A data-knowledge driven intelligent avoidance decision-making method for UCAVs in multi-stage air combat missions

In recent years, Unmanned Combat Aerial Vehicles (UCAVs) have achieved rapid development, and have played a great role in local combat due to the advantages of strong survivability, low cost-effectiveness ratio, and flexible configuration, etc. Autonomous air combat decision-making of UCAVs is one of the key technologies contributing to the winning of air combat, and reasonable evasion of incoming targets is the primary condition for air combat decision-making. Along with the development of information technology, modern air combat missions tend to be multi-level and multi-stage, with intricate and complex situations, diverse and varied target types, and scarce and intermittent intelligence information, which together constitute a highly challenging combat environment. In such a context, the improvement of the survival rate of high-value unmanned combat vehicles under the premise of accomplishing all stages of the combat mission has far-reaching implications and significant value for the realization of combat objectives, the maintenance of the overall level of combat capabilities, and the ultimate achievement of combat victory. Facing the multi-stage air combat mission, the critical factor to improve the survival rate of UCAV is the situational awareness and decision-making capability of the model. Reinforcement learning has strong decision-making ability, but the algorithm performance is constrained by the perception ability, and the traditional reinforcement learning method has slow strategy convergence and low data utilization.

For these issues, this paper proposed a data-knowledge driven intelligent avoidance decision-making method by utilizing the flight characteristics of sustainable large maneuvers of UCAVs. Facing the multi-stage air combat mission, the method employed MDP to formally model the avoidance decision-making process of UCAV in the joint air-sea-land combat, introduced the self-attention mechanism of blocked interaction with self-enemy situation based on reinforcement learning, and constructed a data-knowledge driven strategy updating method. Through the sample mixing of offline database-online interaction chain and the strategy mixing of knowledge decision base-avoidance agent, the rule knowledge was extracted from the existing historical data and decision-making experience, which guided the unmanned combat aircraft to generate accurate and efficient avoidance solutions in the process of continuous interaction and feedback with the multi-stage air combat mission environment.

Based on a simulation platform, this paper analyzes the avoidance performance and evaluates the air combat effectiveness of the proposed model, and the results show that this model not only improves the avoidance success rate against incoming targets, but also achieves a more competitive combat effectiveness compared with the traditional reinforcement learning DQN and rule-based model. Specifically, compared to the DQN and rule-based models, the proposed model not only accelerates the convergence speed while increasing the overall avoidance success rate by about 10% and 18%, but also dramatically improves the survival time of the fighters in the air combat process, which provides the living force support for the completion of the multi-stage mission. Besides, the experimental results also reveal the fact that tactical maneuver strategy is the most effective defense against incoming missiles during air-air combat, while it is difficult to evade anti-aircraft missiles from sea vessels and ground vehicles because of their own fast speed and strong end-to-end guidance capability, which is a pain point and difficulty for long-range pre-planning of intelligent decision.

These results show that the data-knowledge driven intelligent avoidance decision-making method can improve the survival rate and mission completion of the UCAV, realizing efficient avoidance decision-making under the multi-stage air combat mission, which demonstrates it has a certain theoretical significance and reference value for the intelligent modeling of the decision-making behavior of the UCAV.