisher
Cambridge University Press (CUP)
Subject
Ocean Engineering,Oceanography
Reference31 articles.
1. Rigoll G. , Eickeler S. and Muller S. (2000). Person Tracking in Real-World Scenarios Using Statistical Methods. Proceedings of the International Conference on Automatic Face and Gesture Recognition, Grenoble, France, 342–347.
2. Lu G. (1991). Quality Control for Differential Kinematic GPS Positioning. Thesis, Dept. of Geomatics, University of Calgary, UCGE Report No. 20042.
3. Performance Assessment of a Multi-Sensor Personal Navigator Supported by an adaptive knowledge based system;Grejner-Brzezinska;Proceedings of ISPRS,2008
4. Stubberud S. C. , Lobbia R. N. and Owen M. (1995). An Adaptive Extended Kalman Filter Using Artificial Neural Networks. Proceedings of the 34th IEEE Conference on Decision and Control. New Orleans, LA, 1852–1856.
5. On-line detection of abrupt changes in the carrier-phase measurements of GPS
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献