The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Robust Adaptive Central Difference Particle Filter
|
|
Author(s): Li Xue (School of Physics and Electronics Information Engineering, Ningxia University, Yin Chuan, China), Shesheng Gao (School of Automatics, Northwestern Polytechnical University, Xi'an, China), Yongmin Zhong (School of Aerospace, Mechanical and Manufacturing Engineering, RMIT Univeristy, Bundoora, Australia), Reza Jazar (School of Aerospace, Mechanical and Manufacturing Engineering, RMIT Univeristy, Bundoora, Australia)and Aleksandar Subic (School of Aerospace, Mechanical and Manufacturing Engineering, RMIT Univeristy, Bundoora, Australia)
Copyright: 2014
Volume: 2
Issue: 1
Pages: 16
Source title:
International Journal of Robotics Applications and Technologies (IJRAT)
Editor(s)-in-Chief: Dan Zhang (York University, Canada)
DOI: 10.4018/ijrat.2014010102
Purchase
|
Abstract
This paper presents a new robust adaptive central difference particle filtering method for nonlinear systems by combining the concept of robust adaptive estimation with the central difference particle filter. This method obtains system state estimate and covariances using the principle of robust estimation. Subsequently, the importance density is obtained by adjusting the state estimate and covariances through the equivalent weight function and adaptive factor constructed from predicted residuals to control the contributions to the new state estimation from measurement and kinematic model. The proposed method can not only minimize the variance of the importance density distribution to resist the disturbances of systematic noises, but it also fully takes advantage of present measurement information to avoid particle degeneration. Experiments and comparison analysis with the existing methods demonstrate the improved filtering accuracy of the proposed method.
Related Content
|
Rana Seif Fathalla, Wafa Saad Alshehri.
© 2020.
16 pages.
|
|
Adel Alti.
© 2020.
10 pages.
|
|
Sandip Palit, Soumadip Ghosh.
© 2020.
9 pages.
|
|
Amiya Bhusan Bagjadab, Sushree Bibhuprada B. Priyadarshini.
© 2020.
13 pages.
|
|
Soumadip Ghosh, Arnab Hazra, Abhishek Raj.
© 2020.
9 pages.
|
|
Rana Fathalla.
© 2020.
18 pages.
|
|
Umesh Kokate, Arviand V. Deshpande, Parikshit N. Mahalle.
© 2020.
18 pages.
|
|
|