Performance Comparison on Three Time Delay Estimation Algorithms Using Experiments

Junhao Li; Wenhong Liu

doi:doi:10.11648/j.com.20170503.12

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Performance Comparison on Three Time Delay Estimation Algorithms Using Experiments

Junhao Li, Wenhong Liu

Published in Communications (Volume 5, Issue 3)

Received: 7 August 2017 Accepted: Published: 7 August 2017

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Abstract

Time delay estimation (TDE) is applied in many areas. Its estimation performance plays an important role in many actual systems, such as malfunction sound location. In this paper, estimation performances of three TDE algorithms, correlation, covariance, and fractional lower order covariance, are compared. Traditional, additive noises in the actual collected signals are described by Gaussian distribution. However, they have often impulsiveness in practice, and are modeled as α-stable distribution. First, correlation, covariance, and fractional lower order covariance method are analyzed in theory. Then, computer simulation experiments are carried out. Computer sound card records pure audio signals, different pulse intensity noises added to simulate actual environments. Next, results of three algorithms for time delay estimation were obtained in different signal to noise ratio (SNR) conditions. Under the same conditions, estimated RMS (root-mean-square) errors of three algorithms are analyzed and compared. Experimental results show that under low SNR and strong impulsive noise environments, fractional lower order covariance method indicates best estimation performance.

Published in	Communications (Volume 5, Issue 3)
DOI	10.11648/j.com.20170503.12
Page(s)	24-28
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Time Delay Estimation, α-Stable Distribution, Impulsive Noise, Fractional Lower Order Covariance

References

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[3]	LI Wei-hong, TANG Hai-bing, GONG Wei-guo. Study on Time Delay Estimation Method for Abnormal Sound Source Location in Public Places [J]. Journal of Instrumentation, 2012, (04): 750-756.
[4]	Li Xuemei, Tao Ran, Wang Yue. Time delay estimation technology research [J]. Radar Science and Technology, 2010, (04): 362-371.
[5]	ZHANG Duan-jin, ZHANG Zhong-hua, GUO Jian-jun, ZHANG De-jing. New method for time delay estimation based on fourth-order cumulant [J]. Journal of Zhengzhou University (Engineering Science), 2010, (01): 103-106.
[6]	Yang Tao. Time Delay Estimation Method in Indoor Speech Source Location Technology [J]. Computer Engineering and Applications, 2009, (20): 246-248.
[7]	Zhang Zhonghua, Zhang Duanjin, Guo Jianjun, Geng Yan. Multi-path delay estimation based on third-order cumulants [J]. Information and Electronic Engineering, 2009, (02): 94-98.
[8]	LI Li-fang, LIU Qing-hua. Study on time delay estimation method under impulse noise [J]. Electro-acoustic Technology, 2008, (08): 57-59.
[9]	Liu Wenhong. Time delay estimation method and application of impulse noise [D]. Dalian University of Technology, 2007.
[10]	Wang Peng, Zhang Xiaotong, Xu Liyuan, He Jie, Xu Jinwu. High field near-field ranging algorithm based on adaptive time delay estimation [J]. Journal of Computers, 2016, (39): 1-19.
[11]	Zhang Liang, Liu Zhiguang, Xiao Yanfan, Li Tiejun. Design of fault sound source location system for electromechanical equipment [J]. Instrument Technique and Sensor, 2016, (02): 49-52.
[12]	Li Ning, Cao Zhen, Deng Zhongliang, Han Ke. Time Estimation Method of Time Difference in Interference Source Location [J]. Systems Engineering and Electronics, 2016, (05): 994-997.
[13]	Liu Xingyan. Research and application of generalized correlation delay estimation in passive time difference location (TDOA) [D]. Lanzhou Jiaotong University, 2015.
[14]	Time delay estimation in unknown Gaussian spatially correlated noise. Nikias C L, Pan R. IEEE Trans. ASSP. 1988.
[15]	Adaptive estimation of time delay in sampled system. Etter D M, Stearn S D. IEEE Transactions on Acoustics Speech and Signal Processing. 1981.
[16]	Signal processing with Alpha-stable distributions. Nikias C L, Shao M. 1995.

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APA Style

Junhao Li, Wenhong Liu. (2017). Performance Comparison on Three Time Delay Estimation Algorithms Using Experiments. Communications, 5(3), 24-28. https://doi.org/10.11648/j.com.20170503.12

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ACS Style

Junhao Li; Wenhong Liu. Performance Comparison on Three Time Delay Estimation Algorithms Using Experiments. Communications. 2017, 5(3), 24-28. doi: 10.11648/j.com.20170503.12

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AMA Style

Junhao Li, Wenhong Liu. Performance Comparison on Three Time Delay Estimation Algorithms Using Experiments. Communications. 2017;5(3):24-28. doi: 10.11648/j.com.20170503.12

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@article{10.11648/j.com.20170503.12,
  author = {Junhao Li and Wenhong Liu},
  title = {Performance Comparison on Three Time Delay Estimation Algorithms Using Experiments},
  journal = {Communications},
  volume = {5},
  number = {3},
  pages = {24-28},
  doi = {10.11648/j.com.20170503.12},
  url = {https://doi.org/10.11648/j.com.20170503.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.com.20170503.12},
  abstract = {Time delay estimation (TDE) is applied in many areas. Its estimation performance plays an important role in many actual systems, such as malfunction sound location. In this paper, estimation performances of three TDE algorithms, correlation, covariance, and fractional lower order covariance, are compared. Traditional, additive noises in the actual collected signals are described by Gaussian distribution. However, they have often impulsiveness in practice, and are modeled as α-stable distribution. First, correlation, covariance, and fractional lower order covariance method are analyzed in theory. Then, computer simulation experiments are carried out. Computer sound card records pure audio signals, different pulse intensity noises added to simulate actual environments. Next, results of three algorithms for time delay estimation were obtained in different signal to noise ratio (SNR) conditions. Under the same conditions, estimated RMS (root-mean-square) errors of three algorithms are analyzed and compared. Experimental results show that under low SNR and strong impulsive noise environments, fractional lower order covariance method indicates best estimation performance.},
 year = {2017}
}

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TY - JOUR
T1 - Performance Comparison on Three Time Delay Estimation Algorithms Using Experiments
AU - Junhao Li
AU - Wenhong Liu
Y1 - 2017/08/07
PY - 2017
N1 - https://doi.org/10.11648/j.com.20170503.12
DO - 10.11648/j.com.20170503.12
T2 - Communications
JF - Communications
JO - Communications
SP - 24
EP - 28
PB - Science Publishing Group
SN - 2328-5923
UR - https://doi.org/10.11648/j.com.20170503.12
AB - Time delay estimation (TDE) is applied in many areas. Its estimation performance plays an important role in many actual systems, such as malfunction sound location. In this paper, estimation performances of three TDE algorithms, correlation, covariance, and fractional lower order covariance, are compared. Traditional, additive noises in the actual collected signals are described by Gaussian distribution. However, they have often impulsiveness in practice, and are modeled as α-stable distribution. First, correlation, covariance, and fractional lower order covariance method are analyzed in theory. Then, computer simulation experiments are carried out. Computer sound card records pure audio signals, different pulse intensity noises added to simulate actual environments. Next, results of three algorithms for time delay estimation were obtained in different signal to noise ratio (SNR) conditions. Under the same conditions, estimated RMS (root-mean-square) errors of three algorithms are analyzed and compared. Experimental results show that under low SNR and strong impulsive noise environments, fractional lower order covariance method indicates best estimation performance.
VL - 5
IS - 3
ER -

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Author Information

Junhao Li

School of Electronic and Information, Shanghai Dianji University, Shanghai, China
Wenhong Liu

School of Electrical Engineering, Shanghai Dianji University, Shanghai, China

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APA Style

Junhao Li, Wenhong Liu. (2017). Performance Comparison on Three Time Delay Estimation Algorithms Using Experiments. Communications, 5(3), 24-28. https://doi.org/10.11648/j.com.20170503.12

Copy | Download

ACS Style

Junhao Li; Wenhong Liu. Performance Comparison on Three Time Delay Estimation Algorithms Using Experiments. Communications. 2017, 5(3), 24-28. doi: 10.11648/j.com.20170503.12

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AMA Style

Junhao Li, Wenhong Liu. Performance Comparison on Three Time Delay Estimation Algorithms Using Experiments. Communications. 2017;5(3):24-28. doi: 10.11648/j.com.20170503.12

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@article{10.11648/j.com.20170503.12,
  author = {Junhao Li and Wenhong Liu},
  title = {Performance Comparison on Three Time Delay Estimation Algorithms Using Experiments},
  journal = {Communications},
  volume = {5},
  number = {3},
  pages = {24-28},
  doi = {10.11648/j.com.20170503.12},
  url = {https://doi.org/10.11648/j.com.20170503.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.com.20170503.12},
  abstract = {Time delay estimation (TDE) is applied in many areas. Its estimation performance plays an important role in many actual systems, such as malfunction sound location. In this paper, estimation performances of three TDE algorithms, correlation, covariance, and fractional lower order covariance, are compared. Traditional, additive noises in the actual collected signals are described by Gaussian distribution. However, they have often impulsiveness in practice, and are modeled as α-stable distribution. First, correlation, covariance, and fractional lower order covariance method are analyzed in theory. Then, computer simulation experiments are carried out. Computer sound card records pure audio signals, different pulse intensity noises added to simulate actual environments. Next, results of three algorithms for time delay estimation were obtained in different signal to noise ratio (SNR) conditions. Under the same conditions, estimated RMS (root-mean-square) errors of three algorithms are analyzed and compared. Experimental results show that under low SNR and strong impulsive noise environments, fractional lower order covariance method indicates best estimation performance.},
 year = {2017}
}

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