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A Study on Resource Allocations of Multi Function Radar in a Warship

함정의 다기능레이더(MFR) 자원할당 방안에 관한 연구

  • 박영만 (해군사관학교 국방경영학과) ;
  • 이진호 (해군사관학교 국방경영학과) ;
  • 조현진 (해군사관학교 전기전자공학과) ;
  • 박경주 (해군사관학교 해양학과) ;
  • 김하철 (해군사관학교 전기전자공학과) ;
  • 임요준 (해군사관학교 군사학과) ;
  • 김해근 (해군사관학교 작전학과) ;
  • 이호철 (국방과학연구소 함정전투체계개발단) ;
  • 정석문
  • Received : 2018.12.31
  • Accepted : 2019.03.20
  • Published : 2019.03.31

Abstract

A warship equipped with Multi Function Radar(MFR) performs operations by evaluating the degree of threats based on threats' symptom and allocating the resource of MFR to the corresponding threats. This study suggests a simulation-based approach and greedy algorithm in order to effectively allocate the resource of an MFR for warships, and compares these two approaches. As a detection probability function depending on the amount of allocations to each threat, we consider linear and exponential functions. Experimental results show that both the simulation-based approach and greedy algorithm allocate resource similarly to the randomly generated threats, and the greedy algorithm outperforms the simulation-based approach in terms of computational perspective. For a various cases of threats, we analyze the results of MFR resource allocation using the greedy algorithm.

다기능레이더(MFR)를 장착한 함정의 작전수행은 적의 위협에 대한 징후를 바탕으로 위협의 정도를 판단하고 이를 바탕으로 MFR 자원을 위협별로 할당하는 것으로 작전을 시작한다. 본 연구는 MFR 탐지체계를 가진 함정의 임무 시작 시 필요한 위협별 MFR 자원할당 문제에 대하여 시뮬레이션을 이용한 기법과 Greedy 기법을 이용한 MFR 자원할당 방안을 제시하여 그 결과를 비교분석하였다. 분석시 자원할당에 따른 탐지확률 함수가 선형인 경우와 지수형인 경우를 고려하여 실험을 수행하였다. 실험 결과 시뮬레이션 기법과 Greedy기법의 결과는 서로 비슷한 자원할당 결과를 보여주고 있으며, Greedy 기법은 시뮬레이션 기법에 비하여 그 수행시간이 아주 짧아 실제 임무 수행 시에 이용 가능한 기법으로 판단된다. 여러 가지 위협의 정도에 대해 Greedy 기법을 이용하여 MFR 자원할당 결과를 분석하였다.

Keywords

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Fig. 1. A panels of MFR(Bridger & Ruiz, 2006)

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Fig. 2. The detection probability function of resourceallocation of MFR

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Fig. 3. Simulation algorithm for MFR resource allocation

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Fig. 4. Greedy algorithm for MFR resource allocation

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Fig. 5-A. The result of MFR resource allocation for different type of threats(Pa=0.1, Pb=0.3)

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Fig. 5-B. The result of MFR resource allocation for different type of threats(Pa=0.3, Pb=0.3)

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Fig. 5-C. The result of MFR resource allocation for different type of threats(Pa=0.5, Pb=0.3)

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Fig. 5-D. The result of MFR resource allocation for different type of threats(Pa=0.7, Pb=0.3)

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Fig. 5-E. The result of MFR resource allocation for different type of threats(Pa=0.9, Pb=0.3)

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Fig. 6-A. The result of MFR resource allocation for different type of threats(Pa=0.5, Pb=0.1)

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Fig. 6-B. The result of MFR resource allocation for different type of threats(Pa=0.5, Pb=0.3)

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Fig. 6-C. The result of MFR resource allocation for different type of threats(Pa=0.5, Pb=0.5)

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Fig. 6-D. The result of MFR resource allocation for different type of threats(Pa=0.5, Pb=0.7)

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Fig. 6-E. The result of MFR resource allocation for different type of threats(Pa=0.5, Pb=0.9)

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Fig. 7-A. The result of MFR resource allocation for different type of threats(Pa=0.1, Pb=0.3)

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Fig. 7-B. The result of MFR resource allocation for different type of threats(Pa=0.3, Pb=0.3)

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Fig. 7-C. The result of MFR resource allocation for different type of threats(Pa=0.5, Pb=0.3)

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Fig. 7-D. The result of MFR resource allocation for different type of threats(Pa=0.7, Pb=0.3)

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Fig. 7-E. The result of MFR resource allocation for different type of threats(Pa=0.9, Pb=0.3)

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Fig. 8-A. The result of MFR resource allocation for different type of threats(Pa=0.5, Pb=0.1)

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Fig. 8-B. The result of MFR resource allocation for different type of threats(Pa=0.5, Pb=0.3)

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Fig. 8-C. The result of MFR resource allocation for different type of threats(Pa=0.5, Pb=0.5)

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Fig. 8-D. The result of MFR resource allocation for different type of threats(Pa=0.5, Pb=0.7)

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Fig. 8-E. The result of MFR resource allocation for different type of threats(Pa=0.5, Pb=0.9)

Table 1. Parameters of MFR resource allocation

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Table 2. A comparison of the result of MFR resource allocation for different type of threats in case of probability function(1)

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Table 3. The result of MFR resource allocation for different type of threats in case of probability function (2)

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References

  1. Agency for Defense Development (2018), Ulsan Batch-III Multi Function Radar, www.jdep.plani. co.kr.
  2. Bridger, W. W., M. D. Ruiz (2006), Total ownership cost reduction case study: AEGIS radar phase shifters, Technical Report, Naval Postgraduate School.
  3. Cormen, T. H., Leiserson, C. E., Rivest, R. L. and Stein, C., (1990), Introduction to Algorithms, MIT Press.
  4. DAMIR, FY 2017 President's Budget (2016), Selected Acquisition Report : DDG 1000 Zumwalt Class Destroyer, Defense Acquisition Management Information Retrieval.
  5. Hanwhasystem, (2018) https://www.hanwhasystems.com/front/pr/newsView.do (Accessed Dec 30. 2018).
  6. Jeong, B.-M., D.-S. Jang, H.-L. Choi, J.-E. Roh, (2012), "Decision of track update-rate for efficient usage of resource on multi-functiton radar", In Proceedings of the Korean Society for Aeronautical and Space Science, 307-311.
  7. Jeong, K.-W. (2014), 교전용 장거리 다기능 레이다 기술 동향 및 발전방향, 한국전자파학회지 전자파기술, 25(2), 21-29.
  8. Jeong, N.-H., S.-H. Lee, M.-S. Kang, C.-W. Gu, C.-H. Kim, K.-T. Kim (2018), Target Prioritization for Multi-Function Radar using Artificial Neural Network Based on Steepest Descent Method, The Journal of Institute of Electromagnetic Engineering and Science, 29(1), 68-76.
  9. Jeong, S.-J., D.-S. Jang, H.-L. Choi, J.-H. Yang (2014) "Task Scheduling and Multiple Operation Analysis of Multi-Function Radars", Journal of The Korean Society for Aeronautical and Space Science, 42(3), 254-262. https://doi.org/10.5139/JKSAS.2014.42.3.254
  10. Ko, J.-Y., S.-S. Park, H.-L. Choi, J.-M. Ahn, S.-W. Lee, D.-H. Lee, J.-S. Yoon (2017), Implementation of Airborne Multi-Function Radar Including Attitude Maneuvering, The Journal of Institute of Electromagnetic Engineering and Science, 28(3), 225-236.
  11. Kim, H.-J., J.-Y. Park, D.-H. Kim, S.-J. Kim (2013) "A Study of Fuzzy Inference System Based Task Prioritization for the Improvement of Tracking Performance in Multi-Function Radar", The Journal of Korean Institute of Electromagnetic Engineering and Science, 24(2), 198-206. https://doi.org/10.5515/KJKIEES.2013.24.2.198
  12. Lee, J., Y.-J. Lim, Y.-S. Kim, H. Cho, Y.-M. Park, H.-C. Kim, K. Park, S.-M. Chung (2018), "A Study on MOE for Analyzing Effectiveness of a Surface Warship Detection System", In Proceedings of the 2018 Fall Conference on the Korea Institute of Military Science and Technology, 459-460.
  13. Lee, J.-H., S.-G. Lee, D.-S. Park, B.-L. Cho (2014) "Effective Beam Structure for Multi-Target Detection and Tracking in the Active Electrically Scanned Array Radar", The Journal of Korean Institute of Electromagnetic Engineering and Science, 25(10), 1069-1076. https://doi.org/10.5515/KJKIEES.2014.25.10.1069
  14. Naval Technology(2018), Holland Class Offshore Patrol Vessels, www.naval-technology.com.
  15. Park, J.-W., D.-S. Jang, H.-L. Choi, M.-J. Tahk, J.-E. Roh, S.-J. Kim (2013) "Integrated Simulator of Airborne Multi-function Radar Resource Manager and Environment Model", Journal of The Korean Society for Aeronautical and Space Science, 41(7), 577-587. https://doi.org/10.5139/JKSAS.2013.41.7.577
  16. Wagner, D., H. Mylander, W. Charles (1999), Naval Operations Analysis, Naval Institute Press.