隨著資訊科技的發展，物聯網產業近年來蓬勃興起，其中追蹤定位為物聯網的一大核心應用，可依據特定物件或人的所在位置提供相應之適地性服務(Location-Based Service, LBS)。以現有的定位技術來說，全球導航衛星系統(Global Navigation Satellite System, GNSS)已普遍應用於室外定位，然而其在室內等訊號遮蔽嚴重的環境下並無法發揮作用，使得建構室內定位系統需要仰賴其他定位技術。其中，低功耗藍牙(Bluetooth Low Energy, BLE)具有低成本、低功耗、長時間運作等優點，且多數行動裝置皆搭載此規格之藍牙晶片，因此本研究擬應用低功耗藍牙技術實現室內定位系統。藍牙所發送之接收訊號強度指標(Received Signal Strength Indicator, RSSI)可透過距離模型將之轉換為距離觀測量，進而使用三邊交會(Trilateration)定出目標位置，然而RSSI易受環境影響導致訊號衰弱與不穩定，使得轉換距離與實際距離不符，進而降低其定位精度。
本研究將提出差分距離改正以降低RSSI所遭受之環境影響，其概念類似於差分衛星定位系統(Differential GNSS, DGNSS)及網路即時動態定位(Network Real Time Kinematic, Network RTK)，為推估環境因子所造成之距離誤差，將設置參考站(主站)計算改正量或內/外插改正量圖，透過補償方式提升範圍內接收站之定位精度。本研究除提出一系列的優化策略外，更針對差分距離改正比較三種不同模式，包含使用單主站、多主站、多主站加上除錯模式，前兩種模式先於較小場地進行效益分析，而後於較大場地測試完整之定位系統。實驗成果發現，多主站相較於單主站的改善能力更佳，可降低超過40%之定位誤差，而多主站加上除錯模式應用於較大場地可提升50%以上之定位精度，且使用改正後之距離可減少三邊交會的計算量。綜上所述，本研究所提出之定位系統不僅可降低其環境影響並大幅提升三邊交會之定位成果，更能加速定位時的運算效率，實驗整體精度落在1-3公尺左右，符合室內行人導航之精度需求。

With the emerging development of the technology, the industry of Internet of Things (IoT) has boomed rapidly in recent years. Tracking, one of the important applications in IoT, can be used to provide the corresponding Location-Based Service (LBS) according to the position of a specific object or a person. Nowadays, Global Navigation Satellite System (GNSS) has been commonly utilized in outdoor positioning; however, it fails to provide the accurate positions indoors owing to the interruption of the satellite signals. Therefore, another positioning technology is required to construct an indoor positioning system. Bluetooth Low Energy (BLE), which possesses the advantages of low cost, low power consumption, long-term operation, and support by most smart devices, is a desirable technology to realize indoor positioning in this study. The Received Signal Strength Indicator (RSSI) transmitted by BLE can be converted to the distance measurement through the distance model, and the trilateration can be further adopted to determine the target’s location. Nevertheless, RSSI is sensitive to the environmental factors, which make the signal weak and unstable. It will result in the poor distance estimation and insufficient positioning accuracy.
This research proposes a novel method named Differential Distance Correction to reduce the environmental impact on RSSI, which concept is similar to Differential GNSS (DGNSS) and Network Real Time Kinematic (Network RTK). In order to estimate the distance error caused by the environment, the reference station is established at the known coordinate calculating the residual or generating the residual map. The estimated residual is used to correct the distance measurement of the rover and promote its positioning accuracy. This research not only presents a series of enhanced strategies but also compares three different modes of Differential Distance Correction, including utilizing a single reference station, multiple reference stations, and multiple reference stations along with the outlier detection. The performance analysis of the former two modes are first conducted in a smaller scenario, and the proposed positioning system with the last mode is completely applied in a larger scenario. According to the experimental results, the improvement of using multiple reference stations is better than that of using a single reference station with more than 40 % reduction of the positioning error. Besides, the positioning accuracy is increased over 50% by applying multiple reference stations with outlier detection in the larger scenario. Moreover, the computation load of trilateration is also decreased when utilizing the corrected distance measurements. In summary, the proposed positioning system in this study can not only reduce the environmental influence with significant improvement of the positioning results in trilateration, but also speed up the computational efficiency in positioning. The overall accuracy falls in the range around 1-3 meters, which is sufficient for indoor pedestrian navigation.

[1] Adewumi, O. G., Djouani, K., & Kurien, A. M. (2013). RSSI based Indoor and Outdoor Distance Estimation for Localization in WSN. In Proceedings of 2013 IEEE International Conference on of Industrial Technology (ICIT), Cape Town, South Africa, 1534-1539.
[2] Al-Ammar, M. A., Alhadhrami, S., Al-Salman, A., Alarifi, A., Al-Khalifa, H. S., Alnafessah, A., & Alsaleh, M. (2014). Comparative Survey of Indoor Positioning Technologies, Techniques, and Algorithms. In Proceedings of 2014 International Conference on Cyberworlds (CW), Santander, Spain, 245-252.
[3] Bajaj, R., Ranaweera, S. L., & Agrawal, D. P. (2002). GPS: Location-Tracking Technology. Computer, 35(4), 92-94.
[4] Boukerche, A., Oliveira, H. A. B., Nakamura, E. F., & Loureiro, A. A. F. (2007). Localization Systems for Wireless Sensor Networks. IEEE Wireless Communications, 14(6), 6-12.
[5] Bose, A., & Foh, C. H. (2007). A Practical Path Loss Model for Indoor Wifi Positioning Enhancement. In Proceedings of the 6th International Conference on Information, Communications & Signal Processing, Singapore, 1-5.
[6] Bluetooth Special Interest Group. (2010). Specification of the Bluetooth System, Covered Core Package, Version 4.0. Kirkland, WA, USA.
[7] Bertuletti, S., Cereatti, A., Della, U., Caldara, M., & Galizzi, M. (2016). Indoor Distance Estimated from Bluetooth Low Energy Signal Strength: Comparison of Regression Models. In Proceedings of 2016 IEEE Sensors Applications Symposium (SAS), Catania, Italy, 1-5.
[8] Basiri, A., Lohan, E. S., Moore, T., Winstanley, A., Peltola, P., Hill, C., Amirian, P., & Silva, P. (2017). Indoor Location Based Services Challenges, Requirements and Usability of Current Solutions. Computer Science Review, 24, 1-12.
[9] Cook, B., Buckberry, G., Scowcroft, I., Mitchell, J., & Allen, T. (2005). Indoor Location using Trilateration Characteristics. In Proceedings of 2015 London Communications Symposium, London, UK, 147-150.
[10] Chiang, K. W., Liao, J. K., Tsai, G. J., & Chang, H. W. (2015). The Performance Analysis of the Map-Aided Fuzzy Decision Tree Based on the Pedestrian Dead Reckoning Algorithm in an Indoor Environment. Sensors, 16(1), 34.
[11] Čabarkapa, D., Grujić, I., & Pavlović, P. (2015). Comparative Analysis of the Bluetooth Low-Energy Indoor Positioning Systems. In Proceedings of the 12th International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services (TELSIKS), Nis, Serbia, 76-79.
[12] Chruszczyk, Ł., Zając, A., & Grzechca, D. (2016). Comparison of 2.4 and 5 GHz WLAN Network for Purpose of indoor and outdoor location. International Journal of Electronics and Telecommunications, 62(1), 71-79.
[13] Cunningham, A. (2016). “Bluetooth 5” spec coming next week with 4x more range and 2x better speed. Retrieved from https://arstechnica.com/gadgets/2016/06/bluetooth-5-spec-coming-next-week-with-2x-more-range-and-4x-better-speed/
[14] Chriki, A., Touati, H., & Snoussi, H. (2017). SVM-based indoor localization in Wireless Sensor Networks. In Proceedings of the 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, Spain, 1144-1149.
[15] De Blas, A., & López-de-Ipiña, D. (2017). Improving Trilateration for Indoors Localization using BLE Beacons. In Proceedings of the 2nd International Multidisciplinary Conference on Computer and Energy Science (SpliTech), Split, Croatia, 1-6.
[16] Eissfeller, B., Dotterbock, D., Junker, S., & Stober, C. (2011). Online GNSS Data Processing—Status and Future Developments. In Proceedings of Photogrammetric week 2011, Stuttgart, Germany, 111-122.
[17] Feng, Y., & Wang, J. (2007). Exploring GNSS RTK Performance Benefits with GPS and Virtual Galileo Measurements. In Proceedings of 2007 National Technical Meeting of The Institute of Navigation (ION NTM), San Diego, CA, USA, 22-24.
[18] Faragher, R., & Harle, R. (2014). An Analysis of the Accuracy of Bluetooth Low Energy for Indoor Positioning Applications. In Proceedings of the 27th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2014), Tampa, Florida, 201-210.
[19] Ghaziani, M. (2013). Improved Positioning by Distance-based Differential GPS. Master’s Thesis, Middle East Technical University, Ankara, Turkey.
[20] Huh, J. H., & Seo, K. (2017). An Indoor Location-Based Control System Using Bluetooth Beacons for IoT Systems. Sensors, 17(12), 2917.
[21] Huang, W. Q., Ding, C., Wang, S. Y., Lin, J., Zhu, S. Y., & Cui, Y. (2017). Design and Realization of an Indoor Positioning Algorithm Based on Differential Positioning Method. In Proceedings of 2017 International Conference on Wireless Algorithms, Systems, and Applications (WASA), Guilin, China, 546-558.
[22] Johansson, P., Kazantzidis, M., Kapoor, R., & Gerla, M. (2001). Bluetooth: An Enabler for Personal Area Networking. IEEE Network, 15(5), 28-37.
[23] Jung, J., Kang, D., & Bae, C. (2013). Distance Estimation of Smart Device Using Bluetooth. In Proceedings of the 8th International Conference on Systems and Networks Communications (ICSNC), Venice, Italy, 13-18.
[24] Jianyong, Z., Haiyong, L., Zili, C., & Zhaohui, L. (2014). RSSI based Bluetooth Low Energy Indoor Positioning. In Proceedings of 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Busan, Korea, 526-533.
[25] Jin, S., Cardellach, E., & Xie, F. (2014). GNSS Remote Sensing: Chapter 1. Introduction to GNSS. Retrieved from https://link.springer.com/content/pdf/10.1007%2F978-94-007-7482-7_1.pdf
[26] Ji, M., Kim, J., Jeon, J., & Cho, Y. (2015). Analysis of Positioning Accuracy Corresponding to the Number of BLE Beacons in Indoor Positioning System. In Proceedings of the 17th International Conference on Advanced Communication Technology (ICACT), Pyeongchang, South Korea, 92-95.
[27] Khodayari, S., Maleki, M., & Hamedi, E. (2010). A RSS-based fingerprinting method for positioning based on historical data. In Proceedings of 2010 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), Ottawa, ON, Canada, 306-310.
[28] Kuo, Y. T., Liao, J. K., & Chiang, K. W. (2017). BLE-based Indoor Positioning using Differential Distance Correction Technique. In Proceedings of 2017 International Symposium on GNSS (ISGNSS), Hong Kong, China.
[29] Kontakt.io. (2017). The complete Beacon industry report: Current technology and recent history. Retrieved from http://kontakt.io/blog/beacons-2017-forecast-7-experts-share-their-thoughts/
[30] Karaim, M., Elsheikh, M., & Noureldin, A. (2018). Multifunctional Operation and Application of GPS: Chapter 4. GNSS Error Sources. Retrieved from https://www.intechopen.com/books/multifunctional-operation-and-application-of-gps/gnss-error-sources
[31] Liu, H., Darabi, H., Banerjee, P., & Liu, J. (2007). Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(6), 1067-1080.
[32] Lachapelle, G., & Alves, P. (2009). Multiple Reference Station Approach: Overview and Current Research. Journal of Global of Positioning Systems, 1(2), 133-136.
[33] Liu, R., & Yang, Z. (2011). Research on Pseudo-range Differential Positioning Algorithm of COMPASS. In Proceedings of the 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), Zhengzhou, China, 4260-4263.
[34] Liao, J. K. (2017). Using Smartphones for Enhanced Integrated System Strategies for Indoor Pedestrian Positioning. Ph.D. Thesis, National Cheng Kung University, Tainan, Taiwan.
[35] Murphy, J. W., & William, S. (2007). Determination of a Position using Approximate Distances and Trilateration. Master’s thesis, Colorado School of Mines, Golden, Colorado, USA.
[36] Mautz, R. (2012). Indoor Positioning Technologies. Ph.D. Thesis, ETH Zürich, Zürich, Switzerland.
[37] Mackensen, E., Lai, M., & Wendt, T. M. (2012). Bluetooth Low Energy (BLE) based Wireless Sensors. In Proceedings of IEEE Sensors, Taipei, Taiwan, 1-4.
[38] Mainetti, L., Patrono, L., & Sergi, I. (2014). A survey on indoor positioning systems. In Proceedings of the 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, 111-120.
[39] Mazan, F., & Kovarova, A. (2016). Optimizing Artificial Neural Network for Beacon Based Indoor Localization. In Proceedings of the 17th International Conference on Computer Systems and Technologies, Palermo, Italy, 261-268.
[40] Nygård, N. (2015). Indoor Positioning utilizing Bluetooth Smart: A Comparative Study between Trilateration and Fingerprinting. Master’s Thesis, KTH Royal Institute of Technology, Stockholm, Sweden.
[41] Nield, D. (2016). Bluetooth 5: everything you need to know. Retrieved from http://www.techradar.com/news/networking/bluetooth-5-everything-you-need-to-know-1323060
[42] Ouassou, M., Jensen, A. B., Gjevestad, J. G., & Kristiansen, O. (2015). Next Generation Network Real-Time Kinematic Interpolation Segment to Improve the User Accuracy. International Journal of Navigation and Observation, 2015(3), 1-15.
[43] Piwowarczyk, K., Korbel, P., & Kacprzak, T. (2013). Analysis of the Influence of Radio Beacon Placement on the Accuracy of Indoor Positioning System. In Proceedings of 2013 Federated Conference on Computer Science and Information Systems (FedCSIS), Kraków, Poland, 889-894.
[44] Rizos, C., & Han, S. (2003). Reference Station Network based RTK Systems-Concepts and Progress. Wuhan University Journal of Natural Sciences, 8(2), 566-574.
[45] Raghavan, A. N., Ananthapadmanaban, H., Sivamurugan, M. S., & Ravindran, B. (2010). Accurate Mobile Robot Localization in Indoor Environments using Bluetooth. In Proceedings of 2010 IEEE International Conference on Robotics and Automation (ICRA), Anchorage, Alaska, 4391-4396.
[46] Radius Networks. (2015). Getting distance estimates between a device and a beacon. Retrieved from https://altbeacon.github.io/android-beacon-library/distance-calculations.html
[47] Röbesaat, J., Zhang, P., Abdelaal, M., & Theel, O. (2017). An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study. Sensors, 17(5), 951.
[48] Song, Z., Jiang, G., & Huang, C. (2011). A Survey on Indoor Positioning Technologies. In Proceedings of Theoretical and Mathematical Foundations of Computer Science, Heidelberg, Germany, 198-206.
[49] Salas, A. C. (2014). Indoor Positioning System based on Bluetooth Low Energy. Master’s Thesis, Polytechnic University of Catalonia, Barcelona, Spain.
[50] Shao, M., & Sui, X. (2015). Study on Differential GPS Positioning Methods. In Proceedings of 2015 International Conference on Computer Science and Mechanical Automation (CSMA), Zhejiang, China, 223-225.
[51] Sung, Y. (2016). RSSI-based Distance Estimation Framework using a Kalman Filter for Sustainable Indoor Computing Environments. Sustainability, 8(11), 1136.
[52] Sohn, D. H., Park, K. D., & Tae, H. (2017). Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time. Sensors, 17(4), 834.
[53] Thaljaoui, A., Val, T., Nasri, N., & Brulin, D. (2015). BLE localization using RSSI measurements and iRingLA. In Proceedings of 2015 IEEE International Conference on Industrial Technology (ICIT), Seville, Spain, 2178-2183.
[54] Velux. (2018). Modern indoor living can be bad for your health: New YouGov survey for VELUX sheds light on risks of the "Indoor Generation". Retrieved from https://www.prnewswire.com/news-releases/modern-indoor-living-can-be-bad-for-your-health-new-yougov-survey-for-velux-sheds-light-on-risks-of-the-indoor-generation-300648499.html
[55] Wei, T., & Bell S. (2011). Indoor Localization Method Comparison: Fingerprinting and Trilateration Algorithm. In Proceedings of 2011 Spatial Knowledge and Information (SKI), Quebec, Canada, 66-68.
[56] Wang, Y., Yang, Q., Zhang G., & Zhang P. (2016). Indoor Positioning System using Euclidean Distance Correction Algorithm with Bluetooth Low-Energy Beacon. In Proceedings of 2016 Internet of Things and Applications (IOTA), Pune, India, 243-247.
[57] Yang, Z., & Liu, Y. (2010). Quality of trilateration: Confidence based iterative localization. IEEE Transactions on Parallel and Distributed Systems, 21(5), 631-640.
[58] Zhou, Z. M. (2015). The Performance Analysis of Seamless Navigation Systems using Smartphones. Master’s Thesis, National Cheng Kung University, Tainan, Taiwan.
[59] Zhuang, Y., Yang, J., Li, Y., Qi, L., & El-Sheimy, N. (2016). Smartphone-Based Indoor Localization with Bluetooth Low Energy Beacons. Sensors, 16(5), 1-20.
[60] Zou, H., Jiang, H., Luo, Y., Zhu, J., Lu, X., & Xie, L. (2016). BlueDetect: An iBeacon-Enabled Scheme for Accurate and Energy-Efficient Indoor-Outdoor Detection and Seamless Location-Based Service. Sensors, 16(2), 268.