Stochastically Resilient Extended Kalman Filtering for Discrete-time Nonlinear Systems with Sensor Failures

Authors

Xin Wang, Southern Illinois University EdwardsvilleFollow
Edwin E. Yaz, Marquette UniversityFollow

Document Type

Article

Language

eng

Format of Original

9 p.

Publication Date

2014

Publisher

Taylor & Francis

Source Publication

International Journal of Systems Science

Source ISSN

0020-7721

Original Item ID

doi: 10.1080/00207721.2013.879257

Abstract

Missing sensor data is a common problem, which severely influences the overall performance of modern data-intensive control and computing applications. In order to address this important issue, a novel resilient extended Kalman filter is proposed for discrete-time nonlinear stochastic systems with sensor failures and random observer gain perturbations. The failure mechanisms of multiple sensors are assumed to be independent of each other with different failure rates. The locally unbiased robust minimum mean square filter is designed for state estimation under these conditions. The performance of the proposed estimation method is verified by means of numerical Monte Carlo simulation of two different nonlinear stochastic systems, involving a sinusoidal system and a Lorenz oscillator system.

Comments

International Journal of Systems Science, Vol. 45, No. 7 (2014): 1393-1401. DOI.

Recommended Citation

Wang, Xin and Yaz, Edwin E., "Stochastically Resilient Extended Kalman Filtering for Discrete-time Nonlinear Systems with Sensor Failures" (2014). Electrical and Computer Engineering Faculty Research and Publications. 85.
https://epublications.marquette.edu/electric_fac/85