【摘要】：隨著慣性導(dǎo)航技術(shù)的快速發(fā)展,MEMS陀螺儀的應(yīng)用也日益廣泛起來(lái)。如何提高M(jìn)EMS陀螺儀輸出數(shù)據(jù)的精度,已成為國(guó)內(nèi)外專(zhuān)家學(xué)者研究的熱點(diǎn)問(wèn)題。針對(duì)這一問(wèn)題,本文在基于信號(hào)采集系統(tǒng)的基礎(chǔ)上,采集了陀螺儀輸出的原始數(shù)據(jù),并對(duì)其進(jìn)行了誤差分析,然后基于時(shí)間序列分析方法建立了陀螺儀隨機(jī)漂移的數(shù)學(xué)模型。最后,應(yīng)用不同的濾波技術(shù)對(duì)陀螺儀的零點(diǎn)漂移與動(dòng)態(tài)誤差進(jìn)行了研究與仿真分析,從而確定了最終的信號(hào)處理方法。首先,本文介紹了信號(hào)采集系統(tǒng)的基本原理,并進(jìn)一步分析了其硬件結(jié)構(gòu)與軟件組成,然后設(shè)計(jì)了一種以STM32為核心控制器的信號(hào)采集系統(tǒng)。接著對(duì)MEMS陀螺儀的工作原理作了詳細(xì)說(shuō)明,同時(shí)分析了其零點(diǎn)調(diào)整機(jī)制。另外,確定了陀螺儀原始信號(hào)的采樣頻率,并采集了陀螺儀的原始輸出數(shù)據(jù),接著在MATLAB環(huán)境下采用Allan方差分析法對(duì)其誤差項(xiàng)進(jìn)行了辨識(shí),并確定了其各誤差項(xiàng)的系數(shù)。其次,本文在深入理解平穩(wěn)隨機(jī)過(guò)程統(tǒng)計(jì)特性的基礎(chǔ)上,對(duì)陀螺儀的輸出數(shù)據(jù)進(jìn)行了必要的預(yù)處理,并確定用時(shí)間序列分析法建立陀螺儀隨機(jī)漂移的誤差模型,同時(shí)確定了模型定階以及參數(shù)估計(jì)的方法。然后,本文分析了傳統(tǒng)卡爾曼濾波器與二階低通濾波器的設(shè)計(jì)思想、工作原理以及數(shù)學(xué)模型,接著對(duì)陀螺儀輸出數(shù)據(jù)的統(tǒng)計(jì)特性進(jìn)行了檢驗(yàn),并經(jīng)過(guò)實(shí)驗(yàn)確定了其誤差模型的階數(shù)與參數(shù),最終以AR(1)模型作為其誤差模型。最后分別使用卡爾曼濾波器與二階低通濾波器在MATLAB環(huán)境下對(duì)陀螺儀的靜態(tài)數(shù)據(jù)進(jìn)行了仿真分析,通過(guò)分析比較濾波結(jié)果,確定用傳統(tǒng)的卡爾曼濾波器穩(wěn)定零點(diǎn)。最后,本文分析了帶有確定性輸入項(xiàng)的卡爾曼濾波器的工作原理與數(shù)學(xué)模型,接著用二階低通濾波器處理陀螺儀的動(dòng)態(tài)數(shù)據(jù),分析實(shí)驗(yàn)結(jié)果發(fā)現(xiàn),雖然可以在一定程度上減輕誤差,但是存在著濾波滯后的問(wèn)題,因此本文設(shè)計(jì)了一種新的算法,即擴(kuò)展的卡爾曼濾波器與二階低通濾波器融合的算法,通過(guò)對(duì)二階濾波的結(jié)果進(jìn)行差分,然后把差分結(jié)果作為卡爾曼濾波器的確定性輸入,實(shí)驗(yàn)結(jié)果表明:該算法可以很好的處理陀螺儀的動(dòng)態(tài)誤差。
[Abstract]:With the rapid development of inertial navigation technology, the application of MEMS gyroscope is becoming more and more extensive. How to improve the precision of output data of MEMS gyroscopes has become a hot issue for experts and scholars at home and abroad. In order to solve this problem, based on the signal acquisition system, the original data of gyroscope output are collected, and the error is analyzed. Then, the mathematical model of random drift of gyroscope is established based on the time series analysis method. Finally, the zero drift and dynamic error of gyroscope are studied and simulated by using different filtering techniques, and the final signal processing method is determined. Firstly, this paper introduces the basic principle of the signal acquisition system, and further analyzes its hardware structure and software composition, then designs a signal acquisition system with STM32 as the core controller. Then the working principle of MEMS gyroscope is explained in detail, and its zero adjustment mechanism is analyzed. In addition, the sampling frequency of the original signal of the gyroscope is determined, and the original output data of the gyroscope are collected. Then, the error terms are identified by the Allan variance analysis method under the MATLAB environment, and the coefficients of each error term are determined. Secondly, on the basis of deeply understanding the statistical characteristics of stationary stochastic process, this paper preprocesses the output data of gyroscope and establishes the error model of random drift of gyroscope by time series analysis. At the same time, the order of the model and the method of parameter estimation are determined. Then, the design idea, working principle and mathematical model of the traditional Kalman filter and the second-order low-pass filter are analyzed, and the statistical characteristics of the output data of the gyroscope are tested. The order and parameters of the error model are determined by experiments. Finally, the AR (1) model is used as its error model. Finally, the static data of gyroscope are simulated by using Kalman filter and second-order low-pass filter in MATLAB environment, and the stable zero point of gyro is determined by comparing the filtering results with the traditional Kalman filter. Finally, the working principle and mathematical model of Kalman filter with deterministic input term are analyzed, and the dynamic data of gyroscope are processed by second-order low-pass filter. Although the error can be reduced to a certain extent, there is the problem of filtering lag. Therefore, a new algorithm is designed in this paper, which is the fusion algorithm of extended Kalman filter and second-order low-pass filter. Through the difference of the second order filter result and the difference result as the deterministic input of the Kalman filter, the experimental results show that the algorithm can deal with the dynamic error of the gyroscope very well.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN96;TN713

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本文編號(hào)：2298857