Beamforming of Joint Polarization-Space Matched Filtering for Conformal Array

Due to the polarization mismatch of the antenna, the received signal suffers from energy loss. The conventional beamforming algorithms could not be applied to the conformal array because of the varying curvature. In order to overcome the energy loss of the received signal, a novel joint polarization-space matched filtering algorithm for cylindrical conformal array is proposed. First, the snapshot data model of the conformal polarization sensitive array is analyzed. Second, the analytical expression of polarization sensitive array beamforming is derived. Linearly constrained minimum variance (LCMV) beamforming technique is facilitated for the cylindrical conformal array. Third, the idea of joint polarization-space matched filtering is presented, and the principle of joint polarization-space matched filtering is discussed in detail. Theoretical analysis and computer simulation results verify that the conformal polarization sensitive array is more robust than the ordinary conformal array. The proposed algorithm can improve the performance when signal and interference are too close. It can enhance the signal-to-noise ratio (SNR) by adjusting the polarization of the elements of the conformal array, which matches the polarization of the incident signal.

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Application of Prewhitening Beamformer with Linear Constraints for Correlated EEG Signal Source Estimation

Advances in Bioinformatics and Biomedical Engineering - Biomedical Engineering and Cognitive Neuroscience for Healthcare ◽

10.4018/978-1-4666-2113-8.ch025 ◽

2013 ◽

pp. 243-254

Author(s):

Teruyoshi Sasayama ◽

Shoji Hamada ◽

Tetsuo Kobayashi

Keyword(s):

Primary Motor Cortex ◽

Signal To Noise Ratio ◽

Minimum Variance ◽

Linear Constraints ◽

Eeg Signal ◽

Source Estimation ◽

Linearly Constrained ◽

Highly Correlated ◽

Signal Sources ◽

Correlated Signals

To investigate the effect of signal correlation, the authors compared the relative abilities to estimate the source of an ERD/ERS signal among minimum variance beamformer (MVBF), linearly constrained (LC)-MVBF, prewhitening beamformer (PWBF), and LC-PWBF during the measurement of correlated signals. In numerical simulations, equivalent current dipoles were placed in the primary motor cortex to detect the modulation of µ and ß rhythms. It was confirmed that the location bias of LC-PWBF was smaller than that of MVBF, LC-MVBF, and PWBF. These results suggest that LC-PWBF is useful for estimating the location of signal sources that are highly correlated and have low signal-to-noise ratio.

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Accurate diffraction imaging for detecting small-scale geologic discontinuities

Geophysics ◽

10.1190/geo2017-0545.1 ◽

2018 ◽

Vol 83 (5) ◽

pp. S447-S457 ◽

Cited By ~ 4

Author(s):

Peng Lin ◽

Suping Peng ◽

Jingtao Zhao ◽

Xiaoqin Cui ◽

Wenfeng Du

Keyword(s):

Signal To Noise Ratio ◽

Migration Velocity ◽

Minimum Variance ◽

Seismic Interpretation ◽

Small Scale ◽

Imaging Method ◽

Velocity Analysis ◽

Diffraction Imaging ◽

Correlation Properties ◽

Beamforming Technique

Seismic diffractions contain valuable information regarding small-scale inhomogeneities or discontinuities, and therefore they can be used for seismic interpretation in the exploitation of hydrocarbon reservoirs. Velocity analysis is a necessary step for accurate imaging of these diffractions. A new method for diffraction velocity analysis and imaging is proposed that uses an improved adaptive minimum variance beamforming technique. This method incorporates the minimum variance, coherence factor, and correlation properties to improve the signal-to-noise ratio and enhance correlations. Our method can make seismic diffractions become better focused in semblance panels, allowing for the optimal migration velocity for diffractions to be accurately picked. Synthetic and field examples demonstrate that the migration velocity for the diffractions can differ from that for the reflections. The results suggest that the diffraction velocity analysis and imaging method is feasible for accurately locating and identifying small-scale discontinuities, which leads to the possibility of using this approach for practical application and seismic interpretation.

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Background subtraction in STEM energy-loss mapping

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100112774 ◽

1984 ◽

Vol 42 ◽

pp. 568-569

Author(s):

R.D. Leapman ◽

K.E. Gorlen ◽

C.R. Swyt

Keyword(s):

Energy Loss ◽

Signal To Noise Ratio ◽

Statistical Error ◽

Scanning Transmission Electron Microscope ◽

Reference Image ◽

Inverse Power Law ◽

Transmission Electron ◽

Least Squares Fit ◽

Scanning Transmission ◽

Single Number

The determination of elemental distributions by electron energy loss spectroscopy necessitates removal of the non-characteristic spectral background from a core-edge at each point in the image. In the scanning transmission electron microscope this is made possible by computer controlled data acquisition. Data may be processed by fitting the pre-edge counts, at two or more channels, to an inverse power law, AE-r, where A and r are parameters and E is energy loss. Processing may be performed in real-time so a single number is saved at each pixel. Detailed analysis, shows that the largest contribution to noise comes from statistical error in the least squares fit to the background. If the background shape remains constant over the entire image, the signal-to-noise ratio can be improved by fitting only one parameter. Such an assumption is generally implicit in subtraction of the “reference image” in energy selected micrographs recorded in the CTEM with a Castaing-Henry spectrometer.

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