A time-frequency method for increasing the signal-to-noise ratio in system identification with exponential sweeps

Time-frequency peak filtering (TFPF) may efficiently suppress random noise and hence improve the signal-to-noise ratio. However, the errors are not always satisfactory when applying the TFPF to fast-varying seismic signals. We begin with an error analysis for the TFPF by using the spread factor of the phase and cumulants of noise. This analysis shows that the nonlinear signal component and non-Gaussian random noise lead to the deviation of the pseudo-Wigner-Ville distribution (PWVD) peaks from the instantaneous frequency. The deviation introduces the signal distortion and random oscillations in the result of the TFPF. We propose a weighted reassigned smoothed PWVD with less deviation than PWVD. The proposed method adopts a frequency window to smooth away the residual oscillations in the PWVD, and incorporates a weight function in the reassignment which sharpens the time-frequency distribution for reducing the deviation. Because the weight function is determined by the lateral coherence of seismic data, the smoothed PWVD is assigned to the accurate instantaneous frequency for desired signal components by weighted frequency reassignment. As a result, the TFPF based on the weighted reassigned PWVD (TFPF_WR) can be more effective in suppressing random noise and preserving signal as compared with the TFPF using the PWVD. We test the proposed method on synthetic and field seismic data, and compare it with a wavelet-transform method and [Formula: see text] prediction filter. The results show that the proposed method provides better performance over the other methods in signal preserving under low signal-to-noise ratio.

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Threshold Noise Reduction Research of Improved EEMD Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.226-228.237 ◽

2012 ◽

Vol 226-228 ◽

pp. 237-240 ◽

Cited By ~ 1

Author(s):

Mei Jun Zhang ◽

Hao Chen ◽

Chuang Wang ◽

Qing Cao

Keyword(s):

Noise Reduction ◽

Signal To Noise Ratio ◽

Frequency Noise ◽

Signal To Noise ◽

Time Frequency ◽

Noise Ratio ◽

Reduction Effect ◽

Eemd Method ◽

Simulation Signal ◽

Improved Eemd

In order to extract effectively detection signals in the noise background for non-stationary signal.On the basis of EEMD, improved EEMD is put forward, the improve EEMD threshold noise reduction is researched in this paper.The simulation signal compared the noise reduction effect of the wavelet,EMD,EEMD,and the improved EEMD. The improved EEMD threshold noise reduction have the best noise reduction result , the highest signal-to-noise ratio, the smallest standard deviation error.After the improved EEMD threshold noise reduction , the measurement signal time domain waveform smooth. More high frequency noise was obviously reduced in Hilbert time- frequency spectrum. Signal-to-noise ratio significantly improve, and signal characteristics are very clear.

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An Approach to ARMA System Identification at a Very Low Signal-To-Noise Ratio

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. ◽

10.1109/icassp.2005.1415958 ◽

2006 ◽

Author(s):

Shaikh Anowarul Fattah ◽

W.-P. Zhu ◽

M. Omair Ahmad

Keyword(s):

System Identification ◽

Signal To Noise Ratio ◽

Signal To Noise ◽

Noise Ratio

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Dual-Tree Wavelet Packet Transform for Ultrasonic Data Transmission Through Metal Structures

Volume 1: Processing ◽

10.1115/msec2015-9463 ◽

2015 ◽

Author(s):

Jinjiang Wang ◽

Robert X. Gao ◽

Xinyao Tang ◽

Zhaoyan Fan ◽

Peng Wang

Keyword(s):

Signal Processing ◽

Data Transmission ◽

Signal To Noise Ratio ◽

Wavelet Packet ◽

Experimental Studies ◽

Wavelet Packet Transform ◽

Signal To Noise ◽

Metallic Structures ◽

Time Frequency ◽

Noise Ratio

Data communication through metallic structures is generally encountered in manufacturing equipment and process monitoring and control. This paper presents a signal processing technique for enhancing the signal-to-noise ratio and high-bit data transmission rate in ultrasound-based wireless data transmission through metallic structures. A multi-carrier coded-ultrasonic wave modulation scheme is firstly investigated to achieve high-bit data rate communication while reducing inter-symbol inference and data loss, due to the inherent signal attenuation, wave diffraction and reflection in metallic structures. To improve the signal-to-noise ratio, dual-tree wavelet packet transform (DT-WPT) has been investigated to separate multi-carrier signals under noise contamination, given its properties of shift-invariance and flexible time frequency partitioning. A new envelope extraction and threshold setting strategy for selected wavelet coefficients is then introduced to retrieve the coded digital information. Experimental studies are performed to evaluate the effectiveness of the developed signal processing method for manufacturing.

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Detection of Life Characteristic Signals Based on High Order Statistics

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.239-240.807 ◽

2012 ◽

Vol 239-240 ◽

pp. 807-810

Author(s):

Jian Jun Li ◽

Jian Feng Zhao

Keyword(s):

Order Statistics ◽

Signal To Noise Ratio ◽

Low Frequency ◽

Time Estimation ◽

High Order ◽

Signal To Noise ◽

Time Frequency ◽

Noise Ratio ◽

Short Time ◽

High Order Statistics

Life parameters signal has characteristics of extremely low frequency, low signal-to-noise ratio, and the easy submerged in strong clutter noises. The method for detecting life signal based on filter bank and high order statistics is presented, in which neither the Gaussian supposition of the observed signal, nor a prior information about the waveform and arrival time of the observed signal is necessary. The principle of method is to separate the spectrum of input signal into many narrow frequency bands, whose Sub-band signal is followed by a short-time estimation of higher-order statistics so as to suppress Gaussian noises. Simulated results show that the method not only can completely descript life signals in the time-frequency domain, but improve the signal-to-noise ratio and the ability of detecting algorithm. Moreover, the method is effective and practical.

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