High-frequency filter parameter design method and device, computer equipment and storage medium

Abstract

The invention discloses a high-frequency filter parameter design method and device, computer equipment and a storage medium, and relates to the technical field of filter parameter optimization. Comprising the following steps that a circuit characteristic topological finite element model of a target high-frequency filter is established, a passband frequency range is analyzed to determine an optimization target parameter range, and optimization target parameters comprise a zero point angular frequency, a pole angular frequency, a damping coefficient and a system order; constructing a transfer function based on the optimized target parameters, and optimizing the target function by using a genetic algorithm to obtain an optimal target parameter combination; and analyzing the amplitude response and phase response of the high-frequency filter according to the optimal parameter combination, calculating a passband gain deviation coefficient and a group delay jitter coefficient, correcting and optimizing target parameters to obtain accurate values, and finishing the design of the high-frequency filter in combination with a system order. According to the method, the design accuracy of the high-frequency filter is improved, and the high-precision performance requirement is met.

Description

Technical Field

[0001] The present invention relates to the technical field of filter parameter optimization, and in particular to a high-frequency filter parameter design method, device, computer equipment and storage medium. Background Art

[0002] High-frequency filters play a vital role in modern communication systems, radar equipment, wireless transmission, and electronic measurement. Their performance is directly related to the signal transmission quality and stability of the system. With the development of high-frequency electronic equipment towards miniaturization, integration, and high performance, higher requirements are placed on the design of high-frequency filters. In practical applications, high-frequency filters need to efficiently transmit signals within a specific passband frequency range while suppressing interference signals outside the passband. To achieve this goal, it is necessary not only to consider the amplitude response characteristics of the filter, but also to optimize its phase response characteristics to ensure the signal integrity and transmission stability of the system.

[0003] Traditional high-frequency filter design methods are usually based on classical filtering theory (such as Butterworth, Chebyshev or elliptic function filter design), and filter parameters are selected through empirical formulas or specific standard graphs. Although these methods are mature, they have certain limitations. For example, traditional methods are mostly based on amplitude response in design, and it is difficult to take into account both phase response and group delay performance at the same time; in addition, the selection of parameters often depends on the designer's experience, lacking flexibility and intelligent means, especially in complex high-frequency circuits. As the order of the filter system increases, the debugging process becomes more cumbersome and inaccurate. Modern high-frequency filters often need to meet multiple indicators (such as passband gain flatness, group delay jitter, etc.), and traditional design methods are difficult to fully meet these complex requirements.

[0004] Most existing methods only stay at the theoretical calculation stage when optimizing parameters, and lack the ability to comprehensively adjust the actual circuit performance (such as amplitude response, phase response, and group delay characteristics), resulting in a certain gap between the final design results and actual needs. Therefore, there is an urgent need for a method that can design high-frequency filters more accurately and intelligently to solve the optimization deficiencies in traditional methods and the performance deviation problems in practical applications.

[0005] In the prior art, the publication number CN114818509B discloses a filter parameter design method, device, computer equipment and storage medium, which are as follows: based on the current operation mode of the power grid simulation model, an initialization population is generated according to the external parameters of the filter to be optimized; wherein the external parameters include the filter placement position and filter structure determined by the power grid simulation model of the full wiring mode; an artificial intelligence model is used to process the initialization population to obtain the local optimal individual corresponding to the current operation mode; the current operation mode is switched until each operation mode of the power grid simulation model is traversed, and the local optimal individuals corresponding to each operation mode are obtained, and the local optimal cluster of each local optimal individual is obtained; based on the objective function value of each individual of the local optimal cluster, the global optimal individual is obtained; the global optimal individual is used to characterize the internal parameters of the filter to be optimized. However, this method mainly generates optimization results based on the power grid operation mode and the external parameters of the filter (such as placement position and structure), and does not fully consider the key frequency characteristics of the filter (such as passband range, amplitude response, phase response, group delay, etc.) in the optimization process. This may cause the final designed filter to be difficult to meet high-precision requirements in frequency characteristics, especially in high-frequency scenarios. Therefore, the accuracy and effectiveness of the optimization results are reduced.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The object of the present invention is to provide a method, device, computer equipment and storage medium for designing high-frequency filter parameters to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for designing high-frequency filter parameters, the specific steps comprising:

[0010] Establishing a circuit characteristic topology finite element model of a target high-frequency filter, analyzing the circuit characteristic topology finite element model of the target high-frequency filter according to a given passband frequency range, determining an optimization target parameter range, and constructing a transfer function of the target high-frequency filter based on the optimization target parameters, wherein the optimization target parameters include zero angular frequency, pole angular frequency, damping coefficient, and system order;

[0011] Based on the optimization target parameter range, several groups of optimization target parameter combinations within the optimization target parameter range are generated, and according to the transfer function of the target high-frequency filter under different optimization target parameter combinations, an optimization objective function is established, wherein each group of optimization target parameter combinations corresponds to a data of a different type of target parameter;

[0012] A group of optimization target parameter combinations is taken as an individual, the target parameters in the combination are taken as genes, and the established optimization objective function is taken as the fitness function, and the optimal target parameter combination is obtained through a genetic algorithm, wherein the optimal target parameter combination is the optimization target parameter combination with the highest fitness function value;

[0013] Based on the corresponding optimized target parameters in the optimal target parameter combination, the amplitude response and phase response of the high-frequency filter under the current parameters are obtained, and the passband gain deviation coefficient and the group delay jitter coefficient are calculated according to the amplitude response and phase response under the optimal parameters;

[0014] Based on the passband gain deviation coefficient and the group delay jitter coefficient, the corresponding optimization target parameters in the optimal target parameter combination are corrected to obtain the precise values ​​of the zero angular frequency, the pole angular frequency and the damping coefficient. The precise value data obtained are combined with the system order in the optimal target parameter combination as the parameters of the high-frequency filter to complete the design of the high-frequency filter.

[0015] Furthermore, the circuit characteristic topology finite element model of the target high-frequency filter is analyzed according to the given passband frequency range, wherein the given passband frequency range is calibrated as [f L ,f H ], f L represents the lower limit of the passband frequency, f H Indicates the upper limit of the passband frequency, that is, the range of frequency signals that can be passed;

[0016] Combined with the given passband frequency range, the circuit characteristic topology finite element model of the target high-frequency filter is analyzed to determine the optimization target parameter range, and the transfer function of the target high-frequency filter is constructed based on the optimized target parameters. The transfer function of the target high-frequency filter is specifically based on the formula:

[0017]

[0018] Where G(s) is the transfer function of the target high-frequency filter, s represents the complex Laplace variable, ω zi represents the angular frequency of the i-th zero point, ω pq represents the angular frequency of the qth pole, ζ q Represents the damping coefficient of the qth pole, where i is the index of the zero point, q is the index of the pole, m and n are the total number of zeros and poles respectively, i∈[0,m], q∈[1,n], where the number of poles is specifically the order of the high-frequency filter system.

[0019] Further, based on the optimization target parameter range, several groups of optimization target parameter combinations within the optimization target parameter range are generated. The logic for generating the corresponding optimization target parameter combination according to the value range of each optimization parameter is as follows: randomly generating several different data within the value range of each optimization parameter as optional values ​​of the optimization parameter, and randomly selecting a value from all optional values ​​of the optimization parameter as the data of the optimization parameter of this type in the optimization target parameter combination;

[0020] According to the transfer function of the target high-frequency filter under different optimization target parameter combinations, an optimization objective function is established, wherein the specific formula for the optimization objective function is:

[0021]

[0022] Where Y is the optimization objective function, |G(jω)| represents the amplitude response, G(jω) is the frequency response function of the filter, specifically the transfer function in the complex frequency domain, which is obtained by Fourier transforming the transfer function G(s), where ω L and ω H is the lower and upper limits of the angular frequency, and the specific determination is based on the formula:

[0023] ω L =f L *2π

[0024] ω H =f H *2π

[0025] In the formula, f L and f H are the lower and upper passband frequency limits respectively.

[0026] Furthermore, a set of optimization target parameter combinations is taken as an individual, and the target parameters in the combination are taken as genes. The specific logic is as follows: all data in the target parameters in the combination are encoded as corresponding genes, and the target parameters of the same type are alleles to each other, that is, there are 4 genes corresponding to a set of optimization target parameter combinations, including zero angular frequency, pole angular frequency, damping coefficient and system order. All optimization target parameter combinations are encoded to obtain a number of individuals; an initial population is constructed based on all the obtained individuals, and the initial population is calibrated as and u represents the index of different individual chromosomes in the initial population, and u = 1, 2, ..., D, each individual chromosome S u There are 4 genes, which correspond to the parameter values ​​of zero angular frequency, pole angular frequency, damping coefficient and system order respectively.

[0027] Furthermore, the specific logic for obtaining the optimal target parameter combination through the genetic algorithm is: cyclically performing selection, crossover and mutation operations on the initial population to generate an iterative population containing R new individual chromosomes, and judging whether the maximum number of iterations has been reached. When the maximum number of iterations is greater than the maximum number of iterations, the individual with the highest fitness in the current population is selected as the optimal optimization parameter combination; otherwise, the generated iterative population is used as the initial population for iterative operations until the iteration termination condition is met, and the optimization target parameter combination with the largest fitness value that appears during the iteration process is selected as the optimal target parameter combination, wherein the iteration termination condition is the set maximum number of iterations.

[0028] Furthermore, according to the amplitude response and phase response under the current parameters, the passband gain deviation coefficient and the group delay jitter coefficient are calculated, wherein the formula for calculating the passband gain deviation coefficient is:

[0029]

[0030] Where PC represents the passband gain deviation coefficient, |G O (jω)| represents the amplitude response of the high-frequency filter under the optimal parameters, G id represents the ideal passband gain;

[0031] The formula for calculating the group delay jitter coefficient is:

[0032]

[0033] Where SD is the group delay jitter coefficient, τ g (ω) is the group delay of the high-frequency filter under the optimal parameters, τ id is the desired constant group delay value, where τ g (ω) The specific calculation formula is:

[0034]

[0035] Where δ(ω) is the phase response of the high frequency filter.

[0036] Furthermore, based on the passband gain deviation coefficient and the group delay jitter coefficient, the corresponding optimization target parameters in the optimal target parameter combination are corrected to obtain the precise value of the zero angular frequency, the precise value of the pole angular frequency and the precise value of the damping coefficient, wherein the formula for calculating the precise value of the zero angular frequency is:

[0037]

[0038] In the formula, ω z ′ is the exact value of the zero-point angular frequency, ω z is the zero angular frequency corresponding to the optimal target parameter combination, k 1Adjust the proportional constant for the zero angular frequency;

[0039] The formula for calculating the exact value of the pole angular frequency is:

[0040]

[0041] In the formula, ω′ p is the exact value of the pole angular frequency, ω p is the corresponding pole angular frequency in the optimal target parameter combination, k 2 Adjust the proportionality constant for the pole corner frequency;

[0042]

[0043] In the formula, ζ′ is the exact value of the damping coefficient, ζ is the corresponding damping coefficient in the optimal target parameter combination, and k 3 Adjust the proportionality constant for the damping factor.

[0044] The present invention also provides a high-frequency filter parameter design device, which is used to execute the above-mentioned high-frequency filter parameter design method, including:

[0045] An optimization parameter determination module is used to establish a circuit characteristic topology finite element model of a target high-frequency filter, analyze the circuit characteristic topology finite element model of the target high-frequency filter according to a given passband frequency range, determine the optimization target parameter range, and construct a transfer function of the target high-frequency filter based on the optimization target parameters, wherein the optimization target parameters include zero angular frequency, pole angular frequency, damping coefficient and system order;

[0046] An optimization objective function establishment module is used to generate a plurality of optimization objective parameter combinations within the optimization objective parameter range based on the optimization objective parameter range, and to establish an optimization objective function according to the transfer function of the target high frequency filter under different optimization objective parameter combinations, wherein each group of optimization objective parameter combinations corresponds to a data of a different type of objective parameter;

[0047] An optimal parameter acquisition module is used to take a group of optimization target parameter combinations as an individual, the target parameters in the combination as genes, and the established optimization objective function as the fitness function, and obtain the optimal target parameter combination through a genetic algorithm, wherein the optimal target parameter combination is the optimization target parameter combination with the highest fitness function value;

[0048] The time domain characteristic correction module is used to obtain the amplitude response and phase response of the high-frequency filter under the current parameters based on the corresponding optimization target parameters in the optimal target parameter combination, and calculate the passband gain deviation coefficient and group delay jitter coefficient according to the amplitude response and phase response under the optimal parameters;

[0049] The precise design parameter acquisition module is used to correct the corresponding optimization target parameters in the optimal target parameter combination based on the passband gain deviation coefficient and the group delay jitter coefficient, and obtain the precise value of the zero angular frequency, the precise value of the pole angular frequency and the precise value of the damping coefficient. The precise value data obtained is combined with the system order in the optimal target parameter combination as the parameters of the high-frequency filter to complete the design of the high-frequency filter.

[0050] The present invention also provides a high-frequency filter parameter computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned high-frequency filter parameter design method when executing the computer program.

[0051] The present invention also provides a high-frequency filter parameter storage medium, on which a computer program is stored. When the computer program is executed by a processor, the high-frequency filter parameter design method described above is implemented.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] First, this method can accurately describe the frequency response characteristics of the filter by establishing a circuit characteristic topology finite element model of the target high-frequency filter. The analysis method based on the finite element model can ensure that each set of parameter combinations in the filter design can truly reflect the physical characteristics of the filter in different frequency ranges. This advantage effectively overcomes the errors caused by model simplification in traditional methods, making the design results closer to actual application requirements. Secondly, the genetic algorithm is used to globally optimize the optimization target parameters (including zero angular frequency, pole angular frequency, damping coefficient and system order). By simulating natural selection and genetic mechanisms, the genetic algorithm can quickly search for the optimal target parameter combination, solving the problem that traditional methods are prone to fall into local optimality in multi-objective optimization. By introducing the fitness function, high-frequency filter parameters with better performance are screened out. In addition, after obtaining the optimal target parameter combination, the passband gain deviation coefficient and group delay jitter coefficient are calculated through further analysis of the amplitude response and phase response, and the parameters are accurately corrected based on these performance indicators. Ensure that high-precision performance requirements can also be met in actual circuits. Improve the reliability of filter design. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0055] Figure 2 It is a schematic diagram of the overall structure of the device of the present invention. DETAILED DESCRIPTION

[0056] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0057] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0058] Example:

[0059] See also Figure 1 , the present invention provides a technical solution:

[0060] A method for designing high-frequency filter parameters, the specific steps comprising:

[0061] Step 1: Establish a circuit characteristic topology finite element model of the target high-frequency filter, analyze the circuit characteristic topology finite element model of the target high-frequency filter according to a given passband frequency range, determine the optimization target parameter range, and construct the transfer function of the target high-frequency filter based on the optimization target parameters, wherein the optimization target parameters include zero angular frequency, pole angular frequency, damping coefficient and system order.

[0062] The specific steps for establishing the circuit characteristic topology finite element model of the target high-frequency filter include: drawing the connection topology diagram of the inductor and capacitor components, and clarifying the contribution of each component to the filter frequency response; taking the physical dimensions of the filter (such as inductor value, capacitor value, microstrip line length and width) as variable parameters, and establishing a parameterized circuit topology model to ensure that the design parameters can be flexibly adjusted in the subsequent optimization process.

[0063] The circuit characteristic topology finite element model analysis of the target high-frequency filter is carried out according to the given passband frequency range, where the given passband frequency range is calibrated as [f L ,f H ], f L represents the lower limit of the passband frequency, f H It represents the upper limit of the passband frequency, that is, the range of frequency signals that can be passed; the given passband frequency range is set according to the actual application object.

[0064] Combined with the given passband frequency range, the circuit characteristic topology finite element model of the target high-frequency filter is analyzed to determine the optimization target parameter range, and the transfer function of the target high-frequency filter is constructed based on the optimized target parameters. The transfer function of the target high-frequency filter is specifically based on the formula:

[0065]

[0066] Where G(s) is the transfer function of the target high-frequency filter, s represents the complex Laplace variable, ω zi represents the angular frequency of the i-th zero point, ω pq represents the angular frequency of the qth pole, ζ q Represents the damping coefficient of the qth pole, where i is the index of the zero point, q is the index of the pole, m and n are the total number of zeros and poles respectively, i∈[0,m], q∈[1,n], where the number of poles is specifically the order of the high-frequency filter system.

[0067] The optimization target parameter range can be obtained by consulting relevant public information and combining expert experience.

[0068] Step 2: Based on the optimization target parameter range, generate several groups of optimization target parameter combinations within the optimization target parameter range, and establish an optimization objective function according to the transfer function of the target high-frequency filter under different optimization target parameter combinations, where each group of optimization target parameter combinations corresponds to a data of different types of target parameters.

[0069] Based on the optimization target parameter range, several groups of optimization target parameter combinations within the optimization target parameter range are generated. The logic for generating the corresponding optimization target parameter combination according to the value range of each optimization parameter is as follows: randomly generate several different data within the value range of each optimization parameter as optional values ​​of the optimization parameter, and randomly select a value from all optional values ​​of the optimization parameter as the data of the optimization parameter of this type in the optimization target parameter combination;

[0070] According to the transfer function of the target high-frequency filter under different optimization target parameter combinations, an optimization objective function is established, wherein the specific formula for the optimization objective function is:

[0071]

[0072] Where Y is the optimization objective function, |G(jω)| represents the amplitude response, G(jω) is the frequency response function of the filter, specifically the transfer function in the complex frequency domain, which is obtained by Fourier transforming the transfer function G(s), where ω L and ω H is the lower and upper limits of the angular frequency, and the specific determination is based on the formula:

[0073] ω L =f L *2π

[0074] ω H =f H *2π

[0075] In the formula, f L and f H are the lower and upper passband frequency limits respectively.

[0076] in, This formula indicates that in the angular frequency range [ω L ,ω H ] is the cumulative energy of the filter output signal. Because |G(jω)| 2 It represents the intensity of the signal energy output by the filter at the angular frequency ω, so its integration over a certain frequency band can be regarded as the accumulation of total energy. L ,f H ] is the passband frequency range. In the passband, we hope that the signal passing is maximized, that is, the passband gain is as high and stable as possible, and the energy output is as large as possible. The larger Y is, the stronger the filter output energy in the passband, that is, the better the passband performance and the higher the energy passing efficiency, which means that the higher the energy passing rate of the signal in the passband, the better the performance of the high-frequency filter in the passband range.

[0077] Step 3: Take a group of optimization target parameter combinations as an individual, the target parameters in the combination as genes, and the established optimization objective function as the fitness function, and obtain the optimal target parameter combination through a genetic algorithm, wherein the optimal target parameter combination is the optimization target parameter combination with the highest fitness function value.

[0078] A set of optimization target parameter combinations is regarded as an individual, and the target parameters in the combination are regarded as genes. The specific logic is as follows: all data in the target parameters in the combination are encoded as corresponding genes, and the target parameters of the same type are alleles to each other, that is, there are 4 genes corresponding to a set of optimization target parameter combinations, including zero angular frequency, pole angular frequency, damping coefficient and system order. All optimization target parameter combinations are encoded to obtain several individuals; an initial population is constructed based on all the obtained individuals, and the initial population is calibrated as and u represents the index of different individual chromosomes in the initial population, and u = 1, 2, ..., D, each individual chromosome S u There are 4 genes, which correspond to the parameter values ​​of zero angular frequency, pole angular frequency, damping coefficient and system order respectively.

[0079] The initial population is cyclically selected, crossed over and mutated to generate an iterative population containing R new individual chromosomes, and it is determined whether the maximum number of iterations has been reached. If it is greater than the maximum number of iterations, the individual with the highest fitness in the current population is selected as the optimal optimization parameter combination. Otherwise, the generated iterative population is used as the initial population for iterative operation until the iteration termination condition is met. The optimization target parameter combination with the largest fitness value that appears during the iteration process is selected as the optimal target parameter combination, wherein the iteration termination condition is the set maximum number of iterations.

[0080] The initial population is cycled through selection, crossover and mutation operations, where the logic of the selection operation is: select individuals in the population according to the fitness in the sequence, and select two individuals from the current sequence. The formula for calculating the probability of being selected is:

[0081]

[0082] In the formula, f(S u ) represents the fitness of the qth individual in the initial population, pro(S u ) is the probability of the uth individual chromosome being selected, where u = 1, 2, ..., D, and D is a positive integer. The roulette method is used to select individuals. The system randomly generates a random number in the interval [0, 1]. According to the generated probability of being selected, the random number is distributed in the selection interval of the corresponding individual chromosome to determine the individual to be selected in this round;

[0083] The selection operation imitates the evolutionary theory of biologist Darwin. Individuals with better adaptability to nature will have a better chance of obtaining mating rights and passing on their good genes to their offspring. In each generation of genetic evolution, a part of the individuals need to be selected from the current population to evolve into the next generation of population.

[0084] The mainstream crossover operations now include: single-point crossover, multi-point crossover, etc. Among them, single-point crossover is to randomly select the same gene position on the chromosomes of the parent individuals as the crossover point, and exchange the gene fragments on the right side of the chromosome crossover point of the parents, so that two new individuals are obtained, each of which contains part of the genetic information of the parents;

[0085] The logic of the crossover operation is: perform a crossover operation on two selected individuals, exchange the two selected individual chromosomes from a certain gene position and the chromosome fragment after the gene position, and obtain two crossed individual chromosomes, wherein the crossover operation is performed on the two selected individuals, and the logic based on which the crossover operation is performed is: use a single-point crossover method to perform the crossover operation.

[0086] The logic of mutation operation is: the mutation operation is performed on the two new individuals generated by the crossover operation, according to the set mutation probability R. m The selected gene is mutated into one of its alleles, and the gene at the gene position is replaced with the mutated gene, thereby obtaining a new individual.

[0087] The mutation probability R m When set to 0.6, the mutation operation can ensure and enrich the diversity of the population, that is, it can increase the search range of the algorithm and prevent it from falling into the local optimal solution, which will slow down or even stop the evolution of the population.

[0088] The iteration termination condition is the maximum number of iterations that is set. The maximum number of iterations can be dynamically adjusted according to the number of genes in the individual, the size of the population, and the diversity of individuals in the population. The specific calculation formula is:

[0089]

[0090] In the formula, CS max is the maximum number of iterations adjusted dynamically, CS 0 is the number of iterations set initially, ZQ is the number of individuals in the population, SL represents the number of genes in an individual, and DL avg is the average Euclidean distance between individuals in a population, and is used to represent population diversity. The formula for calculating the average Euclidean distance is a conventional technical method and will not be described in detail here.

[0091] The average Euclidean distance DL between individuals in the population avg The larger the value, the higher the diversity. The higher the population diversity, the more the algorithm can explore the global optimal solution, so the number of iterations can be reduced to save computing resources. Therefore, the average Euclidean distance between individuals in the population is inversely proportional to the maximum number of iterations adjusted dynamically.

[0092] The more genes there are, the larger the problem scale is, and the number of iterations needs to be appropriately increased. Therefore, the logarithmic function ln(1+SL) is used to indicate that when the number of genes is large, the nonlinear growth should be appropriately enhanced to reflect the sensitivity of the number of iterations to the complexity of the problem;

[0093] Square root of population size It shows that the effect of population size on the maximum number of iterations is sublinear (that is, when the population size doubles, the maximum number of iterations will not double), which avoids too high number of iterations when the population size is too large.

[0094] Step 4: Based on the corresponding optimization target parameters in the optimal target parameter combination, obtain the amplitude response and phase response of the high-frequency filter under the current parameters, and calculate the passband gain deviation coefficient and group delay jitter coefficient according to the amplitude response and phase response under the optimal parameters.

[0095] According to the amplitude response and phase response under the current parameters, the passband gain deviation coefficient and the group delay jitter coefficient are calculated. The formula for calculating the passband gain deviation coefficient is:

[0096]

[0097] Where PC represents the passband gain deviation coefficient, |G O (jω)| represents the amplitude response of the high-frequency filter under the optimal parameters, G id Represents the ideal passband gain; the passband gain deviation coefficient describes the flatness of the gain response within the passband. The smaller the value, the flatter the passband.

[0098] The formula for calculating the group delay jitter coefficient is:

[0099]

[0100] Where SD is the group delay jitter coefficient, τ g (ω) is the group delay of the high-frequency filter under the optimal parameters, τ id is the desired constant group delay value, where τ g (ω) The specific calculation formula is:

[0101]

[0102] Where δ(ω) is the phase response of the high frequency filter.

[0103] The group delay jitter coefficient describes the jitter of the group delay within the passband. The smaller the value, the smoother the group delay.

[0104] Step 5: Based on the passband gain deviation coefficient and the group delay jitter coefficient, the corresponding optimization target parameters in the optimal target parameter combination are corrected to obtain the precise values ​​of the zero angular frequency, the pole angular frequency and the damping coefficient. The precise value data obtained is combined with the system order in the optimal target parameter combination as the parameters of the high-frequency filter to complete the design of the high-frequency filter.

[0105] Based on the passband gain deviation coefficient and the group delay jitter coefficient, the corresponding optimization target parameters in the optimal target parameter combination are corrected to obtain the precise value of the zero angular frequency, the precise value of the pole angular frequency and the precise value of the damping coefficient. The formula for calculating the precise value of the zero angular frequency is:

[0106]

[0107] In the formula, ω z ′ is the exact value of the zero-point angular frequency, ω z is the zero angular frequency corresponding to the optimal target parameter combination, k 1 Adjust the proportional constant for the zero angular frequency;

[0108] The passband gain deviation coefficient is the sum of the squares of the deviations between the actual filter gain and the expected gain in the passband. The larger the gain deviation, the more uneven the frequency response in the passband, and the higher the insertion loss (i.e., signal energy loss). The distribution of zeros directly affects the stopband position. By appropriately increasing the zero angular frequency, the zero can be kept out of the passband range, thereby reducing interference in the passband. The larger the gain deviation coefficient, the gain deviation coefficient can be reduced by increasing the zero angular frequency, making the passband flatter. Therefore, the passband gain deviation coefficient is proportional to the exact value of the zero angular frequency, and is calculated through the exponential square root. It means that the correction amplitude increases significantly with the increase of the passband gain deviation coefficient.

[0109] The position of the zero point mainly affects the phase response. A too high zero point angular frequency may cause a sharp change in the passband phase response, further exacerbating the group delay jitter. Properly reducing the zero point angular frequency to make it close to the passband boundary can weaken the interference to the passband phase and reduce the group delay jitter. Therefore, the precise value of the zero point angular frequency is inversely proportional to the group delay jitter coefficient. The logarithm ln(1+SD) shows that as the group delay jitter increases, the amplitude of the adjustment of the precise value of the zero point angular frequency gradually decreases.

[0110] The proportional constant k is adjusted 1 It is greater than 0 and less than 0.2, and is used to control the adjustment range, indicating that adjustments and corrections are made without destroying the optimal performance.

[0111] The formula for calculating the exact value of the pole angular frequency is:

[0112]

[0113] In the formula, ω′ p is the exact value of the pole angular frequency, ω p is the corresponding pole angular frequency in the optimal target parameter combination, k 2 Adjust the proportionality constant for the pole corner frequency;

[0114] The distribution of poles has a significant effect on the passband gain and the steepness of the frequency response. By appropriately reducing the pole angular frequency, the gain compensation in the passband can be increased and the passband insertion loss can be reduced. Therefore, the passband gain deviation coefficient is inversely proportional to the exact value of the pole angular frequency. Represents an inverse relationship.

[0115] The pole position directly affects the phase response within the passband. If the pole angular frequency is too low, the phase change may be too concentrated, increasing the group delay jitter. Properly increasing the pole angular frequency and moving it away from the passband can slow down the phase change rate within the passband, thereby reducing the group delay jitter. Therefore, the group delay jitter coefficient is proportional to the precise value of the pole angular frequency.

[0116] The pole angular frequency is adjusted by the proportional constant k 2 Usually greater than 0 and less than 0.1.

[0117] The formula for calculating the exact value of the damping coefficient is:

[0118]

[0119] In the formula, ζ′ is the exact value of the damping coefficient, ζ is the corresponding damping coefficient in the optimal target parameter combination, and k 3 Adjust the proportionality constant for the damping factor.

[0120] The increase in gain deviation is usually reflected in the uneven frequency response in the passband, which may be related to the too small damping coefficient. A damping coefficient that is too small will cause sharp gain fluctuations at the edge of the passband. Increasing the damping coefficient can smooth the frequency response and reduce the fluctuation of the passband gain. Therefore, the passband gain deviation coefficient is proportional to the exact value of the damping coefficient. At the same time, in order to introduce over-correction due to excessive parameter deviation, 1+PC 2 As a denominator, it prevents overcorrection;

[0121] Too large a damping coefficient may introduce a large group delay non-uniformity. Properly reducing the damping coefficient can increase the flexibility of the phase response and more accurately control the group delay within the passband. Therefore, the group delay jitter coefficient is inversely proportional to the exact value of the damping coefficient. -SD It means that as the group delay jitter coefficient increases, the precise value of the damping coefficient decreases significantly.

[0122] Damping coefficient adjustment proportional constant k 3 Usually greater than 0 and less than 0.05.

[0123] See also Figure 2 The present invention also provides a high-frequency filter parameter design device, which is used to execute the above-mentioned high-frequency filter parameter design method, including:

[0124] An optimization parameter determination module is used to establish a circuit characteristic topology finite element model of a target high-frequency filter, analyze the circuit characteristic topology finite element model of the target high-frequency filter according to a given passband frequency range, determine the optimization target parameter range, and construct a transfer function of the target high-frequency filter based on the optimization target parameters, wherein the optimization target parameters include zero angular frequency, pole angular frequency, damping coefficient and system order;

[0125] An optimization objective function establishment module is used to generate a plurality of optimization objective parameter combinations within the optimization objective parameter range based on the optimization objective parameter range, and to establish an optimization objective function according to the transfer function of the target high frequency filter under different optimization objective parameter combinations, wherein each group of optimization objective parameter combinations corresponds to a data of a different type of objective parameter;

[0126] An optimal parameter acquisition module is used to take a group of optimization target parameter combinations as an individual, the target parameters in the combination as genes, and the established optimization objective function as the fitness function, and obtain the optimal target parameter combination through a genetic algorithm, wherein the optimal target parameter combination is the optimization target parameter combination with the highest fitness function value;

[0127] The time domain characteristic correction module is used to obtain the amplitude response and phase response of the high-frequency filter under the current parameters based on the corresponding optimization target parameters in the optimal target parameter combination, and calculate the passband gain deviation coefficient and group delay jitter coefficient according to the amplitude response and phase response under the optimal parameters;

[0128] The precise design parameter acquisition module is used to correct the corresponding optimization target parameters in the optimal target parameter combination based on the passband gain deviation coefficient and the group delay jitter coefficient, and obtain the precise value of the zero angular frequency, the precise value of the pole angular frequency and the precise value of the damping coefficient. The precise value data obtained is combined with the system order in the optimal target parameter combination as the parameters of the high-frequency filter to complete the design of the high-frequency filter.

[0129] The present invention also provides a high-frequency filter parameter computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned high-frequency filter parameter design method when executing the computer program.

[0130] The present invention also provides a high-frequency filter parameter storage medium, on which a computer program is stored. When the computer program is executed by a processor, the high-frequency filter parameter design method described above is implemented.

[0131] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0132] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0133] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A high frequency filter parameter design method, characterized in that: The specific steps include: Establishing a circuit characteristic topology finite element model of a target high-frequency filter, analyzing the circuit characteristic topology finite element model of the target high-frequency filter according to a given passband frequency range, determining an optimization target parameter range, and constructing a transfer function of the target high-frequency filter based on the optimization target parameters, wherein the optimization target parameters include zero angular frequency, pole angular frequency, damping coefficient, and system order; Based on the optimization target parameter range, several groups of optimization target parameter combinations within the optimization target parameter range are generated, and according to the transfer function of the target high-frequency filter under different optimization target parameter combinations, an optimization objective function is established, wherein each group of optimization target parameter combinations corresponds to a data of a different type of target parameter; A group of optimization target parameter combinations is taken as an individual, the target parameters in the combination are taken as genes, and the established optimization objective function is taken as the fitness function, and the optimal target parameter combination is obtained through a genetic algorithm, wherein the optimal target parameter combination is the optimization target parameter combination with the highest fitness function value; Based on the corresponding optimized target parameters in the optimal target parameter combination, the amplitude response and phase response of the high-frequency filter under the current parameters are obtained, and the passband gain deviation coefficient and the group delay jitter coefficient are calculated according to the amplitude response and phase response under the optimal parameters; Based on the passband gain deviation coefficient and the group delay jitter coefficient, the corresponding optimization target parameters in the optimal target parameter combination are corrected to obtain the precise values ​​of the zero angular frequency, the pole angular frequency and the damping coefficient. The precise value data obtained are combined with the system order in the optimal target parameter combination as the parameters of the high-frequency filter to complete the design of the high-frequency filter.

2. A high frequency filter parameter design method according to claim 1, characterized in that: The circuit characteristic topology finite element model analysis of the target high-frequency filter is carried out according to the given passband frequency range, where the given passband frequency range is calibrated as [f L ,f H ], f L represents the lower limit of the passband frequency, f H Indicates the upper limit of the passband frequency, that is, the range of frequency signals that can be passed; Combined with the given passband frequency range, the circuit characteristic topology finite element model of the target high-frequency filter is analyzed to determine the optimization target parameter range, and the transfer function of the target high-frequency filter is constructed based on the optimized target parameters. The transfer function of the target high-frequency filter is specifically based on the formula: Where G(s) is the transfer function of the target high-frequency filter, s represents the complex Laplace variable, ω zi represents the angular frequency of the i-th zero point, ω pq represents the angular frequency of the qth pole, ζ q Represents the damping coefficient of the qth pole, where i is the index of the zero point, q is the index of the pole, m and n are the total number of zeros and poles respectively, i∈[0,m], q∈[1,n], where the number of poles is specifically the order of the high-frequency filter system.

3. A high frequency filter parameter design method according to claim 1, characterized in that: Based on the optimization target parameter range, several groups of optimization target parameter combinations within the optimization target parameter range are generated. The logic for generating the corresponding optimization target parameter combination according to the value range of each optimization parameter is as follows: randomly generate several different data within the value range of each optimization parameter as optional values ​​of the optimization parameter, and randomly select a value from all optional values ​​of the optimization parameter as the data of the optimization parameter of this type in the optimization target parameter combination; According to the transfer function of the target high-frequency filter under different optimization target parameter combinations, an optimization objective function is established, wherein the specific formula for the optimization objective function is: Where Y is the optimization objective function, |G(jω)| represents the amplitude response, G(jω) is the frequency response function of the filter, specifically the transfer function in the complex frequency domain, which is obtained by Fourier transforming the transfer function G(s), where ω L and ω H is the lower and upper limits of the angular frequency, and the specific determination is based on the formula: oh L =f L *2p oh H =f H *2p In the formula, f L and f H are the lower and upper passband frequency limits respectively.

4. A high frequency filter parameter design method according to claim 3, characterized in that: A set of optimization target parameter combinations is regarded as an individual, and the target parameters in the combination are regarded as genes. The specific logic is as follows: all data in the target parameters in the combination are encoded as corresponding genes, and the target parameters of the same type are alleles to each other, that is, there are 4 genes corresponding to a set of optimization target parameter combinations, including zero angular frequency, pole angular frequency, damping coefficient and system order. All optimization target parameter combinations are encoded to obtain several individuals; an initial population is constructed based on all the obtained individuals, and the initial population is calibrated as and u represents the index of different individual chromosomes in the initial population, and u = 1, 2, ..., D, each individual chromosome S u There are 4 genes, which correspond to the parameter values ​​of zero angular frequency, pole angular frequency, damping coefficient and system order respectively.

5. A high frequency filter parameter design method according to claim 4, characterized in that: The specific logic for obtaining the optimal target parameter combination through the genetic algorithm is: cyclically perform selection, crossover and mutation operations on the initial population to generate an iterative population containing multiple new individual chromosomes, determine whether the maximum number of iterations has been reached, and when it is greater than the maximum number of iterations, select the individual with the highest fitness in the current population as the optimal optimization parameter combination; otherwise, use the generated iterative population as the initial population for iterative operations until the iteration termination condition is met, and select the optimization target parameter combination with the largest fitness value that appears during the iteration as the optimal target parameter combination, wherein the iteration termination condition is the set maximum number of iterations.

6. A high frequency filter parameter design method according to claim 5, characterized in that: According to the amplitude response and phase response under the current parameters, the passband gain deviation coefficient and the group delay jitter coefficient are calculated. The formula for calculating the passband gain deviation coefficient is: Where PC represents the passband gain deviation coefficient, |G O (jω)| represents the amplitude response of the high-frequency filter under the optimal parameters, G id represents the ideal passband gain; The formula for calculating the group delay jitter coefficient is: Where SD is the group delay jitter coefficient, τ g (ω) is the group delay of the high-frequency filter under the optimal parameters, τ id is the desired constant group delay value, where τ g (ω) The specific calculation formula is: Where δ(ω) is the phase response of the high frequency filter.

7. A high frequency filter parameter design method according to claim 6, characterized in that: Based on the passband gain deviation coefficient and the group delay jitter coefficient, the corresponding optimization target parameters in the optimal target parameter combination are corrected to obtain the precise value of the zero angular frequency, the precise value of the pole angular frequency and the precise value of the damping coefficient. The formula for calculating the precise value of the zero angular frequency is: In the formula, ω′ z is the exact value of the zero-point angular frequency, ω z is the zero-point angular frequency corresponding to the optimal target parameter combination, k1 is the zero-point angular frequency adjustment proportional constant; The formula for calculating the exact value of the pole angular frequency is: In the formula, ω ′ p is the exact value of the pole angular frequency, ω p is the pole angular frequency corresponding to the optimal target parameter combination, k2 is the pole angular frequency adjustment proportional constant; In the formula, ζ ′ is the exact value of the damping coefficient, ζ is the corresponding damping coefficient in the optimal target parameter combination, and k3 is the proportional constant for adjusting the damping coefficient.

8. A high-frequency filter parameter design device, characterized in that: The high-frequency filter parameter design device is used to execute the high-frequency filter parameter design method according to any one of claims 1 to 7, comprising: An optimization parameter determination module is used to establish a circuit characteristic topology finite element model of a target high-frequency filter, analyze the circuit characteristic topology finite element model of the target high-frequency filter according to a given passband frequency range, determine the optimization target parameter range, and construct a transfer function of the target high-frequency filter based on the optimization target parameters, wherein the optimization target parameters include zero angular frequency, pole angular frequency, damping coefficient and system order; An optimization objective function establishment module is used to generate a plurality of optimization objective parameter combinations within the optimization objective parameter range based on the optimization objective parameter range, and to establish an optimization objective function according to the transfer function of the target high frequency filter under different optimization objective parameter combinations, wherein each group of optimization objective parameter combinations corresponds to a data of a different type of objective parameter; An optimal parameter acquisition module is used to take a group of optimization target parameter combinations as an individual, the target parameters in the combination as genes, and the established optimization objective function as the fitness function, and obtain the optimal target parameter combination through a genetic algorithm, wherein the optimal target parameter combination is the optimization target parameter combination with the highest fitness function value; The time domain characteristic correction module is used to obtain the amplitude response and phase response of the high-frequency filter under the current parameters based on the corresponding optimization target parameters in the optimal target parameter combination, and calculate the passband gain deviation coefficient and group delay jitter coefficient according to the amplitude response and phase response under the optimal parameters; The precise design parameter acquisition module is used to correct the corresponding optimization target parameters in the optimal target parameter combination based on the passband gain deviation coefficient and the group delay jitter coefficient, and obtain the precise value of the zero angular frequency, the precise value of the pole angular frequency and the precise value of the damping coefficient. The precise value data obtained is combined with the system order in the optimal target parameter combination as the parameters of the high-frequency filter to complete the design of the high-frequency filter.

9. A high-frequency filter parameter computer device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the high-frequency filter parameter design method according to any one of claims 1 to 7 is implemented.

10. A high-frequency filter parameter storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the high-frequency filter parameter design method according to any one of claims 1 to 7 is implemented.

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