Vision-Based 3D Aerial Target Detection and Tracking for Maneuver Decision in Close-Range Air Combat

Abstract

Automatic maneuver decision in close-range air combat depends on the situation awareness of the 3D aerial space. Optimal decision could only be made when the 3D state (e.g. 3D position, orientation and velocity) of the target aircraft is accurately provided. Together with the state of the aircraft in our side, optimal maneuver decision could be made by maximizing the situation advantage or utilizing deep reinforcement learning. On the other hand, vision-based 3D sensing methods are ideal for acquiring the 3D state of the target aircraft in close-range air combat, since radar and other sensors work badly in such short range. In this paper, we propose a novel pipeline for vision-based maneuver decision in close-range air combat. The proposed pipeline contains three main modules: 3D target detection based on Augmented Autoencoder, 3D target tracking based on segmentation and optimization, and maneuver decision based on advantage maximization and Deep Q Networks (DQN). The proposed method effectively handles the difficulties in air combat environment, such as fast movement, occlusion from cloud, etc. Experiments demonstrate that our method could robustly detect and track the target aircraft in complex environment, which provides strong priors for maneuver decision and helps to significantly improve the winning rate of short-range air combat.INDEX TERMS 3D target detection and tracking, reinforcement learning, maneuver decision, air combat.

A Review of Deep Reinforcement Learning Methods and Military Application Research

“…Based on this, Zhong et al [47] used visual sensor information as input data and a DQN network to achieve autonomous decision-making for air combat maneuvers in 3D space. Bo et al [48] used the more advanced TD3 algorithm as a decision network and obtained better decision results.…”