基于MEMS陀螺儀隨機(jī)漂移誤差補(bǔ)償?shù)难芯?/H1>
發(fā)布時(shí)間：2018-05-29 20:22
本文選題：MEMS陀螺儀 + Allan方差�。� 參考：《中北大學(xué)》2017年碩士論文
【摘要】：MEMS(Micro.Electromechanical.System)陀螺儀以體積小、壽命長(zhǎng)、成本低、耐沖擊和功耗低等特征,廣泛應(yīng)用于眾多民用領(lǐng)域,如汽車導(dǎo)航、機(jī)器人姿態(tài)測(cè)量系統(tǒng)、拍照設(shè)備的防抖平臺(tái)、虛擬體感游戲、電子玩具等;此外,日后可以預(yù)見的無人機(jī)等偵查設(shè)備等武器系統(tǒng)、必然向著小型化、智能化、數(shù)字化與高機(jī)動(dòng)化方向發(fā)展,因此,MEMS陀螺儀具有巨大的發(fā)展價(jià)值與廣闊的前景,其優(yōu)良的特性使其受到世界各國(guó)的廣泛關(guān)注,并且已被列為21世紀(jì)振興發(fā)展的關(guān)鍵技術(shù)之一。然而MEMS陀螺儀的精度相對(duì)較低,使其成為制導(dǎo)控制系統(tǒng),微型導(dǎo)航系統(tǒng)等領(lǐng)域的發(fā)展瓶頸。提高陀螺精度的途徑有兩種:第一,從硬件構(gòu)造上提升系統(tǒng)性能;第二,從算法角度入手,切實(shí)有效地縮小陀螺的隨機(jī)漂移誤差,提高測(cè)量精度。本文從軟件的角度入手,研究了MEMS陀螺儀的.噪聲特性,就MEMS陀螺儀的隨機(jī)漂移.誤差補(bǔ)償.技術(shù)展開了研究。本文首先介紹了陀螺的的幾項(xiàng)性能指標(biāo)與內(nèi)部工作原理,用Allan方差法對(duì)陀螺的噪聲特性進(jìn)行了相關(guān)分析。之后系統(tǒng)介紹了時(shí)間序列法建模的的相關(guān)理論,為增強(qiáng)試驗(yàn)數(shù)據(jù)的可靠性,特采集10組數(shù)據(jù)運(yùn)用拉伊達(dá)準(zhǔn)則去除奇異點(diǎn)法對(duì)陀螺的輸出數(shù)據(jù)進(jìn)行預(yù)處理,接著應(yīng)用逐步回歸的方法對(duì)漂移趨勢(shì)進(jìn)行擬合,將其轉(zhuǎn)換為零均值的平穩(wěn)數(shù)據(jù)。最后計(jì)算ACF,PCF,建立了自回歸的AR模型。接著運(yùn)用卡爾曼濾波對(duì)10組陀螺的隨機(jī)漂移誤差進(jìn)行處理。實(shí)驗(yàn)結(jié)果充分表明:基于時(shí)間序列的卡爾曼濾波法在陀螺隨機(jī)漂移的誤差補(bǔ)償中的應(yīng)用是有效的。對(duì)于MEMS陀螺的動(dòng)態(tài)誤差處理方面,以轉(zhuǎn)臺(tái)作為標(biāo)定,設(shè)定陀螺進(jìn)行不同速率的勻速運(yùn)動(dòng),此時(shí)經(jīng)典卡爾曼濾波器在解決動(dòng)態(tài)確定性誤差時(shí),依舊可行有效。而當(dāng)陀螺做變速運(yùn)動(dòng)時(shí),設(shè)計(jì)了一種自適應(yīng)卡爾曼濾波器能夠有效地抑制陀螺的動(dòng)態(tài)漂移,提高陀螺精度。
[Abstract]:Due to its small size, long life, low cost, low impact resistance and low power consumption, MEMS Micro.Electromechanic.System. gyroscope is widely used in many civil fields, such as automobile navigation, robot attitude measurement system, anti-shake platform of photo equipment, virtual body feeling game, etc. Electronic toys and so on; in addition, weapon systems such as reconnaissance equipment such as unmanned aerial vehicles, which can be foreseen in the future, are bound to develop towards miniaturization, intelligence, digitization and high motorization. Therefore, MEMS gyroscopes have great development value and broad prospects. Because of its excellent characteristics, it has attracted worldwide attention and has been listed as one of the key technologies for revitalizing development in the 21 ~ (st) century. However, the precision of MEMS gyroscope is relatively low, which makes it a bottleneck in the field of guidance control system and micro navigation system. There are two ways to improve the gyroscope precision: first, to improve the system performance from the hardware structure; second, to reduce the gyroscope random drift error effectively and improve the measurement accuracy from the point of view of algorithm. In this paper, the MEMS gyroscope is studied from the point of view of software. The noise characteristics of the MEMS gyroscope are random drift. Error compensation. The technology has been studied. In this paper, several performance indexes and internal working principles of gyroscope are introduced, and the noise characteristics of gyroscope are analyzed by Allan variance method. In order to enhance the reliability of the test data, 10 groups of data are collected and the singular point removal method is used to pre-process the output data of the gyroscope. Then the drift trend is fitted by stepwise regression method and converted to the stationary data of zero mean value. Finally, the autoregressive AR model is established by calculating the ACFG PCF. Then Kalman filter is used to deal with the random drift error of 10 groups of gyroscopes. The experimental results show that the Kalman filtering method based on time series is effective in the error compensation of gyroscope random drift. For the dynamic error processing of MEMS gyroscope, the gyroscope is calibrated to set the gyroscope moving at different speed, and the classical Kalman filter is still feasible and effective in solving the dynamic deterministic error. When the gyroscope moves at variable speed, an adaptive Kalman filter is designed to effectively suppress the dynamic drift of the gyroscope and improve the precision of the gyroscope.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212;TN96
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 趙婧婧;王寶麗;姚喜妍;;基于ARMA模型的運(yùn)城市果蔬肉類價(jià)格預(yù)測(cè)研究[J];安徽農(nóng)業(yè)科學(xué);2014年25期
2 谷豐;周楹君;何玉慶;韓建達(dá);;非線性卡爾曼濾波方法的實(shí)驗(yàn)比較[J];控制與決策;2014年08期
3 曾凡祥;李勤英;;卡爾曼濾波在動(dòng)態(tài)測(cè)量系統(tǒng)中的應(yīng)用[J];北京測(cè)繪;2014年03期
4 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(shù)與應(yīng)用;2014年05期
5 高偉偉;王廣龍;高鳳岐;高爽;賈波;;穩(wěn)瞄穩(wěn)向系統(tǒng)FOG隨機(jī)信號(hào)處理方法研究[J];中國(guó)測(cè)試;2014年02期
6 郝萬亮;孫付平;;基于Allan方差的陀螺隨機(jī)誤差分析[J];測(cè)繪與空間地理信息;2014年03期
7 袁豹;陸游;;基于總體最小二乘的MEMS陀螺儀標(biāo)定方法研究[J];勘察科學(xué)技術(shù);2014年01期
8 呂品;劉建業(yè);賴際舟;秦國(guó)慶;;光纖陀螺的隨機(jī)誤差性能評(píng)價(jià)方法研究[J];儀器儀表學(xué)報(bào);2014年02期
9 陸振宇;文華;龍玉其;;MEMS陀螺儀濾波算法設(shè)計(jì)[J];傳感器與微系統(tǒng);2013年10期
10 劉洲洲;王禹輝;王國(guó)樹;;基于卡爾曼自適應(yīng)濾波算法的機(jī)動(dòng)目標(biāo)仿真研究[J];微處理機(jī);2013年05期
相關(guān)碩士學(xué)位論文 前9條
1 杜少鶴;MEMS陀螺儀組合系統(tǒng)及濾波算法設(shè)計(jì)[D];哈爾濱工業(yè)大學(xué);2015年
2 霍元正;MEMS陀螺儀隨機(jī)漂移誤差補(bǔ)償技術(shù)的研究[D];東南大學(xué);2015年
3 吳峰;兩軸平臺(tái)穩(wěn)定系統(tǒng)中MEMS陀螺誤差建模與分析[D];天津大學(xué);2012年
4 徐凱;MEMS陀螺誤差補(bǔ)償?shù)乃惴ㄑ芯縖D];沈陽理工大學(xué);2012年
5 劉永;小波分析在MEMS陀螺信號(hào)降噪中的應(yīng)用研究[D];國(guó)防科學(xué)技術(shù)大學(xué);2011年
6 高海豹;無線傳感器網(wǎng)絡(luò)的數(shù)據(jù)傳輸[D];電子科技大學(xué);2010年
7 胡志強(qiáng);激光陀螺誤差模型研究[D];西北大學(xué);2008年
8 王勇;基于DSP的MEMS陀螺信號(hào)處理平臺(tái)設(shè)計(jì)[D];西北工業(yè)大學(xué);2007年
9 李建竹;水下晃動(dòng)平臺(tái)姿態(tài)估計(jì)系統(tǒng)研究與設(shè)計(jì)[D];西北工業(yè)大學(xué);2007年
，
本文編號(hào)：1952265
本文鏈接：http://sikaile.net/kejilunwen/zidonghuakongzhilunwen/1952265.html
本文選題：MEMS陀螺儀 + Allan方差�。� 參考：《中北大學(xué)》2017年碩士論文
【摘要】：MEMS(Micro.Electromechanical.System)陀螺儀以體積小、壽命長(zhǎng)、成本低、耐沖擊和功耗低等特征,廣泛應(yīng)用于眾多民用領(lǐng)域,如汽車導(dǎo)航、機(jī)器人姿態(tài)測(cè)量系統(tǒng)、拍照設(shè)備的防抖平臺(tái)、虛擬體感游戲、電子玩具等;此外,日后可以預(yù)見的無人機(jī)等偵查設(shè)備等武器系統(tǒng)、必然向著小型化、智能化、數(shù)字化與高機(jī)動(dòng)化方向發(fā)展,因此,MEMS陀螺儀具有巨大的發(fā)展價(jià)值與廣闊的前景,其優(yōu)良的特性使其受到世界各國(guó)的廣泛關(guān)注,并且已被列為21世紀(jì)振興發(fā)展的關(guān)鍵技術(shù)之一。然而MEMS陀螺儀的精度相對(duì)較低,使其成為制導(dǎo)控制系統(tǒng),微型導(dǎo)航系統(tǒng)等領(lǐng)域的發(fā)展瓶頸。提高陀螺精度的途徑有兩種:第一,從硬件構(gòu)造上提升系統(tǒng)性能;第二,從算法角度入手,切實(shí)有效地縮小陀螺的隨機(jī)漂移誤差,提高測(cè)量精度。本文從軟件的角度入手,研究了MEMS陀螺儀的.噪聲特性,就MEMS陀螺儀的隨機(jī)漂移.誤差補(bǔ)償.技術(shù)展開了研究。本文首先介紹了陀螺的的幾項(xiàng)性能指標(biāo)與內(nèi)部工作原理,用Allan方差法對(duì)陀螺的噪聲特性進(jìn)行了相關(guān)分析。之后系統(tǒng)介紹了時(shí)間序列法建模的的相關(guān)理論,為增強(qiáng)試驗(yàn)數(shù)據(jù)的可靠性,特采集10組數(shù)據(jù)運(yùn)用拉伊達(dá)準(zhǔn)則去除奇異點(diǎn)法對(duì)陀螺的輸出數(shù)據(jù)進(jìn)行預(yù)處理,接著應(yīng)用逐步回歸的方法對(duì)漂移趨勢(shì)進(jìn)行擬合,將其轉(zhuǎn)換為零均值的平穩(wěn)數(shù)據(jù)。最后計(jì)算ACF,PCF,建立了自回歸的AR模型。接著運(yùn)用卡爾曼濾波對(duì)10組陀螺的隨機(jī)漂移誤差進(jìn)行處理。實(shí)驗(yàn)結(jié)果充分表明:基于時(shí)間序列的卡爾曼濾波法在陀螺隨機(jī)漂移的誤差補(bǔ)償中的應(yīng)用是有效的。對(duì)于MEMS陀螺的動(dòng)態(tài)誤差處理方面,以轉(zhuǎn)臺(tái)作為標(biāo)定,設(shè)定陀螺進(jìn)行不同速率的勻速運(yùn)動(dòng),此時(shí)經(jīng)典卡爾曼濾波器在解決動(dòng)態(tài)確定性誤差時(shí),依舊可行有效。而當(dāng)陀螺做變速運(yùn)動(dòng)時(shí),設(shè)計(jì)了一種自適應(yīng)卡爾曼濾波器能夠有效地抑制陀螺的動(dòng)態(tài)漂移,提高陀螺精度。
[Abstract]:Due to its small size, long life, low cost, low impact resistance and low power consumption, MEMS Micro.Electromechanic.System. gyroscope is widely used in many civil fields, such as automobile navigation, robot attitude measurement system, anti-shake platform of photo equipment, virtual body feeling game, etc. Electronic toys and so on; in addition, weapon systems such as reconnaissance equipment such as unmanned aerial vehicles, which can be foreseen in the future, are bound to develop towards miniaturization, intelligence, digitization and high motorization. Therefore, MEMS gyroscopes have great development value and broad prospects. Because of its excellent characteristics, it has attracted worldwide attention and has been listed as one of the key technologies for revitalizing development in the 21 ~ (st) century. However, the precision of MEMS gyroscope is relatively low, which makes it a bottleneck in the field of guidance control system and micro navigation system. There are two ways to improve the gyroscope precision: first, to improve the system performance from the hardware structure; second, to reduce the gyroscope random drift error effectively and improve the measurement accuracy from the point of view of algorithm. In this paper, the MEMS gyroscope is studied from the point of view of software. The noise characteristics of the MEMS gyroscope are random drift. Error compensation. The technology has been studied. In this paper, several performance indexes and internal working principles of gyroscope are introduced, and the noise characteristics of gyroscope are analyzed by Allan variance method. In order to enhance the reliability of the test data, 10 groups of data are collected and the singular point removal method is used to pre-process the output data of the gyroscope. Then the drift trend is fitted by stepwise regression method and converted to the stationary data of zero mean value. Finally, the autoregressive AR model is established by calculating the ACFG PCF. Then Kalman filter is used to deal with the random drift error of 10 groups of gyroscopes. The experimental results show that the Kalman filtering method based on time series is effective in the error compensation of gyroscope random drift. For the dynamic error processing of MEMS gyroscope, the gyroscope is calibrated to set the gyroscope moving at different speed, and the classical Kalman filter is still feasible and effective in solving the dynamic deterministic error. When the gyroscope moves at variable speed, an adaptive Kalman filter is designed to effectively suppress the dynamic drift of the gyroscope and improve the precision of the gyroscope.
【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212;TN96
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 趙婧婧;王寶麗;姚喜妍;;基于ARMA模型的運(yùn)城市果蔬肉類價(jià)格預(yù)測(cè)研究[J];安徽農(nóng)業(yè)科學(xué);2014年25期
2 谷豐;周楹君;何玉慶;韓建達(dá);;非線性卡爾曼濾波方法的實(shí)驗(yàn)比較[J];控制與決策;2014年08期
3 曾凡祥;李勤英;;卡爾曼濾波在動(dòng)態(tài)測(cè)量系統(tǒng)中的應(yīng)用[J];北京測(cè)繪;2014年03期
4 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(shù)與應(yīng)用;2014年05期
5 高偉偉;王廣龍;高鳳岐;高爽;賈波;;穩(wěn)瞄穩(wěn)向系統(tǒng)FOG隨機(jī)信號(hào)處理方法研究[J];中國(guó)測(cè)試;2014年02期
6 郝萬亮;孫付平;;基于Allan方差的陀螺隨機(jī)誤差分析[J];測(cè)繪與空間地理信息;2014年03期
7 袁豹;陸游;;基于總體最小二乘的MEMS陀螺儀標(biāo)定方法研究[J];勘察科學(xué)技術(shù);2014年01期
8 呂品;劉建業(yè);賴際舟;秦國(guó)慶;;光纖陀螺的隨機(jī)誤差性能評(píng)價(jià)方法研究[J];儀器儀表學(xué)報(bào);2014年02期
9 陸振宇;文華;龍玉其;;MEMS陀螺儀濾波算法設(shè)計(jì)[J];傳感器與微系統(tǒng);2013年10期
10 劉洲洲;王禹輝;王國(guó)樹;;基于卡爾曼自適應(yīng)濾波算法的機(jī)動(dòng)目標(biāo)仿真研究[J];微處理機(jī);2013年05期
相關(guān)碩士學(xué)位論文 前9條
1 杜少鶴;MEMS陀螺儀組合系統(tǒng)及濾波算法設(shè)計(jì)[D];哈爾濱工業(yè)大學(xué);2015年
2 霍元正;MEMS陀螺儀隨機(jī)漂移誤差補(bǔ)償技術(shù)的研究[D];東南大學(xué);2015年
3 吳峰;兩軸平臺(tái)穩(wěn)定系統(tǒng)中MEMS陀螺誤差建模與分析[D];天津大學(xué);2012年
4 徐凱;MEMS陀螺誤差補(bǔ)償?shù)乃惴ㄑ芯縖D];沈陽理工大學(xué);2012年
5 劉永;小波分析在MEMS陀螺信號(hào)降噪中的應(yīng)用研究[D];國(guó)防科學(xué)技術(shù)大學(xué);2011年
6 高海豹;無線傳感器網(wǎng)絡(luò)的數(shù)據(jù)傳輸[D];電子科技大學(xué);2010年
7 胡志強(qiáng);激光陀螺誤差模型研究[D];西北大學(xué);2008年
8 王勇;基于DSP的MEMS陀螺信號(hào)處理平臺(tái)設(shè)計(jì)[D];西北工業(yè)大學(xué);2007年
9 李建竹;水下晃動(dòng)平臺(tái)姿態(tài)估計(jì)系統(tǒng)研究與設(shè)計(jì)[D];西北工業(yè)大學(xué);2007年
，本文編號(hào)：1952265
本文鏈接：http://sikaile.net/kejilunwen/zidonghuakongzhilunwen/1952265.html
