Optimization of Unmanned Air Vehicle Tactical Formation in War Games

Filho, Geraldo Mulato de Lima; R., Kuroswiski, Andre; Medeiros, Felipe Leonardo Lôbo; Voskuijl, Mark; Monsuur, Herman; Passaro, Angelo

doi:10.1109/access.2022.3152768

Cited by 15 publications

(10 citation statements)

References 24 publications

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“…ASA has helped develop extensive academic work in the aerospace and defense context relating to different fields of study, such as artificial intelligence, machine learning, data science, and optimization. These applications can be seen in [4,2,12,5,6,3,8,9], being detailed as follows.…”

Section: Applicationsmentioning

confidence: 99%

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ASA: A Simulation Environment for Evaluating Military Operational Scenarios

Dantas¹,

Costa²,

Gomes³

et al. 2022

Preprint

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The Aerospace Simulation Environment (Ambiente de Simulação Aeroespacial -ASA in Portuguese) is a custom-made objectoriented simulation framework developed mainly in C++ that enables the modeling and simulation of military operational scenarios to support the development of tactics and procedures in the aerospace context for the Brazilian Air Force. This work describes the ASA framework, bringing its distributed architecture for managing multiple simulation machines, a data analysis platform for post-processing simulation data, the capability of loading models at simulation runtime, and a batch mode execution platform to perform multiple independent executions simultaneously. In addition, we present a list of recent works using the ASA framework as a simulation tool in the air combat context.

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Section: Applicationsmentioning

confidence: 99%

“…In [9], the authors integrated the ASA infrastructure into an external optimization framework to apply six different metaheuristics to optimize Unmanned Aircraft Vehicles' tactical formations, considering enemy variables such as firing distance and initial position in a BVR combat scenario.…”

Section: Applicationsmentioning

confidence: 99%

ASA: A Simulation Environment for Evaluating Military Operational Scenarios

Dantas¹,

Costa²,

Gomes³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Unmanned aerial vehicles (UAVs) have several advantages over manned aircraft [5]. In the case of maritime patrol, those advantages include greater autonomy, greater stealth, and a much lower operating cost.…”

Section: Introductionmentioning

confidence: 99%

Time-Critical Maritime UAV Mission Planning Using a Neural Network: An Operational View

et al. 2022

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An operational planning procedure for a time-critical maritime unmanned aerial vehicle (UAV) search mission is introduced and evaluated. The mission is the fast identification of a target vessel. The triggering report only contains information regarding the category and displacement of a vessel carrying out a prohibited activity, resembling operational situations. A neural network trained to classify vessels is combined with vessel clustering to reduce waypoints in the flight plan. The UAV's onboard sensors provide input for the neural network regarding each vessel in the search area, resulting in a prioritization of vessels to be visited. As the accuracy of the classification and the possibilities for clustering depend on several operational factors as well as on the UAV's sensor degradation, we investigate three methodologies to identify which planning procedure to use in various operational situations. The results show that our robust and agile approach can help a UAV find the unknown target vessel as soon as possible.INDEX TERMS Artificial intelligence, optimization methods, unmanned aerial vehicles (UAV), decision support systems.

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“…The results of wartime resource requirement analysis (WRRA) are used as the basis for writing various war documents related to inventory requirements and military force, such as the joint strategic objective plan (JSOP) and oil war stockpile documents, according to the directive of the Korea Ministry of Defense. Although war-game simulation can provide relatively reliable results (De Lima Filho et al, 2022;Mittal & Davidson, 2020;Turnitsa, Blais, & Tolk, 2021), it is impossible to provide the results of judging the expected loss after simulating the war-game within a short period of time (e.g., Brathen, Seehuus, & Mevassvik, 2021;Hujer, Kratky, & Farlik, 2020).…”

Section: ⅰ Introductionmentioning

confidence: 99%

Development of standard loss analysis model using big data

Lee¹,

Lim²,

Lee³

et al. 2022

J. Adv. Mil. Stud.

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The Republic of Korea Army conducts simulations during peacetime using ground operation resource requirements analysis model (GORRAM) to determine potential losses when at war based on the latest operation plan. Although war-game simulation can yield reliable results, it takes considerable amount of time and effort to build a database and generate scenarios. Therefore, a study is required to supplement the detailed war-game simulation method to quickly determine expected losses. Using data built-in GORRAM, we tested the significance of four factors using beta regression analysis. While multiple regression is most commonly used to model the causality, beta regression is a powerful method for modeling response variables in the (0,1) range, such as the loss ratio. We verified that three factors, namely ‘topography’, ‘operational posture’, and ‘friend/foe power ratio’ were related to loss. This study proposes a new method for calculating the expected loss in real-time, overcoming a limitation of existing war-game simulation methods.

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Optimization of Unmanned Air Vehicle Tactical Formation in War Games

Cited by 15 publications

References 24 publications

ASA: A Simulation Environment for Evaluating Military Operational Scenarios

ASA: A Simulation Environment for Evaluating Military Operational Scenarios

Time-Critical Maritime UAV Mission Planning Using a Neural Network: An Operational View

Development of standard loss analysis model using big data

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