• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.4
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• pp.5-12
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• 2008

There are some ways to improve precision of conventional munitions by low-cost method. 2D Course Correction Munition(CCM) is one of those ways, which is a 155mm projectile integrated with 2D Guidance Fuze(GF) instead of conventional fuze. 2D GF can correct the projectile trajectory and minimize range and deflection errors from its aimpoint using canard control. In this paper 2D CCM system concept is introduced and its course correction capability is analyzed using PRODAS.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.11-18
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• 2021

This paper shows the applicability of deep learning algorithm in predicting target position and getting correction value of impact point in order to improve the accuracy of naval gun firing. Predicting target position, the proposed model using LSTM model and RN structure is expected to be more accurate than existing method using kalman filter. Getting correction value of impact point, the another proposed model suggests a reinforcement model that manages factors which is related in ballistic calculation as data set, and learns using the data set. The model is expected to reduce error of naval gun firing. Combining two models, a ballistic calculation model for improving accuracy of naval gun firing based on deep learning algorithm was designed.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.9
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• pp.839-847
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• 2011

This paper proposes a new guidance law for increasing the lethality of munitions. The well known PNG (Proportional Navigation Guidance) is inadequate for the munitions because of some weaknesses. Even if the munition does not have the impact point error, the acceleration command is non zero because the line-of-sight changes at all times in flight. Therefore, we use a difference between a target and an impact point. This proposed guidance law is similar to PNG in the form, but this guidance law concentrates a correction rate of flight path angle instead of the LOS (Line of Sight) rate. The correction of flight path angle is defined as the amount of impact point error. This impact point error can be calculated by neural networks rapidly. Finally, we show that the simulation results prove the suitability of this law.