On model, algorithms, and experiment for micro-doppler-based recognition of ballistic targets
journal contributionposted on 2017-09-06, 13:39 authored by Adriano Rosario Persico, Carmine Clemente, Domenico Gaglione, Christos V. Ilioudis, Jianlin Cao, Luca Pallotta, Antonio De Maio, Ian Proudler, John J. Soraghan
The ability to discriminate between ballistic missile warheads and confusing objects is an important topic from different points of view. In particular, the high cost of the interceptors with respect to tactical missiles may lead to an ammunition problem. Moreover, since the time interval in which the defense system can intercept the missile is very short with respect to target velocities, it is fundamental to minimize the number of shoots per kill. For this reason, a reliable technique to classify warheads and confusing objects is required. In the efficient warhead classification system presented in this paper, a model and a robust framework is developed, which incorporates different micro-Doppler-based classification techniques. The reliability of the proposed framework is tested on both simulated and real data.
This work was supported by the Engineering and Physical Sciences Research Council under Grant EP/K014307/1.
- Mechanical, Electrical and Manufacturing Engineering
Published inIEEE Transactions on Aerospace and Electronic Systems
Pages1088 - 1108
CitationPERSICO, A.R. ... et al., 2017. On model, algorithms, and experiment for micro-doppler-based recognition of ballistic targets. IEEE Transactions on Aerospace and Electronic Systems, 53 (3), pp. 1088 - 1108.
- VoR (Version of Record)
Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/
NotesPublished open access with a CC BY licence by IEEE.