A unified approach to state estimation problems under data and model uncertainties

Author

Sigalov, Daniel ; Michaeli, Tomer ; Oshman, Yaakov

Author_Institution

Appl. Math., Technion - Israel Inst. of Technol., Haifa, Israel

fYear

2012

fDate

9-12 July 2012

Firstpage

2569

Lastpage

2576

Abstract

We present a unified approach to the problem of state estimation under measurement and model uncertainties. The approach allows formulation of problems such as maneuvering target tracking, target tracking in clutter, and multiple target tracking using a single state-space system with random matrix coefficients. Consequently, all may be solved efficiently using a single IMM algorithm or using a linear optimal filter, derived elsewhere, thus replacing the need for deriving a unique algorithm for each problem.

Keywords

matrix algebra; recursive filters; state estimation; state-space methods; uncertain systems; IMM algorithm; data uncertainties; interacting multiple model; linear optimal recursive filter; maneuvering target tracking; model uncertainties; multiple target tracking; random matrix coefficients; single state-space system; state estimation problems; unified approach; Clutter; Covariance matrix; Mathematical model; Noise; Noise measurement; Target tracking; Time measurement; Maneuvering target tracking; clutter and data association; hybrid systems; multiple target tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion (FUSION), 2012 15th International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4673-0417-7

Electronic_ISBN

978-0-9824438-4-2

Type

conf

Filename

6290466