Aiming at the problem of low accuracy of flight trajectory prediction caused by the high speed of fighters, the diversity of tactical maneuvers, and the transient nature of situational change in close range air combat, this paper proposes an enhanced CNN-LSTM network as a fighter flight trajectory prediction method. Firstly, we extract spatial features from fighter trajectory data using CNN, aggregate spatial features of multiple fighters using the social-pooling module to capture geographic information and positional relationships in the trajectories, and use the attention mechanism to capture mutated trajectory features in air combat; subsequently, we extract temporal features by using the memory nature of LSTM to capture long-term temporal dependence in the trajectories; and finally, we merge the temporal and spatial features to predict the flight trajectories of enemy fighters. Extensive simulation experiments verify that the proposed method improves the trajectory prediction accuracy compared to the original CNN-LSTM method, with the improvements of 32% and 34% in ADE and FDE indicators.

Quickly and accurately predicting the flight trajectory of a blue army fighter in close-range air combat helps a red army fighter gain a dominant situation, which is the winning factor in later air combat. However,due to the high speed and even hypersonic capabilities of advanced fighters, the diversity of tactical maneuvers,and the instantaneous nature of situational transitions,it is difficult to meet the requirements of practical combat applications in terms of prediction accuracy.To improve prediction accuracy,this paper proposes a spatio-temporal graph attention network (ST-GAT) using encoding and decoding structures to predict the flight trajectory. The encoder adopts a parallel structure of Transformer and GAT branches embedded with the multi-head self-attention mechanism in each front end. The Transformer branch network is used to extract the temporal characteristics of historical trajectories and capture the impact of the fighter's historical state on future trajectories, while the GAT branch network is used to extract spatial features in historical trajectories and capture potential spatial correlations between fighters.Then we concatenate the outputs of the two branches into a new feature vector and input it into a decoder composed of a fully connected network to predict the future position coordinates of the blue army fighter.The computer simulation results show that the proposed network significantly improves the prediction accuracy of flight trajectories compared to the enhanced CNN-LSTM network (ECNN-LSTM), with improvements of 47% and 34% in both ADE and FDE indicators,providing strong support for subsequent autonomous combat missions.

Autonomous maneuvering decision-making in unmanned serial vehicles (UAVs) is crucial for executing complex missions involving both individual and swarm UAV operations. Leveraging the successful deployment of recommendation systems in commerce and online applications, this paper pioneers a framework tailored for UAV maneuvering decisions. This novel approach harnesses recommendation systems to enhance decision-making in UAV maneuvers. Our framework incorporates a comprehensive six-degree-of-freedom dynamics model that integrates gravitational effects and defines mission success criteria. We developed an integrated learning recommendation system capable of simulating varied mission scenarios, facilitating the acquisition of optimal strategies from a blend of expert human input and algorithmic outputs. The system supports extensive simulation capabilities, including various control modes (manual, autonomous, and hybrid) and both continuous and discrete maneuver actions. Through rigorous computer-based testing, we validated the effectiveness of established recommendation algorithms within our framework. Notably, the prioritized experience replay deep deterministic policy gradient (PER-DDPG) algorithm, employing dense rewards and continuous actions, demonstrated superior performance, achieving a 69% success rate in confrontational scenarios against a versatile expert algorithm after 1000 training iterations, marking an 80% reduction in training time compared to conventional reinforcement learning methods. This framework not only streamlines the comparison of different maneuvering algorithms but also promotes the integration of multi-source expert knowledge and sophisticated algorithms, paving the way for advanced UAV applications in complex operational environments.

Situation assessment (SA) supports maneuver decisions in dynamic three-dimensional robotic interactions, yet explicit methods depend on subjective factors and weights, whereas implicit learning-based methods are difficult to interpret. We propose a traceable benchmarking framework that standardizes motion dynamics, scenarios, capture-tagging rules, round-score metrics, and the decision carrier. Unified-Dimensional Situation Assessment (UDSA) converts heterogeneous advantages to a common altitude-equivalent scale, and Situation Assessment-Based Balanced Expert Decision-Making (SABED) enables controlled closed-loop comparison. Across five independent seeds, UDSA achieved mean round scores of 0.790 ± 0.010 against expert weighting and 0.710 ± 0.016 against Criteria Importance Through Intercriteria Correlation (CRITIC). Scenario-stratified, stress, and symmetric self-play summaries further supported robustness and label invariance. The released package enables reproduction of UDSA scoring, equivalence conversion, trajectory assessment, and the reported statistical summaries.