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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 項潔 | |
| dc.contributor.author | Wen-Chih Hsieh | en |
| dc.contributor.author | 謝文芝 | zh_TW |
| dc.date.accessioned | 2021-06-08T07:07:25Z | - |
| dc.date.copyright | 2008-09-02 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-08-13 | |
| dc.identifier.citation | [1] S. A., M. Grigoras, V. Tresp, and C. Hoffmann. GPPS: A gaussian process positioning system for cellular networks. In In 17th Annual Conf. on Neural Information Processing Systems (NIPS’03), 2003.
[2] P. Bahl and V. N. Padmanabhan. RADAR: An inbuilding rf-based user location and tracking system. In Proceedings of the Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, volume 2, pages 775–784, Tel Aviv, Israel, March 2000. [3] P. Bahl, N. Padmanabhan Venkata, and A. Balachandran. Enhancements to the radar user location and tracking system. Technical report, Microsoft Research, 2000. [4] G. Borriello, A. Liu, T. Offer, C. Palistrant, and R. Sharp. Walrus: wireless acoustic location with room-level resolution using ultrasound. In MobiSys’05, pages 191–203, 2005. [5] B. Ferris, D. Fox, and N. Lawrence. Wifi-slam using gaussian process latent variable models. In M. M. Veloso, editor, IJCAI, pages 2480–2485, 2007. [6] B. Ferris, D. Haehnel, and D. Fox. Gaussian processes for signal strength-based location estimation. In Proc. of Robotics: Science and Systems, 2006. [7] J. Hightower and G. Borriella. Location systems for ubiquitous computing. ”IEEE Computer”, 34(8):57–66, 2001. [8] J. Hightower and G. Borriello. Particle filters for location estimation in ubiquitous computing: A case study. In The 6th International Conference on Ubiquitous Computing (UbiComp’04), 2004. [9] Y. Ji, S. Biaz, S. Pandey, and P. Agrawal. Ariadne: a dynamic indoor signal map construction and localization system. In MobiSys ’06: Proceedings of the 4th international conference on Mobile systems, applications and services, pages 151–164, New York, NY, USA, 2006. ACM. [10] J. Ko, D. Klein, D. Fox, and D. Hahnel. GP-UKF: Unscented kalman filters with gaussian process prediction and observation models. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007. [11] J. Ko, D. J. Klein, D. Fox, and D. H‥ahnel. Gaussian processes and reinforcement learning for identification and control of an autonomous blimp. In ICRA, pages 742–747, 2007. [12] A. M. Ladd, K. Bekris, A. Rudys, G. Marceau, L. E. Kavraki, and D. S. Wallach. Roboticsbased location sensing using wireless Ethernet. In Proceedings of the Eighth ACM International Conference on Mobile Computing and Networking(MOBICOM), Atlanta, GA, Sept. 2002. [13] A. LaMarca, J. Hightower, I. E. Smith, and S. Consolvo. Self-mapping in 802.11 location systems. In Proceedings of the 7th International Conference on Ubiquitous Computing (UbiComp’05), pages 87–104, 2005. [14] P. L’Ecuyer. Ssj: Stochastic simulation in java. [15] S. Lee. Lock maker: Improving room-level localization using spatial constraints. Master’s thesis, 2007. [16] J. Letchner, D. Fox, and A. LaMarca. Largescale localization from wireless signal strength. In Procedings of the National Conference on Artificial Intelligence AAAI, 2005. [17] T. Manesis and N. Avouris. Survey of position location techniques in mobile systems. In Proceedings of the 7th Conference on Human-Computer Interaction with Mobile Devices and Services(Mobile HCI), pages 291–294, Salzburg, Austria, September 2005. ACM. [18] J. J. Pan, Q. Yang, and S. J. Pan. Online co-localization in indoor wireless networks by dimension reduction. In AAAI, pages 1102–1107. AAAI Press, 2007. [19] C. E. Rasmussen and C. Williams. Code from the rasmussen and williams: Gaussian processes for machine learning book. [20] C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. [21] S. Y. Seidel and T. S. Rappaport. 914 mhz path loss prediction models for indoor wireless communications in multifloored buildings. IEEE Trans. Antennas Propagation, 40(2):207–217, 1992. [22] V. Seshadri, G. V. Zaruba, and M. Huber. A bayesian sampling approach to indoor localization of wireless devices using received signal strength indication. In Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications, pages 75–84, Washington, DC, USA, 2005. IEEE Computer Society. [23] N. P. Shwetak, N. T. Khai, and D. A. Gregory. Powerline positioning: A practical sub-roomlevel indoor location system for domestic use. In Ubicomp, pages 441–458, 2006. [24] D. H. Wilson and C. G. Atkeson. Simultaneous tracking and activity recognition (STAR) using many anonymous, binary sensors. In H.-W. Gellersen, R. Want, and A. Schmidt, editors, Pervasive, volume 3468, pages 62–79. Springer, 2005. [25] M. Youssef and A. Agrawala. The horus wlan location determination system. In in: Third International Conference on Mobile Systems, Applications,and Services (MobiSys). University of Maryland, College Park, 2005. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/26356 | - |
| dc.description.abstract | 在室內定位系統中，如果能判斷出被追蹤物的房間位置，我們將可以提供許多有用的服務。然而目前利用訊號強度所做的定位系統準確度大約為兩米左右，若是直接轉為房間資訊，往往會產生錯誤的結果。
本實驗以particle filters機率模型結合likelihood model 與motion model，以Gaussian processes來產生訊號強度的可能性模型(likelihood model)，我們不是在likelihood model中考慮位置對訊號強度造成的影響，我們也加入了方向對訊號強度造成的影響，由於訊號強度與被追蹤物所面對的方向相關，加入了方向的資訊後定位的正確率也因此而提升。另外，並在空間資訊的幫助下我們考慮被追蹤物合理的移動模式讓motion model變得更加合理。在判斷房間位時，我們利用在房間中particle散布的情況和環境中房間的相連情形來決定被追蹤物的房間位置。由於一個人無法劇烈的在不同房間中移動，我們利用likelihood threshold來限制被追蹤物房間位置的改變。 我們以片段錯誤率、序列編輯距離、延遲時間來評估系統的正確性並發現我們提出的方法雖然有大約一秒左右的延遲時間，在片段錯誤率、序列編輯距離的表現上都優於其它方法。 | zh_TW |
| dc.description.abstract | Signal strength-based method is widely adopted in localization nowadays. Because wireless signal strength is unstable, localization deviations are usually larger than one meter. For indoor localization systems, it is essential to provide room-level accuracy. However, with localization deviations larger than one meter, it is difficult to provide accurate room-level location. To solve this problem, we take advantage of spatial information and implement the concept of particle filters. Morever, we apply the Gaussian processes in the likelihood model to predict the mean and variance of the signal strength at any location and direction without the requirement of collecting the wholde training data. Unlike traditional methods of computing likelihood model, which merely consider the influence of location in signal strength, our system takes both location and direction into account. To avoid serious mistakes in localization results, we introduce a threshold in our system to restrict the transition of the tracked person’s room-level location and using spatial information to increase accuracy and efficiency of the constraint. We design a flow to determine whether the system should change the room location in all situations.
To evaluate the results, we compare our system with other methods and apply frame error rate, word error rate and delay time as the performance metric. As a result, within tolerable delay increase, our system performs the best in the frame error rate and word error rate among all localization methods. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T07:07:25Z (GMT). No. of bitstreams: 1 ntu-97-R95922124-1.pdf: 1794614 bytes, checksum: b15245b1a271615288df6caaab5c7a56 (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | Acknowledgments ii
Abstract v List of Figures xi List of Tables xiii Chapter 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Research Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Chapter 2 Preliminaries 5 2.1 Particle filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Gaussian processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Chapter 3 Related Work 11 3.1 Indoor Localization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Signal Strength Localization Approaches . . . . . . . . . . . . . . . . . 12 3.2.1 Spatial Information Utilizing . . . . . . . . . . . . . . . . . . . . 14 3.2.2 Room-level Localization . . . . . . . . . . . . . . . . . . . . . . 14 Chapter 4 Room Level Localization System 17 4.1 Overview of Room-level Localization System . . . . . . . . . . . . . . . 17 4.2 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 4.2.1 Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2.2 Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.2.3 Requirement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.3 Proposed Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.3.1 Room-level Localization . . . . . . . . . . . . . . . . . . . . . . 20 Chapter 5 Implementation 23 5.1 Implementing Particle Filters . . . . . . . . . . . . . . . . . . . . . . . . 23 5.1.1 Without Spatial Information Motion . . . . . . . . . . . . . . . . 25 5.1.2 With Spatial Information . . . . . . . . . . . . . . . . . . . . . . 25 5.2 Implementing Gaussian Processes . . . . . . . . . . . . . . . . . . . . . 26 5.3 Determining in Which Room the Tracked People are . . . . . . . . . . . 31 Chapter 6 Evaluation 35 6.1 Experiment Environment . . . . . . . . . . . . . . . . . . . . . . . . . . 35 6.2 Training Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . 36 6.3 Localization Accuracy Comparison . . . . . . . . . . . . . . . . . . . . 38 6.4 Room-level Localization Accuracy Comparison . . . . . . . . . . . . . . 41 6.4.1 Frame Error Rate . . . . . . . . . . . . . . . . . . . . . . . . . . 42 6.4.2 Word Error Rate . . . . . . . . . . . . . . . . . . . . . . . . . . 42 6.4.3 Delay Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Chapter 7 Conclusion 51 7.1 Summary of Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 7.2 Summary of Contributions . . . . . . . . . . . . . . . . . . . . . . . . . 52 7.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Bibliography 53 | |
| dc.language.iso | zh-TW | |
| dc.subject | 房間定位 | zh_TW |
| dc.subject | 無線電波訊號強度指標 | zh_TW |
| dc.subject | 高斯進程 | zh_TW |
| dc.subject | 粒子濾波 | zh_TW |
| dc.subject | Gaussian processes | en |
| dc.subject | particle filters | en |
| dc.subject | RSSI | en |
| dc.subject | room-level | en |
| dc.subject | indoor localization | en |
| dc.title | 以空間資訊改善房間定位準確度之研究 | zh_TW |
| dc.title | Using spatial information to improve the accuracy of room-level localization | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 許永真 | |
| dc.contributor.oralexamcommittee | 許均南,李育杰,黃寶儀 | |
| dc.subject.keyword | 高斯進程,粒子濾波,無線電波訊號強度指標,房間定位, | zh_TW |
| dc.subject.keyword | indoor localization,particle filters,RSSI,room-level,Gaussian processes, | en |
| dc.relation.page | 55 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2008-08-13 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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