Classification between birds and UAVs using recurrent neural networks
Publicerad
Författare
Typ
Examensarbete för masterexamen
Program
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In this thesis, we investigate recurrent neural networks (RNN) for classifying birds
and drones from radar tracks. The data set consisted of 425 drone tracks and 4000
bird tracks, and included all features measured by the radar system. On this data
set, we have empirically evaluated whether it is better to use Long Short-Term
Memory (LSTM) units or Gated Recurrent Units (GRU) and how the sequence
length affects performance, for both filter based classification and smoothed based
classification where the tracks were re-sampled for a regular sampled interval of the
targets maneuver. Lastly, a hybrid architecture consisting of multiple parallel GRU
networks which is connected to a naive Bayes classifier was implemented and tested
on the data.
Our results show that classification is possible with an accuracy of around 90% for
filter based classification using a GRU architecture and approximately 88% when
classifying by smoothed flight-paths using the hybrid architecture.
This thesis has shown that it is possible to distinguish drones from birds with recurrent
neural network architectures, using their detection history alone. For further
improvement of the classifier, more domain knowledge and a revise of data set labels
is needed in the case of the hybrid classifier.
Beskrivning
Ämne/nyckelord
LSTM, GRU, Radar tracking, Time series classification