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희박신호 기법을 이용한 초 분해능 지연시간 추정 알고리즘

Super-resolution Time Delay Estimation Algorithm using Sparse Signal Reconstruction Techniques

  • 박형래 (한국항공대학교 항공전자정보공학부)
  • Park, Hyung-Rae (Department of Electronics and Information Engineering, Korea Aerospace University)
  • 투고 : 2017.03.06
  • 심사 : 2017.07.26
  • 발행 : 2017.08.25

초록

본 논문에서는 희박신호 (sparse signal) 기법을 이용하여 대역확산 (spread spectrum) 신호의 지연시간을 추정하는 초 분해능 지연시간 추정 방식을 제안한다. 지금까지 대역확산 신호의 지연시간 추정은 코릴레이션 방식이 주로 이용되어 왔으나 이 방식은 신호들이 한 PN 칩(pseudo-noise chip) 이내의 시간 차로 입사하는 경우에는 지연시간을 정확히 추정할 수 없으며 보다 정확한 추정을 위해 코릴레이션 출력에 대한 추가적인 프로세싱이 필요하다. 최근 들어 희박 신호 (sparse signal) 알고리즘이 도래각 추정 분야에서 각광을 받고 있으며 그 중 SPICE 알고리즘이 가장 대표적이다. 따라서, 본 논문에서는 SPICE 알고리즘을 이용하는 초 분해능 지연시간 추정 알고리즘을 개발하고 ISO/IEC 24730-2.1 RTLS 시스템에 적용하여 MUSIC 알고리즘과 성능을 비교, 분석한다.

In this paper a super-resolution time delay estimation algorithm that estimates the time delays of spread spectrum signals using sparse signal reconstruction approach is introduced. So far, the correlation method has been mostly used to estimate the time delays of spread spectrum signals. However it fails to accurately estimate the time delays in the case where the signals are spaced within approximately 1 PN chip duration and a further processing should be applied to the correlation outputs in order to enhance the resolution capability. Recently sparse signal approaches attract much interest in the area of directions-of-arrival estimation, of which SPICE is the most representative. Thus we introduce a super-resolution time delay estimation algorithm based on the SPICE approach and compare its performance with that of MUSIC algorithm by applying them to the ISO/IEC 24730-2.1 RTLS system.

키워드

참고문헌

  1. International Standard ISO/IEC 24730-2, Information Theory-Real Time Locating System (RTLS), ISO/IEC, 2006.
  2. B. C. Bae and Y. S. Nam, "Localization using extended Kalman filter based on chirp spread spectrum ranging," J. KIEE, vol. 49-SC, no. 4, pp. 45-54, July, 2012.
  3. R. O. Schmidt, "Multiple emitter location and signal parameter estimation," IEEE Trans. Antennas Propagat., vol. AP-34, no. 3, pp. 276-280, Mar., 1986.
  4. R. Roy and T. Kailath, "ESPRIT -Estimation of signal parameters via rotational invariance techniques," IEEE Trans. ASSP, vol. 37, no. 7, pp. 984-995, Jul., 1989.
  5. J. H. Shin, S. I. Myong, E. Y. Chang, and H. R. Park, "A super-resolution time delay estimation algorithm for spread spectrum signals," J. KICS, vol. 37A, no. 2, pp. 119-127, Feb., 2012.
  6. J. H. Shin, H. Y. Park, and E. Y. Chang, "An ESPRIT-based super-resolution time delay estimation algorithm for real-time locating systems," J. KICS, vol. 38A, no. 4, pp. 310-317, Apr., 2013. https://doi.org/10.7840/kics.2013.38A.4.310
  7. F. X. Ge, D. Shen, Y. Peng, and V. O. K. Li, "Super-resolution time delay estimation in multipath environments," IEEE Trans. Circuits and Systems, vol. 54, no. 9, pp. 1977-1986, Sept., 2007. https://doi.org/10.1109/TCSI.2007.904693
  8. D. Malioutov, M. Cetin, and A. S. Willsky, "A sparse signal reconstruction perspective for source localization with sensor arrays," IEEE Trans. Signal Process., vol. 53, no. 8, pp. 3010-3022, Aug., 2005. https://doi.org/10.1109/TSP.2005.850882
  9. T. Yardibi, J. Li, P. Stoica, M. Xue, and A. Baggeroer, "Source localization and sensing: a nonparametric iterative adaptive approach based on weighted least squares," IEEE Trans. Aerosp. Electron. Syst., vol. 46, no. 1, pp. 425-443, Feb., 2010. https://doi.org/10.1109/TAES.2010.5417172
  10. P. Stoica, B. Prabhu, and J. Li, "SPICE: a sparse covariance-based estimation method for array processing," IEEE Trans. Signal Process., vol. 59, no. 2, pp. 629-638, Feb., 2011. https://doi.org/10.1109/TSP.2010.2090525
  11. P. Stoica, B. Prabhu, and J. Li, "New method of sparse parameter estimation in separable models and its use for spectral analysis of irregularly sampled data," IEEE Trans. Signal Process., vol. 59, no. 1, pp. 35-47, Jan., 2011. https://doi.org/10.1109/TSP.2010.2086452
  12. P. Stoica, D. Zachariah, and J. Li, "Weighted SPICE: a unifying approach for hyperparameter-free sparse estimation," Digit. Signal Process., vol. 33, no. 6, pp.1-12, Jun., 2014. https://doi.org/10.1016/j.dsp.2014.06.010
  13. T. J. Shan, M. Wax, and T. Kailath, "On spatial smoothing for direction-of-arrival estimation of coherent signals," IEEE Trans. ASSP, vol. 33, no. 8, pp. 806-811, Aug., 1985. https://doi.org/10.1109/TASSP.1985.1164649
  14. R. T. Williams, S. Prasad, A. K. Mahalanabis, and L. H. Sibul, "An improved spatial smoothing technique for bearing estimation in a multipath environment," IEEE Trans. ASSP, vol. 36, no. 4, pp. 425-432, 1988. https://doi.org/10.1109/29.1546
  15. H. R. Park and J. Li, "Sparse covariance-based high resolution time delay estimation for spread spectrum signals," Electron. Letters, vol. 51, no. 2, pp. 155-157, Feb., 2015. https://doi.org/10.1049/el.2014.4130
  16. H. R. Park and J. H. Shin, "Eigen-analysis based super-resolution time delay estimation algorithms for spread spectrum signals," J. KICS, vol. 38A, no. 12, pp. 1013-1020, Dec., 2013. https://doi.org/10.7840/kics.2013.38A.12.1013
  17. J. Sturm, "Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones," Optim. Methods Softw., vol. 11, no. 1, pp. 625-653, 1999. https://doi.org/10.1080/10556789908805766
  18. M. Wax and T. Kailath, "Detection of signals by information theoretic criterion," IEEE Trans. Acoust., Speech, Signal Processing, vol. 33, no. 2, pp. 387-392, 1985. https://doi.org/10.1109/TASSP.1985.1164557