Passive filter parameter accurate measurement method, device and electronic equipment

Abstract

The present application relates to the technical field of passive filter measurement, and in particular to a method, device and electronic device for accurately measuring parameters of a passive filter, and specifically comprises: constructing an instability factor of the passive filter parameters based on the fluctuation characteristics of data in a passband upper limit sequence, a passband lower limit sequence and a cutoff frequency sequence of the passband frequency of the passive filter and the similarity of fluctuations between the sequences; constructing an unexpected response characteristic value of the passive filter based on the abnormality degree of abnormal points among all extreme points in a frequency response curve, avoiding the influence of unexpected resonance peaks or notches, reflecting the overall characteristics of the unexpected response of the filter in the entire frequency range, and eliminating the influence of parameter measurement errors caused by non-ideal factors; correcting the attenuation value of the passive filter in combination with the instability factor; improving the accuracy of the detection result of the attenuation value of the passive filter, realizing quantitative compensation for the change in filter performance, thereby improving the accuracy and reliability of the parameter measurement result of the passive filter.

Description

Technical Field

[0001] The present application relates to the technical field of passive filter measurement, and in particular to a method, device and electronic equipment for accurately measuring parameters of a passive filter. Background Art

[0002] Passive filters play a vital role in electronic circuits. They achieve frequency-selective filtering of signals through passive components such as resistors, capacitors, and inductors. With the development of technology, the requirements for filter performance have become increasingly stringent, which has prompted the advancement of parameter measurement technology for these components. In the early days, the parameter measurement of filters mainly relied on analog instruments and manual calculations. These methods have limitations in accuracy and efficiency. With the emergence of digital technology and automated test equipment, measurement technology has been significantly improved. Modern measurement methods, such as the use of network analyzers, can provide more accurate frequency response and parameter extraction, thereby ensuring that the filter design meets strict performance standards. Parameter measurement can help engineers verify and optimize filter design, ensuring that filters can provide high-quality signal processing in applications such as communications, audio processing, medical equipment, and industrial control systems, which not only improves the overall performance of the system, but also helps reduce interference and improve signal reliability.

[0003] In the frequency response measurement of passive filters, non-ideal characteristics of the filter, such as parasitic capacitance and inductance, are common problems. They mainly originate from limitations in the design and manufacturing process and will affect the filtering effect of the filter, thereby reducing the accuracy and reliability of the measurement results when measuring the filter parameters, such as attenuation value. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a method, device and electronic equipment for accurately measuring the parameters of a passive filter. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present application provides a method for accurately measuring parameters of a passive filter, the method comprising the following steps:

[0006] Collect the passband frequency range, cutoff frequency and the latest attenuation value of the filter signal of the passive filter at each moment, obtain the frequency response curve of the filter signal during data collection, and construct the passband upper limit sequence, passband lower limit sequence and cutoff frequency sequence;

[0007] Based on the fluctuation characteristics of the data in the passband upper limit sequence, the passband lower limit sequence and the cutoff frequency sequence, the fluctuation intensity of the passband upper limit sequence, the passband lower limit sequence and the cutoff frequency sequence is constructed; combined with the similarity of fluctuations between the sequences, the instability factor of the passive filter parameters is constructed;

[0008] Based on the abnormal degree of abnormal points among all extreme points in the frequency response curve, abnormal impact factors of the maximum value sequence and the minimum value sequence are constructed; based on the abnormal impact factors, unexpected response characteristic values ​​of the passive filter are calculated; and the real-time non-ideal impact index of the passive filter is constructed in combination with the instability factor of the passive filter parameters;

[0009] The attenuation value of the passive filter is corrected according to the real-time non-ideal influence index of the passive filter, and the parameters of the passive filter are accurately measured.

[0010] In one embodiment, the construction of the passband upper limit sequence, the passband lower limit sequence and the cutoff frequency sequence is:

[0011] The upper limit and lower limit of the passband frequency range at each moment are obtained, the upper limits at all moments are combined into a passband upper limit sequence, the lower limits at all moments are combined into a passband lower limit sequence; the cutoff frequencies at all moments are combined into a cutoff frequency sequence.

[0012] In one embodiment, the process of obtaining the fluctuation intensity is:

[0013] Calculate the standard deviation of all data in the passband upper limit sequence; use the passband upper limit sequence as the input of the conditional heteroskedasticity algorithm, and output the conditional variance at the latest moment; use the fusion value of the conditional variance and the standard deviation as the volatility intensity of the passband upper limit sequence;

[0014] Based on the passband lower limit sequence and the cutoff frequency sequence, respectively, the fluctuation intensities of the passband lower limit sequence and the cutoff frequency sequence are obtained by adopting the same calculation method as the fluctuation intensity of the passband upper limit sequence.

[0015] In one embodiment, the process of obtaining the instability factor of the passive filter parameter is as follows:

[0016] Calculate the correlation between any two sequences among the passband upper limit sequence, the passband lower limit sequence and the cutoff frequency sequence; record the average of all the correlations of any sequence as the fluctuation similarity of any sequence;

[0017] The instability factor of the passive filter parameters is recorded as A, and the calculation expression of A is: , where n is the number of sequences, is the volatility intensity of the ith sequence, is the fluctuation similarity of the ith sequence.

[0018] In one embodiment, obtaining the abnormal impact factor of the maximum value sequence includes:

[0019] All the maximum values ​​in the frequency response curve form a maximum sequence, and all the minimum values ​​form a minimum sequence; an anomaly detection algorithm is used to obtain the outliers in the maximum sequence and the local outlier factors of each outlier; a Gaussian kernel function is used to statistically estimate the density of each outlier;

[0020] The expression of the abnormal impact factor of the maximum value sequence is: , where m is the abnormal impact factor of the maximum value sequence, L is the number of abnormal values ​​in the maximum value sequence, is the density estimate of the jth outlier in the maximum sequence, is the local outlier factor of the jth outlier in the maximum sequence;

[0021] Based on the minimum value sequence, the abnormal impact factor of the minimum value sequence is obtained in the same way as the abnormal impact factor of the maximum value sequence.

[0022] In one embodiment, the process of obtaining the unexpected response characteristic value of the passive filter is:

[0023] The mean of the abnormal influencing factors of the maximum value sequence and the minimum value sequence is taken as the unexpected response eigenvalue of the passive filter.

[0024] In one embodiment, the real-time non-ideal impact index of the passive filter is the product of the instability factor of the passive filter parameters and the unexpected response characteristic value.

[0025] In one embodiment, the attenuation value of the passive filter after correction is expressed as: , where is the corrected attenuation value; F is the attenuation value at the latest moment; B is the real-time non-ideal impact index of the passive filter; To adjust the parameters, is the sigmoid function.

[0026] In a second aspect, the embodiment of the present application further provides a device for accurately measuring parameters of a passive filter, including:

[0027] Data acquisition module: collects the passband frequency range, cutoff frequency and the latest attenuation value of the filter signal of the passive filter at each moment, obtains the frequency response curve of the filter signal during data acquisition, and constructs the passband upper limit sequence, passband lower limit sequence and cutoff frequency sequence;

[0028] Fluctuation analysis module: Based on the fluctuation characteristics of the data in the passband upper limit sequence, passband lower limit sequence and cutoff frequency sequence, the fluctuation intensity of the passband upper limit sequence, passband lower limit sequence and cutoff frequency sequence is constructed; combined with the similarity of fluctuations between sequences, the instability factor of the passive filter parameters is constructed;

[0029] Anomaly detection module: construct the abnormal impact factor of the maximum value sequence and the minimum value sequence based on the abnormal degree of the abnormal point among all extreme value points in the frequency response curve; calculate the unexpected response characteristic value of the passive filter based on the abnormal impact factor; construct the real-time non-ideal impact index of the passive filter in combination with the instability factor of the passive filter parameters;

[0030] Parameter correction module: corrects the attenuation value of the passive filter according to the real-time non-ideal influence index of the passive filter, and accurately measures the parameters of the passive filter.

[0031] In a third aspect, an embodiment of the present application further provides an electronic device for accurately measuring parameters of a passive filter, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the computer program.

[0032] The embodiments of the present application have at least the following beneficial effects:

[0033] The present application proposes a method, device and electronic device for accurately measuring the parameters of a passive filter. By collecting the passband frequency range, cutoff frequency and frequency response curve of the passive filter, a passband upper limit sequence, a passband lower limit sequence and a cutoff frequency sequence are constructed; based on the fluctuation characteristics of the data in the sequence and the similarity of the fluctuations between the sequences, the instability factor of the passive filter parameters is constructed, the influence of the parameter fluctuation of the passive filter is analyzed, the instability degree of the filter parameters in the measurement process is evaluated, and the changes in the frequency response parameters caused by non-ideal factors such as parasitic capacitance and inductance are analyzed; based on the abnormality degree of the abnormal points among all extreme points in the frequency response curve, the unexpected response characteristic value of the passive filter is constructed, the influence of unexpected resonance peaks or notches is avoided, the overall characteristics of the unexpected response of the filter in the entire frequency range are reflected, and the influence of parameter measurement errors caused by non-ideal factors are eliminated; the attenuation value of the passive filter is further corrected in combination with the instability factor; the accuracy of the detection result of the attenuation value of the passive filter is improved, and the quantitative compensation of the filter performance change is realized, thereby improving the accuracy and reliability of the parameter measurement result of the passive filter. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1A flowchart of a method for accurately measuring parameters of a passive filter provided by an embodiment of the present application;

[0036] Figure 2 Schematic diagram of the relationship between parasitic capacitance and parasitic inductance and passband and cutoff frequency;

[0037] Figure 3 It is a schematic diagram of the resonance peak and notch in the frequency response curve;

[0038] Figure 4 It is a structural schematic diagram of a device for accurately measuring parameters of a passive filter. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the method, device and electronic device for accurately measuring the parameters of the passive filter proposed in the present application, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0041] The specific scheme of the method, device and electronic equipment for accurately measuring parameters of the passive filter provided by the present application is described in detail below with reference to the accompanying drawings.

[0042] See also Figure 1 , which shows a flow chart of the steps of a method for accurately measuring parameters of a passive filter provided by an embodiment of the present application, the method comprising the following steps:

[0043] Step S1, collecting the passband frequency range, cutoff frequency and the latest attenuation value of the filter signal of the passive filter at each moment, obtaining the frequency response curve of the filter signal during data collection, and constructing the passband upper limit sequence, passband lower limit sequence and cutoff frequency sequence.

[0044] The input end of the passive filter is connected to the signal generator, and the output end is connected to the spectrum analyzer and the network analyzer. The signal generator can generate signals of various frequencies, the network analyzer can test the passband frequency range, cutoff frequency and attenuation value of the passive filter output signal, and the spectrum analyzer can synchronously output the frequency response curve of the filtered signal, where the horizontal axis of the frequency response curve is the frequency and the vertical axis is the signal amplitude.

[0045] The signal generated by the signal generator is filtered through a filter, and the passband frequency range, cutoff frequency and attenuation value of the filtered signal at each moment are collected by a network analyzer. Preferably, in the embodiment of the present application, the data acquisition frequency is set to 50Hz, and the passband frequency range, cutoff frequency and attenuation value of the 5000 moments closest to the latest moment are obtained. In other embodiments of the present application, the data acquisition frequency and data acquisition range can be set by the implementer, and this embodiment does not make specific restrictions.

[0046] Obtain the upper limit and lower limit of the passband frequency range at each moment, record the sequence composed of the upper limits of the passband frequency range at all moments as the passband upper limit sequence, and record the sequence composed of the lower limits of the passband frequency range at all moments as the passband lower limit sequence;

[0047] The sequence of cutoff frequencies at all moments in ascending time order is recorded as the cutoff frequency sequence;

[0048] At the same time, the frequency response curve of the filtered signal during data acquisition is obtained through a spectrum analyzer.

[0049] It should be understood that the arrangement of the elements in the above three sequences in chronological order is only one implementation method of the present application. As other implementation methods, the implementer does not need to sort them completely in chronological order. The implementer may decide based on the actual application situation. This application does not impose any special restrictions.

[0050] Step S2, constructing the fluctuation intensity of the passband upper limit sequence, the passband lower limit sequence and the cutoff frequency sequence based on the fluctuation characteristics of the data in the passband upper limit sequence, the passband lower limit sequence and the cutoff frequency sequence; combining the similarity of fluctuations between the sequences, constructing the instability factor of the passive filter parameters.

[0051] The non-ideal characteristics of passive filters, such as parasitic capacitance and inductance, can significantly affect the measurement of their frequency response. These parasitic elements interact with the main elements in the design, changing the resonant frequency, shifting the frequency range of the passband and stopband, changing the bandwidth, and causing attenuation or gain non-uniformity in the passband and stopband.

[0052] These parasitic elements will change the actual circuit parameters of the filter, resulting in changes in the expected performance of the passband frequency range, cutoff frequency, and attenuation gain value. Specifically, parasitic capacitance may cause the passband to become wider and the cutoff frequency to decrease, while parasitic inductance may cause the passband to become narrower and the cutoff frequency to increase. The effects of parasitic capacitance and parasitic inductance on the passband and cutoff frequency are as follows: Figure 2 As shown, Figure 2 In addition, they may also cause gain fluctuations in the passband and insufficient attenuation in the stopband, thus causing fluctuations in the cutoff frequency and passband bandwidth.

[0053] (1) Construct the fluctuation intensity of the passband upper limit sequence based on the fluctuation characteristics of the data in the passband upper limit sequence:

[0054] Calculate the standard deviation of all data in the passband upper limit sequence, recorded as the first standard deviation, which reflects the degree of chaos in the data distribution in the sequence;

[0055] The passband upper limit sequence is used as the input of the ARCH model, and the output is the conditional variance of the passband upper limit sequence at the latest moment. Since the elements in the passband upper limit sequence are arranged in ascending order of time, the latest moment is the moment corresponding to the last data in the passband upper limit sequence;

[0056] The fusion value of the conditional variance of the passband upper limit sequence at the latest moment and the first standard deviation is used as the fluctuation intensity of the passband upper limit sequence. Based on the passband lower limit sequence and the cutoff frequency sequence, the fluctuation intensity of the passband lower limit sequence and the cutoff frequency sequence are obtained by adopting the same calculation method as the fluctuation intensity of the passband upper limit sequence.

[0057] It should be noted that, for the calculation of the conditional variance at the latest moment, the present application only provides a method for calculating the conditional variance. There are many existing algorithms for calculating the conditional variance. In other embodiments of the present application, the implementer may also use other conditional heteroscedasticity algorithms to calculate the conditional variance at the latest moment, and the present application does not make specific restrictions. The fusion described in the present application is to combine multiple variables. The specific fusion method can be determined according to actual conditions during the application process, and the present application does not make special restrictions.

[0058] Preferably, in one embodiment of the present application, the fluctuation intensity of the passband upper limit sequence may be the product of the conditional variance of the passband upper limit sequence at the latest moment and the first standard deviation;

[0059] In other embodiments of the present application, the fluctuation intensity of the passband upper limit sequence may be the sum of the normalized value of the conditional variance of the passband upper limit sequence at the latest moment and the normalized value of the first standard deviation.

[0060] (2) Calculate the Pearson correlation coefficient between any two sequences among the passband upper limit sequence, the passband lower limit sequence and the cutoff frequency sequence; calculate the average of all the Pearson correlation coefficients of the passband upper limit sequence, which is recorded as the fluctuation similarity; based on the Pearson correlation coefficients of the passband lower limit sequence and the cutoff frequency sequence, respectively, the same calculation method as the fluctuation similarity of the passband upper limit sequence is used to obtain the fluctuation similarity of the passband lower limit sequence and the cutoff frequency sequence.

[0061] It should be noted that for the calculation of the correlation between the above three sequences, this application only provides one correlation calculation method, which is the Pearson correlation coefficient. There are many existing correlation calculation methods, and implementers can also use other correlation algorithms to calculate the correlation between the above three sequences. This application does not make specific restrictions.

[0062] (3) Based on the fluctuation intensity and fluctuation similarity of the passband upper limit sequence, passband lower limit sequence and cutoff frequency sequence, the instability factor of the passive filter parameters is constructed, and the expression is:

[0063] , where A is the instability factor of the passive filter parameters; n is the number of sequences. The sequences calculated in this application are the passband upper limit sequence, the passband lower limit sequence and the cutoff frequency sequence, so the value of n is 3; is the fluctuation intensity of the ith sequence; is the fluctuation similarity of the ith sequence.

[0064] The fluctuation intensity reflects the instability of the data changes in each sequence, and expresses the degree of change of the frequency response parameters of the passive filter under actual working conditions due to non-ideal factors such as parasitic elements. The larger the value, the stronger the volatility of the data in a single sequence, which reflects that the non-ideal characteristics of the filter as a whole are more significant, and the instability factor A increases accordingly; the fluctuation similarity reflects the synchronization of fluctuations of different parameters, and reveals whether the upper and lower limits of the passband frequency and the cutoff frequency show similar fluctuation patterns when they change. The larger the value is, the more similar the fluctuation patterns between the upper and lower limits of the passband frequency and the cutoff frequency are, indicating that the non-ideal characteristics of the filter have a high consistency between different parameters. It is more likely that the filter parameter changes are caused by the same non-ideal factors, and the instability factor A increases accordingly.

[0065] The instability factor of the passive filter parameters reflects the degree of overall performance change of the passive filter in actual application due to non-ideal factors, such as parasitic capacitance and inductance. The larger the instability factor, the more the performance of the filter is affected by non-ideality, and the more further optimization or adjustment is needed.

[0066] Step S3, constructing abnormal impact factors of the maximum value sequence and the minimum value sequence based on the abnormal degree of abnormal points in all extreme value points in the frequency response curve; calculating the unexpected response characteristic value of the passive filter based on the abnormal impact factor; and constructing the real-time non-ideal impact index of the passive filter in combination with the instability factor of the passive filter parameters.

[0067] When the performance parameters of a passive filter, such as the passband frequency, cutoff frequency, and attenuation value, fluctuate, it will cause unexpected resonant peaks or notches on the filter's frequency response curve. Resonant peaks usually appear as unexpected signal enhancements in the passband, while notches appear as unexpected signal attenuation in the stopband. Non-ideal characteristics of the filter, such as the presence of parasitic capacitance and inductance, will change the actual circuit parameters of the filter, thereby causing abnormal enhancement or attenuation of the signal at certain frequency points, which manifests as resonant peaks and notches. The resonant peaks and notches in the frequency response curve are as follows: Figure 3 shown.

[0068] (1) The frequency response curve output by the spectrum analyzer is used as the input of the extreme point detection algorithm, and the output is all the maximum and minimum values ​​in the frequency response curve;

[0069] (2) Arrange all the maximum values ​​in the frequency response curve in ascending order of frequency, and the resulting sequence is recorded as the maximum value sequence; arrange all the minimum values ​​in the frequency response curve in ascending order of frequency, and the resulting sequence is recorded as the minimum value sequence;

[0070] (3) The maximum value sequence and the minimum value sequence are used as the input of the LOF anomaly detection algorithm, where the number of adjacent points k is set to 20, the proportion of abnormal points is set to 0.05, and the outputs are the outliers in the maximum value sequence and the minimum value sequence and the local outlier factor of each outlier;

[0071] It is understandable that for the detection of outliers in maximum value sequences and minimum value sequences, the present application only provides one outlier detection method. There are many existing outlier detection methods, and implementers may also use other outlier detection algorithms to perform outlier detection in maximum value sequences and minimum value sequences. The present application does not make any specific restrictions.

[0072] (4) Further, all outliers in the maximum value sequence and the minimum value sequence are used as inputs of the Gaussian kernel function, and the output is the density estimation of each outlier in the maximum value sequence and the minimum value sequence. It should be noted that for the density estimation of outliers, this application only provides a kernel density estimation method. There are many existing kernel density estimation methods. The implementer may also use other kernel density estimation algorithms to perform density estimation of outliers, and this application does not make specific restrictions.

[0073] (5) Based on the local outlier factor and density estimation of the outliers in the maximum sequence, the abnormal impact factor of the maximum sequence is calculated. The expression is: , where m is the abnormal impact factor of the maximum value sequence, L is the number of abnormal values ​​in the maximum value sequence, is the density estimate of the jth outlier in the maximum sequence, is the local outlier factor of the jth outlier in the maximum sequence;

[0074] Based on the local outlier factor and density estimation of the outliers in the minimum sequence, the abnormal impact factor of the minimum sequence is obtained in the same way as the abnormal impact factor of the maximum sequence, denoted as n. m and n reflect the intensity of the abnormal resonance peak or notch on the filter frequency response curve.

[0075] (6) The mean of the abnormal influencing factors of the maximum value sequence and the minimum value sequence is taken as the unexpected response characteristic value of the passive filter, which reflects the overall characteristics of the unexpected response of the filter in the entire frequency range.

[0076] (7) Based on the instability factor and the unexpected response characteristic value, the real-time non-ideal impact index of the passive filter is calculated, and the expression is: , where B is the real-time non-ideal impact index of the passive filter, A is the instability factor of the passive filter parameters; c is the unexpected response characteristic value of the passive filter.

[0077] The instability factor A of the passive filter parameters reflects the non-ideal characteristics of the filter parameters in the measurement process by quantifying the instability of the passive filter parameters. The larger the A value, the greater the volatility of the filter parameters, such as the passband frequency range and the cutoff frequency, during the measurement process, and the fluctuation patterns between these parameters are highly consistent, which means that the filter is affected by non-ideal factors, such as parasitic elements, and B increases accordingly; when the unexpected response characteristic value c of the passive filter is larger, it means that the unexpected signal enhancement or weakening phenomenon in the actual application of the filter is more obvious, and the probability of filter parameter changes caused by non-ideal factors in the filter design or manufacturing process, such as parasitic capacitance and inductance, is greater, and the B value increases accordingly.

[0078] Step S4, correcting the attenuation value of the passive filter according to the real-time non-ideal influence index of the passive filter, and accurately measuring the parameters of the passive filter.

[0079] Passive filters are mainly composed of resistors, capacitors and inductors, which are used to perform frequency selective processing on signals. However, these components are not ideal components in practical applications. They will introduce parasitic capacitance and inductance. These parasitic components will interact with the main components in the design, causing the actual frequency response of the filter to be inconsistent with expectations. For example, parasitic capacitance may form an additional resonant loop with the inductor, changing the cutoff frequency of the filter, thereby affecting its attenuation characteristics. In addition, the presence of parasitic inductance will increase the equivalent series resistance of the filter, resulting in increased signal loss. These non-ideal characteristics reduce the accuracy of the filter when attenuating specific frequency signals, thereby affecting the accuracy of the measurement results. In high-precision measurement systems, these errors may cause inaccurate data and affect the overall performance of the system.

[0080] The attenuation value collected at the latest moment is corrected according to the real-time non-ideal influence index of the passive filter. The expression is:

[0081] , where is the corrected attenuation value; F is the attenuation value at the latest moment; B is the real-time non-ideal impact index of the passive filter; To adjust the parameters, the function is to adjust The value of is a sigmoid function. Preferably, in the embodiment of the present application, The value of is set to 1.5. In other embodiments of the present application, The implementer can set the value according to the actual situation, and this application does not impose any specific restrictions.

[0082] By quantifying and compensating for the filter performance changes caused by non-ideal factors (such as parasitic capacitance and inductance), the real-time non-ideal impact index B, combined with the nonlinear mapping of the sigmoid function, can more accurately reflect the performance fluctuations of the filter under actual working conditions, improve the accuracy of the attenuation value measurement results, reduce the parameter measurement errors caused by non-ideal characteristics, and improve the accuracy of passive filter parameter measurement.

[0083] See also Figure 4 , Figure 4 1 is a schematic diagram of the structure of the device for accurately measuring parameters of a passive filter provided in an embodiment of the present application. In this embodiment, each unit included in the terminal is used to execute each step in the embodiment corresponding to the method for accurately measuring parameters of a passive filter. Figure 4 , the measuring device includes:

[0084] Data acquisition module: collects the passband frequency range, cutoff frequency and the latest attenuation value of the filter signal of the passive filter at each moment, obtains the frequency response curve of the filter signal during data acquisition, and constructs the passband upper limit sequence, passband lower limit sequence and cutoff frequency sequence;

[0085] Fluctuation analysis module: Based on the fluctuation characteristics of the data in the passband upper limit sequence, passband lower limit sequence and cutoff frequency sequence, the fluctuation intensity of the passband upper limit sequence, passband lower limit sequence and cutoff frequency sequence is constructed; combined with the similarity of fluctuations between sequences, the instability factor of the passive filter parameters is constructed;

[0086] Anomaly detection module: construct the abnormal impact factor of the maximum value sequence and the minimum value sequence based on the abnormal degree of the abnormal point among all extreme value points in the frequency response curve; calculate the unexpected response characteristic value of the passive filter based on the abnormal impact factor; construct the real-time non-ideal impact index of the passive filter in combination with the instability factor of the passive filter parameters;

[0087] Parameter correction module: corrects the attenuation value of the passive filter according to the real-time non-ideal influence index of the passive filter, and accurately measures the parameters of the passive filter.

[0088] Based on the same inventive concept as the above method, an embodiment of the present application also provides an electronic device for accurately measuring parameters of a passive filter, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods for accurately measuring parameters of a passive filter when executing the computer program.

[0089] In summary, the embodiment of the present application provides a method for accurately measuring the parameters of a passive filter, by collecting the passband frequency range, cutoff frequency and frequency response curve of the passive filter, constructing a passband upper limit sequence, a passband lower limit sequence and a cutoff frequency sequence; based on the fluctuation characteristics of the data in the sequence and the similarity of the fluctuations between the sequences, the instability factor of the passive filter parameters is constructed, the influence of the parameter fluctuations of the passive filter is analyzed, the instability of the filter parameters during the measurement process is evaluated, and the changes in the frequency response parameters caused by non-ideal factors such as parasitic capacitance and inductance are analyzed; based on the abnormality of the abnormal points among all extreme points in the frequency response curve, the unexpected response characteristic value of the passive filter is constructed, the influence of unexpected resonant peaks or notches is avoided, the overall characteristics of the unexpected response of the filter in the entire frequency range are reflected, and the influence of parameter measurement errors caused by non-ideal factors are eliminated; the attenuation value of the passive filter is further corrected in combination with the instability factor; the accuracy of the detection result of the attenuation value of the passive filter is improved, and the quantitative compensation of the filter performance change is realized, thereby improving the accuracy and reliability of the parameter measurement result of the passive filter.

[0090] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of the present application are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0091] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0092] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for accurately measuring parameters of a passive filter, characterized in that: The method comprises the following steps: Collect the passband frequency range, cutoff frequency and the latest attenuation value of the filter signal of the passive filter at each moment, obtain the frequency response curve of the filter signal during data collection, and construct the passband upper limit sequence, passband lower limit sequence and cutoff frequency sequence; Based on the fluctuation characteristics of the data in the passband upper limit sequence, the passband lower limit sequence and the cutoff frequency sequence, the fluctuation intensity of the passband upper limit sequence, the passband lower limit sequence and the cutoff frequency sequence is constructed; combined with the similarity of fluctuations between the sequences, the instability factor of the passive filter parameters is constructed; Based on the abnormal degree of abnormal points among all extreme points in the frequency response curve, abnormal impact factors of the maximum value sequence and the minimum value sequence are constructed; based on the abnormal impact factors, unexpected response characteristic values ​​of the passive filter are calculated; and the real-time non-ideal impact index of the passive filter is constructed in combination with the instability factor of the passive filter parameters; The attenuation value of the passive filter is corrected according to the real-time non-ideal influence index of the passive filter, and the parameters of the passive filter are accurately measured; The acquisition process of the unexpected response characteristic value of the passive filter is: The mean of the abnormal impact factors of the maximum value sequence and the minimum value sequence is taken as the unexpected response characteristic value of the passive filter; The real-time non-ideal impact index of the passive filter is the product of the instability factor of the passive filter parameter and the unexpected response characteristic value; The corrected attenuation value of the passive filter is expressed as: , where is the corrected attenuation value; F is the attenuation value at the latest moment; B is the real-time non-ideal impact index of the passive filter; To adjust the parameters, is the sigmoid function.

2. The method for accurately measuring parameters of a passive filter according to claim 1, characterized in that: The construction of the passband upper limit sequence, passband lower limit sequence and cutoff frequency sequence is as follows: The upper limit and lower limit of the passband frequency range at each moment are obtained, the upper limits at all moments are combined into a passband upper limit sequence, the lower limits at all moments are combined into a passband lower limit sequence; the cutoff frequencies at all moments are combined into a cutoff frequency sequence.

3. The method for accurately measuring parameters of a passive filter according to claim 1, wherein: The process of obtaining the fluctuation intensity is as follows: Calculate the standard deviation of all data in the passband upper limit sequence; use the passband upper limit sequence as the input of the conditional heteroskedasticity algorithm, and output the conditional variance at the latest moment; The fused value of the conditional variance and the standard deviation is used as the fluctuation intensity of the passband upper limit sequence; Based on the passband lower limit sequence and the cutoff frequency sequence, respectively, the fluctuation intensities of the passband lower limit sequence and the cutoff frequency sequence are obtained by adopting the same calculation method as the fluctuation intensity of the passband upper limit sequence.

4. The method for accurately measuring parameters of a passive filter according to claim 1, wherein: The process of obtaining the unstable factor of the passive filter parameters is as follows: Calculate the correlation between any two sequences among the passband upper limit sequence, the passband lower limit sequence and the cutoff frequency sequence; record the average of all the correlations of any sequence as the fluctuation similarity of any sequence; The instability factor of the passive filter parameters is recorded as A, and the calculation expression of A is: , where n is the number of sequences, is the volatility intensity of the ith sequence, is the fluctuation similarity of the ith sequence.

5. The method for accurately measuring parameters of a passive filter according to claim 1, wherein: The acquisition of the abnormal impact factor of the maximum value sequence includes: All the maximum values ​​in the frequency response curve form a maximum sequence, and all the minimum values ​​form a minimum sequence; an anomaly detection algorithm is used to obtain the outliers in the maximum sequence and the local outlier factors of each outlier; a Gaussian kernel function is used to statistically estimate the density of each outlier; The expression of the abnormal impact factor of the maximum value sequence is: , where m is the abnormal impact factor of the maximum value sequence, L is the number of abnormal values ​​in the maximum value sequence, is the density estimate of the jth outlier in the maximum sequence, is the local outlier factor of the jth outlier in the maximum sequence; Based on the minimum value sequence, the abnormal impact factor of the minimum value sequence is obtained in the same way as the abnormal impact factor of the maximum value sequence.

6. A device for accurately measuring parameters of a passive filter, implementing the method as claimed in claim 1, characterized in that: The device comprises: Data acquisition module: collects the passband frequency range, cutoff frequency and the latest attenuation value of the filter signal of the passive filter at each moment, obtains the frequency response curve of the filter signal during data acquisition, and constructs the passband upper limit sequence, passband lower limit sequence and cutoff frequency sequence; Fluctuation analysis module: Based on the fluctuation characteristics of the data in the passband upper limit sequence, passband lower limit sequence and cutoff frequency sequence, the fluctuation intensity of the passband upper limit sequence, passband lower limit sequence and cutoff frequency sequence is constructed; combined with the similarity of fluctuations between sequences, the instability factor of the passive filter parameters is constructed; Anomaly detection module: construct the abnormal impact factor of the maximum value sequence and the minimum value sequence based on the abnormal degree of the abnormal point among all extreme value points in the frequency response curve; calculate the unexpected response characteristic value of the passive filter based on the abnormal impact factor; construct the real-time non-ideal impact index of the passive filter in combination with the instability factor of the passive filter parameters; Parameter correction module: corrects the attenuation value of the passive filter according to the real-time non-ideal influence index of the passive filter, and accurately measures the parameters of the passive filter.

7. An electronic device for accurately measuring parameters of a passive filter, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

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