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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.
Hounkpevi, Franck O. and Yaz, Edwin E., "Robust Minimum Variance Linear State Estimators for Multiple Sensors with Different Failure Rates" (2007). Electrical and Computer Engineering Faculty Research and Publications. 742.
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