Novel classification algorithm for ballistic target based on HRRP frame

Nowadays the identification of ballistic missile warheads in a cloud of decoys and debris is essential for defence systems in order to optimize the use of ammunition resources, avoiding to run out of all the available interceptors in vain. This paper introduces a novel solution for the classification of ballistic targets based on the computation of the inverse Radon transform of the target signatures, represented by a high resolution range profile frame acquired within an entire period of the main rotation of the target. Namely, the precession for warheads and the tumbling for decoys are taken into account. Following, the pseudo-Zernike moments of the resulting transformation are evaluated as the final feature vector for the classifier. The extracted features guarantee robustness against target's dimensions and rotation velocity, and the initial phase of the target's motion. The classification results on simulated data are shown for different polarizations of the electromagnetic radar waveform and for various operational conditions, confirming the validity of the algorithm

Introducing responsibly self-healing into the incident management lifecycle

In this paper we propose an approach for adopting the self-healing paradigm in complex networking environments. We argue that a straightforward application of self-healing capabilities may have an adverse effect on incident response due to the ill-understanding of the state of the system under protection. We sketch how the use of the Cynefin framework leverages the understanding of complex systems at the appropriate level of detail. In particular, we show how the framework can help to understand how the environment operates and to identify ways to improve its resilience and ability to recover from failures