Novel approach for ballistic targets classification from HRRP frame
Persico, Adriano Rosario and Ilioudis, Christos V. and Clemente, Carmine and Soraghan, John; (2017) Novel approach for ballistic targets classification from HRRP frame. In: 2017 IEEE Sensor Signal Processing for Defence (SSPD). IEEE, GBR. ISBN 9781538616635 (https://doi.org/10.1109/SSPD.2017.8233248)
Preview |
Text.
Filename: Persico_etal_IEEE_2017_Novel_approach_for_ballistic_targets_classification_from.pdf
Accepted Author Manuscript Download (548kB)| Preview |
Abstract
Nowadays the challenge of the identification of Ballistic Missile (BM) warheads in a cloud of decoys and debris is essential for the defence system in order to optimize the use of ammunition resources avoiding to run out of all the available interceptors in vain. In this paper a novel approach for the classification of ballistic threats from the High Resolution Range Profile (HRRP) frame is presented. The algorithm is based on the computation of the inverse Radon Transform (IRT) of the HRRP frame as target signature, and on the evaluation of pseudo-Zernike moments, as final feature vector. Firstly, the algorithm is presented emphasizing the characteristics of the HRRP frame due to target micro-motions. Then, the classification results on simulated data are shown for various operational conditions.
ORCID iDs
Persico, Adriano Rosario ORCID: https://orcid.org/0000-0002-1761-598X, Ilioudis, Christos V. ORCID: https://orcid.org/0000-0002-7164-6461, Clemente, Carmine ORCID: https://orcid.org/0000-0002-6665-693X and Soraghan, John ORCID: https://orcid.org/0000-0003-4418-7391;-
-
Item type: Book Section ID code: 66922 Dates: DateEvent6 December 2017Published6 September 2017AcceptedNotes: © 2017 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. Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 12 Feb 2019 15:14 Last modified: 20 Dec 2024 01:07 URI: https://strathprints.strath.ac.uk/id/eprint/66922