Improved Kalman-based attitude estimation framework for UAVs via an antenna array

Abstract

Accurate attitude estimation is crucial for Unmanned Aerial Vehicles (UAVs) in order to facilitate automated activities such as landing or trajectory tracking. Recently antenna array based communication systems have been installed in UAVs. This array structure can also be applied for attitude estimation by computing the line-of-sight (LOS) path between the base station and UAV. In this paper, we propose a complete framework for attitude estimation by exploiting 3D LOS vector obtained from the antenna array system. We present all the steps to incorporate the estimated LOS vector into the TRIaxial Attitude Determination (TRIAD), QUaternion ESTimator (QUEST) and Kalman algorithms. As an additional contribution, the error covariance matrix of the LOS vector is analytically calculated by first finding the phase shift mean squared error using the known perturbation model from Singular Value Decomposition and assuming that the antenna array measured data error can be modeled as a circularly symmetric white noise. We evaluate five array configurations via Monte Carlo simulations. We show that array configurations that provide orthogonal components of the LOS vector achieve a better performance. The usage of more than three pairs of antennas to improve the estimation of the LOS vector is also proposed for low and intermediate signal-to-noise ratio regimes.