Suboptimal State Estimation for Uncertain Multisensor Discrete-Time Linear Stochastic Systems
Deepak Tyagi, Vladimir Shin, Kiseon Kim, Georgy Shevlyakov
The problem of recursive estimation for uncertain multisensor linear discrete-time systems is considered. A new suboptimal filtering algorithm is herein proposed. It is based on the fusion formula for an arbitrary number of local Kalman filters. Each local Kalman filter is fused by the minimum mean square error criterion. The suboptimal gains and weights do not depend on current observations; therefore the proposed filter can easily be implemented in real-time. The examples given, demonstrate the efficiency and high-accuracy of the proposed filter.