基于单信标的无人水下器导航算法及改进

陈祥国; 尚 凡; 蓝 强

doi:10.11993/j.issn.2096-3920.2021.04.009

基于单信标的无人水下器导航算法及改进

doi: 10.11993/j.issn.2096-3920.2021.04.009

国防科技大学气象海洋学院, 湖南长沙, 410073

详细信息

作者简介:
陈祥国(1979-), 男, 博士, 助理研究员, 主要研究方向为智能优化算法及海洋环境大数据等.

中图分类号: U666.1 TB56
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- 文章访问数: 125
- HTML全文浏览量: 19
- PDF下载量: 67
- 被引次数: 0
出版历程
- 收稿日期: 2020-12-08
- 修回日期: 2021-03-02
- 刊出日期: 2021-08-31

Model and Algorithm Improvement for Unmanned Undersea Vehicle Navigation Based on Single Beacon

College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China

摘要

摘要: 基于单信标的无人水下航行器(UUV)导航算法具有所需潜标数量少、系统海上布放标校简单、维护成本较低等优点, 结合UUV惯性导航系统后能够满足未来广域、低成本水下导航需求。针对单信标导航算法收敛速度较慢、非全局可观测等问题, 提出增加目标径向速度观测量的单信标水下导航算法; 分析了引入目标径向速度观测后的系统全局观测性, 给出新的导航初值估计及跟踪方法; 使用扩展卡尔曼滤波算法, 建立4状态变量-3观测量(4SV-3OB)单信标导航模型, 并通过数值仿真试验, 得出4SV-3OB模型相较于传统单信标导航模型收敛时间减小约75%。仿真结果表明, 增加目标径向速度作为系统观测量可有效简化单信标导航算法初值估计及跟踪流程, 并能够显著减小导航算法收敛时间。
- 无人水下航行器 /
- 单信标 /
- 水下导航 /
- 全局观测性 /
- 扩展卡尔曼滤波
Abstract: In the single beacon-based unmanned undersea vehicle(UUV) navigation algorithm, a small number of beacons, simple deployment and calibration at sea, and low maintenance costs are required. If combined with the inertial navigation system, this algorithm is expected to fulfill the needs for wide-area and low-cost underwater navigation. To solve the problems regarding its slow convergence speed and non-global observability, a single beacon underwater navigation algorithm, which increased target radial velocity observations, is proposed. The global observability of the system after the introduction of the target radial velocity observation is thereafter analyzed, and a new method to estimate the initial value and track the navigation is derived. Afterward, by combining this method with the extended Kalman filter algorithm, a 4-state 3-observation(4SV-3OB) single beacon navigation model is established. Finally, numerical simulations indicate that the convergence time of the 4SV-3OB is approximately 75% lower than that of the traditional single beacon navigation model. Simulation results show that adding the target radial velocity as a system observation can effectively simplify the process of initial value estimation and tracking in the single beacon navigation algorithm and can significantly reduce the convergence time of the navigation algorithm.
- unmanned undersea vehicle /
- single beacon /
- underwater navigation /
- global observability /
- extended Kalman filter

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参考文献(14)

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