基于自适应差异卡尔曼过的GNSS/SINS/DVL集成导航算法
Zhao Zhan1, Changjian Liu1, Kaidi Jin2
1Information Engineering University, Zhengzhou, China.
PloS one
|February 5, 2026
概括
本研究引入了自适应差异卡尔曼过 (ADKF) 方法,以提高无人潜艇 (UUV) 的导航精度. 与标准卡尔曼过器相比,ADKF方法在复杂的海洋环境中提高了参数估计的稳定性和准确性.
科学领域:
- 海上导航技术 技术
- 机器人技术和自主系统
- 信号处理和控制理论
背景情况:
- 综合导航系统,结合全球导航卫星系统/Strapdown惯性导航系统/多普勒速度记录器 (GNSS/SINS/DVL),对海运公司至关重要.
- 无人驾驶水下车辆 (UUV) 在动态海上航行中面临重大挑战,原因是观测异常和动态模型不准确.
- 标准卡尔曼过器 (KF) 在复杂的海洋环境中难以保持准确性,影响UUV导航参数估计.
研究的目的:
- 为GNSS/SINS/DVL集成导航数据开发一种改进的过方法,专门用于UUVs.
- 为了解决标准卡尔曼过器在处理动态海上导航复杂性的局限性.
- 提高UUV在具有挑战性的海洋环境中导航参数估计的准确性和稳定性.
主要方法:
- 实现一个自适应差异卡尔曼过 (ADKF) 算法.
- 来自GNSS/SINS/DVL子系统的集成导航数据的处理.
- 使用实验数据对ADKF与标准卡尔曼波器 (KF) 的比较分析.
主要成果:
- 拟议的ADKF方法在导航参数估计中显著提高了准确性.
- 与标准KF相比,ADKF算法在参数估计中显示出更好的稳定性.
- 实验结果证实了ADKF在复杂的海洋环境中的有效性.
结论:
- 适应差异卡尔曼过 (ADKF) 方法是针对UUVs的综合导航数据后处理的强大解决方案.
- 在UUV导航的准确性和稳定性方面,ADKF提供了比标准KF更好的性能.
- 开发的方法非常适合在具有挑战性的海洋条件下提高UUV导航的可靠性.
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