Linear recursive discrete-time estimators using covariance information under uncertain observations
Signal Processing - From signal processing theory to implementation
Automatica (Journal of IFAC)
Brief paper: Optimal linear estimation for systems with multiple packet dropouts
Automatica (Journal of IFAC)
Control and Estimation of Systems with Input/Output Delays
Control and Estimation of Systems with Input/Output Delays
Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part II
Multi-sensor optimal information fusion Kalman filter
Automatica (Journal of IFAC)
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In this paper, the optimal centralized and distributed fusion estimation problems in the linear minimum variance (LMV) sense are investigated for multi-sensor systems with multiple packet dropouts. For discrete time-varying linear stochastic systems with multiple sensors of different packet dropout rates, the LMV centralized fusion estimators (CFEs) including filter, predictor and smoother are presented in virtue of the method of innovation analysis. However, CFEs can bring expensive computational cost and poor reliability due to augmentation. To reduce the computational cost and improve the reliability, the distributed fusion estimators (DFEs) are given based on the well-known optimal fusion estimation algorithm weighted by scalars in the LMV sense, which have the parallel structures. Estimation error cross-covariance matrices between any two sensor subsystems are derived to obtain the distributed fusion estimators. A numerical example shows the effectiveness of the proposed algorithms.