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

Luca Pallotta; Carmine Clemente; Alessandro Raddi; Gaetano Giunta

doi:10.1109/RadarConf2043947.2020.9266458

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 proceeding › Conference 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 language	English
Title of host publication	2020 IEEE Radar Conference (RadarConf20)
Place of Publication	Piscataway, NJ.
Publisher	IEEE
Number of pages	6
ISBN (Electronic)	9781728189420
ISBN (Print)	9781728189437
DOIs	https://doi.org/10.1109/RadarConf2043947.2020.9266458
Publication status	Published - 4 Dec 2020
Event	IEEE Radar Conference 2020 - Florence, Florence, Italy Duration: 21 Sep 2020 → 25 Sep 2020

Conference

Conference	IEEE Radar Conference 2020
Country	Italy
City	Florence
Period	21/09/20 → 25/09/20

Keywords

micro doppler
automatic target recognition
drones classification
spectral kurtosis

Access to Document

10.1109/RadarConf2043947.2020.9266458

Pallotta-etal-IEEE-RADAR-2020-A-feature-based-approach-for-loaded-unloaded-dronesAccepted author manuscript, 406 KB

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

@inproceedings{c79d7150adfb4538b115f97548d9df03,

title = "A feature based approach for loaded/unloaded drones classification exploiting micro-doppler signatures",

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",

keywords = "micro doppler, automatic target recognition, drones classification, spectral kurtosis",

author = "Luca Pallotta and Carmine Clemente and Alessandro Raddi and Gaetano Giunta",

note = "{\textcopyright} 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; IEEE Radar Conference 2020 ; Conference date: 21-09-2020 Through 25-09-2020",

year = "2020",

month = dec,

day = "4",

doi = "10.1109/RadarConf2043947.2020.9266458",

language = "English",

isbn = "9781728189437",

booktitle = "2020 IEEE Radar Conference (RadarConf20)",

publisher = "IEEE",

}

TY - GEN

T1 - A feature based approach for loaded/unloaded drones classification exploiting micro-doppler signatures

AU - Pallotta, Luca

AU - Clemente, Carmine

AU - Raddi, Alessandro

AU - Giunta, Gaetano

N1 - © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2020/12/4

Y1 - 2020/12/4

N2 - 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

AB - 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

KW - micro doppler

KW - automatic target recognition

KW - drones classification

KW - spectral kurtosis

U2 - 10.1109/RadarConf2043947.2020.9266458

DO - 10.1109/RadarConf2043947.2020.9266458

M3 - Conference contribution book

SN - 9781728189437

BT - 2020 IEEE Radar Conference (RadarConf20)

PB - IEEE

CY - Piscataway, NJ.

T2 - IEEE Radar Conference 2020

Y2 - 21 September 2020 through 25 September 2020

ER -