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

fYear

2014

fDate

1-5 June 2014

Firstpage

2567

Lastpage

2570

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems (ISCAS), 2014 IEEE International Symposium on

Conference_Location

Melbourne VIC

Print_ISBN

978-1-4799-3431-7

Type

conf

DOI

10.1109/ISCAS.2014.6865697

Filename

6865697

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1769417