Data fusion of GPS sensors using Particle Kalman Filter for ship dynamic positioning system

被引:0
|
作者
Jaros, Krzysztof [1 ]
Witkowska, Anna [1 ]
Smierzchalski, Roman [1 ]
机构
[1] Gdansk Univ Technol, Fac Elect & Control Engn, Gdansk, Poland
关键词
Particle Kalman Filter; data fusion; navigation; probabilistic method;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Depending on standards and class, dynamically positioned ships make use of different numbers of redundant sensors to determine current ship position. The paper presents a multi-sensor data fusion algorithm for the dynamic positioning system which allows it to record the proper signal from a number of sensors (GPS receivers). In the research, the Particle Kalman Filter with data fusion was used to estimate the position of the vessel. The presented algorithms generate a virtual measurement using three measurements from independent sensors. The performance of the Particle Kalman Filter algorithm was evaluated in simulation tests for two specific cases: in regular operation and when the signal of one sensor disappears.
引用
收藏
页码:89 / 94
页数:6
相关论文
共 50 条
  • [31] A robust unscented Kalman filter and its application in estimating dynamic positioning ship motion states
    Peng, Xiuyan
    Zhang, Biao
    Rong, Lihong
    JOURNAL OF MARINE SCIENCE AND TECHNOLOGY, 2019, 24 (04) : 1265 - 1279
  • [32] A ToA/IMU Indoor Positioning System by Extended Kalman Filter, Particle Filter and MAP Algorithms
    Chen, Xuechen
    Song, Shupeng
    Xing, Jihong
    2016 IEEE 27TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR, AND MOBILE RADIO COMMUNICATIONS (PIMRC), 2016, : 2532 - 2538
  • [33] Quadrotors Data Fusion using a Particle Filter
    Mercado, D. A.
    Castillo, P.
    Lozano, R.
    2014 INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS (ICUAS), 2014, : 890 - 894
  • [34] A Fuzzy Weighted Kalman Filter for GPS Positioning Precision Enhancement
    Shokri, Shayan
    Mosavi, Mohammad Reza
    2019 7TH IRANIAN JOINT CONGRESS ON FUZZY AND INTELLIGENT SYSTEMS (CFIS), 2019, : 66 - 70
  • [35] Attitude Estimation of a Simulated Flight and GPS Positioning with Kalman Filter
    Querino Filho, Luiz C.
    Rodrigues Filho, Julio F.
    da Silva, Natassya B. F.
    Branco, Kalinka Castelo
    2015 INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS (ICUAS'15), 2015, : 742 - 750
  • [36] A study of a Kalman filter for improving the accuracy of stationary GPS positioning
    Sato, Go
    Asai, Takashi
    Sakamoto, Tadashi
    Hase, Tomohiro
    Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers, 2000, 54 (09): : 1330 - 1338
  • [37] Combination of GSM and GPS signals for mobile positioning and location service using Kalman filter
    Hamani, S.
    Oussalah, M.
    Hall, P.
    WORLD CONGRESS ON ENGINEERING 2007, VOLS 1 AND 2, 2007, : 1524 - 1529
  • [38] Improving GPS positioning accuracy using weighted Kalman Filter and variance estimation methods
    Shokri S.
    Rahemi N.
    Mosavi M.R.
    CEAS Aeronautical Journal, 2020, 11 (02) : 515 - 527
  • [39] UWB/GPS Sensor Fusion Using Kalman Filter for Outdoor Autonomous Robot
    Shin, Junhyeok
    Jung, Hoeryoung
    2022 22ND INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2022), 2022, : 448 - 451
  • [40] Dynamic positioning method for parallel machine based on Kalman filtering data fusion
    Gu, Ling
    Guan, Ronggen
    Jixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering, 2007, 43 (07): : 195 - 201