• Korean Management Science Review
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• v.32 no.1
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• pp.49-63
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• 2015

This paper investigates an aircraft-target assignment problem in consideration of deconfliction. The aircraft-target assignment problem is the problem to assign available aircrafts and weapons to targets that should be attacked, where the objective function is to minimize the total expected damage of aircrafts. Deconfliction is the way of dividing airspaces for aircraft flight to ensure the safety while performing the mission. In this paper, mixed integer programming model is suggested, where it considers deconfliction between aircrafts. However, the suggested MIP model is non-linear and limited to get solution for large size problem. The 2-phase decomposition model is suggested for efficiency and computation, where in the first phase target area is divided into sectors for deconfliction and in the second phase aircrafts and weapons are assigned to given targets for minimizing expected damage of aircraft. The proposed decomposition model shows outperforms the model developed for comparison in the computational experiment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.88-102
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• 2016

This paper considers the allocation and engagement scheduling problem of interceptor missiles, and the problem was formulated by using MIP (mixed integer programming) in the previous research. The objective of the model is the maximization of total intercept altitude instead of the more conventional objective such as the minimization of surviving target value. The concept of the time window was used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. The MIP formulation of the problem is very complex due to the complexity of the real problem itself. Hence, the finding of an efficient optimal solution procedure seems to be difficult. In this paper, an efficient genetic algorithm is developed by improving a general genetic algorithm. The improvement is achieved by carefully analyzing the structure of the formulation. Specifically, the new algorithm includes an enhanced repair process and a crossover operation which utilizes the idea of the PSO (particle swarm optimization). Then, the algorithm is throughly tested on 50 randomly generated engagement scenarios, and its performance is compared with that of a commercial package and a more general genetic algorithm, respectively. The results indicate that the new algorithm consistently performs better than a general genetic algorithm. Also, the new algorithm generates much better results than those by the commercial package on several test cases when the execution time of the commercial package is limited to 8,000 seconds, which is about two hours and 13 minutes. Moreover, it obtains a solution within 0.13~33.34 seconds depending on the size of scenarios.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.131-140
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• 2020

The characteristics of ballistic missiles are changing rapidly but studies have mostly focused on fragmentary flight trajectory analysis estimating the changing characteristics of some types, while there is a lack of research on comprehensive and efficient ballistic search, detection and prediction for missiles including the new types that have been gaining attention lately. This paper analyzes the flight trajectory characteristics of ballistic missiles at various ranges considering flight path angle adjustment, specific impulse and drag force with altitude based on the optimized equations of motion reflecting the parameters of North Korea's general and new types of ballistic missiles. The flight trajectory characteristics of representative ranges for each ballistic missile were analyzed by adjusting the flight path angle in the minimum energy method, lofted method, and depressed method. In addition, High value target can attacked by ballistic missiles considering flight path angle adjustment at various points. It's expected to be used to Threat Evaluation and Weapon Allocation, and deployment of defense systems by interpreting the analysis of the latest Iskander-class ballistic missiles and the new multiple rocket launcher.

• Convergence Security Journal
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• v.23 no.3
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• pp.65-71
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• 2023

The importance of combat drones has been highlighted through the recent outbreak of the Russia-Ukraine war. The combat drones play a significant role as a a game changer that alters the conventional wisdom of traditional warfare. Many pundits expect the role of combat swarm drones would be more crucial in the future warfare. In this regard, this paper aims to analyze the development of artificial intelligence-enabled combat swarm drones. To transform the human-operated swarm drones into fully autonomous weaponry system our suggestions are as follows. Developments of (1) AI algorithms for optimized swarm drone operations, (2) decentralized command and control system, (3) inter-drones' mission analysis and allocation technology, (4) enhanced drone communication security and (5) set up of ethical guideline for the autonomous system. Specifically, we suggest the development of AI algorithms for drone collision avoidance and moving target attacks. Also, in order to adjust rapidly changing military environment, decentralized command and control system and mission analysis allocation technology are necessary. Lastly, cutting-edging secure communication technology and concrete ethical guidelines are essential for future AI-enabled combat swarm drones.