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Zbl 1123.93085
Hounkpevi, Franck O.; Yaz, Edwin E.
Robust minimum variance linear state estimators for multiple sensors with different failure rates. (English)
[J] Automatica 43, No. 7, 1274-1280 (2007). ISSN 0005-1098

Summary: Linear minimum variance unbiased state estimation is considered for systems with uncertain parameters in their state space models and sensor failures. The existing results are generalized to the case where each sensor may fail at any sample time independently of the others. For robust performance, stochastic parameter perturbations are included in the system matrix. Also, stochastic perturbations are allowed in the estimator gain to guarantee resilient operation. An illustrative example is included to demonstrate performance improvement over the Kalman filter which does not include sensor failures in its measurement model.

MSC 2000:: *93E11 Filtering in stochastic control
93E03 General theory of stochastic systems
93A30 Mathematical modelling of systems
93C73 Perturbations in control systems

Keywords: sensor failure; estimation with missing observations; multiple sensor system; stochastic modeling; minimum variance estimation; stochastic robustness; estimation under uncertainty; Kalman filtering

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