臺灣博碩士論文加值系統

超寬頻感測器（Ultra-wideband, UWB）被廣泛的使用在室內定位領域，超寬頻感測器的室內定位通常是指移動端的定位，移動端通過無線電與基站通信並計算移動端和周圍每個基站之間的距離。本文提出了一個數學模型，分別分析了移動端在2D和3D空間定位的誤差，通過數學模型探討了影響移動端定位誤差的因素，分析得到了最佳的基站安裝位置。根據所得出的最佳的基站位置分佈，移動端在2D和3D空間的定位誤差均大幅度降低。然而，在室內的環境中超寬頻感測器容易受障礙物或人的影響產生異常值，單獨使用超寬頻感測器的定位結果容易發生跳變，而基於慣性感測單元（Inertial Measurment Unit, IMU）的慣性導航系統可以輸出連續的定位結果卻存在累積誤差的問題，長時間定位結果容易發散。基於以上的分析，本文提出一套有效的系統框架，將超寬頻感測器和慣性感測器兩者緊密結合共同定位，不僅避免了慣性感測器存在的累積誤差問題而且能避免超寬頻感測器異常值對定位連續性的影響。然而，融合超寬頻感測器和慣性感測器雖然能夠更加精確和穩定的定位，但是研究發現當超寬頻感測器移動端安裝於加速度較大的載具上時，載具自身的運動加速度也會影響定位精度，表现为隨著運動加速度變大定位精度隨之降低。因為过程中的姿態角是通過重力加速度在水準方向的分量進行修正，當載具快速运动時在水準方向上也會產生比較大的運動加速度，而加速度感測器難以將運動加速度與重力加速度的水準分量分開，導致姿態角估測誤差增加，進一步影響到定位精度。為解決上述問題，本文建立了一套在比較大運動加速度下的姿態角誤差估測模型，提出一種加速度自適應擴展卡爾曼濾波器 （Acceleration Adaptive Extended Kalman Fliter, ACCAEKF）的方法。ACCAEKF根據加速度大小自動調節預測誤差的協方差矩陣，能有效修正存在较大運動加速度時的定位誤差。此外本文還發現在比較小的運動加速度或者靜止狀態下，加速度量測雜訊會影響ACCAEKF的定位穩定性。針對這種現象，本文通過對加速度引入的誤差協方差矩陣進行了詳細的推導，提出一種優化方法將雜訊協方差矩陣從誤差協方差矩陣中減去，有效避免低運動加速度下量測雜訊對定位穩定性的影響。本文還針對超寬頻感測器容易在多障礙物的室內環境產生非視距通訊（Non-light of Sight, NLOS）的問題做了優化，非視距通訊可以通過支持向量機（Support Vector Machine, SVM）對超寬頻感測器的信道衝擊響應（Channel Impulse Responses, CIR）和訊號強弱（Received Signal Strength Indicator, RSSI）的特徵進行分類，支持向量機是一種經典的監督機器學習（Machine Learning, ML）算法，適用於分類和回歸問題。本文應用對視距通訊（Light of Sight, LOS）和非視距通訊的分類來驗證ACCAEKF在穿越複雜的室內環境時的定位性能，在實驗中獲得了比較高的定位精度。最後，本文通過MATLAB模擬實驗分別模擬機器人在2D空間和3D空間下基站位置的各種不同的分佈情形併計算定位誤差以此來驗證本文提出的最佳的基站位置分佈，除此之外，本文利用超寬頻感測器多基站的室內定位系統實際做了實驗，實驗結果表明本文找出的最佳基站位置分佈能顯著提高系統定位精度。本文基於上文提出的最佳的基站分佈，應用ACCAEKF進行模擬和室內定位實際實驗，实验結果表明本文提出方法在載具低速運動和加速度劇烈變化的情況下都能得到很好的定位效果。

Ultra-wideband (UWB) sensors have been widely applied in the field of indoor positioning. The indoor positioning of UWB usually refers to the localization of the mobile node that interacts with the anchors through radio for calculating the distance between the mobile node and each of surrounding anchors. In this paper, a mathematical model is proposed to analyze the positioning error of mobile node in 2-dimensional (2D) and 3-dimensional (3D) respectively, and the factors affecting the positioning error of mobile node are investigated through the proposed mathematical models. The best installation positions of surrounding anchors were obtained based on the mathematical models. The positioning error of mobile node in both 2D and 3D were significantly reduced based on the proposed optimal anchor location distribution. Although the ultra-wideband (UWB) sensor's ranging accuracy reaches the centimeter in the light of sight (LOS) environment, it is easily influenced by obstacles or people resulting in outliers. Because of the inertial measurement unit (IMU) sensor-based inertial positioning system has cumulative errors, the long-time positioning tends to diverge. In this paper, we propose an effective system framework that tightly integrates UWB and IMU to location, which not only avoids the cumulative error problem of IMU, but also avoids the impact of UWB outliers. However, it is found that when the linear acceleration of the vehicle becomes larger, the variable linear acceleration will affect the positioning performance and the larger the linear acceleration will lead to the larger the positioning errors. As we know, the attitudes or Euler angles are corrected by the horizontal component of gravitational acceleration, if there is a relatively large linear acceleration generates in the horizontal, it is difficult to distinguish this from the horizontal component of gravitational acceleration. This leads to an increase in attitude estimation error, which will further affect the positioning accuracy. To solve the above problems, this paper develops a model for estimating attitude error and proposes an acceleration adaptive extended Kalman filter (ACCAEKF), which automatically adjusts the prediction noise variance according to the variable acceleration. Moreover, the study also found that the measurement noise will affect the performance at rest or at low-speed. To avoid the influence of the measurement noise on the system, this paper provides a detailed derivation of the noise matrix introduced in ACCAEKF, and proposes an optimization method to subtract the noise matrix from the prediction error matrix. Moreover, UWB is prone to non-light of sight (NLOS) in indoor environments with multiple obstructions, NLOS can be identification and mitigation by the referenced support vector machine (SVM), which is a classic supervised machine learning (ML) algorithm for both classification and regression problems. This paper also applied LOS and NLOS classification to verify the localization performance of ACCAEKF in traversing complex environments, the higher positioning accuracy was also obtained. Finally, the relationship between the root mean square positioning error (RMSPE) and the positioning accuracy was verified by simulating various anchor location distributions for 2D and 3D positioning. Both computer simulations and practical experiments indicated that the RMSPE was highly correlated with positioning accuracy. Therefore, the accuracy of UWB-based indoor positioning was improved by minimizing the RMSPE through the identification of the optimal distribution of anchor locations. In this paper, we also applied ACCAEKF for simulations and experiments based on the optimal anchors distribution, the simulation and experiment results show that the ACCAEKF proposed in this paper could obtain good positioning performance in the case of low-speed motion or drastic change of acceleration.

摘　要 i
ABSTRACT iv
誌 謝 vii
目 錄 viii
表目錄 x
圖目錄 xii
第一章 緒論 1
1.1 前言 1
1.2 研究動機與目的 4
1.3 文獻探討 6
1.4 本章小結 11
1.5 論文規劃 12
第二章 感測器及硬體電路介紹 14
2.1 座標系統介紹 16
2.2 座標系統轉換 18
2.3 感測器原理與校正 21
2.3.1 加速度計 22
2.3.2 陀螺儀 23
2.3.3 磁力計 27
2.3.4 超寬頻感測器 29
2.4 本章小結 34
第三章 超寬頻感測器基站最佳位置分佈 35
3.1 2D定位基站分佈對精度的影響 35
3.2 3D定位基站分佈對精度的影響 40
3.3 本章小結 47
第四章 緊密結合慣性感測器和超寬頻感測器的自動衰減加速度自適應EKF 48
4.1 系統模型 50
4.1.1 運動模型 50
4.1.2 位置誤差狀態（9狀態EKF）的動力學方程 52
4.1.3 量測模型 54
4.2 加速度自適應慣性感測器和超寬頻感測器的融合EKF 57
4.2.1 預測階段 59
4.3 濾波器量測更新階段 65
4.4 本章小結 66
第五章 實驗結果與分析 68
5.1 最佳的基站位置分佈模擬實驗 69
5.1.1 2D定位精度與基站之間的夾角和距離關係的實驗 69
5.1.2 3D定位精度與基站之間的夾角和距離關係的實驗 84
5.1.3 常見的基站位置擺放誤區實例分析 104
5.1.4 關於最佳基站位置擺放研究的幾種方法比較 108
5.2 緊密結合慣性感測器和超寬頻感測器的室內定位系統模擬實驗 120
5.2.1 模擬多感測器融合擴展式卡爾曼濾波架構的定位表現 120
5.2.2 與其他方法的比較 128
5.3 實驗環境介紹 136
5.4 實際實驗結果 142
5.4.1 基站最佳位置分佈實驗 142
5.4.2 移動端處於靜態ACCAEKF算法的定位實驗 148
5.4.3 移動端處於傾斜的直線動態定位實驗 153
5.4.4 動態矩形定位實驗 158
5.4.5 基於SVM方法分類LOS和NLOS環境的實驗 162
5.4.6 多個障礙物之間沿著直線軌跡行走的ACCAEKF定位實驗 168
5.5 本章小結 171
第六章 結論與未來展望 172
6.1 結論 172
6.2 未來展望 173
附錄A 175
參考文獻 177

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