A resilient Extended Kalman Filter for discrete-time nonlinear stochastic systems with sensor failures
作者： Xin Wang , E. E. Yaz , Chung Seop Jeong , Y. I. Yaz
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摘要: Missing sensor data is a common problem which severely influences the overall performance of today's dataintensive applications. In order to address this important issue, resilient Extended Kalman Filter proposed for discrete-time nonlinear stochastic system and measurement equations with failures random gain perturbations. The failure mechanisms multiple sensors are assumed be independent each other different rates. A generalized designed have robustness against resilience perturbations in filter gain. Lorenz oscillator, benchmark chaotic system, used demonstrate effectiveness approach.
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