Accessibility of offshore structures is strongly affected by local weather and wave conditions. During rough wave climates, vessel motions prevent people to be transferred safely from and to an offshore structure. To increase accessibility, Ampelmann developed a motion compensating gangway system. The system uses a 6 DoF motion sensor to measure the vessel motions in order to compensate for them. This motion sensor has a sufficient accuracy to compensate the Ampelmann, but it has some drawbacks. The motion sensor is expensive - it is the most expensive part of the whole Ampelmann - and the sensor is on the blacklists of many boarder securities since the accuracy is high enough to guide a missile.

Due to these drawbacks, it is interesting for Ampelmann to investigate alternative sensors. During onshore tests the currently used motion sensor is compared with an alternative. It was found that the rotations were measured accurately by both sensors, but that the transla- tion measurement was significantly worse with the alternative motion sensor. Through these findings, the focus of this research is on estimating the translations. Also, in literature was found that using multiple sensors improves the performance of the measurement. This research investigates using multiple inertial measurement sensors for estimating the translations.

Noisy acceleration measurements are simulated to replicate real IMU measurements up to five IMUs. A mass-spring-damper model is used to generate a excited sine, which represents a wave. Through a Kalman filter with a Wiener model the translations are estimated from the accelerations. A pullback method is introduced, where a virtual zero measurement is added to compensate for the drift introduced when double integrating accelerations. Five different fusion techniques are investigated and compared. Through the VAF and the average error the performances of the methods are compared. The direct fusion method performed the worst. The best results for the average error were found when using the post-prediction fusion method with 3 IMUs. The best results for the VAF were found using the post-correction fusion method with 2 IMUs. Also real ship motions that were measurement offshore were transformed into one acceleration measurement, which were used in the direct fusion method and the averaging fusion method (since there is only one measurement available). Both methods gave the exact same results.

The different fusion techniques are also applied on real IMU measurements on a test setup. The test setup is a translational sled on a rails. One IMU is used to get real data measure- ments. Rotating this IMU with repect to the translational movement of the sled allows us to get two measurements of the IMU. Again the direct fusion method performed the worst. The best results were found when using the post-prediction fusion method with 3 IMUs.

When comparing the simulations and the real tests with the several fusion methods, the same trend is found in both the VAF and the average error. When using the post-prediciton fusion method or the post-correction fusion method an almost similar VAF is found in both simulation and test. Though the average error for each method was significantly smaller in simulation than found in tests.

Based on the outcome of the simulations and the tests it can be concluded that two fusion methods are found to give good results for estimating translations from low-cost IMUs. These methods use a Kalman filter with a Wiener model. Also, a pullback method is used to compensate for drift. Before implementing this motion sensor into the Ampelmann system, further research should be done on the performance of the methods on real ship motions.