A Nonlinear Filter for Efficient Attitude Estimation of Unmanned Aerial Vehicle (UAV)

Abstract

Autonomous estimation of the state is of key importance in UAVs, as the measurement systems may experience faults and failures. Thus estimation techniques must provide estimates of the most important variables used in the control algorithms for safe, autonomous, unmanned flights. In this paper, a filter with low computational complexity for attitude estimation of a quadrotor UAV is introduced, with a model suitable for Fault-Tolerant Observation. The new filtration method, called the Square Root Unscented Complementary Kalman Filter (SRUCKF), is based on the commonly-known Kalman Filter (KF) in its nonlinear version, namely the Square Root Unscented Kalman Filter (SRUKF). The fundamental equation of the KF is modified so that the complementary feature of the filter is exalted. The new filter introduces characteristics that are analyzed on the basis of its application in quadrotor state estimation. Finally, the results are compared to an ordinary filter of the same type (using the Unscented Transformation). The presented studies indicate that the newly derived filter (SRUCKF) handles strong nonlinearities and gives results similar to those obtained from the SRUKF. Furthermore, it introduces lower computational burden, as the undergoing process uses diagonal matrices in its crucial places. In the paper, the estimation algorithms are tailored to a quadrotor UAV (Crazyflie 2.0), for which a quaternion-based model is proposed. The contribution of the paper lies in a Kalman-based novel state observer and its application in Fault-Tolerant Observation (FTO).