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Advanced Methods for 3D Magnetic Localization in Industrial Process Distributed Data-Logging with a Sparse Distance Matrix

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)

Abstract

Wireless sensor networks/data-logging devices are increasingly applied for distributed measurement and acquiring additional contextual data. These have been applied in large scale indoor and outdoor systems with solutions based on RF, light based and ultra sound based systems. Data-loggers in liquid filled containers pose new challenges for localization because of the high reflectivity of containers and high attenuation due to the liquids obstructing communication between wireless nodes. Magnetic localization techniques have been used in many places including military research [14]. This approach was adapted for use in liquid filled containers. In this project, two prototypes, a laboratory and an industrial installation have been conceived and served for acquisition of experimental data for localization. In our paper, we exploit the sparsity met in the particular magnetic MEMS sensor swarm localization concept by introducing NLMR which is a simplified form of Sammon’s mapping (NLM) and we combine it with different meta-heuristics and soft-computing techniques, e.g., gradient descent, Simulated Annealing and PSO. We compare this with Multilateration and conventional NLM localization technique. Our approach has improved the localization from a mean error of 20 cm in the first cut analysis for the industrial setup using conventional NLM down to 11 cm without and to 9 cm with apriori knowledge. Future improvements are to be expected from a thorough calibration of all system components. in [5]. The modified algorithm is capable of distributed localization producing mean localization error of 10 cm for the Warstein experiment data.

Keywords

Magnetic sensor localization Sammon’s mapping NLMR Particle swarm optimization PSO Indoor localization 

Notes

Acknowledgments

This work was partly supported by the Federal Ministry of Education and Research (BMBF) in the program mst-AVS, in the project ROSIG grant no. 16SV3604 of the PAC4PT consortium (Partners were UST GmbH, IMST GmbH, Krohne, Warsteiner, and microTEC Ges. für Mikrotechn. GmbH (Coord.). The industrial environment data, employed here for the algorithmic studies, has been acquired and first-cut analysed by conventional NLM in the project work by S. Carrella and D. Groben. All algorithms were implemented using python (numpy, scipy, and matplotlib packages) and Matlab.

References

  1. 1.
    Kalmus, H.P.: A new guiding and tracking system. IRE Trans. Aerosp. Navig. Electron. 9, 7–10 (1962)CrossRefGoogle Scholar
  2. 2.
    Sammon, J.W.: A nonlinear mapping for data structure analysis, IEEE Trans. Comput. C-18, 401–409 (1969)Google Scholar
  3. 3.
    Raab, F.E., et al.: Magnetic position and orientation tracking system, IEEE Trans. Aerosp. Electron. Syst. 5, 709–717 (1979). ISSN 18237843Google Scholar
  4. 4.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. Proc, IEEE International Conference on Neural Networks, IJCNN (1995)Google Scholar
  5. 5.
    König, A.: Interactive visualisation and analysis of hierarchical neural projections for data mining. In IEEE TNN, Special Issue on Neural Networks for, Data Mining and Knowledge Discovery, pp. 615–624, 11(3), May, 2000Google Scholar
  6. 6.
    König, A.K.: "Dimensionality reduction techniques for interactive visualization, exploratory data analysis, and classification", Pattern Recognition in Soft Computing Paradigm, World Scientific, N.R. Pal (eds.), 2: 1–37, 2001Google Scholar
  7. 7.
    Paperno, E., Sasada, I., Leonovich, E. A new method for magnetic position and orientation tracking. IEEE Trans. Magn. 37(4), JULY 2001Google Scholar
  8. 8.
    Prigge, E.A.: A positioning system with no line-of-sight restrictions for cluttered environments. Stanford University, Dissertation (2004)Google Scholar
  9. 9.
    Callmer, J., Skoglund, M.: F. Silent localization of underwater sensors using magnetometers. EURASIP J. Adv. Sig. Process, Gustafsson (2010)Google Scholar
  10. 10.
    Blankenbach, J., Norrdine, A.: Position estimation using artificial generated magnetic fields, : International Conference on Indoor Positioning and Indoor Navigation (IPIN), 15–17 September 2010. Zürich, Switzerland (2010)Google Scholar
  11. 11.
    Blankenbach, J., Norrdine, A., Hellmers, H.: Adaptive signal processing for a magnetic indoor positioning system geodetic institute, Technische Universität Darmstadt - Short paper IPINGoogle Scholar
  12. 12.
    Placidi, G., Franchi, D., Maurizi, A., Sotgiu, A.: Review on patents about magnetic localisation systems for in-vivo catheterizations INFM c/o Dept. of Health Sci., Uni. of LAquila, Via Vetoio Coppito 2, 67100 LAquila, ItalyGoogle Scholar
  13. 13.
    Ascension Technology Corporation Products Application: [Online]. http://www.ascension-tech.com/medical/pdf/TrakStarWRTSpecSheet.pdf, checked Nov. 5 2012
  14. 14.
    Polhemus: [Online]. Available: http://www.polhemus.com/?page=Military_Why_Magnetic_Tracking, checked Nov. 5 2012
  15. 15.
    Iswandy, K., önig, A.K.: Soft-computing techniques to advance non-linear mappings for multi-variate data visualization and wireless sensor localization. e-Newsletter IEEE SMC Soc., Issue #29, Dec. 2009Google Scholar
  16. 16.
    Carrella, S., Iswandy, K., Lutz, K., önig, A.K.: 3D-Localization of low-power wireless sensor nodes based on AMRSensors in industrial and AmI applications, VDE Verlag GmbH Berlin Offenbach, pp. 522–529, 2010Google Scholar
  17. 17.
    Iswandy, K., Carrella, S., önig, A.K.: Localization system for low power sensor nodes deployed in liquid-filled industrial containers based on magnetic sensing. Tagungsband XXIV Messtechnisches Symposium des AHMT, 23.-25. September, pp. 108–121, 2010Google Scholar
  18. 18.
    Carrella, S., Iswandy, K., önig, A.K.: A system for localization of wireless sensor nodes in industrial applications based on sequentially emitted magnetic fields sensed by tri-axial AMR sensorsGoogle Scholar
  19. 19.
    Carrella, S., Iswandy, K., önig, A.K.: System for 3D localization and synchronization of embedded wireless sensor nodes based on AMR sensors in industrial environments, Proceedings Sensor+test 2011Google Scholar
  20. 20.
    Reinecke, S., öpping, U.P., Hampel, U.: Autonome sensorpartikel zur räumlichen Parametererfassung in gro\(\beta \)skaligen Behältern, Sensor & Test 2012Google Scholar
  21. 21.
    Pending Patent Application: Method and apparatus for determining the spatial coordinates of at least one sensor node in a container, filing date: 18.05.2010, Int. Pub. 24.11.2011, Int.No.: WO 2011/144325 A2.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Institute of Integrated Sensor SystemsTU KaiserslauternKaiserslauternGermany

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