4 February 2013 Countermeasure effectiveness against an intelligent imaging infrared anti-ship missile
Author Affiliations +
Optical Engineering, 52(2), 026401 (2013). doi:10.1117/1.OE.52.2.026401
Ship self defense against heat-seeking anti-ship missiles is of great concern to modern naval forces. One way of protecting ships against these threats is to use infrared (IR) offboard countermeasures. These decoys need precise placement to maximize their effectiveness, and simulation is an invaluable tool used in determining optimum deployment strategies. To perform useful simulations, high-fidelity models of missiles are required. We describe the development of an imaging IR anti-ship missile model for use in countermeasure effectiveness simulations. The missile model’s tracking algorithm is based on a target recognition system that uses a neural network to discriminate between ships and decoys. The neural network is trained on shape- and intensity-based features extracted from simulated imagery. The missile model is then used within ship-decoy-missile engagement simulations, to determine how susceptible it is to the well-known walk-off seduction countermeasure technique. Finally, ship survivability is improved by adjusting the decoy model to increase its effectiveness against the tracker.
© 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)
Greer J. Gray, Nabil Aouf, Mark A. Richardson, Brian Butters, Roy H. Walmsley, "Countermeasure effectiveness against an intelligent imaging infrared anti-ship missile," Optical Engineering 52(2), 026401 (4 February 2013). http://dx.doi.org/10.1117/1.OE.52.2.026401


Infrared imaging

Neural networks

Infrared radiation

Detection and tracking algorithms

Binary data

Algorithm development


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