scholarly journals Distribution Characteristics of Ground Echo Amplitude and Recognition of Signal Grazing Angle

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8315
Author(s):  
Guangwei Zhang ◽  
Ping Li ◽  
Guolin Li ◽  
Ruili Jia
Keyword(s):  
Grazing Angle ◽  
Recognition Rate ◽  
Amplitude Distribution ◽  
Statistical Characteristics ◽  
Radar Clutter ◽  
Ground Clutter ◽  
Network Training ◽  
Terahertz Technology ◽  
Glancing Angle ◽  
Grazing Angles

With the continuous advancement of electronic technology, terahertz technology has gradually been applied on radar. Since short wavelength causes severe ground clutter, this paper studies the amplitude distribution statistical characteristics of the terahertz radar clutter based on the measured data, and provides technical support for the radar clutter suppression. Clutter distribution is the function of the radar glancing angle. In order to achieve targeted suppression, in this paper, selected axial integral bispectrum (selected AIB) feature is selected as deep belief network (DBN)input to complete the radar glancing angle recognition and the network structure, network training method, robustness are analyzed also. The ground clutter amplitude distribution can follow normal distribution at 0~45° grazing angles. The Weibull distribution and G0 distribution can describe the amplitude probability density function of ground clutter at grazing angles 85° and 65°. The recognition rate of different signal grazing angles can reach 91% on three different terrains. At the same time, the wide applicability of the selected AIB feature is verified. The analysis results of ground clutter amplitude characteristics play an important role in the suppression of radar ground clutter.

RAY CHAOS IN UNDERWATER ACOUSTIC WAVEGUIDES

2008 ◽  
Vol 18 (09) ◽  
pp. 2693-2700 ◽  
Author(s):  
A. L. VIROVLYANSKY
Keyword(s):  
Random Perturbation ◽  
Hamiltonian Formalism ◽  
Statistical Characteristics ◽  
Acoustic Waveguides ◽  
Ray Trajectories ◽  
Grazing Angles ◽  
Ray Path ◽  
Wave Induced ◽  
Ray Chaos ◽  
Analytical Expressions

The chaotic motion of a ray path in a deep water acoustic waveguide with internal-wave-induced fluctuations of the sound speed is investigated. A statistical approach for the description of chaotic rays is discussed. The behavior of ray trajectories is studied using Hamiltonian formalism expressed in terms of action-angle variables. It is shown that the range dependence of the action variable of chaotic ray can be approximated by a random Wiener process. On the basis of this result, analytical expressions for probability density functions of ray parameters are derived. Distributions of coordinates, momenta (grazing angles), and actions of sound rays are evaluated. Numerical simulation shows that statistical characteristics of ray parameters weakly depend on a particular realization of random perturbation giving rise to ray chaos.

scholarly journals Noise-Transfer2Clean: Denoising cryo-EM images based on noise modeling and transfer

2021 ◽  
Author(s):  
Hongjia Li ◽  
Hui Zhang ◽  
Xiaohua Wan ◽  
Zhidong Yang ◽  
Chengmin Li ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Main Idea ◽  
Noise Model ◽  
Statistical Characteristics ◽  
Natural Image ◽  
Macromolecular Complex ◽  
Signal Restoration ◽  
Denoising Method

Motivation: Cryo-electron microscopy (cryo-EM) is a widely-used technology for ultrastructure determination, which constructs the three-dimensional (3D) structures of protein and macromolecular complex from a set of two-dimensional (2D) micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryo-EM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods. Results: Here, we introduce the Noise-Transfer2Clean (NT2C), a denoising deep neural network (DNN) for cryo-EM to enhance image contrast and restore specimen signal, whose main idea is to improve the denoising performance by correctly discovering the noise model of cryo-EM images and transferring the statistical nature of noise into the denoiser. Especially, to cope with the complex noise model in cryo-EM, we design a contrast-guided noise and signal re-weighted algorithm to achieve clean-noisy data synthesis and data augmentation, making our method authentically achieve signal restoration based on noise's true properties. To our knowledge, NT2C is the first denoising method that resolves the complex noise model in cryo-EM images. Comprehensive experimental results on simulated datasets and real datasets show that NT2C achieved a notable improvement in image denoising and specimen signal restoration, comparing with the state-of-art methods. A real-world case study shows that NT2C can improve the recognition rate on hard-to-identify particles by 19% in the particle picking task.

Scattering of Two-Dimensional Sea Surface at Low Grazing Angles with Physical Optics Method

2012 ◽  
Vol 571 ◽  
pp. 372-376
Author(s):  
Zhuo Chen ◽  
Zhen Sen Wu ◽  
Yong Zhang ◽  
Jun Jing Xue ◽  
Wen Hua Ye
Keyword(s):  
Numerical Calculation ◽  
Cross Section ◽  
Incident Wave ◽  
Electromagnetic Scattering ◽  
Grazing Angle ◽  
Sea Surface ◽  
Physical Optics ◽  
Two Dimensional ◽  
Grazing Angles ◽  
Numerical Calculation Method

As the classic analytical methods cannot calculate the electromagnetic scattering characteristics at Low Grazing Angle (LGA), a novel numerical method is presented by utilizing the Radar Cross Section (RCS) of the low grazing two-dimensional PM sea spectrum based on the triangles-based Physical Optics (PO) method in this paper. The calculated RCS is compared with the numerical calculation method of different targets and frequencies of incident wave. The results show that PO method is more accurate, especially under LGA condition, and it has a better agreement with the results calculated by commercial software FEKO.

scholarly journals An Analysis of X-Band Calibrated Sea Clutter and Small Boat Reflectivity at Medium-to-Low Grazing Angles

2008 ◽  
Vol 2008 ◽  
pp. 1-14 ◽  
Author(s):  
P. L. Herselman ◽  
C. J. Baker ◽  
H. J. de Wind
Keyword(s):  
Grazing Angle ◽  
Sea Clutter ◽  
Small Boat ◽  
Empirical Modelling ◽  
Detection And Tracking ◽  
Radar Systems ◽  
X Band ◽  
Radar Waveforms ◽  
Grazing Angles ◽  
Marine Navigation

The coherent temporal characteristics of medium-to-low grazing angle sea clutter and small boat reflectivity are considered for different radar waveforms under a range of environmental conditions and geometrical configurations. Accurate empirical modelling of sea clutter enables the inference of the local sea conditions from radar returns, pertinent for port safety and navigation. Understanding the dynamics and associated reflectivity of small boats, in addition to empirical sea clutter models, allows the development of advanced detection and tracking algorithms, which will improve the performance of surveillance and marine navigation radar against small boats. Work presented is based on the empirical analysis of data recorded with two calibrated, coherent, pulsed radar systems at X-band frequencies. Specifically, target echoes from small boats are included in the datasets and subsequent analysis.

A Recognition Algorithm for M-QAM Based on Amplitude Distribution

2011 ◽  
Vol 128-129 ◽  
pp. 20-24
Author(s):  
Lu Yuan Tan ◽  
Qian Wang ◽  
Xiao Yan ◽  
Kai Yu Qin
Keyword(s):  
Carrier Frequency ◽  
Carrier Frequency Offset ◽  
Recognition Rate ◽  
Amplitude Distribution ◽  
Frequency Offset ◽  
Recognition Algorithm ◽  
Automatic Recognition ◽  
Correct Recognition ◽  
Nonlinear Amplification ◽  
Correct Recognition Rate

An automatic recognition algorithm for M-QAM based on the amplitude distribution is proposed. This algorithm uses the normalized amplitude distribution to achieve automatic recognition for M-QAM signals, and enhances the correct recognition rate through the nonlinear amplification. Compared with the recognition algorithm based on amplitude moment, this algorithm does not need those prior conditions, such as carrier frequency offset, code element rate, amplitude factor and so on. The simulation confirmed that, when SNR≥16dB the correct recognition rate of this algorithm is greater than 90%.

Sign in / Sign up

Export Citation Format

Share Document