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Journal of Defense Modeling and Simulation : Applications, Methodology, Technology
Journal Prestige (SJR): 0.208
Citation Impact (citeScore): 1
Number of Followers: 3  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1548-5129 - ISSN (Online) 1557-380X
Published by Sage Publications Homepage  [1174 journals]
  • Fog, friction, and control in organized conflict: punctuated transitions
           to instability

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      Authors: Rodrick Wallace
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      We explore the effects of Clausewitzian fog and friction using a data rate theorem–based model of the phase transition from control to failure for inherently unstable systems that include, but are not limited to, the many possible modalities of organized conflict. Fog-and-friction challenge any and all cognitive structures facing dynamic patterns of threat or opportunity, whether control is manifested through an institution, a machine entity, or some composite. The fundamental nature of challenge appears independent of the degree of sophistication of those institutions, entities, or composites, and of the technical modalities employed. The dialog/Zweikampf of organized conflict is—and will remain—an intimate and most human enterprise. Implications for other existential threats of inherently unstable circumstance, like pandemic disease or climate change, are evident.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-08-05T11:16:13Z
      DOI: 10.1177/15485129221115740
       
  • How a machine can understand the command intent

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      Authors: Maarten Schadd, Anne Merel Sternheim, Romy Blankendaal, Martin van der Kaaij, Olaf Visker
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      With recent technological advances, commanders request the support of artificial intelligence (AI)-enabled systems during mission planning. Future AI systems may test a wide range of courses of action (COAs) and use a simulator to test each COA’s effectiveness in a war game. The COA’s effectiveness is however dependent on the commanders’ intent. The question arises to what degree a machine can understand the commanders’ intent' Currently, the intent has to be programmed manually, costing valuable time. Therefore, we tested whether a tool can understand a freely written intent so that a commander can work with an AI system with minimal effort. The work consisted of letting a tool understand the language and grammar of the commander to find relevant information in the intent; creating a (visual) representation of the intent to the commander (back brief); and creating an intent-based computable measure of effectiveness. We proposed a novel quantitative evaluation metric for understanding the commanders’ intent and tested the results qualitatively with platoon commanders of the 11th Airmobile Brigade. They were positively surprised with the level of understanding and appreciated the validation feedback. The computable measure of effectiveness is the first step toward bridging the gap between the command intent and machine learning for military mission planning.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-08-05T11:13:27Z
      DOI: 10.1177/15485129221115736
       
  • Interoperability analysis via agent-based simulation

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      Authors: Melissa Pescatore, Paul Beery
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      This paper demonstrates an approach for the use of agent-based simulation, supported by model-based systems engineering products, to analyze interoperability. To demonstrate the approach, a representative maritime search-and-rescue (SAR) operation is simulated in the agent-based simulation program Map-Aware Non-Uniform Automata (MANA). The MANA SAR model is used to assess interoperability decisions at organizational, operational, and technical levels and to highlight dependencies between decisions at each level of interoperability. Analysis indicates that, within the MANA SAR model, organizational interoperability decisions have the largest impact on operational performance but that organizational challenges may be overcome with substantial investment at both the operational and technical levels of interoperability.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-08-05T11:09:42Z
      DOI: 10.1177/15485129221111171
       
  • Network characterization and simulation via mixed properties of the
           Barabási–Albert and Erdös–Rényi degree distribution

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      Authors: Fairul Mohd-Zaid, Christine Schubert Kabban, Richard F Deckro, Wright Shamp
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      Social network analysis (SNA) is a tool for the operations researcher to understand, monitor, and exploit social and military structures which are key in the intelligence community. However, in order to study and influence a network of interest, the network must first be characterized; preferably to a known network model that captures a mixture of graphical properties exhibited by the social network of interest. In this work, we present a novel statistical method for both characterizing networks via a Binomial-Pareto maximum-likelihood approach and simulating the characterized network using a graph of mixed Barabási–Albert (BA, scale-free) and Erdös–Rényi (ER, randomness) properties. Characterization is performed through a combination of hypothesis tests and method of moments parameter estimation on Pareto and Doubly Truncated Binomial distributions. Application on real-world networks suggests that such networks may be characterized with a mixture of scale-free and random properties as modeled through BA and ER graphs. We demonstrate that our simulation methods are able to capture the degree distribution and density of the networks examined. These results demonstrate that this work establishes a statistical framework upon which network characterization and simulation may be accomplished, thus enabling the adaptation of such methods when generating, manipulating, and observing networks of interest.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-08-05T11:06:22Z
      DOI: 10.1177/15485129221110893
       
  • Strategic maneuver and disruption with reinforcement learning approaches
           for multi-agent coordination

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      Authors: Derrik E Asher, Anjon Basak, Rolando Fernandez, Piyush K Sharma, Erin G Zaroukian, Christopher D Hsu, Michael R Dorothy, Thomas Mahre, Gerardo Galindo, Luke Frerichs, John Rogers, John Fossaceca
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      Reinforcement learning (RL) approaches can illuminate emergent behaviors that facilitate coordination across teams of agents as part of a multi-agent system (MAS), which can provide windows of opportunity in various military tasks. Technologically advancing adversaries pose substantial risks to a friendly nation’s interests and resources. Superior resources alone are not enough to defeat adversaries in modern complex environments because adversaries create standoff in multiple domains against predictable military doctrine-based maneuvers. Therefore, as part of a defense strategy, friendly forces must use strategic maneuvers and disruption to gain superiority in complex multi-faceted domains, such as multi-domain operations (MDOs). One promising avenue for implementing strategic maneuver and disruption to gain superiority over adversaries is through coordination of MAS in future military operations. In this paper, we present overviews of prominent works in the RL domain with their strengths and weaknesses for overcoming the challenges associated with performing autonomous strategic maneuver and disruption in military contexts.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-08-05T11:01:36Z
      DOI: 10.1177/15485129221104096
       
  • Open-set low-shot classification leveraging the power of instance
           discrimination

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      Authors: Spiridon Kasapis, Geng Zhang, Jonathon M Smereka, Nickolas Vlahopoulos
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      In search, exploration, and reconnaissance operations of autonomous ground vehicles, an image recognition capability is needed for specifically classifying targeted objects (relevant classes) and at the same time identifying as unknown (irrelevant and unseen) objects that do not belong to any known classes, as opposed to falsely classifying them in one of the relevant classes. This paper integrates an unsupervised learning feature extraction framework based on the Instance Discrimination method with an Open-Set Low-Shot (IDLS) classifier for creating the desired new capability. Unlabeled images from the vehicle’s operating environment are used for training the feature extractor while a modest number (less than 40) images for each relevant class and unlabeled irrelevant images are used for training the Open-Set Low-Shot (OSLS) classifier in a manner that enables recognition of images unseen during training as irrelevant. The value and the accuracy of the new IDLS approach are demonstrated through a thorough comparison with alternative unsupervised and fully supervised methods.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-08-04T11:12:23Z
      DOI: 10.1177/15485129221111172
       
  • A look back at the past 44 years of live virtual and constructive (LVC)
           simulation and lessons for cyberspace LVC

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      Authors: Michael G. Lilienthal
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.

      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-07-06T08:44:03Z
      DOI: 10.1177/15485129221109574
       
  • A live mindset in Live Virtual Constructive simulations: a spin-up for
           future LVC air combat training

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      Authors: Sanna Aronsson, Henrik Artman, Mikael Mitchell, Robert Ramberg, Rogier Woltjer
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      Combining Live, Virtual, and Constructive (LVC) aircraft in the same training scenario holds promise for developing and enhancing fighter pilot training. The simulator study reported here builds on joint pilot-researcher co-design work of beyond visual range LVC training (LVC-T) scenarios to provide training value to pilots in both Live and Virtual aircraft. One fourship of pilots simulated Live entities by acting under peacetime restrictions, while other pilots acted as during regular Virtual training. The objective was to investigate pilots’ reflections on the implications of LVC-T and on the methodology used to provide hands-on experience of a plausible LVC-T scenario. The purpose is to inform the design and use of future LVC in air combat training from the perspective of training value. Results indicate that pilots are positive toward the LVC scenario design, especially the dynamics that a large-scale scenario brings to training of decision making. They indicate a high degree of presence, the need for specific regulations to enforce flight safety, and that restrictions put on the simulated Live entities had implications for the other pilots. In addition to regular Live (L) and simulator (V + C) training, LVC-T may enhance pilots’ repertoires and decision-making patterns.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-07-01T10:27:50Z
      DOI: 10.1177/15485129221106204
       
  • Making simulations future proof

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      Authors: George Stone
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.

      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-06-10T01:33:34Z
      DOI: 10.1177/15485129221097725
       
  • An experiment in tactical wargaming with platforms enabled by artificial
           intelligence

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      Authors: Danielle C Tarraf, J Michael Gilmore, D Sean Barnett, Scott Boston, David R Frelinger, Daniel Gonzales, Alexander C Hou, Peter Whitehead
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      In this report, researchers experimented with how postulated artificial intelligence/machine learning (AI/ML) capabilities could be incorporated into a wargame. We modified and augmented the rules and engagement statistics used in a commercial tabletop wargame to enable (1) remotely operated and fully autonomous combat vehicles and (2) vehicles with AI/ML-enabled situational awareness to show how the two types of vehicles would perform in company-level engagement between Blue (US) and Red (Russian) forces. The augmented rules and statistics we developed for this wargame were based in part on the US Army’s evolving plans for developing and fielding robotic and AI/ML-enabled weapon and other systems. However, we also portrayed combat vehicles with the capability to autonomously detect, identify, and engage targets without human intervention, which the Army does not presently envision. The rules we developed sought to realistically portray the capabilities and limitations of AI/ML-enabled systems, including their vulnerability to selected enemy countermeasures, such as jamming. Future work could improve the realism of both the gameplay and representation of AI/ML-enabled systems, thereby providing useful information to the acquisition and operational communities in the US Department of Defense.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-05-05T09:25:17Z
      DOI: 10.1177/15485129221097103
       
  • Who hacked my holodeck'

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      Authors: Katherine L Morse
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.

      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-05-03T12:21:04Z
      DOI: 10.1177/15485129221092783
       
  • NER-based military simulation scenario development process

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      Authors: Junhua Zhou, Xiaoqing Li, Shaoping Wang, Xiao Song
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      For combat simulation, the simulation scenario serves as the foundation and data source. It is not, however, easy to develop military simulation scenario because the developing process of these texts was time-consuming. To solve this problem, in this paper, we propose a distant supervised method for developing military simulation scenarios based on named entity recognition (NER) method. This method consists of three phases: extracting the key elements of simulation scenario, recognizing named entities of the text, and generating an executable simulation scenario. First, we analyze the two types of scenarios involved in the development process of military simulation scenarios: operational scenario and executable scenario. Second, we train a NER model on operational scenario corpus. Then, we compare our distant supervised-based NER method with the other NER methods, and we achieve an overall improvement of F1 score of 9.01%. Finally, to demonstrate the feasibility of our approach, we use a case study to implement a combat simulation scenario development progress.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-05-02T09:59:14Z
      DOI: 10.1177/15485129221094842
       
  • Small is beautiful

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      Authors: James Ryseff, Michael Bond
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.

      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-05-02T05:53:38Z
      DOI: 10.1177/15485129221096478
       
  • Machine learning to combat cyberattack: a survey of datasets and
           challenges

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      Authors: Arvind Prasad, Shalini Chandra
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      The ever-increasing number of multi-vector cyberattacks has become a concern for all levels of organizations. Attackers are infecting Internet-enabled devices and exploiting them to carry out attacks. These devices are unwittingly becoming part of carrying out cyberattacks. Many studies have proposed machine learning–based promising solutions to stamp out cyberattacks preemptively. We review the machine learning techniques and highlight some promising solutions in recent studies. This study provides the advantage of experimenting with the developed solutions on modern datasets. This survey aims to provide an insightful organization of current developments in cybersecurity datasets and give suggestions for further research. We identified the most frightful cyberattacks and suitable datasets having records related to the attack. This paper discusses modern datasets such as CICIDS2017, CSE-CIC-IDS-2018, CIC-DDoS2019, UNSW-NB15, UNSW-TonIOT, UNSW-BotIoT, DoHBrw2020, and ISCX-URL-2016, which include records of recent sophisticated cyberattacks. This paper will focus on these modern datasets, retrieve detailed knowledge, and experiment with the most commonly used machine learning algorithms. We identify datasets as a significant centric topic that can be addressed with innovative machine learning approaches and solutions to defend against cyberattacks.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-05-01T01:41:10Z
      DOI: 10.1177/15485129221094881
       
  • “Growth directions for the JDMS community”

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      Authors: Harrison Schramm
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      The Defense Modeling and Simulation community as been working to improve decisonmaking for the past 75 years. This invited editorial highlights several open problems in our field, which I believe we need to collectively make progress against in order to maintain our relevance into the next era.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-04-28T03:36:12Z
      DOI: 10.1177/15485129221096472
       
  • “Neuroscience” models of institutional conflict under fog, friction,
           and adversarial intent

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      Authors: Rodrick Wallace
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      Adapting recent formal perspectives on shared interbrain activity in social communication, we explore a model of “East Asian” implication of an effect on an adversary, and take a general approach to degrading an opponent’s rate of cognition. These developments represent surprisingly routine application of the asymptotic limit theorems of information and control theories to organized conflict, modified by the necessity of making “adiabatic” approximations allowing the theorems to work sufficiently well. The resulting probability models provide a rigorous foundation for constructing statistical tools for the analysis of real-time, real-world data involving contention on “Clausewitz Landscapes” of fog, friction, and deadly adversarial intent.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-04-25T12:03:45Z
      DOI: 10.1177/15485129221090592
       
  • On games and simulators as a platform for development of artificial
           intelligence for command and control

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      Authors: Vinicius G Goecks, Nicholas Waytowich, Derrik E Asher, Song Jun Park, Mark Mittrick, John Richardson, Manuel Vindiola, Anne Logie, Mark Dennison, Theron Trout, Priya Narayanan, Alexander Kott
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios with significant parallels to military applications: multiple participants manage resources and make decisions that command assets to secure specific areas of a map or neutralize opposing forces. These characteristics have attracted the artificial intelligence (AI) community by supporting development of algorithms with complex benchmarks and the capability to rapidly iterate over new ideas. The success of AI algorithms in real-time strategy games such as StarCraft II has also attracted the attention of the military research community aiming to explore similar techniques in military counterpart scenarios. Aiming to bridge the connection between games and military applications, this work discusses past and current efforts on how games and simulators, together with the AI algorithms, have been adapted to simulate certain aspects of military missions and how they might impact the future battlefield. This paper also investigates how advances in virtual reality and visual augmentation systems open new possibilities in human interfaces with gaming platforms and their military parallels.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-03-09T11:31:43Z
      DOI: 10.1177/15485129221083278
       
  • A metric for quantifying nonlinearity in k-dimensional complex-valued
           functions

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      Authors: Larry C Llewellyn, Michael R Grimaila, Douglas D Hodson, Scott Graham
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      Modeling and simulation is a proven cost-efficient means for studying the behavioral dynamics of modern systems of systems. Our research is focused on evaluating the ability of neural networks to approximate multivariate, nonlinear, complex-valued functions. In order to evaluate the accuracy and performance of neural network approximations as a function of nonlinearity (NL), it is required to quantify the amount of NL present in the complex-valued function. In this paper, we introduce a metric for quantifying NL in multi-dimensional complex-valued functions. The metric is an extension of a real-valued NL metric into the k-dimensional complex domain. The metric is flexible as it uses discrete input–output data pairs instead of requiring closed-form continuous representations for calculating the NL of a function. The metric is calculated by generating a best-fit, least-squares solution (LSS) linear k-dimensional hyperplane for the function; calculating the L2 norm of the difference between the hyperplane and the function being evaluated; and scaling the result to yield a value between zero and one. The metric is easy to understand, generalizable to multiple dimensions, and has the added benefit that it does not require a closed-form continuous representation of the function being evaluated.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-03-03T11:40:56Z
      DOI: 10.1177/15485129221080399
       
  • Landmine detection via multivariate image analysis

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      Authors: Paulo Maranhão, Leandro Andraos, Rodrigo Guedes, Eugenio Epprecht
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      Despite the technological advances in sensors, landmine action still causes many deaths and mutilations in several countries worldwide, and the search for efficient methods to detect mines has become a relevant issue. Thus, this work proposes a landmine detection approach using multivariate analysis of infrared (IR) images. In addition, it is proposed to monitor these images through a control chart to help identify the mine. Experiments following this approach have indicated effectiveness in the detection of landmines, enabling their safe removal.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-03-02T05:03:30Z
      DOI: 10.1177/15485129221082048
       
  • Fast procedure for vulnerability simulation of armored vehicles

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      Authors: Morten Rikard Jensen, Steven Grate, Kshitiz Khanna
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      The presented work discusses performing a V/L (Vulnerability/Lethality) analysis on an armored tank based on an RHA (Rolled Aomogeneous Armour) equivalence. It is shown how the approach works using small examples verified with hand calculations. Other more complex examples include a set of welded plates protecting a HYBRID III 50 Percentile Male ATD (Anthropomorphic Test Device). Considered response parameters are the VAA (Vulnerable Area Assessment) damage maps, expected protection capability plot, and damage area fractions. Furthermore, different V/L analyses of the Russian T-80 tank are displayed. It is illustrated how to apply the concept of explicit finite element models to find the critical RHA equivalent armor thickness at normal impact. It is done with terminal ballistic models for three materials: RHA, Aluminum 5083-H116, and Armox 500T.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-02-16T10:28:43Z
      DOI: 10.1177/15485129221078988
       
  • Performance analysis of cooperative NOMA system for defense application
           with relay selection in a hostile environment

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      Authors: Indrajeet Kumar, Aman Kumar, Ritesh Kumar Mishra
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      In the current scenario, where all the services have become online, the demand for increased capacity, low symbol error rate (SER), better data rate, and low latency with high quality of services has been expected from the service providers. Non-orthogonal multiple access (NOMA) is the key enabling technology by which all the above requirements could be achieved. In this work, the physical layer security of a cooperative NOMA system has been improved. The present approach of the decode-and-forward (DF) method consists of one base station (BS), N relays, and two users. Here, a two-stage relay method has been proposed, and analytical results are generated to show that this two-stage relay scheme has been found to achieve the lowest outage probability among all possible relay schemes with the highest attainable diversity gain. In a high signal-to-noise ratio (SNR), the asymptotic and closed-form expressions for outage probability have been obtained. The outage probability for N relays is also computed, which shows that the outage probability decreases as the number of relays get increased. Meanwhile, the Monte-Carlo simulation shows that cooperative NOMA with the proposed two-stage relay technique outperforms cooperative NOMA using the traditional max-min approach.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-02-15T08:31:56Z
      DOI: 10.1177/15485129221079721
       
  • Multiple maneuver model of cooperating ground combat troops

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      Authors: Jan Nohel, Petr Stodola, Zdeněk Flasar, Marian Rybanský
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      The article describes the possibilities of using the Maneuver Control System CZ (MCS CZ) in creating a multiple maneuver of a group of cooperating units. It is a tactical information system designed to support the decision-making of commanders in order to effectively deploy their units on the battlefield. The system evaluates the geographical and hydrometeorological battlefield influences as well as the enemy’s situation to calculate the maneuver axes of a unit composed of up to three teams with the same goal. To verify the calculations of mathematical-algorithmic models of the system, two measurements were performed on model situations. In the first of them, a group of three unmanned systems carried out an attack on an identified delivery system of weapons of mass destruction. The second model situation included a hasty attack by three infantry squads on anti-aircraft equipment. In both cases, the resulting group of maneuvers used the fastest and safe axes of advance, spatially synchronized according to tactical principles. In both cases, the enemy’s weapon systems were quickly attacked and destroyed immediately after their identification from three directions. The commander’s decision-making process was largely replaced by the MCS CZ calculations in model situations, which significantly shortened its duration.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-02-15T08:30:04Z
      DOI: 10.1177/15485129221078939
       
  • Gaming AI without AI

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      Authors: Aaron B Frank
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      War games have played an essential role in the development of military force structures, strategies, operational concepts, and more. Military organizations are currently confronting uncertainties over the ways in which Artificial Intelligence (AI) may affect warfare at nearly every level, from combat tactics to operational concepts to force structure to deterrence and national and international security. This paper explores how game designers and players can approach questions regarding how AI may be employed in alternative contexts allowing for insight into how emerging and imagined technologies may affect warfare at many different levels of analysis. It identifies six application areas of AI technologies that games should consider—(1) principal–agent relations, (2) organizational and operational complexity, (3) attention management, (4) exploratory analysis, (5) information exploitation and model validation, and (6) adaptive behavior in open-ended systems—and suggests conceptual and practical strategies for investigating them in games that can be played in the absence of real-world systems and algorithms that perform these functions.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-02-14T05:19:51Z
      DOI: 10.1177/15485129221074352
       
  • Effect of combat textile cloth on human radar cross section for microwave
           camouflage applications

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      Authors: Renan Richter, Daniel Gonçalves, Newton A S Gomes
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      This article proposes an analysis of the effect of combat textile cloth on human radar cross section (RCS) for microwave camouflage applications. Based on numerical simulations for the definition of the geometric profile of the human body and the measurement of electromagnetic parameters of the fabric, made by cotton and polyester, the work reveals that the uniform has a direct influence on the absorption of electromagnetic waves from a hypothetical ground surveillance radar (9.375 GHz). The results of 360° measuring human RCS showed that it can be attenuated, on average, by approximately 8 dB due to the effect of the material.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-02-11T11:27:03Z
      DOI: 10.1177/15485129221077432
       
  • Artificial intelligence for wargaming and modeling

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      Authors: Paul K Davis, Paul Bracken
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      In this paper, we discuss how artificial intelligence (AI) could be used in political-military modeling, simulation, and wargaming of conflicts with nations having weapons of mass destruction and other high-end capabilities involving space, cyberspace, and long-range precision weapons. AI should help participants in wargames, and agents in simulations, to understand possible perspectives, perceptions, and calculations of adversaries who are operating with uncertainties and misimpressions. The content of AI should recognize the risks of escalation leading to catastrophe with no winner but also the possibility of outcomes with meaningful winners and losers. We discuss implications for the design and development of families of models, simulations, and wargames using several types of AI functionality. We also discuss decision aids for wargaming, with and without AI, informed by theory and exploratory work using simulation, history, and earlier wargaming.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-02-08T11:54:21Z
      DOI: 10.1177/15485129211073126
       
  • Analysis of user pairing non-orthogonal multiple access network using deep
           Q-network algorithm for defense applications

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      Authors: Shankar Ravi, Gopal Ramchandra Kulkarni, Samrat Ray, Malladi Ravisankar, V Gokula krishnan, D S K Chakravarthy
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      Non-orthogonal multiple access (NOMA) networks play an important role in defense communication scenarios. Deep learning (DL)-based solutions are being considered as viable ways to solve the issues in fifth-generation (5G) and beyond 5G (B5G) wireless networks, since they can provide a more realistic solution in the real-world wireless environment. In this work, we consider the deep Q-Network (DQN) algorithm-based user pairing downlink (D/L) NOMA network. We have applied the convex optimization (CO) technique and optimized the sum rate of all the wireless users. First, the near-far (N-F) pairing and near-near and far-far (N-N and F-F) pairing strategies are investigated for the multiple numbers of users, and a closed-form (CF) expression of the achievable rate is derived. After that, the optimal power allocation (OPA) factors are derived using the CO technique. Through simulations, it has been demonstrated that the DQN algorithms perform much better than the deep reinforcement learning (DRL) and conventional orthogonal frequency-division multiple access (OFDMA) schemes. The sum-rate performance significantly increases with OPA factors. The simulation results are in close agreement with the analytical results.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-01-27T11:21:53Z
      DOI: 10.1177/15485129211072548
       
  • Promises and pitfalls of computational modelling for insurgency conflicts

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      Authors: Koen van der Zwet, Ana I Barros, Tom M van Engers, Peter M A Sloot
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      Insurgency conflicts pose significant challenges to societies globally. The increase of insurgency conflicts creates a need to understand how insurgencies arise, and to identify societal drivers of insurgencies or effective strategies to counter them. In this paper, we analyze the contributions of computational modeling methods for the analysis of insurgent conflicts. We formalize a specific literature-based analysis framework using the identified key factors and drivers, which enables the evaluation of specific models in this domain. Through a systematic literature search, we identify 64 computational models to apply our framework. We highlight the development and contributions of various methodologies through an in-depth analysis of 13 high-quality models. The evaluation of these computational models revealed promising directions and future topics to design specific simulation models for all identified factors. In addition, our analysis revealed specific pitfalls concerning validity issues for each of the modeling methods.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-01-24T09:01:18Z
      DOI: 10.1177/15485129211073612
       
  • Multi-day evaluation of space domain awareness architectures via decision
           analysis and multi-objective optimization

    • Free pre-print version: Loading...

      Authors: Albert R Vasso, Richard G Cobb, John M Colombi, Bryan D Little, David W Meyer
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      The US Government is the world’s de facto provider of space object cataloging data, but it is challenged to maintain pace in an increasingly complex space environment. This work advances a multi-disciplinary approach to better understand and evaluate an underexplored solution recommended by national policy in which current collection capabilities are augmented with non-traditional sensors. System architecting techniques and extant literature identified likely needs, performance measures, and potential contributors to a conceptualized Augmented Network (AN). Multiple hypothetical architectures of ground- and space-based telescopes with representative capabilities were modeled and simulated on four separate days throughout the year, then evaluated against performance measures and constraints using Multi-Objective Optimization. Decision analysis and Pareto optimality identified a small, diverse set of high-performing architectures while preserving design flexibility. Should decision-makers adopt the AN approach, this research effort indicates (1) a threefold increase in average capacity, (2) a 55% improvement in coverage, and (3) a 2.5-h decrease in the average maximum time a space object goes unobserved.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-01-07T01:12:43Z
      DOI: 10.1177/15485129211067767
       
  • Simulation of chromatic and achromatic assessments for camouflage textiles
           and combat background

    • Free pre-print version: Loading...

      Authors: Md Anowar Hossain
      Abstract: The Journal of Defense Modeling and Simulation, Ahead of Print.
      Chromatic and achromatic (AC) assessments of camouflage textiles have been critical to the defense researchers for concealment, detection, recognition, and identification (CDRI) of target signature against multidimensional combat background (CB). AC assessment and camouflage measurement techniques are simulated and experimented for assessment of camouflage textiles against CB. This model has been demonstrated for color measurement spectrophotometer, scanning electron microscopy (SEM), digital imaging, hyperspectral imaging, and image processing software (ImageJ) for the advancement and establishment of AC camouflage textiles assessment. The chromatic variations of 48 artificial target objects (TOBs) have been synthesized by image processing; the technique can be implemented for defense CB-CDRI assessment. Microstructural variation versus optical signal of woodland, desertland and stoneland CB materials have been elucidated by SEM magnification. The achromatic variation of CB materials have been demonstrated for the replacement of optical signal against modern remote sensing device to the imaging sensor. Color difference (ΔE), microstructural variations, pixel variations to imaging signal and standard deviation of CB materials have been represented for remote sensing surveillance of defense applications against TOB-CB-CDRI. Technical simulation of color, texture, gloss, and pixel intensity has been derived for AC-CDRI assessment of camouflage textiles in TOBs-CB environment.
      Citation: The Journal of Defense Modeling and Simulation
      PubDate: 2022-01-05T07:22:36Z
      DOI: 10.1177/15485129211067759
       
 
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