Energy based signal parameter estimation method and a comparative study of different frequency estimators

Lin, Hsiung-Cheng; Ding, Kang

doi:10.1016/j.ymssp.2010.08.009

Cited by 26 publications

(5 citation statements)

References 19 publications

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“…If the discrete value spectrum is very close to the true value, the deviation effect will be more obvious than the correction effect. As shown in Table 1, by comparison, it can be concluded that the correction ability of the energy centrobaric correction method [9] , and the ratio correction method is weak at low SNR. In contrast, the spectrum correction algorithm based on sinc function fitting has high precision and strong ability to process weak signals.…”

Section: Error Comparison Of Three Different Algorithmsmentioning

confidence: 98%

Research on spectrum correction algorithm based on coherent wind lidar

Ge¹,

Zhao²,

Ma³

et al. 2023

International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022)

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Coherent wind lidar can detect atmospheric wind field information in real time and accurately. It plays an important role in military aviation applications, numerical weather prediction, meteorological climatology research, wind energy resource utilization and other fields.Coherent wind lidar is based on Doppler effect to realize wind speed measurement. Therefore, the estimation accuracy of Doppler frequency shift directly determines the performance of coherent wind lidar system.In this paper, after the FFT transform of the signal, the spectrum correction effect of the energy centrobaric correction method, the ratio correction method and the spectrum correction algorithm based on sinc function fitting is verified by simulation. Finally, the spectrum correction algorithm based on sinc function fitting is selected to correct the frequency of the measured Doppler signal. The correction effect of the algorithm is obvious, and the purpose of reducing the error and improving the analysis accuracy is achieved.

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Section: Error Comparison Of Three Different Algorithmsmentioning

confidence: 98%

Research on spectrum correction algorithm based on coherent wind lidar

Ge¹,

Zhao²,

Ma³

et al. 2023

International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022)

View full text Add to dashboard Cite

show abstract

“…In this type of spectrum analysis, when the signal has poor spectrum accuracy due to errors such as the fence effect, the accurate frequency is often difficult to obtain. In this case, the frequency correction method [24,25] can be used to obtain the true value of the required correction frequency.…”

Section: Reorganization Of Sampling Points Based On Resmentioning

confidence: 99%

Reconstruction of Periodic Band Limited Signals from Non-Uniform Samples with Sub-Nyquist Sampling Rate

Wang

Liu

Wang

2020

Sensors

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Important state parameters, such as torque and angle obtained from the servo control and drive system, can reflect the operating condition of the equipment. However, there are two problems with the information obtained through the network from the control and drive system: the low sampling rate, which does not meet the sampling theorem and the nonuniformity of the sampling points. By combing equivalent sampling and nonuniform signal reconstruction theory, this paper proposes a reconstruction method for signal obtained from servo system in periodic reciprocating motion. Equivalent sampling combines the low rate and nonuniform samples from multiple periods into one single period, so that the equivalent sampling rate is far increased. Then, the nonuniform samples with high density are further resampled to meet the reconstruction conditions. This step can avoid the amplitude error in the reconstructed signal and increase the possibility of successful reconstruction. Finally, the reconstruction formula derived from basis theory is applied to recover the signal. This method has been successfully verified by the simulation signal of the robot swing process and the actual current signal collected on the robot arm testbed.

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“…The Slepians functions are defined [ 52 , 66 , 67 ] as the solutions of the integral Equation

for eigenvalues

. There are infinite eigenvalues, all of them real numbers, positive and smaller than 1,

…”

Section: The Slepian Functions For Fault Diagnosis Of Rotating Elementioning

confidence: 99%

Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window

Burriel-Valencia

Puche-Panadero

Martínez-Román

et al. 2018

Sensors

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The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current’s spectrogram with a significant reduction of the required computational resources.

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Energy based signal parameter estimation method and a comparative study of different frequency estimators

Cited by 26 publications

References 19 publications

Research on spectrum correction algorithm based on coherent wind lidar

Research on spectrum correction algorithm based on coherent wind lidar

Reconstruction of Periodic Band Limited Signals from Non-Uniform Samples with Sub-Nyquist Sampling Rate

Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window

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