A Jump-Markov Regularized Particle Filter for the estimation of ambiguous sensor faults

Sensor or actuator faults occurring on a Unmanned Aerial Vehicle (UAV) can
compromise the system integrity. Fault diagnosis methods is then becoming a required feature
for those systems. In this paper, the focus is on fault estimation for a fixed-wing UAVs in the
presence of simultaneous sensor faults. The altitude measurements of a UAV are commonly
obtained from the combination of two different types of sensors: a Global Navigation Satellite
System (GNSS) receiver and a barometer. Both sensors are subject to additive abrupt faults.
To deal with the multimodal nature of the faulty modes, a Jump-Markov Regularized Particle
Filter (JMRPF) is proposed in this paper to estimate the barometric altitude and GNSS altitude
measurement faults, including the case when both faults occur simultaneously. This method is
based on a regularization step that improves the robustness thanks to the approximation of
the conditional density by a kernel mixture. In addition, the new jump strategy estimates the
correct failure mode in 100 % of the 100 simulations performed in this paper. This approach is
compared with an Interacting Multiple Model Kalman Filter (IMM-KF) and the results show
that the JMRPF outperforms the IMM-KF approach, particularly in the ambiguous case when
both sensors are simultaneously subject to additive abrupt faults.