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
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