Using Autoencoders To Learn Interesting Features For Detecting Surveillance Aircraft

2018/09/26 by Teresa Nicole Brooks, Brooks, Teresa Nicole
Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Target Tracking and Data Fusion in Sensor Networks #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.1809.10333

openalex publication_date 2018/09/27 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28

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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