• 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.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.107-110
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• 1991

When the noise has both additive and signal-dependent components, locally optimum detector test statistics are obtained for detection of weak composite signals using the generalized Neyman-Pearson lemma. In order to consider the non-additive noise as well as purely-additive noise, a generalized observation model is used in this paper. The locally optimum detector test statistics are derived for several different cases according to the relative strengths of the known signal component, the random signal component, and the signal-dependent noise component. Schematic diagrams of the locally optimum detector structures are also included.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8C
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• pp.709-712
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• 2010

In this paper, the two sample locally optimum rank detector is obtained in the weakly dependent noise with non-zero temporal correlation between noise observations. The test statistic of the locally optimum rank detector is derived from the Neyman-Pearson lemma suitable for the two sample observation models, where it is assumed that reference observations are available in addition to regular observations. Two-sample locally optimum rank detecter shows the same performance with the one-sample locally optimum rank detector asymptotically. The structure of the two-sample rank detector is simpler than that of the one-sample rank detector because the sign statistic is not processed separately.

• 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%.