Tracking nonlinear systems using higher order moments

Author

Turner, K.J. ; Faruqi, F.A. ; Brown, C.L.

Author_Institution

Signal Processing Res. Centre, Queensland Univ. of Technol., Brisbane, Qld., Australia

fYear

1997

fDate

21-23 Jul 1997

Firstpage

52

Lastpage

56

Abstract

A sub-optimal nonlinear time-recursive filter is developed which considers an arbitrary number of moments of the conditional density. The filter assumes a quadratic truncation of the system dynamics and measurement functions and retains N moments, thus requiring knowledge of up to 2N+2 a priori moments and N+1 moments of the measurement noise process, which may be non-Gaussian. Prediction and update relations are given for moments of arbitrary order along with mechanisms which facilitate their closed forms. Numerical examples are given for both scalar and vector systems and show promising results

Keywords

higher order statistics; nonlinear dynamical systems; prediction theory; recursive filters; time-varying filters; tracking filters; closed forms; conditional density; higher order moments; measurement functions; measurement noise process; nonlinear systems; prediction; quadratic truncation; scalar systems; sub-optimal nonlinear time-recursive filter; system dynamics; update relations; vector systems; Australia; Density measurement; Filtering; Filters; Gaussian processes; Noise measurement; Nonlinear dynamical systems; Nonlinear systems; Signal processing; State estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Higher-Order Statistics, 1997., Proceedings of the IEEE Signal Processing Workshop on

Conference_Location

Banff, Alta.

Print_ISBN

0-8186-8005-9

Type

conf

DOI

10.1109/HOST.1997.613486

Filename

613486