• The Journal of the Acoustical Society of Korea
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• v.35 no.6
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• pp.411-418
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• 2016

In this paper, we developed a new method estimating the location of ballistic missile launching using infrasound signals. Infrasound signal generated from the North Korea's ballistic missile launch was used as source data and its signal was recorded at KMA (Korea Meteorological Administration) infrasound stations located at Cheorwon and Yanggu. Time-frequency analysis, TDOA (Time Delay of Arrival) method and spherical trigonometry were applied for data processing of signal recorded and occurrence location detection. We could confirm the outstanding performance of the algorithm estimating source location which was only 3 km apart from the actual launching site.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.1
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• pp.18-27
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• 2020

In order to estimate the launch point of a high-speed vehicle, predicting the various characteristics of the vehicle's movement, such as drag and thrust, must be preceded by the estimation. To predict the various parameters regarding the vehicle's characteristics, we build the IMM filter specialized in predicting the parameters of the post-launch phase based on flight dynamics. Then we estimate the launch point of the high-speed vehicle using Inverse Dynamics. In addition, we assume the arbitrary error level of the radar for accuracy of the prediction. We organize multiple-dimensioned IMM structures, and figure out the optimal value of parameters by comparing the various IMM structures. After deriving the optimal value of parameters, we verify the launch point estimation error under certain error level.

• Journal of IKEEE
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• v.24 no.4
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• pp.921-930
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• 2020

The Counter-battery radar estimates the origin and impact point of the artillery by tracking the trajectory of the shell. In addition, it has the ability of identifying the type of weapon. Depending on the position between the shell and the radar, the detected signals appear differently. This has ambiguity to distinguish the type of shells. This paper compares fuzzy logic and artificial intelligence, which classifies type of shell using the parameter of signal processing step. According to the research result, artificial intelligence can improve identification rate of type of shell. The data used in the experiment was obtained from a live fire detection test.