Algorithm of set-valued state estimation for strapdown inertial navigation systems

Abstract

The article deals with set-valued state estimation for strap down inertial navigation systems when there is no statistical information about an initial state, disturbances and noises but the sets of their possible values are available. The suggested algorithm uses an implicit description of feasible sets with the linear inequality system that describes sets of possible values of initial state, disturbances and noises, and the linear equations system received from the system model. The algorithm involves solving a row of linear programming problems. The comparison of set-valued estimates and Kalman filter estimates was performed.