• Journal of the Korea Society for Simulation
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• v.29 no.2
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• pp.105-117
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• 2020

Analysis of combat effectiveness is required to consider the concept of tactical cooperative engagement between manned-unmanned weapon systems, in order to predict the required operational capabilities of future weapon systems that meets the concept of 'effect-based synchronized operations.' However, analytical methods such as mathematical and statistical models make it difficult to analyze the effects of complex systems under nonlinear warfare. In this paper, we propose a combat simulation model that can simulate the concept of cooperative engagement between manned-unmanned combat entities based on wireless communications. First, we model unmanned combat entities, e.g., unmanned ground vehicles and drones, and manned combat entities, e.g., combatants and artillery, considering the capabilities required by the future ground system. We also simulate tactical behavior in which all entities perform their mission while sharing battlefield situation information through wireless communications. Finally we explore the feasibility of the proposed model by analyzing combat effectiveness such as target acquisition rate, remote control success rate, reconnaissance lead time, survival rate, and enemy's loss rate under a small-unit armor reconnaissance scenario. The proposed model is expected to be used in war-game combat experiments as well as analysis of the effects of manned-unmanned ground weapons.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.17-27
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• 2014

As the importance of Cooperative Engagement Capability and network-centric warfare has been dramatically increasing, it is necessary to develop distributed tracking systems. Under the development of distributed tracking systems, it requires tracking filters and data fusion theory for nonlinear systems. Therefore, in this paper, the problem of nonlinear track fusion, which is suitable for distributed networks, is formulated, four algorithms to solve the problem of nonlinear track fusion are introduced, and performance of introduced algorithms are analyzed. It is a main problem of nonlinear track fusion that cross-covarinaces among multiple platforms are unknown. Thus, in order to solve the problem, two techniques are introduced; a simplification technique and a approximation technique. The simplification technique that help to ignore cross-covariances includes two algorithms, i.e. the sample mean algorithm and the Millman formula algorithm, and the approximation technique to obtain approximated cross-covariances utilizes two approaches, by using analytical linearization and statistical linearization based on the sigma point approach. In simulations, BCS fusion is the most efficient scheme because it reduces RMSE by approximating cross-covariances with low complexity.

• Journal of the military operations research society of Korea
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• v.37 no.1
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• pp.87-97
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• 2011

In this paper, we propose a new close combat expert system to overcome the difference of combat damage assessments between combat units belong to their own model in Combined Arms Integrated Interoperability System(CAIIS) which will be deployed in the early future. When it happens to engage in a battle among combat units belong to their own model in CAIIS, the result of damage assessment is different severely. This is related to CAIIS's confidence and need to be overcome. We propose the expert system for close combat damage assessment with a decision tree. Simulation results show that the proposed expert system is valid well. Because the proposed expert system is made not as an independent system but as an inner module type of CAIIS, CAIIS will be simpler system than we expect. And we will hope to reduce the cost of CAIIS.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5C
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• pp.355-364
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• 2012

Target processing mechanisms are necessary to collect target information, real-time data fusion, and tactical environment recognition for cooperative engagement ability. Among these mechanisms, the target tracking starts from predicting state of speed, acceleration, and location by using sensors' measurements. However, it can be a problem to give the reliability because the measurements have a certain uncertainty. Thus, a technique which uses multiple sensors is needed to detect the target and increase the reliability. Also, data fusion technique is necessary to process the data which is provided from heterogeneous sensors for target tracking. In this paper, a target tracking algorithm is proposed based on probabilistic data association(PDA) by fusing radar and ESM sensor measurements. The radar sensor's azimuth and range measurements and the ESM sensor's bearing-only measurement are associated by the measurement fusion method. After gating associated measurements, state estimation of the target is performed by PDA filter. The simulation results show that the proposed algorithm provides improved estimation under linear and circular target motions.