Robust estimation of instantaneous phase using a time-frequency adaptive filter

Geophysics ◽  
2013 ◽  
Vol 78 (1) ◽  
pp. O1-O7 ◽  
Author(s):  
Wen-kai Lu ◽  
Chang-Kai Zhang
Keyword(s):  
Adaptive Filter ◽  
Signal To Noise Ratio ◽  
Amplitude Spectrum ◽  
Estimation Method ◽  
Frequency Component ◽  
Data Sets ◽  
Time Frequency ◽  
Instantaneous Phase ◽  
The Hilbert Transform ◽  
Short Time

The instantaneous phase estimated by the Hilbert transform (HT) is susceptible to noise; we propose a robust approach for the estimation of instantaneous phase in noisy situations. The main procedure of the proposed method is applying an adaptive filter in time-frequency domain and calculating the analytic signal. By supposing that one frequency component with higher amplitude has higher signal-to-noise ratio, a zero-phase adaptive filter, which is constructed by using the time-frequency amplitude spectrum, enhances the frequency components with higher amplitudes and suppresses those with lower amplitudes. The estimation of instantaneous frequency, which is defined as the derivative of instantaneous phase, is also improved by the proposed robust instantaneous phase estimation method. Synthetic and field data sets are used to demonstrate the performance of the proposed method for the estimation of instantaneous phase and frequency, compared by the HT and short-time-Fourier-transform methods.

scholarly journals Noise masking method based on an effective ratio mask estimation in Gammatone channels

2018 ◽  
Vol 7 ◽  
Author(s):  
Feng Bao ◽  
Waleed H. Abdulla
Keyword(s):  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Wiener Filter ◽  
Power Spectra ◽  
Auditory Scene Analysis ◽  
Accurate Estimation ◽  
Noise Power ◽  
Noise Masking ◽  
Time Frequency ◽  
Mask Estimation

In computational auditory scene analysis, the accurate estimation of binary mask or ratio mask plays a key role in noise masking. An inaccurate estimation often leads to some artifacts and temporal discontinuity in the synthesized speech. To overcome this problem, we propose a new ratio mask estimation method in terms of Wiener filtering in each Gammatone channel. In the reconstruction of Wiener filter, we utilize the relationship of the speech and noise power spectra in each Gammatone channel to build the objective function for the convex optimization of speech power. To improve the accuracy of estimation, the estimated ratio mask is further modified based on its adjacent time–frequency units, and then smoothed by interpolating with the estimated binary masks. The objective tests including the signal-to-noise ratio improvement, spectral distortion and intelligibility, and subjective listening test demonstrate the superiority of the proposed method compared with the reference methods.

Automated phase attribute-based picking applied to reflection seismics

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. V141-V150 ◽  
Author(s):  
Emanuele Forte ◽  
Matteo Dossi ◽  
Michele Pipan ◽  
Anna Del Ben
Keyword(s):  
Signal To Noise Ratio ◽  
Phase Method ◽  
Data Sets ◽  
Reflection Seismic ◽  
Instantaneous Phase ◽  
Whole Process ◽  
Specific Analysis ◽  
Reflection Seismics ◽  
Selection Of ◽  
Energy Package

We have applied an attribute-based autopicking algorithm to reflection seismics with the aim of reducing the influence of the user’s subjectivity on the picking results and making the interpretation faster with respect to manual and semiautomated techniques. Our picking procedure uses the cosine of the instantaneous phase to automatically detect and mark as a horizon any recorded event characterized by lateral phase continuity. A patching procedure, which exploits horizon parallelism, can be used to connect consecutive horizons marking the same event but separated by noise-related gaps. The picking process marks all coherent events regardless of their reflection strength; therefore, a large number of independent horizons can be constructed. To facilitate interpretation, horizons marking different phases of the same reflection can be automatically grouped together and specific horizons from each reflection can be selected using different possible methods. In the phase method, the algorithm reconstructs the reflected wavelets by averaging the cosine of the instantaneous phase along each horizon. The resulting wavelets are then locally analyzed and confronted through crosscorrelation, allowing the recognition and selection of specific reflection phases. In case the reflected wavelets cannot be recovered due to shape-altering processing or a low signal-to-noise ratio, the energy method uses the reflection strength to group together subparallel horizons within the same energy package and to select those satisfying either energy or arrival time criteria. These methods can be applied automatically to all the picked horizons or to horizons individually selected by the interpreter for specific analysis. We show examples of application to 2D reflection seismic data sets in complex geologic and stratigraphic conditions, critically reviewing the performance of the whole process.

scholarly journals A new spectrum feature discovered for a category of signals produced by massive and random micro-sources

2018 ◽  
Vol 210 ◽  
pp. 05010
Author(s):  
Xiaodong Zhuang ◽  
Nikos Mastorakis
Keyword(s):  
Statistical Study ◽  
Amplitude Spectrum ◽  
Time Spectrum ◽  
Frequency Component ◽  
Main Category ◽  
Frequency Components ◽  
Spectrum Feature ◽  
Short Time ◽  
The Relationship ◽  
System Characteristics

A statistical study is implemented on the short-time spectrum of one main category of random signals. For the signals with massive and random micro-sources, a new statistic feature of the short-time amplitude spectrum is discovered, which reveals the relationship between the amplitude’s average and its standard for each frequency component. Moreover, the association between the amplitude distributions for different frequency components is also studied. A model representing such association is presented, which accords well with the statistic feature discovered. The analysis result has potential application in signal classification, and also in the study of system characteristics underlying the observed signal.

scholarly journals Selection of window for inter-pulse analysis of simple pulsed radar signal using the short time Fourier transform

2015 ◽  
Vol 4 (4) ◽  
pp. 531 ◽  
Author(s):  
Ashraf Adamu Ahmad ◽  
Abdullahi Daniyan ◽  
David Ocholi Gabriel
Keyword(s):  
Fourier Transform ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Side Lobe ◽  
Radar Signal ◽  
Short Time Fourier Transform ◽  
Time Frequency ◽  
Window Functions ◽  
Pulse Analysis ◽  
Short Time

The electronic intelligence (ELINT) system is used by the military to detect, extract information and classify incoming radar signals. This work utilizes short time Fourier transform (STFT) - time frequency distribution (TFD) for inter-pulse analysis of the radar signal in order to estimate basic radar signal time parameters (pulse width and pulse repetition period). Four well-known windows functions of different and unique characteristics were used for the localization of STFT to determine their various effects on the analysis. The window functions are Hamming, Hanning, Bartlett and Blackman window functions. Monte Carlo simulation is carried out to determine the performance of the signal analysis in presence of additive white Gaussian noise (AWGN). Results show that the lower the transition of main lobe width and higher the peak side lobe, the better the performance of the window function irrespective of time parameter being estimated. This is because 100 percent probability of correct estimation is achieved at signal to noise ratio of about -2dB for Bartlett, 4dB for both Hamming and Hanning, and 9dB for Blackman.

scholarly journals Radar Emitter Recognition Based on the Energy Cumulant of Short Time Fourier Transform and Reinforced Deep Belief Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3103 ◽  
Author(s):  
Xuebao Wang ◽  
Gaoming Huang ◽  
Zhiwen Zhou ◽  
Wei Tian ◽  
Jialun Yao ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Deep Belief Network ◽  
Short Time Fourier Transform ◽  
Belief Network ◽  
Time Frequency ◽  
Low Snr ◽  
Emitter Recognition ◽  
Short Time

To cope with the complex electromagnetic environment and varied signal styles, a novel method based on the energy cumulant of short time Fourier transform and reinforced deep belief network is proposed to gain a higher correct recognition rate for radar emitter intra-pulse signals at a low signal-to-noise ratio. The energy cumulant of short time Fourier transform is attained by calculating the accumulations of each frequency sample value with the different time samples. Before this procedure, the time frequency distribution via short time Fourier transform is processed by base noise reduction. The reinforced deep belief network is proposed to employ the input feature vectors for training to achieve the radar emitter recognition and classification. Simulation results manifest that the proposed method is feasible and robust in radar emitter recognition even at a low SNR.

Detection of Life Characteristic Signals Based on High Order Statistics

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.

scholarly journals The Wigner-Ville Distribution Based on the Linear Canonical Transform and Its Applications for QFM Signal Parameters Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yu-E Song ◽  
Xiao-Yan Zhang ◽  
Chun-Heng Shang ◽  
Hong-Xia Bu ◽  
Xiao-Yan Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Parameters Estimation ◽  
Accurate Estimation ◽  
Linear Canonical Transform ◽  
Estimation Of Parameters ◽  
Position Information ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Definition Of ◽  
Short Time

The Wigner-Ville distribution (WVD) based on the linear canonical transform (LCT) (WDL) not only has the advantages of the LCT but also has the good properties of WVD. In this paper, some new and important properties of the WDL are derived, and the relationships between WDL and some other time-frequency distributions are discussed, such as the ambiguity function based on LCT (LCTAF), the short-time Fourier transform (STFT), and the wavelet transform (WT). The WDLs of some signals are also deduced. A novel definition of the WVD based on the LCT and generalized instantaneous autocorrelation function (GWDL) is proposed and its applications in the estimation of parameters for QFM signals are also discussed. The GWDL of the QFM signal generates an impulse and the third-order phase coefficient of QFM signal can be estimated in accordance with the position information of such impulse. The proposed algorithm is fast because it only requires 1-dimensional maximization. Also the new algorithm only has fourth-order nonlinearity thus it has accurate estimation and low signal-to-noise ratio (SNR) threshold. The simulation results are provided to support the theoretical results.

scholarly journals Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. H29-H37 ◽  
Author(s):  
Bradley Matthew Battista ◽  
Camelia Knapp ◽  
Tom McGee ◽  
Vaughn Goebel
Keyword(s):  
Signal Processing ◽  
Seismic Reflection ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Seismic Reflection Data ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Reflection Data ◽  
Mode Decomposition ◽  
The Hilbert Transform

Advancements in signal processing may allow for improved imaging and analysis of complex geologic targets found in seismic reflection data. A recent contribution to signal processing is the empirical mode decomposition (EMD) which combines with the Hilbert transform as the Hilbert-Huang transform (HHT). The EMD empirically reduces a time series to several subsignals, each of which is input to the same time-frequency environment via the Hilbert transform. The HHT allows for signals describing stochastic or astochastic processes to be analyzed using instantaneous attributes in the time-frequency domain. The HHT is applied herein to seismic reflection data to: (1) assess the ability of the EMD and HHT to quantify meaningful geologic information in the time and time-frequency domains, and (2) use instantaneous attributes to develop superior filters for improving the signal-to-noise ratio. The objective of this work is to determine whether the HHT allows for empirically-derived characteristics to be used in filter design and application, resulting in better filter performance and enhanced signal-to-noise ratio. Two data sets are used to show successful application of the EMD and HHT to seismic reflection data processing. Nonlinear cable strum is removed from one data set while the other is used to show how the HHT compares to and outperforms Fourier-based processing under certain conditions.

Underwater Target Recognition Using Time-Frequency Analysis and Elliptical Fuzzy Clustering Classifications

2009 ◽  
Author(s):  
Hui Ou ◽  
John S. Allen ◽  
Vassilis L. Syrmos
Keyword(s):  
Clustering Algorithm ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Formation Mechanisms ◽  
Data Sets ◽  
Time Frequency ◽  
Underwater Target ◽  
Clustering Pattern ◽  
Acoustic Responses

A novel underwater target recognition approach has been developed based on the use of Wigner-type Time-Frequency (TF) analysis and the elliptical Gustafson-Kessel (GK) clustering algorithm. This method is implemented for the acoustic backscattered signals of the targets, and more precisely from the examination of echo formation mechanisms in the TF plane. For each of the training signals, we generate a clustering distribution which represents the signal’s TF characteristics by a small number of clusters. A feature template is created by combining the clustering distributions for the signals from the same training target. In the classification process, we calculate the clustering distribution of the test signal and compare it with the feature templates. The target is discriminated in terms of the best match of the clustering pattern. The advantages of GK clustering are that it allows elliptical-shaped clusters, and it automatically adjusts their shapes according to the distribution of the TF feature patterns. The recognition scheme has been applied to discriminate four spherical shell targets filled with different fluids. The data sets are the simulated acoustic responses from these targets, including the interferences caused by the seafloor interaction. [J. A. Fawcett, W. L. J. Fox, and A. Maguer, J. Acoust. Soc. Am. 104, 3296–3304 (1998)]. To evaluate the system robustness, white Gaussian noise is added to the acoustic responses. More than 95% of correct classification is obtained for high Signal-to-Noise Ratio (SNR), and it is maintained around 70% for very low SNRs.

scholarly journals Widely Linear Adaptive Instantaneous Frequency Estimation in Vector Hydrophones

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3348 ◽  
Author(s):  
Panpan Peng ◽  
Liang An
Keyword(s):  
Instantaneous Frequency ◽  
Frequency Estimation ◽  
Linear Method ◽  
Estimation Method ◽  
Frequency Resolution ◽  
Linear Filter ◽  
Instantaneous Frequency Estimation ◽  
Time Frequency ◽  
Short Time ◽  
Widely Linear

To solve the problem that the time-frequency resolution of Short-Time Fourier Transform (STFT) is constrained by the window length and the moving step of the short time window, and to utilize the merits of a widely linear method, a novel instantaneous frequency estimation method in vector hydrophone was proposed. In this paper, a complex variable was constructed. It is composed of sound pressure and particle velocity as its real part and imaginary part, respectively. The constructed variable was approved to be second order noncircular (improper). For the modelling of noncircular signals, the standard linear estimation is not adequate and the pseudo-covariance matrix should also be taken into consideration. As a result, a widely linear adaptive instantaneous frequency estimation algorithm and its three solutions based on the augmented complex least mean square (ACLMS) method are presented to estimate the instantaneous frequency in vector hydrophones. The results of simulations and laboratory experiments prove that this approach based on a widely linear model performs better compared to STFT and strict linear filter methods.

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