Sensor validation for flight control by particle filtering

In this paper, we address the problem of adaptive sensor validation for flight control. The model-based approaches are developed, where the sensor system is modeled by a Markov switch dynamic state-space model. To handle the nonlinearity of the problem, two different particle filters: mixture Kalman filter (MKF) and stochastic M-algorithm (SMA) are proposed. Simulation results are presented to compare the effectiveness and complexity of MKF and SMA methods.