learning based

Military Academy drone course training methods

Zhu Jiaxuan

【Abstract】The rapid development of modern military technology, particularly the widespread application of drone technology, has brought new challenges and opportunities to military education and training. This article, through in-depth research on the integration of drone technology and artificial intelligence learning, explores how to innovate the training model of drone courses in military academies, enhance the practical capabilities and tactical literacy of students, and contribute to the development of the military education field.

【Keywords】Learning | Military Academy | Drone Course | Training Methods

Today, drone technology has been widely applied in the military field, demonstrating significant potential in reconnaissance, communication, and other areas. The continuous maturation and application of artificial intelligence technology have brought new possibilities to military academy drone course training. By introducing artificial intelligence learning algorithms, it is possible to achieve automatic monitoring and personalized guidance of students' learning processes, thereby improving training quality. Artificial intelligence technology can also be used for the analysis and simulation of drone flight data, helping students better master flight skills and tactical applications. Research on learning-based military academy drone course training methods will promote the close integration of drone course training with modern warfare needs, providing technical support and theoretical guidance for cultivating more outstanding drone operators and commanders.

I. Characteristics of Military Academy Drone Usage

(1) Use real drone models

Military academy drone courses typically use real drone models for training, ensuring that students become familiar with and master the operation skills of various drones during their training. With the wide variety of drone models, some military academies have adopted more challenging teaching methods to prepare students for complex battlefield environments. For example, in the comprehensive equipment manufacturing practical training project offered by the College of Intelligent Science at the National University of Defense Technology, graduating students are required to design and manufacture drones from scratch. In this project, students not only need to consider the basic design requirements of drones but also the application scenarios in complex battlefield environments. To address these challenges, students must also design drones with shock absorption and foldable features to better adapt to various environments and mission requirements in practical applications. By training with real drone models, students can better understand the structure and working principles of drones, enhancing their operational skills and adaptability. This practical training method not only strengthens students' actual operational abilities but also cultivates their problem-solving skills and innovative thinking.

(2) Simulating Realistic Task Environments

The training missions for military academy UAVs typically simulate real-world operational environments, such as intelligence reconnaissance, firepower strikes, electronic warfare decoys, airborne early warning, anti-missile interception, and signal relaying. These missions aim to cultivate students' abilities in mission planning and execution in complex battlefield environments. By simulating real-world scenarios, students can engage with and respond to various complex combat tasks in a virtual setting, enhancing their military literacy and adaptability. In simulated intelligence reconnaissance missions, students must use UAVs to conduct reconnaissance, gather intelligence information on target areas, and promptly transmit it back to the command center, providing support for subsequent operational decisions. In firepower strike missions, students need to accurately mark target locations, select appropriate weaponry, and execute precise strikes, showcasing their outstanding combat skills and command abilities. Electronic warfare decoy missions test students' understanding and application of electronic countermeasures, training them to respond to enemy interference and ensure communication security through simulated electronic confrontation environments. Additionally, airborne early warning missions require students to promptly detect potential threats and issue warnings, ensuring friendly forces' safety; anti-missile interception missions challenge students' rapid response and interception skills, safeguarding friendly forces from missile attacks. Signal relay missions demand that students can establish signal transmission links, ensuring smooth communication and improving command efficiency.

(III) Adoption of Advanced Training Techniques

The training techniques for military academy drones typically employ advanced simulation and virtual reality technologies to enhance the authenticity and efficiency of training. For instance, the Shijiazhuang campus of the Army Engineering University conducted practical flight training for drones, involving various models and sorties, with some models and sorties being the first flights after acceptance, and some models achieving night flights for the first time. These advanced training techniques play a crucial role in military academy drone training, providing students with a more realistic and practical training environment. The use of simulation technology allows students to experience complex flight operations and emergency handling in a relatively safe environment, improving the safety and efficiency of training. By simulating real mission environments, students can practice flight skills, respond to emergencies, and execute tasks in simulated scenarios, thereby enhancing their practical combat capabilities and adaptability. The introduction of virtual reality technology enables students to feel as if they are actually performing flight operations, enhancing the immersion and realism of training, which helps deepen students' understanding and mastery of flight operations.

II. Practical Applications in Drone Courses

Navigation and Path Planning. Technology can help drones achieve intelligent path planning, effectively planning flight paths based on environmental information and task requirements to ensure flight safety and efficiency. For example, the system from the University of Colorado can help robots find the direction of their walking paths, guiding quadcopters in navigation using technologies such as reinforcement learning algorithms.

Object detection and tracking. Through technology, drones can achieve automatic detection and tracking of targets, enhancing the accuracy and efficiency of task execution, while reducing the need for human intervention, enabling drones to perform stably even in complex environments. For example, a research team from the University of California, Berkeley, proposed a hybrid deep reinforcement learning algorithm that combines data to guide quadcopter navigation.

Autonomous flight control. Utilizing technology, drones can achieve more intelligent flight control, enhance their adaptability and flight stability in various complex environments, and improve the success rate of mission execution.

Image recognition and object recognition. Through technology, drones can achieve rapid identification of image information and accurate positioning of targets, helping drones to more accurately identify targets during missions, thereby improving the efficiency and accuracy of task execution. For example, during drone flight, obstacle recognition and avoidance can be achieved through image recognition technology.

Data Analysis and Visualization. With the help of technology, a large amount of data generated during the execution of drone missions can be analyzed and visualized, providing more accurate data support for teaching and training, helping students better understand the operational mechanisms of drones and the process of mission execution. For example, deep learning technology can be used to analyze and visualize drone flight data, thereby better understanding the flight status and performance of drones.

III. Training Methodology for Military Academy UAV Courses Based on Learning

(1) Establishment of Drone Combat Simulation Scenarios

Utilizing technology to construct virtual training environments can provide more realistic combat simulation scenarios for military academy drone course training. In such environments, students can engage with various simulated missions, including reconnaissance, bombing, and air-to-ground strike combat scenarios, thereby enhancing their operational skills and tactical strategies. Within the virtual training environment, technology can accurately simulate various terrains, including plains, mountains, deserts, and other different landscapes, and set different weather conditions according to actual battlefield environments, such as sunny, overcast, and rainy days, to increase the diversity and challenges of the training. This setup allows students to face various complex situations in the simulation environment and learn flight skills and emergency handling abilities under different terrains and weather conditions. The virtual training environment can also simulate enemy targets, including enemy drones and ground targets, enabling students to conduct reconnaissance and strike training in simulated combat scenarios.

(II) Utilizing Technology for Tactical Analysis

The system is capable of extracting key data from the trainees' training performance, including flight paths, action execution, reaction times, and other aspects. This data can be processed in real-time through algorithms, generating detailed analysis reports. In practical simulations, the technology helps identify the strengths and weaknesses of the trainees by comparing their performance with the standards of excellent pilots. The system can point out areas where the trainees need improvement and provide personalized guidance suggestions. For example, for a specific flight maneuver in a task, the system can analyze the trainee's execution, point out the issues, and propose improvement measures to help the trainee enhance their skill level. The technology also assists in decision-making and tactical optimization. By simulating the execution effects of different tactical plans, the system can offer trainees a variety of tactical options and predict the possible outcomes of different tactical schemes.

(III) Utilizing Technology for Drone Training Assessment

Technology can monitor students' performance in real-time during training, such as behaviors in posture control and flight path planning. By analyzing this data, personalized training feedback can be generated for individual students, pointing out issues in their operations and providing corresponding improvement suggestions. This real-time, personalized feedback not only helps students correct errors promptly but also enhances training efficiency, allowing students to improve their drone operation skills more quickly. Based on students' performance data, the most effective training paths and methods can be identified, providing guidance for instructors to help them better adjust course content and teaching methods, thereby achieving optimal teaching outcomes. Through the application of technology, the training methods for military academy drone courses will become more scientific and efficient, contributing to the enhancement of students' drone operation skills and further development of military academy drone courses.

IV. Conclusion

In the modern military context, the role of drones in reconnaissance, target strikes, and intelligence collection is becoming increasingly prominent. As a crucial base for cultivating future military talents, military academies need to continuously optimize their curriculum and training methods to adapt to the evolving war situation. By incorporating learning, it is possible to better simulate real combat scenarios, enhance students' response capabilities and tactical awareness, and contribute to the cultivation of outstanding drone operators and commanders. The practical application of learning in military academy drone course training helps explore the broader application prospects of artificial intelligence technology in the field of education, providing new ideas for future teaching and education.

references

Zhu Yaling, Zhou Jin, Zhao Jianyu, et al. Second Classroom Teaching for Military Academy UAV Major Oriented Towards Outcome-Based Education. Journal of Aerospace Early Warning Research,,()-.

Cui Xiaojia, Zhang Yun, Yan Yunbin, et al. Research on the Training of Talent for UAV Equipment Support. China Educational Technology & Equipment, ()-.

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Editor of this issue: Chen Luyang

This issue reviewed by: Lin Yingxi

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Article Source: "China Military-to-Civilian Magazine"

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