惯性测量单元的信息融合及测试
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