• Journal of the Korea Society for Simulation
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• v.28 no.2
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• pp.89-95
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• 2019

This paper introduces the simulation concepts and technical approach of underwater weapon system performance analysis simulator, especially focused on probabilistic target detection concepts. We calculated the signal excess (SE) value using SONAR equation, then derived the probability density function(PDF) for target presence($H_1$) or absence($H_0$) cases, respectively. With the Neyman-Pearson detector criterion, we got the probability of detection($P_D$) while satisfying the given probability of false alarm($P_{FA}$). At every instance of simulation, target detection is decided in the probabilistic perspective. With the proposed detection implementation, we improved the model fidelity so that it could support the tactical decision during the operation.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.258-266
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• 2013

We propose adaptive detection algorithm to reduce a false alarm by considering the characteristics of the random noise on the detection system based on a seismic sensor. The proposed algorithm consists of the first step detection using kernel function and the second step detection using detection classes. Kernel function of the first step detection is obtained from the threshold of the Neyman-Pearon decision criterion using the probability density functions varied along the noise from the measured signal. The second step detector consists of 4 step detection class by calculating the occupancy time of the footstep using the first detected samples. In order to verify performance of the proposed algorithm, the detection of the footsteps using measured signal of targets (walking and running) are performed experimentally. The detection results are compared with a fixed threshold detector. The first step detection result has the high detection performance of 95% up to 10m area. Also, the false alarm probability is decreased from 40% to 20% when it is compared with the fixed threshold detector. By applying the detection class(second step detector), it is greatly reduced to less than 4%.