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

机译：传感器异常通过蒙特卡罗和最大似然方法卷起三角翼飞机的滚动动力学检测和恢复

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摘要

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.

机译：本文研究了通用Delta-翼飞机卷动态模型的传感器异常检测，估计和恢复问题。所提出的算法采用颗粒滤波和最大似然方法来检测和估计异常。然后使用估计的异常来校正传感器读数。假设系统模型和传感器输出都被噪声损坏，这不一定是高斯。提出了仿真结果以显示所提出的算法的性能。

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《IFAC World Congress》|2018年|p12704-13371|共6页
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Mohammad Deghat; Evangelia Lampiri;
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中图分类 TP273-53;
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4. Sensor Anomaly Detection and Recovery in the Roll Dynamics of a Delta-Wing Aircraft via Monte Carlo and Maximum Likelihood Methods [C] . Mohammad Deghat, Evangelia Lampiri IFAC World Congress . 2018

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

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