High-precision radio frequency signal amplitude calculation method based on frequency division multiple bands

Abstract

The invention discloses a high-precision radio frequency signal amplitude calculation method based on frequency division multiple bands, which is characterized by comprising the following steps of: processing an input radio frequency signal, selecting a proper extraction coefficient in a software mode to perform sampling rate reduction on the signal under a fixed ADC (Analog to Digital Converter) sampling frequency and a fast Fourier transform frequency, and calculating the amplitude of the high-precision radio frequency signal. And a window function is superposed to carry out fast Fourier transform, so that high-precision real-time amplitude calculation is realized. The method comprises the following steps: performing frequency mixing, low-pass filtering and direct-current component removal operation on an input radio-frequency signal, performing signal frequency estimation by using a zero crossing point method, dividing the input signal into different frequency bands, performing sampling rate reduction operation according to extraction coefficients of the different frequency bands, superposing a window function, then performing fast Fourier transform, and finally performing frequency division on the input radio-frequency signal. The amplitude of the signal can be calculated according to the real part and the imaginary part obtained through calculation, and a high-precision signal amplitude calculation method is provided for radio frequency signal power calculation.

Description

Technical Field

[0001] The present invention relates to the fields of radio frequency signal processing and power calculation, and in particular to a high-precision radio frequency signal amplitude calculation method based on frequency division multi-band. Background Art

[0002] RF signal power is a physical quantity that indicates the strength of an RF signal. RF power meters are essential power measurement instruments in fields such as communications, radar, and aerospace, used to accurately measure RF signal power. With the rapid development of modern communication technologies such as 5G and the Internet of Things, performance requirements for RF power meters are continuously increasing, including higher measurement accuracy, wider frequency coverage, and greater adaptability. This requires the selection of appropriate power measurement methods and front-end data acquisition systems to improve measurement accuracy and real-time performance.

[0003] There are many traditional power measurement methods, but all have certain problems. Professional power meters and spectrum analyzers can directly measure power, but they are expensive and inflexible, making them unsuitable for industrial applications. The traditional voltage-current method measures the voltage and current of a signal and then calculates power using a power formula, but this also requires equipment to measure voltage and current, making it cumbersome. The thermal effect method uses the thermal effect of a signal on a resistor to calculate power by measuring temperature changes, but the measurement speed is slow and the measurement is easily affected by temperature. Therefore, in the field of RF signal power measurement, research on high-speed sampling of the input signal and power measurement methods that meet real-time and high-precision requirements is crucial.

[0004] Fast Fourier transforms (FFTs) can calculate the amplitude, frequency, and phase of an input signal, while power can be calculated based on the voltage and resistance in a circuit. Therefore, the FFT can be used to calculate the voltage amplitude in a circuit, and combined with the known resistance value to calculate the signal power. This not only eliminates the need for additional equipment but also facilitates deployment on FPGAs for industrial measurement. Traditional FFT calculations suffer from low frequency resolution, resulting in frequency information being concentrated in low-frequency points, making it difficult to distinguish. This affects the accuracy of amplitude calculations and significantly impacts power calculations.

[0005] Therefore, designing a suitable high-precision amplitude calculation method for RF signals to reduce the amplitude calculation error caused by the low resolution of frequency domain information and ensure the accuracy of power calculation is an important technical problem that needs to be solved urgently. Summary of the Invention

[0006] The present invention addresses the shortcomings of current RF signal amplitude calculation technology and proposes a high-precision RF signal amplitude calculation method based on frequency division and multi-band. This method estimates the frequency of the input signal and divides it into multiple bands. It performs different multiples of downsampling rate processing according to different frequency bands, then calculates the amplitude of the signal superimposed on a flat-top window. Finally, the calculated result is transmitted to a host computer via an Ethernet port for power calculation, thereby achieving the real-time, stable, and high-precision requirements of amplitude calculation in the field of RF signal power calculation. This method is suitable for RF signal amplitude calculation and can use different fast Fourier transform sampling points. It can significantly reduce the error of the amplitude calculation result caused by the low resolution of frequency domain information, improve the accuracy of power measurement instruments, and significantly reduce the cost of instruments. It has good application prospects and commercial development value.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] S1: Before performing fast Fourier transform, it is necessary to first determine the target frequency band f for fast Fourier transform. min ~ f max , the sampling frequency f of the fast Fourier transform s and the number of sampling points N.

[0009] S2: input RF signal F RF First, mixing is required to reduce the signal frequency, F RF With the local oscillator signal F LO Multiply, and take the difference between the two signal frequencies to get the mixed intermediate frequency signal F IF , F IF Then pass through the low-pass filter to remove F IF The signal with higher intermediate frequency makes the signal frequency at the maximum frequency f of the fast Fourier transform max Next, an AC coupling circuit is used to remove the DC component in the signal to obtain signal Data1, thereby avoiding the influence of the DC signal on the amplitude calculation.

[0010] S3: The ADC samples the hardware-conditioned signal Data1 at N points and performs a secondary DC removal process using software to further filter out the DC component in the signal. The ADC then calculates the average Data1Ave of the maximum value Data1Max and the minimum value Data1Min of the signal Data1, which is the DC component of the signal. This DC component Data1Ave is subtracted from the input signal Data1 to obtain the DC-removed signal Data2.

[0011] S4: The signal Data2 after the DC is removed twice is subjected to frequency estimation using a zero-crossing method, and then frequency-divided into multiple frequency bands.

[0012] S5: No sampling rate processing is performed on the relatively high-frequency signals in the target frequency band of the fast Fourier transform. Different decimation downsampling coefficients m are selected for the relatively medium and low-frequency signals to perform downsampling rate processing, that is, sampling is performed once every m clock cycles, reducing the signal sampling rate by m times.

[0013] S6: Superimpose a flat-top window on the downsampled signal, and then perform a fast Fourier transform to calculate the amplitude.

[0014] S7: The calculated results are transmitted to the host computer through the Ethernet port for power calculation.

[0015] In step S1, the signal is sampled and fast Fourier transformed, which requires determining the target frequency band of the fast Fourier transform and determining the maximum signal processing frequency f max and the fast Fourier transform sampling frequency f s , these two parameters should satisfy the Nyquist theorem, that is, f s ≥ 2f max Otherwise, signal distortion and spectrum aliasing will occur. The Fast Fourier Transform also requires determining the number of sampling points, N. To ensure the accuracy of amplitude calculations, the number of sampling points must be an integer power of 2 and greater than or equal to 1024. This improves the resolution of the spectrum information, thereby ensuring high accuracy in amplitude calculations. The Fast Fourier Transform calculation time is less than 100 μs, ensuring high real-time performance of the system and reducing data processing delays.

[0016] In step S2, the frequency of the input RF signal is too high, which is not conducive to the subsequent ADC signal sampling and fast Fourier transform calculation, so it is necessary to perform mixing and other processing on the RF signal to reduce the signal frequency. RF After mixing with the local oscillator signal F LO Multiply and take the difference between the two signal frequencies to get the intermediate frequency signal F after mixing IF , the formula is:

[0017] ,

[0018] Among them, F RF is the frequency of the input RF signal, F LO is the frequency of the local oscillator signal input, F IF is the frequency of the intermediate frequency signal after mixing.

[0019] To make F IF Completely within the fast Fourier transform target frequency band, F IF It also needs to pass through the cutoff frequency f max A low-pass filter is used to filter out relatively high-frequency signals in the target frequency band, and then an AC coupling circuit is used to remove the DC component in the signal to obtain signal Data1.

[0020] In step S3, the ADC samples the hardware-mixed signal Data1 to obtain N-point data. However, the signal sampled by the ADC may still have a DC offset, which will affect the amplitude calculation performed by the fast Fourier transform. Therefore, a secondary DC removal operation is required in software to improve the accuracy of the amplitude calculation. Since the RF signal is a sine wave signal, the N-point sine wave signal data collected can be dynamically traversed in real time to obtain the maximum value Data1Max and the minimum value Data1Min. The average value Data1Ave of Data1Max and Data1Min is taken as the DC component of signal Data1. The DC component Data1Ave is subtracted from Data1 to obtain signal Data2, completing the secondary DC removal operation.

[0021] The step S4 of frequency-dividing the signal Data2 into multiple bands specifically includes the following steps:

[0022] Step S41: According to Data1Max and Data1Ave described in step S3, set the positive zero-point noise threshold +T and the negative zero-point noise threshold -T, where T is set to i% of the signal peak value after DC removal, that is, (Data1Max – Data1Ave) * i%, where i is 5%~15%, and set the initial sampling time last_time, the initial value is 0, one data is sampled in each clock cycle, and last_time is increased by 1;

[0023] Step S42: Signal Data2 is input and the system first enters the initial state. If the first data is greater than +T, the system enters the waiting state for positive crossing. If the first data is less than -T, the system enters the waiting state for negative crossing.

[0024] Step S43: In the waiting state for positive crossing, if the input signal Data2 is less than the noise threshold -T, a zero point positive crossing is completed, the sampling time last_fall_time at this time is recorded, and the state of waiting for negative crossing of zero point is entered;

[0025] Step S44: In the waiting state for negative crossing, if the input signal Data2 is greater than the noise threshold + T, a zero point negative crossing is completed, the sampling time last_rise_time at this time is recorded, and the state of waiting for zero point positive crossing is entered;

[0026] Step S45: After completing one positive crossing and one negative crossing, calculate the half period of the signal Data2: half_period = |last_rise_time - last_fall_time|. Perform five positive and negative crossings in succession to obtain five half periods. Take the average and multiply by 2 to obtain the signal estimation period. Calculate the signal estimation frequency fe , the formula is:

[0027] ,

[0028] Among them, period is the number of clocks in a cycle, f s is the fast Fourier transform sampling frequency;

[0029] Step S46: Frequency-divide the signal Data2 into multiple bands according to the estimated frequency so that the signal frequency is evenly distributed. s ~ 0.0008f s The signals between 0.0008f are classified as the first frequency band signals. s ~0.004f s The signals between 0.004f are classified as the second frequency band signals. s ~ 0.02f s The signals between 0.02f are classified as the third frequency band signals. s ~ 0.05f s The signals between 0.05f are classified as the fourth frequency band signals. s ~ 0.5f s The signal between is classified as the fifth frequency band signal, and the signal frequency is less than 0.00015f s The signal is not considered within the scope of consideration, and the signal frequency is greater than or equal to 0.5f s When , Nyquist theorem is not satisfied and it is not considered.

[0030] In step S5, to appropriately downsample signal Data2, different decimation coefficients m are set for signals in different frequency bands. The decimation coefficient for the first frequency band is set to 300; the decimation coefficient for the second frequency band is set to 60; the decimation coefficient for the third frequency band is set to 10; the decimation coefficient for the fourth frequency band is set to 4; and the decimation coefficient for the fifth frequency band is set to 1, meaning no decimation. This ensures a reasonable and uniform decimation coefficient setting, ensuring wide applicability. Subsequently, during fast Fourier transform sampling, sampling is performed once every m clock cycles according to the pre-set decimation coefficient m, achieving a downsampling rate of m times, resulting in signal Data3.

[0031] In step S6, the window function coefficients of the flat-top window are cached in a single-port ROM in advance for subsequent use. After the downsampling process is completed, the signal Data3 is output, the signal Data3 is multiplied by the flat-top window coefficients, and then a fast Fourier transform is performed to obtain the real part Re and the imaginary part Im of the spectrum. The absolute value of the amplitude is calculated based on the real and imaginary part information and normalized. The amplitude Amp calculation formula is:

[0032] ,

[0033] Among them, Re is the real part amplitude, Im is the imaginary part amplitude, and N is the number of fast Fourier transform sampling points.

[0034] In step S7, after the FPGA side calculates the relevant signal amplitude, the data is transmitted to the host computer through the Ethernet port for power calculation and display.

[0035] In step S5, the signal is frequency-divided into multiple bands, and then the sampling rate is reduced. The amplitude of the signal is calculated in combination with a flat-top window to solve the problem of low resolution of frequency domain information of fast Fourier transform and improve the accuracy of amplitude calculation.

[0036] Compared with the prior art, the present invention has the following beneficial technical effects and significant technical progress:

[0037] The input RF signal undergoes mixing, AC coupling, and low-pass filtering to remove the DC component and keep it within the target frequency band of the Fast Fourier Transform (FFT). The zero-crossing method is used to estimate the frequency of the input signal, followed by frequency division and multi-band operation. Appropriate decimation coefficients are selected to reduce the sampling rate of signals in different frequency bands, positioning the calculated spectrum curve in the middle of the result. This improves the resolution of the frequency domain information and facilitates subsequent amplitude calculations. Different window functions have different effects on the FFT amplitude calculation. A flat-top window has a slightly wider main lobe and higher amplitude accuracy, so it is used for amplitude calculation. While ensuring high precision and real-time performance, the FFT calculation of the RF signal and the signal amplitude are achieved, ultimately ensuring the accuracy of power calculations, demonstrating high practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a system block diagram of the present invention;

[0039] Figure 2 This is a diagram of the hardware board of the present invention;

[0040] Figure 3 It is the algorithm flow chart of the present invention;

[0041] Figure 2 In the figure, 1 Ethernet port, 2 power supply port, 3 Xilinx XC7K325T FPGA chip, 4 ADC2 chip, 5 front-end hardware conditioning circuit, 6 code burning port, 7 ADC1 chip, 8 hardware trigger port, 9 reference signal 2, 10 measurement signal 2, 11 reference signal 1, 12 measurement signal 1. DETAILED DESCRIPTION

[0042] The present invention will be described in detail below with reference to the accompanying drawings and examples. The following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those skilled in the art, several variations and improvements can be made without departing from the scope of the present invention, and these all fall within the scope of protection of the present invention.

[0043] Example 1

[0044] See Figure 1 The sampling instruction can be provided by an external hardware trigger signal or by the host computer. The host computer sends the sampling signal and sampling time and other related instructions to the slave computer. The main control module of the slave computer is composed of FPGA. The power supply circuit realizes the power supply of the system. The clock chip provides a precise clock for the ADC and FPGA, ensuring the integrity and real-time performance of the collected data. The input signal of the board includes two pairs of measurement signals and reference signals. The amplitude calculation is mainly completed through the measurement signal. The reference signal is used to cooperate with the measurement signal to calculate the phase information of the input signal.

[0045] The present invention comprises the following steps:

[0046] Step 1: After the acquisition command is sent, signal acquisition begins. The acquisition work is mainly completed by the ADC chip. The RF measurement signal enters the ADC after passing through hardware conditioning modules such as mixing, AC coupling, and low-pass filtering, and then undergoes fast Fourier transform N-point sampling.

[0047] Step 2: FPGA inputs the collected signal to the algorithm implementation module for amplitude calculation.

[0048] Step 3: The calculated amplitude information is uploaded to the host computer through the Ethernet port for power calculation and display.

[0049] See Figure 2 The figure shows the power calculation hardware board of the present invention. The board includes an Ethernet port, a XilinxXC7K325T FPGA chip, two ADC chips with a sampling rate of 200MHz, a power supply port, four signal input ports, a hardware trigger port and a front-end hardware conditioning circuit.

[0050] See Figure 3 ,The algorithm flow of the present invention includes hardware part and software part.

[0051] The hardware part mainly includes the following steps:

[0052] Step 1: Determine the target frequency band f for fast Fourier transform min ~ f max , the sampling frequency f of the fast Fourier transform s And the number of sampling points N. Ensure the maximum frequency of the input signal fmax and the fast Fourier transform sampling frequency f s It should satisfy the Nyquist theorem, that is, f s ≥ 2f max In order to improve the resolution of frequency domain information and ensure the accuracy of amplitude calculation, the number of sampling points should be an integer power of 2 and greater than or equal to 1024 points. This embodiment takes an ADC with a sampling rate of 200M as an example. The sampling frequency f of the fast Fourier transform is s The frequency band is 200MHz, the number of sampling points N is 1024 points, and the target frequency band of the fast Fourier transform is 30KHz~30MHz.

[0053] Step 2: RF signal F RF The input frequency is too high, which is not conducive to the subsequent ADC signal sampling and fast Fourier transform calculation, so it is necessary to mix the RF signal to reduce the signal frequency. RF With the local oscillator signal F LO After mixing, the difference between the two signal frequencies is obtained to obtain the mixed intermediate frequency signal F IF , the formula is:

[0054] ,

[0055] Among them, F RF is the frequency of the input RF signal, F LO is the frequency of the local oscillator signal input, F IF is the frequency of the intermediate frequency signal after mixing;

[0056] To make F IF Completely within the fast Fourier transform target frequency band, F IF Need to pass the cutoff frequency f max =30MHz low-pass filter to filter out the mixing signal F IF The relatively high frequency signal is then passed through an AC coupling circuit to remove the DC component in the signal to obtain signal Data1.

[0057] Step 3: The ADC samples the signal Data1 and obtains data at 1024 sampling points.

[0058] The logic part mainly consists of the following steps:

[0059] Step 1: The signal sampled by the ADC may still contain a DC offset, which will affect the amplitude calculation of the fast Fourier transform. Therefore, a secondary DC removal operation is required in the software to improve the accuracy of the amplitude calculation. Since the RF signal is a sine wave signal, while sampling, the 1024-point sine wave signal data collected can be dynamically traversed in real time to obtain the maximum value Data1Max and the minimum value Data1Min. The average value Data1Ave of Data1Max and Data1Min is taken as the DC component of the input signal. The DC component Data1Ave is subtracted from Data1 to obtain the signal Data2, completing the secondary DC removal operation.

[0060] Step 2: After the DC signal Data2 has been de-DCed twice, the frequency is estimated using the zero-crossing method, and then the signal is divided into multiple frequency bands.

[0061] Frequency division of the signal Data2 into multiple bands specifically includes the following steps:

[0062] Step 2-1: According to Data1Max and Data1Ave described in step 1 of the logic section, set the positive zero noise threshold +T and the negative zero noise threshold -T. Set T to i% of the signal peak value after DC removal, that is, (Data1Max – Data1Ave) *i%, where i is 5% to 15%. Set the initial sampling time last_time to 0. One data is sampled in each clock cycle, and last_time is incremented by 1.

[0063] Step 2-2: Signal Data2 is input and the system first enters the initial state. If the first data is greater than +T, the system enters the waiting state for positive crossing. If the first data is less than -T, the system enters the waiting state for negative crossing.

[0064] Step 2-3: In the waiting state for positive crossing, if the input signal Data2 is less than the noise threshold -T, a zero point positive crossing is completed, the sampling time last_fall_time at this time is recorded, and the state of waiting for negative zero crossing is entered;

[0065] Step 2-4: In the waiting state for negative crossing, if the input signal Data2 is greater than the noise threshold + T, a zero point negative crossing is completed, and the sampling time last_rise_time at this time is recorded, and the state of waiting for zero point positive crossing is entered;

[0066] Step 2-5: After completing one positive crossing and one negative crossing, calculate the half period of signal Data2 half_period = |last_rise_time - last_fall_time|. Perform five positive and negative crossings in succession to obtain five half periods. Take the average and multiply by 2 to obtain the signal estimated period period. Calculate the signal estimated frequency f e , the formula is:

[0067] ,

[0068] Among them, period is the number of clocks in a cycle, f s is the fast Fourier transform sampling frequency;

[0069] Step 2-6: Perform frequency division on the signal Data2 to make the signal frequency evenly distributed. s ~ 0.0008f s , that is, the signal between 30KHz ~ 160KHz is classified as the first frequency band signal; the signal with a frequency of 0.0008f s ~ 0.004f s , that is, the signal between 160KHz ~ 800KHz is classified as the second frequency band signal; the frequency between 0.004f s ~ 0.02f s , that is, the signal between 800KHz ~ 4MHz is classified as the third frequency band signal; the frequency between 0.02f s ~0.05f s , that is, the signal between 4MHz ~ 10MHz is classified as the fourth frequency band signal; the maximum fast Fourier transform signal frequency of the embodiment is set to 30MHz, which is less than 0.5f s , so the frequency is set at 0.05f s ~ 30MHz, that is, signals between 10MHz ~ 30MHz are classified as fifth frequency band signals; the signal frequency is less than 0.00015f s , that is, the 30KHz signal is not considered, and the signal frequency is greater than or equal to 0.5f s When , Nyquist theorem is not satisfied and it is not considered.

[0070] Step 3: Set different decimation factors, m, for signals in different frequency bands. The decimation factor for the first frequency band is 300; the decimation factor for the second frequency band is 60; the decimation factor for the third frequency band is 10; the decimation factor for the fourth frequency band is 4; and the decimation factor for the fifth frequency band is 1, indicating no decimation. During fast Fourier transform sampling, sampling occurs every m clock cycles (for example, the first frequency band is sampled once every 300 clock cycles), achieving a downsampling rate of m times. The resulting signal is Data3.

[0071] Step 4: Cache the window function coefficients of the flat-top window in advance into a single-port ROM for subsequent use. After the downsampling process is completed, the output signal Data3 is multiplied by the flat-top window coefficients, and then a fast Fourier transform is performed to obtain the real part Re and the imaginary part Im of the spectrum. The absolute value of the amplitude is calculated based on the real and imaginary part information and normalized. The amplitude Amp calculation formula is:

[0072] ,

[0073] Among them, Re is the real part amplitude, Im is the imaginary part amplitude, and N is the number of fast Fourier transform sampling points.

[0074] Step 5: After calculating the relevant signal amplitude on the FPGA side, transmit the data to the host computer through the Ethernet port for power calculation and display.

[0075] The present invention can divide the frequency band after mixing, low-pass filtering and DC removal of the radio frequency signal, select a suitable decimation coefficient to perform a downsampling rate operation, solve the problem of low resolution of frequency domain information of the fast Fourier transform, and then select a flat-top window function suitable for amplitude calculation to superimpose the signal for fast Fourier transform to reduce the influence of spectrum leakage on the accuracy of amplitude calculation, thereby realizing accurate calculation of the radio frequency signal power.

[0076] The above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A high-precision radio frequency signal amplitude calculation method based on frequency division multi-band, characterized in that: The method comprises the following steps: S1: Before performing fast Fourier transform, it is necessary to first determine the target frequency band f of the fast Fourier transform min ~f max , the sampling frequency f of the fast Fourier transform s and the number of sampling points N; S2: input RF signal F RF , after mixing, with the local oscillator signal F LO Multiply them together to get the mixed intermediate frequency signal F IF , F IF The cutoff frequency is f max A low-pass filter is used to filter out F IF The relatively high frequency signal is kept in the target frequency band f min ~ f max Then, the DC component in the filtered signal is removed through an AC coupling circuit to obtain signal Data1; S3: The ADC samples the signal Data1 at N points and performs a secondary software DC removal. The sampled signal is traversed to calculate the average (Data1Ave) of the maximum value (Data1Max) and the minimum value (Data1Min), which is the DC component of the signal Data1. This DC component (Data1Ave) is subtracted from the signal Data1 to obtain the DC-removed signal Data2. S4: Using the zero-crossing method to estimate the frequency of the signal Data2 after DC removal, and performing frequency division on Data2 according to the estimated frequency to divide it into multiple frequency bands; S5: Select an appropriate decimation coefficient m, do not downsample the relatively high-frequency band signals in Data2, and downsample the relatively medium- and low-frequency band signals by a factor of m. After the processing is completed, the signal Data3 is obtained; S6: Superimpose a flat-top window on the downsampled signal Data3, then perform a fast Fourier transform, and calculate the amplitude based on the output frequency domain information; S7: The calculated RF signal amplitude information is transmitted to the host computer through the Ethernet port for power calculation.

2. The high-precision RF signal amplitude calculation method based on frequency division multi-band according to claim 1 is characterized in that: In step S1, the maximum frequency value f of the target frequency band of the fast Fourier transform is max and the fast Fourier transform sampling frequency f s It should satisfy the Nyquist theorem, that is, f s ≥ 2f max To ensure the accuracy of frequency calculation, the number of sampling points should be an integer power of 2 and greater than or equal to 1024, and the fast Fourier transform calculation time should be less than 100us.

3. The high-precision RF signal amplitude calculation method based on frequency division multi-band according to claim 1, characterized in that: In step S2, the input radio frequency signal F RF Mixing is performed with the local oscillator signal F LO Multiply and take the difference between the two signal frequencies to get the intermediate frequency signal F after mixing IF , the formula is: ， Among them, F RF is the frequency of the input RF signal, F LO is the frequency of the local oscillator signal input, F IF is the intermediate frequency signal frequency after mixing; then low-pass filtering is performed according to the determined fast Fourier transform target frequency band, and the cutoff frequency is set to f max The signal after the low-pass filter may contain a DC component signal. The signal is AC-coupled in hardware to remove the DC component signal and obtain signal Data1.

4. The high-precision RF signal amplitude calculation method based on frequency division multi-band according to claim 1, characterized in that: In step S3, the ADC samples the input signal Data1 to obtain data at N points, and simultaneously traverses the N-point data to obtain the maximum value Data1Max and the minimum value Data1Min. The average value Data1Ave of Data1Max and Data1Min is taken as the DC component of the signal Data1, and the DC component Data1Ave is subtracted from the signal Data1 to obtain the signal Data2, thereby realizing the software secondary DC removal operation.

5. The high-precision RF signal amplitude calculation method based on frequency division multi-band according to claim 1, characterized in that: The step S4 specifically includes the following steps: Step S41: According to the Data1Max and Data1Ave of claim 1, set the positive zero noise threshold +T and the negative zero noise threshold -T, where T is set to i% of the signal peak value after DC removal, that is, (Data1Max - Data1Ave) * i%, where i is 5% to 15%, set the initial sampling time last_time, the initial value is 0, and one data is sampled in each clock cycle, and last_time is increased by 1; Step S42: Signal Data2 is input and the system first enters the initial state. If the first data is greater than +T, the system enters the waiting state for positive crossing. If the first data is less than -T, the system enters the waiting state for negative crossing. Step S43: In the waiting state for positive crossing, if the input signal Data2 is less than the noise threshold -T, a zero point positive crossing is completed, the sampling time last_fall_time at this time is recorded, and the state of waiting for negative crossing of zero point is entered; Step S44: In the waiting state for negative crossing, if the input signal Data2 is greater than the noise threshold + T, a zero point negative crossing is completed, the sampling time last_rise_time at this time is recorded, and the state of waiting for zero point positive crossing is entered; Step S45: After completing one positive crossing and one negative crossing, calculate the half period of the signal Data2: half_period = |last_rise_time - last_fall_time|. Perform five positive and negative crossings in succession to obtain five half periods. Take the average and multiply by 2 to obtain the signal estimation period. Calculate the signal estimation frequency f e , the formula is: ， Among them, period is the number of clocks in a cycle, f s is the fast Fourier transform sampling frequency; Step S46: Frequency-divide the signal Data2 into multiple bands according to the estimated frequency, and divide the frequency at 0.00015f s ~ 0.0008f s The signals between 0.0008f are classified as the first frequency band signals. s ~ 0.004f s The signals between 0.004f are classified as the second frequency band signals. s ~ 0.02f s The signals between 0.02f are classified as the third frequency band signals. s ~0.05f s The signals between 0.05f are classified as the fourth frequency band signals. s ~ 0.5f s The signals between are classified as fifth frequency band signals; Signal frequency is less than 0.00015f s The signal is not considered within the scope of consideration, and the signal frequency is greater than 0.5f s When , Nyquist theorem is not satisfied and it is not considered.

6. The high-precision RF signal amplitude calculation method based on frequency division multi-band according to claim 1, characterized in that: In step S5, different decimation coefficients m are set for signals of different frequency bands. The decimation coefficient of the first frequency band signal is set to 300; the decimation coefficient of the second frequency band signal is set to 60; the decimation coefficient of the third frequency band signal is set to 10; and the decimation coefficient of the fourth frequency band signal is set to 4. The decimation coefficient of the fifth frequency band signal is set to 1, that is, no decimation is performed. When performing fast Fourier transform sampling, sampling is performed once every m clock cycles, achieving an m-fold downsampling rate effect, and obtaining signal Data3.

7. The high-precision RF signal amplitude calculation method based on frequency division multi-band according to claim 1, characterized in that: In step S6, the window function coefficients of the flat-top window are cached in a single-port ROM in advance. After the downsampling process is completed, the signal Data3 is output, Data3 is multiplied by the flat-top window, and the calculated result is subjected to fast Fourier transform calculation to obtain the real part Re and the imaginary part Im of the spectrum. The absolute value is calculated based on the real and imaginary part information and normalized. The amplitude Amp is calculated as follows: ， Among them, Re is the real part amplitude, Im is the imaginary part amplitude, and N is the number of fast Fourier transform sampling points.

8. The high-precision RF signal amplitude calculation method based on frequency division multi-band according to claim 1, characterized in that: In step S7, after the FPGA side calculates the relevant signal amplitude, the data is transmitted to the host computer through the Ethernet port for power calculation and display.

9. The high-precision RF signal amplitude calculation method based on frequency division multi-band according to claim 6, characterized in that: The signal is frequency-divided into multiple bands, and then the sampling rate is reduced. The amplitude of the signal is calculated in combination with a flat-top window to solve the problem of low resolution of frequency domain information in fast Fourier transform and improve the accuracy of amplitude calculation.

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