近年來，人們對於室內定位的需求層面日益擴大，現今室外定位主流的GPS系統因其應用於室內易受遮蔽訊號以及多路徑效應影響，使得定位成效不彰，鑒於此現象，目前眾多研究致力於行人航位推算(Pedestrian Dead Reckoning, PDR)系統的分析，以降低定位誤差為核心目標進行探討。
目前常見的行人航位推算研究大多數固定單一手機姿態去分析，限制行人以手持模式下紀錄數據，若隨意改變其設備姿態將造成誤差增大，本文基於上述缺失去改進，應用三種常見的設備攜帶模式進行分析，實現一套多模式行人航位推算系統。架構中主要使用三軸線性加速度計(Three-axis Accelerometer)、三軸陀螺儀(Three-axis Gyroscope)、重力感測器(Gravity Sensor)以及旋轉向量感測器(Rotation Vector Sensor)四個慣性感測單元(Inertial measurement unit, IMU)作為研究的數據來源，基於不同模式在步伐檢測區塊提出各自的分析方法，並使用機器學習演算法進行模式的分類輔助其運作，而在步伐長度估算區塊中取代傳統的經驗模型，構建神經網路模型運行迴歸分析，並實現一種非線性特徵轉換方式以增進模型的成效，能夠適應不同使用者的行走習慣，屬於一種自適應式步長演算法。實驗結果顯示，本文提出的架構在各區塊皆有顯著的進展，並且能夠有效降低整體的定位誤差。

The demand for indoor localization has expanded greatly in recent years. When the pedestrians are in the building, the received signal will be blocked and affect the performance of GPS positioning. As a result, the numerous researches are devoted to the analysis of pedestrian dead reckoning system.
In this article, a multimode pedestrian dead reckoning system is proposed based on different device placements to improve positioning error. We have used the data which includes accelerometer, gyroscope, gravity sensor and rotation vector sensor in the framework.
In the step detection block, we propose different analysis methods based on different phone poses and work successfully with machine learning classifier. Furthermore, in the step length estimation block, we haven’t used an empirical model. Instead, we have built a neural network sequence model for regression analysis. In the prior works, the features are extracted from the raw acceleration and angular velocity signal. However, the non-linear feature transformation method is adopted to generate tree features in this article. We design an adaptive step length algorithm considering gait diversity. Experimental results show that the proposed architecture has made much progress in each block, and can effectively reduce the overall positioning error.

第一章、緒論 1
1.1 研究背景 1
1.2 研究動機與目的 3
1.3 論文架構 4
第二章、相關研究 5
2.1 行人航位推算(Pedestrian Dead Reckoning, PDR) 5
2.1.1 概述 5
2.1.2 步伐檢測 (Step detection) 6
2.1.3 步伐長度估算 (Step Length Estimation) 9
2.1.4 方位估計 (Heading Estimation) 11
2.2 神經網路模型 11
2.2.1 雙向長短期記憶神經網路 (Bi-directional LSTM) 11
2.2.2 時間卷積神經網路(Temporal Convolution Network, TCN) 13
2.3 特徵轉換 (Feature Transformation) 16
2.4 巴特沃斯濾波 18
第三章、空間座標系統 20
3.1 座標系定義 20
3.1.1 地心地固座標系(Earth-Centered Earth-Fixed Frame, ECEF) 20
3.1.2 導航坐標系(Navigation Frame) 21
3.1.3 附體坐標系(Body Frame) 21
3.2 座標系旋轉 22
3.2.1 方向餘弦法 22
3.2.1 四元數法 24
3.2.3 尤拉角法 26
3.3 座標系轉換 27
第四章、系統架構 29
4.1 概述 29
4.2 設備模式 29
4.3 改進的行人航位推算 31
4.3.1 步伐檢測 31
4.3.2 步伐長度估算 36
4.3.3 方位估計 41
第五章、模擬與結果 43
5.1 資料集 43
5.2 模擬結果 43
5.2.1 設備模式 43
5.2.2 步伐檢測 48
5.2.3 步伐長度估算 51
5.2.4 方位估計 57
第六章、結論與未來工作 63
參考文獻 64

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