Abstract
A new nonlinear attitude filter called the sequential optimal attitude recursion filter is developed. This routine is based on maximum likelihood estimation and a sequentialization of the Wahba problem that has been extended to include nonattitude states. The algorithm can accept either individual unit vector measurements or quaternion measurements. This new algorithm is compared with existing attitude filtering routines, including the multiplicative extended Kalman filter, filter QUEST, REQUEST, optimal REQUEST, and extended QUEST.
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