Velocity-Aided Underwater Navigation System Using Receding Horizon Kalman Filter

This paper discusses the problems related to constructing a receding horizon filter for underwater inertial navigation systems which are subject to external disturbances. Noises are assumed to be bounded, additive, and contained in both state and measurement equations. An estimator is designed according to the sliding-window strategy to minimize the receding horizon estimation cost function. The derived filter is applied to a velocity-aided inertial navigation system. Simulations show that the derived filter is more accurate than the standard Kalman filter (KF) for underwater navigation systems subject to temporary unknown disturbances

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