Virtual Metrology Filter-Based Algorithms for Estimating Constant Ocean Current Velocity

被引:1
|
作者
Huang, Yongjiang [1 ,2 ]
Liu, Xixiang [1 ,2 ]
Shao, Qiantong [1 ,2 ]
Wang, Zixuan [1 ,2 ]
机构
[1] Southeast Univ, Sch Instrument Sci & Engn, Nanjing 210096, Peoples R China
[2] Minist Educ, Key Lab Microinertial Instrument & Adv Nav Technol, Nanjing 210096, Peoples R China
关键词
AUV; SINS/DVL integrated navigation system; ocean current velocity; virtual metrology filter; INITIAL ALIGNMENT; SINS/DVL;
D O I
10.3390/rs15164097
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The strap-down inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation system are widely used for autonomous underwater vehicles (AUVs). Whereas DVL works in the water tracking mode, the velocity provided by DVL is relative to the current layer and cannot be directly used to suppress the divergence of SINS errors. Therefore, the estimation and compensation of the ocean current velocity play an essential role in improving navigation positioning accuracy. In recent works, ocean currents are considered constant over a short term in small areas. In the common KF algorithm with the ocean current as a state vector, the current velocity cannot be estimated because the current velocity and the SINS velocity error are coupled. In this paper, two virtual metrology filter (VMF) methods are proposed for estimating the velocity of ocean currents based on the properties that the currents remain unchanged at the adjacent moments. New measurement equations are constructed to decouple the current velocity and the SINS velocity error, respectively. Simulations and lake tests show that both proposed methods are effective in estimating the current velocity, and each has its advantages in estimating the ocean current velocity or the misalignment angle.
引用
收藏
页数:21
相关论文
共 50 条
  • [1] KALMAN FILTER-BASED ALGORITHMS FOR ESTIMATING DEPTH FROM IMAGE SEQUENCES
    MATTHIES, L
    KANADE, T
    SZELISKI, R
    INTERNATIONAL JOURNAL OF COMPUTER VISION, 1989, 3 (03) : 209 - 236
  • [2] Estimation of constant ocean current velocity based on SINS/DVL integrated navigation with an augmented observable quantities filter
    Huang, Yongjiang
    Liu, Xixiang
    Wang, Zixuan
    Wu, Xiaoqiang
    OCEAN ENGINEERING, 2023, 284
  • [3] Optimizations for filter-based join algorithms in MapReduce
    Rababa, Salahaldeen
    Al-Badarneh, Amer
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 40 (05) : 8963 - 8980
  • [4] Correlation Filter-based Object Tracking Algorithms
    Zhao, Songke
    Sun, Kewei
    Ji, Yuanfa
    Guo, Ning
    Jia, Xizi
    2020 IEEE 3RD INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND SIGNAL PROCESSING (ICICSP 2020), 2020, : 57 - 62
  • [5] Dynamic Kalman filter-based velocity tracker for Intelligent vehicle
    Khan, Md Asif
    Singh, Tegveer
    Azim, Akramul
    Burhanpurkar, Vivek
    Perrin, Rodolphe
    IECON 2021 - 47TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2021,
  • [6] Iterative and sequential Kalman filter-based speech enhancement algorithms
    Gannot, S
    Burshtein, D
    Weinstein, E
    IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, 1998, 6 (04): : 373 - 385
  • [7] Dynamic visual servoing with Kalman filter-based depth and velocity estimator
    Chang, Ting-Yu
    Chang, Wei-Che
    Cheng, Ming-Yang
    Yang, Shih-Sian
    INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2021, 18 (03)
  • [8] Lead-Lag Filter-Based Damping of Virtual Synchronous Machines
    Mandrile, Fabio
    Mallemaci, Vincenzo
    Carpaneto, Enrico
    Bojoi, Radu
    IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2023, 59 (06) : 6900 - 6913
  • [9] LORA: a local ensemble transform Kalman filter-based ocean research analysis
    Ohishi, Shun
    Miyoshi, Takemasa
    Kachi, Misako
    OCEAN DYNAMICS, 2023, 73 (3-4) : 117 - 143
  • [10] LORA: a local ensemble transform Kalman filter-based ocean research analysis
    Shun Ohishi
    Takemasa Miyoshi
    Misako Kachi
    Ocean Dynamics, 2023, 73 : 117 - 143