DVL数据失效时辅助SINS的神经网络改进算法
作者:
作者单位:

西安测绘研究所,陕西 西安 710054

作者简介:

欧阳明达(1986-),男,博士,工程师,主要从事水下组合导航研究。

中图分类号:

U666.7

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)


Improved Neural Network Algorithm for Assisting SINS When DVL Data Fails
Author:
Affiliation:

Xi’an Research Institute of Surveying and Mapping,Xi’an 710054 ,China

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    摘要:

    SINS/DVL 水下组合导航时,受外界因素影响,DVL 信号不稳定和丢失情况时有发生,容易造成定位结果不连续或精度减弱。将 DVL 正常时段采集数据作为训练样本,采用径向基函数神经网络算法(RBF) 对 DVL 丢失时段信号进行填补。为降低系统噪声影响,选择采用扩展卡尔曼滤波(EKF)和自适应渐消 Sage-Husa 扩展卡尔曼滤波(SHEKF)2 种模式进行组合导航计算,得到不同计算结果。分析表明,RBF 算法能够用于处理 DVL 信号丢失情况,相同条件下,SHEKF 滤波模式能够得到更优计算结果,E 方向上位置误差相比 EKF 滤波减少约 50%。

    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.

    参考文献
    [1] 许江宁.浅析水下PNT体系及其关键技术[J].导航定位与授时,2017,4(1):1-6.
    [2] 郭银景,孔芳,张曼琳,等.自主水下航行器的组合导航系统综述[J].导航定位与授时,2020,7(5):107-119.
    [3] 杨洋,王征,胡致远,等.无人水下航行器编队控制研究现状及技术综述[J].舰船电子工程,2022,42(2):1-7,94.
    [4] ROMANOVAS M,ZIEBOLD R,LAN?A L.A method for IMU/GNSS/Doppler Velocity Log integration in marine applications[C]//2015 International Association of Institutes of Navigation World Congress(IAIN).Prague:IEEE,2015.
    [5] PAULL L,SAEEDI S,SETO M,et al.AUV navigation and localization:a review[J].Oceanic Engineering,2013,39(1):131-149.
    [6] ALAHYARI A,ROZBAHANI S G,HABIBZADEH A,et al.INS/DVL positioning system using Kalman filter[J].Australian Journal of Basic and Applied Sciences,2011,5(9):1123-1129.
    [7] LEE C M,LEE P M,HONG S W,et al.Underwater navigation system based on inertial sensor and Doppler velocity log using indirect feedback Kalman filter[J].International Journal of Offshore and Polar Engineering,2005,15(2):88-95.
    [8] 梁钟泓,谢元平,张永健,等.基于协方差变换的 INS/DVL 全球组合导航算法[J].中国惯性技术学报,2022,30(2):159-167.
    [9] 王连钊,徐博,李盛新,等.基于状态变换的DVL辅助SINS初始对准方法[J].中国惯性技术学报,2022,30(2):202-209.
    [10] 潘绍华,徐晓苏,张亮.基于卡方检测和相关向量机的DVL异常信息处理机制[J].中国惯性技术学报,2022,30(4):461-468.
    [11] ZHU J P,LI A,QIN F J,et al.A hybrid method for dealing with DVL faults of SINS/DVL integrated navigation system[J].IEEE Sensors Journal,2022,22(16):15844-15854.
    [12] LIU P J,WANG B,LI G H,et al.SINS/DVL integrated navigation method with current compensation using RBF neural network[J].IEEE Sensors Journal,2022,22(14):14366-14377.
    [13] LI W L,CHEN M J,ZHANG C,et al.A novel neural network-based SINS/DVL integrated navigation approach to deal with DVL malfunction for underwater vehicles[J].Mathematical Problems in Engineering,2020,2020:1-14.
    [14] SUN J,WANG F.An effective LS-SVM/AKF aided SINS/DVL integrated navigation system for underwater vehicles[J].Peer-to-Peer Networking and Applications,2022,15(3):1437-1451.
    [15] JIN K D,CHAI H Z,SU C H,et al.A compensation algorithm with motion constraint in DVL/SINS tightly coupled positioning[J].Marine Geodesy,2022,45(4):380-406.
    [16] LI W L,CHEN M J,ZHANG C,et al.A novel neural network-based SINS DVL integrated navigation approach to deal with DVL malfunction for underwater vehicles[J].Mathematical Problems in Engineering,2020(1):1-14.
    [17] LI D,XU J N,HE H Y,et al.An underwater integrated navigation algorithm to deal with DVL malfunctions based on deep learning[J].IEEE Access,2021,9:82010-82020.
    [18] 王健,鲁金瑞,郑栋,等.水下复杂环境下基于 SINS/USBL/DVL 多源信息融合的组合导航算法[J].导航定位与授时,2022.1(1):76-84.
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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

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  • 收稿日期:2024-04-07
  • 在线发布日期: 2024-09-07
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