Steady-state Kalman filtering with an error bound☆
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2023, IFAC-PapersOnLineNetwork-based event-triggered H<inf>∞</inf> filtering for discrete-time singular Markovian jump systems
2018, Signal ProcessingCitation Excerpt :In recent two decades, the state estimation problem has been extensively studied and has been found various applications in systems where states are unmeasurable. Kalman filtering technique, as one of the most distinguished ways to deal with the state estimation problem, has been fully studied [1,2]. However, Kalman filtering is based on the assumptions that the dynamics system under discussion is exactly known and its disturbances are stationary Gaussian noises with known statistics [3].
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Supported in part by the Air Force Office of Scientific Research under contract F49620-86-C-0002.
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