Aerial Radar Target Classification using Artificial Neural Networks

Guy Ardon, Or Simko, Akiva Novoselsky

Abstract

In this paper, we propose a new algorithm for classification of aerial radar targets by using Radar Cross Section (RCS) time-series corresponding to target detections of a given track. RCS values are obtained directly from SNR values, according to the radar equation. The classification is based on analysing the behaviour of the RCS time-series, which is the unique “fingerprint” of an aerial radar target. The classification process proposed in this paper is based on training a fully-connected neural network on features extracted from the RCS time-series and its corresponding Intrinsic Mode Functions (IMFs). The training is based on a database containing RCS signatures of various aerial targets. The algorithm has been tested on a large and diverse set of simulative flight trajectories, and its performance has been compared with that of several different methods. We have found that the proposed neural network-based classifier performed better on our database.

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Paper Citation


in Harvard Style

Ardon G., Simko O. and Novoselsky A. (2020). Aerial Radar Target Classification using Artificial Neural Networks.In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 136-141. DOI: 10.5220/0008911701360141


in Bibtex Style

@conference{icpram20,
author={Guy Ardon and Or Simko and Akiva Novoselsky},
title={Aerial Radar Target Classification using Artificial Neural Networks},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2020},
pages={136-141},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008911701360141},
isbn={978-989-758-397-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Aerial Radar Target Classification using Artificial Neural Networks
SN - 978-989-758-397-1
AU - Ardon G.
AU - Simko O.
AU - Novoselsky A.
PY - 2020
SP - 136
EP - 141
DO - 10.5220/0008911701360141