Information fusion estimators for systems with multiple sensors of different packet dropout rates

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.

Research highlights
► For discrete time-varying linear stochastic systems with multiple sensors of different packet dropout rates, information fusion estimators are investigated in the linear minimum variance (LMV) sense.
► Centralized fusion estimators (CFEs) are presented by the innovation analysis method. CFEs can bring expensive computational cost and poor reliability.
► Distributed fusion estimators (DFEs) are given based on the scalar-weighted fusion algorithm.
► DFEs have the parallel structures and can reduce the computational cost and improve the reliability. Estimation error cross-covariance matrices between any two sensor subsystems are derived.