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Article

Learning-Based Anomaly Detection and Monitoring for Swarm Drone Flights

1
Korea Aerospace Research Institute, Daejeon 34133, Korea
2
Department of Aerospace Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
3
School of Mechanical Engineering, Pusan National University, Busan 46241, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(24), 5477; https://doi.org/10.3390/app9245477
Received: 22 October 2019 / Revised: 11 December 2019 / Accepted: 11 December 2019 / Published: 13 December 2019
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs))
This paper addresses anomaly detection and monitoring for swarm drone flights. While the current practice of swarm flight typically relies on the operator’s naked eyes to monitor health of the multiple vehicles, this work proposes a machine learning-based framework to enable detection of abnormal behavior of a large number of flying drones on the fly. The method works in two steps: a sequence of two unsupervised learning procedures reduces the dimensionality of the real flight test data and labels them as normal and abnormal cases; then, a deep neural network classifier with one-dimensional convolution layers followed by fully connected multi-layer perceptron extracts the associated features and distinguishes the anomaly from normal conditions. The proposed anomaly detection scheme is validated on the real flight test data, highlighting its capability of online implementation. View Full-Text
Keywords: swarm drone; anomaly detection; clustering; labeling; classification swarm drone; anomaly detection; clustering; labeling; classification
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MDPI and ACS Style

Ahn, H.; Choi, H.-L.; Kang, M.; Moon, S. Learning-Based Anomaly Detection and Monitoring for Swarm Drone Flights. Appl. Sci. 2019, 9, 5477. https://doi.org/10.3390/app9245477

AMA Style

Ahn H, Choi H-L, Kang M, Moon S. Learning-Based Anomaly Detection and Monitoring for Swarm Drone Flights. Applied Sciences. 2019; 9(24):5477. https://doi.org/10.3390/app9245477

Chicago/Turabian Style

Ahn, Hyojung, Han-Lim Choi, Minguk Kang, and SungTae Moon. 2019. "Learning-Based Anomaly Detection and Monitoring for Swarm Drone Flights" Applied Sciences 9, no. 24: 5477. https://doi.org/10.3390/app9245477

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