Knowledge-Driven Accurate Opponent Trajectory Prediction for Gun-Dominated Autonomous Air Combat

Abstract

The autonomous air combat (AAC) technique has been a lasting topic for decades. Accurate opponent trajectory prediction provides a fundamental basis for decision making in AAC. In this work, we propose a knowledge-driven scheme for the opponent trajectory prediction problem in one-versus-one gun-dominated within-visual-range (WVR) air combat. A dedicated air combat engagement database is first constructed via skilled human pilots flying WVR air combat. Two baseline algorithms following rule-based and learning-based paradigms are developed and optimized. The knowledge-driven scheme begins with defining handcrafted features extracted from the opponent history movements, and principal components analysis is adopted to compress/refine the features. A generalized regression neural network is then developed to compensate for the residual of the rule-based trajectory prediction method, wherein the refined features are used as inputs, and the residual compensation are used as outputs. Via extensive simulation tests, the proposed scheme shows a more accurate performance as compared with the two other baseline algorithms. To demonstrate the applicability of the proposed scheme, an automatic gun-firing strategy for commencing gun attack in AAC is also illustrated, which justifies the proposed scheme.