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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-888d541b-5216-4139-ab81-2d19dfc8836d

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Archiwum Fotogrametrii, Kartografii i Teledetekcji

Tytuł artykułu

Particle swarm optimization algorithm based low cost magnetometer calibration

Autorzy Ali, A.  Siddharth, S.  Syed, Z.  El-Sheimy, N. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a microprocessor provide inertial digital data from which position and orientation is obtained by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the absolute user heading based on Earth’s magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are corrupted by several errors including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO) based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometer. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. The estimated bias and scale factor errors from the proposed algorithm improve the heading accuracy and the results are also statistically significant. Also, it can help in the development of the Pedestrian Navigation Devices (PNDs) when combined with the INS and GPS/Wi-Fi especially in the indoor environments.
Słowa kluczowe
PL sztuczna inteligencja   systemy   pomiar   nawigacja   algorytm   sensor  
EN artificial intelligence   systems   measurement   navigation   algorithm   sensor  
Wydawca Zarząd Główny Stowarzyszenia Geodetów Polskich
Czasopismo Archiwum Fotogrametrii, Kartografii i Teledetekcji
Rocznik 2011
Tom Vol. 22
Strony 9--23
Opis fizyczny Bibliogr. 11 poz.
Twórcy
autor Ali, A.
autor Siddharth, S.
autor Syed, Z.
autor El-Sheimy, N.
Bibliografia
1. Åkesson, B. M., Jørgensen, J. B., Poulsen N. K., and Jørgensen, S. B., 2008. A generalized auto-covariance least-squares method for Kalman filter tuning. Journal of Process Control, 18(78), pp. 769-779.
2. Crassidis, J. L., Lai, K.L., and Harman, R. R., 2005. Real time attitude-independent Tyree axis magnetometer calibration. Journal of Guidance Control, 28(1), pp. 115120.
3. Elbeltagi, E., Hegazy, T., and Grierson, D., 2005. Comparison among five evolutionarybased optimization algorithms. Advanced Engineering Informatics, 19(1), pp. 43-53.
4. Gebre-Egziabher, D., and Elkaim, G. H., 2006. Calibration of strapdown magnetometers in magnetic field domain. ASCE Journal of Aerospace Engineering, 19 (2) , pp 6-16.
5. Guo, P., Qiu, H., Yang, Y., and Ren, Z. 2008. The Soft Iron and Hard Iron Calibration Method using Extended Kalman Filter for Attitude and Heading Reference System. In: Proceedings of IEEE/ION PLANS, Monterey, CA, USA, pp 1167-1174.
6. Hernane, Y., Hernane, S., and Benyettou, M., 2010. PFPSO: An optimised filtering approach based on sampling. Journal of Applied Science, 10(6), pp. 494-499.
7. Kennedy, J., and Eberhart, R., 1995 . Particle Swarm Optimization. In: Proceeding of IEEE International Conference on Neural Networks (ICNN), Perth, Australia, Vol. IV, pp.1942-1948.
8. Kennedy, J., Eberhart, R., and Shi, Y., 2001. Swarm Intelligence. Morgan and Kauffman Publishers, San Francisco.
9. Kwon, W., Roh, K.-S., and Sung, H.-K., 2006. Particle Filter-based Heading Estimation using Magnetic Compasses for Mobile Robot Navigation. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, Orlando, Florida.
10. Reeves, C. R., 1993. Modern Heuristic Techniques for Combinatorial Problems. Blackwell Scientific Publications.
11. Siddharth, S., Ali, A. S., Goodall, C. L., and El-Sheimy, N., 2011. Investigating Aspects of Heading Estimation Error in Pedestrian Navigation. In: Proceedings of the ION ITM 2011 International Technical Meeting, San Diego, CA, USA, pp. 635-642.
Kolekcja BazTech
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