Using Autoencoders To Learn Interesting Features For Detecting Surveillance Aircraft
This work addresses a domain-specific problem for aviation security, but it appears incremental as it applies an existing method (LSTM autoencoder) to a new dataset (ADS-B data).
The paper tackled the problem of detecting surveillance aircraft by using an LSTM-based sequence autoencoder to learn features from ADS-B flight data, which involves variable-length time series and irregular sampling, but no concrete results or numbers were reported in the 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.