| 引用本文: | 朱兵,常国宾,何泓洋,等.SINS/DVL/AST水下组合导航中的鲁棒信息融合方法.[J].国防科技大学学报,2020,42(5):107-114.[点击复制] |
| ZHU Bing,CHANG Guobin,HE Hongyang,et al.Robust information fusion method in SINS/DVL/AST underwater integrated navigation[J].Journal of National University of Defense Technology,2020,42(5):107-114[点击复制] |
|
| |
|
|
| 本文已被：浏览 6481次 下载 5408次 |
| SINS/DVL/AST水下组合导航中的鲁棒信息融合方法 |
| 朱兵1,3,常国宾2,何泓洋3,许江宁3 |
|
(1. 北京跟踪与通信技术研究所, 北京 100094;2. 中国矿业大学 环境与测绘学院, 江苏 徐州 221116;3. 海军工程大学 电气工程学院, 湖北 武汉 430033)
|
| 摘要: |
| 捷联式惯导系统由于自主性强等优势成为自主式水下航行器长航时、长航程导航的主要手段。针对水下环境中外部多导航传感器如多普勒计程仪提供的测速信息和水声单应答器提供的位置信息容易受到非高斯噪声污染的问题,提出基于马氏距离算法的联邦鲁棒卡尔曼滤波算法。在联邦鲁棒卡尔曼滤波算法中,通过马氏距离算法引入膨胀因子,对量测噪声协方差阵进行膨胀,以实现非高斯条件下水下组合导航系统鲁棒性的提升。同时基于子滤波器的滤波性能对信息分配系数进行自适应调整以确保水下组合导航系统的高精度。基于江试试验实测数据进行水下组合导航半物理仿真试验,试验结果表明:相比于传统的联邦卡尔曼滤波算法,联邦鲁棒卡尔曼滤波算法可在非高斯条件下实现更高精度、更加稳定的组合导航；能够满足水下组合导航系统对容错性和鲁棒性的要求。 |
| 关键词: 捷联式惯导系统 组合导航 非高斯 联邦滤波 鲁棒 |
| DOI：10.11887/j.cn.202005016 |
| 投稿日期：2019-04-15 |
| 基金项目:国家自然科学基金资助项目(41774005,41804076,42004067) |
|
| Robust information fusion method in SINS/DVL/AST underwater integrated navigation |
| ZHU Bing1,3, CHANG Guobin2, HE Hongyang3, XU Jiangning3 |
|
(1. Beijing Institute of Tracking and Telecommunication Technology, Beijing 100094, China;2. School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;3. College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China)
|
| Abstract: |
| Strapdown inertial navigation system has been the main navigation and positioning method for long voyage and long-endurance underwater navigation. In order to solve the problem that velocity information provided by Doppler velocity log and position information provided by acoustic single transponder are easily contaminated by non-Gaussian noise, a federated robust Kalman filter algorithm was proposed. In the proposed method, the Mahalanobis distance algorithm was used to introduce a inflated factor to inflate the measurement noise covariance, which can improve the robustness of integrated navigation system. At the same time, the information distribution coefficient was adaptively tuned based on the performance of the sub-filter, which can guarantee the accuracy of integrated navigation system. The semi-physical simulation test for underwater integrated navigation was carried out by the federated robust Kalman filter algorithm and traditional federated Kalman filter algorithm based on measured data of the river test. The experiment results demonstrate that the federated robust Kalman filter algorithm has better performance in underwater integrated navigation compared with the traditional federated Kalman filter algorithm under the non-Gaussian condition and it can meet the requirements of fault tolerance and robustness for underwater integrated navigation system. |
| Keywords: strapdown inertial navigation system integrated navigation non-Gaussian federated Kalman filter robust |
|
|
|
|
|
