Classification of single and multi propelled miniature drones using multilayer perceptron artificial neural network

Nir Regev, Ilia Yoffe, Dov Wulich

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations

Abstract

The last few years have seen a surge in drone popularity - not only in military and industrial applications, but also as for mainstream recreation uses. Despite its commercial success - or perhaps as a result of it - there are growing concerns on the risk they pose to aerial security as well as invasion of privacy. Detecting and classifying flying objects have always been a big research topic in the radar community. The challenge of detecting, tracking and classifying these small, sometimes multi-propelled flying objects is two fold. The first is the low radar cross section, which makes it hard for radars to pick up such a low energy echo. The second, once the echo has been detected, is classifying what kind of target it is (i.e bird, single propeller UAV or a multi-propeller drone). Tackling this challenge begins with giving the mathematical and physical model for the micro-Doppler (uDoppler) effect of a drone's radar returns. Then, this model will be used to train a multilayer per-ceptron (MLP) artificial neural network (ANN) to accurately classify a drone. Moreover, it will be shown that the MLP can be used also for regressing on the drone's propeller parameters such as blade length and frequency of rotation and to determine how many blades and propellers the drone consists of. All this will be attained from the baseband signal of the radar return. The work presented is essentially a proof-of-concept that radar-based complex target classification can be done effectively, while opening a new field of research of radar drones classification.

Original languageEnglish
Title of host publicationIET Conference Publications
PublisherInstitution of Engineering and Technology
EditionCP728
ISBN (Electronic)9781785614217, 9781785615030, 9781785616624, 9781785616723, 9781785616990, 9781785617072
ISBN (Print)9781785615078, 9781785615153
DOIs
StatePublished - 1 Jan 2017
Event2017 International Conference on Radar Systems, Radar 2017 - Belfast, United Kingdom
Duration: 23 Oct 201726 Oct 2017

Publication series

NameIET Conference Publications
NumberCP728
Volume2017

Conference

Conference2017 International Conference on Radar Systems, Radar 2017
Country/TerritoryUnited Kingdom
CityBelfast
Period23/10/1726/10/17

Keywords

  • Artificial neural network
  • Drones
  • Micro doppler
  • Multilayer perceptron
  • Radar

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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