Distributed receding horizon Kalman filter

In this paper a distributed version of the Kalman filter is proposed. In particular, the estimation problem is reduced to the optimization of a cost function that depends on the system dynamics and the latest output measurements and state estimates which is distributed among the local subsystems by means of dual decomposition. The techniques presented in the paper are applied to estimate the position of mobile agents.