Weighted measurement fusion estimation for stochastic uncertain systems with multiple sensors of different missing measurement rates
Limin Wu · Control theory & applications · 2014
This paper is concerned with the optimal linear estimation problem for a multisensor linear discrete-time stochastic uncertain system with missing measurements. Different sensors have different missing measurement rates. Firstly, multiplicative noises are transferred to additive noises. Then, based on full-rank decomposition of a matrix and weighted least-squares theory, the weighted measurement fusion estimation algorithms with small computational burden are developed. The steady-state property of the weighted measurement fusion estimation algorithms is analyzed. A sufficient condition for the existence of the steady state is given. The weighted measurement fusion estimators proposed here have the same accuracy as the centralized fusion estimators, i.e., they have the global optimality. A simulation example shows the effectiveness of the algorithms.