Abstract
Magnetic field fluctuations in modern buildings can derive from both natural and man-made sources, which typically include steel, reinforced concrete structures, and electric power systems, etc. Since the anomalies of the magnetic field inside the building are nearly static and have sufficient local variability, this provides a unique magnetic clue which could be utilized for global self-localization. In this research, we propose TACO, an algorithm that uses Ant Colony Optimization (ACO) and Multi-Position TraceBack Algorithm (MTA) to solve one dimensional magnetic data localization problem. TACO employs a set of novel techniques to resolve ambiguity in locations: ACO is used to generate candidate locations and MTA to make full use of both historical positions and users moving direction information. The evaluation results show that TACO could achieve high localization accuracy, when appropriate previous position information is provided.
This work was supported in part by the National Natural Science Foundation of China (61374214), the Major Projects of Ministry of Industry and Information Technology (2014ZX03006003-002), the National High Technology Research and Development Program of China (2013AA12A201) and Taiyuan-Zhongguancun Cooperation special Project (130104).
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References
Larry, C.B., Kenneth, J.L.: True navigation and magnetic maps in spiny lobsters. Nature 421(6918), 60–63 (2003)
Robert, J.G., Lauren, E.F., Robert, J.G.: Human cryptochrome exhibits light-dependent magnetosensitivity. Nat. Commun. 2, 356–362 (2011)
Treiber, C.D., Salzer, M.C.: Clusters of iron-rich cells in the upper beak of pigeons are macrophages not magneto sensitive neurons. Nature 484(7394), 367–370 (2012)
Burnett, J., Yaping, P.D.: Mitigation of extremely low frequency magnetic fields from electrical installations in high-rise buildings. Build. Environ. 37(8), 769–775 (2002)
Odawara, S., Haraguchi, Y., Muramatsu, K., Yamazaki, K., Hirosato, S.: Magnetic field analyses of architectural components using homogenization technique. IEEE Trans. Magn. 46(8), 3313–3316 (2010)
Bao, G.C., Ming, D.L.: Quantitative analyses of magnetic field distributions for buildings of steel structure. In: 6th International Conference of ICEF (Electromagnetic Field Problems and Applications), pp, 1–4, Dalian (2012)
Paolinelli, D.C., Cunha, D., Cota, A.B.: Study of the simultaneous effects of the hot band grain size and cold rolling reduction on the structure and magnetic properties of nonoriented 3% si steel. IEEE Trans. Magn. 48(4), 1401–1404 (2012)
Suksakulchai, S., Thongchai, S., Wilkes, D.M., Kawamura, K.: Mobile robot localization using an electronic compass for corridor environment. In: International Conference on Systems, Man, and Cybernetics. pp, 3354–3359, Nashville (2000)
Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte carlo localization for mobile robots. In: International Conference on Robotics and Automation, pp, 1322–1328, Detroit (1999)
Thrun, S., Burgard, W., Fox, D.: Probabilistic robotics. Commun. of the ACM - Robots: Intell. Versatility Adaptively 45(3), 52–57 (2002)
Haverinen, J., Kemppainen, A.: A global self-localization technique utilizing local anomalies of the ambient magnetic field. In: IEEE International Conference on Robotics and Automation, pp, 3142–3147, Kobe (2009)
Grand, E.L., Thrun, S.: 3-Axis magnetic field mapping and fusion for indoor localization. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp, 358–364, Hamburg (2012)
Blankenbach, J., Norrdine, A.: Position estimation using artificial generated magnetic fields. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–5, Zurich (2010)
Blankenbach, J., Norrdine, A., Hellmers H.: A robust and precise 3D indoor positioning system for harsh environments. In: International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp, 1–8, Sydney (2012)
Yuan, X.M., Zhi, Y.W.: Grid Task scheduling based on chaotic ant colony optimization algorithm. In: 2nd International Conference on Computer Science and Network Technology, pp. 469–472, Changchun (2012)
Ping, J.Z., Gang, J.L., Wen, X.G.: Research of path planning for mobile robot based on improved ant colony optimization algorithm. In: 2nd International Conference on Advanced Computer Control, pp. 241–245, Shenyang (2010)
Fang, L., Antsaklis, P.J., Montestruque, L.A., McMickell, M.B.: Design of a wireless assisted pedestrian dead reckoning system the navmote experience. IEEE Trans. Instrum. Meas. 54(6), 2342–2358 (2005)
Anshul, R., Chintalapudi, K.K., Padmanabhan, V.N., Rijurekha, S.: Zee: zero-effort crowdsourcing for indoor localization. In: 18th Annual International Conference on Mobile Computing and Networking (MOBICOM), pp. 293–304, Istanbul (2012)
Weimann, F., Abwerzger, G., Wellenhof, B.H.: A pedestrian navigation system for urban and indoor environments. In: 20th International Technical Meeting of the Satellite Division of the Institute of Navigation, pp. 1380–1389, Fort Worth (2007)
Robert, W.L., Judd, T.: Dead Reckoning Navigation System Using Accelerometer to Measure Foot Impacts. US Patent No. 5583776 (1996)
Fan, L., Shui, Z.C., Zhong, D.G., Gong, J., Xing, L.C., Feng, Z.: A reliable and accurate indoor localization method using phone inertial sensors. In: 14th ACM Conference on Ubiquitous Computing, pp. 421–430, Pittsburgh (2012)
Storms, W., Shockley, J., Raquet, J.: Magnetic field navigation in an indoor environment. In: Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS) Conference, pp. 1–10, Kirkkonummi (2010)
Shibuhisa, N., Sato, J., Takahashi, T., Ide, I., Murase, H., Kojima, Y., Takahashi, A.: Accurate vehicle localization using DTW between range data map and laser scanner data sequences. In: Intelligent Vehicles Symposium Conference, pp. 975–980, Istanbul (2007)
Fan, J.X., Mike, L., Fei, C.K., Ben, Z., Hsu, J., Jie, L., Bin, C., Feng, Z.: Design and evaluation of a wireless magnetic-based proximity detection platform for indoor applications. In: 11th International Conference on Information Processing in Sensor Networks, pp, 221–232, Beijing (2012)
Gutjahr, W.J.: First steps to the runtime complexity analysis of ant colony optimization. Comput. Oper. Res. 35(9), 2711–2727 (2008)
Neumann, F., Sudholt, D., Witt, C.: Computational complexity of ant colony optimization and its hybridization. Innovations Swarm Intell. 248, 91–120 (2009)
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Li, H., Luo, H., Zhao, F., Li, X. (2015). TACO: A Traceback Algorithm Based on Ant Colony Optimization for Geomagnetic Positioning. In: Sun, L., Ma, H., Fang, D., Niu, J., Wang, W. (eds) Advances in Wireless Sensor Networks. CWSN 2014. Communications in Computer and Information Science, vol 501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46981-1_20
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