Imperial College London
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Professor Washington Yotto Ochieng, EBS, FREng

Faculty of Engineering, Department of Civil and Environmental Engineering

Head of Department of Civil and Environmental Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6104w.ochieng Website

 
 
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Assistant

 

Ms Maya Mistry +44 (0)20 7594 6100

 
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Location

 

441/442Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ochieng:2017:10.1049/iet-rsn.2016.0427,
author = {Ochieng, W},
doi = {10.1049/iet-rsn.2016.0427},
journal = {IET Radar, Sonar and Navigation},
pages = {847--853},
title = {Integrated method for the UAV navigation sensor anomaly detection},
url = {http://dx.doi.org/10.1049/iet-rsn.2016.0427},
volume = {11},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - © 2016, The Institution of Engineering and Technology. The rapid development of unmanned aerial vehicles (UAVs) has made great progress for its widespread uses in military and civilian applications in recent years. On-board integrated navigation sensors are essential for UAV flight control systems in that they must operate with robustness and reliability. To achieve this, timely and effectively anomaly detection capabilities for the estimated UAV status from the integrated navigation sensors are required to ensure the UAV flight safety. Extraction of the anomaly information from the real-time navigation sensors and designing a robust and reliable anomaly detection algorithm are major issues for the UAV navigation sensor anomaly detection. This study introduces a novel integrated algorithm for detecting UAV on-board navigation sensor anomaly, by combining particle filter (PF) estimated state residuals with fuzzy inference system (FIS) decision system. The residual information is obtained based on the difference between the collected Global Positioning System measurements and high accuracy PF estimates. The indicators derived from the PF residuals are further made as inputs for the FIS system to output the different anomaly levels. The simulation and filed test results have demonstrated the effectiveness and efficiency of the proposed anomaly detection method in terms of timeliness, recall and precision.
AU  - Ochieng,W
DO  - 10.1049/iet-rsn.2016.0427
EP  - 853
PY  - 2017///
SN  - 1751-8784
SP  - 847
TI  - Integrated method for the UAV navigation sensor anomaly detection
T2  - IET Radar, Sonar and Navigation
UR  - http://dx.doi.org/10.1049/iet-rsn.2016.0427
VL  - 11
ER  -
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