The Journal of Defense Modeling and Simulation: Applications,Methodology,Technology

Increases in computational power have contributed to growing interest in using modeling and simulation (M&S) to better understand and address blast injury for US service members. The development of an M&S capability that can comprehensively simulate human injury, lethality, and impairment due to blast injury threats in the military environment requires a large, coordinated integration of many models and simulations. This contribution describes how various lessons learned from the defense M&S domain were used to support the future development of an envisioned Modeling Capability for blast injury by providing simulation functionality for simulation-based experimentation. It first addresses conceptually the interoperability challenges when more than one simulation can be or must be applied and possibly composed for the experiment. This leads to the development of a proposed concept of operations for the application of the Modeling Capability and the development of a framework of services needed to allow the identification of an applicable simulation solution, selecting the subset of those simulations, composing them for the experiment, and assessing the results. As a nascent endeavor, anticipated challenges for implementing these concepts are discussed, leveraging lessons learned regarding interoperability, composability, use of services, repositories, and development of simulation compositions to conduct simulation-based experiments.

This paper extends the emerging target information gathering domain by introducing a camouflaging component, investigating the benefit of using multiple UAVs, and studying the impact of allowing re-visits. Previous work in this area addresses the UAV Orienteering Problem for target detection where targets emerge according to non-homogeneous space–time Poisson processes. Our extension considers that emerged targets will camouflage themselves to become undetectable after a period of time and nodes can be revisited. Routes for single or multiple UAVs are generated using the Team Orienteering Problem with Time Windows and evaluated using simulation. In addition, a framework is developed for comparing routes, and the value added by increasing the solving time is investigated. Our computational testing reveals increasing the number of time windows used increases the expected route value. A factorial analysis is conducted which indicates the network topology, the number of time windows used, and the coefficient of variation for camouflage time generally have significant effects on the expected number of targets detected regardless of the number of UAVs used. In addition, increasing the amount of time spent solving the problem does not always increase the number of expected target detected.

Military operations are increasingly taking place in urban environments; the effective reconnaissance of such environments is critical to their success. This paper deals with modeling the reconnaissance of a built-up area using a swarm of heterogeneous cooperating unmanned aerial vehicles. The model consists of two phases. In the first, a set of waypoints is generated from which the reconnaissance of the walls and roofs of buildings and roads located in the area of intelligence responsibility is performed; the waypoints are deployed in such a manner that the comprehensive reconnaissance of all objects takes place while keeping their number as small as possible. In the second phase, the flight trajectories of the available unmanned aerial vehicles are planned with the purpose of minimizing the overall operation time; the intention is thus to load each vehicle evenly. The proposed model is validated on a set of six scenarios with varying complexity; each scenario is based on typical tactical situations. This paper is a contribution to the research being intensively conducted in the field of robotic systems for civilian and military applications.

In long, stressful operational periods, military personnel face numerous challenges that may compromise their performance, an especially important one being fatigue. Current literature supports the view that behavioral, physiological, and cognitive factors are all predictive of the level of fatigue in individuals. However, much of the work on modeling fatigue has taken a narrow approach, relying only on a handful of modalities to measure fatigue. This paper aims to fill the void by providing an extensive overview of the current literature on both computationally measuring and modeling fatigue. We provide up-to-date and practical advice on which models are best suited for different situations and highlight directions for future work.

In this paper, an innovative discrete dynamical model is presented, which is used to predict the kind of strategic behavior the participants should adopt to win a battle. For study purposes, a computer model is developed to reveal the most critical factors that strategically affect combat and the relationship of dependence between the warring parties. Besides, it can predict the outcome of a battle under specific scenarios. Furthermore, the proposed dynamical system is applied in Midway’s air–naval battle, which was one of the most decisive battles of World War II (WWII). It was a significant turning point in the history of naval warfare in the Pacific Ocean since the victory of the United States marked an end to Japanese expansionist policy, and these are the reason this battle was chosen. The numerical results of the analysis were presented, and the key factors (e.g., persons, decisions, and weather conditions represented by the critical values of model parameters) were highlighted, defining the outcome of the conflict.

High energy lasers (HELs) are evolving to provide an effective solution for air and missile defense. The emergence of this technology comes at a similar time to the development of cooperative and collaborative defense systems that collect and communicate data to inform decisions. This paper proposes a stochastic jump method for modeling the performance of networked HELs, defending against aerial threats which follow a queueing methodology. By drawing on an existing method that quantifies performance using the sum of sojourn times in a stochastic jump process, the model can predict the probability of survival when multiple effectors are tasked in defending against an arbitrary number of threats. The model can be applied more generally to processes with both waiting time–dependent service and finite existence. Furthermore, a new HEL counteraction probability model is developed to enable the demonstration and comparison of three different system collaboration methods in a future warfare application. Results suggest the prevailing superimposing laser strategy may be less effective than simple one-to-one allocation of lasers to threats. There may also be merit in targeting separate components of a threat’s structure.

Military performance must be evaluated, and one of the most critical concepts to measure involves the lethal capabilities of a military force. However, there are multiple challenges that complicate any accurate performance assessment, including theoretical issues of measurement due to statistical irregularities and practical limitations due to the military context. Here, we describe the lethality paradox, which states that measuring lethality could be a self-defeating exercise despite its necessity. Specifically, the value of any collected metric may be inherently reduced by the act of measurement while, also, creating operational vulnerabilities for a military force. This paradox is conceived as an extension of Goodhart’s Law and incorporates the same challenges of a personnel gaming a set standard rather than developing the skill set supposedly measured by this standard. Our discussion identifies the limitations and applications of Goodhart’s Law to lethality while also concluding with several proposed solutions to different paradoxical challenges.

Small arms combat modeling is one method to describe raw human performance data in terms of lethality. This process uses a series of Monte Carlo simulations based on observed data to convert measurements of speed and accuracy into a quantifiable chance of winning a combat engagement. A major issue within these modeling efforts involves the assumptions of incorporating wounded personnel. Realistic combat will have scenarios where shots fired strike adversaries without killing them, and therefore, this element cannot be ignored. However, there are at least four significant assumptions made during the modeling and simulation effort when incorporating wounded personnel: (1) assigning damage inflicted by shots, (2) tracking wounded personnel, (3) reducing combat effectiveness of wounded personnel, and (4) burdening other fighters in the simulation. Here, we outline the challenges posed by each assumption and discuss possible solutions. Whatever the final decision for a particular modeling effort, the assumptions made should always be clearly documented in the “Methods” section. Wounded personnel will likely require several such assumptions be made that could affect the outcome; nevertheless, wounded personnel should be represented in some capacity in any small arms combat modeling effort.

Military command and control spaces are complex work environments and critical facilities for a military mission. This paper describes a newly developed modeling tool called SPACE (Spatial layout Planning and Analysis for Communication Effectiveness) for assisting layout design of such workplaces. As a Human Factors tool, SPACE provides common functions required for workspace modeling, including rapid workspace prototyping, versatile design visualization, and algorithmic layout assessment. One of its key features is a layout evaluation algorithm that enables objective assessment of floor plans based on their impact on operator communication and interaction efficiency. In this paper, the main functionalities of SPACE are explained using a case study where models were constructed to compare three layout options for a Joint Intelligence Center. The results revealed the pros and cons of each layout in facilitating team interaction involving different sensory domains. While all three layouts were deemed acceptable, an inward-facing boardroom style design was predicted to be optimal as it best balanced the need for direct sightline access, non-technology-mediated verbal conversations, and the physical effort associated with movement to collaborators’ workstations. This study demonstrated the usefulness of modeling and simulation to provide quantitative auditable data for supporting evidence-based decision-making in military system design.

We analyze the concept of multiple unmanned aerial vehicles (UAVs) on a shared tether (MUST), where the UAVs act as control nodes for the shape of the tether, enabling the system to maneuver around obstacles without tangling or colliding for increased flexibility compared with single-tethered UAVs. MUST use cases are gathered from stakeholders in the military, public safety, and commercial domains. We present a model for the tether shape to use in collision checking and a model for the interactions among tether weight, size, and power, which we exercise to determine the maximum tether segment sizes. We apply three probabilistic path-planning algorithms from the literature to MUSTs, using a novel local planner and constraint set. In simulation, we show that probabilistic planners are a feasible approach to path planning for MUSTs with curved tether segments. We also show the first manual piloting modality of MUSTs.

Multi-agent systems are of ever-increasing importance in a contested space environment—use of multiple, cooperative satellites potentially increases positive mission outcomes on orbit, while autonomy becomes an ever-increasing requirement to increase reaction time to dynamic situations and lower the burden on space operators. This research explores multi-agent satellite swarm Guidance, Navigation, and Control (GNC) using deep reinforcement learning (DRL). DRL policies are trained to provide guidance inputs to agents in multi-agent swarm environments for completing complex, teamwork-focused objectives in geosynchronous orbit. An example scenario is explored for a group of satellite agents maneuvering to triangulate an object that is non-stationary in the relative orbit frame. Reward shaping is used to encourage learning guidance that positions swarm members to maximize triangulation accuracy, using angles-only observations for navigation relative to the target. Results show the policies successfully learn guidance through reward shaping to improve triangulation accuracy by a significant factor.