Learning-Based Anomaly Detection and Monitoring for Swarm Drone Flights

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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.
Publisher
MDPI
Issue Date
2019-12
Language
English
Article Type
Article
Citation

APPLIED SCIENCES-BASEL, v.9, no.24, pp.5477

ISSN
2076-3417
DOI
10.3390/app9245477
URI
http://hdl.handle.net/10203/272342
Appears in Collection
AE-Journal Papers(저널논문)
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