自适应无迹卡尔曼滤波算法在水下组合导航系统中的应用
Application of Adaptive Unscented Kalman Filter in Underwater Integrated Navigation System
肖鹏飞 1许至尊 1白虎林 1刘洺辛2
作者信息
- 1. 广东海洋大学船舶与海运学院,广东 湛江 524005
- 2. 广东海洋大学船舶与海运学院,广东 湛江 524005;广东海洋大学电子与信息工程学院,广东 湛江 524088
- 折叠
摘要
[目的]解决水下组合导航系统中先验噪声与实际噪声分布不匹配时,融合滤波性能下降问题,提高自主式水下航行器导航精度.[方法]提出一种自适应无迹卡尔曼滤波算法(AUKF),在融合算法中引入自适应因子;重构系统状态方程中速度项与状态变量的结合方式,解决系统方差不一致问题.通过仿真实验和半物理实验验证该算法的有效性.[结果与结论]与无迹卡尔曼滤波算法相比,在平均位置估计偏差上,AUKF算法的纬度均方根误差(RMSE)降低27%,经度RMSE降低27%,高度RMSE降低25%.AUKF在面对偏差对系统状态的扰动时能够有效抑制滤波发散,从而有效地提高自主式水下航行器的导航精度.
Abstract
[Objective]To solve the problem of degraded fusion filtering performance in underwater integrated navigation systems when the prior noise distribution does not match the actual noise distribution,and improve the navigation accuracy of autonomous underwater vehicles.[Method]This paper proposed an improved robust adaptive UKF(AUKF)filtering algorithm by introducing adaptive factors to improve the UKF filtering algorithm.The combination of velocity term and state variable in the system state equation was reconstructed to solve the inconsistency problem of system variance.The effectiveness of the proposed algorithm wasverified by ship experiment and semi-physical simulation.[Result and Conclusion]The experimental results show that compared with UKF algorithm,the latitude RMSE,longitude RMSE and height RMSE of AUKF algorithm are reduced by 27%,27%and 25%in terms of average position estimation deviation.Therefore,the improved robust AUKF can effectively suppress the filter divergence in the face of the disturbance of the system,andimprove the navigation accuracy of autonomous underwater vehicle(AUV)in underwater environment.
关键词
组合导航/无迹卡尔曼滤波/自适应因子/捷联惯性导航/多普勒测速仪Key words
integrated navigation/unscented Kalman filter/adaptive factor/strapdown inertial navigation/Dopper velocity logger引用本文复制引用
基金项目
国家自然科学基金面上项目(62171143)
广东省自然科学基金面上项目(2021A1515011948)
广东省普通高校重点领域专项(2021ZDZX1060)
出版年
2024
国家科技期刊平台
NSTL
万方数据