Beyond visual range combat occurs with the detection and tracking of an enemy aircraft (target) beyond the range of the pilot’s vision ( Liang et al.
Machine learning multilayer perceptron Strategic Options Development and Analysisīeyond visual range (BVR) air combat has become the most important type of contemporary air combat. The ANN was trained with a data set with 1093 registered shoots in military exercises, and it shows 78.0% accuracy with the cross-validation procedure. Such features help us to develop an artificial neural network (ANN) for shoot-down prediction. The strategic options development and analysis (SODA) method is applied to select the main features available in the on-board sensor systems of the shooter aircraft required to launch a missile successfully. This paper discusses an approach to evaluate the probability of shoot-down of an unmanned combat air vehicle (UCAV) in a BVR air combat, based on a decision support system model that makes use of parameters available from the onboard sensors of the shooter UCAV.
The decision involves combining knowledge about altitude, speed, distance, onboard sensor systems information, aircraft type, and type of missile on the aircraft, as well as intelligence on the opponent’s behavior. In a beyond visual range (BVR) air combat, one of the challenges is identifying the best time to launch a missile, which is a decision that must be made quickly.
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