• DocumentCode
    3446786
  • Title

    Observability analysis for cooperative navigation system in autonomous underwater vehicles

  • Author

    Li, Wenbai ; Liu, Mingyong ; Lei, Xiaokang ; Xu, Fei

  • Author_Institution
    Coll. of Marine, Northwestern Polytech. Univ., Xi´´an, China
  • Volume
    1
  • fYear
    2010
  • fDate
    29-31 Oct. 2010
  • Firstpage
    155
  • Lastpage
    161
  • Abstract
    Precise navigation of autonomous underwater vehicles (AUV) is one of the most important challenges for marine applications. Current 2-dimensional (2D) navigation systems (algorithms) are effective to autonomous surface vehicles (ASV) operations, but still have some limitations for AUV navigation in 3D underwater environment. In this paper, a 3D range navigation algorithm is proposed that is potentially useful for small AUVs that have severe volume and power constraints. This algorithm enables an AUV to compute its trajectory in the presence of unknown currents and simultaneously estimate the currents by using range measurements from a single known location (beacon). The principal work here is the observability analysis of the proposed navigation system that characterizes observable AUV trajectories using novel approaches towards uniform observability of linear time varying systems. Then, implementation of the 3D range navigation algorithm as a discrete time extended Kalman filter is given based on the observability results and kinematic model of the system. Finally, a set of comparative simulation results verify the validity of the navigation algorithm and the system´s observability related to trajectories.
  • Keywords
    Kalman filters; cooperative systems; discrete time systems; linear systems; marine control; navigation; observability; position control; remotely operated vehicles; time-varying systems; underwater vehicles; 3D underwater environment; autonomous underwater vehicle; cooperative navigation system; discrete time system; extended Kalman filter; linear time varying system; marine application; observability analysis; trajectory algorithm; Navigation; Trajectory; autonomous underwater vehicle; extended Kalman filter; navigation system; observability; unknown currents;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on
  • Conference_Location
    Xiamen
  • Print_ISBN
    978-1-4244-6582-8
  • Type

    conf

  • DOI
    10.1109/ICICISYS.2010.5658655
  • Filename
    5658655