Abstract
Particle filters are among the most effective filtering algorithms for nonlinear and non-Gaussian models. When the state dimension is high, they are known to suffer from weight degeneracy. Sequential Markov chain Monte Carlo (SMCMC) methods have been proposed as an alternative sequential inference technique that can perform better in high dimensional state spaces. In this paper, we propose to construct a composite Metropolis-Hastings (MH) kernel within the SMCMC framework using invertible particle flow. Simulation results show that the proposed kernel significantly increases the acceptance rate and improves estimation accuracy compared with state-of-the-art filtering algorithms, in high dimensional simulation examples.
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.
