Network-based Metric for Measuring Combat Effectiveness

Lee, Youngwoo; Lee, Taesik

doi:10.14429/dsj.64.5534

Cited by 23 publications

(11 citation statements)

References 10 publications

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“…The motif idea is significant to our work as it serves as the functional unit in the framework 19 . Lee and Lee used motifs in the calculations for combat effectiveness 20 . We use motifs as basic task units in our planning model.…”

Section: Basic Motifs To Perform Tasksmentioning

confidence: 99%

Solving A Multi-objective Mission Planning Problem for UAV Swarms with An Improved NSGA-III Algorithm

Liu¹,

Wang²,

Li³

et al. 2018

IJCIS

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Restricted communication in unmanned aerial vehicle (UAV) swarms means that configuration needs to vary dynamically with changing tasks. We propose a mission planning model that uses a motif, a grouping of related functions, as the basic task unit. The planning model automatically generates a mission planning scheme from a task priority execution order given as an input. The selection of the best scheme from among possible solutions is a multiobjective optimization problem with calculation complexity rapidly increasing with the number of tasks. To address this difficulty, we enhance the NSGA-III algorithm by adding adaptive genetic operators when generating the offspring population. We apply the improved NSGA-III algorithm to optimize mission planning schemes with changing task priority execution orders. We validated the feasibility and effectiveness of the improved algorithm via a case study.

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Section: Basic Motifs To Perform Tasksmentioning

confidence: 99%

Solving A Multi-objective Mission Planning Problem for UAV Swarms with An Improved NSGA-III Algorithm

Liu¹,

Wang²,

Li³

et al. 2018

IJCIS

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“…Lee and Lee proposed an attack opportunity model to measure combat effectiveness (Lee and Lee 2014). The number of attack opportunities created by a force is proportional to the force's power at enemy's casualties.…”

Section: Metric For Combat Effectivenessmentioning

confidence: 99%

Measuring and visualizing combat effectiveness

Lee¹,

Lee²

2015

2015 Winter Simulation Conference (WSC)

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Combat effectiveness created by deployment of force is a key factor in leading successful combat operations. Measurements of combat effectiveness should consider the overall capabilities of the resources involved, and should capture spatial aspects to keep regional advantages for a combat operation. This study focuses on developing an analytical metric to measure combat effectiveness by adapting network representation of a combat environment, and visualizes it in a map format. Attack opportunity structures and their numerical values are computed for each unit area in a battlefield, which represents the force's possibility of creating attack opportunities in that location. In this study, we consider a defense operation where the force's initial deployment strategy plays a critical role in how the combat proceeds. Applying the proposed metric to a simulation of a defense scenario, we find that the spatial distribution of combat effectiveness is highly correlated with the combat results. 1.3148 978-1-4673-9743-8/15/$31.00 ©2015 IEEE

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“…According to the OODA loop theory [6] and the F2T2EA theory of the kill chain [7], the operational plan can be divided into different steps. This paper analyzes the function requirements of the equipment in each step, combines with the idea of the network model [27]- [29], and combines different types of node connections into different types of function chains to meet the needs of each operational phase. The method based on function chain can reduce the connection and interaction between nodes, and is not easily affected by communication interference.…”

Section: Introductionmentioning

confidence: 99%

An Operation Planning Generation and Optimization Method for the New Intelligent Combat SoS

Wang

Zhu

et al. 2019

IEEE Access

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With the rapid development of emerging technologies, the unmanned and intelligent degree of combat forces in the combat system of systems(SoS) has been continuously improved, making the operational equipment more diverse. How to better describe the multiple functions and forces hybrid combat SoS becomes a difficult problem. Changes in combat forces have transformed the operational style from single platform to cluster, which puts forward higher demands on the timeliness of the development of course of action(COA), which is the key step in the formulation of operational planning. It is of great military value to study the model of the hybrid new complex combat SoS, as well as the generation and optimization of the operational plan under the new combat model. This paper extends the FINC (Force, Intelligence, Networking, C2) model of heterogeneous networks, adds a variety of new intelligent unmanned combat force nodes, and proposes a new Force Intelligent Network C2 and Autonomous Model (FINCA 2) which represents more different types of function nodes and communication relationships between nodes. Based on this, this paper proposes six types of 12 kinds functionally simple, frequently used, and practically function chains (FC) for the new combat SoS. Through the Joint Mission Thread (JMT) method, the operational mission is decomposed to get specific operational actions, action relationships, and requirements list of function chains for the actions, and complete the matching of actions and resources. Based on the idea of sigmoid function in neural network, the multi-objective optimization algorithm NSGA-III is improved to find the Pareto frontier of the action order scheme that satisfies the task association relationship. Finally, the method proposed in this paper is verified by the case of a brigade-level combat SoS participating in border operations. INDEX TERMS Intelligent combat SoS, FINCA 2 , function chain, JMT, improved adaptive NSGA-III.

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Network-based Metric for Measuring Combat Effectiveness

Cited by 23 publications

References 10 publications

Solving A Multi-objective Mission Planning Problem for UAV Swarms with An Improved NSGA-III Algorithm

Solving A Multi-objective Mission Planning Problem for UAV Swarms with An Improved NSGA-III Algorithm

Measuring and visualizing combat effectiveness

An Operation Planning Generation and Optimization Method for the New Intelligent Combat SoS

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