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This thesis has explored if it is feasible to produce a good estimation of the rotational position of a stepper motor by using sensor fusion schemes to merge a sensorless position estimation (based on the back electromotive force) with the measurement from a magnetic rotational position sensor. The purpose was to find a cheaper alternative for position feedback in closed loop control from conventionally used rotational encoders and resolvers. Beyond the sensor fusion a suitable position control logic was also developed to verify the concept of a low cost closed loop hybrid stepper motor solution for high precision position applications. The sensor fusion and position control were simulated offline to first test the feasibility of the implementation, after which laboratory tests were performed to assess online performance. The extended Kalman filter implemented improved the performance of the magnetic rotational position sensor which was used exclusively at lower speeds (between 0-75 rpm) by decreasing its root-mean-square error by almost half from 0.0733 unfiltered to 0.0370 filtered (in mechanical degrees). When fusing both position signals at higher rotational speeds (75-400rpm) did the extended Kalman filter clearly improve position estimation accuracy compared to the single sources. It is not meaningful however to discuss the numeric improvement of the filter at these working points as this result is not conclusive but based on some fortunate conditions. This is because the two signals used for the fusion is diverging towards positive and negative error respectively for increasing rotational speeds making the fused estimate result in between. This basically means that the result from the fusion is outperforming two very bad signals, and is then not meaningful to use as a measure of how well the fusion is actually performing. Further work on the raw signals used for fusion need to be performed before a proper assessment on the fusion performance could be made.