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Time delay estimation between two receivers using basis pursuit denoising

Basis pursuit denoising을 사용한 두 수신기 간 시간 지연 추정 알고리즘

Jun-Seok Lim1*
,
MyoungJun Cheong2
임 준석1*
,
정 명준2
1세종대학교 전자정보통신공학과
2국방과학연구소
*교신저자. *Corresponding Author. 
31 July 2017. pp. 285-291
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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.

Keywords
Time delay estimation
Sparse signal processing
L1-norm
GCC (Generalized Cross Correlation)

두 개 수신기에 들어오는 신호 간의 시간 지연 값을 추정하기 위한 방법들이 연구되고 있다. 그중에서 채널 추정 기법을 기반으로 한 방법의 경우는 두 수신기의 입력 신호간의 상대적인 지연을 채널의 임펄스 응답처럼 추정하 는 방법이다. 이 경우에는 해당 채널의 특성이 희소 채널의 특성을 가지고 있다. 기존의 방법들은 채널의 희소성을 이용 하지 못하고 있는 방법이 대부분이다. 본 논문에서는 채널의 희소성을 이용하기 위하여 희소 신호 최적화 방법의 하나 인 BPD(Basis Pursuit Denoising) 최적화 기법을 사용한 시간 지연 추정 방법을 제안한다. 제안한 방법을 기존의 일반 상호 상관(Generalized Cross Correlation, GCC) 방법과 적응 소유치 분해법 및 희소 신호 추정법의 일종인 RZA-LMS(Reweighted Zero-Attracting Least Mean Square)들과 비교하여, 백색 가우시안 신호원과 유색 신호원 및 해양 포유류 신호원에 대해서 비교 실험을 하였다. 그 결과 갑자기 추정성능이 열화되는 문턱 현상이 늦게 나타나거 나 훨씬 줄어드는 것을 보였다.

키워드
시간 지연 추정, , ,
희소성 신호 처리
L1-놈
일반적 상호 상관
References
1
E. Tiana-Roig, F. Jacobsen, and E. Grande, “Beamforming with a circular microphone array for localization of environmental noise sources,” J. Acoust. Soc. Am. 128, 3535–42 (2010).
2
J. Shin, H. Park, and E. Chang, “An ESPRIT-based super-resolution time delay estimation algorithm for real-time locating systems” (in Korean), J. KICS. 38, 310-317 (2013).
3
J. Shin, S. Myong, E. Chang, and H. Park, “A super-resolution time delay estimation algorithm for spread spectrum signals” (in Korean), J. KICS, 37, 119-127 (2012).
4
J. Lim and W. Hong, “An adaptive time delay estimation method based on canonical correlation analysis” (in Korean), J. Acoust. Soc. Kr. 32, 548-555 (2013).
5
P. Feintuch, N. Bershad, and F. Reed, “Time delay estimation using the LMS adaptive lter-dynamic beha-viour,” IEEE Trans. Acoust. Speech Signal Process. 29, 571-576 (1981).
6
K. Ho, Y. Chan, and P. Ching, “Adaptive time-delay estimation in nonstationary signal and noise power environments,” IEEE Trans. Signal Process. 41, 2289-2299 (1993).
7
H. So, P. Ching, and Y. Chan, “A new algorithm for explicit adaptation of time delay,” IEEE Trans. Signal Process. 42, 1816-1820 (1994).
8
8.S. Dooley and A. Nandi, “Adaptive subsample time delay estimation using Lagrange interpolators,” IEEE Signal Process. Lett. 6, 65-57 (1999).
9
G. Carter, Coherence and Time Delay Estimation: An Applied Tutorial for Research, Development, Test and Evaluation Engineers (IEEE press, New York, 1993), pp. 1-28.
10
R. Baraniuk, “Compressive sensing,” IEEE Signal Process. Mag. 25, 21-30 (2007).
11
J. Lim and W. Hong, “Adaptive time delay estimation using l1 constraint” (in Korean), J. Acoust. Soc. Kr. Suppl.1(s) 32, 272-275 (2013).
12
J. Benesty, “Adaptive eigenvalue decomposition algo-rithm for passive acoustic source localization,” J. Acoust. Soc. Am. 107, 384-391 (2000).
13
Y. Chen, Y. Gu, and A. Hero, “Sparse LMS for system identification,” Proc. ICASSP, 3125-3128 (2009).
14
H. Lee, S. Park, and S. Park, “Introduction to compressive sensing,” Mag. IEIE. 38, 19-30 (2011).
15
R. Tibshirani, “Regression shrinkage and selection via the LASSO,” J. Royal Statist. Soc. B. 21, 279-289 (1996).
16
The MOSEK Optimization Tools Version 2.5. User’s Manual and Reference, http://www.mosek.com, 2002.
17
E. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory. 52, 489-509 (2006).
18
SPGL1, a solver for large scale sparse reconstruction, http://www.cs.ubc.ca/labs/scl/sp gl1/, 2008.
19
1st International Workshops on the Detection and Localization of Marine Mammals Using Passive Acou-stics, http://www.mobysound.org/workshops.html, 2017.
Information
  • Publisher :The Acoustical Society of Korea
  • Publisher(Ko) :한국음향학회
  • Journal Title :The Journal of the Acoustical Society of Korea
  • Journal Title(Ko) :한국음향학회지
  • Volume : 36
  • No :4
  • Pages :285-291
  • Received Date : 2017-05-30
  • Revised Date : 2017-07-03
  • Accepted Date : 2017-07-31
  • DOI :https://doi.org/10.7776/2017.36.4.279
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