近年來微機電系統之慣性測量單元(MENS-IMU)的運用越來越廣泛，IMU被運用於智慧型手機等隨身裝置，也被運用於感知人肢體即時的姿態。為了能更精準估測姿態，校正IMU成為一項不可或缺的程序。然而，校正程序通常在無擾動環境下進行。由於無擾動環境情況的建構困難度較高。因此，在本論文中，考慮到外部環境擾動的校正。提出迭代具權重的萊文貝格－馬夸特方法(Iterative Weighted Levenberg-Marquardt Algorithm, IWLMA)以克服外部環境擾動問題，並透過實際的實驗數據驗證所提出IWLMA方法的適用性。另外，本論文亦提出以9自由度為基底的互補濾器之姿態航向參考系統(Attitude Heading Reference System, AHRS)演算法，互補濾波器演算法相較卡爾曼濾波器演算法需求更少的計算量，因此適合實現於微處理器，所提出AHRS演算法使用四元數來計算載具的姿態，相較於使用尤拉角描述姿態，以四元數描述姿態不具有奇異性問題。根據所估測的姿態資訊，再結合加速規量測的數值進行積分可估測載具速度，並將加速規估測的速度值與光流法模組PX4FLOW所估測的速度值透過速度融合演算法進行資料融合，提升速度估測之正確性。本研究通過設計三軸旋轉平台(Gimbal)驗證所提出AHRS演算法的精度。

Implementation of low-cost Microelectromechanical Systems Inertia Measurement Unit (MENS-IMU) has been more and more popular over the past few years. Recently, IMU has been used for wide varieties of consumer electronics, including smartphones, smart watches and so on. Under some situations IMU has been also used to detect the gesture of the human limbs. To precisely estimate the motion information, the calibration process of the IMU is indispensable. In general, the calibration procedures are usually conducted under a clean and perturbation free environment. However, such a perfect situations may not always be available. As a result, in this paper, the calibration subject to external perturbation is considered. An iterative weighted Levenberg-Marquardt algorithm (IWLMA) is proposed to cope with the perturbation issue. Experiment data are conducted to verify the proposed IWLMA method under the imperfect situations. The 9 degree of freedom (DOF) complementary filter based attitude heading reference system (AHRS) algorithm method will be introduced. Complementary filter consumes less computation effort than Kalman filter, as a result, it is suitable to implemented on the microprocessor. Quaternion is used in the proposed AHRS algorithm to calculate the attitude of a vehicle, unlike Euler angles, quaternion can describe the attitude of a vehicle without singularity problem. According to the estimated attitude information, the velocity can be calculated by using integration of the data from accelerometer. The estimated velocity from accelerometer and the estimated velocity from optical flow module PX4FLOW will be fused together by using the velocity fusion algorithm, thus improve accuracy of the estimated velocity. Three axis rotational platform (Gimbal) has been designed and built to verify the performance the proposed AHRS algorithm.

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