本文選題：MEMS + 航姿系統(tǒng)　； 參考：《哈爾濱工程大學(xué)》2014年碩士論文

【摘要】：二十一世紀(jì)的高科技水平促進(jìn)了微電子技術(shù)和微機(jī)械加工工藝的進(jìn)步,在其推動下MEMS慣性傳感器應(yīng)運(yùn)而生并且逐漸成為世界慣性領(lǐng)域中的研究熱點(diǎn)。以MEMS慣性測量單元為主體的航姿參考系統(tǒng)以其微型化、輕質(zhì)量、低成本等優(yōu)勢廣泛應(yīng)用于機(jī)器人、無人機(jī)、汽車、艦船、潛艇、通訊設(shè)備、生物醫(yī)學(xué)等多個領(lǐng)域。微慣性系統(tǒng)由于其低精度和誤差隨時(shí)間累積等因素導(dǎo)致系統(tǒng)誤差大且易發(fā)散,因此它不能夠獨(dú)立完成姿態(tài)測量工作。本研究在MEMS慣性測量單元的基礎(chǔ)上以磁強(qiáng)計(jì)、GPS為輔助,融合三者信息形成優(yōu)勢互補(bǔ)的航姿系統(tǒng)。本文首先介紹了航姿系統(tǒng)中常用的參考坐標(biāo)系以及磁強(qiáng)計(jì)測量原理,在此基礎(chǔ)上研究了利用MEMS加速度計(jì)和磁強(qiáng)計(jì)輸出信息進(jìn)行解析式初始對準(zhǔn)的方法。此外,介紹了 MEMS航姿系統(tǒng)的多種姿態(tài)更新算法并對各個方法進(jìn)行對比分析,最終選擇四元數(shù)法進(jìn)行姿態(tài)更新。在基本理論學(xué)習(xí)的基礎(chǔ)上本文對MEMS陀螺儀、MEMS加速度計(jì)以及磁強(qiáng)計(jì)進(jìn)行誤差分析并且分別建立了 MEMS陀螺儀輸出誤差模型、MEMS加速度計(jì)輸出誤差模型以及磁強(qiáng)計(jì)輸出誤差模型。然后,根據(jù)各誤差模型分別設(shè)計(jì)了陀螺儀角增量標(biāo)定方案、加速度計(jì)六位置標(biāo)定方案,同時(shí)研究了基于橢球假設(shè)的磁強(qiáng)計(jì)誤差標(biāo)定方案,進(jìn)而計(jì)算出各器件誤差參數(shù)并進(jìn)行相應(yīng)補(bǔ)償,為后續(xù)研究奠定堅(jiān)實(shí)的基礎(chǔ)。在航姿系統(tǒng)算法研究過程中首先建立了基于大失準(zhǔn)角的MEMS慣性系統(tǒng)非線性誤差方程,在有GPS信號的情況下,以針對非線性系統(tǒng)的EKF濾波算法進(jìn)行MIMU、GPS、磁強(qiáng)計(jì)三者的信息融合,在此基礎(chǔ)上利用GPS位置、速度和磁強(qiáng)計(jì)解算的磁航向與慣性系統(tǒng)自身解算的位置、速度、航向之差為EKF濾波的觀測量,進(jìn)而估計(jì)系統(tǒng)姿態(tài),之后通過仿真驗(yàn)證本算法的有效性。在無GPS信號情況下,利用改進(jìn)的基于四元數(shù)誤差的自適應(yīng)卡爾曼濾波算法實(shí)現(xiàn)MIMU、磁強(qiáng)計(jì)組合姿態(tài)估計(jì)。利用陀螺儀進(jìn)行姿態(tài)解算的同時(shí)以加速度計(jì)和磁強(qiáng)計(jì)為輔助,提出了基于加速度計(jì)和磁強(qiáng)計(jì)的二階量測更新算法,以及針對外部加速度的自適應(yīng)算法,并且通過Matlab仿真驗(yàn)證了本算法的合理性。在仿真的基礎(chǔ)上,分別設(shè)計(jì)了實(shí)驗(yàn)室靜態(tài)試驗(yàn)、室外車載試驗(yàn)以及松花江江試試驗(yàn)。其中,靜態(tài)試驗(yàn)結(jié)果較為理想;室外車載試驗(yàn)過程中汽車一直行駛在平穩(wěn)路面,因此解算姿態(tài)具有一定的精度;而江試結(jié)果則進(jìn)一步說明了在低動態(tài)條件下MIMU/GPS/磁強(qiáng)計(jì)組合方法能夠保證姿態(tài)有效跟蹤。
[Abstract]:The high technology level in 21 century has promoted the progress of microelectronics technology and micromachining technology. The MEMS inertial sensor has emerged as the times require and gradually become the research hotspot in the inertial field of the world. The attitude reference system based on MEMS inertial measurement unit is widely used in many fields, such as robot, UAV, automobile, ship, submarine, communication equipment, biomedicine and so on with its advantages of miniaturization, light weight and low cost. Due to its low precision and error accumulation with time, the micro inertial system can not complete the attitude measurement independently because of its large error and easy divergence. In this paper, based on MEMS inertial measurement unit, magnetometer GPS is used as the aid, and the three information are fused to form an attitude and navigation system with complementary advantages. In this paper, the reference coordinate system and the measuring principle of magnetometer are introduced. Based on this, the analytical initial alignment method using MEMS accelerometer and magnetometer output information is studied. In addition, various attitude updating algorithms of MEMS attitude and attitude system are introduced and compared with each other. Finally, the quaternion method is selected for attitude updating. Based on the basic theoretical study, the error analysis of MEMS accelerometer and magnetometer is carried out. The output error model of MEMS gyroscope and the output error model of MEMS accelerometer and magnetometer are established respectively. Then, according to each error model, the angular increment calibration scheme of gyroscope and the six-position calibration scheme of accelerometer are designed, and the calibration scheme of magnetometer error based on ellipsoid hypothesis is studied. The error parameters of each device are calculated and compensated accordingly, which lays a solid foundation for further research. The nonlinear error equation of MEMS inertial system based on large misalignment angle is first established in the research process of attitude and heading system. In the case of GPS signal, the information fusion of MIMUGPS and magnetometer is carried out for the EKF filtering algorithm of nonlinear system. On the basis of this, the magnetic heading calculated by GPS position, velocity and magnetometer and the position, velocity and course difference of the inertial system itself are used to estimate the attitude of the system by EKF filter, and the validity of the algorithm is verified by simulation. In the case of no GPS signal, an improved adaptive Kalman filter algorithm based on quaternion error is used to realize the combined attitude estimation of MIMUand magnetometer. By using gyroscopes to calculate the attitude of accelerometers and magnetometers, a second-order measurement updating algorithm based on accelerometers and magnetometers is proposed, and an adaptive algorithm for external acceleration is proposed. The rationality of the algorithm is verified by Matlab simulation. On the basis of simulation, laboratory static test, outdoor vehicle test and Songhua River test were designed. Among them, the static test result is more ideal, the vehicle has been driving on the smooth road during the outdoor vehicle test process, so the calculation attitude has certain precision; The result of Jiang test further shows that the combination of MIMUP / GPS / magnetometer can ensure effective attitude tracking under low dynamic conditions.
【學(xué)位授予單位】：哈爾濱工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN96;TP212

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本文編號：2007461