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 Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

2021 ◽  
Vol 13 (10) ◽  
pp. 265
Author(s):  
Jie Chen ◽  
Bing Han ◽  
Xufeng Ma ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Low Frequency ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Underwater Target

Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.

Assisted traveltime picking of crosshole GPR data

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. J35-J48 ◽  
Author(s):  
Bernard Giroux ◽  
Abderrezak Bouchedda ◽  
Michel Chouteau
Keyword(s):  
Time Window ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Information Criterion ◽  
High Signal ◽  
Data Sets ◽  
Signal To Noise ◽  
Arrival Times ◽  
Common Time ◽  
Waveform Similarity

We introduce two new traveltime picking schemes developed specifically for crosshole ground-penetrating radar (GPR) applications. The main objective is to automate, at least partially, the traveltime picking procedure and to provide first-arrival times that are closer in quality to those of manual picking approaches. The first scheme is an adaptation of a method based on cross-correlation of radar traces collated in gathers according to their associated transmitter-receiver angle. A detector is added to isolate the first cycle of the radar wave and to suppress secon-dary arrivals that might be mistaken for first arrivals. To improve the accuracy of the arrival times obtained from the crosscorrelation lags, a time-rescaling scheme is implemented to resize the radar wavelets to a common time-window length. The second method is based on the Akaike information criterion(AIC) and continuous wavelet transform (CWT). It is not tied to the restrictive criterion of waveform similarity that underlies crosscorrelation approaches, which is not guaranteed for traces sorted in common ray-angle gathers. It has the advantage of being automated fully. Performances of the new algorithms are tested with synthetic and real data. In all tests, the approach that adds first-cycle isolation to the original crosscorrelation scheme improves the results. In contrast, the time-rescaling approach brings limited benefits, except when strong dispersion is present in the data. In addition, the performance of crosscorrelation picking schemes degrades for data sets with disparate waveforms despite the high signal-to-noise ratio of the data. In general, the AIC-CWT approach is more versatile and performs well on all data sets. Only with data showing low signal-to-noise ratios is the AIC-CWT superseded by the modified crosscorrelation picker.

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.

Extended stable factor method for the inverse Q-filter

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. T155-T163
Author(s):  
Yong Li ◽  
Gulan Zhang ◽  
Jing Duan ◽  
Chengjie He ◽  
Hao Du ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Time Frequency ◽  
Stable Factor ◽  
Factor Method ◽  
Data Application ◽  
Two Parameters ◽  
Limit Assumption

The commonly used stable factor methods for the inverse [Formula: see text]-filter achieve good performance in seismic data processing; however, the constant gain-limit assumption in these methods is not associated with the effective frequency band of seismic data and cannot obtain desirable results with high resolution and high signal-to-noise ratio (S/N). Our extended stable factor method for the inverse [Formula: see text]-filter extends these methods by introducing two parameters and constant or self-adaptive gain limit to achieve the desirable high-resolution and high-S/N result. The extended stable factor method for the inverse [Formula: see text]-filter can be implemented in the frequency or time-frequency domain; the latter implementation achieves a higher S/N. Analysis of synthetic signals and field seismic data application illustrate that our method can produce a desirable high-resolution and high-S/N result.

A new technique for first-arrival picking of refracted seismic data based on digital image segmentation

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. V79-V89 ◽  
Author(s):  
Wail A. Mousa ◽  
Abdullatif A. Al-Shuhail ◽  
Ayman Al-Lehyani
Keyword(s):  
Image Segmentation ◽  
Seismic Data ◽  
Convex Sets ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Color Image Segmentation ◽  
High Signal ◽  
Data Sets ◽  
First Arrival

We introduce a new method for first-arrival picking based on digital color-image segmentation of energy ratios of refracted seismic data. The method uses a new color-image segmentation scheme based on projection onto convex sets (POCS). The POCS requires a reference color for the first break and one iteration to segment the first-break amplitudes from other arrivals. We tested the segmentation method on synthetic seismic data sets with various amounts of additive Gaussian noise. The proposed method gives similar performance to a modified version of Coppens’ method for traces with high signal-to-noise ratio and medium-to-large offsets. Finally, we applied our method and used as well the modified first-arrival picking method based on Coppens’ method to pick the first arrivals on four real data sets, where both were compared to the first breaks that were picked manually and then interpolated. Based on an assessment error of a 20-ms window with respect to manual picks that are interpolated, we find that our method gives comparable performance to Coppens’ method, depending on the data difficulty of picking first arrivals. Therefore, we believe that our proposed method is a good new addition to the existing methods of first-arrival picking.

scholarly journals Central velocity dispersion catalogue of LAMOST-DR7 galaxies

2020 ◽  
Vol 498 (4) ◽  
pp. 5704-5719
Author(s):  
Nicola R Napolitano ◽  
Giuseppe D’Ago ◽  
Crescenzo Tortora ◽  
Gang Zhao ◽  
A-Li Luo ◽  
...  
Keyword(s):  
Galaxy Evolution ◽  
Velocity Dispersion ◽  
Signal To Noise Ratio ◽  
Matter Content ◽  
High Signal ◽  
Data Sets ◽  
Statistical Errors ◽  
The Galaxy ◽  
First Results ◽  
Galaxy Sample

ABSTRACT The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) is a major facility to carry out spectroscopic surveys for cosmology and galaxy evolution studies. The seventh data release of the LAMOST ExtraGAlactic Survey (LEGAS) is currently available and including redshifts of 193 361 galaxies. These sources are spread over $\sim 11\, 500$ deg2 of the sky, largely overlapping with other imaging (SDSS and HSC) and spectroscopic (BOSS) surveys. The estimated depth of the galaxy sample, r ∼ 17.8, the high signal-to-noise ratio, and the spectral resolution R = 1800, make the LAMOST spectra suitable for galaxy velocity dispersion (VD) measurements, which are invaluable to study the structure and formation of galaxies and to determine their central dark matter content. We present the first estimates of central VD of $\sim 86\, 000$ galaxies in LAMOST footprint. We have used a wrap-up procedure to perform the spectral fitting using ppxf, and derive VD measurements. Statistical errors are also assessed by comparing LAMOST VD estimates with the ones of SDSS and BOSS over a common sample of $\sim 51\, 000$ galaxies. The two data sets show a good agreement, within the statistical errors, in particular when VD values are corrected to 1 effective radius aperture. We also present a preliminary mass–σ relation and find consistency with previous analyses based on local galaxy samples. These first results suggest that LAMOST spectra are suitable for galaxy VD measurements to complement the available catalogues of galaxy internal kinematics in the Northern hemisphere. We plan to expand this analysis to next LAMOST data releases.

scholarly journals Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation

2021 ◽  
Vol 11 (4) ◽  
pp. 1942
Author(s):  
Yunseong Lee ◽  
Chanhong Park ◽  
Taeyoung Kim ◽  
Yeongyoon Choi ◽  
Kiseon Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Signal To Noise Ratio ◽  
Poor Performance ◽  
Direction Of Arrival ◽  
Estimation Accuracy ◽  
High Signal ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Low Snr

Source enumeration is an important procedure for radio direction-of-arrival finding in the multiple signal classification (MUSIC) algorithm. The most widely used source enumeration approaches are based on the eigenvalues themselves of the covariance matrix obtained from the received signal. However, they have shortcomings such as the imperfect accuracy even at a high signal-to-noise ratio (SNR), the poor performance at low SNR, and the limited detection number of sources. This paper proposestwo source enumeration approaches using the ratio of eigenvalue gaps and the threshold trained by a machine learning based clustering algorithm for gaps of normalized eigenvalues, respectively. In the first approach, a criterion formula derived with eigenvalue gaps is used to determine the number of sources, where the formula has maximum value. In the second approach, datasets of normalized eigenvalue gaps are generated for the machine learning based clustering algorithm and the optimal threshold for estimation of the number of sources are derived, which minimizes source enumeration error probability. Simulation results show that our proposed approaches are superior to the conventional approaches from both the estimation accuracy and numerical detectability extent points of view. The results demonstrate that the second proposed approach has the feasibility to improve source enumeration performance if appropriate learning datasets are sufficiently provided.

scholarly journals Time-Frequency Analysis and Target Recognition of HRRP Based on CN-LSGAN, STFT, and CNN

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jianghua Nie ◽  
Yongsheng Xiao ◽  
Lizhen Huang ◽  
Feng Lv
Keyword(s):  
Least Squares ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Feature Learning ◽  
Generative Adversarial Network ◽  
Time Frequency ◽  
Adversarial Network

Aiming at the problem of radar target recognition of High-Resolution Range Profile (HRRP) under low signal-to-noise ratio conditions, a recognition method based on the Constrained Naive Least-Squares Generative Adversarial Network (CN-LSGAN), Short-time Fourier Transform (STFT), and Convolutional Neural Network (CNN) is proposed. Combining the Least-Squares Generative Adversarial Network (LSGAN) with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), the CN-LSGAN is presented and applied to the HRRP denoise. The frequency domain and phase features of HRRP are gained by STFT in order to facilitate feature learning and also match the input data format of the CNN. These experimental results show that the CN-LSGAN has better data augmentation performance and can effectively avoid the model collapse compared to the generative adversarial network (GAN) and LSGAN. Also, the method has better recognition performance than the one-dimensional CNN method and the Long Short-Term Memory (LSTM) network method.

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