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Under-sampling waveform frequency estimation method and device under strong noise interference

A technology of frequency estimation and sampling frequency, applied in the field of frequency estimation of under-sampled waveforms, it can solve the problems of large error, deviation, and unsuitable frequency estimation of reconstruction results, and achieve strong adaptability, high accuracy, and improve anti-noise performance. Effect

Inactive Publication Date: 2016-01-20
TIANJIN UNIV
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Problems solved by technology

[0008] However, the above CRT method requires the remainder error to be strictly controlled within M / 4
Under the condition of strong noise interference, some residuals will have large deviations, sometimes exceeding M / 4. At this time, the error of the reconstruction result is large, and the closed CRT is invalid, which is not suitable for frequency estimation under strong noise interference.

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  • Under-sampling waveform frequency estimation method and device under strong noise interference
  • Under-sampling waveform frequency estimation method and device under strong noise interference
  • Under-sampling waveform frequency estimation method and device under strong noise interference

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Embodiment 1

[0056] A frequency estimation method for undersampled waveforms under strong noise interference, see figure 1 , the frequency estimation method includes the following steps:

[0057] 101: respectively with f s1 ~ f sL For the sampling rate, L channels of low-speed sampling are performed on high-frequency signals containing noise interference to obtain L channels of sampling signals x i (n)(i=1,...,L);

[0058] 102: L channels of signal x obtained by sampling at a low rate i (n) Perform Fast Fourier Transform (FFT), and obtain f for each channel si (1≤i≤L) sample points and store them;

[0059] 103: Use the L-channel FFT spectrum distribution characteristics to extract the frequency corresponding to the peak position of the amplitude spectrum as the noise-containing remainder of each channel output, and set the residual error of each channel to be Δr i , that is Among them, r i is the signal frequency f 0 Modulo each sampling rate f si remainder obtained after.

...

Embodiment 2

[0066] Below in conjunction with specific calculation formula, example the scheme in embodiment 1 is described in detail:

[0067] 201: L-channel low-speed undersampling;

[0068] Let the received noisy signal be x(t)=aexp(j2πf 0 t)+ω(t), f 0 is the signal frequency, that is, the measured frequency. Perform L-way undersampling on it, and the sampling frequency is f s1 ~ f sL , sampling frequency f si (1≤i≤L) requires that the common divisor is M, and f si The factor Γ obtained after dividing by the common divisor M i It is pairwise prime. Then each sampling signal x i (n) is:

[0069]

[0070] 202: Perform FFT transformation on each channel of the sampled signal, analyze the spectrum characteristics, and extract the remainder;

[0071] For the L signal x obtained after sampling i (n), respectively do the sampling frequency f si Point FFT transformation, and obtain the magnitude spectrum of the i-th path, find out the frequency value corresponding to the largest ...

Embodiment 3

[0094] Below in conjunction with concrete experimental data, accompanying drawing, the scheme in embodiment 1 and 2 is carried out feasibility verification, see the following description for details:

[0095] Analysis of results

[0096] Experiment 1 screening error remainder

[0097] It may be advisable to take L=6 channels of undersampling and K=4 channels with small error residuals as an example to illustrate the detailed process of screening error residuals. Assume that there is a large error in the remainder of the second path, that is, the error Δr 2 >M / 4, and set the sampling frequency as f s1 =18M,f s2 =19M,f s3 =23M,f s4 =25M,f s5 =29M,f s6 =31M, where M=801 is f s1 ~ f s6 greatest common divisor of . The signal frequency is set to f 0 =N=250000Hz.

[0098] According to the closed CRT algorithm of literature [10], the correct remainder set is r={48946496105019700177101690}, and the correct folded integer n i The set is n={171613121010}, and the set of err...

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Abstract

The invention discloses an under-sampling waveform frequency estimation method and a device under strong noise interference. The method comprises the steps as follows: sampling a high-frequency signal containing noise interference through L channels at low speed to obtain L sampling signals; carrying out sampling frequency fsi point FFT transform of the L sampling signals; extracting the frequency corresponding to the peak position of amplitude spectrum as the output of each noise-containing remainder based on the FFT spectrum distribution characteristic of each of the L sampling signals; and using the sampling rate of each channel and a screened error remainder to obtain the frequency estimation of the original high-frequency signal based on an improved Chinese remainder theorem under the condition of low signal-to-noise ratio. The device comprises an acquisition module, a transformation module, an extraction module, a frequency estimation module, and an output drive and display circuit used for output. Frequency measurement of a high-frequency signal after multi-channel under-sampling is realized, and the anti-noise robustness of under-sampling signal frequency estimation is improved under the condition of low signal-to-noise ratio.

Description

technical field [0001] The present invention relates to the field of digital signal processing, in particular to a method and device for estimating the frequency of undersampled waveforms under strong noise interference, and in particular to performing multi-channel undersampling on high-frequency signals under strong noise interference, and using samples to perform remainder A method for detecting errors and making high-precision measurements of the frequency of high-frequency signals. Background technique [0002] Frequency estimation and detection problems of high-frequency signals widely exist in the engineering field, such as phase resolution of radar communication arrival signals in literature [1][2] and array signal processing problems in sensor networks. The Nyquist sampling theorem requires that more than 2 sampling points need to be collected in one signal cycle. However, in fields such as radio engineering, it is more and more common to process signals with high f...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R23/02G01R23/16G01S7/02
Inventor 黄翔东白瑞朋靳旭康吕卫
Owner TIANJIN UNIV
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