Abstract
The geomagnetic field has been wildly advocated as an effective signal for fingerprint-based indoor localization due to its omnipresence and local distinctive features. Prior survey-based approaches to collect magnetic fingerprints often required surveyors to walk at constant speeds or rely on a meticulously calibrated pedometer (step counter) or manual training. This is inconvenient, error-prone, and not highly deployable in practice. To overcome that, we propose Maficon, a novel and efficient pedometer-free approach for geomagnetic fingerprint database construction. In Maficon, a surveyor simply walks at casual (arbitrary) speed along the survey path to collect geomagnetic signals. By correlating the features of geomagnetic signals and accelerometer readings (user motions), Maficon adopts a self-learning approach and formulates a quadratic programming to accurately estimate the walking speed in each signal segment and label these segments with their physical locations. To the best of our knowledge, Maficon is the first piece of work on pedometer-free magnetic fingerprinting with casual walking speed. Extensive experiments show that Maficon significantly reduces walking speed estimation error (by more than 20%) and hence fingerprint error (by 35% in general) as compared with traditional and state-of-the-art schemes.
- Martin Andersen, Joachim Dahl, and Lieven Vandenberghe. 2005. CVXOPT: A Python Package for Convex Optimization. UCLA.Google Scholar
- Stéphane Beauregard and Harald Haas. 2006. Pedestrian dead reckoning: A basis for personal positioning. In Proceedings of the 3rd Workshop on Positioning, Navigation and Communication. Shaker, Hannover, Germany, 27–35.Google Scholar
- Donald J. Bemdt and James Clifford. 1994. Using dynamic time warping to find patterns in time series. In Proceedings of 1994 Workshop on Knowledge Discovery in Databases, Vol. 10. AAAI, Seattle, WA, 359–370. Google ScholarDigital Library
- Christopher Bishop. 2006. Pattern Recognition and Machine Learning. Springer, New York, NY. Google ScholarDigital Library
- Stephen P. Boyd and Lieven Vandenberghe. 2004. Convex Optimization. Cambridge University Press, Cambridge, UK. QA402.5 .B69 2004 Google ScholarDigital Library
- Agata Brajdic and Robert Harle. 2013. Walk detection and step counting on unconstrained smartphones. In Proceedings of 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 225–234. https://doi.org/10.1145/2493432.2493449 Google ScholarDigital Library
- Jaewoo Chung, Matt Donahoe, Chris Schmandt, Ig-Jae Kim, Pedram Razavai, and Micaela Wiseman. 2011. Indoor location sensing using geo-magnetism. In Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services. ACM, 141–154. https://doi.org/10.1145/1999995.2000010 Google ScholarDigital Library
- W. Du, P. Tong, and M. Li. 2021. UniLoc: A unified mobile localization framework exploiting scheme diversity. IEEE Transactions on Mobile Computing 20, 7 (July 2021), 2505–2517. https://doi.org/10.1109/TMC.2020.2979857Google ScholarCross Ref
- Rudolf J. Freund, William J. Wilson, and Donna L. Mohr. 2010. Statistical Methods (3rd ed.). Academic Press, Cambridge, MA.Google Scholar
- Stefan Gradl, Markus Zrenner, Dominik Schuldhaus, Markus Wirth, Tomek Cegielny, Constantin Zwick, and Bjoern M. Eskofier. 2018. Movement speed estimation based on foot acceleration patterns. In Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 3505–3508. https://doi.org/10.1109/EMBC.2018.8513042Google Scholar
- Suining He and S.-H. Gary Chan. 2016. Tilejunction: Mitigating signal noise for fingerprint-based indoor localization. IEEE Transactions on Mobile Computing 15, 6 (June 2016), 1554–1568. https://doi.org/10.1109/TMC.2015.2463287Google ScholarCross Ref
- Suining He and S.-H. Gary Chan. 2016. Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons. IEEE Communications Surveys & Tutorials 18, 1 (2016), 466–490. https://doi.org/10.1109/COMST.2015.2464084Google ScholarDigital Library
- Suining He, S.-H. Gary Chan, Lei Yu, and Ning Liu. 2015. Calibration-free fusion of step counter and wireless fingerprints for indoor localization. In Proceedings of 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 897–908. https://doi.org/10.1145/2750858.2804254 Google ScholarDigital Library
- Suining He and Kang G. Shin. 2017. Geomagnetism for smartphone-based indoor localization: Challenges, advances, and comparisons. Computing Surveys 50, 6 (Dec. 2017), 97:1–97:37. https://doi.org/10.1145/3139222 Google ScholarDigital Library
- Paulien Hogeweg and Ben Hesper. 1984. The alignment of sets of sequences and the construction of phyletic trees: An integrated method. Journal of Molecular Evolution 20, 2 (June 1984), 175–186. https://doi.org/10.1007/BF02257378Google ScholarCross Ref
- Konstantin Klipp, Helge Rosé, Jonas Willaredt, Oliver Sawade, and Ilja Radusch. 2018. Rotation-invariant magnetic features for inertial indoor-localization. In Proceedings of 2018 International Conference on Indoor Positioning and Indoor Navigation. IEEE, 1–10. https://doi.org/10.1109/IPIN.2018.8533842Google ScholarCross Ref
- Binghao Li, Thomas Gallagher, Andrew G. Dempster, and Chris Rizos. 2012. How feasible is the use of magnetic field alone for indoor positioning? In Proceedings of 2012 International Conference on Indoor Positioning and Indoor Navigation. IEEE, 1–9. https://doi.org/10.1109/IPIN.2012.6418880Google ScholarCross Ref
- Zhenguang Liu, Li Cheng, Anan Liu, Luming Zhang, Xiangnan He, and Roger Zimmermann. 2017. Multiview and multimodal pervasive indoor localization. In Proceedings of the 25th ACM International Conference on Multimedia. ACM, 109–117. https://doi.org/10.1145/3123266.3123436 Google ScholarDigital Library
- Jun-geun Park, Ami Patel, Dorothy Curtis, Seth Teller, and Jonathan Ledlie. 2012. Online pose classification and walking speed estimation using handheld devices. In Proceedings of 2012 ACM Conference on Ubiquitous Computing. ACM, 113–122. https://doi.org/10.1145/2370216.2370235 Google ScholarDigital Library
- Anshul Rai, Krishna Kant Chintalapudi, Venkata N. Padmanabhan, and Rijurekha Sen. 2012. Zee: Zero-effort crowdsourcing for indoor localization. In Proceedings of the 18th Annual International Conference on Mobile Computing and Networking. ACM, 293–304. https://doi.org/10.1145/2348543.2348580 Google ScholarDigital Library
- Aawesh Shrestha and Myounggyu Won. 2018. DeepWalking: Enabling smartphone-based walking speed estimation using deep learning. In Proceedings of 2018 IEEE Global Communications Conference. IEEE, 1–6. https://doi.org/10.1109/GLOCOM.2018.8647857Google ScholarCross Ref
- Yuanchao Shu, Cheng Bo, Guobin Shen, Chunshui Zhao, Liqun Li, and Feng Zhao. 2015. Magicol: Indoor localization using pervasive magnetic field and opportunistic WiFi sensing. IEEE Journal on Selected Areas in Communications 33, 7 (July 2015), 1443–1457. https://doi.org/10.1109/JSAC.2015.2430274Google ScholarDigital Library
- Yuanchao Shu, Kang G. Shin, Tian He, and Jiming Chen. 2015. Last-mile navigation using smartphones. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. ACM, 512–524. https://doi.org/10.1145/2789168.2790099 Google ScholarDigital Library
- Yoonseon Song, Seungchul Shin, Seunghwan Kim, Doheon Lee, and Kwang H. Lee. 2007. Speed estimation from a tri-axial accelerometer using neural networks. In Proceedings of 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 3224–3227. https://doi.org/10.1109/IEMBS.2007.4353016Google Scholar
- Kalyan Pathapati Subbu, Brandon Gozick, and Ram Dantu. 2013. LocateMe: Magnetic-fields-based indoor localization using smartphones. ACM Transactions on Intelligent Systems and Technology 4, 4 (Oct. 2013), 73:1–73:27. https://doi.org/10.1145/2508037.2508054 Google ScholarDigital Library
- S. Tanaka, K. Motoi, M. Nogawa, and K. Yamakoshi. 2004. A new portable device for ambulatory monitoring of human posture and walking velocity using miniature accelerometers and gyroscope. In Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 1. IEEE, 2283–2286. https://doi.org/10.1109/IEMBS.2004.1403663Google ScholarCross Ref
- Guohua Wang, Xinyu Wang, Jing Nie, and Liwei Lin. 2019. Magnetic-based indoor localization using smartphone via a fusion algorithm. IEEE Sensors Journal 19, 15 (Aug. 2019), 6477–6485. https://doi.org/10.1109/JSEN.2019.2909195Google ScholarCross Ref
- He Wang, Souvik Sen, Ahmed Elgohary, Moustafa Farid, Moustafa Youssef, and Romit Roy Choudhury. 2012. No need to war-drive: Unsupervised indoor localization. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services. ACM, 197–210. https://doi.org/10.1145/2307636.2307655 Google ScholarDigital Library
- Lusheng Wang and Tao Jiang. 1994. On the complexity of multiple sequence alignment. Journal of Computational Biology 1 (Jan. 1994), 337–348. https://doi.org/10.1089/cmb.1994.1.337Google ScholarCross Ref
- Hang Wu, Suining He, and S.-H. Gary Chan. 2017. Efficient sequence matching and path construction for geomagnetic indoor localization. In Proceedings of 2017 International Conference on Embedded Wireless Systems and Networks. ACM, 156–167.Google ScholarDigital Library
- Hang Wu, Suining He, and S.-H. Gary Chan. 2017. A graphical model approach for efficient geomagnetism-pedometer indoor localization. In Proceedings of IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems. IEEE, 371–379. https://doi.org/10.1109/MASS.2017.11Google ScholarCross Ref
- Zhuoling Xiao, Hongkai Wen, Andrew Markham, and Niki Trigoni. 2015. Indoor tracking using undirected graphical models. IEEE Transactions on Mobile Computing 14, 11 (Nov. 2015), 2286–2301. https://doi.org/10.1109/TMC.2015.2398431 Google ScholarDigital Library
- Hongwei Xie, Tao Gu, Xianping Tao, Haibo Ye, and Jian Lv. 2014. MaLoc: A practical magnetic fingerprinting approach to indoor localization using smartphones. In Proceedings of 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 243–253. https://doi.org/10.1145/2632048.2632057 Google ScholarDigital Library
- Sangki Yun, Yi-Chao Chen, and Lili Qiu. 2015. Turning a mobile device into a mouse in the air. In Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services. ACM, 15–29. https://doi.org/10.1145/2742647.2742662 Google ScholarDigital Library
- Jiaping Zhao and Laurent Itti. 2018. shapeDTW: Shape dynamic time warping. Pattern Recognition 74 (Feb. 2018), 171–184. https://doi.org/10.1016/j.patcog.2017.09.020 Google ScholarDigital Library
- Pengfei Zhou, Mo Li, and Guobin Shen. 2014. Use it free: Instantly knowing your phone attitude. In Proceedings of the 20th Annual International Conference on Mobile Computing and Networking. ACM, 605–616. https://doi.org/10.1145/2639108.2639110 Google ScholarDigital Library
- Wiebren Zijlstra. 2004. Assessment of spatio-temporal parameters during Unconstrained Walking. European Journal of Applied Physiology 92, 1 (June 2004), 39–44. https://doi.org/10.1007/s00421-004-1041-5Google ScholarCross Ref
Index Terms
- Pedometer-free Geomagnetic Fingerprinting with Casual Walking Speed
Recommendations
TDNN speed estimator applied to stator oriented IM sensorless drivers
AbstractThe direct measurement of speed in induction motors is costly and requires maintenance. Thus, sensorless techniques for estimating or predicting the speed in three-phase induction motors represent a feasible and economical solution. This work ...
An ANN speed observer applied to three-phase induction motor
IDEAL'12: Proceedings of the 13th international conference on Intelligent Data Engineering and Automated LearningThis work proposes an artificial neural network approach to estimate the induction motor speed applied to three-phase induction motor. The induction motor speed is the important variable in an industrial process. However, the direct measurement of speed ...
Speed sensorless rotor flux estimation in vector controlled induction motor drive
CONTROL'05: Proceedings of the 2005 WSEAS international conference on Dynamical systems and controlThis paper presents a speed sensorless rotor flux estimation algorithm in a vector controlled induction motor drive. The proposed method is based on observing a newly defined state which replaces the unknown terms containing rotor flux and speed on ...
Comments