Geomagnetic Reference Map Denoising Based on Singular Entropy

2014 ◽  
Vol 543-547 ◽  
pp. 912-916
Author(s):  
Zhan Long Zhu ◽  
Gong Liu Yang ◽  
Yan Yong Wang ◽  
Yuan Yuan Liu
Keyword(s):  
Information Entropy ◽  
Signal To Noise Ratio ◽  
Singular Values ◽  
Signal To Noise ◽  
Simulation Experiments ◽  
Noise Disturbance ◽  
Asymptotic Characteristic ◽  
Reference Map ◽  
Simulation Results ◽  
Matching Probability

To weaken the noise disturbance of GRM and improve the matching precision and matching probability of inertial/geomagnetic system, this paper proposed a method for denoising based on SVD. Firstly, from the perspective of information entropy, the singular entropy is introduced and the inner link between singular entropy and signal-to-noise ratio (SNR) is analyzed. Secondly, the method based on the asymptotic characteristic of the probabilities associated with the different singular values order (SVO) is proposed. Lastly, by utilizing practical GRM, the denoising analysis about the proposed method is demonstrated and later simulation experiments of GMN are accomplished. Simulation results show that the method is feasible and reliable.

scholarly journals Cooperative Spectrum Sensing Based on Convolutional Neural Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4440
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Neural Networks ◽  
Spectrum Sensing ◽  
Convolutional Neural Networks ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Majority Voting ◽  
Signal To Noise ◽  
Hidden Terminal ◽  
Simulation Results ◽  
Sensing Performance

Cooperative spectrum sensing (CSS) is an important topic due to its capacity to solve the issue of the hidden terminal. However, the sensing performance of CSS is still poor, especially in low signal-to-noise ratio (SNR) situations. In this paper, convolutional neural networks (CNN) are considered to extract the features of the observed signal and, as a consequence, improve the sensing performance. More specifically, a novel two-dimensional dataset of the received signal is established and three classical CNN (LeNet, AlexNet and VGG-16)-based CSS schemes are trained and analyzed on the proposed dataset. In addition, sensing performance comparisons are made between the proposed CNN-based CSS schemes and the AND, OR, majority voting-based CSS schemes. The simulation results state that the sensing accuracy of the proposed schemes is greatly improved and the network depth helps with this.

An Improved Acquisition Method for High-Order BOC-Modulated Signals Based on Fractal Geometry

2013 ◽  
Vol 846-847 ◽  
pp. 1185-1188 ◽  
Author(s):  
Hua Bing Wu ◽  
Jun Liang Liu ◽  
Yuan Zhang ◽  
Yong Hui Hu
Keyword(s):  
Fractal Geometry ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
High Order ◽  
Main Peak ◽  
Signal To Noise ◽  
Binary Offset Carrier ◽  
Simulation Results ◽  
Modulated Signals ◽  
Acquisition Method

This paper proposes an improved acquisition method for high-order binary-offset-carrier (BOC) modulated signals based on fractal geometry. We introduced the principle of our acquisition method, and outlined its framework. We increase the main peak to side peaks ratio in the BOC autocorrelation function (ACF), with a simple fractal geometry transform. The proposed scheme is applicable to both generic high-order sine-and cosine-phased BOC-modulated signals. Simulation results show that the proposed method increases output signal to noise ratio (SNR).

ROBUST SVD FILTERING FOR LOW SIGNAL-TO-NOISE RATIO SEISMIC DATA

Geophysics ◽  
2021 ◽  
pp. 1-51
Author(s):  
Chao Wang ◽  
Yun Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Singular Values ◽  
New Method ◽  
Signal To Noise ◽  
Coherent Noise ◽  
Edge Preserving ◽  
Singular Vectors ◽  
Reduced Rank ◽  
Noise Ratio

Reduced-rank filtering is a common method for attenuating noise in seismic data. As conventional reduced-rank filtering distinguishes signals from noises only according to singular values, it performs poorly when the signal-to-noise ratio is very low, or when data contain high levels of isolate or coherent noise. Therefore, we developed a novel and robust reduced-rank filtering based on the singular value decomposition in the time-space domain. In this method, noise is recognized and attenuated according to the characteristics of both singular values and singular vectors. The left and right singular vectors corresponding to large singular values are selected firstly. Then, the right singular vectors are classified into different categories according to their curve characteristics, such as jump, pulse, and smooth. Each kind of right singular vector is related to a type of noise or seismic event, and is corrected by using a different filtering technology, such as mean filtering, edge-preserving smoothing or edge-preserving median filtering. The left singular vectors are also corrected by using the filtering methods based on frequency attributes like main-frequency and frequency bandwidth. To process seismic data containing a variety of events, local data are extracted along the local dip of event. The optimal local dip is identified according to the singular values and singular vectors of the data matrices that are extracted along different trial directions. This new filtering method has been applied to synthetic and field seismic data, and its performance is compared with that of several conventional filtering methods. The results indicate that the new method is more robust for data with a low signal-to-noise ratio, strong isolate noise, or coherent noise. The new method also overcomes the difficulties associated with selecting an optimal rank.

Highly Accurate Frequency Interpolation of Apodized FFT Magnitude-Mode Spectra

1998 ◽  
Vol 52 (1) ◽  
pp. 134-138 ◽  
Author(s):  
Yutaka Goto
Keyword(s):  
Simple Formula ◽  
Previous Investigation ◽  
Signal To Noise Ratio ◽  
Damping Ratio ◽  
Frequency Error ◽  
Signal To Noise ◽  
Window Functions ◽  
Optimal Values ◽  
Simulation Results ◽  
True Frequency

“Generalized” interpolation (called GIα here) of fast Fourier transform (FFT) spectra apodized by a family of sinα ( X) windows has previously been proposed. The GIα gives the highly accurate interpolated frequency by calculating the simple formula of frequency determination with the use of two squared ratios between three magnitudes nearest to the peak maximum on the apodized FFT spectrum. Although the value of window parameter α, limited to integer values, has been used for the GIα, we show in the present paper that the GIα with a real α value also gives an extremely good estimate of the true frequency from the sinα ( X)-apodized spectra. Thus, we intend to apply the GIα with the optimal values of α to FFT spectra apodized by any other window functions that are often used in Fourier spectroscopy. Simulation results show that the GIα is easier and more accurate than the KCe interpolation, which uses a family of interpolating functions [ KCe(ω) = ( aω2 + bω + c)e] proposed by Keefe and Comisarow. Finally, in the presence of noise we examine effects of damping and windowing on the frequency interpolation of FFT spectra. Because damping and windowing reduce the signal-to-noise ratio (SNR), we define anew the relative SNR by the ratio of the SNR of the apodized spectrum of a damped sinusoid to the SNR of the unapodized spectrum of an undamped sinusoid. Numerical calculation shows that the relative SNR varies, owing to damping rather than windowing. In fact, the observed frequency error roughly increases as the damping ratio increases for any window functions, as is expected from our previous investigation that the frequency error based upon the GIα is inversely proportional to the SNR. However, no obvious differences between the various window functions are observed in the presence of noise.

Digital Modulation Recognition in Different Environments

2014 ◽  
Vol 1049-1050 ◽  
pp. 2084-2087 ◽  
Author(s):  
Rong Li
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Automatic Recognition ◽  
Modulation Recognition ◽  
Signal To Noise ◽  
Digital Modulation ◽  
Multipath Interference ◽  
Modulation Signal ◽  
Simulation Results ◽  
Recognition Success

For the using of multi-modulation, the precondition of receiving and demodulating signal is to determine the type of the modulation, so automatic recognition of modulation signal has significant influence on the analysis of the signals. In this paper, digital modulation recognition is studied respectively in different environment of White Gaussian Noise (WGN), stationary interference and multipath interference. The simulation results show that the recognition success rate is the highest in stationary interference environment and the lowest in multipath interference environment with the same signal to noise ratio (SNR).

scholarly journals Superposition Coded Modulation Based Faster-Than-Nyquist Signaling

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Shuangyang Li ◽  
Baoming Bai ◽  
Jing Zhou ◽  
Qingli He ◽  
Qian Li
Keyword(s):  
Bit Error Rate ◽  
Spectral Efficiency ◽  
Detection Method ◽  
Signal To Noise Ratio ◽  
Coded Modulation ◽  
Signal To Noise ◽  
Data Rates ◽  
Orthogonal Signaling ◽  
Simulation Results ◽  
Constrained Capacity

A structure of faster-than-Nyquist (FTN) signaling combined with superposition coded modulation (SCM) is considered. The so-called FTN-SCM structure is able to achieve the constrained capacity of FTN signaling and only requires a low detection complexity. By deriving a new observation model suitable for FTN-SCM, we offer the power allocation based on a proper detection method. Simulation results show that, at any given spectral efficiency, the bit error rate (BER) curve of FTN-SCM lies clearly outside the minimum signal-to-noise ratio (SNR) boundary of orthogonal signaling with a larger alphabet. The achieved data rates are also close to the maximum data rates of the certain shaping pulse.

scholarly journals Optimization of Direct Direction Finding Method with Two-Dimensional Correlative Processing of Spatial Signal

2018 ◽  
Vol 8 ◽  
pp. 46-53
Author(s):  
Vitaliy V. Tsyporenko ◽  
Valentyn G. Tsyporenko
Keyword(s):  
Main Parameter ◽  
Noise Immunity ◽  
Signal To Noise Ratio ◽  
Direction Finding ◽  
High Accuracy ◽  
Signal To Noise ◽  
High Noise ◽  
Spatial Shift ◽  
Simulation Results ◽  
Finding Method

In this article, the main parameter of the correlative-interferometric direction finding method with twodimensional correlative processing of spatial signal in the aperture of a linear antenna array (AA) is determined as the value of spatial shift within the AA aperture. The corresponding objective function is also formed. Analytical optimization of this parameter is presented and a comparative analysis of analytical calculations based on simulation results is conducted. In the simulation, a range of dependencies of the middle square deviation of estimation of direction on the value of the spatial shift for a signal-to-noise ratio of 0 dB, for minimum 3-sample and 4-sample Blackman-Harris windows of the spectral analysis, is received. The value of the middle square deviation of estimation of direction will be minimal and will equal 0.02 degrees using a minimum 3-sample Blackman-Harris window with the −67 dB level of side lobes. It offers high noise immunity and high accuracy of direction finding.

scholarly journals An Algorithm for Automatic Recogniton of Digital QAM Modulations

2019 ◽  
Vol 25 (3) ◽  
pp. 36-41
Author(s):  
Alexandru-Daniel Luţă ◽  
Paul Bechet
Keyword(s):  
Lower Order ◽  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Automatic Recognition ◽  
Signal To Noise ◽  
Small Influence ◽  
Qam Modulation ◽  
Digital Modulations ◽  
Simulation Results ◽  
Noise Ratio

Abstract This paper proposes a new Matlab-developed algorithm for automatic recognition of digital modulations using the constellation of states. Using this technique the automatic distinction between four digital modulation schemes (8-QAM, 16-QAM, 32-QAM and 64-QAM) was made. It has been seen that the efficiency of the algorithm is influenced by the type of modulation, the value of the signal-to-noise ratio and the number of samples. In the case of an AWGN noise channel the simulation results indicated that the value of SNR (signal-to-noise ratio) has a small influence on the recognition rate for lower-order QAM (8-QAM and 16-QAM). The length of the signal may change essentially the recognition rate of this algorithm especially for modulations with a high number of bits per symbol. Consequently, for the 64-QAM modulation in a case of 25dB signal-to-noise ratio the recognition rate is doubled if the sample rate is incresed from 5400 to 80640.

scholarly journals Novel ultrasound transmission and reconstruction techniques for synthetic transmit aperture imaging

2021 ◽  
Author(s):  
Ping Gong
Keyword(s):  
Signal To Noise Ratio ◽  
Singular Values ◽  
Single Element ◽  
Ultrasound Images ◽  
Transmission Scheme ◽  
Signal To Noise ◽  
Spatial Compounding ◽  
Image Quality Enhancement ◽  
Noise Ratio ◽  
Value Decomposition

This dissertation describes ultrasound algorithms developed for synthetic transmit aperture (STA) imaging during the transmission and the image reconstruction stages. Images generated using these algorithms demonstrate image quality enhancement both theoretically and experimentally. The advanced algorithms also improve the application of STA imaging. Due to the single element transmission pattern, the low signal-to-noise ratio is a major limitation for STA imaging. A delay-encoded transmission scheme (DE-STA) was designed in this dissertation to encode all the transmissions. The decoded RF signals were equivalent to the standard STA signals, but with a higher SNR. Improved image qualities were observed under DE-STA transmission in terms of lateral resolution (+28%), peak-signal-to-noise ratio (PSNR, +7 dB) and target contrast-to-noise ratio (CNR, +360%) compared to those acquired with the standard STA mode. The stability of DE-STA was analyzed and verified under various noise levels by the special distribution of the singular values of the encoding matrix through singular value decomposition (SVD) (i.e. all the singular values were the same except for the first one and the last one). A more efficient decoding process was also derived based on pseudo-inversion (PI) and the computation complexity was reduced by 2/3. Speckle and undesired sidelobe signals can reduce the lesion CNR and detectability in ultrasound images. Typically, the CNR can be increased by spatial compounding (SC) or frequency compounding (FC) during reconstruction. We proposed methods to implement a 2-dimentional (2-D) aperture domain filter in the SC/FC processes, referred to as filtered spatial compounding (FSC) and filtered frequency compounding (FFC), for synthetic transmit aperture (STA) imaging. Both techniques reduced the sidelobe interference and provided improved lesion CNR. Consequently, the lesion signal-to-noise ratio (lSNR) in FSC and FFC increased (up to +130%), compared to that in the standard delay-and-sum (DAS) method. This dissertation investigates all these proposed advanced ultrasound algorithms, with the end goal of implementing these methods in STA imaging to extend its application in clinic.

scholarly journals Analysis of the reliability of LoRa

2020 ◽  
Author(s):  
Qahhar Muhammad Qadir
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Coverage Probability ◽  
Stochastic Geometry ◽  
Signal To Noise Ratio ◽  
Low Complexity ◽  
Signal To Noise ◽  
Coverage Probabilities ◽  
Form Model ◽  
Simulation Results

This letter studies the performance of a single gateway LoRa system in the presence of different interference considering the imperfect orthogonality effect. It utilizes concepts of stochastic geometry to present a low-complexity approximate closed-form model for computing the success and coverage probabilities under these challenging conditions. Monte Carlo simulation results have shown that LoRa is not as theoretically described as a technology that can cover few to ten kilometers. It was found that in the presence of the combination of signal-to-noise ratio (SNR) and imperfect orthogonality between spreading factors (SF), the performance degrades dramatically beyond a couple of kilometers. However, better performance is observed when perfect orthogonality is considered and SNR is not included. Furthermore, the performance is annulus dependent and slightly improves at the border of the deployment cell annuli. Finally, the coverage probability declines exponentially as the average number of end devices grows.

Sign in / Sign up

Export Citation Format

Share Document