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  本文選題：MEMS + IMU初始對準　； 參考：《南昌大學》2013年碩士論文

【摘要】：隨著現(xiàn)代科學技術的發(fā)展,人們對定位與導航的信息需求日益膨脹,尤其是在室內(nèi)環(huán)境下,如商場、圖書館、停車場、礦井等,經(jīng)常需要快速而準確地了解用戶或者設備的位置信息。由于室內(nèi)環(huán)境的復雜多變,電磁波傳播存在衰減、多徑和干擾等因素的影響,提取的信號特征在精度和穩(wěn)定性方面難以得到保證。因此,基于各種無線網(wǎng)絡(如WiFi, ZigBee等)的室內(nèi)定位技術目前還不能在所有的環(huán)境中都有穩(wěn)定準確的定位結(jié)果。 慣性測量單元(Inertial Measure Unit, IMU)以其自主導航、不受環(huán)境干擾等優(yōu)點,受到導航領域的廣泛關注和重點研究。本文圍繞IMU室內(nèi)行人航跡推算問題進行研究,重點研究了基于微機械(Micro-Electro-Mechanical Systems, MEMS) IMU的室內(nèi)行人航跡推算(Pedestrian Dead Reckoning, PDR)算法、仿真以及性能分析,主要包括以下三個方面： 1. MEMS IMU的初始對準：初始對準在導航算法中占有十分重要的地位,它的效果將直接影響導航參數(shù)的精度。由于MEMS IMU的硬件性能限制,單獨采用MEMS IMU的對準精度比較差,尤其是在航向角方面。因此,本文將研究磁力計和MEMS IMU的信息融合方法,從而實現(xiàn)MEMS IMU的初始對準； 2.行人運動模型：IMU的運動狀態(tài)是室內(nèi)行人航跡推算算法中的關鍵問題,決定著擴展卡爾曼濾波中觀測信息的準確使用。因此,本文將研究利用加速度和角速度建立行人運動模型,高效地判斷IMU的運動狀態(tài)。當檢測到IMU處于靜止狀態(tài)時,采用foot-mounted方法和擴展卡爾曼濾波對導航參數(shù)誤差和傳感器誤差進行準確的估計和及時補償； 3.室內(nèi)行人航跡推算算法：本文基于MEMS IMU硬件平臺和MATLAB仿真平臺,采用IMU捷聯(lián)算法進行導航參數(shù)的解算,同時結(jié)合行人運動模型、foot-mounted和擴展卡爾曼濾波等方法實現(xiàn)誤差估計和補償,設計室內(nèi)行人航跡推算算法。通過多次實驗和仿真表明：位置誤差占總路程的0.5%-2.0%,實現(xiàn)了在室內(nèi)較高精度的行人航跡推算。
[Abstract]:With the development of modern science and technology, people need more and more information about positioning and navigation, especially in indoor environment, such as shopping mall, library, parking lot, mine, etc. It is often necessary to quickly and accurately understand the location of the user or device. Because of the complexity of indoor environment, the attenuation of electromagnetic wave propagation, the influence of multi-path and interference, it is difficult to ensure the accuracy and stability of the extracted signal features. Therefore, indoor localization technology based on various wireless networks (such as WiFi, ZigBee, etc.) can not have stable and accurate localization results in all environments. Inertial Measurement Unit (IMU) has been widely concerned and studied in navigation field for its advantages of autonomous navigation and non-interference with environment. This paper focuses on the research of indoor pedestrian trajectory estimation in IMU. Micro-Electro-Mechanical Systems (MEMS) IMU-based indoor pedestrian track estimation (PDR) algorithm, simulation and performance analysis are studied, including the following three aspects: 1. Initial alignment of MEMS IMU: initial alignment plays an important role in navigation algorithm, and its effect will directly affect the accuracy of navigation parameters. Because of the limitation of hardware performance of MEMS IMU, the alignment accuracy of MEMS IMU alone is poor, especially in the aspect of heading angle. Therefore, this paper will study the information fusion method of magnetometer and MEMS IMU, so as to realize the initial alignment of MEMS IMU. 2. The movement state of the pedestrian model: IMU is a key problem in the indoor footpath estimation algorithm, which determines the accurate use of the observation information in the extended Kalman filter (EKF). Therefore, in this paper, the acceleration and angular velocity are used to establish pedestrian motion model to judge the motion state of IMU efficiently. When the foot-mounted method and extended Kalman filter are used to estimate and compensate the errors of navigation parameters and sensors accurately. Indoor footpath calculation algorithm: based on MEMS IMU hardware platform and MATLAB simulation platform, the IMU strapdown algorithm is used to solve the navigation parameters. At the same time, the error estimation and compensation are realized by combining the pedestrian movement model foot-mounted and extended Kalman filter. Calculation algorithm of pedestrian track in Design Room. Through many experiments and simulations, it is shown that the position error accounts for 0.5- 2.0% of the total distance, and a high precision pedestrian track calculation is realized.
【學位授予單位】：南昌大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：P227.9

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本文編號：2118024