Filter detection method and apparatus, electronic device, and storage medium

Abstract

The present disclosure provides a filter detection method, device, electronic equipment and storage medium. The filter detection method comprises: obtaining an analog noisy periodic signal and a noise-free periodic signal; performing fixed-point operation on the noisy periodic signal based on an output bit width of a filter to obtain a fixed-point periodic signal; inputting the fixed-point periodic signal into the filter for processing to obtain an output signal; sampling the fixed-point periodic signal, the output signal and the noise-free periodic signal respectively to obtain pre-filtering data, post-filtering data and original data; and obtaining a first detection result of the filter based on the pre-filtering data, the post-filtering data and the original data. The filter detection method can avoid cross-software operation, the data processing process is simple, and the process of filter performance detection can be simplified.

Description

Technical Field

[0001] This disclosure pertains to the field of integrated circuit digital circuit design and relates to filter detection methods, particularly to filter detection methods, apparatus, electronic devices, and storage media. Background Technology

[0002] The raw data sampled by lidar contains noise and impurities, requiring filtering. CIC filters (cascaded integrator comb filters), as high-precision low-pass filters, are simple in structure and consume few resources, making them commonly used filters in digital circuit signal processing. Before using them as radar data processors, it is necessary to screen and test their filtering performance, including their allowed cutoff frequency (maximum frequency) and the proportion of noise after filtering. Testing the performance of CIC filters generally requires 1. excitation generation and 2. spectrum analysis. The traditional method for generating the raw excitation signal involves generating a fixed signal and a high-frequency interference signal in Matlab software, superimposing them to simulate the actual signal, and then saving it as a text file (usually in .csv or .txt format) through fixed-point conversion of floating-point numbers. The excitation file output by Matlab is then read using the `$readmem` function in Verilog (hardware description language). This method requires modifying and updating the excitation file in the Matlab script every time the excitation needs to be changed; furthermore, data analysis also requires Matlab, making it quite cumbersome. Alternatively, the signal can be generated by calling the Cordic (Corrected Rotation Digital Computer Algorithm) IP core. However, Cordic cannot generate real circuit signals with noise, the noise function still needs to be constructed separately, and the data analysis process still requires the use of third-party software. Both of these methods are quite cumbersome. Therefore, current filter detection methods suffer from the problems of complex cross-software operations and cumbersome data exchange in filter analysis. Summary of the Invention

[0003] The purpose of this disclosure is to provide a filter detection method, apparatus, electronic device, and storage medium to solve the problems of complex cross-software operation and cumbersome data exchange in existing filter detection methods.

[0004] In a first aspect, embodiments of this disclosure provide a filter detection method, comprising: acquiring a simulated noisy periodic signal and a noiseless periodic signal; performing fixed-point conversion on the noisy periodic signal based on the output bit width of the filter to obtain a fixed-point periodic signal; inputting the fixed-point periodic signal into the filter for processing to obtain an output signal of the filter; sampling the fixed-point periodic signal, the output signal, and the noiseless periodic signal respectively to obtain data before filtering, data after filtering, and original data; and obtaining a first detection result of the filter based on the data before filtering, the data after filtering, and the original data.

[0005] In one embodiment of this disclosure, a method for obtaining a first detection result of the filter based on the pre-filter data, the post-filter data, and the original data includes: performing difference processing on the pre-filter data, the post-filter data, and the original data respectively to obtain pre-filter difference data and post-filter difference data; obtaining the variance of the pre-filter difference data and the variance of the post-filter difference data based on the pre-filter difference data and the post-filter difference data; and obtaining the first detection result based on the variance of the pre-filter difference data and the variance of the post-filter difference data.

[0006] In one embodiment of this disclosure, obtaining the first detection result based on the variance of the difference data before filtering and the variance of the difference data after filtering includes: obtaining a variance comparison result based on the variance of the difference data before filtering and the variance of the difference data after filtering; and obtaining the first detection result based on the variance comparison result.

[0007] In one embodiment of this disclosure, the filter detection method further includes: obtaining the actual cutoff frequency of the filter based on the fixed-point periodic signal and the output signal; and obtaining a second detection result of the filter based on the actual cutoff frequency and the target cutoff frequency.

[0008] In one embodiment of this disclosure, obtaining the actual cutoff frequency of the filter based on the fixed-point periodic signal and the output signal includes: processing the fixed-point periodic signal and the output signal to obtain fixed-point periodic signal waveform data and output signal waveform data; and obtaining the actual cutoff frequency based on the fixed-point periodic signal waveform data and the output signal waveform data.

[0009] In one embodiment of this disclosure, obtaining a second detection result of the filter based on the actual cutoff frequency and the target cutoff frequency includes: comparing the actual cutoff frequency and the target cutoff frequency to obtain a comparison result of the actual cutoff frequency and the target cutoff frequency; and obtaining the second detection result based on the comparison result.

[0010] In one embodiment of this disclosure, a method for obtaining simulated noisy periodic signals and noiseless periodic signals includes: generating the noiseless periodic signal based on a signal generation function; and superimposing a Gaussian white noise function on the noiseless periodic signal to generate the simulated noisy periodic signal.

[0011] Secondly, embodiments of this disclosure provide a filter detection device, comprising: a signal acquisition module for acquiring simulated noisy periodic signals and noiseless periodic signals; a signal localization module for localizing the noisy periodic signals based on the output bit width of the filter to obtain localized periodic signals; a signal filtering module for inputting the localized periodic signals into the filter for processing to obtain the output signal of the filter; a signal sampling module for sampling the localized periodic signals, the output signal, and the noiseless periodic signals respectively to obtain data before filtering, data after filtering, and original data; and a result acquisition module for obtaining a first detection result of the filter based on the data before filtering, the data after filtering, and the original data.

[0012] Thirdly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the filter detection method described in any of the first aspects.

[0013] Fourthly, embodiments of this disclosure provide an electronic device, including: a memory; and a processor coupled to the memory and configured to perform the filter detection method according to any one of the first aspects.

[0014] As described above, the filter detection method, apparatus, electronic device, and storage medium of this application have the following beneficial effects:

[0015] The described filter detection method involves localizing a noisy periodic signal, filtering the localized periodic signal using a filter to obtain an output signal, and further sampling the localized periodic signal, the output signal, and the noise-free periodic signal to obtain pre-filter data, post-filter data, and original data. Based on these data, the filter detection result is obtained. This filter detection method avoids cross-software operations, simplifies the data processing, and streamlines the filter detection process. Attached Figure Description

[0016] Figure 1 The diagram shown is a schematic representation of the hardware structure of the filter detection method according to an embodiment of this disclosure.

[0017] Figure 2The flowchart shown is a filter detection method according to an embodiment of this disclosure.

[0018] Figure 3 The flowchart shown is a process for obtaining the first detection result of the filter based on the pre-filter data, the post-filter data, and the original data according to an embodiment of this disclosure.

[0019] Figure 4 The flowchart shown is an embodiment of this disclosure of obtaining the first detection result of the filter based on the variance of the difference data before filtering and the variance of the difference data after filtering.

[0020] Figure 5 The flowchart shown is a filter detection method according to an embodiment of this disclosure.

[0021] Figure 6 The flowchart shown is an embodiment of this disclosure of obtaining the actual cutoff frequency of the filter based on the fixed-point periodic signal and the output signal.

[0022] Figure 7 This is a flowchart illustrating how, according to an embodiment of this disclosure, a second detection result of the filter is obtained based on the filter's actual cutoff frequency and target cutoff frequency.

[0023] Figure 8 The flowchart shown is a method for obtaining simulated noisy periodic signals and noiseless periodic signals according to an embodiment of this disclosure.

[0024] Figure 9 The diagram shown is an architectural schematic of the filter detection method according to an embodiment of this disclosure.

[0025] Figure 10 The diagram shows a comparison of signal waveforms in embodiments of this disclosure.

[0026] Figure 11 The diagram shown is a schematic representation of the filter detection device according to an embodiment of this disclosure. Detailed Implementation

[0027] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. This disclosure can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0028] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. Therefore, the illustrations only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0029] The principles and implementation methods of the filter detection method according to the embodiments of this disclosure will be described in detail below, so that those skilled in the art can understand the filter detection method according to the embodiments of this disclosure without creative effort.

[0030] The technical solutions of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The filter detection method provided in the embodiments of this disclosure can be run on a computer terminal or similar device. Taking the operation on the computer terminal as an example, Figure 1 This is a hardware structure block diagram of the computer terminal, such as... Figure 1 As shown, a computer terminal may include a processor and a memory, wherein the processor may be a central processing unit and the memory is used to store data. Figure 1 The computer terminal shown is for illustrative purposes only and is not intended to limit the specific structure of the computer terminal.

[0031] In some implementations, the computer terminal may be a computer device equipped with digital simulation verification tools.

[0032] In some embodiments, the computer terminal may further include: communication transmission equipment and input / output equipment.

[0033] In some embodiments, the memory may be used to store computer programs, such as application software programs and modules. The memory may include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory may further include memory remotely located relative to the processor, which can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks (LANs), mobile communication networks, and combinations thereof.

[0034] In some embodiments, the communication transmission device can be used to receive or send data via a network, which may include a wireless network provided by the communication provider of the computer terminal. The communication transmission device may include a NIC (Network Interface Controller), which can be connected to other network devices via a base station to communicate with the Internet.

[0035] Figure 2This is a flowchart illustrating a filter detection method according to an embodiment of the present disclosure. Figure 2 As shown, the filter detection method includes:

[0036] S11, acquire the simulated noisy periodic signal and the noise-free periodic signal.

[0037] Optionally, the simulated noisy periodic signal and noiseless periodic signal can refer to the noisy periodic signal and noiseless periodic signal actually operating in the analog filter. This periodic signal can be a sine, cosine, or triangular wave periodic signal.

[0038] Optionally, the filter detection method can be implemented in a digital simulation verification platform. The method for obtaining simulated noisy periodic signals and noiseless periodic signals includes: generating the noisy periodic signals and noiseless periodic signals by calling external functions through a direct programming interface within the digital simulation verification platform.

[0039] Optionally, the digital simulation verification platform, i.e. the test platform, includes a declared SystemVerilog (system-level verification language) DPI-C (Direct Programming-C language) interface, which allows SystemVerilog to call functions in the C program, and the external functions can be functions in the C program.

[0040] S12, the noisy periodic signal is fixed-pointed based on the output bit width of the filter to obtain the fixed-pointed periodic signal.

[0041] Optionally, the method for fixing-pointing the noisy periodic signal based on the output bit width of the filter to obtain the fixed-pointed periodic signal includes: obtaining a quantization step size based on the noisy periodic signal and the output bit width; performing saturation processing on the noisy periodic signal to obtain a saturated periodic signal; and obtaining the fixed-pointed periodic signal based on the saturated periodic signal and the quantization step size. The output bit width may refer to the bit width of the filter output data.

[0042] Optionally, the quantization step size can be expressed as:

[0043] in, This indicates the quantization step size. This represents the maximum amplitude value of the noisy periodic signal. This indicates the output bit width.

[0044] Optionally, saturating the noisy periodic signal to obtain a saturated periodic signal may include: setting the amplitude of the noisy periodic signal that is greater than the maximum amplitude value as the maximum amplitude value, setting the amplitude of the noisy periodic signal that is less than the minimum amplitude value as the minimum amplitude value, and keeping the amplitude of the noisy periodic signal that does not exceed the maximum amplitude value and is not less than the minimum amplitude value unchanged.

[0045] Optionally, the amplitude value of the fixed-point periodic signal can be expressed as:

[0046]

[0047] in, This represents the amplitude value of the fixed-point periodic signal. This represents the amplitude value of the periodic signal subjected to saturation processing. Indicates rounding, used to round down... Convert to the nearest integer fixed-point value.

[0048] Optionally, the filter may include a CIC filter. The fixed-pointing is used to ensure that the bit width of the noisy periodic signal is consistent with that of the fixed-pointed periodic signal.

[0049] S13, the fixed-point periodic signal is input into the filter for processing to obtain the output signal of the filter.

[0050] S14, sample the fixed-point periodic signal, the output signal and the noise-free periodic signal respectively to obtain the data before filtering, the data after filtering and the original data.

[0051] Optionally, the method of sampling the fixed-point periodic signal, the output signal, and the noise-free periodic signal respectively to obtain the data before filtering, the data after filtering, and the original data includes: sampling the fixed-point periodic signal, the output signal, and the noise-free periodic signal respectively to obtain the data before filtering, the data after filtering, and the original data containing multiple sampling points.

[0052] S15, obtain the first detection result of the filter based on the data before filtering, the data after filtering, and the original data.

[0053] Optionally, the first detection result may include whether the filtering performance of the filter meets or does not meet the preset standard, and the first detection result may be a noise suppression degree detection result based on variance comparison.

[0054] Optionally, the following steps are performed in the digital simulation verification platform: the noisy periodic signal is fixed-pointed based on the output bit width of the filter; the fixed-pointed periodic signal is input into the filter for processing; the fixed-pointed periodic signal, the output signal, and the noise-free periodic signal are sampled respectively; and the first detection result of the filter is obtained based on the data before filtering, the data after filtering, and the original data.

[0055] As described above, the filter detection method includes: acquiring simulated noisy periodic signals and noiseless periodic signals; performing fixed-point normalization on the noisy periodic signals based on the output bit width of the filter to obtain fixed-point normalized periodic signals; processing the fixed-point normalized periodic signals by the filter to obtain an output signal; sampling the fixed-point normalized periodic signals, the output signal, and the noiseless periodic signals respectively to obtain pre-filter data, post-filter data, and original data; and obtaining a first detection result of the filter based on the pre-filter data, the post-filter data, and the original data.

[0056] The described filter detection method involves localizing a noisy periodic signal, filtering the localized periodic signal using a filter to obtain an output signal, and further sampling the localized periodic signal, the output signal, and the noise-free periodic signal to obtain pre-filter data, post-filter data, and original data. Based on these data, the filter detection result is obtained. This filter detection method avoids cross-software operations, simplifies the data processing, and streamlines the filter detection process.

[0057] Figure 3 This is a flowchart illustrating an embodiment of the present disclosure of a method for obtaining a first detection result of the filter based on the pre-filter data, the post-filter data, and the original data. Figure 3 As shown, the method includes:

[0058] S21, perform difference processing on the data before filtering, the data after filtering, and the original data respectively to obtain difference data before filtering and difference data after filtering.

[0059] S22, Based on the difference data before filtering and the difference data after filtering, obtain the variance of the difference data before filtering and the variance of the difference data after filtering.

[0060] S23, obtain the first detection result based on the variance of the difference data before filtering and the variance of the difference data after filtering.

[0061] Figure 4This is a flowchart illustrating an embodiment of the present disclosure of obtaining a first detection result of the filter based on the variance of the difference data before filtering and the variance of the difference data after filtering. For example... Figure 4 As shown, the method includes:

[0062] S31, Based on the variance of the difference data before filtering and the variance of the difference data after filtering, obtain the variance comparison result.

[0063] Optionally, the variance comparison result can be either the variance of the difference data before filtering and the variance of the difference data after filtering exceeding a first preset threshold, or the variance of the difference data before filtering and the variance of the difference data after filtering not exceeding a first preset threshold. When the variance of the difference data before filtering and the variance of the difference data after filtering exceed the first preset threshold, the variance of the difference data after filtering can be considered smaller. When the variance of the difference data before filtering and the variance of the difference data after filtering do not exceed the first preset threshold, the variance of the difference data after filtering can be considered larger. The first preset threshold can be flexibly set according to actual conditions, and this embodiment does not explicitly limit it.

[0064] S32, obtain the first detection result based on the variance comparison result.

[0065] Optionally, when the variance comparison result shows that the variance of the difference data before filtering and the variance of the difference data after filtering exceed a first preset threshold, the first detection result indicates that the filtering performance meets the preset standard; when the variance of the difference data before filtering and the variance of the difference data after filtering do not exceed the first preset threshold, the first detection result indicates that the preset standard has not been met. The first detection result can represent the result of the filter performance detection.

[0066] Figure 5 This is a flowchart illustrating a filter detection method according to an embodiment of the present disclosure. Figure 5 As shown, the method includes:

[0067] S41, Based on the fixed-point periodic signal and the output signal, obtain the actual cutoff frequency of the filter.

[0068] S42, based on the actual cutoff frequency and the target cutoff frequency of the filter, obtain the second detection result of the filter.

[0069] Optionally, the target cutoff frequency can be expressed as:

[0070]

[0071] in, Indicates the target cutoff frequency. Indicates the sampling frequency of the filter. This refers to the decimation factor. The decimation factor can be a downsampling factor determined based on the relationship between the filter's operating clock frequency and the sampling frequency.

[0072] Figure 6 This is a flowchart illustrating how, according to an embodiment of this disclosure, the actual cutoff frequency of the filter is obtained based on the fixed-point periodic signal and the output signal. For example... Figure 6 As shown, the method includes:

[0073] S51, the fixed-point periodic signal and the output signal are processed to obtain fixed-point periodic signal waveform data and output signal waveform data.

[0074] S52, based on the fixed-point periodic signal waveform data and the output signal waveform data, obtain the actual cutoff frequency.

[0075] Optionally, the fixed-point periodic signal can gradually decrease in frequency starting from a high frequency. The filter has the characteristic of suppressing high frequencies, and the actual cutoff frequency can be obtained based on the intact waveform data in the output signal waveform data. The actual cutoff frequency can refer to the frequency at which the output signal waveform data switches from a suppressed state to a pass state.

[0076] Optionally, the method for obtaining the actual cutoff frequency based on the fixed-point periodic signal waveform data and the output signal waveform data includes: obtaining the gain of each frequency point in the output signal waveform data based on the fixed-point periodic signal waveform data and the output signal waveform data; and obtaining the actual cutoff frequency based on the frequency point corresponding to the gain. The gain can be the ratio of the amplitude in the output signal waveform data corresponding to that frequency point to the amplitude in the fixed-point periodic signal waveform data, and the actual cutoff frequency can be the frequency corresponding to a gain of 0.707.

[0077] Figure 7 This is a flowchart illustrating how, according to an embodiment of this disclosure, a second detection result of the filter is obtained based on the filter's actual cutoff frequency and target cutoff frequency. For example... Figure 7 As shown, the method includes:

[0078] S61, compare the actual cutoff frequency and the target cutoff frequency to obtain a comparison result between the actual cutoff frequency and the target cutoff frequency.

[0079] Optionally, the comparison result can be that the difference between the actual cutoff frequency and the target cutoff frequency does not exceed a second preset threshold, or that the difference between the actual cutoff frequency and the target cutoff frequency exceeds the second preset threshold. The second preset threshold can be flexibly set according to the actual situation, and this embodiment does not explicitly limit it.

[0080] S62, obtain the second detection result based on the comparison result.

[0081] Optionally, the second detection result can be either meeting or failing to meet a preset standard. When the difference between the actual cutoff frequency and the target cutoff frequency does not exceed a second preset threshold, the second detection result is considered to have met the preset standard; when the difference exceeds the second preset threshold, the second detection result is considered to have failed to meet the preset standard. The second detection result can represent the result of detecting the filter's filtering effect, and it can be a frequency response characteristic detection result based on a cutoff frequency comparison.

[0082] Figure 8 This is a flowchart illustrating a method for obtaining simulated noisy periodic signals and noise-free periodic signals according to an embodiment of this disclosure. For example... Figure 8 As shown, the method includes:

[0083] S71, generate the noise-free periodic signal based on the signal generation function.

[0084] Optionally, the frequency of the noise-free periodic signal gradually decreases from a high frequency. The signal generation function can be flexibly set according to the actual situation, which will not be elaborated in this embodiment.

[0085] Optionally, the signal generation function can be:

[0086]

[0087] in, This represents the instantaneous value of the signal at any time t. Indicates the amplitude of the signal. The initial frequency is represented by t, and time is represented by t. This represents the rate of change of frequency.

[0088] S72, the Gaussian white noise function is superimposed on the noise-free periodic signal to generate the simulated noisy periodic signal.

[0089] In one embodiment of this disclosure, the filter detection method generates a simulated real excitation original signal by calling a C function in the verification environment -> the filter processes the original signal -> the signal output by the filter is sent to the performance analysis module to calculate the signal-to-noise ratio and cutoff frequency of the filtered signal. The specific technical approach includes: 1) Generating original signal data with frequency variations in the testbench based on the low-pass characteristics of the CIC filter; 2) Constructing a Gaussian white noise function in C language and superimposing it on the original periodic signal to simulate a noisy periodic signal in the actual circuit; 3) Connecting the CIC filter output data to the performance analysis module, comparing the original noisy signal with the CIC output signal, subtracting them from the original noiseless ideal signal, and performing digital circuit fixed-pointing to obtain two sets of data; 4) Calculating the variance of these two sets of data values ​​to determine the degree of improvement in the signal-to-noise ratio of the CIC output data. The smaller the variance of the CIC filter output data sets, the better the CIC filtering performance; 5) Observing the Verdi waveform window and comparing the output data with the original signal data one by one to determine its actual cutoff frequency; then calculating the theoretical cutoff frequency using the formula f / (2*decimation factor) (f is the CIC filter sampling frequency). Comparing the two, the closer the values ​​are, the better the CIC filtering effect and performance are considered. This method can realize the performance analysis and verification of the CIC filter through digital simulation on a hardware platform.

[0090] In one embodiment of this disclosure, Figure 9 The diagram shown is an architectural schematic of the filter detection method according to an embodiment of this disclosure. Figure 9 As shown, the filter detection method includes:

[0091] 1) Write a C source program to define the required signal excitation types as functions, with adjustable periods that vary over time;

[0092] 2) Write a Gaussian white noise function, which is essentially a normal distribution function following N(0,1). Set the signal-to-noise ratio (dB) as the input, and superimpose the mathematically ideal signal with the noise function.

[0093] 3) Set up the Testbench (test platform) verification environment and declare the DPI-C interface of SystemVerilog to allow SystemVerilog to call functions in C programs;

[0094] 4) In Testbench, call the signal generation function in the C source program, write the excitation module, and ensure that the signal gradually decreases in frequency from high frequency, while superimposing the noise function onto the original signal;

[0095] 5) Confirm the bit width of the output data of the designed CIC filter, and convert the original noisy signal to a fixed point using digital circuit floating-point conversion to ensure that it is consistent with the bit width of the CIC filter output data;

[0096] 6) Connect the generated noisy raw signal to the input of the CIC filter and detect the output data;

[0097] 7) Sample the output signal of the CIC filter and record the data before and after filtering respectively. Subtract the original noise-free ideal signal from the data and calculate the variance of the difference between the two sets of data. The smaller the variance of the filtered data set, the better the performance of the CIC filter.

[0098] 8) Connect the CIC filter under test as the DUT (Design Under Test) to the Testbench, perform VCS (VCS simulation tool) simulation, open Verdi (Verdi debugging tool), drag the data signals before and after filtering (which have been digitally fixed-pointed) into the waveform window, and observe the difference between the two signals;

[0099] 9) Since the original signal gradually decreases in frequency from high frequency, the CIC filter output will exhibit high frequency suppression characteristics in the early stage of simulation. When the frequency decreases to below the CIC filter cutoff frequency, a better waveform can be obtained. Record the frequency at this point.

[0100] 10) Calculate the theoretical cutoff frequency (f is the sampling frequency of the CIC filter) using the CIC filter cutoff frequency calculation formula f / (2*decimation factor), and compare it with the cutoff frequency shown in the actual waveform to obtain the analysis results.

[0101] Figure 10 The diagram shows a comparison of signal waveforms in an embodiment of this disclosure. The waveform comparison is as follows: Figure 10 As shown, this embodiment will not elaborate further.

[0102] This embodiment replaces the traditional filter analysis with a purely digital design verification environment and hardware design, which suffers from complex cross-software operations and cumbersome data communication. This simplifies the CIC filter performance evaluation process and facilitates real-time modification and optimization of the CIC filter.

[0103] The scope of protection of the filter detection method described in this disclosure is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this disclosure is included within the scope of protection of this disclosure.

[0104] Figure 11 This is a schematic diagram illustrating the structure of a filter detection device according to an embodiment of the present disclosure. Figure 11 As shown, the device includes:

[0105] The signal acquisition module 1110 is used to acquire analog noisy periodic signals and noiseless periodic signals.

[0106] The signal localization module 1120 is used to localize the noisy periodic signal based on the output bit width of the filter to obtain the localized periodic signal.

[0107] The signal filtering module 1130 is used to input the fixed-point periodic signal into the filter for processing in order to obtain the output signal of the filter.

[0108] The signal sampling module 1140 is used to sample the fixed-point periodic signal, the output signal and the noise-free periodic signal respectively to obtain the data before filtering, the data after filtering and the original data.

[0109] The result acquisition module 1150 is used to acquire the first detection result of the filter based on the data before filtering, the data after filtering, and the original data.

[0110] The signal acquisition module 1110 and Figure 2 The steps S11 shown correspond one-to-one, and the signal positioning module 1120 and Figure 2 The steps S12 shown correspond one-to-one, and the signal filtering module 1130 and Figure 2 The steps S13 shown correspond one-to-one, and the signal sampling module 1140 and Figure 2 The steps S14 shown correspond one-to-one, and the result acquisition module 1150 and Figure 2 The steps S15 shown correspond one-to-one.

[0111] In the several embodiments provided in this disclosure, it should be understood that the disclosed apparatus or method can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0112] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this disclosure, depending on actual needs. For example, the functional modules / units in the various embodiments of this disclosure may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0113] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0114] This embodiment provides an electronic device, which includes a memory and a processor coupled to the memory and configured to perform a filter detection method.

[0115] This disclosure also provides a computer-readable storage medium. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0116] This disclosure also provides a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this disclosure are generated. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0117] When the computer program product is executed by a computer, the computer performs the method described in the foregoing method embodiments. The computer program product can be a software installation package; when the foregoing method is required, the computer program product can be downloaded and executed on the computer.

[0118] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0119] The above embodiments are merely illustrative of the principles and effects of this disclosure and are not intended to limit this disclosure. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this disclosure. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this disclosure should still be covered by the claims of this disclosure.

Claims

1. A filter detection method, characterized in that, include: Acquire simulated noisy periodic signals and noise-free periodic signals; The noisy periodic signal is fixed-pointed based on the output bit width of the filter to obtain the fixed-pointed periodic signal. The fixed-point periodic signal is input into the filter for processing to obtain the output signal of the filter; The fixed-point periodic signal, the output signal, and the noise-free periodic signal are sampled respectively to obtain data before filtering, data after filtering, and original data. The first detection result of the filter is obtained based on the data before filtering, the data after filtering, and the original data.

2. The filter detection method according to claim 1, characterized in that, The method for obtaining the first detection result of the filter based on the pre-filter data, the post-filter data, and the original data includes: The data before filtering, the data after filtering, and the original data are respectively subjected to difference processing to obtain the difference data before filtering and the difference data after filtering; Based on the difference data before filtering and the difference data after filtering, obtain the variance of the difference data before filtering and the variance of the difference data after filtering; The first detection result is obtained based on the variance of the difference data before filtering and the variance of the difference data after filtering.

3. The filter detection method according to claim 2, characterized in that, Obtaining the first detection result based on the variance of the difference data before filtering and the variance of the difference data after filtering includes: Based on the variance of the difference data before filtering and the variance of the difference data after filtering, obtain the variance comparison result; The first detection result is obtained based on the variance comparison result.

4. The filter detection method according to claim 1, characterized in that, The filter detection method further includes: Based on the fixed-point periodic signal and the output signal, the actual cutoff frequency of the filter is obtained; Based on the actual cutoff frequency and the target cutoff frequency of the filter, the second detection result of the filter is obtained.

5. The filter detection method according to claim 4, characterized in that, Based on the fixed-point periodic signal and the output signal, obtaining the actual cutoff frequency of the filter includes: The fixed-point periodic signal and the output signal are processed to obtain fixed-point periodic signal waveform data and output signal waveform data; The actual cutoff frequency is obtained based on the fixed-point periodic signal waveform data and the output signal waveform data.

6. The filter detection method according to claim 4, characterized in that, Based on the actual cutoff frequency and the target cutoff frequency of the filter, the second detection result of the filter is obtained as follows: The actual cutoff frequency and the target cutoff frequency are compared to obtain a comparison result between the actual cutoff frequency and the target cutoff frequency; The second detection result is obtained based on the comparison result.

7. The filter detection method according to claim 1, characterized in that, Methods for obtaining simulated noisy periodic signals and noise-free periodic signals include: The noise-free periodic signal is generated based on the signal generation function; The Gaussian white noise function is superimposed on the noise-free periodic signal to generate the simulated noisy periodic signal.

8. A filter detection device, characterized in that, include: The signal acquisition module is used to acquire analog noisy periodic signals and noiseless periodic signals; The signal localization module is used to localize the noisy periodic signal based on the output bit width of the filter to obtain the localized periodic signal. A signal filtering module is used to input the fixed-point periodic signal into the filter for processing in order to obtain the output signal of the filter. The signal sampling module is used to sample the fixed-point periodic signal, the output signal and the noise-free periodic signal respectively to obtain data before filtering, data after filtering and original data. The result acquisition module is used to acquire the first detection result of the filter based on the data before filtering, the data after filtering, and the original data.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the filter detection method according to any one of claims 1-7.

10. An electronic device, characterized in that, include: Memory; A processor, coupled to the memory, is configured to perform the filter detection method according to any one of claims 1-7.

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