Title :
Convergence analysis of multiple imputations particle filters for dealing with missing data in nonlinear problems
Author :
Zhang, Xiao-Ping ; Khwaja, A.S. ; Luo, J.-A. ; Housfater, A.S. ; Anpalagan, Alagan
Author_Institution :
Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON, Canada
Abstract :
We apply multiple imputations particle filter (MIPF) to deal with non-linear state estimation problem in the presence of missing data. We use imputations to replace the missing data. We present the convergence analysis of MIPF and show that it is almost surely convergent.We also present examples with a nonstationary growth model and dual-sensor bearing-only tracking, which demonstrate that MIPF can effectively deal with missing data in nonlinear problems.
Keywords :
convergence; particle filtering (numerical methods); state estimation; MIPF; convergence analysis; dual-sensor bearing-only tracking; missing data; multiple imputations particle filters; nonlinear state estimation problem; nonstationary growth model; Approximation methods; Convergence; Data models; Kalman filters; Mathematical model; State estimation;
Conference_Titel :
Circuits and Systems (ISCAS), 2014 IEEE International Symposium on
Conference_Location :
Melbourne VIC
Print_ISBN :
978-1-4799-3431-7
DOI :
10.1109/ISCAS.2014.6865697
