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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃寶儀 | |
| dc.contributor.author | Yi-Hsien Lin | en |
| dc.contributor.author | 林依仙 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:18:49Z | - |
| dc.date.available | 2017-08-27 | |
| dc.date.copyright | 2012-08-27 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-08-17 | |
| dc.identifier.citation | [1] P. Bahl and V. N. Padmanabhan, “RADAR: An In-Building RF-based User Location and Tracking System”, in Proceedings of IEEE INFOCOM 2000, vol.2, pp.775-784, March, 2000.
[2] R.H. Wu, Y.H. Lee, H.W. Tseng, Y.G. Jan, M.H. Chuang, “Study of Characteristics of RSSI Signal”, IEEE International Conference on Industrial Technology, 2008. [3] A. Purohit, Z. Sun, F. Mokaya, P. Zhang, “SensorFly : Controlled-mobile Sensing Platform for Indoor Emergency Response Applications” in IPSN, Chicago, IL, USA, April 12-14, 2011. [4] Glanzer, G., Bernoulli, T., Wiessflecker, T., Walder, “Semi-autonomous Indoor Positioning Using MEMS-based Inertial Measurement Units and Building Information”, in Proceedings of the WPNC’09, Hannover, Germany, 2009. [5] Xiaoping Yun, Eric R. Bachmann, Hyatt Moore IV, James Calusdian, “Self-contained Position Tracking of Human Movement Using Small Inertial/Magnetic Sensor Modules”, IEEE International Conference on Robotics and Automation Roma, Italy, April 2007. [6] Ted Tsung-Te Lai, Wei-Ju C hen, Kuei-Han Li, Polly Huang, Hao-Hua Chu, ” TriopusNet: Automating Wireless Sensor Network Deployment and Replacement in Pipeline Monitoring”, IPSN, Beijing, China, 2012. [7] Raul Feliz, Eduardo Zalama, Jaime Gomez , Garcia-Bermejo, “Pedestrian tracking using inertial sensors”, PHYSICAL AGENTS, VOL. 3 NO. 1 JAN. 2009. [8] M. L. Han, T. Tsuchiya, and K. Koyanagi, “Orientation-aware indoor localization path loss prediction model for wireless sensor networks,” NBiS, pp. 169–178, 2008. [9] B. J. Dil, P. J. M. Havinga, “RSS-Based Localization with Different Antenna Orientations”, in Australian Telecommunication Networks and Applications Conference, Auckland, New Zealand, 2010. [10] T. H. Lin, I. H. Ng, S. Y. Lau, K. M. Chen, P. Huang, “A Microscopic Examination of an RSSI-Signature-Based Indoor Localization System” in HotEmNets’08, Virginia, USA, Jun. 2008. [11] Thomas King, Stephan Kopf, Thomas Haenselmann, Chrestian Lubberger, Wolfgang Effelsberg, “COMPASS: A Probabilistic Indoor Positioning System Based on 802.11 and Digital Compasses”, in WiNTECH’06, Los Angeles, California, USA, Sep. 2006. [12] J. Borenstein, L. Ojeda, S. Kwanmuang, “Heuristic Reduction of Gyro Drift in IMU-based Personnel Tracking Systems” in SPIE Defense, Security and Sensing Conference, Orlando, Florida, USA, April 13-17, 2009. [13] S. K. Hong, S. Park, “Minimal-Drift Heading Measurement using a MEMS Gyro for Indoor Mobile Robots” in Sensors, vol. 8, pp. 7287-7299, Nov. 2008. [14] G. Aslan, A. Saranlı, 'Characterization and Calibration of MEMS Inertial Measurement Units” in European Signal Processing Conference, Lausanne, Switzerland, August 25-29, 2008. [15] L. Wang, F. Wang, “Intelligent Calibration Method of Low Cost MEMS Inertial Measurement Unit for an FPGA-based Navigation System” in Intelligent Engineering and Systems, vol. 4, no. 2, 2011. [16] Ren Bo, Zhang Deming, Li Huan, “MEMS Gyroscope Random Error Modeling and Filtering”, in Applied Mechanics and Materials, vol. 29-32, pp. 829-834, 2010. [17] C. Marselli, D. Daudet, H. P. Amann, F. Pellandini, “Application of Kalman filtering to noise reduction on microsensor signals”, in Proceedings of the Colloque interdisciplinaire en instrumentation, C2I, 443-450, 1998. [18] Qintuo Zhang, Zhenfan Tan, Lidong Guo, “Compensation of Temperature Drift of MEMS Gyroscope Using BP Neural Network”, ICIECS, Wuhan, China, 2009. [19] Dunzhu Xia *, Shuling Chen, Shourong Wang, Hongsheng Li, “Microgyroscope Temperature Effects and Compensation-Control Methods”, in sensors, vol.9, pp. 8349-8376, 2009. [20] Don G. Kim, Sung K. Hong, “The Compensation of Nonlinear Thermal Bias Drift of Resonant Rate Sensor (RRS) Using Fuzzy Logic”, Sens. Actuat. A-Phys. 1999, 78, 143-148. [21] Yi Jiing Song, “Orientation-Aware Localization System for Long Term Elder Care”, Master, National Taiwan University, Taiwan, 2010. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63775 | - |
| dc.description.abstract | Nowadays, the gyroscopes are used in many mobile systems to sense the orientation change. In sensor networks, the sensors are requested to be small and low-cost. Unfortunately, this kind of gyro usually has the drift problem and the integral heading angle could not be used in application directly. Some calibration methods need to be taken before the gyro output rate being integral to be the heading angle. In our work, we focus on noise filtering, input voltage and temperature calibration, and scalar factor.
Besides, because our target application is RSSI finger printing based indoor localization system, which using RSSI finger print to locate person, it is found that the RSSI values are impacted by the human’s direction. Thus, we need to use gyro to get the orientation information even though the gyro output is not that reliable. One of the characters of the indoor moving pattern is that human turning is restricted to right angle in buildings. With this limit, we further proposed a turn detection method that assumes the user can only face four directions. The algorithm output the estimated heading angle and the turning state that determine if it is now turning. With this algorithm, we can estimate the heading angle accurately when we walked along the hallway of our EE building with our daily life paths. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:18:49Z (GMT). No. of bitstreams: 1 ntu-101-R99921069-1.pdf: 3631283 bytes, checksum: 1c723e25bf30676f88a136c09ef02f94 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | 摘要 i
Abstract ii Content iv List of Figures vi List of Tables viii Chapter 1 Introduction 1 Chapter 2 Related Work 4 Chapter 3 Implementation 8 3.1 Hardware 8 3.2 Angle Integration 9 3.3 Drift 10 3.3.1 Noise 11 3.3.2 Environmental Influence 13 3.3.3 Scalar factor 17 3.4 Walking 18 3.5 Turn Detection Algorithm 19 Chapter 4 Evaluation 26 4.1 Calibration Results 26 4.2 Turn Detection Algorithm Results 31 4.3 Walking Experiment 32 4.3.1 Walking Around the Hallway 33 4.3.2 Restroom 34 4.3.3 Elevator 37 4.4 Performance Analysis 38 4.4.1 Estimation Accuracy 39 4.4.2 Accuracy of Turning State Detection 42 4.4.3 Delay 43 4.4.4 Limitation of Distance 44 4.5 Energy Consumption 45 Chapter 5 Discussion 46 Chapter 6 Conclusion 50 Reference 52 | |
| dc.language.iso | en | |
| dc.title | 使用IDG500與ISZ500陀螺儀之方向感測 | zh_TW |
| dc.title | Orientation Sensing with InvenSense IDG500 and ISZ500 Gyroscope | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 藍崑展,朱浩華,陳伶志 | |
| dc.subject.keyword | 陀螺儀,偏移,溫度, | zh_TW |
| dc.subject.keyword | gyroscope,drift,temperature, | en |
| dc.relation.page | 55 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-08-17 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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| ntu-101-1.pdf 目前未授權公開取用 | 3.55 MB | Adobe PDF |
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