Magnetic Fault–Tolerant Navigation Filter for a UAV

Abstract

The heading state of an unmanned aerial vehicle (UAV) often diverges due to magnetic field disturbances, although the magnetic field can provide information about the heading. In this paper, we propose a novel magnetic fault-tolerant navigation filter (MFTNF) for UAVs, which combines a primary filter and a magnetometer-free attitude and heading reference system (AHRS) as a secondary backup filter to obtain an accurate and robust heading under an unknown magnetic disturbance. In the MFTNF framework, the primary filter that uses the magnetic field is the main filter used in normal situations for estimating the attitude, position, and velocity states. When an anomaly in the magnetic field is detected, the magnetometer-free AHRS replaces the heading of the primary filter. To detect magnetic disturbances, a novel decision-making algorithm is proposed in this paper. Flight experiments with UAVs demonstrate that the MFTNF is accurate and reliable under an unknown magnetic disturbance and that in such circumstances, the conventional approaches typically exhibit degraded accuracy in estimating the heading and the position. In addition, the advantage of the proposed MFTNF is that it always produces a robust heading estimate, regardless of the degree of magnetic interference, due to the magnetometer-free AHRS.