Stochastic analysis of reduced order GNSS based attitude determination algorithm

GNSS based attitude determination algorithms require highly accurate data about the geometry of receiver´s antennas. Due to a variety of factors, such as heating and gravity, somewhat mechanical distortions occur in the baselines´ configuration. In order to improve the performance of attitude estimation algorithm, it is of importance to determine the baseline biases arising from these distortions. However, notably in real-time applications computation of full-order models which include baseline biases may lead to a significant computational burden to the filter, resulting in a decrease in performance of algorithms. In this paper we´ve performed an analysis of the attitude estimation algorithm for the reduced-order models. Based on stochastic measure of observability we´ve examined the performance of the Kalman filter.