Abstract
This research aims at further completing our novel Generic Multisensor Integration Strategy (GMIS) with the systematic development of three alternate attitude models, i.e., roll-pitch-heading (RPH), direction cosine matrix (DCM), and quaternion. The GMIS' potential for a true sensor level data fusion is leveraged to its full extent here by facilitating comprehensive error analysis framework in Kalman filtering. A comparative analysis between the solutions resulted from the GMIS associated with each attitude model have been analysed and compared through real road test data. The attitude models were found to perform very consistently, exhibiting the same behaviours in the residuals of the process noise and measurement vectors along with the estimated variance components. Besides, an analysis was conducted to investigate how each attitude model reacts to a sudden trajectory variation captured by the IMU. Each attitude model still performed consistently, but the DCM model in particular exhibited resistance to absorbing erroneous observations into its process noise estimates.
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