| 引用本文: | 金红新,杨涛,王小刚,等.多传感器信息融合理论在无人机相对导航中的应用.[J].国防科技大学学报,2017,39(5):90-95.[点击复制] |
| JIN Hongxin,YANG Tao,WANG Xiaogang,et al.Application of multi sensor information fusion in UAV relative navigation method[J].Journal of National University of Defense Technology,2017,39(5):90-95[点击复制] |
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| 多传感器信息融合理论在无人机相对导航中的应用 |
| 金红新1,2, 杨涛1, 王小刚3, 周国峰4, 姚旺4 |
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(1. 国防科技大学 空天科学学院， 湖南 长沙 410073;2.
2. 中国运载火箭技术研究院 战术武器事业部， 北京 100076;3. 哈尔滨工业大学 航天学院， 黑龙江 哈尔滨 155600;4.2. 中国运载火箭技术研究院 战术武器事业部， 北京 100076)
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| 摘要: |
| 为提升无人机的作战效能和作战指标，提升无人机的相对导航精度和导航系统可靠性，以无人机编队的相对导航系统为研究背景，基于容积卡尔曼滤波算法和信息滤波算法，研究容积信息滤波算法。此外，还采用多传感器信息融合理论，利用分布式信息融合结构构建了无人机相对导航滤波器，对来自惯导、视觉和卫星的信息进行融合，获取无人机间的相对位置、速度和姿态信息。该方法提升了无人机相对导航的导航精度、导航可靠性和滤波稳定性，容积信息滤波算法的应用避免了传统滤波算法在高维系统中出现的数值不稳定以及精度降低等问题。数学仿真结果表明，该方法提高了无人机编队之间相对导航的精度和可靠性，证明了算法的有效性。 |
| 关键词: 无人机 多传感器信息融合 相对导航 |
| DOI：10.11887/j.cn.201705015 |
| 投稿日期：2016-06-20 |
| 基金项目:国家自然科学基金资助项目（61304236） |
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| Application of multi sensor information fusion in UAV relative navigation method |
| JIN Hongxin1,2, YANG Tao1, WANG Xiaogang3, ZHOU Guofeng4, YAO Wang4 |
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(1. College of Aeronautics and Astronautics, National University of Defense Technology, Changsha 410073, China;2.
2. Tactical Weapons Division, China Academy of Launch Vehicle Technology, Beijing 100076, China;3. School of Astronautics, Harbin Institute of Technology, Harbin 155600， China;4.2. Tactical Weapons Division, China Academy of Launch Vehicle Technology, Beijing 100076, China)
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| Abstract: |
| In order to improve the operational effectiveness and operational indicators of the UAV (unmanned aerial vehicle), and to enhance the accuracy and reliability of the UAV relative navigation system, a novel relative navigation method was proposed. Under the background of relative navigation system, the cubature information filter based on the cubature Kalman filter and information filter was studied. Moreover, an INS/GPS/VisNav relative navigation filter was designed by making use of the multi-sensor information fusion theory and distributed information fusion structure to fuse the information from INS, VisNav and GPS, and then the relative position, velocity and attitude were obtained. By making use of this algorithm, the accuracy, reliability and stability of the reliability navigation system were all improved. In addition, the accuracy decrease and numerical instability which often occur to traditional filter were avoided by cubature information filter. Mathematical simulation results indicate that the method can improve the accuracy and reliability of the UAV relative navigation system, and the proposed algorithm is verified. |
| Keywords: unmanned aerial vehicle multi-sensor information fusion relative navigation |
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