Stochastic analysis of reduced order GNSS based attitude determination algorithm

Abstract

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.