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.
Persico, A.R., Clemente, C., Gaglione, D., Ilioudis, C.V., Cao, J., Pallotta, L., 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), 1088-1108 [10.1109/TAES.2017.2665258].
On Model, Algorithms, and Experiment for Micro-Doppler-Based Recognition of Ballistic Targets
Pallotta L.;
2017-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
