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arXiv · 1809.10333

Using Autoencoders To Learn Interesting Features For Detecting Surveillance Aircraft

Teresa Nicole Brooks
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Abstract

This paper explores using a Long short-term memory (LSTM) based sequence autoencoder to learn interesting features for detecting surveillance aircraft using ADS-B flight data. An aircraft periodically broadcasts ADS-B (Automatic Dependent Surveillance - Broadcast) data to ground receivers. The ability of LSTM networks to model varying length time series data and remember dependencies that span across events makes it an ideal candidate for implementing a sequence autoencoder for ADS-B data because of its possible variable length time series, irregular sampling and dependencies that span across events.

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Teresa Nicole Brooks. 2018-09-27. Using Autoencoders To Learn Interesting Features For Detecting Surveillance Aircraft. https://arxiv.org/abs/1809.10333

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