基于MEMS-IMU的航姿测量系统技术研究

发布时间：2018-05-30 10:09

本文选题：航姿测量系统 + 微机电系统　；参考：《哈尔滨工程大学》2014年硕士论文

【摘要】：MEMS技术的出现,引领了惯性导航技术的巨大变革,使惯性导航系统向着低成本、低功耗、微型化的方向发展。特别是MEMS-IMU的出现,使得惯性导航系统向着集传感器与处理器于一身的“微系统”方向发展。鉴于MEMS技术在航空航天及军事领域的巨大应用潜力,对MEMS-IMU的研究成为目前惯性导航领域的研究热点。本文以基于MEMS-IMU的捷联式航姿测量系统的硬件平台为依托,着重研究MEMS-IMU的误差建模及标定技术、航姿测量系统的初始对准及基于自适应Kalman滤波的姿态更新算法。首先,本文简要介绍了课题研究背景及意义,阐述了 MEMS惯性器件的发展历程及其在相关领域的巨大应用优势;简要介绍了航姿测量系统中测量单元的构成方案,并确定使用加速度计和磁强计来辅助陀螺仪进行测姿,这种测姿方案优势明显,通过使用加速度计和磁强计分别敏感重力向量和地磁向量来辅助陀螺仪测姿,很好的弥补了陀螺仪测姿时误差随时间积累的缺陷。然后,给出了航姿测量系统所使用的坐标系定义及姿态角定义,阐述了航姿测量系统的捷联矩阵的建立及即时修正算法;介绍了本文所依托硬件平台的基本结构,特别是对MEMS-IMU模块做了详细介绍,分析了其性能特点及数据采集过程;这为后续的课题研究奠定了基础。接着,针对MEMS惯性器件的特性,分析了其误差来源,并根据本系统要求选取零偏、刻度因子和非正交安装误差系数作为主要误差源,建立了相应的误差模型;对于MEMS-IMU的标定,也针对陀螺仪、加速度计和磁强计的不同特点分别给出了具体的方法步骤;通过对陀螺仪和加速度计进行标定实验,给出了标定结果,并根据标定结果验证了 MEMS-IMU的建模及标定方法是可行的。然后,介绍了 Kalman滤波基本理论并做了简要分析,针对本课题的滤波算法流程分析了系统的初始对准过程,给出了初始捷联矩阵的求解方法;在Kalman滤波基本原理的基础上,本文使用四元数误差和陀螺仪零偏误差作为状态变量,以加速度计误差和磁强计误差作为观测变量,完成了航姿测量系统的自适应Kalman滤波器设计,实现了姿态角的最优估计,并给出了滤波器自适应参数的调节规则。最后,通过仿真实验具体分析了陀螺仪零偏对系统性能的影响;对系统在受扰情况下的性能进行了定性测试;并进一步确定了滤波器的自适应参数值;对系统的动静态性能测试表明系统可以在中低动态环境下稳定工作,验证了系统算法是切实可行的。
[Abstract]:The emergence of MEMS technology leads to a great change in inertial navigation technology and makes inertial navigation system develop in the direction of low cost, low power consumption and miniaturization. In particular, the emergence of MEMS-IMU makes inertial navigation system (ins) develop towards a "microsystem" which combines sensors and processors. In view of the great application potential of MEMS technology in aerospace and military fields, the research on MEMS-IMU has become a hotspot in inertial navigation field. Based on the hardware platform of strapdown attitude measurement system based on MEMS-IMU, this paper focuses on the error modeling and calibration technology of MEMS-IMU, the initial alignment of attitude measurement system and the attitude updating algorithm based on adaptive Kalman filter. First of all, this paper briefly introduces the research background and significance of the subject, describes the development of MEMS inertial devices and its great application advantages in related fields, and briefly introduces the configuration of the measurement unit in the attitude measurement system. It is determined to use accelerometers and magnetometers to assist gyroscopes in attitude determination. This kind of attitude determination scheme has obvious advantages. By using accelerometers and magnetometers to sensitive gravity vectors and geomagnetic vectors respectively to assist gyroscopes in attitude determination, A good remedy for the gyroscope attitude measurement error with time accumulation of defects. Then, the definition of coordinate system and attitude angle used in attitude measurement system is given, the establishment of strapdown matrix of attitude measurement system and the algorithm of instant correction are expounded, and the basic structure of hardware platform based on this paper is introduced. In particular, the MEMS-IMU module is introduced in detail, and its performance characteristics and data acquisition process are analyzed, which lays a foundation for further research. Then, according to the characteristics of MEMS inertial devices, the error sources are analyzed, and the zero offset, calibration factor and non-orthogonal installation error coefficient are selected as the main error sources according to the requirements of the system, and the corresponding error model is established for the calibration of MEMS-IMU. According to the different characteristics of gyroscopes, accelerometers and magnetometers, the specific methods and steps are given, and the calibration results are given through the calibration experiments of gyroscopes and accelerometers. According to the calibration results, the modeling and calibration method of MEMS-IMU is proved to be feasible. Then, the basic theory of Kalman filter is introduced and briefly analyzed. The initial alignment process of the system is analyzed according to the filtering algorithm flow of this subject, and the solution method of initial strapdown matrix is given, which is based on the basic principle of Kalman filtering. In this paper, quaternion error and gyroscope zero bias error are used as state variables, accelerometer error and magnetometer error are used as observation variables, adaptive Kalman filter design of attitude measurement system is completed, and the optimal attitude angle estimation is realized. The regulation of adaptive parameters of filter is also given. Finally, the effect of gyroscope bias on the system performance is analyzed by simulation experiments, the system performance under disturbance is tested qualitatively, and the adaptive parameters of the filter are further determined. The dynamic and static performance test of the system shows that the system can work stably in low and low dynamic environment, and the algorithm is feasible.
【学位授予单位】：哈尔滨工程大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN96

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