本研究以藍芽作為室內定位技術、樹莓派作為藍芽訊號接收裝置、USBeacon作為藍芽訊號發射裝置於一510公分×204公分之實驗場域進行定位實驗。測量藍芽強度接收訊號與實際距離並找出其之間轉換模型，開發出一室內定位系統演算法，結合非線性最小平方法以及角度定位法，並提出防止因干擾導致量測誤差所造成定位系統演算法無法收斂之校正系統。另以位置指紋辨識法在同一場域進行定位實驗，於離線階段建立訊號地圖存放於資料庫，以權重近鄰演算法進行位置估計，並於模擬實驗中對多點進行模擬，統計K值並找出最佳參數以有效提升定位精度。根據無障礙精度、環境變化敏感度、離線工作量、參考點數目四點因素於非線性最小平方法及位置指紋辨識法兩種定位演算法進行優缺點的比較：於無障礙時，指紋辨識法的精度明顯優於非線性最小平方法，但其離線工作量繁瑣且若環境發生改變時受其影響甚大。相較而言，非線性最小平方法有著可接受的精度，且若環境改變並不會大幅的下滑其表現，對於環境改變有著較高的穩定度。

This research applies Raspberry pi as the signal receiving device and USBeacon as the signal transmitting device to implement the indoor position system (IPS) based on Bluetooth at a 510cm×204cm space. The received signal strength (RSS) and the actual distance are measured and the conversion model between them is constructed. In this research, an IPS algorithm based on the nonlinear least square and the arrival angle is proposed, and a calibration method is developed to prevent the IPS algorithm from divergence due to the measurement errors caused by the interference. In addition, fingerprinting location method is used to conduct the experiment in the same space. At the offline stage, it creates a signal map and stores it in the database. After collecting data at the online stage, the RSS is compared to the signal map, and it uses weighted K-nearest neighbor (WKNN) algorithm to estimate the position of the target. In the simulation, multiple locations are estimated, and the best K value is found to effectively improve the performance. The pros and cons of nonlinear least square and fingerprint location are compared in terms of position accuracy, environmental sensitivity, off-line workload, and number of reference point. When there is no barrier, the accuracy of the fingerprint location is significantly better than the nonlinear least square, but its offline workload is cumbersome and will be greatly affected if the environment changes. In contrast, the nonlinear least square has acceptable accuracy, and if the environment changes, its performance will not be greatly reduced, and it has a high degree of stability to environmental changes.

[1] R. Mautz, "Indoor positioning technologies [Ph. D. thesis]," ETH Zürich, Zürich, Switzerland, 2012.
[2] A. Lindemann, B. Schnor, J. Sohre, and P. Vogel, "Indoor positioning: A comparison of WiFi and Bluetooth Low Energy for region monitoring," in International Conference on Health Informatics, 2016, vol. 6: SCITEPRESS, pp. 314-321.
[3] C. Chen, Y. Chen, H.-Q. Lai, Y. Han, and K. R. Liu, "High accuracy indoor localization: A WiFi-based approach," in 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP), 2016: IEEE, pp. 6245-6249.
[4] C.-H. Lim, Y. Wan, B.-P. Ng, and C.-M. S. See, "A real-time indoor WiFi localization system utilizing smart antennas," IEEE Transactions on Consumer Electronics, vol. 53, no. 2, pp. 618-622, 2007.
[5] Z. Wang, P. Sokliep, C. Xu, J. Huang, L. Lu, and Z. Shi, "Indoor position algorithm based on the fusion of wifi and image," in 2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI), 2019: IEEE, pp. 212-216.
[6] J. Chen, G. Ou, A. Peng, L. Zheng, and J. Shi, "An INS/WiFi indoor localization system based on the Weighted Least Squares," Sensors, vol. 18, no. 5, p. 1458, 2018.
[7] Z. Wang, Z. Yang, and T. Dong, "A review of wearable technologies for elderly care that can accurately track indoor position, recognize physical activities and monitor vital signs in real time," Sensors, vol. 17, no. 2, p. 341, 2017.
[8] A. Poulose and D. S. Han, "UWB indoor localization using deep learning LSTM networks," Applied Sciences, vol. 10, no. 18, p. 6290, 2020.
[9] M. Ridolfi et al., "Experimental evaluation of UWB indoor positioning for sport postures," Sensors, vol. 18, no. 1, p. 168, 2018.
[10] H. D. Chon, S. Jun, H. Jung, and S. W. An, "Using RFID for accurate positioning," Positioning, vol. 1, no. 08, 2004.
[11] H. Xu, M. Wu, P. Li, F. Zhu, and R. Wang, "An RFID indoor positioning algorithm based on support vector regression," Sensors, vol. 18, no. 5, p. 1504, 2018.
[12] A. Montaser and O. Moselhi, "RFID indoor location identification for construction projects," Automation in Construction, vol. 39, pp. 167-179, 2014.
[13] S. Park and S. Hashimoto, "Autonomous mobile robot navigation using passive RFID in indoor environment," IEEE Transactions on industrial electronics, vol. 56, no. 7, pp. 2366-2373, 2009.
[14] C. Lee et al., "Indoor positioning system based on incident angles of infrared emitters," in 30th Annual Conference of IEEE Industrial Electronics Society, 2004. IECON 2004, 2004, vol. 3: IEEE, pp. 2218-2222.
[15] E. Martín-Gorostiza, M. A. García-Garrido, D. Pizarro, D. Salido-Monzú, and P. Torres, "An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors," Sensors, vol. 19, no. 11, p. 2519, 2019.
[16] Z. Chen, Q. Zhu, and Y. C. Soh, "Smartphone inertial sensor-based indoor localization and tracking with iBeacon corrections," IEEE Transactions on Industrial Informatics, vol. 12, no. 4, pp. 1540-1549, 2016.
[17] J. So and Y. Kim, "Interference-aware frequency hopping for Bluetooth in crowded Wi-Fi networks," Electronics Letters, vol. 52, no. 17, pp. 1503-1505, 2016.
[18] A. A. Kalbandhe and S. C. Patil, "Indoor positioning system using bluetooth low energy," in 2016 international conference on computing, analytics and security trends (CAST), 2016: IEEE, pp. 451-455.
[19] M. Terán, J. Aranda, H. Carrillo, D. Mendez, and C. Parra, "IoT-based system for indoor location using bluetooth low energy," in 2017 IEEE Colombian Conference on Communications and Computing (COLCOM), 2017: IEEE, pp. 1-6.
[20] Y.-C. Pu and P.-C. You, "Indoor positioning system based on BLE location fingerprinting with classification approach," Applied Mathematical Modelling, vol. 62, pp. 654-663, 2018.
[21] S. Kajioka, T. Mori, T. Uchiya, I. Takumi, and H. Matsuo, "Experiment of indoor position presumption based on RSSI of Bluetooth LE beacon," in 2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE), 2014: IEEE, pp. 337-339.
[22] M. E. Rida, F. Liu, Y. Jadi, A. A. A. Algawhari, and A. Askourih, "Indoor location position based on bluetooth signal strength," in 2015 2nd International Conference on Information Science and Control Engineering, 2015: IEEE, pp. 769-773.
[23] G. Li, E. Geng, Z. Ye, Y. Xu, J. Lin, and Y. Pang, "Indoor positioning algorithm based on the improved RSSI distance model," Sensors, vol. 18, no. 9, p. 2820, 2018.
[24] C. Yang and H.-R. Shao, "WiFi-based indoor positioning," IEEE Communications Magazine, vol. 53, no. 3, pp. 150-157, 2015.
[25] M. Ke, Y. Xu, A. Anpalagan, D. Liu, and Y. Zhang, "Distributed TOA-based positioning in wireless sensor networks: A potential game approach," IEEE Communications Letters, vol. 22, no. 2, pp. 316-319, 2017.
[26] K. Yu and Y. J. Guo, "Statistical NLOS identification based on AOA, TOA, and signal strength," IEEE Transactions on Vehicular Technology, vol. 58, no. 1, pp. 274-286, 2008.
[27] S. Tiwari et al., "Practical result of wireless indoor position estimation by using hybrid TDOA/RSS algorithm," in CCECE 2010, 2010: IEEE, pp. 1-5.
[28] P. Du, S. Zhang, C. Chen, A. Alphones, and W.-D. Zhong, "Demonstration of a low-complexity indoor visible light positioning system using an enhanced TDOA scheme," IEEE Photonics Journal, vol. 10, no. 4, pp. 1-10, 2018.
[29] F. M. Ghannouchi, D. Wang, and S. Tiwari, "Accurate wireless indoor position estimation by using hybrid TDOA/RSS algorithm," in 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2012), 2012: IEEE, pp. 437-441.
[30] M. Srbinovska, V. Dimcev, C. Gavrovski, and Z. Kokolanski, "Localization techniques in wireless sensor networks using measurement of received signal strength indicator," FACULTY OF ELECTRICAL ENGINEERING UNIVERSITY OF BANJA LUKA, vol. 15, no. 1, pp. 67-71, 2011.
[31] C. Li, Z. Qiu, and C. Liu, "An improved weighted k-nearest neighbor algorithm for indoor positioning," Wireless Personal Communications, vol. 96, no. 2, pp. 2239-2251, 2017.
[32] M. Li, L. Zhao, D. Tan, and X. Tong, "BLE fingerprint indoor localization algorithm based on eight-neighborhood template matching," Sensors, vol. 19, no. 22, p. 4859, 2019.
[33] N. Paulino, L. M. Pessoa, A. Branquinho, and E. Gonçalves, "Evaluating a Novel Bluetooth 5.1 AoA Approach for Low-Cost Indoor Vehicle Tracking via Simulation," in 2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 2021: IEEE, pp. 259-264.
[34] F. Ye, R. Chen, G. Guo, X. Peng, Z. Liu, and L. Huang, "A low-cost single-anchor solution for indoor positioning using ble and inertial sensor data," IEEE Access, vol. 7, pp. 162439-162453, 2019.
[35] K. Zhao et al., "Optimization of time synchronization and algorithms with TDOA based indoor positioning technique for Internet of things," Sensors, vol. 20, no. 22, p. 6513, 2020.
[36] J. L. Crassidis and J. L. Junkins, Optimal estimation of dynamic systems. Chapman & Hall/CRC Boca Raton, FL, 2004.