scholarly journals An Algorithm for Estimating the Signal Frequency at the Output of a Channel with a Controlled Information Flow under Phase Noise Conditions

2021 ◽  
Vol 28 (4) ◽  
pp. 452-461
Author(s):  
Leonid Nikolaevich Kazakov ◽  
Evgenii Pavlovich Kubyshkin ◽  
Ilya Victorovich Lukyanov
Keyword(s):  
Phase Noise ◽  
Frequency Estimation ◽  
Estimation Error ◽  
Harmonic Signal ◽  
Frequency Synthesizers ◽  
Broadband Noise ◽  
Signal Frequency ◽  
High Signal ◽  
Radio Network ◽  
Estimation Algorithms

Research in the field of efficient frequency estimation algorithms is of great interest. The reason for this is the redistribution of the role of additive and phase noise in many modern radio-engineering applications. An example is the area of measuring radio devices, which usually operate at high signal-to-noise ratios (SNR). The estimation error is largely determined not by the broadband noise, but by the frequency and phase noise of the local oscillators of the receiving and transmitting devices. In particular, earlier works \\cite{Nikiforov} proposed an efficient computational algorithm for estimating the frequency of a quasi-harmonic signal based on the iterative calculation of the autocorrelation sequence (ACS). In \\cite{Volkov}, this algorithm was improved and its proximity to the Rao-Cramer boundary was shown (the sources of this noise are master oscillators and frequency synthesizers). Possibilities of frequency estimation in radio channels make it possible to significantly expand the functionality of the entire radio network. This can include, for example, the problem of adaptive distribution of information flows of a radio network. This also includes the tasks of synchronization and coherent signal processing. For these reasons, more research is needed on this algorithm, the calculation of theoretical boundaries and their comparison with the simulation results.

scholarly journals A High-Resolution Algorithm for Supraharmonic Analysis Based on Multiple Measurement Vectors and Bayesian Compressive Sensing

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2559 ◽  
Author(s):  
Shuangyong Zhuang ◽  
Wei Zhao ◽  
Qing Wang ◽  
Zhe Wang ◽  
Lei Chen ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Electromagnetic Interference ◽  
Frequency Estimation ◽  
Estimation Error ◽  
Observation Time ◽  
Frequency Resolution ◽  
Inner Product ◽  
Signal Frequency ◽  
Multiple Measurement ◽  
Multiple Measurement Vectors

Supraharmonics emitted by electrical equipment have caused a series of electromagnetic interference in power systems. Conventional supraharmonic analysis algorithms, e.g., discrete Fourier transform (DFT), have a relatively low frequency resolution with a given observation time. Our previous work supplied a significant improvement on the frequency resolution based on multiple measurement vectors and orthogonal matching pursuit (MMV-OMP). In this paper, an improved algorithm for supraharmonic analysis, which employs Bayesian compressive sensing (BCS) for further improving the frequency resolution, is proposed. The performance of the proposed algorithm on the simulation signal and experimental data show that the frequency resolution can be improved by about a magnitude compared to that of the MMV-OMP algorithm, and the signal frequency estimation error is about 20 times better. In order to identify the signals in two adjacent frequency grids with one resolution, a normalized inner product criterion is proposed and verified by simulations. The proposed algorithm shows a potential for high-accuracy supraharmonic analysis.

Adaptive Signal Frequency Estimation

2019 ◽  
pp. 41-49
Author(s):  
V.V. Chudnikov ◽  
B.I. Shakhtarin
Keyword(s):  
Signal Sequence ◽  
Frequency Estimation ◽  
Harmonic Signal ◽  
Descent Method ◽  
Estimation Methods ◽  
Signal Frequency ◽  
Accurate Estimation ◽  
Frequency Error ◽  
Gradient Descent Method ◽  
Harmonic Components

The paper introduces an adaptive algorithm for estimating the frequencies of harmonic components in the signal against the background of additive white noise. This method is iterative, which distinguishes it from the periodogram and parametric spectral estimation methods. The key feature of the algorithm is that it gives a reasonably accurate estimation only for the preset number of harmonic components included in the signal under study. In the original discrete signal, a frequency search was performed at each time sample using the gradient descent method. Frequency estimation is made when the frequency error value tends to a certain value. The search is based on the representation of the value of the current sample of the harmonic signal of a known frequency through the two previous values. Knowing the number of components included in the original signal sequence, it is possible to form the resulting sequence containing only residual noise samples. A mathematical model of the algorithm is given, its work is simulated for different conditions of application, the accuracy of the algorithm, i.e., frequency estimation, and the number of iterations for various signal-to-noise ratios are shown.

scholarly journals Influence of A/D Quantization in an Interpolated DFT Based System of Power Control with A Small Delay

2014 ◽  
Vol 21 (3) ◽  
pp. 423-432 ◽  
Author(s):  
Józef Borkowski ◽  
Dariusz Kania ◽  
Janusz Mroczka
Keyword(s):  
Time Window ◽  
Frequency Estimation ◽  
Estimation Error ◽  
Estimation Method ◽  
Quantization Noise ◽  
Estimation Accuracy ◽  
Signal Frequency ◽  
Practical Applications ◽  
Dominant Component ◽  
Signal Value

Abstract Fast and accurate grid signal frequency estimation is a very important issue in the control of renewable energy systems. Important factors that influence the estimation accuracy include the A/D converter parameters in the inverter control system. This paper presents the influence of the number of A/D converter bits b, the phase shift of the grid signal relative to the time window, the width of the time window relative to the grid signal period (expressed as a cycle in range (CiR) parameter) and the number of N samples obtained in this window with the A/D converter on the developed estimation method results. An increase in the number b by 8 decreases the estimation error by approximately 256 times. The largest estimation error occurs when the signal module maximum is in the time window center (for small values of CiR) or when the signal value is zero in the time window center (for large values of CiR). In practical applications, the dominant component of the frequency estimation error is the error caused by the quantization noise, and its range is from approximately 8×10-10 to 6×10-4.

scholarly journals Statistical Learning with a Nuisance Component (Extended Abstract)

2020 ◽  
Author(s):  
Dylan J. Foster ◽  
Vasilis Syrgkanis
Keyword(s):  
Statistical Learning ◽  
Domain Adaptation ◽  
Estimation Error ◽  
Nuisance Parameter ◽  
Individual Performance ◽  
Excess Risk ◽  
Risk Minimization ◽  
Target Parameter ◽  
Estimation Algorithms ◽  
The Impact

We provide excess risk guarantees for statistical learning in a setting where the population risk with respect to which we evaluate a target parameter depends on an unknown parameter that must be estimated from data (a "nuisance parameter"). We analyze a two-stage sample splitting meta-algorithm that takes as input two arbitrary estimation algorithms: one for the target parameter and one for the nuisance parameter. We show that if the population risk satisfies a condition called Neyman orthogonality, the impact of the nuisance estimation error on the excess risk bound achieved by the meta-algorithm is of second order. Our theorem is agnostic to the particular algorithms used for the target and nuisance and only makes an assumption on their individual performance. This enables the use of a plethora of existing results from statistical learning and machine learning literature to give new guarantees for learning with a nuisance component. Moreover, by focusing on excess risk rather than parameter estimation, we can give guarantees under weaker assumptions than in previous works and accommodate the case where the target parameter belongs to a complex nonparametric class. We characterize conditions on the metric entropy such that oracle rates---rates of the same order as if we knew the nuisance parameter---are achieved. We also analyze the rates achieved by specific estimation algorithms such as variance-penalized empirical risk minimization, neural network estimation and sparse high-dimensional linear model estimation. We highlight the applicability of our results in four settings of central importance in the literature: 1) heterogeneous treatment effect estimation, 2) offline policy optimization, 3) domain adaptation, and 4) learning with missing data.

scholarly journals Dynamical decoupling of laser phase noise in compound atomic clocks

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Sören Dörscher ◽  
Ali Al-Masoudi ◽  
Marcin Bober ◽  
Roman Schwarz ◽  
Richard Hobson ◽  
...  
Keyword(s):  
Phase Noise ◽  
Phase Measurement ◽  
Local Oscillator ◽  
Frequency Instability ◽  
Laser Frequency ◽  
High Signal ◽  
Frequency Noise ◽  
Atomic Clocks ◽  
Dynamical Decoupling ◽  
Measurement Protocol

Abstract The frequency stability of many optical atomic clocks is limited by the coherence of their local oscillator. Here, we present a measurement protocol that overcomes the laser coherence limit. It relies on engineered dynamical decoupling of laser phase noise and near-synchronous interrogation of two clocks. One clock coarsely tracks the laser phase using dynamical decoupling; the other refines this estimate using a high-resolution phase measurement. While the former needs to have a high signal-to-noise ratio, the latter clock may operate with any number of particles. The protocol effectively enables minute-long Ramsey interrogation for coherence times of few seconds as provided by the current best ultrastable laser systems. We demonstrate implementation of the protocol in a realistic proof-of-principle experiment, where we interrogate for 0.5 s at a laser coherence time of 77 ms. Here, a single lattice clock is used to emulate synchronous interrogation of two separate clocks in the presence of artificial laser frequency noise. We discuss the frequency instability of a single-ion clock that would result from using the protocol for stabilisation, under these conditions and for minute-long interrogation, and find expected instabilities of σy(τ) = 8 × 10−16(τ/s)−1/2 and σy(τ) = 5 × 10−17(τ/s)−1/2, respectively.

scholarly journals Crack identification using piezoelectric testing on carbon steel pipe for transverse, longitudinal and hole defects with low excitation frequency

2018 ◽  
Vol 7 (2.14) ◽  
pp. 171
Author(s):  
Ahmed N. Abdalla ◽  
Kharudin Ali ◽  
Johnny Koh Siaw Paw ◽  
Chong Kok Hen ◽  
Tan Jian Ding ◽  
...  
Keyword(s):  
Carbon Steel ◽  
Excitation Frequency ◽  
High Amplitude ◽  
Steel Pipe ◽  
Signal Frequency ◽  
Crack Identification ◽  
High Signal ◽  
Excitation Signal ◽  
Longitudinal Defect ◽  
Transverse Defect

AC excitation signal is most widely used in Non Destructed Testing (NDT) devices for Piezoelectric Technique (PZT) method in an inspec-tion. This paper is presenting the application of piezoelectric with end to end method for defect identification for Carbon Steel Pipe (CSP) where the frequency is used around 1kHz until 6kHz for standard pipe, transverse defect pipe, longitudinal defect pipe and hole defect pipe. From here, the identification of defect signal by based on the signal pick value and different pick signal between ordinary pipe (without defect) and defects pipe are analysis. The result shows that the standard pipe will give the high amplitude of signal compare the defect pipe by based on the type of defect, size of defect and depth of defect. Findings from the comparative study, validate the application of piezoelec-tric show that the different amplitude of the signal is directly proportional with excitation signal frequency and through the experiment, the longitudinal defect is contributed the different high signal until 79.7% compared to the hole and transverse defect 74.4 %.

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