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Title: On model, algorithms, and experiment for micro-doppler-based recognition of ballistic targets
Authors: Persico, Adriano Rosario
Clemente, Carmine
Gaglione, Domenico
Ilioudis, Christos V.
Cao, Jianlin
Pallotta, Luca
De Maio, Antonio
Proudler, Ian
Soraghan, John J.
Issue Date: 2017
Publisher: IEEE
Citation: PERSICO, 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.
Abstract: 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.
Description: Published open access with a CC BY licence by IEEE.
Sponsor: This work was supported by the Engineering and Physical Sciences Research Council under Grant EP/K014307/1.
Version: Published
DOI: 10.1109/TAES.2017.2665258
URI: https://dspace.lboro.ac.uk/2134/26395
Publisher Link: https://doi.org/10.1109/TAES.2017.2665258
ISSN: 0018-9251
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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