This paper addresses the information fusion state estimation problem for multisensor stochastic uncertain systems with missing measurements and unknown measurement disturbances. The missing measurements of sensors are described by Bernoulli distributed random variables. Measurements of sensors are subject to external disturbances whose any prior information is unknown. Stochastic parameter uncertainties of systems are depicted by multiplicative noises. For such complex systems with multiple sensors, the Kalman-like centralized fusion and distributed fusion state one-step predictors (i.e., prior filters) independent of unknown measurement disturbances are designed based on the linear unbiased minimum variance criterion, respectively. Estimation error cross-covariance matrices between any two local predictors are derived. Their steady-state properties are analyzed. The sufficient conditions for the existence of the steady-state predictors are given.
Two simulation examples show the effectiveness of the proposed algorithms.Index Terms-Multi-sensor, missing measurement, unknown disturbance, multiplicative noise, fusion predictor, linear unbiased minimum variance.
This paper studies the distributed fusion filtering problem for multi-sensor stochastic systems with unknown inputs and one-step random delays. By defining some new variables, the original system with unknown inputs and random delays is equivalently transformed into a stochastic parameterized system.The time-delay is depicted by a Bernoulli distributed random
variable. No prior information about unknown inputs is available.A Kalman-form distributed fusion filter (DFF) independent of unknown inputs is presented based on the linear unbiased minimum variance criterion. The filtering error cross-covariance matrices between any two local filters are derived. A simulation explains the effectiveness of the algorithms.Then, (33) is readily obtained by substituting (34) into P;j (t+ I) = E[X; (t+ I)X; (t+ I)] . D
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