| 引用本文: | 金红新,杨涛,王小刚,等.基于鲁棒高阶容积滤波的无人机相对导航状态估计方法.[J].国防科技大学学报,2017,39(4):139-143.[点击复制] |
| JIN Hongxin,YANG Tao,WANG Xiaogang,et al.Unmanned aerial vehicle relative navigation method based on robust high degree cubature filtering[J].Journal of National University of Defense Technology,2017,39(4):139-143[点击复制] |
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| 基于鲁棒高阶容积滤波的无人机相对导航状态估计方法 |
| 金红新1,2, 杨涛1, 王小刚3, 卢鑫4, 李璞1,2 |
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(1. 国防科技大学 航天科学与工程学院， 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: |
| 由于无人机相对导航系统具有非线性强、噪声非高斯的特点，传统的基于卡尔曼滤波算法设计的相对导航滤波器存在估计失准甚至发散的问题。考虑到高阶容积卡尔曼滤波和最大熵滤波算法分别在解决非线性问题和非高斯问题时的优势，利用最大熵滤波的量测更新方法对高阶容积卡尔曼滤波的测量更新方程进行了改进，将传统的量测更新问题转换成了线性衰退的求解问题，避免了对测量噪声进行高斯假设，同时解决了系统非线性和量测噪声非高斯的问题。进行了相应的数学仿真，仿真结果表明：所提算法的估计精度超过了高阶容积卡尔曼滤波和最大熵滤波算法的，验证了算法的有效性。 |
| 关键词: 无人机 相对导航 鲁棒高阶容积滤波 非高斯噪声 |
| DOI：10.11887/j.cn.201704021 |
| 投稿日期：2017-06-20 |
| 基金项目:国家自然科学基金资助项目（61304236） |
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| Unmanned aerial vehicle relative navigation method based on robust high degree cubature filtering |
| JIN Hongxin1,2, YANG Tao1, WANG Xiaogang3, LU Xin4, LI Pu1,2 |
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(1. College of Aerospace Science and Engineering, 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: |
| Due to the strong-nonlinearity and non-Gaussianity of the UAV (unmanned aerial vehicle) relative navigation system, the accuracy of the traditional relative navigation filter, which is designed based on the Kalman filtering algorithm, decreases or even diverge. In view of the advantages of HCKF (high degree cubature Kalman filter) and MCKF (maximum correntropy Kalman filter) in coping with nonlinear problems and non-Gaussian problems, respectively, the measurement update equation was modified by the measurement update method of MCKF, and the traditional measurement update problem was recast as a linear regression problem. In addition, the Gaussian assumption of the measurement noise was avoided, the system nonlinearity and measurement noise non-Gaussianity were solved at the same time. The simulation was conducted, and the simulation results indicated that RHCF is superior to HCKF and MCKF. Hence, the effectiveness of the proposed algorithm is verified. |
| Keywords: unmanned aerial vehicle relative navigation robust high degree cubature filtering non-Gaussian noise |
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