An Integrated Navigation Algorithm with Asynchronous Fusion forFull-Ocean-Depth Human Occupied Vehicle
ZHANG Zhihui1,2,3, ZHAO Yang1,2, JIANG Chenlin1,2,3, LI Zhigang1,2
1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang 110016, China; 2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China; 3. University of Chinese Academy of Sciences, Beijing 100049, China
Abstract：The asynchronous fusion maybe happen in the integrated navigation of full-ocean-depth human occupied vehicle (HOV), and large error can be caused if the traditional integrated navigation algorithms are used. To solve this problem, an integrated navigation algorithm with asynchronous fusion is proposed based on machine learning (ML) and unscented Kalman filter (UKF). At first, an ML model is established for prediction of the ultra-short baseline(USBL) acoustic positioning system. Then, the model is trained by the observation dataset of USBL acoustic positioning system, and the data in the intervals between updates are predicted by the model. Finally, the updated dataset is fused by using UKF. The results of simulation experiments manifest that compared with the traditional integrated navigation algorithms, the error caused by asynchronous data from USBL acoustic positioning system can be reduced by 17%, by the proposed integrated navigation algorithm with asynchronous fusion, and the accuracy of the whole integrated navigation system is effectively improved.
[1] Liu K Z, Li J, Guo W, et al. Navigation system of a class of underwater vehicle based on adaptive unscented Kalman filter algorithm[J]. Journal of Central South University, 2014, 21(2):550-557. [2] 张恒浩,史静. 组合导航后验方差异步融合算法[J].控制理论与应用,2017,34(12):1561-1567. Zhang H H, Shi J. Integrated navigation algorithm with posterior variance and asynchronous fusion[J]. Control Theory and Applications, 2017, 34(12):1561-1567. [3] 朱倚娴,程向红,周玲,等.组合导航系统中异步多传感器信息融合算法[J].东南大学学报(自然科学版),2018,48(2):195-200. Zhu Y X, Cheng X H, Zhou L, et al. Information fusion algorithm for asynchronous multi-sensors in integrated navigation systems[J]. Journal of Southeast University (Natural Science Edition), 2018, 48(2):195-200. [4] 赫飞,丁姚,张永伟.SINS/GPS/SST/高度表组合导航系统异步融合算法[J]. 海军航空工程学院学报,2015,30(1):17-22.He F, Ding Y, Zhang Y W. Fusion-algorithm of asynchronous in the SINS/GPS/SST/altimeter integrated navigation system[J]. Journal of Naval Aeronautical and Astronautical University, 2015, 30(1):17-22. [5] 李洋漾,李雯,易伟,等.基于DP-TBD的分布式异步粒子滤波融合算法[J]. 现代雷达,2018,40(10):38-44.Li Y Y, Li W, Yi W, et al. A distributed asynchronous particle filtering fusion algorithm via DP-TBD[J]. Modern Radar, 2018, 40(10):38-44. [6] Safari S, Shabani F, Simon D. Multirate multisensor data fusion for linear systems using Kalman filters and a neural network[J]. Aerospace Science and Technology, 2014, 39:465-471. [7] 张勇刚,黄玉龙,武哲民,等.一种高阶无迹卡尔曼滤波方法[J].自动化学报,2014,40(5):838-848. Zhang Y G, Huang Y L, Wu Z M, et al. A high order unscented Kalman filtering method[J]. Acta Automatica Sinica, 2014, 40(5):838-848. [8] Julier S J, Uhlmann J K. Unscented filtering and nonlinear estimation[J]. Proceedings of the IEEE, 2004, 92(3):401-422. [9] Nickel M, Murphy K, Tresp V, et al. A review of relational machine learning for knowledge graphs[J]. Proceedings of the IEEE, 2015, 104(1):11-33. [10] Seber G A F, Lee A J. Linear regression analysis[M]//Process Automation Handbook. Berlin, Germany:Springer, 2012. [11] Sun T, Zhang C H. Scaled sparse linear regression[J]. Biometrika, 2012, 99(4):879-898. [12] 任玉刚,刘保华,丁忠军,等.载人潜水器发展现状及趋势[J].海洋技术学报,2018,37(2):114-122. Ren Y G, Liu B H, Ding Z J, et al. Research on the current status and development trend of manned submersibles[J]. Journal of Ocean Technology, 2018, 37(2):114-122. [13] Liu B, Liu K Z, Wang Y Y, et al. A hybrid deep sea navigation system of LBL/DR integration based on UKF and PSO-SVM[J]. Robot, 2015, 37(5):614-620.
