隨著無線通訊與微電子技術的快速發展，適地性服務的應用也日益劇增，如何量測並提供出精確的定位資訊，將會是適地性服務發展的關鍵因素。近年來，各式各樣的定位技術逐漸成熟，然而對於複雜的室內環境而言，卻易受環境干擾而降低定位準確度，如何設計一套合適的室內定位系統一直是備受矚目的議題。

本研究提出一套室內定位系統，於環境中建置四台接收器，每台接收器具有一支全向性天線與四支指向性天線，於定位時，目標物(智慧型手持裝置)需開啟行動無線基地台模式，由室內定位管理器負責從各接收器偵測目標物的訊號值，再透過取訊號最大值運算、子區域篩選及輔助點判斷等模組來觀察目標物周遭的環境，依照環境條件提供合適的室內定位機制。

本研究具有三種定位演算機制，當目標物位於模糊子區域時，系統會選擇四台接收器來進行四點定位演算法，其平均誤差距離為1.5公尺，定位準確度為87.15 %，然而當環境中具有輔助者存在時，系統會以輔助者來取代接收器進行輔助型四點定位演算法，其方法可將平均誤差距離縮短至1.2公尺，定位準確度提升至91.77 %，藉由輔助者的輔助機制來提升模糊子區域的定位準確度。當目標物位於接收器子區域時，系統會選擇基於第k位最接近的鄰居之指紋定位的指向性定位演算法對目標物進行定位，其平均誤差距離為0.79公尺，定位準確度為96.34 %，由結果得知，當目標物位於接收器子區域時，可提供高定位準確度。

With the rapid development of wireless communication technology and mobile devices, the use of location-based services (LBS) has gradually increased. The accuracy of measured positioning information influences the quality of LBS. In recent years, positioning technology has become increasingly well-known and mature. However, indoor complex environments make accurate positioning difficult, and the development of effective indoor positioning system has very important.

The work proposes an indoor positioning system. Four receivers are established in the experimental environment. Each receiver has one omnidirectional antenna and four directional antennas. In the real-time phase, the target (mobile device) enters mobile access point mode. The indoor positioning manager collects the target’s RSSI values from receivers. The RSSI maxima calculation, subspace selection and assisted-point detection modules estimate characteristics of the environment around the target. The environmental conditions determine the most effective indoor positioning mechanism, of which the developed system has three.

When the target is in obscured subspace, the system selects four-point positioning using four receivers. The average error distance is 1.5 m and the average positioning accuracy is 87.15 %. However, an assistant is present in the environment, then the system will select assisted four-point positioning. Assisted four-point positioning reduces the average error distance to 1.2 m and increase average positioning accuracy to 91.77 %. The support of an assistant enhances positioning accuracy in obscured subspace. When the target in receiver’s subspace, the system selects directional positioning based on k-Nearest Neighbors (KNN) fingerprinting to measure the position of target. The average error distance is 0.79 m and the average positioning accuracy is 96.34 %. The result shows that directional positioning can achieve higher positioning accuracy when the target is in receiver’s subspace.

[1] A. Tian, D. Dong, D. Ning and C.B Fu, “GPS Single Point Positioning Algorithm Based on Least Squares,” Proceedings of the International Symposium on Computational Intelligence and Design, pp. 16-19, 2013.
[2] Y. Ma, Y. Xu and X. Chen, “Wireless Local Area Network Assisted GPS in Seamless Positioning,” Proceedings of the International Conference on Computer Science and Electronics Engineering, pp. 612-615, 2012.
[3] Y.F. Zhou, J. Li and L. Lamont, “Multilateration Localization in the Presence of Anchor Location Uncertainties,” Proceedings of the IEEE Global Communications Conference, pp. 309-314, 2012.
[4] J.L. Liu, Y.H. Wan, B.G. Xu, S.L. Tang, X.K. Ding and Q. Wan, “A Novel Indoor Positioning Method Based on Location Fingerprinting,” Proceedings of the International Conference on Communications, Circuits and Systems, pp. 239-242, 2013.
[5] J.S. Leu and H.J. Tzeng, “Received Signal Strength Fingerprint and Footprint Assisted Indoor Positioning Based on Ambient Wi-Fi Signals,” Proceedings of the IEEE Vehicular Technology Conference, pp. 1-5, 2012.
[6] B. Altintas and T. Serif, “Indoor Location Detection with a RSS-Based Short Term Memory Technique,” Proceedings of the IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 794-798, 2012.
[7] Y.X. Zhao and M. Li, “An Indoor Positioning Algorithm Based on Path Tracking Assistance,” Proceedings of the International Conference on Computational Intelligence and Software Engineering, pp. 1-4, 2010.
[8] U. Alkasi, M. Al Shayokh and H.P. Partal, “An Experimental Comparison Study on Indoor Localization: RF Fingerprinting and Multilateration Methods,” Proceedings of the International Conference on Electronics, Computer and Computation, pp. 255-259, 2013.
[9] P. Chai and L. Zhang, “Indoor Radio Propagation Models and Wireless Network Planning,” Proceedings of the IEEE International Conference on Computer Science and Automation Engineering, pp. 738-741, 2012.
[10] K. Dragünas and K. Borre, “Indoor Multipath Mitigation,” Proceedings of the Ubiquitous Positioning Indoor Navigation and Location Based Service, pp. 1-8, 2010.
[11] Y.H. Geng, J. He, H.K. Deng and K. Pahlavan, “Modeling the Effect of Human Body on TOA Ranging for Indoor Human Tracking with Wrist Mounted Sensor,” Proceedings of the International Symposium on Wireless Personal Multimedia Communications, pp. 1-6, 2013.
[12] R. Kaune, “Accuracy Studies for TDOA and TOA Localization,” Proceedings of the International Conference on Information Fusion, pp. 408-415, 2012.
[13] A.A. Wahab, A. Khattab and Y.A. Fahmy, “Two-Way TOA with Limited Dead Reckoning for GPS-Free Vehicle Localization Using Single RSU,” Proceedings of the International Conference on ITS Telecommunications, pp. 244-249, 2013.
[14] J.Y Yoon, J.W. Kim, W.H Lee and D.S. Eom, “A TDoA-based Localization Using Precise Time-Synchronization,” Proceedings of the International Conference on Advanced Communication Technology, pp. 1266-1271, 2012.
[15] S. Uebayashi, M. Shimizu and T. Fujiwara, “A Study of TDOA Positioning Using UWB Reflected Waves,” Proceedings of the IEEE Vehicular Technology Conference, pp. 1-5, 2013.
[16] J.R. Jiang, C.M. Lin, F.Y. Lin and S.T. Huang, “ALRD: AoA Localization with RSSI Differences of Directional Antennas for Wireless Sensor Networks,” Proceedings of the International Conference on Information Society, pp. 304-309, 2012.
[17] B.D.S. Muswieck, J.L. Russi and M.V.T. Heckler, “Hybrid Method Uses RSS and AoA to Establish a Low-Cost Localization System,” Proceedings of the Argentine Symposium and Conference on Embedded Systems, pp. 1-6, 2013.
[18] S. Shuo, S. Hao and S. Yang, “Design of An Experimental Indoor Position System Based on RSSI,” Proceedings of the International Conference on Information Science and Engineering, pp. 1989-1922, 2010.
[19] M.M. Atia, A. Noureldin and M.J. Korenberg, "Dynamic Propagation Modeling for Mobile Users' Position and Heading Estimation in Wireless Local Area Networks," IEEE Wireless Communications Letters, vol. 1, no. 2, pp. 101-104, 2012.
[20] S. Alamri, D. Taniar and M. Safar, “Indexing Moving Objects in Indoor Cellular Space,” Proceedings of the International Conference on Network-Based Information Systems, pp. 38-44, 2012.
[21] A. Aboodi and T.C. Wan, “Evaluation of WiFi-Based Indoor (WBI) Positioning Algorithm,” Proceedings of the Third FTRA International Conference on Mobile, Ubiquitous, and Intelligent Computing, pp. 260-264, 2012.
[22] J.A. Jiang, X.Y. Zheng, Y.F. Chen, C.H. Wang, P.T. Chen, C.L. Chuang and C.P. Chen, "A Distributed RSS-Based Localization Using a Dynamic Circle Expanding Mechanism," Proceedings of the IEEE Sensors Journal, vol. 13, no. 10, pp. 3754-3766, 2013.
[23] N. Miwa, S. Tagashira, H. Matsuda, T. Tsutsui, Y. Arakawa and A. Fukuda,, “A Multilateration-based Localization Scheme for Adhoc Wireless Positioning Networks Used in Information-oriented Construction,” Proceedings of the IEEE International Conference on Advanced Information Networking and Applications, pp. 690-695, 2013.
[24] Y. Cho, M. Ji, Y. Lee and S. Park, “WiFi AP Position Estimation Using Contribution from Heterogeneous Mobile Devices,” Proceedings of the IEEE/ION Position Location and Navigation Symposium, pp. 562-567, 2012.
[25] H.H. Liu and Y.N. Yang, “WiFi-Based Indoor Positioning for Multi-Floor Environment,” Proceedings of the IEEE Region 10 Conference TENCON, pp. 597-601, 2011.
[26] S. Asano, Y. Wakuda, N. Koshizuka and K. Sakamura, “A Robust Pedestrian Dead-Reckoning Positioning Based on Pedestrian Behavior and Sensor Validity,” Proceedings of the IEEE/ION Position Location and Navigation Symposium, pp. 328-333, 2012.
[27] L. Li and X. Lin, “Apply Pedestrian Dead Reckoning to Indoor Wi-Fi Positioning Based on Fingerprinting,” Proceedings of the IEEE International Conference on Communication Technology, pp. 206-210, 2013.
[28] J. Park, D. Lee and J. Park, “Digital Map Based Pose Improvement for Outdoor Augmented Reality,” Proceedings of the IEEE International Symposium on Mixed and Augmented Reality, pp. 309-310, 2012.
[29] A. Chandra, S. Jain and M.A. Qadeer, “GPS Locator: An Application for Location Tracking and Sharing Using GPS for Java Enabled Handhelds,” Proceedings of the International Conference on Computational Intelligence and Communication Networks, pp. 406-410, 2011.
[30] M.H. Park, W.H. Kim, S.J. Lee and H.C. Kim, “GPS-Tag for Indoor Location Information and Additional User Information Providing,” Proceedings of the International Conference on Advanced Communication Technology, pp. 894-897, 2012.
[31] X. Liu, Q. Man, H. Lu and X. Lin, “Wi-Fi/MARG/GPS Integrated System for Seamless Mobile Positioning,” Proceedings of the IEEE Wireless Communications and Networking Conference, pp. 2323-2328, 2013.
[32] M.H. Park, H.C. Kim, S.J. Lee and K.S. Bae, “Performance Evaluation of Android Location Service at the Urban Canyon,” Proceedings of the International Conference on Advanced Communication Technology, pp. 662-665, 2014.
[33] T. Reinsch, Y. Wang, M. Knechtel, M. Ameling and P. Herzig, “CINA - A Crowdsourced Indoor Navigation Assistant,” Proceedings of the IEEE/ACM International Conference on Utility and Cloud Computing, pp. 500-505, 2013.
[34] O.A. Hammadi, A.A. Hebsi, M.J. Zemerly and J.W.P. Ng,, “Indoor Localization and Guidance Using Portable Smartphones,” Proceedings of the IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, pp. 337-341, 2012.
[35] H. Wang, L. Zhu and A. Chin, “An Indoor Location-Based Social Network for Managing Office Resource and Connecting People,” Proceedings of the International Conference on Ubiquitous Intelligence & Computing and International Conference on Autonomic & Trusted Computing, pp. 481-483, 2010.
[36] W.U.L.J.R. Perera and M.S. Karunarathne, “Enhancing and Speeding-Up Real-Time-Shopping Using an Indoor Map, Intelligent Suggestions and Calculations, Built Upon a Smart Phone Application,” Proceedings of the IEEE International Conference on Industrial and Information Systems, pp. 583-588, 2013.
[37] C.H. Lu, H.H. Kuo, C.W. Hsiao, Y.L. Ho, Y.H. Lin and H.P. Ma, “Localization with WLAN on Smartphones in Hospitals,” Proceedings of the IEEE International Conference on e-Health Networking, Applications & Services, pp. 534-538, 2013.