近年來隨著物聯網（Internet of Things, IoT）的趨勢，愈來愈多適地性服務（Location-Based Service, LBS）已經成為日常生活中不可或缺的重要科技，因此室內定位技術也愈加蓬勃發展。礙於室內環境遮蔽，限制全球導航衛星系統（Global Navigation Satellite System , GNSS）的信號，必須使用其他替代定位技術。其中，磁場是一種低成本且覆蓋範圍廣的方案。雖然磁性材料的存在造成磁場異常，但這些異常也成為獨特的「指紋」，帶來更高的定位精度。基於磁場的定位分成離線訓練階段和在線階段。在離線階段，收集磁場測量值並生成磁場圖。從高精地圖（High Definition map, HD-map）的概念出發，事先構建的磁場圖也必須維持一定的精度，以提供在線階段資訊。因此，除了安裝在手機中的低成本傳感器外，更使用高規格的磁力計採集資料。有了先前的磁場地圖，在線階段就能估計用戶的位置。值得注意的是，由於不同位置的絕對磁場值可能相似，匹配策略應同時考慮相鄰位置。
本研究有兩種場景：行人場景和車輛場景。首先在行人場景中，除了在離線階段由於收集資料耗時所提出的新穎製圖策略，在線階段更是提出一種融合行人航位推算（Pedestrian Dead Reckoning, PDR）和磁場定位的方法來得到用戶位置。根據PDR所提供的軌跡型態，在預先建構的參考地圖中滑動磁序列以找到匹配結果；而磁場地圖的先驗訊息則能校正PDR的航向資訊。兩個方法各發揮所長達到互補效果，得到更好的定位結果。接著在車輛場景中，由於車輛會沿特定路線行駛，只須採集特定區域的離線地圖資料，使得製圖階段更有效率。另一種常見的定位方法是整合 GNSS 和慣性導航系統（Inertial Navigation System, INS）來得到用戶位置。然而，嵌入在手機中的低成本傳感器無法提供高精度的原始觀測量，進而導致軌跡不佳，故使用磁場定位改進原始軌跡。此外，由於磁場定位事先產製地圖的概念與地圖匹配類似，本文進一步討論其方法並與之進行比較。
實驗結果發現，在行人場景下，磁匹配更新和航向校正有效減少陀螺儀的漂移，不同離線地圖分別提高了約71%和64%，且達到桌面級（1-3 公尺）的精度。而在車輛場景下，整體平均誤差為2.4公尺，滿足道路等級（5公尺）的要求，與原始軌跡相比，提升率達81%。

Over the past decades, with the trend of Internet of Things (IoT), indoor positioning has gained much attention because of its various Location-Based Service (LBS). There are many alternative technologies for indoor environments developed due to the limitation of the sheltered Global Navigation Satellite System (GNSS) signal in such difficult environments. Among these technologies, the magnetic-based one is a low-cost, wide coverage scheme. The magnetic field generated by the Earth exists everywhere. Undoubtedly, there must be some magnetic anomalies due to the magnetism material. These anomalies become the unique “fingerprints”, which brings higher positioning accuracy. Magnetic-based positioning is divided into two parts: the offline training phase and the online phase. In the offline phase, magnetic field measurements are collected, and magnetic field maps are generated. In this research, From the thought of High Definition map (HD-map), the high accuracy magnetic field maps are built previously in the offline phase to provide the information for the online phase. Therefore, besides the low-cost sensor mounted in a mobile phone, the high specification magnetometer is used. In the online phase, the location of a user can be estimated by matching the previous magnetic field maps. Notably, since the absolute magnetic field value of different grids might be similar, the matching strategy should take neighbor grid into consideration instead of just one grid. The relative relationship brings benefits to the matching result. There are two scenarios in this research: pedestrian scenario and vehicle scenario.
In the pedestrian scenario, the mapping phase is time-consuming. Therefore, a collecting strategy is proposed. Besides generating a magnetic map, a novel fusion method is proposed in the online phase to give the user location. Owing to the randomness of the trajectory of a pedestrian, Pedestrian Dead Reckoning (PDR), a common indoor positioning method is introduced. With the cooperation of magnetic-based positioning and PDR, the series can be slid in the reference map built in advance to find the matching result.
In the vehicle scenario, since a vehicle will drive in a specific path, the offline map is collected in the specific area to make the mapping phase more effective. With the pre-built map, the location of the user can be estimated by magnetic information. Another common positioning method, which integrates GNSS and Inertial Navigation System (INS) system, is widely used for vehicle positioning. However, the low-cost sensors embedded in the smartphones cannot provide accurate raw data, further cause the bad trajectory. The magnetic positioning results, therefore, is used to improve the raw trajectory. In addition, the map matching approach is also discussed in this thesis since the basic concept of a previous map generation is similar to the magnetic-based positioning.
According to the performance analysis, in the pedestrian scenario, the magnetic matching update and heading correction effectively reduce the situation of the drift of gyroscope with the improvement of about 71% and 64% by different offline map respectively. A desk-level (1-3 meters) accuracy can be achieved. In the vehicle scenario, the error is 2.4 meters on average overall, which meet the accuracy requirement of “which-road” (5 meters). In addition, the improvement can reach up to 81% compared to the INS/GNSS trajectory.

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