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      Basis pursuit denoising을 사용한 두 수신기 간 시간 지연 추정 알고리즘 = Time delay estimation between two receivers using basis pursuit denoising

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      https://www.riss.kr/link?id=A103422085

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      다국어 초록 (Multilingual Abstract)

      Many methods have been studied to estimate the time delay between incoming signals to two receivers. In the case of the method based on the channel estimation technique, the relative delay between the input signals of the two receivers is estimated as an impulse response of the channel between the two signals. In this case, the characteristic of the channel has sparsity. Most of the existing methods do not take advantage of the channel sparseness. In this paper, we propose a time delay estimation method using BPD (Basis Pursuit Denoising) optimization technique, which is one of the sparse signal optimization methods, in order to utilize the channel sparseness. Compared with the existing GCC (Generalized Cross Correlation) method, adaptive eigen decomposition method and RZA-LMS (Reweighted Zero-Attracting Least Mean Square), the proposed method shows that it can mitigate the threshold phenomenon even under a white Gaussian source, a colored signal source and oceanic mammal sound source.
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      Many methods have been studied to estimate the time delay between incoming signals to two receivers. In the case of the method based on the channel estimation technique, the relative delay between the input signals of the two receivers is estimated as...

      Many methods have been studied to estimate the time delay between incoming signals to two receivers. In the case of the method based on the channel estimation technique, the relative delay between the input signals of the two receivers is estimated as an impulse response of the channel between the two signals. In this case, the characteristic of the channel has sparsity. Most of the existing methods do not take advantage of the channel sparseness. In this paper, we propose a time delay estimation method using BPD (Basis Pursuit Denoising) optimization technique, which is one of the sparse signal optimization methods, in order to utilize the channel sparseness. Compared with the existing GCC (Generalized Cross Correlation) method, adaptive eigen decomposition method and RZA-LMS (Reweighted Zero-Attracting Least Mean Square), the proposed method shows that it can mitigate the threshold phenomenon even under a white Gaussian source, a colored signal source and oceanic mammal sound source.

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      참고문헌 (Reference)

      1 신준호, "실시간 위치 추적 시스템을 위한 ESPRIT 기반의 초 분해능 지연 시간 추정 알고리즘" 한국통신학회 38 (38): 310-317, 2013

      2 신준호, "대역 확산 신호를 위한 지연 시간 추정 알고리즘" 한국통신학회 37 (37): 119-127, 2012

      3 P. L. Feintuch, "Time delay estimation using the LMS adaptive lter-dynamic behaviour" 29 (29): 571-576, 1981

      4 "The MOSEK Optimization Tools Version 2.5. User’s Manual and Reference"

      5 Y. Chen, "Sparse LMS for system identification" 3125-3128, 2009

      6 "SPGL1, a solver for large scale sparse reconstruction"

      7 E. Candès, "Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information" 52 : 489-509, 2006

      8 R. Tibshirani, "Regression shrinkage and selection via the LASSO" 21 : 279-289, 1996

      9 H. Lee, "Introduction to compressive sensing" 38 : 19-30, 2011

      10 R. Baraniuk, "Compressive sensing" 25 : 21-30, 2007

      1 신준호, "실시간 위치 추적 시스템을 위한 ESPRIT 기반의 초 분해능 지연 시간 추정 알고리즘" 한국통신학회 38 (38): 310-317, 2013

      2 신준호, "대역 확산 신호를 위한 지연 시간 추정 알고리즘" 한국통신학회 37 (37): 119-127, 2012

      3 P. L. Feintuch, "Time delay estimation using the LMS adaptive lter-dynamic behaviour" 29 (29): 571-576, 1981

      4 "The MOSEK Optimization Tools Version 2.5. User’s Manual and Reference"

      5 Y. Chen, "Sparse LMS for system identification" 3125-3128, 2009

      6 "SPGL1, a solver for large scale sparse reconstruction"

      7 E. Candès, "Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information" 52 : 489-509, 2006

      8 R. Tibshirani, "Regression shrinkage and selection via the LASSO" 21 : 279-289, 1996

      9 H. Lee, "Introduction to compressive sensing" 38 : 19-30, 2011

      10 R. Baraniuk, "Compressive sensing" 25 : 21-30, 2007

      11 G. Carter, "Coherence and Time Delay Estimation: An Applied Tutorial for Research, Development, Test and Evaluation Engineers" IEEE press 1-28, 1993

      12 E. Tiana-Roig, "Beamforming with a circular microphone array for localization of environmental noise sources" 128 : 3535-3542, 2010

      13 임준석, "An Adaptive Time Delay Estimation Method Based on Canonical Correlation Analysis" 한국음향학회 32 (32): 548-555, 2013

      14 K. C. Ho, "Adaptive time-delay estimation in nonstationary signal and=or noise power environments" 41 (41): 2289-2299, 1993

      15 J. Lim, "Adaptive time delay estimation using l1 constraint" 32 (32): 272-275, 2013

      16 S. R. Dooley, "Adaptive subsample time delay estimation using Lagrange interpolators" 6 (6): 65-57, 1999

      17 J. Benesty, "Adaptive eigenvalue decomposition algorithm for passive acoustic source localization" 107 : 384-391, 2000

      18 H. C. So, "A new algorithm for explicit adaptation of time delay" 42 (42): 1816-1820, 1994

      19 "1st International Workshops on the Detection and Localization of Marine Mammals Using Passive Acoustics"

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2026 평가예정 재인증평가 신청대상 (재인증)
      2020-01-01 평가 등재학술지 유지 (재인증) KCI등재
      2017-01-01 평가 등재학술지 유지 (계속평가) KCI등재
      2013-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2010-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2008-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2006-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2004-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2001-07-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      1999-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.23 0.23 0.22
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.2 0.18 0.398 0.07
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