scholarly journals Efficient and Accurate Frequency Estimator under Low SNR by Phase Unwrapping

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Shen Zhou ◽  
Liu Rongfang
Keyword(s):  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Likelihood Estimation ◽  
Accurate Method ◽  
Phase Unwrapping ◽  
Single Frequency ◽  
Low Snr ◽  
Cramer Rao Bound ◽  
Frequency Estimator

In the case of low signal-to-noise ratio, for the frequency estimation of single-frequency sinusoidal signals with additive white Gaussian noise, the phase unwrapping estimator usually performs poorly. In this paper, an efficient and accurate method is proposed to address this problem. Different from other methods, based on fast Fourier transform, the sampled signals are estimated with the variances approaching the Cramer-Rao bound, followed with the maximum likelihood estimation of the frequency. Experimental results reveal that our estimator has a better performance than other phase unwrapping estimators. Compared with the state-of-the-art method, our estimator has the same accuracy and lower computational complexity. Besides, our estimator does not have the estimation bias.

A dichotomous search-based frequency estimator with generic analytical expression

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840095
Author(s):  
Gangbing Zhang ◽  
Lu Jin ◽  
Defeng (David) Huang
Keyword(s):  
Gaussian Noise ◽  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Frequency Interval ◽  
Analytical Expression ◽  
Dichotomous Search ◽  
Simulation Results ◽  
Fine Resolution ◽  
Frequency Estimator

Fine resolution frequency estimation of a single-tone complex sinusoidal signal in the additive white Gaussian noise is of importance in many fields. In this paper, a generic analytical expression is proposed to refine the residual of a dichotomous search, leading to an estimator with much less iterations than the conventional dichotomous search estimator. Compared with other existing estimators, the proposed estimator has a better trade-off between performance and computational complexity. Simulation results demonstrate that the root-mean-square error (RMSE) of the proposed estimator is closer to the Cramer–Rao lower bound (CRLB) than other estimators over the whole frequency interval when the signal-to-noise ratio (SNR) is above a threshold.

scholarly journals A Novel Method Based on Cyclic Spectral Analysis about Carrier Frequency Estimation in the UAV Data Chain

2012 ◽  
Vol 6-7 ◽  
pp. 215-220
Author(s):  
Lin Xi Zhang ◽  
Peng Song ◽  
Ying Jun Zhang
Keyword(s):  
Spectral Analysis ◽  
Carrier Frequency ◽  
Electromagnetic Interference ◽  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Simulation Method ◽  
Phase Lock Loop ◽  
Spectral Density Function ◽  
Low Snr ◽  
Carrier Extraction

When UAV works on the end of the task path, or affected by waves transmission environment and the influence of the electromagnetic interference, the signal of receiver will be made low signal-to-noise ratio (SNR) signal and the phase lock loop (PLL) using for carrier extraction will performance serious decline or loss locks. Therefore, in order to ensure UAV completed a predefined task successfully, low SNR carrier recovery has become one of the key technologies in the UAV data chain. In this paper, based on the basic concept of cyclic spectral analysis, we research and give the cyclic autocorrelation function and the cyclic spectral density function of the BPSK modulation signal. We emphasis a computer simulation method based on the time-smoothing FFT accumulation method and use this method to verify cyclic spectral analysis is still effective to estimate the carrier frequency when the SNR is -10 dB.

Evaluation of pixel-wise geometric constraint-based phase-unwrapping method for low signal-to-noise-ratio (SNR) phase

2016 ◽  
Vol 5 (5-6) ◽  
Author(s):  
Yatong An ◽  
Ziping Liu ◽  
Song Zhang
Keyword(s):  
Signal To Noise Ratio ◽  
Phase Unwrapping ◽  
Geometric Constraints ◽  
Geometric Constraint ◽  
Signal To Noise ◽  
Low Snr ◽  
Digital Fringe Projection ◽  
Phase Map ◽  
Noise Ratio ◽  
New Phase

AbstractThis paper evaluates the robustness of our recently proposed geometric constraint-based phase-unwrapping method to unwrap a low-signal-to-noise ratio (SNR) phase. Instead of capturing additional images for absolute phase unwrapping, the new phase-unwrapping algorithm uses geometric constraints of the digital fringe projection (DFP) system to create a virtual reference phase map to unwrap the phase pixel by pixel. Both simulation and experimental results demonstrate that this new phase-unwrapping method can even successfully unwrap low-SNR phase maps that bring difficulties for conventional multi-frequency phase-unwrapping methods.

Improved SNR Estimation Technique for BPSK and QPSK Signals

2012 ◽  
Vol 239-240 ◽  
pp. 994-999
Author(s):  
Guang Zu Liu ◽  
Jian Xin Wang
Keyword(s):  
Phase Shift ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Estimation Accuracy ◽  
Estimation Technique ◽  
Phase Shift Keying ◽  
Low Snr ◽  
Mathematical Relation ◽  
Shift Keying ◽  
Binary Phase Shift Keying

To improve the estimation accuracy of non-data-aided (NDA) signal-to-noise ratio (SNR) estimators at low SNR value, A novel estimation technique for binary phase-shift keying and quadrature phase-shift keying signals in complex additive white Gaussian noise channel is proposed. The mathematical relation between SNR and the ratio of two simple statistical computations is derived, then SNR is determined by looking up a table. Its accuracy surpasses other NDA estimators, approaching closely to the Cramer-Rao lower bound at SNR > 5dB.

scholarly journals Cognitive Radio Network based on Energy Spotting Method with Enhanced Sensing Accuracy

2019 ◽  
Vol 8 (12) ◽  
pp. 4257-4261
Keyword(s):  
Cognitive Radio ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Research Work ◽  
Radio Networks ◽  
Decision Threshold ◽  
Operating Characteristics ◽  
Low Snr ◽  
Unique System ◽  
Proposed Model

This paper represents the unique system model for cognitive radio based on the energy spotting method to enhance the performance of the accuracy by managing the queue regarding energy-samples and also estimating their average in order to characterize the decision-threshold. Consequently, these typical values summed and estimated over the sum of the samples are repeatedly correlated and analyzed with the recent energy values to determine whether the frequency band is vacant or occupied most accurately. The energy spotting technique’s performance is analyzed and estimated analytically for distinct decision-thresholds. Conventionally Such evaluations interprets that; the advances made to energy spotting algorithm which have enhanced the sensing accuracy in spectrum under the differing signal-to-noise ratio values. Consequently, we shown the utilities and advantages of proposed model that increases the cognitive radio ability. The performance has measured by utilizing the AWGN (Additive White Gaussian Noise) channel and receiver operating-characteristics curves varying under various SNR values alike as: -20 dB, -15 dB, -5 dB, 0 dB, 5db and 10db. With small-tradeoffs among the detection and false-alarm probabilities, the model increases and enhances the ability of spectrum sensing mechanism greatly in the lower SNR situations while tested with number of samples. By that, improving the conventional performance by increasing the sensing accuracy of cognitive radio networks under the low SNR have been the promising achievement of this research work.

Adaptive Algorithm for Determination of Optimal Wavelet Decomposition Level Based on Jarque-Bera Test

2014 ◽  
Vol 644-650 ◽  
pp. 2220-2223
Author(s):  
Yuan Hong Liu ◽  
Ming Zeng ◽  
Yan Sheng Zhang
Keyword(s):  
White Noise ◽  
Wavelet Decomposition ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Nonparametric Test ◽  
Noise Signal ◽  
Test Methods ◽  
Decomposition Level ◽  
Low Snr

Additive Gauss white noise is one of the most commonly observed interferences in practical engineering applications. This paper proposed an algorithm for the adaptive determination of the optimal wavelet decomposition level based on Jarque-Bera test in efforts to solve the filtering problem of additive white Gaussian noise signal. By, The optimal decomposition level of wavelet is determined by testing the white noise which was realized by calculating skewness (S) and kurtosis (K) of the parameters. With signal-to-noise ratio (SNR) as the measurement index, simulation results show that the presented algorithm have higher accuracy, and better filtering effect on low SNR signals compared with nonparametric test methods.

Modal frequencies of bridges from GNSS (GPS) monitoring data: Experimental, statistical evidence

2021 ◽  
Vol 17 (1-2) ◽  
pp. 3-14
Author(s):  
Stathis C. Stiros ◽  
F. Moschas ◽  
P. Triantafyllidis
Keyword(s):  
Signal To Noise Ratio ◽  
Vertical Axis ◽  
Bridge Monitoring ◽  
Low Snr ◽  
True Value ◽  
Gps Satellites ◽  
Wide Range ◽  
Spectral Peaks ◽  
Monitoring Survey ◽  
True Frequency

GNSS technology (known especially for GPS satellites) for measurement of deflections has proved very efficient and useful in bridge structural monitoring, even for short stiff bridges, especially after the advent of 100 Hz GNSS sensors. Mode computation from dynamic deflections has been proposed as one of the applications of this technology. Apart from formal modal analyses with GNSS input, and from spectral analysis of controlled free attenuating oscillations, it has been argued that simple spectra of deflections can define more than one modal frequencies. To test this scenario, we analyzed 21 controlled excitation events from a certain bridge monitoring survey, focusing on lateral and vertical deflections, recorded both by GNSS and an accelerometer. These events contain a transient and a following oscillation, and they are preceded and followed by intervals of quiescence and ambient vibrations. Spectra for each event, for the lateral and the vertical axis of the bridge, and for and each instrument (GNSS, accelerometer) were computed, normalized to their maximum value, and printed one over the other, in order to produce a single composite spectrum for each of the four sets. In these four sets, there was also marked the true value of modal frequency, derived from free attenuating oscillations. It was found that for high SNR (signal-to-noise ratio) deflections, spectral peaks in both acceleration and displacement spectra differ by up to 0.3 Hz from the true value. For low SNR, defections spectra do not match the true frequency, but acceleration spectra provide a low-precision estimate of the true frequency. This is because various excitation effects (traffic, wind etc.) contribute with numerous peaks in a wide range of frequencies. Reliable estimates of modal frequencies can hence be derived from deflections spectra only if excitation frequencies (mostly traffic and wind) can be filtered along with most measurement noise, on the basis of additional data.

scholarly journals Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4623
Author(s):  
Sinead Barton ◽  
Salaheddin Alakkari ◽  
Kevin O’Dwyer ◽  
Tomas Ward ◽  
Bryan Hennelly
Keyword(s):  
Raman Spectra ◽  
Loss Function ◽  
Signal To Noise Ratio ◽  
Biomedical Science ◽  
Clinical Tool ◽  
Signal To Noise ◽  
Low Snr ◽  
Low Intensity ◽  
Spectral Peaks ◽  
Noise Ratio

Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to-noise ratio before application of statistical modeling. A popular method for performing this is Savitsky–Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between denoising and excessive smoothing of spectral peaks, the characteristics of which are critically important for classification purposes. In this paper, we demonstrate how Convolutional Neural Networks may be enhanced with a non-standard loss function in order to improve the overall signal-to-noise ratio of spectra while limiting corruption of the spectral peaks. Simulated Raman spectra and experimental data are used to train and evaluate the performance of the algorithm in terms of the signal to noise ratio and peak fidelity. The proposed method is demonstrated to effectively smooth noise while preserving spectral features in low intensity spectra which is advantageous when compared with Savitzky–Golay filtering. For low intensity spectra the proposed algorithm was shown to improve the signal to noise ratios by up to 100% in terms of both local and overall signal to noise ratios, indicating that this method would be most suitable for low light or high throughput applications.

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