Unscented Kalman Filtering for Greenhouse Climate Control Systems with Missing Measurement
نویسندگان
چکیده
A stochastic unscented Kalman filter is designed in an attempt to solve the state estimation problem of the greenhouse climate control systems with missing measurements. The missing measurements are described by a binary switching sequence satisfying a conditional probability distribution. In order to accommodate the effects of randomly varying arrival of measurement data, the stochastic unscented transformation coupled with certain parts of the classic Kalman filter is applied to estimate the greenhouse states and filter out the noises, where some or all measurements are lost in a random fashion. The simulation results demonstrate the performance degradation of state estimation caused by random measurement data loss.
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