数字海洋与水下攻防2024，Vol.7Issue(4)：397-404,8.DOI:10.19838/j.issn.2096-5753.2024.04.006
DVL数据失效时辅助SINS的神经网络改进算法
Improved Neural Network Algorithm for Assisting SINS When DVL Data Fails
摘要
Abstract
In underwater SINS/DVL integrated navigation,the instability and loss of DVL signal often occur due to external factors,which may easily lead to discontinuous positioning or weakened accuracy.In this paper,the data collected during the normal period of DVL are used as training samples,and the radial basis function neural network algorithm(RBF)is used to fill the signal during the period of DVL loss.To reduce the influence of system noise,two modes of extended Kalman filter(EKF)and adaptive fading Sage-Husa extended Kalman filter(SHEKF)are selected for integrated navigation calculation,and different calculation results are obtained.The analysis shows that RBF algorithm can be used to deal with the loss of DVL signal.Under the same conditions,SHEKF filter mode can get better calculation results,and the position error in the direction of E is reduced by about 50%compared with EKF filter.关键词
水下组合导航/扩展卡尔曼滤波/径向基函数神经网络算法/多普勒计程仪Key words
underwater integrated navigation/extended Kalman filter/radial basis function neural network algorithm/Doppler velocity分类
交通工程引用本文复制引用
欧阳明达,朱文会..DVL数据失效时辅助SINS的神经网络改进算法[J].数字海洋与水下攻防,2024,7(4):397-404,8.基金项目
国家自然科学基金基础科学中心项目"地球时空基准"(42388102). （42388102）
