Sensor validation for flight control by particle filtering

Author

Tao Wei ; Yufei Huang ; Chen, Philip

Author_Institution

Dept. of Electr. Eng., Univ. of Texas at San Antonio, San Antonio, TX, USA

fYear

2005

fDate

4-8 Sept. 2005

Firstpage

1

Lastpage

4

Abstract

In this paper, we address the problem of adaptive sensor validation for flight control. The model-based approaches are developed, where the sensor system is modeled by a Markov switch dynamic state-space model. To handle the nonlinearity of the problem, two different particle filters: mixture Kalman filter (MKF) and stochastic M-algorithm (SMA) are proposed. Simulation results are presented to compare the effectiveness and complexity of MKF and SMA methods.

Keywords

Kalman filters; Markov processes; adaptive control; aerospace control; particle filtering (numerical methods); sensors; state-space methods; stochastic systems; MKF method; Markov switch dynamic state-space model; SMA method; adaptive sensor validation; flight control; mixture Kalman filter; model-based approach; particle filtering; sensor system; stochastic M-algorithm; Adaptation models; Kalman filters; Mathematical model; Prediction algorithms; Stochastic processes; Trajectory; Vectors; Fault Detection and Isolation (FDI); Mixture Kalman Filter (MKF); Monte-Carlo technique; Stochastic M-Algorithm (SMA); particle filter (PF); sensor failure; sensor validation;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Conference, 2005 13th European

Conference_Location

Antalya

Print_ISBN

978-160-4238-21-1

Type

conf

Filename

7078351