Combat Scenarios Research Articles

THE USE OF VIRTUAL REALITY TECHNOLOGIES IN MILITARY EDUCATION

The article examines the topical topic of introducing virtual reality technology into military education to increase the level of effectiveness and safety of training military personnel. In the context of the latest challenges to global security and rapid technological progress, the need to improve the quality of military personnel training has become extremely urgent. The purpose(s) of the article is to analyze the state, problems and prospects of introducing virtual reality into military education in order to increase the level of efficiency and safety of training military personnel. The advantages of using virtual reality technology in the field of military education are outlined, in particular, reproduction of close to reality combat scenarios, immersive simulations and optimization of team interaction. Having analyzed in the article modern approaches to military training and the shortcomings of classical methods, we singled out the prospects of using virtual reality to increase the effectiveness of the educational process, balancing the realism of the experience and the safety of military training. The introduction of virtual reality into the educational process of military education will require investments, the development of specialized software and the training of qualified instructors, although over time these efforts can increase the level of combat readiness of the armed forces. The conclusion of the article emphasizes that the introduction of virtual reality into military education should become one of the key decisions in the framework of the reform of military personnel training. The use of this technology helps to increase the level and speed of knowledge acquisition, the safety of military training and exercises, to develop key skills and readiness to act in a real combat environment. Investments in innovative technologies and the latest training tools are critical to maintaining the combat capability of the armed forces and the successful execution of combat missions. The results of the article will have important implications for commanders, military instructors, and researchers trying to improve the effectiveness and safety of military personnel training. They should be able to use the latest virtual reality technology for greater flexibility and efficiency of the educational process, which will help strengthen the combat capability of the military personnel of the armed forces.

An adaptive operation planning and EBO-BPNN optimization method for decision support systems

Formulating a course of action (COA) before a combat is crucial for operational command. Research in command and control (C2) artificial intelligence is currently focused on using intelligent auxiliary decision-making methods to implement COA. This paper proposes a COA planning method based on line of operation (LOO) and uses planning domain definition language (PDDL) to describe combat scenarios and COA. Following the effect-based optimization (EBO) principle, an effect evaluation model for COA was constructed, and dynamic bayesian networks (DBNs) was used to determine the reasoning and calculate the results of the effect evaluation network. To further improve the execution efficiency of the effect evaluation model in practical applications, the network was optimized through a back propagation neural network (BPNN). Relevant experiments based on the coordinated distributed air defense and anti-missile scenario were carried out using the LOO model to complete the planning of COA. A BPNN evaluation model based on the DBNs evaluation model was built. After training and fine-tuning, it achieved similar evaluation results, with a mean absolute percentage error (MAPE) of less than 0.02%. Compared with the DBNs model, the BPNN model achieved an efficiency improvement of no less than 65%, effectively reducing the consumption of computing resources. This research is the first time to realize the modeled description of COA planning, automatic evaluation, and calculation optimization of COA effects. It can support the development of decision support systems (DSS) and has the potential for practical application.

Multi-source information fusion based on evidence reasoning using double frames of discernment for estimating the number of remaining missiles

In air combat, multi-source information fusion is used to estimate the number of remaining missiles (NRM), which is a key premise for multi-aircraft cooperative decision-making. However, the existing multi-source information fusion methods are difficult to apply because prior probability and labeled data are hard to obtain accurately. This paper proposes a novel multi-source information fusion method using double frames of discernment (MIFDFD) for estimating the NRM, which can efficiently and dynamically fuse discrete uncertain information with time series to extract useful information. In the first frame of discernment, the basic belief assignment (BBA) function is constructed by modifying the soft maximum function to fuse the air combat situation information. The BBA of the second frame of discernment is constructed by fusing the believed degree of the launching missile source and historically estimated NRM by using a transferable belief model. The optimal value of the classification factor is determined by conducting parametric analyses in typical air combat scenarios. Performance experiments show that the MIFDFD method provides a more accurate estimate of the NRM and takes less time than the comparison method.

UAV data link anti-interference via SLHS-SVM-AdaBoost algorithm: Classification prediction and route planning

The ability to predict the anti-interference communications performance of unmanned aerial vehicle (UAV) data links is critical for intelligent route planning of UAVs in real combat scenarios. Previous research in this area has encountered several limitations: Classifiers exhibit low training efficiency, their precision is notably reduced when dealing with imbalanced samples, and they cannot be applied to the condition where the UAV’s flight altitude and the antenna bearing vary. This paper proposes the sequential Latin hypercube sampling (SLHS)-support vector machine (SVM)-AdaBoost algorithm, which enhances the training efficiency of the base classifier and circumvents local optima during the search process through SLHS optimization. Additionally, it mitigates the bottleneck of sample imbalance by adjusting the sample weight distribution using the AdaBoost algorithm. Through comparison, the modeling efficiency, prediction accuracy on the test set, and macro-averaged values of Precision, Recall, and F1-score for SLHS-SVM-AdaBoost are improved by 22.7%, 5.7%, 36.0%, 25.0%, and 34.2%, respectively, compared with Grid-SVM. Additionally, these values are improved by 22.2%, 2.1%, 11.3%, 2.8%, and 7.4%, respectively, compared with particle swarm optimization (PSO)-SVM-AdaBoost. Combining Latin hypercube sampling with the SLHS-SVM-AdaBoost algorithm, the classification prediction model of anti-interference performance of UAV data links, which took factors like three-dimensional space position of UAV and antenna bearing into consideration, is established and used to assess the safety of the classical flying path and optimize the flying route. It was found that the risk of loss of communications could not be completely avoided by adjusting the flying altitude based on the classical path, and that intelligent path planning based on the classification prediction model of anti-interference performance can realize complete avoidance of being interfered while reducing the route length by at least 2.3%, thus benefiting both safety and operation efficiency.

Parameterized evasion strategy for hypersonic glide vehicles against two missiles based on reinforcement learning

In practical combat scenarios, Hypersonic Glide Vehicles (HGV) face the challenge of evading Successive Pursuers from the Same Direction while satisfying the Homing Constraint (SPSDHC). To address this problem, this paper proposes a parameterized evasion guidance algorithm based on reinforcement learning. The three-player optimal evasion strategy is firstly analyzed and approximated by parametrization. The switching acceleration command of HGV optimal evasion strategy considering the upper limit of missile acceleration command is analyzed based on the optimal control theory. The terminal miss of HGV in the case of evading two missiles is analyzed, which means that the three-player optimal evasion strategy is a linear combination of two one-to-one strategies. Then, a velocity control algorithm is proposed to increase the terminal miss by actively controlling the flight speed of the HGV based on the parametrized evasion strategy. The reinforcement learning method is used to implement the strategy in real time and a reward function is designed by deducing homing strategy for the HGV to approach the target, which ensures that the HGV satisfies the homing constraint. Experimental results demonstrate the feasibility and robustness of the proposed parameterized evasion strategy, which enables the HGV to generate maximum terminal miss and satisfy homing constraint when facing single or double missiles.

Research on Information Fusion Method for Human Computer Interaction Interface of Command and Control System in MR Environment

In response to the challenge of high cognitive pressure and difficulty for commanders in cross-layer operations during the transition from traditional two-dimensional command and control system interfaces to mixed reality human-computer interaction interfaces, this paper conducts research on the information fusion display method for the human-computer interaction interface of the mixed reality command and control system. First, based on the findings of previous research, we developed a five-layer human-computer interaction interface for a mixed reality command and control system in typical combat scenarios. An evaluation experiment was designed and the resulting data analyzed. This analysis revealed that there are problems with low work efficiency and high cognitive load in the information acquisition process in cross-layer interaction operations. Consequently, we conducted research into the methods of integrating and displaying interactive interface information, integrating and displaying interactive and display interface information, and integrating and displaying complex situation interface information. The research findings indicate that: (1) The optimal display ratio for mixed reality 2D interface integration is 3:2. (2) The optimal display scheme for mixed reality 2D interface fusion is a combination of the main and secondary interfaces, with the main interface table displaying global information and the secondary interface displaying accurate information. (3) The optimal display scheme for mixed reality 2D and 3D interface fusion is the display of overall class information in 2D and the accurate identification of class information in 3D. The findings of this research can be applied to the design of human-computer interaction interfaces for mixed reality command and control systems in complex battlefield environments.

Who needs a tourniquet? And who does not? Lessons learned from a review of tourniquet use in the Russo-Ukrainian war.

Extremity tourniquets have proven to be lifesaving in both civilian and military settings and should continue to be used by first responders for trauma patients with life-threatening extremity bleeding. This is especially true in combat scenarios in which both the casualty and the first responder may be confronted by the imminent threat of death from hostile fire as the extremity hemorrhage is being treated. Not every extremity wound, however, needs a tourniquet. One of the most important aspects of controlling life-threatening extremity bleeding with tourniquets is to recognize what magnitude of bleeding requires this intervention and what magnitude of bleeding does not. Multiple studies, both military and civilian, have shown that tourniquets are often applied when they are not medically indicated. Overuse of extremity tourniquets has not caused excess morbidity in either the recent conflicts in Iraq and Afghanistan or in the US urban civilian setting. In the presence of prolonged evacuation, however, applying a tourniquet when it is not medically indicated changes tourniquet application from being a lifesaving intervention to one that may cause an avoidable amputation and the development of an array of metabolic derangements and acute kidney injury collectively called prolonged tourniquet application syndrome. The recent literature was reviewed for papers that documented the complications of tourniquet use resulting from the prolonged casualty evacuation times being seen in the current Russo-Ukrainian war. The literature was also reviewed for the incidence of tourniquet application that was found to not be medically indicated, in both the US civilian setting and from Ukraine. Finally, an in-person meeting of the US/Ukraine Tourniquet Working Group was held in Warsaw, Poland, in December of 2023. Unnecessary loss of extremities and life-threatening episodes of prolonged tourniquet application syndrome are currently occurring in Ukrainian combat forces because of nonindicated tourniquet use combined with the prolonged evacuation time seen in the Russo-Ukrainian war. Specific numbers of the complications experienced as a result of tourniquet use by Ukrainian forces in the current conflict are treated as classified information and are not available, but multiple sources from the Ukrainian military medical personnel and from the US advisors providing medical assistance to Ukraine have all agreed that the problem is substantial. Unnecessary tourniquet morbidity might also occur in US forces in a variety of potential future combat scenarios in which evacuation to surgical care is delayed. Prehospital trauma training programs, including but not limited to tactical combat casualty care, place insufficient emphasis on the need to avoid leaving tourniquets in place when they are not medically indicated. This aspect of training should receive emphasis in future Tactical Combat Casualty Care (TCCC) and civilian first responder curriculum development. An interim ad hoc training solution on this topic is available at the websites noted in this articles. Additional training modalities may follow in the near future. Therapeutic/Care Management; Level V.

Research on Autonomous Manoeuvre Decision Making in Within-Visual-Range Aerial Two-Player Zero-Sum Games Based on Deep Reinforcement Learning

In recent years, with the accelerated development of technology towards automation and intelligence, autonomous decision-making capabilities in unmanned systems are poised to play a crucial role in contemporary aerial two-player zero-sum games (TZSGs). Deep reinforcement learning (DRL) methods enable agents to make autonomous manoeuvring decisions. This paper focuses on current mainstream DRL algorithms based on fundamental tactical manoeuvres, selecting a typical aerial TZSG scenario—within visual range (WVR) combat. We model the key elements influencing the game using a Markov decision process (MDP) and demonstrate the mathematical foundation for implementing DRL. Leveraging high-fidelity simulation software (Warsim v1.0), we design a prototypical close-range aerial combat scenario. Utilizing this environment, we train mainstream DRL algorithms and analyse the training outcomes. The effectiveness of these algorithms in enabling agents to manoeuvre in aerial TZSG autonomously is summarised, providing a foundational basis for further research.

Mission-oriented capability evaluation for combat network based on operation loops

With continuous growth in scale, topology complexity, mission phases, and mission diversity, challenges have been placed for efficient capability evaluation of modern combat systems. Aiming at the problems of insufficient mission consideration and single evaluation dimension in the existing evaluation approaches, this study proposes a mission-oriented capability evaluation method for combat systems based on operation loop. Firstly, a combat network model is given that takes into account the capability properties of combat nodes. Then, based on the transition matrix between combat nodes, an efficient algorithm for operation loop identification is proposed based on the Breadth-First Search. Given the mission-capability satisfaction of nodes, the effectiveness evaluation indexes for operation loops and combat network are proposed, followed by node importance measure. Through a case study of the combat scenario involving space-based support against surface ships under different strategies, the effectiveness of the proposed method is verified. The results indicated that the ROI-priority attack method has a notable impact on reducing the overall efficiency of the network, whereas the O-L betweenness-priority attack is more effective in obstructing the successful execution of enemy attack missions.

Penetration Strategy for High-Speed Unmanned Aerial Vehicles: A Memory-Based Deep Reinforcement Learning Approach

With the development and strengthening of interception measures, the traditional penetration methods of high-speed unmanned aerial vehicles (UAVs) are no longer able to meet the penetration requirements in diversified and complex combat scenarios. Due to the advancement of Artificial Intelligence technology in recent years, intelligent penetration methods have gradually become promising solutions. In this paper, a penetration strategy for high-speed UAVs based on improved Deep Reinforcement Learning (DRL) is proposed, in which Long Short-Term Memory (LSTM) networks are incorporated into a classical Soft Actor–Critic (SAC) algorithm. A three-dimensional (3D) planar engagement scenario of a high-speed UAV facing two interceptors with strong maneuverability is constructed. According to the proposed LSTM-SAC approach, the reward function is designed based on the criteria for successful penetration, taking into account energy and flight range constraints. Then, an intelligent penetration strategy is obtained by extensive training, which utilizes the motion states of both sides to make decisions and generate the penetration overload commands for the high-speed UAV. The simulation results show that compared with the classical SAC algorithm, the proposed algorithm has a training efficiency improvement of 75.56% training episode reduction. Meanwhile, the LSTM-SAC approach achieves a successful penetration rate of more than 90% in hypothetical complex scenarios, with a 40% average increase compared with the conventional programmed penetration methods.

Optimising Combat Readiness: Practical Strategies for Integrating Physiological and Psychological Resilience in Soldier Training.

This narrative review examines the intricate psychophysiological interplay between cognitive functions and physical responses within military personnel engaged in combat. It elucidates the spectrum of responses elicited by symmetric and asymmetric warfare alongside specialised combat scenarios, including close-quarters and subterranean warfare. Central to this discourse is the emphasis on integrating training programs beyond physical conditioning to encompass psychological resilience and decision-making efficacy under duress. The exploration further ventures into applying advanced technologies such as virtual reality and wearable devices, highlighting their pivotal role in augmenting training outcomes and supporting soldier health. Through a detailed analysis of psychophysiological variations across different military branches of service, the narrative review advocates for bespoke training regimens and support frameworks tailored to address the unique exigencies of each service branch. Concluding observations stress the importance of evolving military training paradigms, advocating for adopting realistic, immersive training simulations that mirror the complexities of the contemporary battlefield. This synthesis aims to contribute to the ongoing discourse on optimising military training protocols and enhancing the operational readiness and well-being of armed forces personnel. This narrative review is essential for military psychologists, trainers, and policymakers, aiming to bridge the gap between theoretical knowledge and practical implementation in military training programs.

Precision Targeting Companion: Empowering Marksmen with Smart Rifle Scopes

In the realm of modern warfare, the ability to swiftly identify and engage adversaries with precision is indispensable. This paper outlines our objective to contribute to this paradigm shift by undertaking a comprehensive exploration, and design, of accessories crucial for rifles, including smart scopes, auto- matic triggers, and intelligent magazines. Moreover, this paper introduces a groundbreaking innovation - the Precision Targeting Companion, an intuitive system integrated into rifle scopes, aimed at elevating the effectiveness and accuracy of marksmanship in dynamic combat scenarios. At its core, this system is a testament to the fusion of advanced technology with human ingenuity. By harnessing the power of cutting-edge image processing and predictive analytic, the Precision Targeting Companion offers marksmen a seamless and intuitive interface for detecting potential threats and predicting bullet trajectories in real-time. Yet, beyond its computational prowess, this system embodies a deeper understanding of the challenges faced by soldiers and law enforcement officers on the front-lines. It is a trusted companion, empowering users with the confidence to make split-second decisions in high-pressure situations. Key features of the Precision Targeting Companion include: 1. Swift identification of threats: Leveraging sophisticated image processing algorithms, the system swiftly identifies adver- saries within the user’s line of sight, providing crucial situational awareness. 2. Predictive guidance: Through intricate calculations and analysis, the system predicts bullet trajectories, enabling marksmen to make informed decisions and engage targets with precision. 3. Intuitive assistance: The system’s user-friendly interface overlays visual cues on detected targets and cross-hair alignment, offering intuitive guidance and facilitating seamless target tracking. In essence, the Precision Targeting Companion represents more than just a technological advancement; it is a testament to the enduring partnership between man and machine, working in harmony to safeguard lives and achieve mission success on the battlefield. Keywords: Precision Targeting Companion, Smart Rifle Scopes, Human-Centric Design, Tactical Engagement, Threat Detection, Predictive Guidance.

Joint operational situation assessment and firepower allocation method based on enhanced potential energy model

The assessment of battlefield situations and the allocation of firepower are pivotal prerequisites for conducting joint operational planning. In response to the challenges posed by multi-service joint operations, this paper presents a methodology based on enhanced operational potential energy model for the assessment of battlefield situations and the generation of optimal firepower allocation plan. We focuses on the three-fold factors influencing operational effectiveness: spatial, informational, and environmental factors, and completes the enhanced operational potential energy model by integrating the assessment of joint operational situations and the generation of optimal firepower allocation plans. Finally, the effectiveness of this methodology is demonstrated through operational simulations in a joint anti-ship combat scenario.

Dynamic Gaussian mutation beetle swarm optimization method for large-scale weapon target assignment problems

The weapon target assignment is a crucial issue for firepower resources optimization in modern warfare. Such a problem is complicated, multi-constrained, strongly nonlinear, NP-complete and the existing studies did not consider the suitability between different weapons and targets. In this paper, a novel weapon target assignment model is established that involves the weapon-target suitability and is closer to the real combat scenarios. Then, in view of that the conventional weapon target assignment methods are difficult to be applied in the large-scale problems efficiently, this work proposes a dynamic Gaussian mutation beetle swarm optimization algorithm with rule-based chaotic initialization. With the assistance of the dynamic parameter adjustment strategies and Gaussian mutation, the improved algorithm has fast convergence speed and high convergence accuracy, and it can solve the weapon target assignment problems with excellent optimization capabilities. Besides, the rule-based chaotic initialization strategy is embedded in this algorithm to generate high-quality population with better diversity. Finally, two comparative simulation cases of different initialization methods and algorithms for solving the large-scale weapon target assignment problems are designed. The results demonstrate that the proposed approach can provide more superior assignment schemes than its competitors with enhanced efficiency.

Towards Global Explainability of Artificial Intelligence Agent Tactics in Close Air Combat

In this paper, we explore the development of an explainability system for air combat agents trained with reinforcement learning, thus addressing a crucial need in the dynamic and complex realm of air combat. The safety-critical nature of air combat demands not only improved performance but also a deep understanding of artificial intelligence (AI) decision-making processes. Although AI has been applied significantly to air combat, a gap remains in comprehensively explaining an AI agent’s decisions, which is essential for their effective integration and for fostering trust in their actions. Our research involves the creation of an explainability system tailored for agents trained in an air combat environment. Using reinforcement learning, combined with a reward decomposition approach, the system clarifies the agent’s decision making in various tactical situations. This transparency allows for a nuanced understanding of the agent’s behavior, thereby uncovering their strategic preferences and operational patterns. The findings reveal that our system effectively identifies the strengths and weaknesses of an agent’s tactics in different air combat scenarios. This knowledge is essential for debugging and refining the agent’s performance and to ensure that AI agents operate optimally within their intended contexts. The insights gained from our study highlight the crucial role of explainability in improving the integration of AI technologies within air combat systems, thus facilitating more informed tactical decisions and potential advancements in air combat strategies.

Transfer Learning Method from Parameter Scene to Physical Scene Based on Self-Game Theory

Due to the inherent challenges in meeting the comprehensive training requirements of combat scenarios within a digital environment, this paper proposes innovative solutions. Firstly, a parametric scene training method is introduced, aiming to enhance the adaptability of the training process. Secondly, the utilization of the digital environment as a test environment is suggested, which can significantly improve the efficiency of iterative virtual-to-real conversion. Additionally, employing digital simulation as a pre-test environment for the physical setting can effectively reduce deployment costs and enhance the safety of virtual reality migration experiments. The proposed approach involves constructing a training model based on the parameterized environment and implementing a feedback loop utilizing the digital environment agent model. This framework enables continuous verification of the environment’s parameters, ensuring the accuracy of decision-making strategies and evaluating the transferability of the decision-making model.

A Smartphone-Gamified Virtual Reality Exposure Therapy Augmented With Biofeedback for Ailurophobia: Development and Evaluation Study.

To the best of our knowledge, no specialized research has been conducted to address ailurophobia (fear of cats) in Iran or globally. This has driven our project, along with the prevalence of ailurophobia and the absence of a gamified virtual reality exposure therapy (VRET) that incorporates affordable and easily accessible biofeedback (BF) tools. We hypothesize that a gamified VRET augmented with BF will yield more positive effects than a similar device lacking BF. This study primarily focuses on the development and preliminary evaluation of a smartphone-gamified VRET integrated with BF, targeting animal phobia, with a specific case study on ailurophobia. The secondary objectives are using affordable and readily available BF found in devices such as smart bands and smartwatches and creating a mobile virtual reality gamified app to improve patients' adherence to treatments while simultaneously enhancing the app's accessibility, scalability, and outreach. Evaluations encompassed 3 methods. First, we identified the tool's potential positive effects on phobia interventions, exploring 4 effects: intrinsic motivation, simulation of fearful situations, management of stressful circumstances without therapists' presence and mitigation of catastrophic thoughts, and preliminary effects on ailurophobia treatment. Participants were divided into BF and non-BF groups. Second, we gathered user preferences and opinions about the treatment. Third, we conducted heuristic evaluations using 44 heuristics from existing system usability scales assessing user interfaces, virtual reality platforms, and video games' playability. To interpret the data, mean scores; ANOVA, single factor; and ANOVA, 2-factor with replication were used. A total of 29 individuals were identified, of which 10 met the eligibility criteria or were accessible. The smartphone-gamified VRET augmented with BF exhibited better results on the identified effects compared with the non-BF version and contributed to normalizing encounters with cats. Moreover, 41 of the 44 heuristics achieved a percentage above 62%, indicating its potential as a therapeutic product and its ability to enhance patient adherence to treatments. Patient preferences on the treatment and its strengths and weaknesses were provided for further improvement. The tool has the potential to evolve into a comprehensive solution by incorporating various types of cats and their behaviors, simulating environments in which they are commonly found, and enhancing its appeal through an increased sense of adventure without inducing unrealistic fears. By adapting fear elements, the game can be tailored to treat various animal phobias. Phobia-focused games should avoid action and combat scenarios to prevent reinforcement of fear responses. After rigorous evaluation, further exploration is required to provide remote use beyond clinical settings.

Enhancing Assault Maneuvers in Simulated Scenarios of Multiple Invader Kamikaze Drones through the Utilization of a Modified Adaptive Elforce Algorithm

The development of autonomous drone technology has led in their widespread deployment, especially in combat scenarios. One instance of this is the utilization of kamikaze drones, as seen in the Ukraine war. Autonomous defense drones have been used to counter these invading kamikaze drones. This study focuses on simulating scenarios involving invader vs. defender drones, primarily exploring invader drone maneuver motions to maximize damage inflicted on chosen targets. The work we conducted presents an enhanced el-force algorithm that employs Coulomb's Law-based maneuver techniques to improve the effectiveness of multiple kamikaze invader drones when engaging target defended by defender drones. We aim to improve traditional el-force by addressing key challenges such as siege tendencies and unproductive conduct. In addition, we explore various attacking formations to determine the most effective formation. To evaluate the performance of our proposed algorithm, we conducted simulation in a dynamic 3D environment, employing damage inflicted as the evaluation metric. Through rigorous testing, we conclusively demonstrate that our proposed method combining with a circular formation, outperforms alternative attacking maneuvers and formations. Our findings provide insights into optimal maneuver movements and attacking formations, improving the effectiveness of invader drones in engaging and damaging designated targets.

Killing Time: Homosocial Bonding Behind the Front Line in Tim Hetherington’s Infidel

Even in intense combat scenarios, a significant proportion of a soldier's time is spent passing the time. Tim Hetherington was acutely aware of this contradiction; of the 240 pages of Infidel, his account of the time he spent in Combat Outpost Restrepo in the remote Afghan Korengal valley, only about one sixth show soldiers actually in combat. The remaining pages depict soldiers maintaining their base, passing the time playing cards, wrestling each other, and sleeping. This focus on the lacunary moments between skirmishes and contacts marks out the work as attuned to a more complex understanding of the nature of soldiering in front line situations. This paper situates Hetherington's work into a longer historical engagement with the depiction of soldiers ‘killing time’ between moments of combat, and in the context of how the homosocial nature of the proximity in which soldiers live and work contributes to their combat motivation.

SIMULATION MODEL OF A MISSILE STRIKE

Abstract. As modern naval warfare systems and tactics continue to evolve, it is crucial to employ contemporary tools capable of assessing their potential across various combat scenarios. This study model simulates interactions between anti-ship missiles and naval forces during landing operations. Our research seeks to develop a robust method for quantitatively analyzing the impact of anti-ship missiles on naval vessels under diverse combat conditions. The model's scalability allows for the incorporation of emerging naval tactics and procedures, maintaining its relevance and utility in dynamic strategic environments. It supports enhancements in precision through statistical analysis and ongoing assessments of new weaponry and technological advancements. By simulating a broad range of missile types, naval strategies, warship classes, aircraft, reconnaissance systems, and electronic warfare capabilities, the model provides insights into the strategic utility of anti-ship missiles. Our findings aim to optimize the allocation of military resources by accurately determining the necessary missile quantities to thwart enemy naval landings, thereby enhancing the effectiveness and efficiency of defensive operations. Keywords: Anti-ship missiles, missile guidance, naval landing, simulation framework, missile damage estimation.