• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.3
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• pp.67-75
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• 2008

In this paper, we analyze the performance of Follower Noise Jamming(FNJ) considering three practical tracking parameters such as tracking bandwidth, tracking time and tracking success probability. The performance of FNJ is compared with that of Partial-Band Noise Jamming(PBNJ) in terms of Symbol Error Rate(SER) at the communication receiver under the assumed typical operation model. It is observed that the performance of FNJ is non-linearly dependent on the tracking bandwidth, the tracking time and the tracking success probability. As we can easily expect, it is also observed that the performance of FNJ is better than that of the PBNJ. Finally, it is shown that, for a fixed tracking bandwidth, the combinations of the required tracking time and the tracking success probability which satisfy a certain required SER.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.657-659
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• 1998

In this paper we discuss an algorithm to discriminate a target under track against multiple acoustic counter-measure (ACM) sources, based on sequential testings of multiple hypotheses. The ACM sources are separated from the target under track and generate, while drifting, measurements with false range and Doppler information. The purpose of the ACM is to mislead the target tracking and to help the true target evade a pursuer. The proposed algorithm uses as a test statistic a function of both the sequences of processed waveform signature and the innovation sequences from extended Kalman filters to estimate the target dynamics and the drifting positions of the ACM sources. Numerical experiments on various scenarios show that the proposed algorithm discriminates the target faster with a higher probability of success than the algorithm using only the innovation sequences from extended Kalman filters.

• Journal of the Korean Society of Systems Engineering
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• v.10 no.2
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• pp.71-79
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• 2014

To enhance success probability of a system development project, its overall risk level should be minimized through systematically managing schedules, costs, and technical performances. However, Attempts to manage technical performance compared to numerous efforts to control costs and schedules in such projects are deficient. Particularly, a space launch vehicle, a large complex system, development project is much less likely to meet its technical performance objectives due to its technological difficulty, along with schedule delay and cost overrun. The technical performance management (TPM) is a method for tracking and managing technical progress in order to achieve technical performance targets within schedule and budget. In this paper, we investigate applications of the TPM in several space launch vehicle development projects. Then we propose and validate the TPM process to achieve a successful mission in such projects.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.6-14
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• 1996

In this paper we present an algorithm to discriminate an underwater target under track against an acoustic counter-measure(ACM) source, based on a sequential hypothesis test. The ACM source is separated from the target under track and generates, while drifting, measurements with false range and Doppler information. The purpose of the ACM is to mislead the target-tracking and to help the true target evade a pursuer. The algorithm uses as a test statistic a function of the innovation sequences from extended Kalman filters to estimate the target dynamics and the drifting position of the ACM source. Numerical experiments on various scenarios show that the proposed algorithm discriminates the target against an ACM source very fast with a high probability of success.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.721-728
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• 2005

This paper suggests effective object detection system for moving objects with specified color and motion information. The proposed detection system includes the object extraction and definition process which uses MCN(Multiple Color Normalization) and MCWUPC(Moving Color Weighted Unmatched Pixel Count) computation to decide the existence of moving object and object segmentation technique using signature information is used to exactly extract the objects with high probability. Finally, real time detection system is implemented to verify the effectiveness of the technique and experiments show that the success rate of object tracking is more than $89\%$ of total 120 image frames.