小型水下自主航行器（AUV）空间小、功耗低，传感器布置有限，且AUV运动方程高度非线性，传统扩展卡尔曼滤波（EKF）易发散，多传感器数据融合的传统惯性导航方案难以实现。本文利用AUV数学模型进行辅助，设计了基于平方根无迹卡尔曼滤波（SRUKF）的惯性导航系统。利用AUV运动特性计算得到角速度、加速度等信息，融合六轴加速度传感器信号，实现导航系统误差补偿。通过半实物实验和Matlab仿真实验，验证该导航系统可行性。结果表明，该导航系统相比EKF滤波方案精度高、稳定性好，能够满足小型AUV导航定位需求。
The equipment of sensors for miniature Autonomous Underwater Vehicles (AUVs) is restricted because of limited space and low power. And Extend Kalman Filter (EKF) suffers from the potential divergence due to the high nonlinear of AUV dynamic equation. Consequently, traditional navigation system which relies on sensor fusion is difficult to be deployed in AUVs navigation. In this paper, an inertial navigation system (INS) based on Square-Root Unscented Kalman Filter (SRUKF) is proposed, using AUVs model as aiding source. Navigation errors are compensated via fusing six-axis acceleration sensor data and AUV motion data calculated by dynamic equation. The feasibility of this INS is validated by half-real test and Matlab simulation test, in which it shows higher accuracy and better robust than EKF, and navigation demands of miniature AUVs can be satisfied.
2021,43(7): 153-157 收稿日期：2020-06-15
DOI：10.3404/j.issn.1672-7649.2021.07.031
分类号：U666.11
作者简介：王科宇(1995-),男,硕士研究生,主要从事自动化控制研究
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