Abstract
In this paper, a new particle filter based on sequential importance sampling (SIS) is proposed for the on-line estimation of non-Gaussian nonlinear systems. In this filtering method, state parameters separation and an annealing parameter are used to produce importance function. Since the distribution function makes full use of the prior, likelihood, and statistical characteristics of noise and the newest observation data, it is much closer to posterior distributions. Theoretical analysis and simulation show that the performance of proposed particle filter outperforms the standard particle filter and the extended Kalman filter.
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.
