Sensor Anomaly Detection and Recovery in the Roll Dynamics of a Delta-Wing Aircraft via Monte Carlo and Maximum Likelihood Methods<sup>*</sup>

Publication Type:
Conference Proceeding
Citation:
IFAC-PapersOnLine, 2017, 50 (1), pp. 12791 - 12796
Issue Date:
2017-07-01
Metrics:
Full metadata record
© 2017 This paper studies the problem of sensor anomaly detection, estimation and recovery for the roll dynamic model of a generic delta-wing aircraft. The proposed algorithm employs particle filtering and maximum likelihood methods to detect and estimate the anomaly. The estimated anomaly is then used to correct the sensor readings. It is assumed that both the system model and sensor outputs are corrupted by noise, which are not necessarily Gaussian. Simulation results are presented to show the performance of the proposed algorithm.
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