Explainable Basic-Fighter-Maneuver Decision Support Scheme for Piloting Within-Visual-Range Air Combat

In within-visual-range (WVR) air combat, basic fighter maneuvers (BFMs) are widely used. A BFM decision support scheme has been proposed to aid human pilots in the complex air combat engagement. Recent artificial intelligence advances provide novel opportunities for the development of BFM decision support research. This paper commences by establishing an air-combat-engagement database. Key features that pilots rely on for BFM decision-making in WVR air combat are analyzed, which identifies the input and output data essential for the development of the BFM decision support scheme. A Long Short-Term-Memory (LSTM)-based BFM decision support scheme is then proposed to map input (i.e., combat situations) to output (i.e., BFM decision). Additionally, Shapley-Additive-Explanations-based explainability analysis is also employed to assess the importance of each input feature in the LSTM blocks, and to explain the contribution of each feature to the BFM decision. To evaluate the effectiveness of the proposed BFM decision support scheme, WVR air-combat tests are conducted, which justify the effectiveness of the proposed scheme.