置信检验自适应联邦卡尔曼滤波及其水下机器人组合导航应用

陈帅; 王宁; 陈廷凯; 杨毅; 田嘉禾

doi:10.19693/j.issn.1673-3185.02216

置信检验自适应联邦卡尔曼滤波及其水下机器人组合导航应用

doi: 10.19693/j.issn.1673-3185.02216

陈帅^1,,
王宁^1, ,,
陈廷凯^1,,
杨毅²,
田嘉禾¹

1.
大连海事大学船舶电气工程学院，辽宁大连 116026
2.
中车大连电力牵引研发中心有限公司，辽宁大连 116085

基金项目: 辽宁省“兴辽英才计划”资助项目(XLYC1807013)；辽宁省高等学校创新人才支持计划资助项目(LR2017024)；水下机器人技术国防科技重点实验室稳定支持课题资助项目(SXJQR2018WDKT03)

详细信息

作者简介:
陈帅，男，1996年生，硕士生。研究方向：多源信息融合。E-mail: cs773715170@163.com

王宁，男，1983年生，博士，教授，博士生导师。研究方向：无人船，海洋机器人，无人系统自主控制，人工智能。E-mail: n.wang.dmu.cn@gmail.com

陈廷凯，男，1993年生，博士。研究方向：目标识别与检测。E-mail: tingkai.chen@hotmail.com

通信作者:
王宁

中图分类号: U666.1
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出版历程
- 收稿日期: 2020-12-07
- 修回日期: 2021-03-08
- 网络出版日期: 2022-02-26
- 刊出日期: 2022-03-02

Confidence check-adaptive federated Kalman filter and its application in underwater vehicle integrated navigation

CHEN Shuai^1
,,
WANG Ning^{1
, ,},
CHEN Tingkai^1
,,
YANG Yi²,
TIAN Jiahe¹

1.
Marine Electrical Engineering College, Dalian Maritime University, Dalian 116026, China
2.
CRRC Dalian R&D Co., Ltd., Dalian 116085, China

置信检验自适应联邦卡尔曼滤波及其水下机器人组合导航应用由陈帅，等创作，采用知识共享署名4.0国际许可协议进行许可。

摘要

摘要: 目的为解决载体受到扰动时组合导航精度下降的问题，提出一种基于置信检验自适应联邦卡尔曼滤波（CC-AFKF）框架。方法首先，将电子罗盘（EC）、全球定位系统（GPS）与惯性导航系统（INS）相结合；其次，构建置信检验模型，有效滤除INS/GPS和INS/EC子系统中低置信度的量测值，保证量测值的准确性；最后，提出INS/GPS和INS/EC系统自适应调节因子策略，有效调整更新过程中系统噪声协方差。结果通过对具备INS/GPS/EC组合导航系统的水下机器人开展大量相关试验，结果表明，CC-AFKF算法相较于典型的卡尔曼滤波（KF）和联邦卡尔曼滤波（FKF）算法在位置和速度的融合精度上均能至少提高29%。结论研究成果可为松耦合组合导航系统的研究提供相应的方向和思路。
- 置信检验 /
- 自适应联邦卡尔曼滤波 /
- 组合导航 /
- 水下机器人
Abstract: Objectives In order to solve the problem of the reduced accuracy of integrated navigation when a carrier is disturbed, a confidence check-adaptive federated Kalman filter (CC-AFKF) framework is proposed. Methods First, the electronic compass (EC), global positioning system (GPS) and inertial navigation system (INS) are combined. Second, a confidence check model is constructed to effectively filter out low-confidence measurements in the INS/GPS and INS/EC subsystems, and ensure the accuracy of the measured value. Finally, an adaptive adjustment factor strategy for the INS/GPS and INS/EC systems is proposed to effectively adjust system noise covariance during the update process. Results A large number of related tests are carried out through an underwater vehicle equipped with INS/GPS/EC integrated navigation systems. The test results show that the CC-AFKF algorithm proposed in this paper can improve the integrated accuracy of position and velocity by at least 29% compared with typical KF and FKF algorithms. Conclusions The results of this study can provide corresponding directions and ideas for research on loosely coupled integrated navigation systems.
- confidence check /
- adaptive federated Kalman filter /
- integrated navigation /
- underwater vehicle

HTML全文

图 1 INS/GPS/EC组合导航系统结构图

Figure 1. Structure diagram of INS/GPS/EC integrated navigation system

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图 2 置信检验自适应联邦卡尔曼滤波算法流程图

Figure 2. Flow process of confidence check-adaptive federated Kalman filter algorithm

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图 3 水下机器人试验平台

Figure 3. Test platform of underwater vehicle

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图 4 水下机器人的真实运动轨迹

Figure 4. Real trajectory of underwater vehicle

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图 5 原始经纬度信息对比图

Figure 5. Comparison of original latitude and longitude information

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图 6 原始北、东向速度信息对比图

Figure 6. Comparison of original information of eastward and northward speed

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图 7 受扰动后不同框架的经纬度对比图

Figure 7. Comparison of latitude and longitude between different frameworks due to perturbation

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图 8 受扰动后不同框架的北、东向速度对比图

Figure 8. Comparison of northward and eastward speed between different frameworks after perturbation

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图 9 不同框架的经纬度对比图

Figure 9. Comparison of latitude and longitude between different frameworks

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图 10 不同框架的经纬度误差对比图

Figure 10. Comparison of longitude and latitude errors between different frameworks

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图 11 不同框架的位置均方根误差对比图

Figure 11. Comparison of root mean square error of position between different frameworks

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图 12 不同框架的北、东向速度对比图

Figure 12. Comparison of northward and eastward speed between different frameworks

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图 13 不同框架下的北、东向速度误差对比图

Figure 13. Comparison of northward and eastward speed errors between different frameworks

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图 14 不同框架的速度均方根误差对比图

Figure 14. Comparison of root mean square error of speed between different frameworks

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表 1 组合导航模块器件清单

Table 1. Parts list of integrated navigation module

模块名称型号

处理器 PIXHAWK
IMU模块 MPU6050
GPS模块 NEO-6M-GPS
电子罗盘 GY-26

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