Analog filter automation design and circuit construction method

Abstract

The invention discloses an automatic design and circuit construction method of an analog filter, and aims to improve the simulation efficiency and accuracy of filter design, especially the problem of efficient simulation design of the filter. An existing filter simulation design method is generally based on needed parameters and performance indexes, an equivalent circuit is constructed by using discrete components or integrated components and the like, and a large amount of time and computing resources are generally needed. According to the method, element values in the equivalent circuit can be directly solved and selected according to parameter requirements, and a specific filter circuit can be automatically generated by utilizing an electric network theory and related technologies, so that the complexity of designing and building the filter equivalent circuit is greatly reduced, and resources required by simulation are remarkably reduced. The analog filter plays a role in application scenes such as a quick response system, so that the method has important significance for realizing efficient and quick design of a filter device.

Description

Technical Field

[0001] The invention relates to the field of analog filters, and in particular to an automatic design and circuit construction method for analog filters. Background Art

[0002] In modern communication systems, radar systems, and satellite communication systems, analog filters, as key components of RF devices, play a vital role. They are mainly used to select signals in a specific frequency band while suppressing unwanted frequency bands to ensure signal purity and system reliability. With the rapid development of wireless communication technology and the expansion of application scenarios, the performance requirements of analog filters are becoming higher and higher, including the accuracy of frequency response, control of passband width, and the effect of stopband attenuation.

[0003] Traditional analog filter design methods mainly rely on discrete components or integrated components to build circuits. Engineers need to select appropriate circuit components based on the design parameters through experience and trial and error, and then build and debug the circuit. This process is not only time-consuming and labor-intensive, but also has high design complexity, especially when facing multi-parameter, high-order filter design, the design workload and debugging difficulty increase significantly.

[0004] At present, most analog filter design methods are based on classical circuit theory, such as Butterworth filter, Chebyshev filter and elliptic function filter. These methods can meet basic filtering requirements to a certain extent, but their design accuracy and performance are often limited in high-frequency applications and complex systems. In addition, with the expansion of RF application frequency bands and the diversification of requirements, traditional design methods are difficult to respond quickly to market demand, and a more efficient and automated design method is urgently needed.

[0005] In recent years, with the development of computer technology and high-performance computing (HPC) technology, numerical calculation methods have gradually been applied to the field of analog filter design. This method can quickly solve circuit design solutions that meet specific parameter requirements through computer simulation and optimization algorithms, significantly improving design efficiency and accuracy. However, the current research on combining fast calculation technology with electrical network theory to apply to analog filter design is still in its initial stage, and a mature technical system and application method has not yet been formed.

[0006] In summary, the existing analog filter design methods have many deficiencies in efficiency, accuracy and automation. How to use fast computing technology and advanced electrical network theory to achieve efficient, fast and automated analog filter design has become an important technical problem to be solved. The present invention is proposed in this context, aiming to provide an innovative numerical calculation method for efficiently solving analog filter design problems to meet the high requirements of modern radio frequency applications. Summary of the invention

[0007] In view of the limitations of traditional analog filter design methods, the present invention proposes a method for automatic design and circuit construction of analog filters, which is a new calculation method based on fast calculation technology, realizing the efficient and rapid design of analog filters, and applicable to the design of Butterworth filters, Chebyshev filters, inverse Chebyshev filters, etc. with various frequency responses. The technical solution adopted by the present invention is as follows:

[0008] A method for automatic design and circuit construction of analog filters includes the following steps:

[0009] Step 1, select the filter type and filter characteristics according to specific application requirements, and input relevant performance parameters;

[0010] Step 2, determine the design order and adjustment factor of the filter according to filter design theory;

[0011] Step 3, select a standard prototype filter using the design parameters in Step 2, and perform adjustment and parameter optimization;

[0012] Step 4, normalize the actual design requirements to the specifications of the standard prototype filter;

[0013] Step 5, perform numerical solution and optimization calculation to generate the required component parameters, circuit structure or transfer function;

[0014] Step 6, after obtaining the component parameters, set the circuit nodes in combination with electric network theory to complete the construction of the filter circuit;

[0015] Step 7, verify the filter characteristics through simulation and testing, and perform parameter adjustment to meet the design requirements;

[0016] Step 8, output the design results, including the specific component parameters and node parameters of the filter, or the numerator and denominator polynomial coefficients of the transfer function fraction.

[0017] In the above technical solution, further, modular design is adopted, that is, the entire process of the method is implemented based on functional modules. The functional modules include a filter initialization module, a filter design module, a circuit modeling module, and a circuit simulation module; wherein the initialization module is used to calculate and store the parameter information of the filter; the filter design module is used to analyze the design requirements and design the filter structure; the circuit modeling module forms a circuit by setting specific components through the designed circuit structure; the circuit simulation module is used to perform parameter simulation and performance evaluation based on the circuit structure.

[0018] Further, the filter type is low-pass, high-pass, band-pass or band-stop, the filter characteristic is Butterworth filter, Chebyshev filter or inverse Chebyshev filter, and the performance parameters include: cut-off frequency, passband attenuation, stopband attenuation, passband bandwidth, stopband bandwidth, etc.

[0019] Further, when the filter design order is greater than 2, it is decomposed into a cascade form of several filters; at the same time, an adjustment factor is calculated according to the design requirements to adjust the case where the attenuation is not equal to the 3dB bandwidth attenuation when designing the band-pass response, and to adjust the first frequency value of the low-pass prototype filter that satisfies the specified band-stop loss when designing the band-stop response.

[0020] Further, specifically in step 4, each parameter of the standard prototype filter is normalized, its frequency adjustment factor and impedance adjustment factor are calculated, and the response curve of the prototype filter is corrected.

[0021] Further, in step 5, numerical solution and optimization calculation are performed on the selected filter type. For Butterworth and Chebyshev filters, there are two modes. The first mode is to automatically generate the required component parameters and circuit structure according to the input parameters, and the second mode is to output the transfer function. For inverse Chebyshev filters, there is only the second mode, that is, the output transfer function mode.

[0022] Further, step 5 is implemented using the scientific computing language Julia.

[0023] Further, in the first mode, after obtaining the required component parameters, combined with the electric network theory, the circuit nodes are specifically set, and the corresponding components are placed on the corresponding nodes to complete the construction of the filter circuit;

[0024] In the second mode, first calculate the frequency ratio according to the filter frequency. If the filter parameters are not clearly defined in the design, the order is calculated using the frequency ratio, and the formula is as follows:

[0025]

[0026] Where: A s is the stopband attenuation, A p is the passband attenuation, Ω s is the stopband cut-off frequency, Ω p is the passband cut-off frequency;

[0027] After that, according to the passband attenuation and the filter order, calculate the scaling factor scale3dB of the 3dB attenuation, whose function is to adjust the passband attenuation cut-off frequency so that the attenuation of the filter at the passband edge meets the design requirements. The calculation formula is:

[0028]

[0029] After obtaining the above parameters, it is also necessary to calculate the relevant parameters of the input and output impedances, including the input and output impedances, the impedance matching factor, and the square of the input and output reflection coefficients; thereafter, calculate the parameters of the transfer function according to the filter type, that is, the numerator and denominator coefficients of the transfer function.

[0030] Further, in step 7, through simulation and testing, verify the characteristics of the generated filter circuit to ensure that it meets the design requirements. If there are deviations or it does not meet the design requirements, adjust the parameters through iterative optimization until the filter performance reaches the expected standard.

[0031] The present invention proposes an efficient method for designing and constructing analog filters. This method not only significantly improves the design speed and accuracy but also provides reliable technical guidance in the actual circuit construction. It has important application values in fast response systems, communication systems, radar systems, and satellite communication systems, and has broad application prospects in other application scenarios that require high-precision and high-performance filters. The present invention provides a new solution for the design and construction of analog filters, having profound technical impacts and application values. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings described herein are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0033] Figure 1 is a logic diagram of a new method for designing and constructing an analog filter provided by an embodiment of the present invention;

[0034] Figure 2 is a flowchart of a new method for designing and constructing an analog filter provided by an embodiment of the present invention;

[0035] Figure 3 is a comparison diagram of S-parameter results and Matlab results of a band-pass Butterworth filter provided by an embodiment of the present invention;

[0036] Figure 4 is a comparison diagram of group delay parameter results and Matlab results of a band-pass Butterworth filter provided by an embodiment of the present invention;

[0037] Figure 5 is a comparison diagram of S21 parameter results and Matlab results of an inverse Chebyshev 5th-order low-pass filter provided by an embodiment of the present invention;

[0038] Figure 6 is a comparison diagram of S21 parameter results and Matlab results of a Chebyshev 4th-order low-pass filter provided by an embodiment of the present invention. Detailed implementation manners

[0039] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0040] It should be noted that those of ordinary skill in the art explicitly and implicitly understand that the embodiments described in the present invention can be combined with other embodiments without conflict. Unless otherwise defined, the technical terms or scientific terms involved in the present invention should be of the ordinary meaning understood by those with ordinary skills in the technical field to which the present invention belongs. The terms "a", "one kind", "the" and other similar words involved in the present invention do not indicate a limitation in quantity, and can represent a single or plural number. The terms "including", "comprising", "having" and any variations thereof involved in the present invention are intended to cover non-exclusive inclusion; the terms "first", "second", "third", etc. involved in the present invention are only used to distinguish similar objects and do not represent a specific order for the objects.

[0041] The present invention proposes a novel integrated method for automated design and circuit construction of analog filters, which is used to efficiently solve analog filter problems and is applicable to Butterworth filters, Chebyshev filters and inverse Chebyshev filters with four frequency responses of low-pass, high-pass, band-pass and band-stop. This method can combine electrical network theory to accurately calculate the component parameters and node configurations of the filter circuit, ensuring that the circuit response characteristics are consistent with the design requirements. Through simulation and testing for characteristic verification and optimization, specific component parameters and transfer functions are finally output, providing reliable guidance from numerical design to actual circuit construction. It has important application value in fast response systems, communication systems, radar systems and satellite communication systems, provides an efficient and high-precision filter design solution, and has broad application prospects and far-reaching technical impacts.

[0042] Specifically, according to a specific embodiment of the present invention, as Figure 1 、 Figure 2 , the method of the present invention includes the following steps:

[0043] First step: Filter type selection and parameter input: According to specific application requirements, select the filter type (low-pass, high-pass, band-pass, band-stop) and filter characteristics (Butterworth, Chebyshev, inverse Chebyshev), and input relevant performance parameters, such as cut-off frequency, attenuation in the passband and stopband, bandwidth of the passband and stopband, etc.

[0044] Step 2: Determine design parameters such as the filter design order: According to the relevant theories of filter design, when the filter order is greater than 2, its transfer function H can be regarded as a cascade form of first-order and second-order transfer functions, that is:

[0045]

[0046] At the same time, calculate the adjustment factor according to the design requirements to adjust the case where the attenuation is not equal to the 3dB bandwidth during the design of the band-pass response, and optimize the adjustment of the first frequency value that satisfies the specified band-stop loss of the low-pass prototype filter during the design of the band-stop response.

[0047] Step 3: Select a standard prototype filter using the design parameters obtained in Step 2 to perform further calculations by adjusting and optimizing the parameters of the standard prototype filter.

[0048] Step 4: Normalize the actual design requirements to the specifications of the standard prototype filter.

[0049] The basis of filter normalization is that the response of a given filter can scale (shift to) the elements of the reactor to a different frequency range through a frequency scaling factor (FSF). The FSF is the ratio of the reference frequency of the desired response to the corresponding reference frequency of the given filter. Usually, the 3dB point is selected as the reference frequency for low-pass and high-pass filters, and the center frequency is selected as the reference for band-pass filters.

[0050] Step 5: Numerical Solution and Optimization: Numerically solve and optimize the selected filter type. This step utilizes the efficient numerical computing capabilities of the Julia programming language to implement filter design. After determining the requirements and objectives of the filter, the logic and calculation process of the filter are realized through the functions and algorithms of the Julia language. Filter design is a crucial step in analog signal processing, involving a large amount of mathematical calculations and algorithm optimizations. Traditional methods may affect the rapid response of the design process due to insufficient computing speed and efficiency when dealing with complex filter designs. The Julia language significantly improves the operation speed and efficiency of filter design through its high-performance computing capabilities. Specifically, the Julia language, through Just-In-Time (JIT) compilation technology and using the LLVM compiler framework, can achieve computing performance close to that of the C language. The JIT compilation technology of Julia can compile code into machine code during program execution, thus achieving an execution speed close to that of the hardware. The LLVM compiler framework provides a powerful foundation that enables the compiled code to run efficiently on different platforms. In addition, Julia not only performs excellently in single-core performance but also has powerful parallel computing capabilities. Julia has built-in support for multithreading and distributed computing, which can make full use of multi-core processors and computing clusters, enabling users to conveniently distribute computing tasks to multiple processor cores or multiple computing nodes, thereby achieving parallel computing, significantly improving the operation speed and efficiency, and meeting the requirements of analog filter design for rapid response. This parallel computing ability is particularly important for processing large-scale data and complex algorithms because it can decompose computing tasks into multiple subtasks and execute them in parallel, thus greatly shortening the computing time.

[0051] For Butterworth and Chebyshev filters, the algorithm automatically generates the required component parameters, circuit structure, and transfer function according to the input parameters. That is, for these two types of filters, both their transfer functions and the LC circuit structures corresponding to realizing their functions can be output. For inverse Chebyshev filters, only the mode with the output being the transfer function is provided.

[0052] For the first mode, that is, outputting the LC circuit structure realizing its function, impedance scaling is often required. Specifically, impedance scaling can be achieved through a resistor network or a transformer. Resistor networks are usually used for lower-frequency applications, while transformers are more suitable for occasions that need to operate in the high-frequency range. During this process, any linear active or passive network maintains its transfer function, and at the same time, the calculated filter can have a more excellent response curve and filtering performance.

[0053] Step 6: In the first mode, after obtaining the required component parameters, in combination with electrical network theory, specifically set the circuit nodes, place the corresponding components on the respective nodes, and complete the construction of the filter circuit. Electrical network theory realizes the automated conversion from numerical design to actual circuit construction by analyzing and synthesizing the topological structure of the circuit and the characteristics of its components.

[0054] In the second mode, first calculate the frequency ratio based on the filter frequency. If the filter parameters are not clearly defined in the design, use the frequency ratio to calculate the order, and the formula is as follows:

[0055]

[0056] Where:

[0057] A s is the stopband attenuation (dB).

[0058] A p is the passband attenuation (dB).

[0059] Ω s is the stopband cut-off frequency (in radians).

[0060] Ω p is the passband cut-off frequency (in radians).

[0061] After that, according to the passband attenuation and the filter order, calculate the scaling factor scale3dB for 3dB attenuation, whose function is to adjust the passband attenuation cut-off frequency so that the attenuation at the passband edge of the filter meets the design requirements. The calculation formula is:

[0062]

[0063] Where, A p is the passband attenuation (dB), and n is the filter order.

[0064] After obtaining the above parameters, it is also necessary to calculate the relevant parameters of the input and output impedances, including the input and output impedances, the impedance matching factor, and the square of the input and output reflection coefficients. After that, calculate the parameters of the transfer function according to the filter type, that is, the numerator and denominator coefficients of the transfer function.

[0065] Step 7: Filter characteristic verification and adjustment: Through simulation and testing, verify the characteristics of the generated filter circuit to ensure that it meets the design requirements. If there are deviations or the design requirements are not met, perform parameter adjustment through iterative optimization until the filter performance reaches the expected standard.

[0066] Step 8: Output the results required for the design. Specifically, for the mode of designing a specific filter circuit, calculate the specific parameter vectors of each required component and obtain the node parameters of each node in the circuit; for the mode of solving the transfer function, obtain the corresponding coefficients of the numerator and denominator polynomials of the transfer function fraction. Further, the S-parameters, group delay, etc. of the designed filter circuit can be solved using the obtained information.

[0067] In the above solution of the present invention, the Julia language and the integrated technology of circuit design and construction are introduced into the field of analog filter design for the first time. Especially after determining the requirements and objectives of the filter, the scientific computing language Julia is used to implement the logic and calculation process of the filter. Through Just-In-Time compilation and parallel computing capabilities, efficient numerical solution and optimization are achieved.

[0068] In the present invention, the electric network theory is applied to circuit construction, and according to the electric network theory, seamless transformation from design parameters to actual circuit construction is achieved. The specific steps are as follows:

[0069] 1) Component parameter calculation: According to the design requirements of the filter (such as cut-off frequency, passband and stopband characteristics, etc.), use standard prototype filters (such as Butterworth, Chebyshev, and inverse Chebyshev filters) to calculate the specific parameters of each component (such as inductors, capacitors, and resistors) in the filter circuit.

[0070] 2) Circuit building: Use the electric network theory to analyze and determine the overall response characteristics of the circuit. Through the relationship between node voltages and branch currents, establish a mathematical model of the filter circuit. According to the calculated component parameters, place each component accurately at the specific node positions in the circuit. Mark the node positions of the components in the circuit in detail to provide reliable technical guidance for the construction of the actual circuit.

[0071] 3) Verification and optimization: Verify the performance of the circuit in the simulation environment to ensure its response characteristics under the design requirements. According to the simulation results, further optimize the circuit design, adjust the component parameters and node configurations to achieve the best filtering effect.

[0072] In this design method, while meeting the basic design requirements, the present invention also adopts a modular design, which can significantly simplify the design process, accelerate the simulation speed, and achieve a highly modular functional construction. Specifically, the present invention decomposes the filter design process into several independent modules, each module corresponding to different design requirements and objectives, and processes them using a parallel design route. This modular design not only simplifies the design process but also improves the accuracy of the results. Through the independent operation and parallel calculation of the modules, the overall operation speed is significantly increased. The modular design also provides good scalability and maintainability, making the filter design process more flexible and efficient. With the development of technology and the change of requirements, designers can easily modify or replace a specific module without having to redesign the entire filter system. This flexibility enables the filter design to adapt to the ever-changing technical requirements and market demands. In addition, the modular design method makes the entire system easier to maintain and upgrade, improving the reliability and sustainability of the system.

[0073] In an embodiment of the present invention, the filter function module includes a filter initialization module, a filter design module, a circuit modeling module, a circuit simulation module, etc.; wherein the initialization module is used to calculate and store detailed parameter information of the filter; the filter design module is used to analyze design requirements and design the filter structure; the circuit modeling module forms a circuit by setting specific components through the designed circuit structure; the circuit simulation module is used to perform parameter simulation and performance evaluation based on the circuit structure.

[0074] After obtaining the circuit structure or transfer function of the filter by using the method of the present invention, the parameter information such as S-parameters and group delay can be further directly solved, which is convenient for carrying out other work. Specifically, once the circuit structure or transfer function of the filter is determined, the system can quickly and accurately calculate the S-parameters (scattering parameters), that is, the transmission characteristics of the circuit between different ports, by using the built-in numerical calculation function. S-parameters are crucial for understanding the transmission characteristics of the circuit, matching network design, and system integration. In addition, the system can also calculate the group delay, that is, the total delay generated when the signal passes through the filter, which is particularly important for the requirements of timing accuracy and signal processing. Through these automated calculations, engineers can quickly obtain key circuit characteristic parameters without manual deduction or through complex experimental measurements. This not only saves time and resources but also ensures the accuracy and consistency of the results. These calculation results provide important basic data and reference for subsequent work, such as system integration, performance optimization, and product verification. Therefore, this feature of the present invention not only reflects the application advantages of integrated computational design in filter design but also demonstrates the important role of the automated design process in improving work efficiency and result reliability.

[0075] The above examples of the present invention have the following advantages:

[0076] 1) High - efficiency calculation: After the method process, especially by utilizing the high - performance computing power of the Julia language, the computing speed and efficiency of filter design are significantly improved, enabling the handling of more complex design requirements.

[0077] 2) Modular design: Through the construction of modular functions, the design process is simplified and optimized, improving the design accuracy and running speed. At the same time, good scalability and maintainability are provided. Modular design also allows for flexible adjustment and optimization of the design process, enhancing the flexibility and accuracy of the design.

[0078] 3) Precise circuit construction: Based on electrical network theory, the filter circuit is accurately calculated and constructed to ensure that the response characteristics of the circuit are consistent with the design requirements, providing reliable technical support for the actual circuit construction. By detailedly marking the node positions of components in the circuit, clear construction guidance is provided.

[0079] When facing more complex and higher - order design requirements, the technology proposed by the present invention shows more significant advantages. By applying the method of the present invention, designers can more accurately and efficiently meet the various requirements of complex filter design, thus further promoting the development and application of analog filters in various complex applications. The present invention not only solves the problems of time and resource consumption in existing filter design methods, but also improves the overall efficiency and accuracy of filter simulation design through an innovative integrated design method, providing strong technical support for research and applications in related fields.

[0080] Taking a small filter as an example:

[0081] The design requirements of this filter are as follows: Design a 4 - th - order Butterworth band - pass filter with a pass - band attenuation of 10·log2, a pass - band frequency of [397500000, 402500000] in hertz (Hz), an output impedance of 139.9861 in ohms (Ω), and its output mode is to output the transfer function of the filter.

[0082] The first step: From the actually designed band - pass filter, determine the design parameters required in the design, such as pass - band attenuation, stop - band attenuation, pass - band frequency, stop - band range, etc. Band - pass filters can be divided into two categories: narrow - band and wide - band. If the ratio of the upper cut - off frequency to the lower cut - off frequency exceeds 2 (one octave), the filter is considered a wide - band type.

[0083] The second step: According to its specific design order requirements, it can be decomposed into a cascaded form of several filters.

[0084] Step 3: Select a standard prototype filter using the design parameters obtained in Step 2 to perform further calculations by adjusting and optimizing the parameters of the standard prototype filter. After determining the standard filter, its order can be calculated.

[0085] Step 4: Normalize the parameters of the standard prototype filter, calculate its frequency adjustment factor and impedance adjustment factor, and correct the response curve of the prototype filter.

[0086] Step 5: Since the transfer function of the filter is required, the mode is "transfer function acquisition". Calculate the frequency ratio of the filter according to the input frequency, and this parameter is used for subsequent calculation of the design order of the filter.

[0087] Efficiently solve the coefficients of the filter circuit or the specific transfer function. This process is implemented in the Julia language. Here, what needs to be calculated is the transfer function of the Butterworth band-pass filter, so the coefficients of the numerator and denominator polynomials of the transfer function need to be solved.

[0088] The design order of the filter has been clearly given in this example. Therefore, directly calculate the scaling factor of the 3dB attenuation according to the band-pass attenuation and the order, and adjust the passband cut-off frequency. Then calculate the input and output impedances, impedance matching factor, and reflection coefficient.

[0089] On this basis, calculate the positions of the poles. Specifically, adjust and correct the positions of the poles by solving the powers of the reflection coefficients, and solve the phase angles of the poles according to the filter order.

[0090] When calculating the numerator coefficients of the transfer function of the band-pass filter, the formula used is: numcoeff = [1, 2·rtnO2NsiAng·bw, 2·Cn + bw 2 ·rtnO2Nsq, 2·rtnO2NsiAng·bw·Cn, Cn 2

[0091] where rtnONsq is the power of the reflection coefficient, rtn02NsiAng is the sine value of the phase angle of the pole after adjusting the reflection coefficient, bw is the bandwidth of the frequency band, and Cn is the square value of the center frequency of the frequency band. Each parameter is in vector form.

[0092] When calculating the denominator coefficients of the transfer function of the band-pass filter, the formula used is:

[0093] denocoeff = [1, 2·siAng·bw, 2·C n +bw 2 , 2·siAng·bw·C n , C n 2 ​​

[0094] Among them, siAng is the sine value of the pole phase angle, bw is the frequency band width, and Cn is the square value of the center frequency of the frequency band. All parameters are in vector form.

[0095] Step 6: Decompose and calculate the matrix to further accelerate the solution process, and finally obtain the coefficients of the numerator and denominator.

[0096] Step 7: Assemble the transfer function of the filter according to each coefficient. The calculation formula is:

[0097]

[0098] Among them, l is the gain adjustment coefficient, and its calculation formula is:

[0099] l = bw n ·P div

[0100] Among them, P div is the impedance matching factor, bw is the bandwidth, and n is the filter order.

[0101] The rest, rtnONsq is the power of the reflection coefficient, rtn02NsiAng is the sine value of the pole phase angle after adjusting the reflection coefficient, θ k is the pole phase angle, bw is the frequency band width, and Cn is the square value of the center frequency of the frequency band.

[0102]

[0103] Among them, siAng is the sine value of the pole phase angle, bw is the frequency band width, and Cn is the square value of the center frequency of the frequency band. All parameters are in vector form.

[0104] Step 8: Backpropagate the designed parameters to calculate the implemented metrics, and adjust according to the difference from the target metrics to finally obtain the most suitable calculation result.

[0105] Step 9: Obtain the corresponding output result according to the design requirements, calculate the specific parameters of each component of the filter corresponding circuit model, and construct the filter circuit.

[0106] First, normalize the output impedance R of the filter loadNorm ;

[0107] Secondly, check the circuit topology structure and determine the impedance ratio impedScale that needs to be adjusted specifically;

[0108] Furthermore, calculate the numerical values of the components. The specific process is as follows:

[0109] 1. Calculate the impedance ratio:

[0110]

[0111] 2. Calculate the parameters γ = 1 and δ = (γ - R radio ) 1 / 2n , where n is the filter order;

[0112] 3. Calculate b terms and a terms

[0113]

[0114] where k is the serial number of the pole, and the value range is k = 1, 2,..., n, where n is the filter order.

[0115] 4. Calculate the normalized inductance and capacitance values according to b terms and a terms .

[0116] After obtaining the normalized capacitance and inductance, further calculate the scaling factors of the capacitance and inductance, and finally obtain the values of each component of the circuit that meet the design requirements. The normalized component values are based on the normalized frequency and impedance, and need to be further adjusted to meet the actual design requirements.

[0117] In the tenth step, the obtained filter circuit can be used to additionally calculate important parameters such as the S-parameters and group delay of the filter.

[0118] The time consumed by using the new method proposed by the present invention is 47 μs, while the time required to solve the same filter in the commercial software Matlab is 2.516907 s, as shown in Table 1. The method of the present invention is significantly less time-consuming than other commercial software. As Figure 3 、 Figure 4 are respectively the comparison diagrams of the S-parameters and group delay results of the designed filter with the calculation results of the commercial software, and it can be seen that the results are in good agreement. In addition, in some other embodiments of the present invention, the high efficiency and accuracy of the present invention are also confirmed, as shown in Figure 5 、 Figure 6 .

[0119] Table 1 Comparison of the efficiency of building filters by commercial software and the method of the present invention

[0120]

[0121] The above are only the embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail above with general descriptions and specific embodiments, on the basis of the present invention, some modifications or improvements can be made, which are obvious to those skilled in the art. Therefore, these modifications or improvements made without departing from the spirit of the present invention all fall within the scope of protection required by the present invention.

Claims

1. An automated design and circuit construction method for analog filters, characterized in that It includes the following steps: Step 1: Select the filter type and filter characteristics according to specific application requirements, and input relevant performance parameters; Step 2: Determine the design order and adjustment factor of the filter according to filter design theory; Step 3: Select a standard prototype filter using the design parameters in Step 2, and perform adjustment and parameter optimization; Step 4: Normalize the actual design requirements to the specifications of the standard prototype filter; Step 5: Perform numerical solution and optimization calculation to generate the required component parameters, circuit structure or transfer function; Step 6: After obtaining the component parameters, set the circuit nodes in combination with electric network theory to complete the construction of the filter circuit; Step 7: Verify the filter characteristics through simulation and testing, and perform parameter adjustment to meet the design requirements; Step 8: Output the design results, including the specific component parameters and node parameters of the filter, or the numerator and denominator polynomial coefficients of the transfer function fraction.

2. The method for automatic design and circuit construction of the analog filter according to claim 1, wherein Adopt modular design, that is, implement the entire process of the method based on functional modules. The functional modules include a filter initialization module, a filter design module, a circuit modeling module, and a circuit simulation module; among them, the initialization module is used to calculate and store the parameter information of the filter; the filter design module is used to analyze the design requirements and design the filter structure; the circuit modeling module forms a circuit by setting specific components through the designed circuit structure; The circuit simulation module is used to perform parameter simulation and performance evaluation based on the circuit structure.

3. The method for automatic design and circuit construction of an analog filter according to claim 1, characterized in that The filter type is low-pass, high-pass, band-pass or band-stop, and the filter characteristics are Butterworth filter, Chebyshev filter or inverse Chebyshev filter. The performance parameters include: cut-off frequency, attenuation of the passband and stopband, bandwidth of the passband and stopband.

4. The method for automated design and circuit construction of an analog filter according to claim 1, wherein When the filter design order is greater than 2, it is decomposed into a cascade form of several filters; at the same time, calculate the adjustment factor according to the design requirements to adjust the case where the attenuation is not equal to the 3dB bandwidth during the design of the band-pass response, and adjust the first frequency value that satisfies the specified band-stop loss of the low-pass prototype filter during the design of the band-stop response.

5. The method for automated design and circuit construction of an analog filter according to claim 1, wherein Specifically in Step 4, normalize the parameters of the standard prototype filter, calculate its frequency adjustment factor and impedance adjustment factor, and correct the response curve of the prototype filter.

6. The method for automatic design and circuit construction of an analog filter according to claim 1, characterized in that, In Step 5, perform numerical solution and optimization calculation on the selected filter type. For Butterworth and Chebyshev filters, there are two modes. The first mode is to automatically generate the required component parameters and circuit structure according to the input parameters, and the second mode is to output the transfer function. For inverse Chebyshev filters, there is only the second mode, that is, the mode of outputting the transfer function.

7. The method for automatic design and circuit construction of an analog filter according to claim 6, wherein Step 5 is implemented using the scientific computing language Julia.

8. The method for automatic design and circuit construction of an analog filter according to claim 6, characterized in that In the first mode, after obtaining the required component parameters, in combination with electric network theory, specifically set the circuit nodes, place the corresponding components on the corresponding nodes, and complete the construction of the filter circuit; In the second mode, first calculate the frequency ratio according to the filter frequency. If the filter parameters are not clearly defined in the design, use the frequency ratio to calculate the order, and the formula is as follows: Where: A s is the stopband attenuation, A p is the passband attenuation, Ω s is the stopband cut-off frequency, Ω p is the passband cut-off frequency; After that, according to the passband attenuation and the filter order, calculate the scaling factor scale3dB for 3dB attenuation, whose function is to adjust the passband attenuation cut-off frequency so that the attenuation of the filter at the passband edge meets the design requirements. The calculation formula is as follows: After obtaining the above parameters, it is also necessary to calculate the relevant parameters of the input and output impedances, including the input and output impedances, the impedance matching factor, and the square of the input and output reflection coefficients; after that, calculate the parameters of the transfer function according to the filter type, that is, the numerator and denominator coefficients of the transfer function.

9. The method for automatic design and circuit construction of an analog filter according to claim 1, wherein In step 7, verify the characteristics of the generated filter circuit through simulation and testing to ensure that it meets the design requirements. If there are deviations or it does not meet the design requirements, adjust the parameters through iterative optimization until the filter performance reaches the expected standard.

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