光流/惯导多传感器信息融合方法

刘小明; 陈万春; 邢晓岚; 殷兴良

光流/惯导多传感器信息融合方法

1.
北京航空航天大学宇航学院, 北京 100191
2. 空军装备研究院总体所, 北京 100076
3. 中国航天科工集团公司, 北京 100830

详细信息

中图分类号: V 249.122
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- 被引次数: 0
出版历程
- 收稿日期: 2011-01-26
- 网络出版日期: 2012-05-30

Optical flow/INS multi-sensor information fusion

1.
School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2. System Analysis Institute, Air Force Armament Research Academy, Beijing 100076, China
3. China Aerospace Science & Industry Corp, Beijing 100830, China

摘要

摘要: 将光流传感器多点布置在弹体上,建立了纵向平面内的弹体运动学模型和光流传感器量测模型,利用离散卡尔曼滤波器对光流信息和速率陀螺信息进行融合,估计弹体的高度、姿态和速度信息,并利用估计信息进行了超低空飞行的高度控制仿真,仿真结果表明:利用多个光流传感器和一个速率陀螺,可以准确地、实时地估计出弹体的高度、垂直速度、俯仰角、俯仰角速度和攻角等信息,并可利用这些信息实现导弹的超低空突防任务.
- 光流 /
- 光流传感器 /
- 信息融合 /
- 卡尔曼滤波器 /
- 超低空突防
Abstract: Several optical flow sensors were mounted dispersedly on a missile airframe. The airframe kinematics model in the vertical plane and the optical flow sensor measurement model were established. Discrete-time Kalman filters were used to fuse the optical flow data and rate gyroscope data, and estimate the altitude, attitude and velocity of the airframe. A very-low altitude-holding flight simulation was then implemented based on these estimations. The simulation results indicate that several optical flow sensors and a rate gyroscope can be used to estimate the altitude, vertical velocity, pitching angle, pitching angular velocity and attack angle exactly and real-time. These estimations can help to implement very-low penetration missions.
- optical flow /
- optical flow sensor /
- information fusion /
- Kalman filter /
- very-low penetration

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参考文献(13)

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