Sistema de posicionamiento en interiores utilizando señales de radio estaciones FM comerciales y Deep Learning
Enviado: 26-10-2018
|Aceptado: 20-06-2019
|Publicado: 01-01-2020
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Palabras clave:
Estimación de la posición, Frecuencia Modulada, Interiores, Señales de radio, Sistemas de posicionamiento
Agencias de apoyo:
Grupo de investigación AECC (COL0053581) del Instituto Tecnológico Metropolitano
proyecto P14208.
Resumen:
Para dar solución al problema de posicionamiento en interiores, muchos autores han propuesto el uso de diversas técnicas. Desde la simulación de un Sistema de Posicionamiento Global (GPS) a través de antenas Pseudolites, la construcción de campos magnéticos artificiales, el uso de diversos sensores como visión, ultrasonido, unidades de medida inercial, entre otros. Hasta el uso de transmisores y receptores en el rango de la Radio Frecuencia como los presentes en una oficina. Muchos de los sistemas anteriormente mencionados dependen para su correcto funcionamiento de una instalación y configuración de equipos en el lugar de posicionamiento, lo cual puede llevar en ocasiones a costosas implementaciones. Es por esto que para realizar este trabajo hemos propuesto una metodología de posicionamiento en interiores que no requiere de adecuaciones e instalaciones en el lugar de aplicación. La metodología propuesta hace uso de las radio estaciones FM existentes y utiliza algoritmos de Deep Learning como algoritmo de posicionamiento.
Citas:
Adler, S., Schmitt, S., 2014. Path loss and multipath effects in a real world indoor localization scenario. 2014 11th Workshop on Positioning, Navigation and Communication, WPNC 2014 1. https://doi.org/10.1109/WPNC.2014.6843300
Al, M., Amali, A., Khir, M. H., Saad, N. M., Dass, S. C., 2017. WiFi Fingerprinting Indoor Positioning with Multiple Access Points in a Single Base Station using Probabilistic Method. International Journal of Applied Engineering Research 12 (6), 1102-1113.
Ali-Rantala, P., Ukkonen, L., Sydanheimo, L., Keskilammi, M., Kivikoski, M., 2003. Different kinds of walls and their e
ect on the attenuation of radiowaves indoors. IEEE Antennas and Propagation Society International Symposium. Digest. Held in conjunction with: USNC/CNC/URSI North American Radio Sci. Meeting (Cat. No.03CH37450) 3, 1020-1023. https://doi.org/10.1109/APS.2003.1220085
Anastasijevi, A., Neskovi, A., 2012. A practical realisation of kNN indoor positioning model for GSM. In: Telecommunications Forum (TELFOR), 2012 20th. No. m. pp. 1-4. https://doi.org/10.1109/TELFOR.2012.6419576
Batistíc, L., Tomic, M., 2018. Overview of Indoor Positioning System Technologies. 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 473-478. https://doi.org/10.23919/MIPRO.2018.8400090
Blankenbach, J., Norrdine, A., Hellmers, H., 2012. A robust and precise 3d indoor positioning system for harsh environments. In: Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on. IEEE, pp. 1-8. https://doi.org/10.1109/IPIN.2012.6418863
Campos, R., Lovisolo, L., 2015. RF Positioning: Fundamentals, Applications, and Tools:. GNSS technology and applications series. Artech House, Ch. Fundamentals Of RF Fingerprint, pp. 79-109. URL: https://books.google.com.co/books?id=oLTQCgAAQBAJ
Chao, H., Gu, Y., Gross, J., Guo, G., Fravolini, M. L., Napolitano, M. R., 2013. A comparative study of optical flow and traditional sensors in uav navigation. In: American Control Conference (ACC), 2013. IEEE, pp. 3858-3863.
Chen, Y., Lymberopoulos, D., Liu, J., Priyantha, B., 2013. Indoor localization using FM signals. IEEE Transactions on Mobile Computing 12 (8), 1502-1517. https://doi.org/10.1109/TMC.2013.58
Danymol, R., Ajitha, T., Gandhiraj, R., Dec 2013. Real-time communication system design using rtl-sdr and raspberry pi. In: 2013 International Conference on Advanced Computing and Communication Systems. pp. 1-5. https://doi.org/10.1109/ICACCS.2013.6938691
De Angelis, G., Pasku, V., De Angelis, A., Dionigi, M., Mongiardo, M., Moschitta, A., Carbone, P., 2015. An indoor ac magnetic positioning system. IEEE Transactions on Instrumentation and Measurement 64 (5), 1267-1275. https://doi.org/10.1109/TIM.2014.2381353
Do, T.-H., Hwang, J., Yoo, M., 2013. Tdoa based indoor visible light positioning systems. In: Ubiquitous and Future Networks (ICUFN), 2013 Fifth International Conference on. IEEE, pp. 456-458.
Domingo-Perez, F., Lázaro-Galilea, J. L., Martín-Gorostiza, E., Salido-Monzú, D.,Wieser, A., 2014. Evolutionary optimization of sensor deployment for an indoor positioning system with unknown number of anchors. In: Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), 2014. IEEE, pp. 195-202. https://doi.org/10.1109/UPINLBS.2014.7033728
Interaction, M., 2010. Indoor Positioning Using FM Radio. International Journal of Handheld Computing Research (IJHCR) 1 (3), 19-31. https://doi.org/10.4018/jhcr.2010070102
Khalajmehrabadi, A., Member, S., Gatsis, N., Akopian, D., Member, S., 2017. Modern WLAN Fingerprinting Indoor Positioning Methods and Deployment Challenges (c), 1-30. https://doi.org/10.1109/COMST.2017.2671454
Kuflik, T., Lanir, J., Dim, E., Wecker, A., Corra, M., Zancanaro, M., 2012. Indoor Positioning in Cultural Heritage: Challenges and a Solution, 1-5. https://doi.org/10.1109/EEEI.2012.6376935
Le, W., Wang, Z., Wang, J., Zhao, G., Miao, H., 2014. A Novel WIFI Indoor Positioning Method Based on Genetic Algorithm and Twin Support Vector Regression. In: Control and Decision Conference (2014 CCDC), The 26th Chinese. No. 61174059. pp. 4859-4862. https://doi.org/10.1109/CCDC.2014.6853043
Le Dortz, N. and Gain, F. and Zetterberg, P., 2012. Wifi Fingerprint Indoor Positioning System Using Probability Distribution Comparison. Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, 2301-2304. https://doi.org/10.1109/ICASSP.2012.6288374
Lee, C., Chang, Y., Park, G., Ryu, J., Jeong, S.-G., Park, S., Park, J. W., Lee, H. C., Hong, K.-s., Lee, M. H., 2004. Indoor positioning system based on incident angles of infrared emitters. In: Industrial Electronics Society, 2004. IECON 2004. 30th Annual Conference of IEEE. Vol. 3. IEEE, pp. 2218-2222.
Liu, H., Member, S., Darabi, H., Banerjee, P., Liu, J., 2007. Survey of Wireless Indoor Positioning Techniques and Systems 37 (6), 1067-1080. https://doi.org/10.1109/TSMCC.2007.905750
Luo, P., Zhang, M., Zhang, X., Cai, G., Han, D., Li, Q., 2013. An indoor visible light communication positioning system using dual-tone multi-frequency technique. In: Optical Wireless Communications (IWOW), 2013 2nd International Workshop on. IEEE, pp. 25-29. https://doi.org/10.1109/IWOW.2013.6777770
Machaj, J., Brida, P., 2014. Using GSM Signals for Fingerprint-based Indoor Positioning System. In: ELEKTRO, 2014. pp. 64-67. https://doi.org/10.1109/ELEKTRO.2014.6847872
Mainetti, L., Patrono, L., Sergi, I., 2014. A Survey on Indoor Positioning Systems. 2014 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM). https://doi.org/10.1109/SOFTCOM.2014.7039067
Matic, A., Popleteev, A., Osmani, V., Mayora-Ibarra, O., 2010. FM radio for indoor localization with spontaneous recalibration. In: Pervasive and Mobile Computing. Vol. 6. Elsevier B.V., pp. 642-656. URL: http://dx.doi.org/10.1016/j.pmcj.2010.08.005 https://doi.org/10.1016/j.pmcj.2010.08.005
Moghtadaiee, V., Dempster, A. G., 2014. FM Radio Signals. IEEE TRANSACTIONS ON BROADCASTING 60 (2), 336-346. https://doi.org/10.1109/TBC.2014.2322771
Moghtadaiee, V., Dempster, A. G., Lim, S., 2011. Indoor localization using FM radio signals: A fingerprinting approach. 2011 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2011, 3-9. https://doi.org/10.1109/IPIN.2011.6071932
Murata, S., Yara, C., Kaneta, K., Ioroi, S., Tanaka, H., 2014. Accurate indoor positioning system using near-ultrasonic sound from a smartphone. In: Next Generation Mobile Apps, Services and Technologies (NGMAST), 2014 Eighth International Conference on. IEEE, pp. 13-18. https://doi.org/10.1109/NGMAST.2014.17
Mustafah, Y. M., Azman, A. W., Akbar, F., 2012. Indoor uav positioning using stereo vision sensor. Procedia Engineering 41, 575-579. https://doi.org/10.1016/j.proeng.2012.07.214
Niu, J., Wang, B., Cheng, L., Rodrigues, J. J. P. C., 2015. WicLoc : An Indoor Localization System based on WiFi Fingerprints and Crowdsourcing. In: 2015 IEEE International Conference on Communications (ICC). pp. 3008-3013. https://doi.org/10.1109/ICC.2015.7248785
Niwa, H., Kodaka, K., Sakamoto, Y., Otake, M., Kawaguchi, S., Fujii, K., Kanemori, Y., Sugano, S., 2008. Gps-based indoor positioning system with multichannel pseudolite. In: Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on. IEEE, pp. 905-910.
Nuaimi, K. A., Ain, A., Ain, A., 2011. A Survey of Indoor Positioning Systems and Algorithms, 185-190.
Popleteev, A., Osmani, V., Mayora, O., 2012. Investigation of indoor localization with ambient FM radio stations. In: 2012 IEEE International Conference on Pervasive Computing and Communications. pp. 19-23. https://doi.org/10.1109/PerCom.2012.6199864
Regula, G., Gozse, I., Soumelidis, A., 2012. Position estimation using novel calibrated indoor positioning system. In: Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International. IEEE, pp. 1142-1147. https://doi.org/10.1109/I2MTC.2012.6229376
Ruiz, D., Garcia, E., Urena, J., Villadangos, J. M., Garcia, J. J., De Marziani, C., 2014. Performance comparison of correlation-based receive filters in an ultrasonic indoor positioning system. In: Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2014 IEEE International. IEEE, pp. 1548-1551. https://doi.org/10.1109/I2MTC.2014.6861005
Selmi, I., Vervisch-Picois, A., Gottesman, Y., Samama, N., 2012. Optical and radio calibration of the repealite based indoor positioning system. In: Indoor Positioning and Indoor Navigation (IPIN), 2012 International Conference on. IEEE, pp. 1-8. https://doi.org/10.1109/IPIN.2012.6418906
Sertthin, C., Tsuji, E., Nakagawa, M., Kuwano, S.,Watanabe, K., 2009. A switching estimated receiver position scheme for visible light based indoor positioning system. In: Wireless Pervasive Computing, 2009. ISWPC 2009. 4th International Symposium on. IEEE, pp. 1-5. https://doi.org/10.1109/ISWPC.2009.4800561
Siller, M., 2016. A Fingerprinting Indoor Localization Algorithm Based Deep Learning. In: 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN). pp. 1006-1011.
Suzuki, A., Iyota, T., Choi, Y., Kubota, Y., Watanabe, K., Yamane, A., 2009. Measurement accuracy on indoor positioning system using spread spectrum ultrasonic waves. In: Autonomous Robots and Agents, 2009. ICARA 2009. 4th International Conference on. IEEE, pp. 294-297. https://doi.org/10.1109/ICARA.2000.4803981
Swangmuang, N., Krishnamurthy, P., 2008. An effective location fingerprint model for wireless indoor localization. Pervasive and Mobile Computing 4 (6), 836-850. URL: http://dx.doi.org/10.1016/j.pmcj.2008.04.005 https://doi.org/10.1016/j.pmcj.2008.04.005
Tian, Y., Denby, B., Pierre, U., 2014. Hybrid Indoor Localization using GSM Fingerprints , Embedded Sensors and a Particle Filter. In: Wireless Communications Systems (ISWCS), 2014 11th International Symposium on. pp. 542-547. https://doi.org/10.1109/ISWCS.2014.6933413
Torteeka, P., Chundi, X., 2014. Indoor positioning based on Wi-Fi Fingerprint Technique using Fuzzy K-Nearest Neighbor. Applied Sciences and Technology (IBCAST), 2014 11th International Bhurban Conference on, 461-465. https://doi.org/10.1109/IBCAST.2014.6778188
Varshavsky, A., Lara, E. D., Hightower, J., Lamarca, A., Otsason, V., 2007. GSM indoor localization. Pervasive and Mobile Computing 3, 698-720. https://doi.org/10.1016/j.pmcj.2007.07.004
Vervisch-Picois, A., Samama, N., 2012. First experimental performances of the repealite based indoor positioning system. In:Wireless Communication Systems (ISWCS), 2012 International Symposium on. IEEE, pp. 636-640. https://doi.org/10.1109/ISWCS.2012.6328445
Vervisch-Picois, A., Selmi, I., Gottesman, Y., Samama, N., 2010. Current status of the repealite based approach: A sub-meter indoor positioning system. In: Satellite Navigation Technologies and European Workshop on GNSS Signals and Signal Processing (NAVITEC), 2010 5th ESAWorkshop on. IEEE, pp. 1-6. https://doi.org/10.1109/NAVITEC.2010.5707994
Wang, X., Gao, L., Mao, S., Pandey, S., 2015. DeepFi : Deep Learning for Indoor Fingerprinting Using Channel State Information. In: Wireless Communications and Networking Conference (WCNC), 2015 IEEE. pp. 9-12.
Wietrzykowski, J., 2017. Low-Effort Place Recognition with WiFi Fingerprints Using Deep Learning. In: Automation 2017. ICA 2017. https://doi.org/10.1007/978-3-319-54042-957
Xia, S., Liu, Y., Yuan, G., Zhu, M.,Wang, Z., 2017. Indoor Fingerprint Positioning Based on Wi-Fi : An Overview. https://doi.org/10.3390/ijgi6050135
Yamaguchi, S., Mai, V. V., Thang, T. C., Pham, A. T., 2014. Design and performance evaluation of vlc indoor positioning system using optical orthogonal codes. In: Communications and Electronics (ICCE), 2014 IEEE Fifth International Conference on. IEEE, pp. 54-59. https://doi.org/10.1109/CCE.2014.6916679
Yazici, A., Yayan, U., Y¨ucel, H., 2011. An ultrasonic based indoor positioning system. In: Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on. IEEE, pp. 585-589. https://doi.org/10.1109/INISTA.2011.5946154
Zhao, J., Wang, J., 2017. WiFi Indoor Positioning Algorithm Based on Machine Learning. In: Electronics Information and Emergency Communication (ICEIEC), 2017 7th IEEE International Conference on. https://doi.org/10.1109/ICEIEC.2017.8076562
Zhuang, Y., Syed, Z., Georgy, J., 2015. Autonomous smartphone based WiFi positioning system by using access points localization and crowdsourcing. Pervasive and Mobile Computing. URL: http://dx.doi.org/10.1016/j.pmcj.2015.02.001 https://doi.org/10.1016/j.pmcj.2015.02.001
Zou, H., Luo, Y., Lu, X., Jiang, H., Xie, L., 2015. A Mutual Information Based Online Access Point Selection Strategy for WiFi Indoor Localization. In: Automation Science and Engineering (CASE), 2015 IEEE International Conference on. pp. 24-28. https://doi.org/10.1109/CoASE.2015.7294059
