Adaptive filter for linear systems with generalized unknown disturbance in measurements

Author

Yanbo Yang ; Yuemei Qin ; Yan Liang ; Quan Pan ; Feng Yang

Author_Institution

Sch. of Autom., Northwestern Polytech. Univ., Xian, China

fYear

2013

fDate

9-12 July 2013

Firstpage

1336

Lastpage

1341

Abstract

The paper presents the problem of state estimation of linear stochastic time-varying system with generalized unknown disturbance (GUD) existing in the measurements. Such GUD can reflect the effects of sensor bias, deception jamming, navigation bias and so on. An upper-bound filter (UBF) is designed for such systems, and its optimal parameters are derived so that the minimum upper-bounds filter (MUBF) is obtained. The simulation about tracking a target via a biased sensor shows the effectiveness of the proposed filter.

Keywords

adaptive filters; linear systems; state estimation; stochastic systems; GUD; MUBF; adaptive filter; biased sensor; deception jamming; generalized unknown disturbance; linear stochastic time-varying system; minimum upper-bounds filter; navigation bias; optimal parameters; sensor bias; state estimation; upper-bound filter; Noise; Optimized production technology; Robustness; State estimation; Target tracking; Technological innovation; Vectors; Adaptive filtering; discrete time systems; generalized unknown disturbance;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion (FUSION), 2013 16th International Conference on

Conference_Location

Istanbul

Print_ISBN

978-605-86311-1-3

Type

conf

Filename

6641152