State-bounding estimation for nonlinear models with multiple measurements

A hierarchical state bounding estimation method is presented for nonlinear dynamic systems where different sensors offer several measurements of the same state vector, each of which is subject to unknown but bounded disturbances and is equipped with a local processor. For each sampling time, the proposed algorithm proceeds in two stages. At the prediction stage, an approximating outer-bounding ellipsoid is computed for the reachable set of the nonlinear function of the state vector. At the correction stage, the algorithm works at two levels : Each local processor computes the state estimate and its outer-bounding ellipsoid according to the local measurements given by the corresponding sensor. These ellipsoids are transmitted simultaneously from all local processors to the fusion center which synthesizes them to compute the global state bounding ellipsoid. Then it feeds these data back to all the local processors. This feedback allows the local processors to adjust their results by taking into account the measurements of all the other sensors.