제어로봇시스템학회논문지 (Journal of Institute of Control, Robotics and Systems)

  • 제22권7호
  • /
  • Pages.536-542
  • /
  • 2016
  • /
  • 1976-5622(pISSN)
  • /
  • 2233-4335(eISSN)
제어로봇시스템학회 (Institute of Control, Robotics and Systems)
권호 표지
DOI QR코드

DOI QR Code

수동센서를 이용한 효율적인 표적추적을 위한 적응적 자원관리 알고리듬 연구

Efficient Target Tracking with Adaptive Resource Management using a Passive Sensor

  • 김우찬 (한양대학교 전자시스템공학과) ;
  • 이해호 (LIG Nex1 전투체계연구센터) ;
  • 안명환 (LIG Nex1 전투체계연구센터) ;
  • 이범직 (대우조선해양 전투체계파트) ;
  • 송택렬 (한양대학교 전자시스템공학과)
  • Kim, Woo Chan (Electronic Systems Engineering, Hanyang University) ;
  • Lee, Haeho (Combat System R&D Lab, LIG Nex1 Co., Ltd.) ;
  • Ahn, Myonghwan (Combat System R&D Lab, LIG Nex1 Co., Ltd.) ;
  • Lee, Bum Jik (Submarine Combat System Part, Daewoo Shipbuilding & Marine Engineering) ;
  • Song, Taek Lyul (Electronic Systems Engineering, Hanyang University)
  • 투고 : 2016.03.08
  • 심사 : 2016.06.13
  • 발행 : 2016.07.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

To enhance tracking efficiency, a target-tracking filter with a resource management algorithm is required. One of the resource management algorithms chooses or evaluates the proper sampling time using cost functions which are related to the target tracking filter. We propose a resource management algorithm for bearing only tracking environments. Since the tracking performance depends on the system observability, the bearing-only tracking is one of challenging target-tracking fields. The proposed algorithm provides the adaptive sampling time using the variation rate of the error covariance matrix from the target-tracking filter. The simulation verifies the efficiency performance of the proposed algorithm.

키워드

참고문헌

  1. T. L. Song, "Multi-target tracking filters and data association: A survey," Journal of Institute of Control, Robotics and Systems, vol. 20, no. 3, pp. 313-322, 2014. https://doi.org/10.5302/J.ICROS.2014.14.9018
  2. B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter, Artech House, 2004.
  3. T. L. Song, "Observability of target tracking with bearings only measurements," - IEEE Trans. on Aerospace and Electronic System, vol. 32, no. 4, pp. 1468-1472, 1996. https://doi.org/10.1109/7.543868
  4. V. J. Aidala, "Behavior of the Kalman filter applied to bearing only target motion analysis," Advances in - Passive Tracking 1, 1976.
  5. D. Musicki, R. Kaune, and W. Koch, "Mobile emitter geolocation and tracking using TDOA and FDOA measurements," IEEE Trans. on Signal Processing, vol. 58, no. 3, pp. 1863-1874, 2010. https://doi.org/10.1109/TSP.2009.2037075
  6. T. Kirubarajan, Y. Bar-Shalom, W. D. Blair, and S. A. Watson, "IMMPDAF for radar management and tracking benchmark with ECM," IEEE Trans. on Aerospace and Electronic System, vol. 34, no. 4, 1998.
  7. S. L. Coetzee, K. Woodbridge, and C. H. Baker, "Multifunction radar resource management tracking using tracking optimization," Proc. of the International Radar Conference, 2003.
  8. G. V. Keuk and S. S. Blackman, "On phased-array radar tracking and parameter control," IEEE Trans. on Aerospace and Electronic System, vol. 29, no. 1, Jan. 1993.
  9. H. J. Shin, S. M. Hong, and D. H. Hong, "Adaptiveupdate-rate target tracking for phased-array radar," IEE Proc. Radar, Sonar, and Navigation, vol. 142, no. 3, 1995.
  10. K. White, J. Williams, and P. Hoffensetz, "Radar sensor management for detection and tracking," Information Fusion, 11th International Conference on, 2008.
  11. G. Davidson, "Cooperation between tracking and radar resource management," IET International Conference on Radar System, 2007.
  12. T. L. Song and D. Musicki, "Target existence based resource allocation," IEEE Trans. on Signal Processing, vol. 58, no. 9, 2010.
  13. U. Steimel, "About some benefits of a cross bearing TMA (XTMA)," GI Jahrestagung (2), vol. 110 of LNI, pp. 110-114, 2007.
  14. E. Fogel and M. Gavish, "Nth order dynamics target - observability from angle measurements," IEEE Trans. on Aerospace and Electronic System, vol. 24, no. 3, May 1988.
  15. D. Mušicki, T. L. Song, W. C. Kim, and D. Nesic, "Non-linear automatic target tracking in clutter using dynamic Gaussian mixture," IET Radar, Sonar and Navigation, vol. 6, no. 9, pp. 937-944, 2012. https://doi.org/10.1049/iet-rsn.2012.0093
  16. D. Musicki, "Bearings only single-sensor target tracking using Gaussian," Automatica, vol. 45, no. 9, pp. 2088-2092, 2009. https://doi.org/10.1016/j.automatica.2009.05.008
  17. W. C. Kim and T. L. Song, "Adaptive mode transition matrix for variable sampling time," Proc. of Information Fusion (FUSION), 2014 17th International Conference on, 2014.
  18. T. L. Song and J. Speyer, "A stochastic analysis of a modified gain extended Kalman filter with applications to estimation with bearings only measurements," IEEE Transactions on Automatic Control, vol. 30, no. 10, pp. 940-949, 1985. https://doi.org/10.1109/TAC.1985.1103821
  19. E. A. Wan and R. Van Der Merwe, "The unscented Kalman filter for nonlinear estimation," Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC., pp. 153-158, 2000.
  20. I. Arasaratnam and S. Haykin, "Cubature Kalman filters," IEEE Transactions on Automatic Control, vol. 54, no. 6, 2009