Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Improved Target Localization Using Line Fitting in Distributed Sensor Network of Detection-Only Sensor

탐지만 가능한 센서로 구성된 분산센서망에서 라인피팅을 이용한 표적위치 추정기법의 성능향상

  • Ryu, Chang Soo (Div. of Electronics & Information Engineering, Yeungnam College of Science & Technology)
  • 류창수 (영남이공대학교 전자정보계열)
  • Received : 2012.03.07
  • Published : 2012.09.25
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, a target detection based on a distributed sensor network has been much studied in active sonar. Zhou et al. proposed a target localization method using line fitting based on a distributed sensor network which consists of low complexity sensors that only report binary detection results. This method has three advantages relative to ML estimator. First, there is no need to estimate propagation model parameters. Second, the computation is simple. Third, it only use sensors with "detection", which implies less data to be collected by data processing center. However, this method has larger target localization error than the ML estimator. In this paper, a target localization method which modifies Zhou's method is proposed for reducing the localization error. The modified method shows the performance improvement that the target localization error is reduced by 40.7% to Zhou's method in the point of RMSE.

최근에 능동소나 분야에서 분산센서망을 이용하여 표적을 탐지하는 연구가 많이 이루어지고 있다. Zhou 등은 표적의 탐지만 가능한 간단한 구조의 센서들로 구성된 분산센서망에서 라인피팅(line fitting)을 이용하여 표적의 위치를 추정하는 기법을 제안하였다. 이 기법은 ML(Maximum Likelihood) 기법에 비해 3가지 장점을 가지고 있다. 첫째는, 음파전달 모델에 대한 파라미터들을 추정할 필요가 없으며, 둘째는 연산량이 적다. 셋째는 분산센서망에서 센서들이 표적을 탐지했다는 정보만 이용하기 때문에 데이터처리 센터는 적은 량의 데이터만 수집하여도 된다. 그러나 이 기법은 표적의 위치 추정오차가 크다는 단점을 가지고 있다. 본 논문에서는 Zhou의 기법이 가지는 큰 위치 추정오차를 줄이기 위하여 Zhou가 제안한 표적위치 추정기법을 수정하였다. 본 논문에서 제안한 수정된 표적위치 추정기법은 Zhou의 기법보다 40.7%의 위치 추정오차가 감소하는 성능향상을 보였다.

Keywords

References

  1. John R. Benedict, Jr, "Future Underwater Warfare perspectives," Johns Hopkins APL Technical Digest, vol. 21, no. 2, pp. 269-279, 2000.
  2. J. Xiao, A. Ribeiro, Z. Luo and G. B. Ginnakis, "Distribution compression-estimation using wireless sensor networks," IEEE Signal Processing Mag., vol. 23, no. 4, pp. 27-41, Jul. 2006. https://doi.org/10.1109/MSP.2006.1657815
  3. Biao Chen, Lang Tong, and Pramod K. Varshney, "Channel-aware distributed Detection in wireless sensor networks," IEEE Signal Processing Mag., vol. 23, no. 4, pp. 16-26, Jul. 2006.
  4. M. Cetin, L. Chen, J. W. Fisher III, A. T. Ihler, R. L. Moses, M. J. Wainwright, and A. S. Willsky, "Distributed fusion in sensor networks," IEEE Signal Processing Mag., vol. 23, no. 4, pp. 42-55, Jul. 2006. https://doi.org/10.1109/MSP.2006.1657816
  5. J. B. Predd, S. R. Kulkarni, and H. V. Poor, "Distributed Learning in wireless sensor networks," IEEE Signal Processing Mag., vol. 23, no. 4, pp. 56-69, Jul. 2006. https://doi.org/10.1109/MSP.2006.1657817
  6. M. Gastpar, M. Vetterli and P. L. Dragotti "Sensing reality and communicating bits : a dangerous liaison," IEEE Signal Processing Mag., vol. 23, no. 4, pp. 70-83, Jul. 2006.
  7. Q. Zhao, A. Swami, and L. Tong, "The interplay between signal processing and networking in sensor networks," IEEE Signal Processing Mag., vol. 23, no. 4, pp. 84-93, Jul. 2006.
  8. S. Zhou and P. Wilett, "Submarine location estimation via a network of detection-only sensors," IEEE Trans. on signal processing, vol. 55, no. 6, June 2007 pp. 3104-3115. https://doi.org/10.1109/TSP.2007.893970
  9. Xiaohong Sheng and Yu-Hen Hu, "Maximum likelihood multiple-source localization using acoustic energy measurements with wireless sensor networks," IEEE Transaction on Signal Processing, vol. 53, no. 1, Jan., 2005.
  10. J. Chamberland and V. V. Veeravalli, "Asymptotic results for detection in power constrained wireless sensor networks," in Proc. IEEE Int. Symp. Inform. Theory (ISIT'2003), Yokohama, Japan, June-July 2003, p. 230.
  11. J. Chamberland and V. V. Veeravalli, "The impact of fading on decentralized detection in power constrained wireless sensor networks," in Proc. IEEE Int. Conf. Acoust. Speech, Signal Processing (ICASSP'2004), Montreal, Canada, vol. 3, pp. 837-840, May 2004.
  12. J. Chamberland and V. V. Veeravalli, "Asymptotic results for decentralized detection in power constrained wireless sensor networks," IEEE J. Select. Areas Commun., vol. 22, pp. 1007-1015, Aug. 2004. https://doi.org/10.1109/JSAC.2004.830894
  13. J. Xiao and Z-Q. Luo, "Decentralized detection in a bandwidth constrained sensor network," in Proc. IEEE GLOBECOM 2004, Dallas, TX, vol. 1, pp. 123-128, Nov. 2004.
  14. J. Xiao and Z-Q. Luo, "Universal decentralized detection in a bandwidth constrained sensor network," IEEE Trans. Signal Processing, vol. 53, pp. 2617-2624, Aug. 2005. https://doi.org/10.1109/TSP.2005.850334
  15. S. A. Aldosari and J. M. F. Moura, "Detection in decentralized sensor networks," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, Montreal, Canada, vol. 2, pp. 277-280, May 2004.

Cited by

  1. 확장된 깊이-우선 탐색 알고리듬을 적용한 다중표적 위치 좌표 추정 기법 vol.c38, pp.12, 2012, https://doi.org/10.7840/kics.2013.38c.12.1207