Optimum Signal Processing of Three‐Dimensional Arrays Operating on Gaussian Signals and Noise

1962 ◽  
Vol 34 (3) ◽  
pp. 289-297 ◽  
Author(s):  
Finn Bryn
Keyword(s):  
Signal Processing ◽  
Three Dimensional

scholarly journals Prospects of the WSR-88D Radar for Cloud Studies

2011 ◽  
Vol 50 (4) ◽  
pp. 859-872 ◽  
Author(s):  
Valery M. Melnikov ◽  
Dusan S. Zrnić ◽  
Richard J. Doviak ◽  
Phillip B. Chilson ◽  
David B. Mechem ◽  
...  
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Three Dimensional ◽  
High Sensitivity ◽  
Doppler Velocity ◽  
Cloud Data ◽  
Three Dimensional Mapping ◽  
Spectrum Width ◽  
Differential Reflectivity ◽  
Dimensional Mapping

AbstractSounding of nonprecipitating clouds with the 10-cm wavelength Weather Surveillance Radar-1988 Doppler (WSR-88D) is discussed. Readily available enhancements to signal processing and volume coverage patterns of the WSR-88D allow observations of a variety of clouds with reflectivities as low as −25 dBZ (at a range of 10 km). The high sensitivity of the WSR-88D, its wide velocity and unambiguous range intervals, and the absence of attenuation allow accurate measurements of the reflectivity factor, Doppler velocity, and spectrum width fields in clouds to ranges of about 50 km. Fields of polarimetric variables in clouds, observed with a research polarimetric WSR-88D, demonstrate an abundance of information and help to resolve Bragg and particulate scatter. The scanning, Doppler, and polarimetric capabilities of the WSR-88D allow real-time, three-dimensional mapping of cloud processes, such as transformations of hydrometeors between liquid and ice phases. The presence of ice particles is revealed by high differential reflectivities and the lack of correlation between reflectivity and differential reflectivity in clouds in contrast to that found for rain. Pockets of high differential reflectivities are frequently observed in clouds; maximal values of differential reflectivity exceed 8 dB, far above the level observed in rain. The establishment of the WSR-88D network consisting of 157 polarimetric radars can be used to collect cloud data at any radar site, making the network a potentially powerful tool for climatic studies.

Three‐dimensional integration of resonant tunneling structures for signal processing and three‐state logic

1988 ◽  
Vol 52 (25) ◽  
pp. 2163-2164 ◽  
Author(s):  
Robert C. Potter ◽  
Amir A. Lakhani ◽  
Dana Beyea ◽  
Harry Hier ◽  
Erica Hempfling ◽  
...  
Keyword(s):  
Signal Processing ◽  
Resonant Tunneling ◽  
Three Dimensional

scholarly journals Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5466 ◽  
Author(s):  
Xinrui Jiang ◽  
Ye Zhang ◽  
Qi Yang ◽  
Bin Deng ◽  
Hongqiang Wang
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Millimeter Wave ◽  
Gait Recognition ◽  
Three Dimensional ◽  
Human Gait ◽  
Radar Signal ◽  
Learning Technology ◽  
Motion Speed ◽  
Signal Processing Algorithms

At present, there are two obvious problems in radar-based gait recognition. First, the traditional radar frequency band is difficult to meet the requirements of fine identification with due to its low carrier frequency and limited micro-Doppler resolution. Another significant problem is that radar signal processing is relatively complex, and the existing signal processing algorithms are poor in real-time usability, robustness and universality. This paper focuses on the two basic problems of human gait detection with radar and proposes a human gait classification and recognition method based on millimeter-wave array radar. Based on deep-learning technology, a multi-channel three-dimensional convolution neural network is proposed on the basis of improving the residual network, which completes the classification and recognition of human gait through the hierarchical extraction and fusion of multi-dimensional features. Taking the three-dimensional coordinates, motion speed and intensity of strong scattering points in the process of target motion as network inputs, multi-channel convolution is used to extract motion features, and the classification and recognition of typical daily actions are completed. The experimental results show that we have more than 92.5% recognition accuracy for common gait categories such as jogging and normal walking.

scholarly journals Direct 3-D Sparse Imaging Using Non-Uniform Samples Without Data Interpolation

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 321
Author(s):  
Dou Sun ◽  
Bo Pang ◽  
Shiqi Xing ◽  
Yongzhen Li ◽  
Xuesong Wang
Keyword(s):  
Signal Processing ◽  
Large Scale ◽  
Three Dimensional ◽  
Sparse Reconstruction ◽  
Electromagnetic Simulation ◽  
Data Interpolation ◽  
Reconstruction Problem ◽  
Simulation Data ◽  
Processing Scheme ◽  
Imaging Performance

As an emerging technique, sparse imaging from three-dimensional (3-D) and non-uniform samples provides an attractive approach to obtain high resolution 3-D images along with great convenience in data acquisition, especially in the case of targets consisting of strong isolated scatterers. Although data interpolation in k-space and fast Fourier transform have been employed in the existing 3-D sparse imaging methods to reduce the computational complexity, the data-gridding errors induced by local interpolation may usually result in poor imaging performance. In this paper, we directly regard the imaging problem as a joint sparse reconstruction problem from non-uniform data without interpolation in 3-D space. Combining dictionary reduction and Gauss iterative method with the optimized signal processing scheme, a sparse imaging algorithm is proposed to address the difficulty of large-scale computation involved in direct 3-D sparse reconstruction. Benefited from the optimized signal processing scheme and the avoidance of data interpolation, the direct 3-D sparse imaging (DTDSI) method proposed in this paper is of low computation scale and high imaging performance. Experiments of electromagnetic simulation data demonstrate the DTDSI method outperforms baseline methods in terms of resolving ability, lower side-lobes and higher accuracy.

Wavefront Coded Imaging Systems for MEMS Analysis

2002 ◽  
Author(s):  
Daniel L. Barton ◽  
Jeremy A. Walraven ◽  
Edward R. Dowski ◽  
Rainer Danz ◽  
Andreas Faulstich ◽  
...  
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
High Performance ◽  
Imaging System ◽  
Three Dimensional ◽  
Imaging Systems ◽  
Depth Of Field ◽  
Real Time Imaging ◽  
Large Depth ◽  
Wavefront Coding

Abstract A new imaging technique called Wavefront Coding allows real-time imaging of three-dimensional structures over a very large depth. Wavefront Coding systems combine aspheric optics and signal processing to achieve depth of fields ten or more times greater than that possible with traditional imaging systems. Understanding the relationships between traditional and modern imaging system design through Wavefront Coding is very challenging. In high performance imaging systems nearly all aspects of the system that could reduce image quality are carefully controlled. Modifying the optics and using signal processing can increase the amount of image information that can be recorded by microscopes. For a number of applications this increase in information can allow a single image to be used where a number of images taken at different object planes had been used before. Having very large depth of field and real-time imaging capability means that very deep structures such as surface micromachined MEMS can be clearly imaged with one image, greatly simplifying defect and failure analysis.

THREE‐DIMENSIONAL SEISMIC DEPTH COMPUTATION USING SPACE‐SAMPLED VELOCITY LOGS

Geophysics ◽  
1969 ◽  
Vol 34 (1) ◽  
pp. 7-20 ◽  
Author(s):  
J. W. Sattlegger
Keyword(s):  
Signal Processing ◽  
Analytic Function ◽  
Seismic Data ◽  
Three Dimensional ◽  
Velocity Information

Due to the large amount of velocity information provided through adequate signal processing of multicoverage seismic data, approaches for depth computation presuming the velocity to be described by some simple analytic function may no longer be considered adequate. Three approaches to depth computation, are applied to cases of arbitrarily complicated velocity distributions. When we use these approaches, accuracy may be increased to any desired degree or to any degree which seems reasonable with respect to the velocity information available.

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