Abstract
This paper considers continuous-time state estimation for a dynamic system with Gaussian and Poisson white noise. We design a finite-dimensional filter with a lower computational cost than the extended Kalman–Bucy filter: its order is the dimension of the estimated part of the system state vector. The nonlinear structure of the filter is selected using the mean squared optimal unbiased estimation for each infinitesimal period of time. We develop an algorithm to find the structural functions of the filter that is based on the Kolmogorov–Feller equation for the density function. A numerical method to calculate them a priori by sequential Monte Carlo trials is presented, which requires the histograms of the desired functions. Due to its cumbersome form, some numerical and analytical approximations of the suggested filter are also given. They structurally coincide with the corresponding nonlinear extensions of the Kalman–Bucy filter but have a considerably smaller order and calculable parameters.
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 callMore From: Journal of Computer and Systems Sciences International
Disclaimer: 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.
