Recursive update Gaussian particle filter
ZHANG Yong-gang
WANG Gang
HUANG Yu-long
LI Ning
Abstract：In traditional Gaussian particle filter (GPF), sample importance density function is constructed through com-bining the latest measurements based on Gaussian filter (GF). However, in measurement update of the traditional GF, since the measurement value is assimilated directly based on the linear update rule, the constructed sample importance density function may not be approximate to the real posterior distribution under certain conditions. To solve this problem, we propose a new recursive update GF (RUGF) based on the recursive update idea and give out its general framework. On this basis, a new sample importance density function is constructed by using RUGF, based on which a new recursive update GPF (RUGPF) can be derived. Simulation results demonstrate that recursive update idea can assimilate the measurement information commendably, and compared with traditional GPF, the proposed filter has higher estimation accuracy for state estimation in nonlinear systems.
Keywords：Gaussian filterparticle filterrecursive algorithmnonlinear filtering
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 353-360 )