Small pipeline trajectory estimation using MEMS based IMU

27th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS 2014

Volumn 1, Issue , 2014, Pages 154-161

  Sahli, H ^a   Moussa, A ^a   Noureldin, A ^a   El Sheimy, N ^a  

^a UNIVERSITY OF CALGARY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

AIR NAVIGATION; COSTS; DATA FUSION; EXTENDED KALMAN FILTERS; GLOBAL POSITIONING SYSTEM; INERTIAL NAVIGATION SYSTEMS; MAMMALS; MEMS; NAVIGATION SYSTEMS; PIPELINES; TRAJECTORIES; UNITS OF MEASUREMENT;

INERTIAL MEASUREMENT UNIT; INTEGRATED NAVIGATION SYSTEMS; MICRO ELECTROMECHANICAL SYSTEM (MEMS); MULTI-SENSOR DATA-FUSION SYSTEMS; MULTI-SENSOR INTEGRATIONS; PERFORMANCE REQUIREMENTS; PIPELINE INSPECTION GAUGES; SMALL DIAMETER PIPELINES;

SENSOR DATA FUSION;

EID: 84939248499     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. (2011, September) A heavy requirement: Environmentally friendly pipeline weighting. Pipelines International. 48.
2. P. Products and S. Association, An introduction to pipeline pigging: pigging products & services association: Pipes & pipelines international, 1995.
3. J. Tiratsoo, Pipeline Pigging Technology, Second ed.: Butterworth-Heinemann College, 1992.
4. A. Noureldin, T. B. Karamat, M. D. Eberts, and A. El-Shafie, "Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications, " Vehicular Technology, IEEE Transactions on, vol. 58, pp. 1077-1096, 2009.
5. A. Noureldin, T. B. Karamat, and J. Georgy. (2012). Fundamentals of Inertial Navigation, Satellite Positioning and Their Integration.
6. B. M. Scherzinger, "Estimation with Application to Navigation, " University of Calgary, University of Calgary, Lecture Notes 2004.
7. E.-H. Shin, "Estimation Techniques for Low-Cost Inertial Navigation, " University of Calgary, Calgary, Alberta, Canada, 2005.
8. M. G. Petovello, "Lecture Notes for ENGO 620 - Estimation for Navigation, " ed, 2012.
9. R. G. Brown and P. Y. C. Hwang, Introduction to random signals and applied Kalman filtering : With MATLAB exercises and solutions, 3rd ed. New York: Wiley, 1997.
10. A. Gelb, J. F. Kasper, R. A. Nash, C. F. Price, and A. A. Sutherland, Applied Optimal Estimation. US: THE M.I.T. PRESS, 1974.
11. M. S. Grewal and A. P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB: A Wiley-Interscience Publications, 2001.
12. H. Wu and G. Chen, "Suboptimal Kalman filtering for linear systems with Gaussian-sum type of noise, " Mathematical and Computer Modelling, vol. 29, pp. 101-125, 111 1999.
13. J. S. Meditch, Stochastic optimal linear estimation and control. New York: McGraw-Hill, 1969.