Modern Battlefields Research Articles

A visible transparent infrared microwave compatible stealth metasurface with low infrared emissivity

Abstract This article proposes a visible transparent infrared microwave-compatible stealth metasurface with low infrared emissivity, aiming to integrate visible transparency, low infrared radiation, and broadband microwave absorption. The entire structure consists of a dual layer of infrared shielding layer (IRSL) and microwave absorption layer (MAL). Among them, IRSL adopts a frequency selective surface (FSS) structure with high-duty cycle dielectric-metal-dielectric (DMD) to obtain low infrared emissivity and high microwave transmittance. MAL combines the equivalent circuit method (ECM) and particle swarm optimization algorithm (PSO) to design a circular ring metasurface for broadband microwave absorption. To achieve better absorption performance, a transition layer (TL) is introduced to improve impedance matching between IRSL and MAL. Meanwhile, the use of transparent materials throughout the entire structure has the characteristic of visible transparency. In summary, this article proposes a multiband compatible stealth metasurface design method that can meet the stealth requirements of modern battlefields.

An Attention-Guided Complex-Valued Transformer for Intra-Pulse Retransmission Interference Suppression

With the maturation of digital radio frequency memory (DRFM) technology, various intra-pulse retransmission interference methods have emerged. These flexible and changeable retransmission interference methods pose significant challenges to radar detection tasks, particularly in modern battlefields. This paper proposes an attention-guided complex-valued transformer (AGCT) as a solution. First, the encoder maps the received signal contaminated by interference and noise into a high-dimensional space. Then, the dilated convolution block (DCB) group and attention block (AB) group in the mask estimator extract the delicate multi-scale features and large-scale features of the interference, respectively, to obtain a multidimensional space mask. Finally, the decoder restores interference to the time domain and outputs the estimated target echo using residual learning. Considering the characteristics of intra-pulse interference, we introduced the energy attention block (EAB) at the end of the DCBs and the ABs within our network. This addition ensures a heightened focus on extracting interference features. Furthermore, we implemented a curriculum learning strategy during the network training. This approach gradually acclimates the network to fit different types of retransmission interference, starting from simpler to more complex scenarios. Our extensive experiments, conducted under various conditions, have provided compelling evidence of the AGCT’s superior performance. Compared to the comparative network, the AGCT’s advantages are particularly pronounced under more harsh conditions, demonstrating its robustness and effectiveness.

Automatic modulation recognition of radio fuzes using a DR2D-based adaptive denoising method and textural feature extraction

The identification of intercepted radio fuze modulation types is a prerequisite for decision-making in interference systems. However, the electromagnetic environment of modern battlefields is complex, and the signal-to-noise ratio (SNR) of such environments is usually low, which makes it difficult to implement accurate recognition of radio fuzes. To solve the above problem, a radio fuze automatic modulation recognition (AMR) method for low-SNR environments is proposed. First, an adaptive denoising algorithm based on data rearrangement and the two-dimensional (2D) fast Fourier transform (FFT) (DR2D) is used to reduce the noise of the intercepted radio fuze intermediate frequency (IF) signal. Then, the textural features of the denoised IF signal rearranged data matrix are extracted from the statistical indicator vectors of gray-level cooccurrence matrices (GLCMs), and support vector machines (SVMs) are used for classification. The DR2D-based adaptive denoising algorithm achieves an average correlation coefficient of more than 0.76 for ten fuze types under SNRs of −10 dB and above, which is higher than that of other typical algorithms. The trained SVM classification model achieves an average recognition accuracy of more than 96% on seven modulation types and recognition accuracies of more than 94% on each modulation type under SNRs of -12 dB and above, which represents a good AMR performance of radio fuzes under low SNRs.

Preparation of HMX@NPBAs microparticles by coating process with improved mechanical properties, thermal stability, and safety performance

Polymer-bonded explosives (PBXs) played a critical role in lethality and destruction as a vital military explosive in the world. Unfortunately, the current PBXs exhibit poor properties in safety performance and reliability for the weak interfacial surface, which cannot meet requirements of the high security and high reliability of weapons and ammunition in the harsh environment of modern battlefields. Herein, the neutral polymeric bonding agents (NPBAs) were successfully coated on the surface of 1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane (HMX) and proved to enhance the mechanical performance and reliability of PBXs. The scanning electron microscopy images indicated a slight aggregation of NPBAs coated in the HMX surface, the XRD spectra of HMX@NPBAs had an analogous spectrum of HMX, and the X-ray photoelectron spectroscopy spectrum confirmed that the NPBAs played a role on the surface of HMX by coating. Furthermore, the HMX coated with NPBAs had more excellent safety performance, thermal stability, and mechanical properties. The strategy is helpful to strengthen the interaction between HMX and binder matrix, which provides a brilliant idea in modifying for the weak interfacial surface of energetic crystals.

Optimization of Air Defense System Deployment Against Reconnaissance Drone Swarms

Due to their advantages in flexibility, scalability, survivability, and cost-effectiveness, drone swarms have been increasingly used for reconnaissance tasks and have posed great challenges to their opponents on modern battlefields. This paper studies an optimization problem for deploying air defense systems against reconnaissance drone swarms. Given a set of available air defense systems, the problem determines the location of each air defense system in a predetermined region, such that the cost for enemy drones to pass through the region would be maximized. The cost is calculated based on a counterpart drone path planning problem. To solve this adversarial problem, we first propose an exact iterative search algorithm for small-size problem instances, and then propose an evolutionary framework that uses a specific encoding-decoding scheme for large-size problem instances. We implement the evolutionary framework with six popular evolutionary algorithms. Computational experiments on a set of different test instances validate the effectiveness of our approach for defending against reconnaissance drone swarms.

Radar Communication Jamming Signal Recognition Based on the Attention Mechanism

In modern battlefields, radar radiation sources detect targets by transmitting high-power modulated signals. In order to protect the operational effectiveness of the radar, there is a higher requirement for radar communication anti-jamming signal recognition capability. The accurate and fast identification of radar anti-jamming signals is the prerequisite for taking effective anti-jamming measures, which will directly affect the operational effectiveness of radar. At present, the recognition method based on the neural network can automatically extract signal features and obtain good recognition results. Because radar emitter signals have a strong correlation in time sequence, the LSTM network is used in this paper. For better feature extraction and classification of signals, this paper constructs the LSTM attention network, combines the attention mechanism with the LSTM recognition network, and proves the effectiveness of radar communication jamming signal recognition network based on the attention mechanism through experiments.

Russia/Ukraine military conflict: Discussing the maritime element of the confrontation

Within the globalization era, the conduct, resolution, and impact of international conflicts are frequently not limited solely to the belligerent states involved. Conflict influences distant countries and often illustrates the vulnerability of sea access to the security of coastal and landlocked nations, by factoring in that: import/export commodities are impacted; access to vital energy resources is undermined; and/or international resource distribution is threatened. Therefore, the maritime domain (and its military, legal, and commercial components) represents a Russia/Ukraine conflict cornerstone and the epicenter of this analysis. This conflict highlights maritime trade importance and re-establishes the strategic significance of protecting multi-polarity, the “rule of law”, and freedom of the seas within the Black Sea region (BSR), which today represents a very large concentration of power (involving actors like Russia, USA, NATO, EU) and has been the site of ten post-Cold War conflicts. Resultantly, maritime domain objectives and tactical events (on, above, and below the seas) require detailed analysis as hostilities continue, the norms and principles of international law are threatened and/or undermined, and prospective combat end-state(s) are considered. Such will define Russia’s and Ukraine’s future(s), as well as economic-diplomatic stability and the future of rules-based international order across the BSR, which is a vital maritime transport corridor. 
 Amidst increasing maritime emphasis, this conflict also illustrates transformational warfighting facets. In addition to troops, ships, and aircraft, modern battlefields now include issues like: Information Warfare outlets; “lawfare”; cyber threats; and adversaries with unprecedented Artificial Intelligence capabilities. The international community must acknowledge these skills yield warfighting capability to nations lacking capacity. As naval warfare equipment and tactics change, protecting sea lanes, preventing maritime hegemony, and upholding the “rule of law”, remain dominant-and are enhanced by globalization.

Smrtonosni autonomni sistemi oružja (LAWS) - ka strožoj regulativi ili neselektivnoj upotrebi?

Modern battlefields around the globe demonstrated the employment of the next generation of weapons which are colloquially designated as "killer robots", or Lethal Autonomous Weapon Systems (LAWS). Although LAWS, for now, are always under the supervision of the human operator, the technological advancements in Artificial Intelligence allow for such weapon systems to achieve a significant degree of autonomy, including the autonomy over the decision-making process of utilizing the lethal force against the human targets. Due to the lack of global regulation for the research, production, and deployment of LAWS, they are seeing more and more employment in contemporary battlefields, from Libya, Syria, Yemen, and Nagorno-Karabakh to Ukraine. The goals of this article are to understand the limitations of the AI that can be employed in LAWS; to present an overview of the current LAWS via the available public data; to assess the state of the regulations of the LAWS, by employing comparative analysis of strategies and positions towards LAWS from the side of the EU, the USA, China, Russia, and India. The results of this research demonstrate that barring the EU which is in the process of adopting a regulation that will enforce a total ban on the LAWS, the other major powers express a balanced approach towards this issue by reserving rights to develop and employ LAWS for the goals of their national security, per the Article 36 of the 1977 Additional Protocol I to the 1949 Geneva Conventions.

The application of software defined radios in unmanned aerial vehicles

The article addresses the application of software defined radios (SDR) on modern battlefields. Above all, it focuses on the application of the SDR technology in communication with unmanned aerial vehicles. It also reviews some solutions applied in the armed forces of Finland, United States, Poland, Turkey, and Israel. It outlines the potential of these solutions and considers the main direction of development of the SDR technology, which is the cognitive radio.

Research on Anti-Frequency Sweeping Jamming Method for Frequency Modulation Continuous Wave Radio Fuze Based on Wavelet Packet Transform Features

Frequency modulation continuous wave (FMCW) radio fuze is widely used in military equipment, due to its excellent range and anti-jamming ability. However, the widespread use of radio fuze jammers on modern battlefields poses a serious threat to fuzes. In this study, a classification method of targeting and sweeping frequency jamming signals of FMCW radio fuze based on wavelet packet transform features is proposed, which improves the anti-jamming ability of fuze. The wavelet packet transform of the output signal of the radio fuze detector is used to form a feature vector, which is fed into a support vector machine for targeting and jamming signal classification. The experimental results of the measured data show that the proposed method can achieve a high accuracy rate of classification and identification of FMCW radio fuze targets and frequency sweeping jamming signals. The highest recognition accuracy reached is 98.81% ± 0.0037. The lowest false alarm probability is 0.57% ± 0.0043, which indicates its potential application values in the near future.

Optimization of False Target Jamming against UAV Detection

Unmanned aerial vehicles (UAVs) have been widely used for target detection in modern battlefields. From the viewpoint of the opponents, false target jamming is an effective approach to decrease the UAV detection ability or probability, but currently there are few research efforts devoted to this adversarial problem. This paper formulates an optimization problem of false target jamming based on a counterpart problem of UAV detection, where each false target jamming solution is evaluated according to its adversarial effects on a set of possible UAV detection solutions. To efficiently solve the problem, we propose an evolutionary framework, which is implemented with four popular evolutionary algorithms by designing/adapting their evolutionary operators for false target jamming solutions. Experimental results on 12 test instances with different search regions and numbers of UAVs and false targets demonstrate that the proposed approach can significantly reduce the UAV detection probability, and the water wave optimization (WWO) metaheuristic exhibits the best overall performance among the four evolutionary algorithms. To our knowledge, this is the first study on the optimization of false target jamming against UAV detection, and the proposed framework can be extended to more countermeasures against UAV operations.

A Research on Intercommunication Method for Tactical Information Among Heterogeneous Mobile Combat Vehicles

Modern Battle Fields are covered with various weapon systems, such as tanks, infantry vehicles, or artillery vehilces. In such environment, it is important to take it into consideration what kind of efficient method should be developed to communicate with heterogeneous friend combat systems for the exchange of tactical information aqcuired because military operations require coordinated actions. For this purpose, we made a research on tactical information equipments such as Battlefield Management Systems(BMSs) equipped in multiple kinds of Mobile Combat Vehicles(MCVs). The exchange of tactical information could be divieded into separate domains, such as connectivity, messages, and recognition processes by operators. In this article, we will deal with wireless radio connectivity, KVMF messages, and User Interfaces showing shared data.

Why Drones Have Not Revolutionized War: The Enduring Hider-Finder Competition in Air Warfare

Abstract According to the accepted wisdom in security studies, unmanned aerial vehicles, also known as drones, have revolutionizing effects on war and world politics. Drones allegedly tilt the military balance in favor of the offense, reduce existing asymmetries in military power between major and minor actors, and eliminate close combat from modern battlefields. A new theory about the hider-finder competition between air penetration and air defense shows that drones are vulnerable to air defenses and electronic warfare systems, and that they require support from other force structure assets to be effective. This competition imposes high costs on those who fail to master the set of tactics, techniques, procedures, technologies, and capabilities necessary to limit exposure to enemy fire and to detect enemy targets. Three conflicts that featured extensive employment of drones—the Western Libya military campaign of the second Libyan civil war (2019–2020), the Syrian civil war (2011–2021), and the Armenia-Azerbaijan conflict over Nagorno-Karabakh (2020)—probe the mechanisms of the theory. Drones do not by themselves produce the revolutionary effects that many have attributed to them.

Landscape Analysis of a Battlefield of the Austro-Prussian War of 1866 near Hospital Kuks

The article presents the possibilities of landscape analysis based on historical and cartographic sources. The methods and procedures used produced important spatial information about a specific battlefield from the second half of the 19th century. This type of non-destructive research is for the first time applied to a battlefield of the Austro-Prussian War of 1866 in the Hradec Králové region. The results of the analysis helped to find a place for a geophysical survey, which confirmed the location of a relic of field fortifications for the Austrian artillery near the baroque Hospital Kuks near Jaroměř. The results provide an important example for the implementation of similar methods in the research on modern battlefields in the Czech Republic.

Design of A Novel 1553B Data Communication Board Based on DSP and Integrated 1553B Terminal

In order to make the communication cables in airborne equipment not as complicated, large and heavy as before, the MIL-STD-1553B standard was released in the 1970s, thus reducing aircraft weight. Application of 1553B bus is becoming wider and wider. In recent years, UAV is a new force in modern battlefields and local conflicts. At the same time, the 1553B bus is prevalent in the domain of UAV for communication between different devices. A novel 1553B data communication board based on DSP and integrated 1553B terminal is designed in this paper. The principle of 1553B bus is introduced at first. And then it presents the hardware design as well as the software design. In the end, the conclusion part summarizes its applications. Undoubtedly, the 1553B board designed in this paper is convenient to be integrated to the whole system, providing a real-time, reliable and flexible communication solution for UAV.

Multi‐label hybrid radar signal recognition based on a feature pyramid network and class activation mapping

The electromagnetic environment of modern battlefields becomes increasingly complex, and radar receivers may receive multiple radar signals simultaneously. However, current deep learning models can only predict a single class and cannot recognize multi-label mixed radar signals. In this study, a multi-label hybrid radar signal recognition framework based on the feature pyramid network (FPN) and class activation map (CAM) is proposed. The multi-label radar signals are recognized by calculating the average value of the CAM corresponding to each class. The proposed method can recognize, localize and separate mixed radar signals in time-frequency images, which improves the interpretability and transparency of the model. In addition, the FPN is adopted to improve the spatial resolution of the feature maps, and the Mixup data augmentation is utilized to improve the generalization performance of the model. Experiments with eight different modulation types of mixed radar signals show that the recognition accuracy of hybrid radar signals achieves 92.2% at 0 dB.

The Latent Operational Sway of Modern Indian Conventional Weapons on the Armed Forces of Pakistan

Attaining and maintaining operations security remains the primary objective of armed forces on modern battlefields. Due to their flexibility in employment, conventional weapons are the true weapons of war and warfare, and in turn, significantly affect the operational environment based on their offensive as well as defensive capabilities. South Asia being a nuclear flashpoint is still characterized by conventional weapons. This paper intends to investigate the traditional weapon modernization undertaken by India and the likely impact of systems such as the Russian-built T-90S Main Battle Tank and S-400 Missile System, French-built Rafale Omni-role aircraft, and the Indian Navy aircraft carrier INS Vikramaditya on the Armed Forces of Pakistan. The paper aspires to examine how and why these weapons and their acquisitions and deployments are likely to affect the operations security of both rival armed forces and recommend several conventional responses to enhance it, with special regard to Pakistan.

Intelligent Tutoring Systems for Commercial Games: The Virtual Combat Training Center Tutor and Simulation

Game manuals and tutorial scenarios are insufficient for new players to learn games of deep complexity such as highly realistic tactical simulations of modern battlefields. Adding post-game after-action reviews improves the situation, but these typically do not provide guidance during the mission and tend to focus on quantitative feedback, rather than specifics about what the player did wrong and how to improve. Intelligent tutoring system (ITS) technology provides a higher level of interactivity and a more specific qualitative analysis to guide players during game play. This use of an AI technology is demonstrated with the integration of an ITS component with the tactical simulation Armored Task Force (ATF) resulting in a combined system called the the Virtual Combat Training Center (V-CTC). V-CTC simulates the Army's combat training center at Fort Irwin and its instructors, called observer / controllers. The ATF game itself was modified to send an event stream over TCP-IP sockets to the ITS component, which interprets the events and acts accordingly. V-CTC was originally intended for a military context: either classroom use, field instruction, or embedded deployment. However, in non-military games, tutors (or non-player characters acting in that role) may well enhance the gaming experience of players. Such players might otherwise become frustrated with learning very challenging games, or simply fail to appreciate the tactical possibilities and depth of strategy possible in a well-designed game.

Weapons of War, Tools of Justice

Abstract Just as the internal combustion engine revolutionized warfare in the early twentieth century, artificial intelligence is shaping warfare in the Digital Age. Fuelled by data rather than gasoline, artificial intelligence (AI) derivative technologies are driven by innovation in both the military and civilian sectors. Today’s defence scientists and engineers, like their predecessors, develop physical equipment and weapons, but instead of simply making them more powerful and lethal, they are also making them more intelligent and connected. As AI is increasingly integrated into military tools, the digital footprints of modern battlefields will grow exponentially, generating new sources and types of data that will fundamentally alter war crimes investigations. This article examines emerging military applications of AI in order to identify what opportunities and challenges these tools might offer international criminal investigators. Will more sensors and smart devices on the battlefield benefit or burden the investigation of war crimes? Moreover, will intelligent machines add to the fog of war or help us see through it?

Intelligent Design for Simulation Models of Weapon Systems Using a Mathematical Structure and Case-Based Reasoning

The armed forces of major nations have utilized modeling and simulation technologies to develop weapon systems corresponding to changing modern battlefields and reducing the development cycle. However, model design is complex owing to the characteristics of current weapons, which require multiple functions. Therefore, this study proposes a method to support the automated design of weapon system models for simulation. We apply module-based modeling and an intelligent modeling process to our devised method. The former formalizes constituents and constraints regarding an element combination to design the required model, while the latter applies case-based reasoning (CBR) to intelligentize the modeling process based on the results of the former. Using a case study, our proposed method demonstrates that models that respond to operational circumstances can be designed based on simulation results. Consequently, when weapon systems can be represented in formalized structures and constituents, the weapon models can be reusable based on the addition, modification, and replacement of modules in the common structure. The CBR process can provide the models that satisfy the requirements by retrieving similar models and modifying the models. The proposed method is applicable to the process of weapon system design or improvement for changing battlefields.