https://oldena.lpnu.ua/handle/ntb/52498| Title: | Data Processing Methods for Mobile Indoor Navigation |
| Authors: | Roenko, Alexey Sirenko, Feliks Chervoniak, Yevhen Gorovyi, Ievgen |
| Affiliation: | IT-Jim |
| Bibliographic description (Ukraine): | 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). |
| Bibliographic description (International): | 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). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | signal processing sensor fusion indoor navigation BLE navigation IMU navigation |
| Number of pages: | 5 |
| Page range: | 236-240 |
| Start page: | 236 |
| End page: | 240 |
| 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. |
| URI: | https://ena.lpnu.ua/handle/ntb/52498 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | http://doi.org/10.3390/s16091423 |
| References (Ukraine): | [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. [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. [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. [4] G. Trein, N. Singh, and P. Maddila, “Simple approach for indoor mapping using low-cost accelerometer and gyroscope sensors,” DOCPLAYER, 2013. [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 [6] H. Weinberg, Using the ADXL202 in Pedometer and Personal Navigation Applications. Analog Devices, Inc.; Norwood, MA, USA: 2002. [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. [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 [9] S.O.H. Madgwick, An efficient orientation filter for inertial and inertial/magnetic sensor arrays. 2010. [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. [11] S. Mau, “What is the Kalman Filter and How can it be used for Data Fusion?” Robotics Math, pp. 16-811, December 2005. [12] M. Pedley, “Tilt Sensing Using a Three-Axis Accelerometer,” Freescale Semiconductor, AN3461, 2013. [13] R. Zhi, A Drift Eliminated Attitude & Position Estimation Algorithm In 3D. Graduate College Dissertations and Theses, University of Vermont, 2016. [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. [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. [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. [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. [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. [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 [20] P. Del Moral, "Non Linear Filtering: Interacting Particle Solution". Markov Processes and Related Fields. 2 (4) pp. 555–580, 1996. [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. [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. |
| References (International): | [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. [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. [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. [4] G. Trein, N. Singh, and P. Maddila, "Simple approach for indoor mapping using low-cost accelerometer and gyroscope sensors," DOCPLAYER, 2013. [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 [6] H. Weinberg, Using the ADXL202 in Pedometer and Personal Navigation Applications. Analog Devices, Inc.; Norwood, MA, USA: 2002. [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. [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 [9] S.O.H. Madgwick, An efficient orientation filter for inertial and inertial/magnetic sensor arrays. 2010. [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. [11] S. Mau, "What is the Kalman Filter and How can it be used for Data Fusion?" Robotics Math, pp. 16-811, December 2005. [12] M. Pedley, "Tilt Sensing Using a Three-Axis Accelerometer," Freescale Semiconductor, AN3461, 2013. [13] R. Zhi, A Drift Eliminated Attitude & Position Estimation Algorithm In 3D. Graduate College Dissertations and Theses, University of Vermont, 2016. [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. [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. [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. [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. [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. [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 [20] P. Del Moral, "Non Linear Filtering: Interacting Particle Solution". Markov Processes and Related Fields. 2 (4) pp. 555–580, 1996. [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. [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. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
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| 2018_Roenko_A-Data_Processing_Methods_236-240.pdf | 311.47 kB | Adobe PDF | View/Open | |
| 2018_Roenko_A-Data_Processing_Methods_236-240__COVER.png | 576.85 kB | image/png | View/Open |
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