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

Luca Pallotta; Carmine Clemente; Alessandro Raddi; Gaetano Giunta

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

Luca Pallotta, Carmine Clemente, Alessandro Raddi, Gaetano Giunta

Research output: Contribution to conference › Paper

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
Number of pages	6
Publication status	Accepted/In press - 15 Jun 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

https://www.radarconf20.org/

Embargoed Document

Pallotta-etal-IEEE-RADAR-2020-A-feature-based-approach-for-loaded-unloaded-drones
Accepted author manuscript, 406 KB
Embargo ends: 25/09/20

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Pallotta, L., Clemente, C., Raddi, A., & Giunta, G. (Accepted/In press). A feature based approach for loaded/unloaded drones classification exploiting micro-doppler signatures. Paper presented at IEEE Radar Conference 2020, Florence, Italy.

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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",

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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 - 2020/6/15

Y1 - 2020/6/15

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

UR - https://www.radarconf20.org/

M3 - Paper

T2 - IEEE Radar Conference 2020

Y2 - 21 September 2020 through 25 September 2020

ER -