• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.3
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• pp.197-204
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• 2021

If a failure or an abnormal maneuver occurs during the flight test of a missile, the missile is deliberately self-destructed so as not to continue the flight. At this time, debris are produced and it is important to estimate the impact area in real-time whether it is out of the safety area. In this paper, we propose a method to estimate the debris dispersion area and falling time in real-time using a Fully-Connected Neural Network (FCNN). We applied the Unscented Transform (UT) to generate a large amount of training data. UT parameters were selected by comparing with Monte-Carlo (MC) simulation to secure reliability. Also, we analyzed the performance of the proposed method by comparing the estimation result of MC.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.6
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• pp.745-753
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• 2019

Recently, with the development of long-range / high-altitude guided weapon system for defense against ballistic missile, test range and firing altitude for guided weapons are increasing. Due to the increase in the test range and the intercepting altitude, it is expected to increase the range of safety area required for the firing test. Comparing to the foreign countries which have many desert or non-residence, in the domestic circumstances where the population is concentrated and distributed, it is more important to predict the falling area and to set the safety area for safely carry out the long-range / high-altitude intercept test. In this paper, we consider the following three points. The first is the booster fall trajectory modeling, the second is the shroud fall trajectory modeling, and finally, the debris dispersion modeling for the missile intercept. Especially, the AUTODYN model was used to predict debris falling area which produced in the high-speed guided missile intercepting test.