With the burgeoning impact of artificial intelligence on the traditional UAV industry, the pursuit of autonomous UAV flight has emerged as a focal point of contemporary research. Addressing the imperative for advancing critical technologies in autonomous flight, this paper delves into the realm of UAV flight state recognition and trajectory prediction. Presenting an innovative approach focused on improving the precision of unmanned aerial vehicle (UAV) path forecasting via the identification of flight states, this study demonstrates its efficacy through the implementation of two prediction models. Firstly, UAV flight data acquisition was realized in this paper by the use of multi-sensors. Finally, two models for UAV trajectory prediction were designed based on machine learning methods and classical mathematical prediction methods, respectively, and the results before and after flight pattern recognition are compared. The experimental results show that the prediction error of the UAV trajectory prediction method based on multiple flight modes is smaller than the traditional trajectory prediction method in different flight stages.

In response to the challenges of estimating missile launch timing during close-range unmanned autonomous air combat in the future, this paper proposes an autonomous attack decision-making method based on hierarchical virtual Bayesian reinforcement learning (HVBRL). First, a six-degree-of-freedom (6-DOF) high-fidelity aircraft dynamics model along with missile dynamics and guidance rate models are constructed. Second, the HVBRL algorithm is introduced, where the low-level algorithm outputs control parameters and the high-level algorithm generates control commands. Given that the number of missile hits on a target under specific conditions follows a binomial distribution, a simple prior knowledge can be introduced through its conjugate prior, the Beta distribution, to avoid prolonged exploration of ineffective areas. Moreover, carrying only a limited number of missiles and predicting the number of hits by multiple virtual missiles in specific states through a neural network circumvents the computational complexity issue associated with carrying an excessive number of missiles. Finally, this paper presents the low-level training algorithm, the high-level training algorithm, and the high-level self-play training algorithm. Experimental results show that our method significantly reduces the simulation computational complexity. Compared with the Monte Carlo method carrying 1000 missiles, the simulation speed of the high-level training algorithm is increased by 32.75 times, and that of the high-level self-play algorithm is increased by 23 times. Moreover, the estimated missile hit probability with bias can effectively guide the timing of missile launches in close-range air combat, which has significant implications for intelligent autonomous air combat decision-making and operational analysis.

This study introduces a novel framework for intelligent unmanned BVR maneuver control within the context of adversarial games. The emphasis lies on three pivotal aspects: situational awareness, maneuver decision-making, and precise maneuver control. Within this paradigm, our unmanned aerial vehicles (UAVs) can assimilate crucial situational information through constructed situational vectors and execute sophisticated maneuvers, effectively addressing the intricacies of dynamic flight environments and various unpredictable scenarios within the game setting. To achieve granular maneuver control, this research introduces the Priority Heading Polling–Random Observation Weight (PHP-ROW) method, underpinned by deep reinforcement learning. This approach integrates two primary components: (1) the priority heading polling (PHP) mechanism, which governs the extent of flight trajectories while emphasizing heading control, and (2) the random observation weight (ROW) technique, which adeptly moderates the influence of roll angle rewards during the learning phase. The superiority of the PHP-ROW method is showcased by contrasting it against the conventional proximal policy optimization (PPO) algorithm. Conclusively, the utility and efficacy of the presented framework are corroborated through human–machine adversarial game simulations in a hyper-realistic environment. This investigation provides foundational theoretical and empirical contributions to the realm of intelligent unmanned aerial maneuver control, promising significant implications for the evolution of aviation technology in adversarial game contexts.

This paper builds a wearable device that can be used for motion movement recognition based on artificial intelligence technology. Firstly, a human motion data collection device is designed to realise data collection and pre-processing; secondly, the collected data is classified based on artificial intelligence technology to realise pattern recognition of motion states; finally, the results of recognition are analysed and the results show that motion pattern recognition based on artificial intelligence methods is feasible.