Decision Support System for Unmanned Combat Air Vehicle in Beyond Visual Range Air Combat Based on Artificial Neural Networks

ABSTRACT

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. 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. 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 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. Such features help us to develop an artificial neural network (ANN) for shoot-down prediction. 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.

Keywords
Machine learning; multilayer perceptron; Strategic Options Development and Analysis