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- W2110122461 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." @default.
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- W2110122461 date "2014-01-17" @default.
- W2110122461 modified "2023-10-18" @default.
- W2110122461 title "Stochastically resilient extended Kalman filtering for discrete-time nonlinear systems with sensor failures" @default.
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- W2110122461 doi "https://doi.org/10.1080/00207721.2013.879257" @default.
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