Volume 7, Issue 3, June 2018, Page: 28-37
A Robust and Higher Precision Time Delay Estimation Method Facing Low Signal to Noise Ratio Conditions
Junhao Li, School of Electrical Engineering, Shanghai Dianji University, Shanghai, China
Wenhong Liu, School of Electronic Information, Shanghai Dianji University, Shanghai, China
Niansheng Chen, School of Electronic Information, Shanghai Dianji University, Shanghai, China
Guangyu Fan, School of Electronic Information, Shanghai Dianji University, Shanghai, China
Received: Dec. 11, 2018;       Published: Dec. 12, 2018
DOI: 10.11648/j.ijiis.20180703.12      View  703      Downloads  88
Many available signals in the real world are usually weak with impulse noises and/or outliers, and we also need to have higher estimation precision in applications. Our focus of attention is pretty much on integrating robustness and accuracy under lower signal to noise ratio (SNR) with impulse noises. Although traditional fractional adaptive time delay estimation (TDE) methods have higher precision, the results of estimation are unreasonable when the signals contain some impulse noises. While, most proposed robust algorithms later can work well mainly with high SNR. In this paper, considering the practical problem in equipment fault acoustic localization based on TDE methods, an improved robust fractional adaptive time delay estimation method is addressed facing lower SNR conditions. First, the impulse noises are modeled as Alpha stable distribution, and the integer part of TDE is getting by using covariate correlation approach. Then, the integer estimation value is used as initial parameter value of time delay. Covariant sequence is the input of time delay estimator. Next, fractional TDE value is adaptive obtained by iteration under minimum average p norm criterion. Covariant sequence weakens irrelevant noises, meanwhile preserves time delay information between original sequences. Computer simulations and comparative experiments show that improved method has better estimation results. This method is robust and higher precision, and especially under impulse environment and low SNR conditions.
Equipment Acoustic Fault Location, Adaptive Fractional Time Delay Estimation, Lower Signal to Noise Ratio, Robust
To cite this article
Junhao Li, Wenhong Liu, Niansheng Chen, Guangyu Fan, A Robust and Higher Precision Time Delay Estimation Method Facing Low Signal to Noise Ratio Conditions, International Journal of Intelligent Information Systems. Vol. 7, No. 3, 2018, pp. 28-37. doi: 10.11648/j.ijiis.20180703.12
Qian Shie. Acoustic camera——Making our community more quiet [J]. Foreign Electronic Measurement Technology, 2009, 28 (2): 5-8.
Ottermo F, Möllerström E, Nordborg A, et al. Location of aerodynamic noise sources from a 200 kW vertical-axis wind turbine [J]. Journal of Sound & Vibration, 2017, 400: 154-166.
Shi Quan, Guo Dong, Shi Xiaohui, et al. Study on noise source localization of transmission based on microphone array [J]. Journal ofVibration and Shock, 2012, 31 (13): 134-137.
Xie D, Wang M, Zhu J Q, et al. An equipment fault sound location system design [J]. Applied Mechanics & Materials, 2014, 462-463 (462-463): 298-301.
Li J H, Liu W H. Characteristics analysis and modeling of fault sound and background noise of large central air conditioner [J]. Journal of Electrical and Electronic Engineering, 2018, 6 (1): 30-35.
Liu Min, Zeng Yumin, Zhang Ming, et al. Improved algorithm for time delay estimation of speech signal based on quadratic correlation [J]. Journal of Applied Acoustics, 2016, 35 (3): 255-264.
Zhang Q, Zhang L. An improved delay algorithm based on generalized cross correlation [C]//Information Technology and Mechatronics Engineering Conference, IEEE, 2017: 395-399.
Shen Guoqing, Yang Jiedong, Chen Dong, Liu Weilong, Zhang Shiping, An Chain. Study on temperature estimation of boiler acoustic temperature measurement based on quadratic correlation PHAT-β algorithm [J]. Journal of Power Engineering, 2018, 38 (08): 617-623.
Li J H, Liu W H. Performance comparison on three time delay estimation algorithms using experiments, communications [J]. Electrical & Computer Science, 2017, 5 (3): 24-28.
Liu W, Wang Y, Qiu T. Evoked potential latency delay estimation by using covariation correlation approach [C]//International Conference on Bioinformatics and Biomedical Engineering, IEEE, 2008: 652-655.
Sun X, Liu Y, Zhang J, et al. Measurement and analysis of a horizontal-axis washing machine for low-frequency abnormal noise [C]. 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Xi'an, 2016: 735-739.
Jiang Xue, Liu Yuanyuan, Lei Weijia, et al. FPGA implementation of a cross-correlation delay estimator with low SNR [J]. Telecommunication Engineering, 2014, 54 (7): 951-957.
So H C, Ching P C. Performance analysis of ETDGE-an efficient and unbiased TDOA estimator [J]. IEE Proceedings - Radar Sonar and Navigation, 1998, 145 (6): 325-330.
W. Xia, W. Jiang and L. Zhu, "An Adaptive Time Delay Estimator Based on ETDE Algorithm with Noisy Measurements," in Chinese Journal of Electronics, vol. 26, no. 4, pp. 760-767, 7 2017.
Yang X, Liu X, Shen J. The research of the explicit time delay and gain estimation algorithm based on fourth-order cumulants in acoustic pyrometry in the power plant boiler [C]//Chinese Automation Congress, 2017: 6091-6097.
Liu Wenhong, Qiu Tianshuang, Hu Tingting, et al. Non-integer delay estimation method based on fractional lower order moments [J]. Journal on Communications, 2006, 27 (12): 37-42.
Nikias C L, Shao M. Signal processing with Alpha-stable distributions [M]. New York: John Wiley & Sons Inc, 1995.
Ma X Y. Robust signal processing in impulsive noise with stable distributions: estimation, identification and equalization [J]. Pesticide Science, 1988, 23 (3): 259-265.
Browse journals by subject