Multi-sensor optimal information fusion Kalman filter
Automatica (Journal of IFAC)
Optimal fusion reduced-order Kalman estimators for discrete-time stochastic singular systems
Control and Intelligent Systems
Brief paper: Multisensor fusion fault tolerant control
Automatica (Journal of IFAC)
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Based on the optimal fusion criterion weighted by matrices in the linear minimum variance sense, an optimal information fusion steady-state Kalman filter is given for the discrete time-invariant linear stochastic control system measured by multiple sensors with coloured measurement noises, which is equivalent to an optimal information fusion steady-state Kalman predictor with a two-layer fusion structure for system with correlated noises. Furthermore, the steady-state optimal fusion predictor can be obtained only by fusing once after all local subsystems enter the steady-state predictions. The solution of steady-state prediction error cross-covariance matrix between any two subsystems can be obtained by iteration with an arbitratry initial value, whose convergence is proved. Applying it to a tracking system with three sensors shows its effectiveness.