Suboptimal filter for multisensor linear discrete-time systems with observation uncertainties

The focus of this paper is the problem of recursive estimation for uncertain multisensor linear discrete-time systems. We herein propose a new suboptimal filtering algorithm. The basis of the proposed algorithm is the fusion formula for an arbitrary number of local Kalman filters. The proposed suboptimal filter fuses each local Kalman filter by weighted sum with scalar weights. This filter can be implemented in real time because the scalar weights do not depend on current observations in distinction to the optimal adaptive filter, The examples given, demonstrate the effectiveness and high precision of proposed filter.