A feature based approach for loaded/unloaded drones classification exploiting micro-doppler signatures

Luca Pallotta, Carmine Clemente, Alessandro Raddi, Gaetano Giunta

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

14 Downloads (Pure)

Abstract

This paper deals with the problem of loaded/unloaded drones classification. Precisely, exploiting the different micro-Doppler signatures exhibited by a drone with both any load and payloads of different weights, a novel signature extraction procedure is developed for automatic recognition purposes. The developed algorithms is based on a novel adaptation of the spectral kurtosis technique to the problem at hand, specifically the analysis of narrowband and wideband spectrograms of the radar echoes reflected by the drones. In addition, the principal component analysis is used to reduce the feature vector size. The experiments conducted on measured bistatic radar data prove the effectiveness of the proposed method in separating the quoted classes of objects
Original languageEnglish
Title of host publication2020 IEEE Radar Conference (RadarConf20)
Place of PublicationPiscataway, NJ.
PublisherIEEE
Number of pages6
ISBN (Electronic)9781728189420
ISBN (Print)9781728189437
DOIs
Publication statusPublished - 4 Dec 2020
EventIEEE Radar Conference 2020 - Florence, Florence, Italy
Duration: 21 Sep 202025 Sep 2020

Conference

ConferenceIEEE Radar Conference 2020
CountryItaly
CityFlorence
Period21/09/2025/09/20

Keywords

  • micro doppler
  • automatic target recognition
  • drones classification
  • spectral kurtosis

Fingerprint Dive into the research topics of 'A feature based approach for loaded/unloaded drones classification exploiting micro-doppler signatures'. Together they form a unique fingerprint.

Cite this