Abstract
In order to avoid the possible gaps and clusters that arise from random sampling in Monte Carlo (MC) methods, and improve the sampling efficiency and calculation accuracy, the Quasi-Monte Carlo (QMC) methods are to be applied to replace it. The idea in QMC is to use more regularly distributed and deterministic points for sampling an integrand. We propose a new nonlinear filter by applying the QMC sampling methods to the particle filter algorithm. Given certain proposal distributions, a simulation example is presented. The results show that the nonlinear filter based on the QMC methods performs more efficient than that based on the MC methods. The performance provides some references for the real-time application of particle filter in nonlinear / non-Gaussian systems.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
