| DC Field | Value | Language |
|---|
| dc.contributor.author | Roenko, Alexey | |
| dc.contributor.author | Sirenko, Feliks | |
| dc.contributor.author | Chervoniak, Yevhen | |
| dc.contributor.author | Gorovyi, Ievgen | |
| dc.coverage.temporal | 21-25 August 2018, Lviv | |
| dc.date.accessioned | 2020-06-19T12:05:18Z | - |
| dc.date.available | 2020-06-19T12:05:18Z | - |
| dc.date.created | 2018-02-28 | |
| dc.date.issued | 2018-02-28 | |
| dc.identifier.citation | Data Processing Methods for Mobile Indoor Navigation / Alexey Roenko, Feliks Sirenko, Yevhen Chervoniak, Ievgen Gorovyi // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 236–240. — (Dynamic Data Mining & Data Stream Mining). | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/52498 | - |
| dc.description.abstract | The competition on the world market of
smartphones and tablets between the acknowledged leaders on
one side and the numerous newcomers on the other makes
them all look for new solutions that open additional
opportunities for the developers and customers without the
growth of the price. The progress with new opportunities turns
possible when it happens simultaneously in the software and
the hardware. The brightest one example of the above
statement can be observed for the sensors of mobile devices. It
is totally impossible to imagine modern smart devices having
no sensors, as the progress of last decade (SLAM, face ID,
OCR, pattern recognition etc.) was achieved thanks to
considerable improvements of sensors and the algorithms for
their processing. The paper addresses the questions of
characteristics analysis of such mobile sensors as
accelerometer, magnetometer and gyroscope from the point of
view of their application in indoor navigation field. Signals of
BLE beacons and their processing methods are investigated as
well. The sensor fusion task is briefly discussed and several
practical examples are given. | |
| dc.format.extent | 236-240 | |
| dc.language.iso | en | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 | |
| dc.relation.uri | http://doi.org/10.3390/s16091423 | |
| dc.subject | signal processing | |
| dc.subject | sensor fusion | |
| dc.subject | indoor navigation | |
| dc.subject | BLE navigation | |
| dc.subject | IMU navigation | |
| dc.title | Data Processing Methods for Mobile Indoor Navigation | |
| dc.type | Conference Abstract | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2018 | |
| dc.contributor.affiliation | IT-Jim | |
| dc.format.pages | 5 | |
| dc.identifier.citationen | Data Processing Methods for Mobile Indoor Navigation / Alexey Roenko, Feliks Sirenko, Yevhen Chervoniak, Ievgen Gorovyi // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 236–240. — (Dynamic Data Mining & Data Stream Mining). | |
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| dc.relation.references | [5] H. Bao and W.-Ch. Wong “A Novel Map-Based Dead-Reckoning Algorithm for Indoor Localization,” Journal of Sensor and Actuator Network, vol. 3, pp. 44-63, 2014. doi:10.3390 | |
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| dc.relation.references | [7] Q. Tian, Z. Salcic, K.I.-K. Wang, and Y. Pan, “A Multi-Mode Dead Reckoning System for Pedestrian Tracking Using Smartphones,” IEEE Sens. J., vol. 16, pp. 2079–2093, 2016. doi: 10.1109/JSEN.2015.2510364. | |
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| dc.relation.references | [10] R. Mahony, T. Hamel, and J.-M. Pflimlin, “Complementary filter design on the special orthogonal group SO(3),” Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 2005 Seville, Spain, December 12-15, 2005. | |
| dc.relation.references | [11] S. Mau, “What is the Kalman Filter and How can it be used for Data Fusion?” Robotics Math, pp. 16-811, December 2005. | |
| dc.relation.references | [12] M. Pedley, “Tilt Sensing Using a Three-Axis Accelerometer,” Freescale Semiconductor, AN3461, 2013. | |
| dc.relation.references | [13] R. Zhi, A Drift Eliminated Attitude & Position Estimation Algorithm In 3D. Graduate College Dissertations and Theses, University of Vermont, 2016. | |
| dc.relation.references | [14] F. Abyarjoo, A. Barreto, J. Cofino, and F. R. Ortega, “Implementing a Sensor Fusion Algorithm for 3D Orientation Detection with Inertial/Magnetic Sensors.” In: Sobh T., Elleithy K. (eds) Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering. Lecture Notes in Electrical Engineering, vol 313. Springer, Cham. 2015, pp. 305-310. | |
| dc.relation.references | [15] F. Zafari, I. Papapanagiotou, M. Devetsikiotis, and T. Hacker “An iBeacon based Proximity and Indoor Localization System,” arXiv:1703.07876v2 [cs.NI] 24 Mar 2017. | |
| dc.relation.references | [16] K. Vadivukkarasi, R. Kumar and Mary Joe, “A Real Time Rssi Based Novel Algorithm to Improve Indoor Localization Accuracy for Target Tracking in Wireless Sensor Networks,” ARPN Journal of Engineering and Applied Sciences, vol. 10, no. 16, pp. 7015-7023, SEPTEMBER 2015. | |
| dc.relation.references | [17] T. Qinglin et al. “A Hybrid Indoor Localization and Navigation System with Map Matching for Pedestrians Using Smartphones.” Ed. Kourosh Khoshelham and Sisi Zlatanova. Sensors (Basel, Switzerland) 15.12 (2015): 30759–30783. PMC. Web. 14 Mar. 2018. | |
| dc.relation.references | [18] A. Masse, S. Lefèvre, R. Binet, S. Artigues, G. Blanchet, and S.Baillarin, “Denoising Very High Resolution Optical Remote Sensing Images: Application and Optimization of Nonlocal Bayes method,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11:3, pp. 691-700, 2018. | |
| dc.relation.references | [19] T. Singhal, A. Harit, and D. N. Vishwakarma, “Kalman Filter Implementation on an Accelerometer sensor data for three state estimation of a dynamic system,” International Journal of Research in Engineering and Technology (IJRET), vol. 1, no. 6, 2012. ISSN 2277 – 4378 | |
| dc.relation.references | [20] P. Del Moral, "Non Linear Filtering: Interacting Particle Solution". Markov Processes and Related Fields. 2 (4) pp. 555–580, 1996. | |
| dc.relation.references | [21] B.I. Ahmad, J. Murphy, P.M. Langdon, and S. J. Godsill, "Filtering perturbed in-vehicle pointing gesture trajectories: Improving the reliability of intent inference", Machine Learning for Signal Processing (MLSP) 2014 IEEE International Workshop on, pp. 1-6, 2014. | |
| dc.relation.references | [22] D. Gusenbauer, C. Isert, and J. Krosche, “Self-contained indoor positioning on off-the-shelf mobile devices,” International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-9, 2010. | |
| dc.relation.referencesen | [1] A. Ali and N. El-Sheimy, "Low-Cost MEMS-Based Pedestrian Navigation Technique for GPS-Denied Areas," Journal of Sensors, vol. 2013, Article ID 197090, 10 pages, 2013. doi:10.1155/2013/197090. | |
| dc.relation.referencesen | [2] T. Zengshan, Z. Yuan, Z. Mu, and L. Yu, "Pedestrian dead reckoning for MARG navigation using a smartphone." EURASIP J. Adv. Sign. Process., vol. 1, pp. 1–9, 2014. | |
| dc.relation.referencesen | [3] A. Ali, and N. El-Sheimy, " Low-cost MEMS-based pedestrian navigation technique for GPS-denied areas," Journal of Sensors, 10 pages, 2013. 10.1155/2013/197090. | |
| dc.relation.referencesen | [4] G. Trein, N. Singh, and P. Maddila, "Simple approach for indoor mapping using low-cost accelerometer and gyroscope sensors," DOCPLAYER, 2013. | |
| dc.relation.referencesen | [5] H. Bao and W.-Ch. Wong "A Novel Map-Based Dead-Reckoning Algorithm for Indoor Localization," Journal of Sensor and Actuator Network, vol. 3, pp. 44-63, 2014. doi:10.3390 | |
| dc.relation.referencesen | [6] H. Weinberg, Using the ADXL202 in Pedometer and Personal Navigation Applications. Analog Devices, Inc.; Norwood, MA, USA: 2002. | |
| dc.relation.referencesen | [7] Q. Tian, Z. Salcic, K.I.-K. Wang, and Y. Pan, "A Multi-Mode Dead Reckoning System for Pedestrian Tracking Using Smartphones," IEEE Sens. J., vol. 16, pp. 2079–2093, 2016. doi: 10.1109/JSEN.2015.2510364. | |
| dc.relation.referencesen | [8] N.-H. Ho, P. H. Truong, and G.-M. Jeong, "Step-Detection and Adaptive Step-Length Estimation for Pedestrian Dead-Reckoning at Various Walking Speeds Using a Smartphone," Sensors, Basel, Switzerland, vol. 16(9), pp. 1423, 2016. http://doi.org/10.3390/s16091423 | |
| dc.relation.referencesen | [9] S.O.H. Madgwick, An efficient orientation filter for inertial and inertial/magnetic sensor arrays. 2010. | |
| dc.relation.referencesen | [10] R. Mahony, T. Hamel, and J.-M. Pflimlin, "Complementary filter design on the special orthogonal group SO(3)," Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 2005 Seville, Spain, December 12-15, 2005. | |
| dc.relation.referencesen | [11] S. Mau, "What is the Kalman Filter and How can it be used for Data Fusion?" Robotics Math, pp. 16-811, December 2005. | |
| dc.relation.referencesen | [12] M. Pedley, "Tilt Sensing Using a Three-Axis Accelerometer," Freescale Semiconductor, AN3461, 2013. | |
| dc.relation.referencesen | [13] R. Zhi, A Drift Eliminated Attitude & Position Estimation Algorithm In 3D. Graduate College Dissertations and Theses, University of Vermont, 2016. | |
| dc.relation.referencesen | [14] F. Abyarjoo, A. Barreto, J. Cofino, and F. R. Ortega, "Implementing a Sensor Fusion Algorithm for 3D Orientation Detection with Inertial/Magnetic Sensors." In: Sobh T., Elleithy K. (eds) Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering. Lecture Notes in Electrical Engineering, vol 313. Springer, Cham. 2015, pp. 305-310. | |
| dc.relation.referencesen | [15] F. Zafari, I. Papapanagiotou, M. Devetsikiotis, and T. Hacker "An iBeacon based Proximity and Indoor Localization System," arXiv:1703.07876v2 [cs.NI] 24 Mar 2017. | |
| dc.relation.referencesen | [16] K. Vadivukkarasi, R. Kumar and Mary Joe, "A Real Time Rssi Based Novel Algorithm to Improve Indoor Localization Accuracy for Target Tracking in Wireless Sensor Networks," ARPN Journal of Engineering and Applied Sciences, vol. 10, no. 16, pp. 7015-7023, SEPTEMBER 2015. | |
| dc.relation.referencesen | [17] T. Qinglin et al. "A Hybrid Indoor Localization and Navigation System with Map Matching for Pedestrians Using Smartphones." Ed. Kourosh Khoshelham and Sisi Zlatanova. Sensors (Basel, Switzerland) 15.12 (2015): 30759–30783. PMC. Web. 14 Mar. 2018. | |
| dc.relation.referencesen | [18] A. Masse, S. Lefèvre, R. Binet, S. Artigues, G. Blanchet, and S.Baillarin, "Denoising Very High Resolution Optical Remote Sensing Images: Application and Optimization of Nonlocal Bayes method," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11:3, pp. 691-700, 2018. | |
| dc.relation.referencesen | [19] T. Singhal, A. Harit, and D. N. Vishwakarma, "Kalman Filter Implementation on an Accelerometer sensor data for three state estimation of a dynamic system," International Journal of Research in Engineering and Technology (IJRET), vol. 1, no. 6, 2012. ISSN 2277 – 4378 | |
| dc.relation.referencesen | [20] P. Del Moral, "Non Linear Filtering: Interacting Particle Solution". Markov Processes and Related Fields. 2 (4) pp. 555–580, 1996. | |
| dc.relation.referencesen | [21] B.I. Ahmad, J. Murphy, P.M. Langdon, and S. J. Godsill, "Filtering perturbed in-vehicle pointing gesture trajectories: Improving the reliability of intent inference", Machine Learning for Signal Processing (MLSP) 2014 IEEE International Workshop on, pp. 1-6, 2014. | |
| dc.relation.referencesen | [22] D. Gusenbauer, C. Isert, and J. Krosche, "Self-contained indoor positioning on off-the-shelf mobile devices," International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-9, 2010. | |
| dc.citation.conference | IEEE second international conference "Data stream mining and processing" | |
| dc.citation.spage | 236 | |
| dc.citation.epage | 240 | |
| dc.coverage.placename | Львів | |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference
|
