本文選題：MEMS陀螺儀 + 隨機漂移誤差　； 參考：《西南大學》2017年碩士論文

【摘要】：近年來,MEMS陀螺儀作為慣性導航技術中十分重要的部分,由于其具有成本低、尺寸小、重量輕、集成度高等一系列優(yōu)點,在慣性導航、工業(yè)控制及電子產(chǎn)品等領域得到廣泛的應用。盡管與傳統(tǒng)類型陀螺儀相比,MEMS陀螺儀具有諸多優(yōu)勢,但由于制造工藝和設計水平等原因,其測量精度相對較低,往往無法滿足實際使用需求,極大的制約了MEMS陀螺儀的發(fā)展和應用。尤其對于陀螺儀的隨機漂移誤差,由于其形成的機理非常復雜,沒有明確的規(guī)律且隨外界環(huán)境變化而變化,不能用常規(guī)的方法補償修正,并且無法完全消除,是限制MEMS陀螺精度提高的主要因素。有鑒于此,本文以目前較常用的低成本MEMS慣性器件MPU-6050中的陀螺儀為研究對象,開展了對MEMS陀螺儀隨機漂移誤差建模分析與修正技術的研究,本文的主要研究內(nèi)容及取得結論如下:首先,為采集陀螺儀的靜態(tài)漂移數(shù)據(jù),以STM32F103C8T6為核心處理器設計并構建了MEMS陀螺儀誤差數(shù)據(jù)采集系統(tǒng),實現(xiàn)了對MPU-6050中陀螺儀靜態(tài)漂移數(shù)據(jù)的采集。其次,在對MEMS陀螺儀隨機漂移誤差建模與濾波技術的研究現(xiàn)狀與發(fā)展動態(tài)調(diào)研分析的基礎上,研究了MEMS陀螺儀靜態(tài)漂移誤差產(chǎn)生的機理及隨機漂移誤差的組成,并采用Allan方差法辨識已采集到的MEMS陀螺儀靜態(tài)漂移誤差。誤差分析結果表明,該陀螺儀的靜態(tài)漂移誤差主要由零偏不穩(wěn)定性、速率隨機游走及速率斜坡三部分噪聲構成。再次,對MEMS陀螺儀靜態(tài)漂移數(shù)據(jù)進行預處理和數(shù)據(jù)檢驗以得到平穩(wěn)隨機的誤差數(shù)據(jù),然后采用AIC準則對模型定階并用Yule-Walker方程確定模型參數(shù),在此基礎上建立陀螺儀隨機漂移誤差的時間序列模型,結合誤差模型設計了卡爾曼濾波器,并對誤差數(shù)據(jù)濾波。對比濾波前后各項指標,經(jīng)卡爾曼濾波后的MEMS陀螺儀隨機漂移誤差數(shù)據(jù)的方差下降為濾波前的11.7%,影響MEMS陀螺儀精度的主要隨機誤差中,零偏不穩(wěn)定性噪聲系數(shù)減少了68.6%,速率隨機游走噪聲系數(shù)減少了67.7%,速率斜坡噪聲系數(shù)減少了68.0%,表明卡爾曼濾波能有效的減少MEMS陀螺儀隨機漂移誤差。最后,針對時間序列和卡爾曼濾波對MEMS隨機漂移數(shù)據(jù)處理中的不足,采用Singer運動模型建立MEMS隨機漂移誤差的模型,結合粒子-卡爾曼組合濾波的方法處理零均值化后的陀螺隨機漂移誤差數(shù)據(jù)。比較濾波前后各項指標,經(jīng)粒子-卡爾曼組合濾波后誤差數(shù)據(jù)的方差下降為濾波前的1.2%,影響MEMS陀螺儀精度的主要隨機誤差中,零偏不穩(wěn)定性噪聲系數(shù)減少了72.8%,速率隨機游走噪聲下降了76.2%,速率斜坡噪聲下降了74.6%,表明粒子-卡爾曼組合濾波的方法能顯著的抑制MEMS陀螺隨機漂移誤差。研究結果表明,本文采用的時間序列建模與卡爾曼濾波、基于Singer模型建模與粒子-卡爾曼濾波組合濾波兩種方案均能有效的抑制MEMS陀螺儀的隨機漂移誤差,且粒子-卡爾曼組合濾波方法的濾波效果要優(yōu)于卡爾曼濾波。研究成果能有效修正MEMS陀螺儀的隨機漂移誤差,對于提高以低成本MEMS陀螺儀作為主要慣性傳感器的慣性導航系統(tǒng)的導航精度具有一定的實用價值。
[Abstract]:In recent years, as a very important part of inertial navigation technology, MEMS gyroscope has been widely used in the fields of inertial navigation, industrial control and electronic products because of its advantages such as low cost, small size, light weight and high integration. Compared with the traditional type gyroscope, MEMS gyroscope has many advantages, but it has a lot of advantages. The measurement precision of the manufacturing process and the design level is relatively low, which often can not meet the actual use demand and greatly restricts the development and application of the MEMS gyroscope. Especially for the gyro random drift error, because the mechanism is very complex, there is no definite law and changes with the external environment, and it can not be used. It is the main factor to limit the precision of MEMS gyroscope, which is the main factor to limit the precision of the gyroscope. In this paper, the research on the modeling analysis and correction of the random drift error of the MEMS gyroscope is carried out in this paper, which is the research object of the gyroscope in the low cost MEMS inertial device MPU-6050. The research content and the conclusions are as follows: first, in order to collect the static drift data of the gyroscope, the MEMS gyroscope error data acquisition system is designed and constructed with STM32F103C8T6 as the core processor. The data collection of the gyro static drift in the MPU-6050 is realized. Secondly, the modeling and filtering of random drift error of the MEMS gyroscope is made. On the basis of research status and development dynamic investigation and analysis, the mechanism of the static drift error of MEMS gyroscope and the composition of random drift error are studied. The static drift error of the MEMS gyroscope has been identified by the Allan variance method. The error analysis results show that the static drift error of the gyroscope is mainly caused by the zero bias instability. Three parts of the rate random walk and the rate slope are made up. Again, the static drift data of the MEMS gyroscope are preprocessed and the data are tested to get the stationary random error data. Then the model parameters are determined by the AIC criterion and the model parameters are determined by the Yule-Walker equation. On this basis, the time of random drift error of the gyroscope is established. The Calman filter is designed with the error model, and the error data filter is designed. The variance of the random drift error data of the MEMS gyroscope after the Calman filter is reduced to 11.7% before and after the Calman filtering. In the main random error that affects the precision of the MEMS gyroscope, the zero bias instability noise coefficient is reduced. 68.6%, the rate of random walk noise reduction is reduced by 67.7% and the rate of rate slope noise reduction is reduced by 68%. It shows that Calman filter can effectively reduce the random drift error of MEMS gyroscope. Finally, the random drift of MEMS drift data is not enough for time series and Calman filtering, and the random drift of MEMS is established by using the Singer motion model. The error model is combined with the particle Calman combination filtering method to deal with the random drift error data of the gyroscope after the zero mean. The variance of the error data is reduced to 1.2% before filtering after the particle Calman combination filtering, and the zero bias instability noise is affected by the main random error of the precision of the MEMS gyroscope. The sound coefficient is reduced by 72.8%, the rate of random walk noise is reduced by 76.2% and the rate of rate slope is reduced by 74.6%. It shows that the method of particle Calman combination filtering can significantly inhibit the random drift error of MEMS gyro. The results show that the time series modeling and Calman filtering adopted in this paper are based on the Singer model modeling and particle Carle. Two schemes of Mann filter combined filter can effectively suppress random drift error of MEMS gyroscope, and the filtering effect of particle Calman combination filtering method is better than Calman filter. The research results can effectively modify the random drift error of MEMS gyroscope, and improve the inertia of the low current MEMS gyroscope as the main inertial sensor. The navigation accuracy of the sex navigation system is of practical value.

【學位授予單位】：西南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN96

【參考文獻】

相關期刊論文 前10條

1 馬正華;盧成俊;戎海龍;賀小捧;;基于Kalman濾波算法的陀螺儀動態(tài)漂移補償研究[J];計算機測量與控制;2016年09期

2 張玉蓮;儲海榮;張宏巍;張明月;陳陽;李銀海;;MEMS陀螺隨機誤差特性研究及補償[J];中國光學;2016年04期

3 代金華;張麗杰;;多準則MEMS陀螺隨機誤差在線建模與實時濾波[J];傳感技術學報;2016年01期

4 王麗平;李杰;祝敬德;;基于Allan方差的MEMS陀螺隨機誤差建模[J];計算機測量與控制;2015年10期

5 王春榮;黃凌山;夏爾冬;吳龍;;基于SVM的微機械陀螺儀誤差研究[J];三明學院學報;2015年02期

6 許昕;何杰;王文;卜繼軍;;微機械陀螺儀的新進展及發(fā)展趨勢[J];壓電與聲光;2014年04期

7 戴冬冰;;基于卡爾曼濾波的陀螺儀數(shù)據(jù)處理[J];數(shù)字技術與應用;2014年05期

8 張霞;;振動式微機械陀螺儀的研究現(xiàn)狀與展望[J];固體電子學研究與進展;2013年05期

9 梁兵;汪同慶;;基于HHT的振動信號趨勢項提取方法[J];電子測量技術;2013年02期

10 史巖;李小民;連光耀;;基于在線增量小波LS-SVM的垂直陀螺儀殘差故障檢測研究[J];計算機測量與控制;2013年01期

相關會議論文 前1條

1 呂志清;張巧云;;振動陀螺的新進展[A];加入WTO和中國科技與可持續(xù)發(fā)展——挑戰(zhàn)與機遇、責任和對策（下冊）[C];2002年

相關博士學位論文 前5條

1 譚秋衡;時間序列的非平穩(wěn)性度量及其應用[D];中國科學院研究生院（武漢物理與數(shù)學研究所）;2013年

2 何昆鵬;MEMS慣性器件參數(shù)辨識及系統(tǒng)誤差補償技術[D];哈爾濱工程大學;2009年

3 劉俊;基于紅外圖像的內(nèi)河運動船舶目標檢測和跟蹤技術研究[D];重慶大學;2008年

4 聶琦;非線性濾波及其在導航系統(tǒng)中的應用[D];哈爾濱工程大學;2008年

5 劉危;基于MEMS的低成本MIMU的應用研究[D];國防科學技術大學;2004年

相關碩士學位論文 前10條

1 馮思奇;飛碟射擊槍體姿態(tài)參數(shù)獲取技術及系統(tǒng)研究[D];中北大學;2016年

2 杜少鶴;MEMS陀螺儀組合系統(tǒng)及濾波算法設計[D];哈爾濱工業(yè)大學;2015年

3 程陽;MEMS慣性器件誤差分析與補償方法研究[D];沈陽理工大學;2015年

4 段棟棟;高精度MEMS陀螺儀的濾波算法研究[D];電子科技大學;2014年

5 李座;馬氏鏈蒙特卡洛方法在金融模型參數(shù)估計中的應用[D];清華大學;2014年

6 羅小娜;時間序列模型辨識研究及其在導彈建模中的應用[D];南昌航空大學;2013年

7 梁麗娟;基于機器人及軍用導航系統(tǒng)的MEMS陀螺儀性能研究[D];北京交通大學;2011年

8 趙靜;基于粒子濾波的陀螺誤差建模及補償技術研究[D];中北大學;2010年

9 陳寶毅;MEMS慣性器件的數(shù)據(jù)采集與誤差分析技術研究[D];哈爾濱工程大學;2009年

10 張勤拓;基于MEMS慣性傳感器的定位定向裝置研究[D];哈爾濱工程大學;2008年

，

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