The Kalman Filter is a common method in navigation applications used to fuse data from the Inertial Measuring Unit with the GPS data to estimate a precise position. Dynamic inversion has proven to be a suitable way of controlling nonlinear systems. Both methods result in complex algorithms with a high count of computation cycles. These algorithms are developed in Matlab/Simulink and usually laboriously implemented onto the hardware. In this work autocode techniques are used to program these algorithms directly from Simulink. The aspect of the implementation in relation to the operating system and the computing power are considered.
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