【摘要】：MEMS (Micro Electromechanical System)陀螺儀相比于其他類型的陀螺儀,具有體積小、功耗低、成本低、質(zhì)量輕、易集成以及生產(chǎn)批量大等優(yōu)點(diǎn)。由于MEMS陀螺儀具有上述優(yōu)勢(shì),已經(jīng)在眾多民用領(lǐng)域得到廣泛應(yīng)用,如汽車導(dǎo)航、汽車安全氣囊脫扣裝置、拍照攝像設(shè)備的防抖動(dòng)平臺(tái)、機(jī)器人姿態(tài)測(cè)量系統(tǒng)、電子玩具、虛擬體感游戲等。在軍事領(lǐng)域,可以預(yù)見未來的武器系統(tǒng)、無人機(jī)等偵查設(shè)備必然向著數(shù)字化、智能化、小型化和高機(jī)動(dòng)化發(fā)展,因此MEMS陀螺儀具有巨大的發(fā)展?jié)摿蛢r(jià)值。但是MEMS陀螺儀的測(cè)量精度相對(duì)較低,這成為微小型導(dǎo)航系統(tǒng)、制導(dǎo)與控制系統(tǒng)等關(guān)鍵技術(shù)的發(fā)展瓶頸。解決這一問題的途徑有兩種：第一,從硬件內(nèi)部結(jié)構(gòu)上針對(duì)性地進(jìn)行分析；第二,從軟件層面入手,從算法角度處理。MEMS陀螺儀的隨機(jī)漂移誤差問題是提升測(cè)量精度這一課題的重要研究內(nèi)容。本文從軟件層面入手,研究了MEMS陀螺儀的靜態(tài)輸出噪聲特性,就MEMS陀螺儀的隨機(jī)漂移誤差補(bǔ)償技術(shù)展開研究。本文介紹了MEMS陀螺儀的若干性能指標(biāo),搭建了MEMS陀螺儀漂移數(shù)據(jù)采集系統(tǒng),用Allan方差方法對(duì)MEMS陀螺儀噪聲特性進(jìn)行了分析。對(duì)MEMS陀螺儀輸出的非平穩(wěn)數(shù)據(jù)進(jìn)行了預(yù)處理,應(yīng)用拉伊達(dá)準(zhǔn)則去除奇異點(diǎn),通過逐步回歸方法擬合漂移趨勢(shì),將其轉(zhuǎn)換為平穩(wěn)、零均值的隨機(jī)漂移數(shù)據(jù)。根據(jù)自相關(guān)系數(shù)ACF、偏相關(guān)系數(shù)PCF、AIC等準(zhǔn)則選取了時(shí)間序列模型和參數(shù),采用基于自回歸AR模型的線性離散系統(tǒng)的卡爾曼濾波方法對(duì)MEMS陀螺隨機(jī)漂移進(jìn)行補(bǔ)償,MATLAB仿真及Allan方差計(jì)算結(jié)果表明,該方法能夠有效地抑制漂移誤差達(dá)50%。針對(duì)基于自回歸AR模型的線性離散系統(tǒng)的卡爾曼濾波方法的不足,應(yīng)用了AR與SVM(Support Vector Machine)支持向量機(jī)混合模型方法。用AR模型描述隨機(jī)漂移數(shù)據(jù)中的線性部分,用SVM支持向量機(jī)方法對(duì)非線性數(shù)據(jù)進(jìn)行處理,經(jīng)過歸一化處理、相空間重構(gòu)、數(shù)據(jù)訓(xùn)練及數(shù)據(jù)預(yù)測(cè)驗(yàn)證等一系列步驟,計(jì)算Allan方差及其它性能指標(biāo),結(jié)果表明：應(yīng)用AR與SVM支持向量機(jī)混合模型方法能夠有效抑制漂移誤差80%以上,相比于基于時(shí)間序列的卡爾曼濾波方法,具有更為良好的去噪效果。
[Abstract]:Compared with other kinds of gyroscopes, MEMS (Micro Electromechanical System) gyroscopes have the advantages of small size, low power consumption, low cost, light weight, easy integration and large batch production. Because of the above advantages, MEMS gyroscope has been widely used in many civil fields, such as automobile navigation, automobile airbag tripping device, anti-jitter platform of camera equipment, robot attitude measurement system, electronic toy, etc. Virtual sense of the game and so on. In the military field, the future of weapon system, UAV and other reconnaissance equipment will inevitably develop towards digitalization, intelligence, miniaturization and high motorization. Therefore, MEMS gyroscope has great development potential and value. However, the measurement accuracy of MEMS gyroscope is relatively low, which has become the bottleneck of the development of micro navigation system, guidance and control system and other key technologies. There are two ways to solve this problem: first, the internal structure of the hardware is analyzed pertinently; Secondly, the problem of random drift error of MEMS gyroscopes is an important part of improving the measurement accuracy from the point of view of algorithm and software. In this paper, the static output noise characteristics of MEMS gyroscopes are studied from the software level, and the compensation technique of random drift error of MEMS gyroscopes is studied. In this paper, some performance indexes of MEMS gyroscope are introduced, the drift data acquisition system of MEMS gyroscope is built, and the noise characteristics of MEMS gyroscope are analyzed by Allan variance method. The non-stationary data from MEMS gyroscope were pretreated, and the singularity was removed by using Ray criterion, and the drift trend was fitted by stepwise regression method, which was transformed into stationary and zero mean random drift data. According to the criterion of autocorrelation coefficient ACF, partial correlation coefficient PCF,AIC and so on, the time series model and parameters are selected, and the Kalman filter method of linear discrete system based on autoregressive AR model is used to compensate the random drift of MEMS gyroscope. The results of MATLAB simulation and Allan variance calculation show that the proposed method can effectively suppress the drift error up to 50. Aiming at the deficiency of Kalman filtering method for linear discrete systems based on autoregressive AR model, the hybrid model method of AR and SVM (Support Vector Machine) support vector machine is applied. AR model is used to describe the linear part of the random drift data, and SVM support vector machine is used to process the nonlinear data. After normalization, phase space reconstruction, data training and data prediction verification, a series of steps, such as normalization, phase space reconstruction, data training and data prediction verification, are presented. The Allan variance and other performance indexes are calculated. The results show that the mixed model method of AR and SVM support vector machine can effectively suppress the drift error by more than 80%, compared with the Kalman filter method based on time series. It has better denoising effect.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN96

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