Multi-band radio measurement calibration method and system

Abstract

The invention relates to the technical field of radio measurement, and discloses a multi-band radio measurement calibration method and system, and the method comprises the steps: S1, carrying out the sampling and preprocessing of a multi-band radio signal, and obtaining a preprocessed signal; s2, performing signal classification to obtain a mixed signal; s3, performing signal reconstruction on the mixed signal to obtain a pure target signal; s4, performing dynamic sub-band division on the pure target signal, introducing a regularization factor into each sub-band, and iteratively calculating a minimum mean square error until a residual error is smaller than a first target value to obtain an initial calibration parameter; s5, establishing logarithmic interval frequency point distribution based on the initial calibration parameter, and obtaining a compensation calibration coefficient; and S6, verifying the compensation calibration coefficient by adopting the standard calibration signal, and when the root-mean-square error exceeds a second target value, repeatedly executing the steps S4-S6 until the precision requirement is met to obtain a target calibration result, so that the interference signal is effectively suppressed, and the calibration precision is improved.

Description

Technical Field

[0001] The present invention relates to the field of radio metrology technology, and in particular to a multi-band radio metrology calibration method and system. Background Art

[0002] With the rapid development of radio communication technology, multi-band radio equipment is widely used in various fields, which puts forward higher requirements for calibration accuracy and efficiency. However, in the actual calibration environment, external electromagnetic interference can significantly affect the quality of the measurement signal, resulting in a decrease in the accuracy of the calibration results.

[0003] Traditional radio metrology calibration methods mainly use a single-band calibration strategy, which is difficult to meet the calibration requirements of multi-band equipment. At the same time, due to the use of fixed signal processing parameters and simple interference suppression algorithms, the calibration accuracy is easily affected in an environment with complex electromagnetic interference. Especially when performing high-precision calibration, external interference can cause large deviations in the measurement results. Summary of the invention

[0004] The present invention provides a multi-band radio metrology calibration method and system, which can effectively suppress interference signals and improve calibration accuracy.

[0005] In a first aspect, the present invention provides a multi-band radio metrology calibration method, the multi-band radio metrology calibration method comprising: S1: sampling and preprocessing the multi-band radio signal to obtain a preprocessed signal; S2: performing fast Fourier transform and signal classification on the preprocessed signal to obtain a mixed signal; S3: constructing the mixed signal into a Hankel matrix, and reconstructing the signal in combination with a correlation coefficient threshold to obtain a pure target signal; S4: dynamically divide the pure target signal into sub-bands, introduce a signal-to-noise ratio adaptive regularization factor in each sub-band, iteratively calculate the minimum mean square error until the residual is less than the first target value, and obtain initial calibration parameters; S5: establishing a logarithmically spaced frequency point distribution based on the initial calibration parameters, and performing linear weighted averaging within the overlapping bandwidth of adjacent sub-frequency bands to obtain a compensation calibration coefficient; S6: Use a standard calibration signal to verify the compensation calibration coefficient. When the root mean square error exceeds the second target value, repeat steps S4-S6 until the accuracy requirement is met to obtain a target calibration result.

[0006] In a second aspect, the present invention provides a multi-band radio metrology calibration system, the multi-band radio metrology calibration system comprising: A sampling module, used for sampling and preprocessing multi-band radio signals to obtain preprocessed signals; A classification module, used for performing fast Fourier transform and signal classification on the preprocessed signal to obtain a mixed signal; A reconstruction module, used to construct the mixed signal into a Hankel matrix, and perform signal reconstruction in combination with a correlation coefficient threshold to obtain a pure target signal; A dynamic sub-band division module is used to dynamically divide the pure target signal into sub-bands, introduce a signal-to-noise ratio adaptive regularization factor in each sub-band, iteratively calculate the minimum mean square error until the residual is less than the first target value, and obtain initial calibration parameters; A linear weighted averaging module, used to establish a logarithmically spaced frequency point distribution based on the initial calibration parameters, and perform linear weighted averaging within the overlapping bandwidth of adjacent sub-bands to obtain a compensation calibration coefficient; The verification module is used to verify the compensation calibration coefficient using a standard calibration signal. When the root mean square error exceeds the second target value, the steps from the dynamic sub-band division module to the verification module are repeatedly executed until the accuracy requirement is met to obtain a target calibration result.

[0007] In the technical solution provided by the present invention, by adopting dynamic sampling and preprocessing strategies, combined with Hanning window functions and bandpass filter groups, the quality of signal acquisition is effectively improved. A signal classification method based on fast Fourier transform and support vector machine classifier accurately identifies effective components and interference components in mixed signals, and significantly improves the accuracy of signal classification; by adopting Hankel matrix decomposition and singular value reconstruction technology, combined with correlation coefficient threshold judgment, effective suppression of interference signals is achieved, and the purity of target signals is ensured; dynamic sub-band division and adaptive regularization factors are introduced, and the minimum mean square error is iteratively optimized to effectively reduce the calculation complexity and improve the calibration efficiency; by logarithmically spaced frequency distribution and linear weighted averaging strategies, a smooth transition between adjacent sub-bands is achieved, and the continuity and stability of the calibration results are ensured; a complete calibration verification and optimization mechanism is established, and the reliability and accuracy of the calibration results are ensured through root mean square error control and iterative optimization, so that the overall calibration accuracy is significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0009] Figure 1A schematic diagram of the steps of a multi-band radio measurement calibration method according to an embodiment of the present invention; Figure 2 Schematic diagram of the structure of a multi-band radio metrology calibration system in an embodiment of the present invention. DETAILED DESCRIPTION

[0010] An embodiment of the present invention provides a multi-band radio metrology calibration method and system. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 An embodiment of the multi-band radio measurement calibration method in the embodiment of the present invention includes: Step S1, sampling and preprocessing the multi-band radio signal to obtain a preprocessed signal; It is understandable that the execution subject of the present invention may be a multi-band radio measurement and calibration system, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0012] Specifically, the sampling frequency is set according to the highest frequency of the signal to be measured to meet the requirements of the Nyquist sampling theorem, ensure the integrity of the signal, and avoid the generation of aliasing effects. On this basis, a high-precision analog-to-digital converter (ADC) is used to digitally sample the multi-band radio signal to obtain a digital sampling signal with high time resolution. The digital sampling signal is framed based on the preset overlap rate and the number of sampling points to obtain a framed signal. The frame length is reasonably selected so that it can cover sufficient signal information and avoid the reduction of spectrum resolution caused by too short a time window. At the same time, an appropriate overlap rate is used to make a certain degree of redundancy between adjacent frames to reduce the boundary effects that occur in subsequent signal processing such as short-time Fourier transform. The framed signal is windowed by the Hanning window function. The Hanning window function has the characteristics of smooth transition in the time domain, thereby effectively reducing the energy of the spectrum side lobe and improving the frequency resolution of the signal. By applying the Hanning window to the framed signal, the signal edge is smoothly transitioned to avoid the spectrum distortion problem caused by the truncation effect. The signal after windowing is input into the analog-to-digital converter (ADC) for quantization processing to obtain a quantized signal that better meets the requirements of digital signal processing. The quantized signal is DC offset corrected, and a high-pass filter or adaptive mean filter is used to eliminate the low-frequency component and restore the true waveform of the signal to obtain a DC offset corrected signal. The corrected signal is bandpass filtered. Five groups of bandpass filters are used, and the center frequency of each filter corresponds to each frequency band of the signal to be measured, so as to ensure that the signals of different frequency bands are extracted and analyzed separately. The design of the bandpass filter must take into account the bandwidth characteristics of the signal to ensure that its passband range matches the spectral characteristics of the target signal, and at the same time have a steeper cutoff characteristic to reduce the influence of out-of-band interference signals. Through this process, filtered signals corresponding to different frequency bands are obtained. The filtered signal is stored in the signal buffer area to obtain a preprocessed signal so that the subsequent processing modules can read and perform corresponding calculations in sequence. The design of the signal buffer area meets the requirements of low latency and high throughput to ensure that the entire system can efficiently process multi-band radio signals while avoiding the impact of cache overflow or read delay on calibration accuracy.

[0013] Step S2, performing fast Fourier transform and signal classification on the preprocessed signal to obtain a mixed signal; Specifically, a fast Fourier transform is performed on the preprocessed signal to convert the time domain signal into a frequency domain signal spectrum, which contains the amplitude and phase information of different frequency components. The amplitude spectrum is used for signal classification and feature extraction, while the phase spectrum is used for subsequent signal reconstruction and coherence analysis. In order to analyze the energy distribution of the signal, the frequency domain signal spectrum is normalized, and the ratio of the target frequency band energy to the full frequency band energy is calculated to obtain the spectrum energy ratio characteristic parameter, which reflects the energy proportion of a specific frequency band in the entire spectrum, and is then used to determine the relative strength of the target signal in the overall spectrum. Based on the frequency domain signal spectrum, the ratio of the spectrum centroid to the spectrum bandwidth is calculated to obtain the spectrum concentration characteristic parameter. The spectrum centroid indicates the position of the "center of gravity" of the signal in the spectrum, which measures whether the frequency distribution of the signal is concentrated in a specific frequency band, while the spectrum bandwidth reflects the spectrum extension degree of the signal, that is, the width of the signal energy distribution. By calculating the ratio of the spectrum centroid to the spectrum bandwidth, the concentration degree of the signal is characterized, thereby distinguishing narrowband signals from broadband signals. At the same time, the main peak and secondary peak of the frequency domain signal spectrum are extracted, and their energy difference is calculated. The main peak is the strongest frequency component in the signal, while the secondary peak is the secondary frequency component caused by modulation, interference or other signal characteristics. The energy difference between the main peak and the secondary peak is calculated and normalized to obtain the main and secondary peak energy difference characteristic parameters, which effectively reflect the modulation characteristics of the signal and the possibility of mixed signals. A three-dimensional feature vector is constructed based on the spectral energy ratio characteristic parameters, spectral concentration characteristic parameters and main and secondary peak energy difference characteristic parameters. In order to ensure the efficiency and accuracy of classification, a support vector machine (SVM) classifier based on the Gaussian kernel function is used to classify the three-dimensional feature vector. SVM is a machine learning method suitable for data classification tasks in high-dimensional feature space. By using the Gaussian kernel function, different categories of signals can be effectively identified in nonlinear space to improve classification accuracy. In the training stage, a set of signal samples of known categories are used to train the SVM to determine the best classification hyperplane, while in the actual classification stage, the SVM projects the input three-dimensional feature vector into the high-dimensional feature space and uses the support vector to determine the category label of the signal. When the SVM classifier completes the classification, the data segments determined to be mixed signals are extracted from the preprocessed signal based on the classification results, and the corresponding time position and frequency characteristics are recorded to obtain complete mixed signal information.

[0014] Step S3, constructing the mixed signal into a Hankel matrix, and reconstructing the signal in combination with the correlation coefficient threshold to obtain a pure target signal; Specifically, the mixed signal is subjected to appropriate matrix transformation so as to decompose the signal structure using linear algebra methods. According to one third of the number of sampling points as the number of rows of the Hankel matrix, the mixed signal is constructed into a Hankel matrix with a specific structure so as to provide a suitable matrix form for the subsequent singular value decomposition. The characteristic of the Hankel matrix is ​​that the elements on its diagonal are the same. This structure can effectively retain the timing information of the signal and highlight the main signal components in the subsequent decomposition process, while suppressing random noise and interference components to obtain the signal Hankel matrix. The Hankel matrix is ​​subjected to singular value decomposition to obtain the main components of the signal. Singular value decomposition decomposes the matrix into left and right singular vectors and their corresponding singular value sequences, where the singular values ​​represent the energy distribution of the signal and the singular vectors correspond to different characteristic modes of the signal. Through this decomposition method, the main components of the signal are effectively extracted and the low-energy noise or interference components therein are identified. The obtained singular value sequence is further analyzed, that is, the singular values ​​are arranged in descending order according to the energy contribution rate, and the cumulative energy contribution rate is calculated. When the cumulative energy contribution rate reaches 95%, it is considered that the main signal features have been fully captured, so these singular values ​​and their corresponding singular vectors are extracted to obtain the signal subspace features. Based on the signal subspace features, the mixed signal is reconstructed to remove noise and irrelevant components to obtain a preliminary reconstructed signal. The interference feature library is used to perform correlation analysis on the preliminary reconstructed signal to identify the interference components therein. By calculating the correlation coefficient between the preliminary reconstructed signal and the known interference pattern in the interference feature library and setting a reasonable correlation coefficient threshold, the signal is decomposed into a target signal component and an interference signal component. A higher correlation coefficient indicates that this part of the signal is affected by the known interference pattern and should be classified as an interference signal component, while the signal part with a lower correlation is retained as the target signal component. The target signal component is subjected to time-frequency analysis to ensure its purity. Time-frequency analysis uses methods such as short-time Fourier transform to accurately characterize the distribution of the signal at different times and frequencies. Through this analysis process, the residual interference signal components are more clearly identified and eliminated through appropriate filtering or nonlinear signal decomposition methods, and finally a pure target signal is obtained.

[0015] Step S4, dynamically divide the pure target signal into sub-bands, introduce a signal-to-noise ratio adaptive regularization factor in each sub-band, iteratively calculate the minimum mean square error until the residual is less than the first target value, and obtain the initial calibration parameters; Specifically, the spectrum analysis of the pure target signal is performed to determine the appropriate subband division scheme. The subband division should be performed in a dynamic manner, that is, adaptive division of N subbands is performed according to the power spectrum density characteristics and energy concentration area of ​​the signal to ensure that each subband fully covers the main characteristic information of the signal, while avoiding the calibration accuracy loss caused by unnecessary spectrum segmentation. After the division is completed, N subband signal sequences are obtained, each subband corresponds to a certain frequency range and contains the corresponding target signal components. Statistical analysis is performed on each subband signal in the subband signal sequence, and the autocorrelation matrix and cross-correlation vector are calculated to extract the relevant feature quantity of the signal. The autocorrelation matrix reflects the time correlation of the signal, while the cross-correlation vector describes the correlation characteristics between different subbands. The signal-to-noise ratio of each subband is calculated based on the relevant feature quantity, and the regularization factor is dynamically generated according to the signal-to-noise ratio value. The regularization factor plays a role in balancing the stability and accuracy of the solution in the optimization calculation. For subbands with high signal-to-noise ratio, a smaller regularization factor is used to improve the accuracy of parameter estimation, while for subbands with low signal-to-noise ratio, a larger regularization factor is required to suppress the influence of noise, thereby improving the robustness of the system. The final optimized parameters will provide the necessary adjustment items for iterative calculation to ensure the convergence and accuracy of the solution. The optimized parameters are substituted into the Wiener-Hopp equation and solved by the conjugate gradient method to obtain the iterative calculation sequence. The Wiener-Hopp equation is an optimal filtering equation with high computational efficiency and theoretical optimality in solving the minimum mean square error problem. Since the equation involves matrix inversion calculation, direct solution leads to high computational complexity, so the conjugate gradient method is used for numerical iterative solution. The conjugate gradient method is an efficient optimization algorithm suitable for solving large linear equations. It converges to a stable solution within a small number of iterations by gradually approaching the optimal solution, thereby improving computational efficiency. During the iterative calculation process, the change of the residual is continuously monitored, and the residual of the current solution is calculated after each iteration. When the residual is less than the preset first target value, it is considered that the solution has reached a converged state, and the final subband calibration parameters are obtained. Local iterative optimization is performed on the frequency points where the error in the subband calibration parameters exceeds the threshold to ensure that the final calibration accuracy meets the expected target. During the local optimization process, the optimization step size should be gradually reduced to avoid numerical oscillation or unstable optimization results caused by too large a step size. The optimization step size is reduced by 0.01 times the initial step size to achieve more precise error correction, so that the final optimization result is closer to the actual calibration parameters. The optimization results of each subband are merged to ensure the continuity and consistency of the calibration parameters within the entire frequency band, and finally obtain accurate initial calibration parameters.

[0016] Step S5, establishing a logarithmically spaced frequency point distribution based on the initial calibration parameters, and performing linear weighted averaging within the overlapping bandwidth of adjacent sub-frequency bands to obtain a compensation calibration coefficient; Specifically, the frequency band of the signal to be tested is divided into M sub-bands, and three calibration frequency points are distributed in each sub-band according to logarithmic intervals to construct a frequency distribution method that is more in line with the physical characteristics of the signal. Compared with the linear equidistant distribution, the logarithmic interval calibration frequency distribution is more adaptable to the uneven characteristics of the frequency distribution of radio signals, and at the same time ensures that the sampling density in the high-frequency area is more reasonable than that in the low-frequency area, thereby improving the calibration accuracy. Through this step, a calibration frequency distribution covering the entire test frequency band is obtained. The initial calibration parameters are interpolated based on the calibration frequency distribution to construct the frequency response function of each sub-band. For the interpolation calculation of each sub-band, not only the amplitude response is interpolated, but also the phase response needs to be interpolated to ensure the integrity of the calibration parameters. In the interpolation process of amplitude and phase, the piecewise cubic spline interpolation method is adopted, which ensures the smoothness of the interpolation while maintaining a high fitting accuracy and avoiding the generation of overfitting problems. Through this process, a frequency response curve is obtained, so that the initial calibration parameters can be extended to the entire calibration frequency distribution and a more accurate frequency response model is formed. A certain overlapping bandwidth is introduced between adjacent sub-bands, and the frequency response is linearly weighted averaged within the overlapping bandwidth to obtain the transition region calibration value, thereby achieving a continuous frequency response transition at the junction of the sub-bands and avoiding the spectrum discontinuity problem caused by independent calculations between sub-bands. The weight distribution within the overlapping bandwidth adopts a linear function form, that is, at the starting position of the overlapping bandwidth, the weight of the lower frequency band is larger, while the weight of the higher frequency band is smaller, and as the frequency increases, the weight gradually reverses, thereby forming a smooth transition calibration value in the entire overlapping region. Through linear weighted average calculation, the mutation during spectrum splicing is effectively eliminated, so that the entire frequency response curve maintains good continuity between different sub-bands. The calibration coefficients of each frequency point are calculated based on the frequency response curve and the transition region calibration value, and a compensation sequence is constructed. The calculation method of the compensation sequence is based on the inverse or inverse fitting model of the frequency response to ensure that the calibration coefficient can effectively compensate for the system error and keep the measurement signal with high accuracy in the entire spectrum range. In the calculation process, the nonlinear effect of the system and the measurement error are considered to optimize the stability and applicability of the compensation coefficient. The compensation sequence is iteratively optimized, that is, the updated compensation sequence is calculated in each iteration cycle and compared with the result of the previous cycle until the change rate of the results of the adjacent iteration cycles is less than the preset target percentage value, the optimization process is terminated, and finally a stable compensation calibration coefficient is obtained. The core of iterative optimization is to dynamically adjust the compensation coefficient to continuously approach the optimal calibration solution. In the iterative process, an adaptive step size adjustment strategy is adopted, so that the optimization process converges quickly with a larger step size in the initial stage, and gradually reduces the step size in the later optimization stage to ensure that the accuracy of the final calibration coefficient reaches the optimal level.

[0017] Step S6, using a standard calibration signal to verify the compensation calibration coefficient, when the root mean square error exceeds the second target value, repeating steps S4-S6 until the accuracy requirement is met to obtain a target calibration result.

[0018] Specifically, a calibration signal is generated using a standard signal source to ensure that the reference signal of the entire measurement process has sufficient stability and accuracy. The standard signal source needs to cover all frequency bands to be measured, and generate a series of equally spaced standard calibration signals in a manner with a nominal frequency interval of 20kHz. This interval selection ensures that there are sufficiently dense measurement points in the entire frequency band, and at the same time does not cause excessive calculation burden, thereby ensuring that the subsequent calibration error calculation and optimization adjustment have high accuracy and operability. After the signal generated by the standard signal source covers all frequency bands to be measured, a standard calibration signal is obtained. The standard calibration signal is compared point by point with the compensation calibration coefficient in terms of frequency and amplitude to accurately calculate the measurement error of each frequency point. The amplitude and frequency of the standard calibration signal are accurately measured, and compared with the measurement results corrected by the compensation calibration coefficient to obtain the error values ​​of each frequency point. An error distribution histogram is generated based on the measurement errors of each frequency point, so as to intuitively display the distribution characteristics of the error, and the root mean square error and maximum error of each sub-frequency band are calculated to obtain the error statistics. The root mean square error is an important indicator for measuring measurement accuracy, reflecting the overall level of error, while the maximum error is used to evaluate the measurement deviation in the most extreme case. The performance of the current compensation calibration coefficient is reflected by calculating the error statistical index. The error statistical results are optimized to determine whether the calibration parameters need to be further adjusted. The calculated root mean square error is compared with the second target value to determine whether the current calibration accuracy meets the requirements. If the root mean square error is lower than the second target value, it means that the optimization of the compensation calibration coefficient has achieved the expected effect, and the final target calibration result is obtained. Based on the optimization judgment result, the frequency band where the root mean square error exceeds the second target value is marked, and the key parameters in the calibration process are recorded to form a frequency band optimization mark. These key parameters include the current calibration coefficient, the trend of the measurement error, the signal-to-noise ratio distribution, and the convergence of the previous rounds of optimization iterations. Through a comprehensive analysis of this information, it is determined which factors affect the frequency band with large errors. After the frequency band that needs to be optimized is determined, return to step S4 according to the frequency band optimization mark, and repeat steps S4-S6 to optimize the calibration parameters of the marked frequency band in a targeted manner. During the optimization process, the calibration accuracy is improved by adjusting the regularization factor of the signal-to-noise ratio adaptation, optimizing the subband division strategy, and improving the minimum mean square error calculation method. At the same time, during the iterative optimization process, a dynamic step size adjustment strategy is adopted to ensure that the optimization process can converge quickly without causing numerical oscillation due to excessive step size. After each iterative optimization, the root mean square error is recalculated and compared with the second target value to determine whether further optimization is needed. When the root mean square error finally converges to a value lower than the second target value, it is considered that the calibration process has achieved the expected accuracy requirements and the final target calibration result is obtained.

[0019] In the embodiment of the present invention, by adopting dynamic sampling and preprocessing strategies, combined with Hanning window function and bandpass filter group, the quality of signal acquisition is effectively improved, and the signal classification method based on fast Fourier transform and support vector machine classifier accurately identifies the effective components and interference components in the mixed signal, and significantly improves the accuracy of signal classification; by adopting Hankel matrix decomposition and singular value reconstruction technology, combined with correlation coefficient threshold judgment, effective suppression of interference signals is achieved, and the purity of target signals is ensured; dynamic sub-band division and adaptive regularization factor are introduced, and the minimum mean square error is iteratively optimized to effectively reduce the computational complexity and improve the calibration efficiency; through logarithmic interval frequency distribution and linear weight averaging strategy, a smooth transition between adjacent sub-bands is achieved, and the continuity and stability of the calibration results are guaranteed; a complete calibration verification and optimization mechanism is established, and the reliability and accuracy of the calibration results are ensured through root mean square error control and iterative optimization, so that the overall calibration accuracy is significantly improved.

[0020] In a specific embodiment, the process of executing step S1 may specifically include the following steps: The sampling frequency is set according to the highest frequency of the signal to be tested, and the multi-band radio signal is digitally sampled based on the sampling frequency to obtain a digital sampling signal; Based on the preset overlap rate and sampling points, the digital sampling signal is framed to obtain a framed signal; Performing windowing processing using a Hanning window function on the framed signal and quantizing it through an analog-to-digital converter to obtain a quantized signal, and performing DC offset correction on the quantized signal to obtain a corrected signal; The correction signal is filtered through five groups of bandpass filters to obtain a filtered signal, and the filtered signal is stored in a signal buffer to obtain a preprocessed signal, wherein the center frequency of each bandpass filter corresponds to each frequency band of the signal to be measured.

[0021] Specifically, the sampling frequency is set according to the highest frequency of the signal to be measured to ensure the integrity of the signal and avoid the influence of aliasing effect. According to the Nyquist sampling theorem, the sampling frequency Must be at least the highest frequency of the signal to be measured twice, that is: in, Indicates the highest frequency of the signal to be measured. is the set sampling frequency. If the signal bandwidth is wide, the sampling rate should be appropriately increased to ensure that no spectrum aliasing occurs during subsequent signal processing. The multi-band radio signal is digitally sampled based on the sampling frequency to obtain a digital sampled signal. The digital sampling process involves analog-to-digital conversion, that is, converting a continuous analog signal into a discrete digital signal. Let the number of sampling points be , then the discrete sampling signal is expressed as: in, Indicates The discrete signal value of sampling points, is the original analog signal, is the sampling interval, that is: Every The digital signal is divided into frames to improve the calculation efficiency of subsequent analysis and reduce the impact of short-term signal mutations on the overall spectrum calculation. The frame division method is based on the preset overlap rate. and number of sampling points , the overlap rate is set between 50% and 75% to ensure that there is enough information sharing between adjacent frames and reduce the impact of signal mutation. If the length of each frame is , then the step size of adjacent frames is: For example, if the frame length is 1024 points, and the overlap rate Set to 50%, the step length of adjacent frames The number of points is 512, and a series of overlapping frame signal sequences are obtained. In order to reduce the spectrum leakage effect and improve the spectrum resolution, the frame signal is windowed using the Hanning window function, and its mathematical expression is: in, is the window value of the Hanning window function, is the current sample point, is the frame length. The windowed signal is expressed as: The Hanning window can effectively reduce the side lobes and improve the accuracy of signal analysis. After windowing, the signal is quantized by the analog-to-digital converter to convert it into a digital value with a fixed number of bits. For example, if the ADC uses a 12-bit resolution, the signal's numerical range is quantized to Different discrete levels are generated, thereby improving the accuracy of subsequent signal processing. DC offset correction is performed on the quantized signal. The DC offset is calculated by taking the average value of the signal. ,Right now: This value is then used to debias the signal, i.e.: in, is the corrected signal. After the signal is corrected, it is filtered by five sets of bandpass filters, whose center frequencies correspond to the frequency bands of the signal to be measured. Assume that the frequency bands of the signal to be measured include , then the transfer function of each bandpass filter is It is expressed as: in, It is The center frequency of the bandpass filter is is the filter order, which is 4 or 6 to ensure good passband characteristics and steep cutoff characteristics. The function of each filter is to extract the signal of the corresponding frequency band and suppress other frequency components. The filtered signal is stored in the signal buffer for further signal analysis and processing. The design of the signal buffer needs to ensure sufficient storage space to avoid data overflow, and the reading rate must match the sampling rate to ensure data integrity.

[0022] In a specific embodiment, the process of executing step S2 may specifically include the following steps: Perform fast Fourier transform on the preprocessed signal to obtain the frequency domain signal spectrum; Calculate the ratio of the target frequency band energy to the full frequency band energy in the frequency domain signal spectrum to obtain the spectrum energy ratio characteristic parameter; The ratio of the spectrum centroid to the spectrum bandwidth is calculated based on the frequency domain signal spectrum to obtain the spectrum concentration characteristic parameter; The energy difference between the main peak and the secondary peak is extracted from the frequency domain signal spectrum and normalized to obtain the energy difference characteristic parameters of the main and secondary peaks; A three-dimensional feature vector is constructed based on a spectrum energy ratio feature parameter, a spectrum concentration feature parameter, and a primary-secondary peak energy difference feature parameter, and the three-dimensional feature vector is input into a support vector machine classifier based on a Gaussian kernel function to obtain a classification result; Based on the classification result, the data segments determined to be mixed signals and their corresponding time positions and frequency characteristics are extracted from the preprocessed signals to obtain the mixed signals.

[0023] Specifically, a fast Fourier transform is performed on the preprocessed signal to convert the signal from the time domain to the frequency domain to obtain spectrum information. The mathematical expression of the fast Fourier transform is expressed as: in, It is The complex representation of the frequency components, It is The time domain signal of sampling points, is the number of Fourier transform points, is an imaginary unit, Represents the frequency index. In actual calculation, the efficient fast Fourier transform algorithm is used to The time complexity of the transformation is reduced, thereby accelerating the spectrum analysis process. The obtained frequency domain signal spectrum contains the amplitude information and phase information of the signal, and the amplitude spectrum is used for feature extraction. After obtaining the frequency domain signal spectrum, the spectrum energy ratio characteristic parameter is calculated, that is, the ratio of the target frequency band energy to the full frequency band energy. The target frequency band energy It is expressed as: in, and Respectively represent the start frequency index and end frequency index of the target frequency band, Represents the frequency domain signal spectrum in The power at each frequency point. Full frequency band energy is the total energy in the entire spectrum, which is calculated as: The characteristic parameter of spectrum energy ratio is expressed as: in, Reflects the ratio of the target frequency band energy to the total signal energy. This parameter is used to measure the significance of the target signal in signal classification. Calculate the spectrum concentration characteristic parameter, which is calculated by the spectrum centroid and spectrum bandwidth The centroid of the spectrum is calculated as follows: in, It is The actual frequency corresponding to each frequency point, while the numerator represents the weighted sum of the signal energy on the frequency axis, and the denominator is the total signal energy, ensuring that the calculated Reflects the location of the signal's "center of gravity". Spectrum bandwidth It is calculated based on the expansibility of signal energy distribution and is calculated using the second-order moment method, namely: The spectrum concentration characteristic parameter is expressed as: in, Reflects the concentration of signal energy in the spectrum. A larger value indicates that the signal energy is more concentrated in a certain frequency, while a smaller value indicates that the signal energy distribution is more dispersed. In order to further extract the modulation characteristics of the signal, the energy difference characteristic parameter of the main peak and the secondary peak is calculated, that is, the amplitude difference between the main peak and the secondary peak in the spectrum, and normalized. He Cifeng The identification of adopts the local maximum search algorithm, where: in, is the index of the main peak, and the secondary peak is the second largest peak after removing the main peak. Calculated as This parameter is used to measure the modulation characteristics of the signal. A larger value indicates that the signal has a distinct single main peak, while a smaller value means that the signal has a stronger modulation component or interference component. , spectrum concentration and the energy difference between the main and secondary peaks Construct a three-dimensional feature vector, that is, The three-dimensional feature vector is input into the support vector machine (SVM) classifier based on Gaussian kernel function for signal classification. The decision boundary of the SVM classifier is solved by the following optimization problem: in, is the classification hyperplane normal vector, is the bias term, is the regularization parameter, It is The class labels of samples, is the input feature vector. Using the Gaussian kernel function: Map the data to a high-dimensional space to enhance the classification capability. Based on the classification results, extract the data segments determined to be mixed signals from the preprocessed signals, and record their corresponding time positions and frequency features to construct a complete mixed signal data set.

[0024] In a specific embodiment, the process of executing step S3 may specifically include the following steps: The mixed signal is constructed with 1 / 3 of the number of sampling points as the number of rows to obtain the signal Hankel matrix; Perform singular value decomposition on the signal Hankel matrix to obtain the singular value sequence and its corresponding left and right singular vectors; Arrange the singular value sequence in descending order according to the energy contribution rate, extract the singular values ​​and their corresponding singular vectors with a cumulative energy contribution rate of 95%, and obtain the signal subspace features; Reconstruct the signal according to the signal subspace characteristics to obtain a preliminary reconstructed signal; Perform correlation analysis on the preliminary reconstructed signal and the interference feature library, and divide the signal components based on the correlation coefficient threshold to obtain the target signal component and the interference signal component; Perform time-frequency analysis on the target signal components and remove the interference signal components to obtain a pure target signal.

[0025] Specifically, the mixed signal is constructed with 1 / 3 of the number of sampling points as the number of rows to form a structured representation of the signal, so that the matrix decomposition method can be used for noise reduction and signal decomposition. The Hankel matrix is ​​a special structured matrix in which the elements on each anti-diagonal line are the same, so for a length of Discrete mixed signal , construct a Hankel matrix of the following form: The number of rows in the matrix is ​​set to , and the number of columns is , ensuring that the time series information of the signal is well preserved in the matrix. The constructed Hankel matrix can provide redundant information in signal processing and help remove random noise and interference signals. The signal Hankel matrix is ​​subjected to singular value decomposition (SVD) to extract the main components of the signal and remove noise. The mathematical expression of singular value decomposition is as follows: in, is the left singular vector matrix, is a right singular vector matrix, and is a diagonal matrix with the diagonal elements are the singular values ​​of the Hankel matrix. These singular values ​​represent the energy distribution of the signal in different modes. Larger singular values ​​correspond to the main components of the signal, while smaller singular values ​​usually correspond to noise and interference. Arrange the singular value sequence in descending order according to the energy contribution rate, and calculate the cumulative energy contribution rate to select the most important signal component. The energy contribution rate is calculated as: in, Before The cumulative energy proportion of singular values, the goal is to find the smallest , so that: That is, ensure that the total energy contributed by the selected singular values ​​reaches at least 95%. and The corresponding singular vectors are extracted from the , and the signal subspace features are constructed to retain the main components of the signal and remove the noise. After obtaining the signal subspace features, the signal is reconstructed to obtain a preliminary reconstructed signal. The reconstruction process is expressed as: in, and Only the front singular vectors, and Only keep the front Singular values. The correlation analysis between the preliminary reconstructed signal and the interference feature library is performed to distinguish the target signal component from the interference signal component. Assume that the reference signal in the interference feature library is ,in Represent different interference signal types and calculate the reconstructed signal Correlation coefficient with each interference signal: in, Reflects the correlation between the current signal and the specific interference signal. Exceeding the set correlation coefficient threshold , it is considered that the signal component is mainly affected by the interference signal, so it is necessary to remove the interference signal component to obtain the division result of the target signal component and the interference signal component. For the extracted target signal component, time-frequency analysis is performed to ensure that the final signal is pure and reliable. The time-frequency analysis uses the short-time Fourier transform (STFT) method, and its mathematical expression is as follows: in, It's time and frequency The time spectrum at is the window function, is the frame shift parameter. The frequency components of the signal at different time positions are analyzed by STFT, so as to identify the residual interference components and further eliminate them to obtain the pure target signal.

[0026] In a specific embodiment, the process of executing step S4 may specifically include the following steps: Divide the pure target signal into N sub-bands to obtain a sub-band signal sequence; Calculate the autocorrelation matrix and the cross-correlation vector for each sub-band signal in the sub-band signal sequence to obtain the relevant feature quantity; The signal-to-noise ratio of each subband is calculated based on the relevant feature quantity, and the regularization factor is dynamically generated according to the signal-to-noise ratio value to obtain the optimization parameter; Substitute the optimized parameters into the Wiener-Hope equation and solve it using the conjugate gradient method to obtain an iterative calculation sequence; Perform residual calculation on the iterative calculation sequence, stop iteration when the residual is less than the first target value, and obtain the subband calibration parameter; The frequency points where the error in the sub-band calibration parameters exceeds the threshold are locally iteratively optimized, and the optimization step size is decreased by 0.01 times the initial step size. The optimization results of each sub-band are merged to obtain the initial calibration parameters.

[0027] Specifically, the pure target signal is The signal characteristics are analyzed independently in different frequency bands, so that the calibration process is optimized for the characteristics of different frequency bands. The total bandwidth is , then the bandwidth of each subband is defined as: in, represents the bandwidth of a single subband, Represents the total number of sub-bands. On this basis, the target signal is decomposed into sub-band signals, each sub-band signal Contains only the frequency components of the corresponding frequency band. The autocorrelation matrix and cross-correlation vector are calculated for each sub-band signal in the sub-band signal sequence to extract the relevant feature quantity. It is used to describe the correlation of signals under different time delays. The calculation method is: in, represents the subband signal, represents its conjugate transpose, represents the mathematical expectation operation. The cross-correlation vector It is used to measure the difference between the subband signal and the expected signal The correlation between them is defined as follows: in, is the reference signal corresponding to the subband. The signal-to-noise ratio of each subband is calculated based on the relevant feature quantity, and the regularization factor is dynamically generated according to the signal-to-noise ratio value to obtain the optimized parameter. The calculation method of the signal-to-noise ratio is: in, is the noise component in the subband signal, and is the signal component. , an adaptive regularization strategy is used to set the regularization factor , for example using the inverse relationship: in, is a tuning parameter, is a small constant to avoid the denominator being zero. The role of the regularization factor is to prevent numerical instability during matrix solution and to improve the convergence of the optimization process. Substitute the optimization parameters into the Wiener-Hope equation and solve it using the conjugate gradient method to obtain an iterative calculation sequence. The basic form of the Wiener-Hope equation is as follows: in, is the subband calibration parameter to be solved. Since the direct solution has high computational complexity, the conjugate gradient method is used for iterative solution. The iterative update formula of the conjugate gradient method is in, is the search direction, is the residual, and is the step size parameter. Initially, let: The step size parameter is updated as follows: The advantage of this algorithm is that it avoids the computational complexity of directly solving the matrix inverse operation, so that the calibration parameters can converge within a limited number of steps. During the conjugate gradient method iteration process, the residual is continuously monitored and calculated. When the residual satisfies: When , the iteration stops and the subband calibration parameters are obtained. is the first target value, which is used to judge the convergence. However, in some cases, some subband calibration parameters still have frequency points with large errors. Local optimization is performed on these frequency points. For the frequency points whose errors exceed the threshold, the error is set to ,like: Then further adjustment of calibration parameters is required. Optimization step size Adopt a step-by-step reduction approach, according to: in, is the initial step size, and the optimization precision is ensured by continuously reducing the step size. The local optimization process is corrected by the gradient descent method, that is, in, is the gradient of the error with respect to the parameter. After all sub-band calibration parameters are optimized, the optimization results of each sub-band are merged to form a complete initial calibration parameter. The merging method adopts a weighted average strategy, namely: in, is the normalized weight coefficient, set as: This ensures that subbands with high signal-to-noise ratios have a larger weight in the final calibration parameters.

[0028] The signal-to-noise ratio of each subband is calculated based on the relevant feature quantity, and the regularization factor is dynamically generated according to the signal-to-noise ratio value to obtain the optimization parameters, including: performing eigenvalue decomposition on the autocorrelation matrix R in the relevant feature quantity to obtain the eigenvalue sequence and eigenvector; calculating the ratio of the main component to the secondary component based on the eigenvalue sequence to obtain the subband signal-to-noise ratio; performing logarithmic transformation and normalization on the subband signal-to-noise ratio to obtain the normalized signal-to-noise ratio; constructing an exponential decay function according to the normalized signal-to-noise ratio, and substituting the signal-to-noise ratio into the function to obtain the initial regularization factor; iteratively updating the initial regularization factor, with the update step size of each iteration being 0.1 times the current value, until the difference between two adjacent iterations is less than 10 -8 , and obtain the final regularization factor; the final regularization factor is combined with the relevant feature quantity to obtain the optimization parameter.

[0029] The optimization parameters are substituted into the Wiener-Hope equation and solved by the conjugate gradient method to obtain an iterative calculation sequence, including: substituting the optimization parameters into the Wiener-Hope equation Rx=P to construct the initial system equation, where R is the autocorrelation matrix and P is the cross-correlation vector, to obtain the set of equations to be solved; calculating the initial residual vector r0 and the initial search direction p0 for the set of equations to be solved to obtain the initial value of the iteration; calculating the step size factor α based on the initial value of the iteration, and updating the current solution vector xk and the residual vector rk to obtain the updated parameters; calculating the conjugate coefficient β for the updated parameters, and updating the search direction pk to obtain a new iterative direction; calculating the residual norm based on the new iterative direction to determine whether it is less than 10 -6, and obtain the convergence criterion; determine whether to continue the iteration according to the convergence criterion, and output the current solution sequence as the iterative calculation sequence when the convergence condition is met.

[0030] In a specific embodiment, the process of executing step S5 may specifically include the following steps: The frequency band of the signal to be measured is divided into M sub-frequency bands, and three calibration frequency points are set in each sub-frequency band according to a logarithmic relationship to obtain a calibration frequency point distribution; The initial calibration parameters are interpolated based on the calibration frequency point distribution to obtain the frequency response function of each sub-band, and the amplitude and phase of the frequency response function are interpolated by piecewise cubic spline to obtain the frequency response curve; An overlapping bandwidth is set between adjacent sub-bands, and a linear weighted average is performed on the frequency response within the overlapping bandwidth to obtain a transition region calibration value; Calculate the calibration coefficient of each frequency point based on the frequency response curve and the transition region calibration value to obtain a compensation sequence; The compensation sequence is iteratively optimized until the result change rate of adjacent iteration cycles is less than the target percentage value, and the compensation calibration coefficient is obtained.

[0031] Specifically, the frequency band of the signal to be tested is divided into Sub-bands are set, and three calibration frequency points are set in each sub-band according to a logarithmic relationship to ensure that the calibration frequency points are reasonably distributed in the entire frequency range. The calibration frequency point setting method of the logarithmic relationship is expressed as: in, Indicates The first Calibration frequency points, Represents 3 calibration frequency points, is the minimum frequency of the entire measurement range, is the maximum frequency. The divided calibration frequency points can provide higher frequency resolution at lower frequencies and more reasonable coverage at high frequencies. After completing the setting of the calibration frequency points, the initial calibration parameters are interpolated based on the calibration frequency points to obtain a complete frequency response function. Since the initial calibration parameters are discrete, the interpolation method is used to extend them to all calibration frequency points. Assume that the initial calibration parameters are ,in If the value is known only at a limited number of calibration frequency points, the piecewise cubic spline interpolation method is used for interpolation calculation. The basic formula of cubic spline interpolation is as follows: in, Indicates that in the sub-band The interpolation function inside is the interpolation coefficient to be determined, is the center frequency of the current sub-band. By constructing continuity and smoothness constraint equations and solving these interpolation coefficients, the obtained frequency response curve has high smoothness and accuracy in different sub-bands. After obtaining the complete frequency response curve, in order to ensure that the calibration parameters between adjacent sub-bands have a smooth transition, an overlapping bandwidth is set between adjacent sub-bands, and the frequency response is linearly weighted averaged in the overlapping area to obtain the calibration value of the transition area. Suppose the frequency responses of two adjacent sub-bands are and , then in the overlapping bandwidth The transition calibration value within is expressed as: Among them, the weight function and It is defined in a linear increasing and decreasing manner, that is, in, is the starting frequency of the overlapping bandwidth. Through linear weighted averaging, the frequency responses of adjacent sub-bands are ensured to transition smoothly in the overlapping area, avoiding the frequency discontinuity problem caused by factor band segmentation. After obtaining the complete frequency response curve and transition area calibration value, the calibration coefficient of each frequency point is calculated to obtain the compensation sequence. Compensation coefficient The calculation of is based on the correction of the measurement error of the reference signal, that is: in, is the ideal frequency response function, and is the frequency response calculated currently. The compensation coefficient is used to adjust the non-ideal characteristics of the measurement system to make the measurement result more accurate. In order to optimize the compensation sequence, an iterative optimization method is used so that the calibration coefficient can gradually approach the optimal value. The compensation coefficient for the iteration is , the following update formula is used for iterative optimization: in, is the learning rate, which should be taken as a small value to ensure convergence stability. After each iteration, the change rate of the results of adjacent iteration cycles is calculated to determine whether the optimization has converged. The change rate is expressed as: if Less than the set target percentage value ,Right now: The iteration is terminated and the final compensation calibration coefficient is obtained.

[0032] Among them, after obtaining the compensation calibration coefficient and before using the standard signal for verification, it also includes: setting a time delay threshold based on the compensation calibration coefficient, periodically sampling the calibration coefficient of each frequency band to obtain a sampling sequence; performing steady-state analysis on the sampling sequence, calculating the oscillation characteristics of the calibration coefficient of each frequency band, and obtaining oscillation parameters; establishing a coupling relationship model between frequency bands according to the oscillation parameters, and extracting key coupling characteristics to obtain a coupling characteristic sequence; performing time domain decomposition on the coupling characteristic sequence, determining the phase difference and delay characteristics between each frequency band, and obtaining a delay characteristic quantity; constructing an adaptive adjustment model of the compensation calibration coefficient based on the delay characteristic quantity to obtain a dynamic compensation function; performing periodic correction on the compensation calibration coefficient according to the dynamic compensation function to obtain a correction coefficient sequence; performing stability assessment based on the correction coefficient sequence, and calculating the system response characteristics to obtain response parameters; optimizing and screening the response parameters, and outputting the compensation calibration coefficient when the system reaches a stable oscillation state.

[0033] In a specific embodiment, the process of executing step S6 may specifically include the following steps: A standard signal source is used to generate a calibration signal with a nominal frequency interval of 20kHz, covering all the frequency bands to be tested, to obtain a standard calibration signal; Compare the standard calibration signal and the compensation calibration coefficient in frequency and amplitude to obtain the measurement error at each frequency point; Generate an error distribution histogram based on the measurement error of each frequency point, and calculate the root mean square error and maximum error of each frequency band to obtain the error statistics; Compare the root mean square error in the error statistics result with the second target value to obtain an optimization judgment result; Based on the optimization judgment result, mark the frequency band whose root mean square error exceeds the second target value, and record the key parameters of the calibration process to obtain the frequency band optimization mark; Return to step S4 according to the frequency band optimization mark, and repeat steps S4-S6 to optimize the calibration parameters of the marked frequency band until the root mean square error is less than the second target value, thereby obtaining the target calibration result.

[0034] Specifically, a standard signal source is used to generate a calibration signal with a nominal frequency interval of 20kHz, and it is ensured that the signal can cover all the frequency bands to be measured, so as to verify the calibration accuracy in the entire measurement range. The frequency distribution of the standard calibration signal is expressed as: in, Indicates The frequency of the calibration frequency point, is the lowest frequency, 20kHz is the frequency interval, is the calibration frequency index, the maximum Depends on the maximum frequency of the measurement system ,Right now: This means that the entire measurement frequency band is divided into equal intervals to ensure that each frequency point can be accurately covered by the standard signal and provide high-precision reference data. The frequency and amplitude of the standard calibration signal are compared with the compensation calibration coefficient to obtain the measurement error of each frequency point. The response at , the ideal response of the standard calibration signal is , then the measurement error at this frequency point is defined as: Meanwhile, the normalized measurement error is defined as: in, represents the absolute error of the measurement system, and represents the relative error. The latter expression more intuitively reflects the relative size of the error at different frequency points. After obtaining the measurement errors of all frequency points, the error data is statistically analyzed. An error distribution histogram is generated based on the error values ​​of all frequency points to observe the overall distribution of the error. The error distribution histogram is constructed by dividing the measurement error by the value of the error distribution. Divide it into several error intervals, and count the number of frequency points in each interval to obtain the probability distribution of the error. Calculate the root mean square error (RMSE) and maximum error of each sub-band to quantify the overall characteristics of the error. The root mean square error calculation formula is: in, Indicates The root mean square error of the sub-bands is is the number of calibration frequency points in this sub-band, is the set of all frequency points contained in the sub-band, and the maximum error is defined as: in, Represents the maximum measurement error in the sub-band. Through these error statistical indicators, the effectiveness of the current compensation calibration coefficient is evaluated and whether further optimization is needed. After obtaining the error statistical results, the root mean square error of each sub-band is compared with the second target value to determine whether the current calibration accuracy meets the requirements. If the root mean square error of a sub-band is greater than the second target value ,Right now: This means that the compensation calibration of this sub-band still has a large error and needs further optimization. Otherwise, the calibration parameters of this sub-band are acceptable and do not need to be optimized again. For the sub-band whose RMS error exceeds the second target value, it is marked based on the optimization judgment result, and the key parameters of the sub-band during the calibration process are recorded for use in the subsequent optimization process. The key parameters include: the initial calibration parameters of the sub-band , Current error statistics , the error convergence trend in the previous rounds of optimization, and the signal-to-noise ratio information. Based on this recorded information, those frequency points with large errors are optimized more specifically. Return to step S4 according to the frequency band optimization mark, and repeat steps S4-S6 to optimize the calibration parameters of the marked frequency band. Recalculate the calibration parameters for the sub-bands with large root mean square errors and adjust them through the minimum mean square error optimization method. During the optimization process, an adaptive step size strategy is adopted to avoid oscillation caused by too large an optimization step size or slow convergence caused by too small an optimization step size. The step size adjustment strategy is defined as: in, is the optimization step size of the sub-band, is the initial step size. In this way, the frequency band with larger error adopts larger step size to speed up the optimization, while the frequency band with smaller error adopts smaller step size to maintain stable convergence. After each optimization iteration, recalculate the root mean square error and check whether it satisfies: If it is satisfied, the optimization process of the sub-band ends, otherwise the optimization continues until the error converges to the target range.

[0035] The above describes the multi-band radio measurement calibration method in the embodiment of the present invention. The following describes the multi-band radio measurement calibration system in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a multi-band radio metrology calibration system includes: A sampling module, used for sampling and preprocessing multi-band radio signals to obtain preprocessed signals; A classification module, used for performing fast Fourier transform and signal classification on the preprocessed signal to obtain a mixed signal; The reconstruction module is used to construct the mixed signal into a Hankel matrix and reconstruct the signal in combination with the correlation coefficient threshold to obtain a pure target signal; A dynamic sub-band division module is used to dynamically divide the pure target signal into sub-bands, introduce a signal-to-noise ratio adaptive regularization factor in each sub-band, iteratively calculate the minimum mean square error until the residual is less than the first target value, and obtain the initial calibration parameters; A linear weighted averaging module is used to establish a logarithmically spaced frequency distribution based on initial calibration parameters and perform linear weighted averaging within the overlapping bandwidth of adjacent sub-bands to obtain compensation calibration coefficients; The verification module is used to verify the compensation calibration coefficient using a standard calibration signal. When the root mean square error exceeds the second target value, the steps from the dynamic sub-band division module to the verification module are repeatedly executed until the accuracy requirement is met to obtain the target calibration result.

[0036] Through the coordinated cooperation of the above-mentioned components, by adopting dynamic sampling and preprocessing strategies, combined with Hanning window function and bandpass filter group, the quality of signal acquisition is effectively improved. The signal classification method based on fast Fourier transform and support vector machine classifier can accurately identify the effective components and interference components in the mixed signal, and significantly improve the accuracy of signal classification; Hankel matrix decomposition and singular value reconstruction technology are adopted, combined with correlation coefficient threshold judgment, to achieve effective suppression of interference signals and ensure the purity of target signals; dynamic sub-band division and adaptive regularization factor are introduced, and the minimum mean square error is iteratively optimized to effectively reduce the computational complexity and improve the calibration efficiency; through logarithmic interval frequency distribution and linear weight averaging strategy, smooth transition between adjacent sub-bands is achieved, ensuring the continuity and stability of calibration results; a complete calibration verification and optimization mechanism is established, and the reliability and accuracy of calibration results are ensured through root mean square error control and iterative optimization, so that the overall calibration accuracy is significantly improved.

[0037] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0038] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0039] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-band radio measurement calibration method, characterized in that: The method comprises: S1: sampling and preprocessing the multi-band radio signal to obtain a preprocessed signal; S2: performing fast Fourier transform and signal classification on the preprocessed signal to obtain a mixed signal; S3: constructing the mixed signal into a Hankel matrix, and reconstructing the signal in combination with a correlation coefficient threshold to obtain a pure target signal; S4: dynamically divide the pure target signal into sub-bands, introduce a signal-to-noise ratio adaptive regularization factor in each sub-band, iteratively calculate the minimum mean square error until the residual is less than the first target value, and obtain initial calibration parameters; S5: establishing a logarithmically spaced frequency point distribution based on the initial calibration parameters, and performing linear weighted averaging within the overlapping bandwidth of adjacent sub-frequency bands to obtain a compensation calibration coefficient; S6: Use a standard calibration signal to verify the compensation calibration coefficient. When the root mean square error exceeds the second target value, repeat steps S4-S6 until the accuracy requirement is met to obtain a target calibration result.

2. The multi-band radio measurement calibration method according to claim 1, characterized in that: The sampling and preprocessing of the multi-band radio signal to obtain the preprocessed signal includes: Setting a sampling frequency according to the highest frequency of the signal to be tested, and digitally sampling the multi-band radio signal based on the sampling frequency to obtain a digital sampling signal; Based on a preset overlap rate and sampling points, the digital sampling signal is subjected to frame processing to obtain a frame signal; Performing windowing processing using a Hanning window function on the framed signal and quantizing it through an analog-to-digital converter to obtain a quantized signal, and performing direct current offset correction on the quantized signal to obtain a corrected signal; The correction signal is filtered through five groups of bandpass filters to obtain a filtered signal, and the filtered signal is stored in a signal buffer to obtain a preprocessed signal, wherein the center frequency of each bandpass filter corresponds to each frequency band of the signal to be measured.

3. The multi-band radio measurement calibration method according to claim 1, characterized in that: The step of performing fast Fourier transform and signal classification on the preprocessed signal to obtain a mixed signal comprises: Performing a fast Fourier transform on the preprocessed signal to obtain a frequency domain signal spectrum; Calculating the ratio of the target frequency band energy to the full frequency band energy in the frequency domain signal spectrum to obtain a spectrum energy ratio characteristic parameter; Calculating the ratio of the spectrum centroid to the spectrum bandwidth based on the frequency domain signal spectrum to obtain a spectrum concentration characteristic parameter; Extracting the energy difference between the main peak and the secondary peak from the frequency domain signal spectrum and performing normalization processing to obtain a main-secondary peak energy difference characteristic parameter; Constructing a three-dimensional feature vector based on the spectrum energy ratio feature parameter, the spectrum concentration feature parameter and the primary and secondary peak energy difference feature parameter, and inputting the three-dimensional feature vector into a support vector machine classifier based on a Gaussian kernel function to obtain a classification result; Based on the classification result, the data segments determined to be mixed signals and their corresponding time positions and frequency characteristics are extracted from the preprocessed signals to obtain mixed signals.

4. The multi-band radio measurement calibration method according to claim 1, characterized in that: The mixed signal is constructed as a Hankel matrix, and the signal is reconstructed in combination with a correlation coefficient threshold to obtain a pure target signal, including: Constructing a Hankel matrix of the mixed signal according to 1 / 3 of the number of sampling points as the number of rows to obtain a signal Hankel matrix; Performing singular value decomposition on the signal Hankel matrix to obtain a singular value sequence and its corresponding left and right singular vectors; Arrange the singular value sequence in descending order according to the energy contribution rate, extract the singular values ​​and their corresponding singular vectors whose cumulative energy contribution rate reaches 95%, and obtain the signal subspace feature; Reconstructing the signal according to the signal subspace characteristics to obtain a preliminary reconstructed signal; Performing correlation analysis on the preliminary reconstructed signal and the interference feature library, and dividing the signal components based on a correlation coefficient threshold to obtain a target signal component and an interference signal component; The target signal components are subjected to time-frequency analysis and interference signal components are eliminated to obtain a pure target signal.

5. The multi-band radio measurement calibration method according to claim 1, characterized in that: The method of dynamically dividing the pure target signal into sub-bands, introducing a signal-to-noise ratio adaptive regularization factor in each sub-band, iteratively calculating the minimum mean square error until the residual is less than the first target value, and obtaining the initial calibration parameters includes: Dividing the pure target signal into N sub-bands to obtain a sub-band signal sequence; Calculating an autocorrelation matrix and a cross-correlation vector for each subband signal in the subband signal sequence to obtain a related feature quantity; Calculating the signal-to-noise ratio of each subband based on the relevant feature quantity, and dynamically generating a regularization factor according to the signal-to-noise ratio value to obtain an optimization parameter; Substituting the optimization parameters into the Wiener-Hopper equation and solving it using the conjugate gradient method to obtain an iterative calculation sequence; Performing residual calculation on the iterative calculation sequence, stopping iteration when the residual is less than a first target value, and obtaining a subband calibration parameter; The frequency points in the sub-band calibration parameters where the errors exceed the threshold are locally iteratively optimized, the optimization step size is decreased by 0.01 times of the initial step size, and the optimization results of each sub-band are combined to obtain the initial calibration parameters.

6. The multi-band radio measurement calibration method according to claim 1, characterized in that: The step of establishing a logarithmically spaced frequency point distribution based on the initial calibration parameters and performing linear weighted averaging within the overlapping bandwidth of adjacent sub-bands to obtain a compensation calibration coefficient includes: The frequency band of the signal to be measured is divided into M sub-frequency bands, and three calibration frequency points are set in each sub-frequency band according to a logarithmic relationship to obtain a calibration frequency point distribution; Performing interpolation processing on the initial calibration parameters based on the calibration frequency point distribution to obtain a frequency response function of each sub-band, and performing piecewise cubic spline interpolation on the amplitude and phase of the frequency response function to obtain a frequency response curve; An overlapping bandwidth is set between adjacent sub-bands, and a linear weighted average is performed on the frequency response within the overlapping bandwidth to obtain a transition region calibration value; Calculate the calibration coefficient of each frequency point based on the frequency response curve and the transition region calibration value to obtain a compensation sequence; The compensation sequence is iteratively optimized until the result change rate of adjacent iteration cycles is less than the target percentage value, thereby obtaining the compensation calibration coefficient.

7. The multi-band radio measurement calibration method according to claim 1, characterized in that: The compensation calibration coefficient is verified by using a standard calibration signal, and when the root mean square error exceeds a second target value, steps S4-S6 are repeated until the accuracy requirement is met to obtain a target calibration result, including: A standard signal source is used to generate a calibration signal with a nominal frequency interval of 20kHz, covering all the frequency bands to be tested, to obtain a standard calibration signal; Comparing the standard calibration signal with the compensation calibration coefficient in frequency and amplitude to obtain the measurement error of each frequency point; Generate an error distribution histogram based on the measurement errors of each frequency point, and calculate the root mean square error and maximum error of each frequency band to obtain error statistics results; Comparing the root mean square error in the error statistical result with the second target value to obtain an optimization determination result; Mark the frequency band whose root mean square error exceeds the second target value based on the optimization determination result, and record the key parameters of the calibration process to obtain a frequency band optimization mark; Return to step S4 according to the frequency band optimization mark, and repeat steps S4-S6 to optimize the calibration parameters of the marked frequency band until the root mean square error is less than the second target value, thereby obtaining a target calibration result.

8. A multi-band radio metrology calibration system, characterized in that: Used to perform the multi-band radio metrology calibration method according to any one of claims 1 to 7, the multi-band radio metrology calibration system comprising: A sampling module, used for sampling and preprocessing multi-band radio signals to obtain preprocessed signals; A classification module, used for performing fast Fourier transform and signal classification on the preprocessed signal to obtain a mixed signal; A reconstruction module, used to construct the mixed signal into a Hankel matrix, and perform signal reconstruction in combination with a correlation coefficient threshold to obtain a pure target signal; A dynamic sub-band division module is used to dynamically divide the pure target signal into sub-bands, introduce a signal-to-noise ratio adaptive regularization factor in each sub-band, iteratively calculate the minimum mean square error until the residual is less than the first target value, and obtain initial calibration parameters; A linear weighted averaging module, used to establish a logarithmically spaced frequency point distribution based on the initial calibration parameters, and perform linear weighted averaging within the overlapping bandwidth of adjacent sub-bands to obtain a compensation calibration coefficient; The verification module is used to verify the compensation calibration coefficient using a standard calibration signal. When the root mean square error exceeds the second target value, the steps from the dynamic sub-band division module to the verification module are repeatedly executed until the accuracy requirement is met to obtain a target calibration result.

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