• DocumentCode
    2024806
  • Title

    Sigma-Point Filters: An Overview with Applications to Integrated Navigation and Vision Assisted Control

  • Author

    Wan, Eric

  • Author_Institution
    Department of Computer Science and Electrical Engineering, OGI School of Science and Engineering, Oregon Health & Science University
  • fYear
    2006
  • fDate
    13-15 Sept. 2006
  • Firstpage
    201
  • Lastpage
    202
  • Abstract
    In this presentation, we first provide an overview of Sigma-Point filtering methods, which include the Unscented Kalman Filter (UKF), Central Difference Kalman Filter (CDKF), and several variants with hybrid extensions to sequential Monte Carlo filtering (e.g., particle filtering). In the second half, we focus on recent applications to integrated navigation systems (INS), which provide state-estimation by combining GPS and inertial measurements. In addition, we present new work on using video data to extract the equivalent state-information (i.e., replacing the INS) for use in closed-loop control of an Unmanned Aerial Vehicle (UAV).
  • Keywords
    Bayesian methods; Data mining; Filtering; Global Positioning System; Inference algorithms; Navigation; Particle filters; Recursive estimation; State estimation; Unmanned aerial vehicles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Nonlinear Statistical Signal Processing Workshop, 2006 IEEE
  • Conference_Location
    Cambridge, UK
  • Print_ISBN
    978-1-4244-0581-7
  • Electronic_ISBN
    978-1-4244-0581-7
  • Type

    conf

  • DOI
    10.1109/NSSPW.2006.4378854
  • Filename
    4378854