摘要惯性测量单元（IMU）是一种广泛应用于航海、航空、物体运动跟踪的必不可少的测量模块。由于测量单元的随机噪声，运动跟踪并不十分精确，需要辅以滤波算法进行数据校准。本论文研究的具体内容是如何利用IMU上的三轴加速度计、三轴陀螺仪和三轴磁力计进行物体空间姿态的解算。利用当前最为流行的扩展卡尔曼滤波（EKF）算法进行物体空间姿态的融合与解算，将该算法与本实验平台进行结合，为该实验平台进行系统建模，并将该算法转化为本实验平台的程序源码，消除低精度传感器因随机误差造成的波动，并估算出最接近准确值的姿态向量。同时又做了一阶互补滤波、二阶互补滤波进行横向对比，利用示波器对姿态结果进行动态显示，以直观、明了地对比不同滤波器的滤波效果，并根据这些对比总结出扩展卡尔曼滤波器、互补滤波器的优缺点。33168
关键词 EKF 扩展卡尔曼滤波 互补滤波 姿态解算
毕业论文设计说明书外文摘要
Title The information fusion of inertial measurement unit and testing
Abstract
Inertial measurement unit (IMU) is a kind of essential measurement module that is widely used in the navigation, aviation, object motion tracking. Due to the random noise measurement unit, motion tracking is not very accurate, need to be supplemented by filtering algorithm. The specific content of this paper is how to use IMU on three-axis accelerometer, three-axis gyroscope and calculation of three axis magnetometer in object space posture. Using the most popular extended kalman filtering (EKF) algorithm and calculating the attitude of the object space fusion, the algorithm combining with the experimental platform, experimental platform for the system modeling, and convert the algorithm into the experiment platform of program source code, eliminate the low accuracy sensors fluctuations caused by the random error, and estimate the position vector of the most close to the accurate values. Again at the same time to do the first complementary filter and second order complementary filter transverse comparison, dynamic posture results to make use of the oscilloscope display, with intuitive, clearly the contrast of different filter filtering effect, and according to the contrast summed up the advantages and disadvantages of extended kalman filter, complementary filter.
Keywords EKF Extended kalman filter Complementary filter Attitude algorithm
目 次
1 引言 1
1.1 研究的目的与意义 1
1.2 IMU与滤波算法国内外研究现状 1
1.3 研究内容 2
1.4 论文结构 2
2 系统硬件平台 3
2.1 准备工作 3
2.1.1 FT232RL接口转换芯片 3
2.1.2 Arduino 开发平台 3
2.1.3 GY-86模块 4
2.2 系统传感器 6
2.3 姿态表达 7
3 滤波器算法 10
3.1 互补滤波器 10
3.2 扩展卡尔曼滤波器 11
3.2.1 过程方程的建立 12
3.2.2 测量方程的建立 13
3.2.3 状态协方差矩阵计算 13
3.2.4 卡尔曼增益计算 13
3.2.5 系统状态更新 14
3.2.6 协方差矩阵更新 14 惯性测量单元的信息融合及测试:http://www.youerw.com/jisuanji/lunwen_30095.html