Title :
Sigma-Point Filters: An Overview with Applications to Integrated Navigation and Vision Assisted Control
Author_Institution :
Department of Computer Science and Electrical Engineering, OGI School of Science and Engineering, Oregon Health & Science University
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;
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
DOI :
10.1109/NSSPW.2006.4378854