Windowing method for reducing influence of spectrum leakage in phase measurement

Abstract

The invention discloses a windowing method for reducing the influence of spectrum leakage in phase measurement in the fusion field, and belongs to the technical field of plasma density diagnosis. As a new energy form, nuclear fusion not only has the remarkable advantages of no pollution, rich resources and the like, but also is widely considered as one of important ways for solving environment and energy crisis faced by human beings. In nuclear fusion research, the electron density of the plasma is an important physical parameter, the change of the electron density can reflect the state of the plasma, and the accurate and stable monitoring of the plasma can help experimenters to improve fusion reaction conditions, so that the efficiency and stability of energy output are improved. Therefore, high-quality measurement of electron density is an indispensable link in experimental research. The physical parameter may be achieved by measuring the phase difference of both after the beam passes through the plasma and in a vacuum (i.e. Not through the plasma). However, when the phase difference is measured by using a spectral analysis method, the problem of spectrum leakage can be generated, and the precision and accuracy of phase data processing can be influenced. Therefore, in order to improve the precision of the phase difference, the invention provides a processing method for windowing two paths of original signals, and the precision requirement of phase measurement in the fusion field can be met.

Description

Technical Field

[0001] The present invention relates to a real-time high-precision data processing technology in plasma diagnostic technology in the field of nuclear fusion, and in particular to a windowing method for reducing the influence of spectrum leakage in phase measurement. Background Art

[0002] In the field of fusion, research and development of efficient, clean and stable controlled nuclear fusion methods have far-reaching significance for the energy crisis and environmental problems facing mankind. Controlled nuclear fusion can not only provide almost unlimited clean energy, but also significantly reduce greenhouse gas emissions, providing an effective solution for global response to climate change. In the process of stable operation of fusion devices, accurate measurement of plasma electron density and real-time density feedback become key diagnostic links to ensure the stability of the device. By adopting appropriate methods and advanced technologies, experimenters can obtain accurate data on plasma electron density to monitor its behavior and changes. The real-time feedback mechanism can help experimenters adjust their operating parameters to ensure that the equipment operates in the best condition, thereby improving the efficiency and reliability of fusion reactions.

[0003] In the process of measuring plasma electron density, when the measuring beam passes through the plasma, the propagation path of the light will change due to the change of the plasma's refractive index, resulting in a change in the optical path, which will produce a phase shift. The relative reference beam will not be affected by the optical path because it has not been refracted by the plasma, so no phase shift will occur. Therefore, the phase difference between the two beams of light becomes a physical quantity that needs to be measured, which can directly reflect the electron density of the plasma. Phase detection is crucial to achieve accurate electron density measurement. The traditional Fourier transform-based phase difference measurement method often faces the problem of spectrum leakage in practical applications. This phenomenon not only reduces the accuracy of data processing, but also affects the real-time processing of the system. This affects the final phase difference accuracy and directly affects the precise control of the plasma electron density in the nuclear fusion device. Therefore, in order to improve the overall performance of the system and improve the phase difference accuracy, it is necessary to find a method to process the two-way data for the spectrum leakage problem, so as to reduce the calculation error of the phase difference and ensure the accuracy and reliability of signal analysis in nuclear fusion research. Summary of the invention

[0004] The present invention proposes a windowing method for reducing the influence of spectrum leakage in phase measurement, which is applied to plasma diagnosis in the field of nuclear fusion. The method can significantly reduce the problem of decreased phase difference accuracy caused by spectrum leakage and improve the accuracy of controlling the plasma electron density of nuclear fusion devices.

[0005] The technical solution adopted by the present invention to solve the above problems is: a windowing method for reducing the influence of spectral leakage in phase measurement, comprising the following steps:

[0006] (1): Determine the number of conversion points N, and respectively obtain the data of five window functions, namely, the rectangular window, triangular window, Hanning window, Hamming window, and Blackman window of N points through the rectwin function, triang function, hann function, hamming function, and blackman function of the Matlab software tool.

[0007] (2): Use the dec2bin function of the Matlab software to convert the five window functions into binary strings.

[0008] (3): Use the fprintf function of the Matlab software to write the binary strings into a.coe file.

[0009] (4): Use the Block Memory Generator IP core of the Vivado software tool to store the.coe file into a ROM.

[0010] (5): Use the multiplier of the Vivado software tool to multiply the signals, and then perform subsequent data processing to obtain the phase information of the Vivado simulation result.

[0011] (6): Compare with the phase information calculated by Matlab to obtain the window function with the highest phase difference accuracy.

[0012] Compared with the traditional method, the advantages of the method of the present invention are:

[0013] This method adopts a windowing method for reducing the influence of spectral leakage in high-precision phase measurement. Based on this method, a real-time digital phase measurement system can realize the windowing process of the original signal with different numbers of conversion points on a fusion device, realize the measurement of the phase difference between the reference signal and the measurement signal during plasma discharge, significantly improve the calculation accuracy of the phase difference, and reduce errors. It not only ensures the accuracy of density feedback, but also provides more accurate data support for fusion research. Description of the Drawings

[0014] Figure 1 It is the system framework diagram of the windowing method for reducing the influence of spectral leakage in high-precision phase measurement of the present invention.

[0015] Figure 2 It is the intercepted periodic signal and its spectrogram.

[0016] Figure 3 It is the intercepted non-periodic signal and its spectrogram.

[0017] Figure 4 Are the time-domain diagrams and frequency-spectrum diagrams of five window functions.

[0018] Figure 5 Are the frequency-spectrum diagram and phase-error curve diagram without using a window function.

[0019] Figure 6 Are the frequency-spectrum diagram and phase-error curve diagram using a rectangular window.

[0020] Figure 7 Are the frequency-spectrum diagram and phase-error curve diagram using a triangular window.

[0021] Figure 8 Are the frequency-spectrum diagram and phase-error curve diagram using a Hanning window.

[0022] Figure 9 Are the frequency-spectrum diagram and phase-error curve diagram using a Hamming window.

[0023] Figure 10 Are the frequency-spectrum diagram and phase-error curve diagram using a Blackman window. Detailed implementation manner

[0024] The method of the present invention will be compared in detail below in combination with the accompanying drawings in specific implementation cases.

[0025] Figure 1 Is the system framework diagram of the windowing method for reducing the influence of spectral leakage in the high-precision phase measurement of the present invention. The system process of the present invention includes the following steps:

[0026] (1): The PC side performs a reset and sends relevant instructions such as sampling time and trigger mode to the lower computer. In this invention, the lower computer is composed of an FPGA main control.

[0027] (2): After receiving the relevant parameters, the board will first judge its working mode. If the working mode is software trigger, the board will directly enter the working state and start executing the corresponding tasks; if the working mode is hardware trigger, the board will maintain the set relevant parameters and enter the standby state, waiting for a 5V external voltage trigger signal to activate its work.

[0028] (3): When in the hardware trigger mode, after detecting the external hardware trigger, the board will not only immediately respond and start working normally, but also feedback the received hardware trigger signal to the PC side. This feedback process ensures that the PC can monitor the operating state of the board in real time.

[0029] (4): The data acquisition module starts to work, which is completed by the ADC chip. This module includes an ADC chip configuration module and a data reception module.

[0030] (5): The FPGA inputs the two received data streams to the data preprocessing module for cross-clock domain processing.

[0031] (6): The two processed signals are multiplied by the selected window function to obtain two new signals.

[0032] (7): The signal is input to the algorithm module for calculating the phase difference, and the phase difference result is fed back to the DAC and the host computer.

[0033] Figure 2 are the intercepted periodic signal and its spectrogram, Figure 3 are the intercepted aperiodic signal and its spectrogram. As shown in the figure, the process of the present invention includes the following steps:

[0034] (1): Assume that the original signal frequency is f = 5Hz, the sine amplitude is 1, and the starting phase is 0, as shown in formula (1).

[0035] y(t) = sin(2*π*f*t) (1)

[0036] (2): After y is sampled and quantized at 100Hz, the discrete sequence of the signal is obtained as shown in formula (2), that is, f s = 100Hz, where n = 0, 1, 2,..., 999.

[0037] y(n) = sin(2*π*f*n / f s ) (2)

[0038] (3): Select 40 data points when n = 0, 1, 2,..., 39. The intercepted data points exactly correspond to two complete periods of the original sequence.

[0039] (4): Use the Matlab tool to plot the intercepted periodic signal and its spectrogram. The plotting result is as Figure 2 shown.

[0040] (5): Select 46 data points when n = 0, 1, 2,..., 45. That is, the intercepted data points are a non-periodic truncation of the original signal.

[0041] (6): Use the Matlab tool to plot the intercepted aperiodic signal and its spectrogram. The plotting result is as Figure 3 shown.

[0042] (7): Compare Figure 2 and Figure 3 , and draw the conclusion that if a periodic truncation is made, there is energy at only one frequency point, that is, there is no spectral leakage phenomenon; if a non-periodic truncation is made, there is also non-negligible energy at the frequency points near the main frequency point, that is, the spectral leakage phenomenon occurs.

[0043] Figures 4 - 10 They are the time-domain diagrams and frequency-spectrum diagrams of five window functions, as well as the frequency-spectrum diagrams and phase-error curves without using window functions, rectangular windows, triangular windows, Hanning windows, Hamming windows, and Blackman windows, respectively. As shown in the figure, the process of the present invention includes the following steps:

[0044] (1): Assume that the waveforms of the input reference signal y_R and the measured signal y_M are as shown in Formula (3) and Formula (4), where

[0045]

[0046]

[0047] (2): Assume that the input signal frequency f i = 8 MHz, the number of sampling points is 256, the sampling rate is 20 M, the digital filtering window is set to 52, and the amplitude of the phase change is 90, and the phase frequency is 200 K.

[0048] (3): Do not perform windowing on the original signal, and perform subsequent phase measurement steps to obtain the phase-difference curve between the final Matlab calculation value and the FPGA simulation. Use the Matlab tool to plot the frequency-spectrum diagram and phase-error curve without using window functions. The plotting results are as Figure 5 shown.

[0049] (4): Perform windowing on the original signal. The types of window functions are rectangular window, triangular window, Hanning window, Hamming window, and Blackman window respectively, and perform subsequent phase measurement steps to obtain the phase-difference curve between the final Matlab calculation value and the FPGA simulation. Use the Matlab tool to plot the frequency-spectrum diagrams and phase-error curves using these five window functions. The plotting results are as Figures 6 - 10 shown. It can be seen from the figure that using the triangular window function can reduce the phase-difference error to a certain extent.

[0050] Through Figures 6 - 10 it can be seen that the method of the present invention can perform windowing transformation on the preprocessed original signal according to the number of FFT conversion points, then perform phase measurement and calculation on the transformed result to obtain the final phase, and send it to the PC side. It can solve the problem of spectral leakage generated by the Fourier transform method, thereby improving the phase accuracy and realizing the precise control of the plasma electron density in the nuclear fusion device.

[0051] The above-mentioned embodiments are only used to explain the specific implementation of the present invention, rather than to limit the present invention. Therefore, any changes made according to the shape and principle of the present invention should be covered within the scope of the present invention.

Claims

1. A windowing method for reducing the influence of spectral leakage in phase measurement, characterized in that: The method includes the following steps: In step (1), after determining the number of conversion points N, the data of five window functions, namely the rectangular window, triangular window, Hanning window, Hamming window, and Blackman window with N points, are obtained through the rectwin function, triang function, hann function, hamming function, and blackman function of the Matlab software tool respectively. In step (2), the five window functions are converted into binary strings using the dec2bin function of the Matlab software. In step (3), the binary strings are written into a.coe file using the fprintf function of the Matlab software. In step (4), the.coe file is stored in the ROM using the Block Memory Generator IP core of the Vivado software tool. In step (5), the multiplier of the Vivado software tool is used to multiply the signals, and then subsequent data processing is performed to obtain the phase information of the Vivado simulation result. In step (6), it is compared with the phase information calculated by Matlab to obtain the window function with the highest phase difference accuracy.

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