Autonomous Capability Evaluation of Ground-Attack UAV Based on Cloud Model and Combined Weight Theory

Abstract

The evaluation for autonomous capability of ground-attack unmanned aerial vehicle (UAV) comes from the demand of reality, which determines the operational use of airborne equipment authority. It essentially entails a multicriteria decision-making process accounting for evaluation model and uncertainties. Firstly, as for the construction of evaluation model, the index model is proposed from four aspects of observation capability, decision capability, action capability, and security capability, namely, ODAS, which analogizes cognitive behavior mechanism of human based on airborne equipment; then, to solve uncertainties of randomness and fuzziness in the process of autonomous capability evaluation, a cloud model approach is proposed, which expresses uncertainties by the certainty degree distribution. Finally, the cloud model-based approach is tested by evaluating typical UAVs and comparing with Hopfield neural network method. The results show that the evaluation of the autonomous capability based on the cloud model is accurate and more representative than the Hopfield neural network method.