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Shallow Neural Networks for Unmanned Aerial Vehicles Data Traffic Classification
Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
Wireless P2P Technologies Research and Development, Falun, Sweden.
2023 (engelsk)Inngår i: Proceedings - 2023 IEEE Future Networks World Forum: Future Networks: Imagining the Network of the Future, FNWF 2023, Institute of Electrical and Electronics Engineers Inc. , 2023Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In this paper, the classification of Unmanned Aerial Vehicles (UAV) data traffic into three distinct classes: analog video, digital OFDM-modulated video, and Additive White Gaus-sian Noise (AWGN) has been performed employing six neural network classifiers including Feed Forward Neural Network (FFNN), Generalized Regression Neural Network (GRNN), and Probabilistic Neural Network (PNN); and Cascade Forward Neural Network (CFNN), Recurrent Neural Network (RNN) and multilayer perceptron neural network (NN). The data set composed of the time domain signal samples for classifiers' training, validation, and testing has been collected in the controlled exper-iment conducted in the office/lab environment with the stationary signal source and receiver. The subset of twenty-four extracted features has been used as input to the neural network classifiers. Feature reduction has been performed using four popular in literature feature selection algorithms: Minimum Redundancy Maximum Relevance (MRMR), Neighborhood Component Anal-ysis (NCA), Relief, and Laplacian score to enhance computational efficiency and prediction speed for hardware implementation and real-time operation on the target CPU. Four features including mean, standard deviation, and median absolute deviation of the time domain signal, and RSSI have been selected. Six neural network classifiers have been trained using both the full and reduced feature sets. Also, two validation algorithms: k-fold cross-validation and hold-out validation have been evaluated. The Recurrent Neural Network (RNN) has demonstrated the highest accuracy using the full feature set and employing cross-validation. The feature reduction has led to a 3 % decrease in accuracy for RNN. Feedforward Neural Network (FFNN) has demonstrated the highest accuracy of 93.51 % with the reduced feature set input using cross-validation on PC in Matlab environment. It has been prototyped on our target hardware CPU using Mathworks Embedded Coder; the generated C code has been deployed on ARM Cortex CPU. FFNN using four feature inputs has demonstrated an accuracy of 91.23 % in real-time testing.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers Inc. , 2023.
Emneord [en]
feed-forward neu-ral network, Supervised machine learning, UAV data traffic classification, Antennas, C (programming language), Classification (of information), Computational efficiency, Recurrent neural networks, Signal receivers, Statistical tests, Support vector machines, Unmanned aerial vehicles (UAV), Aerial vehicle, Data traffic, Features sets, Feed forward, Neural networks classifiers, Neural-networks, Traffic classification, Unmanned aerial vehicle data traffic classification, Multilayer neural networks
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Identifikatorer
URN: urn:nbn:se:mdh:diva-67207DOI: 10.1109/FNWF58287.2023.10520364Scopus ID: 2-s2.0-85194161600ISBN: 9798350324587 (tryckt)OAI: oai:DiVA.org:mdh-67207DiVA, id: diva2:1865988
Konferanse
6th IEEE Future Networks World Forum, FNWF 2023, Baltimore, November 13-15, 2023
Tilgjengelig fra: 2024-06-05 Laget: 2024-06-05 Sist oppdatert: 2024-06-05bibliografisk kontrollert

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