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基于四元数扩展到状态变量的水下滑翔器导航回溯解耦与自适应扩展卡尔曼滤波算法研究

Study of the algorithm of backtracking decoupling and adaptive extended Kalman filter based on the quaternion expanded to the state variable for underwater glider navigation.

作者信息

Huang Haoqian, Chen Xiyuan, Zhou Zhikai, Xu Yuan, Lv Caiping

机构信息

Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.

出版信息

Sensors (Basel). 2014 Dec 3;14(12):23041-66. doi: 10.3390/s141223041.

DOI:10.3390/s141223041
PMID:25479331
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4299052/
Abstract

High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.

摘要

高精度的姿态和位置确定对于水下滑翔机非常重要。当发生俯仰或横滚运动时,三个姿态角(航向角、俯仰角和横滚角)之间的交叉耦合会变得更加严重。这种交叉耦合会使姿态角不准确甚至出现错误。因此,对于实际的水下滑翔机来说,高精度的姿态和位置确定成为一个难题。为了解决这个问题,本文提出了基于四元数扩展到状态变量的回溯解耦和自适应扩展卡尔曼滤波器(BD-AEKF)。回溯解耦可以有效消除俯仰或横滚运动发生时三个姿态之间的交叉耦合。解耦后,基于四元数扩展到状态变量的自适应扩展卡尔曼滤波器(AEKF)进一步平滑滤波输出,以提高姿态和位置确定的精度和稳定性。为了评估所提出的BD-AEKF方法的性能,对俯仰和横滚运动进行了仿真,并将所提出方法的性能与传统方法进行了分析和比较。仿真结果表明所提出的BD-AEKF性能更好。此外,为了进一步验证,设计了一种新的水下导航系统,并进行了三轴无磁转台实验和航行器实验。结果表明,与传统方法相比,所提出的BD-AEKF在消除交叉耦合和减少误差方面是有效的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/0cacee677c2f/sensors-14-23041f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/ffde9317dbc0/sensors-14-23041f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/50ce54efae1e/sensors-14-23041f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/67ccf8598203/sensors-14-23041f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/31f8060af508/sensors-14-23041f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/36c3e0886221/sensors-14-23041f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/e38f085bd906/sensors-14-23041f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/5b870e0de912/sensors-14-23041f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/0cacee677c2f/sensors-14-23041f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/ffde9317dbc0/sensors-14-23041f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/50ce54efae1e/sensors-14-23041f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/67ccf8598203/sensors-14-23041f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/31f8060af508/sensors-14-23041f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/36c3e0886221/sensors-14-23041f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/e38f085bd906/sensors-14-23041f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/5b870e0de912/sensors-14-23041f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8c0/4299052/0cacee677c2f/sensors-14-23041f8.jpg

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