Distributed Kalman Filtering for State Constrained Systems with Multisensor

In practice, the state variables of dynamic systems often have relations, which are always ignored in the application of state estimate method, such as Kalman filter. In this note, the distributed Kalman filtering for discrete dynamic system with state equation constraint is studied. New algorithm is derived in the minimum mean squared error sense by using of Lagrange method. At each step, the unconstrained solution is projected onto the state constraint surface. The distributed constrained Kalman filter (DCKF) avoiding the measurement augmentation and it reduces the computing burden. The precision relation between the new algorithm and some other filters are strictly proved and simulation result shows that new filter is better.