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http://dx.doi.org/10.7232/JKIIE.2016.42.2.086

Gaussian Approximation of Stochastic Lanchester Model for Heterogeneous Forces  

Park, Donghyun (Department of Industrial and Systems Engineering, KAIST)
Kim, Donghyun (Department of Industrial and Systems Engineering, KAIST)
Moon, Hyungil (Department of Industrial and Systems Engineering, KAIST)
Shin, Hayong (Department of Industrial and Systems Engineering, KAIST)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.42, no.2, 2016 , pp. 86-95 More about this Journal
Abstract
We propose a new approach to the stochastic version of Lanchester model. Commonly used approach to stochastic Lanchester model is through the Markov-chain method. The Markov-chain approach, however, is not appropriate to high dimensional heterogeneous force case because of large computational cost. In this paper, we propose an approximation method of stochastic Lanchester model. By matching the first and the second moments, the distribution of each unit strength can be approximated with multivariate normal distribution. We evaluate an approximation of discrete Markov-chain model by measuring Kullback-Leibler divergence. We confirmed high accuracy of approximation method, and also the accuracy and low computational cost are maintained under high dimensional heterogeneous force case.
Keywords
Combat Modelling; Stochastic Lanchester Model; Heterogeneous Forces;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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