A signal jitter separation method, device, medium and equipment

Abstract

The present application discloses a signal jitter separation method, device, medium and equipment, relating to the technical field of digital signal processing. The present application performs jitter separation based on the spectrum separation method, quantifies the serial signal and performs clock recovery, and then resamples using the time interval error data, jitter timestamp data and clock signal. Since it is the best fit for the number of single frequencies, it is necessary to first fit the sequence of the number of multi-tone signals that may be included in the signal, and finally fit the optimal number of single frequencies. Since the optimal model of the Bayesian information criterion is used for fitting, both the goodness of fit of the model and the complexity of the model are considered, avoiding overfitting, ensuring faster calculation while taking into account the accuracy of the calculation, and being more flexible at the same time. After determining the target number of single frequencies, when extracting the mixed single-frequency information, it can greatly reduce the calculation time, more accurately and quickly separate the jitter information, and improve the effect of jitter separation in serial bus communication.

Description

Technical Field

[0001] This application relates to the technical field of digital signal processing, and particularly to a signal jitter separation method, device, medium and equipment. Background Art

[0002] In serial bus communication, with the continuous increase of data communication rate, jitter is one of the main reasons affecting the data transmission of high-speed serial links. In practice, jitter is caused by various reasons, so the components of jitter are also diverse. It is necessary to analyze signal jitter efficiently and accurately, trace the root cause, and take measures based on the jitter analysis results to reduce the impact of jitter; in addition, the transmission system can be diagnosed and debugged through different jitter components.

[0003] According to the characteristics of jitter and its formation reasons, jitter can be divided into random jitter and deterministic jitter. Among them, deterministic jitter can be divided into data-dependent jitter DDJ, periodic jitter and bounded uncorrelated jitter. When there is no crosstalk or bounded uncorrelated jitter, the spectrum separation method can be used to separate random jitter and periodic jitter. After resampling the jitter sequence, the spectrum peak is modeled as a sine curve and the sum of the identified sine curves is returned to calculate the periodic jitter. The specific steps are as follows: clock recovery, and obtaining jitter timestamps; time interval error TIE calculation; resampling the jitter timestamps and TIE; extracting the mixed single-frequency information according to the input amplitude threshold and the maximum number of single frequencies. After extracting the mixed single-frequency signal, the peak-to-peak value of the periodic jitter is obtained by taking the extreme difference of the derived time signal. The root mean square value of the derived time signal is taken to obtain the value of the periodic jitter; the root mean square of the difference between the resampled time signal and the derived time signal is taken to obtain the random jitter .

[0004] When the above method extracts the mixed single-frequency information, the maximum number of single frequencies is artificially specified by the developer. This number will affect the extraction time. The larger the maximum number of single frequencies, the longer the calculation time, and sometimes it will reach more than one second, which will affect the system performance in a high-speed sampling system; if the maximum number of single frequencies is set smaller, the accuracy of jitter separation will be lower; in addition, for different serial signals, the maximum number of single frequencies will be different, and this artificially determined method seriously affects the effect of jitter separation. Summary of the Invention

[0005] In summary, the main purpose of this application is to provide a signal jitter separation method, device, medium and equipment, aiming to solve the problem of poor jitter separation effect in the existing technology for serial bus communication.

[0006] To achieve the above object, the technical solution adopted in the embodiments of the present application is as follows:

[0007] In a first aspect, an embodiment of the present application provides a signal jitter separation method, including the following steps:

[0008] Perform clock recovery on the signal data to obtain a clock signal; wherein, the signal data is the quantization data of a serial signal;

[0009] Resample the signal data according to the time interval error data, jitter timestamp data, and clock signal of the signal data to obtain resampled signal data;

[0010] Fit the number of multi-tone signals in the amplitude value sequence of the resampled signal data to obtain a fitted single-frequency number sequence;

[0011] Based on the optimal model of the Bayesian information criterion, fit the fitted single-frequency number sequence to obtain the target single-frequency number;

[0012] Extract the mixed single-frequency information according to the target single-frequency number, and separate the jitter information according to the extraction result.

[0013] In a possible implementation manner of the first aspect, resampling the signal data according to the time interval error data, jitter timestamp data, and clock signal of the signal data to obtain resampled signal data includes:

[0014] Using the time interval error data of the signal data as the amplitude and the jitter timestamp data as the time, resample the signal data at the sampling interval of the clock signal to obtain resampled signal data.

[0015] In a possible implementation manner of the first aspect, before fitting the number of multi-tone signals in the amplitude value sequence of the resampled signal data to obtain a fitted single-frequency number sequence, the method further includes:

[0016] Determine the fitting method according to the value range of the maximum number of multi-tone signals;

[0017] Fitting the number of multi-tone signals in the amplitude value sequence of the resampled signal data to obtain a fitted single-frequency number sequence includes:

[0018] According to the fitting method, fit the number of multi-tone signals in the amplitude value sequence of the resampled signal data to obtain a fitted single-frequency number sequence.

[0019] In a possible implementation manner of the first aspect, determining the fitting method according to the value range of the maximum number of multi-tone signals includes:

[0020] According to the value of the maximum number of multi-tone signals within the first value range, determine that the fitting method is linear fitting;

[0021] Determine the fitting methods as linear fitting and logarithmic fitting according to the value of the maximum number of multi-tone signals within the second value range.

[0022] In a possible implementation manner of the first aspect, before obtaining the target single-frequency number by fitting the single-frequency number sequence based on the Bayesian information criterion optimal model, the method further includes:

[0023] Obtain the root mean square of random jitter according to the single-frequency number sequence of fitting;

[0024] Obtain the model likelihood function according to the sample quantity and the root mean square of random jitter;

[0025] Obtain the Bayesian information criterion optimal model according to the model calculation formula and the model likelihood function.

[0026] In a possible implementation manner of the first aspect, obtaining the root mean square of random jitter according to the single-frequency number sequence of fitting includes:

[0027] Adopt the method of extracting mixed single-frequency information to separate single-frequency signals from the single-frequency number sequence of fitting to obtain single-frequency signals;

[0028] Obtain the root mean square of random jitter according to the time-domain data of the single-frequency signals.

[0029] In a possible implementation manner of the first aspect, extracting mixed single-frequency information according to the target single-frequency number and separating jitter information according to the extraction result includes:

[0030] Extract mixed single-frequency information according to the target single-frequency number to obtain target single-frequency signals;

[0031] Perform time-domain addition on the target single-frequency signals to obtain an addition result;

[0032] Separate periodic jitter information according to the addition result;

[0033] Separate random jitter information according to the root mean square of the subtraction result between the amplitude value sequence of the resampled signal data and the target single-frequency signals.

[0034] In a second aspect, an embodiment of the present application provides a signal jitter separation device, including:

[0035] A recovery module, which is used to perform clock recovery on signal data to obtain a clock signal; wherein, the signal data is quantization data of a serial signal;

[0036] A resampling module, which is used to resample signal data according to the time interval error data, jitter timestamp data of the signal data, and the clock signal to obtain resampled signal data;

[0037] A first fitting module, which is used to fit the number of multi-tone signals in the amplitude value sequence of the resampled signal data to obtain a fitted single-frequency number sequence;

[0038] A second fitting module, which is used to fit the fitted single-frequency number sequence based on the optimal model of the Bayesian information criterion to obtain the target single-frequency number;

[0039] A separation module, which is used to extract mixed single-frequency information according to the target single-frequency number and separate the jitter information according to the extraction result.

[0040] In a third aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when loaded and executed by a processor, implements the signal jitter separation method provided in any one of the first aspects above.

[0041] In a fourth aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, wherein,

[0042] The memory is used to store a computer program;

[0043] The processor is used to load and execute the computer program so that the electronic device executes the signal jitter separation method provided in any one of the first aspects above.

[0044] Compared with the prior art, the beneficial effects of the present application are:

[0045] A signal jitter separation method, device, medium and equipment proposed in an embodiment of the present application. The method includes: performing clock recovery on signal data to obtain a clock signal, where the signal data is quantization data of a serial signal; resampling the signal data according to the time interval error data, jitter timestamp data and clock signal of the signal data to obtain resampled signal data; fitting the number of multi-tone signals in the amplitude value sequence of the resampled signal data to obtain a fitted single-frequency number sequence; fitting the fitted single-frequency number sequence based on the Bayesian information criterion optimal model to obtain the target single-frequency number; extracting mixed single-frequency information according to the target single-frequency number and separating jitter information according to the extraction result. The present application performs jitter separation based on the spectrum separation method, quantizes the serial signal and performs clock recovery, and then resamples according to the time interval error data, jitter timestamp data and clock signal. Since the best fit is performed on the number of single frequencies, it is necessary to first fit the sequence of the possible number of multi-tone signals in the signal, and finally fit the best number of single frequencies. Since the Bayesian information criterion optimal model is used for fitting, both the goodness of fit of the model and the complexity of the model are considered, avoiding overfitting, ensuring faster calculation operation while taking into account the accuracy of the calculation, and being more flexible at the same time. After determining the target single-frequency number, when extracting mixed single-frequency information, the calculation time can be greatly reduced, and the jitter information can be separated more accurately and quickly, improving the effect of jitter separation in serial bus communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic structural diagram of an electronic device for the hardware operating environment involved in an embodiment of the present application;

[0047] Figure 2 It is a schematic flowchart of the signal jitter separation method provided by an embodiment of the present application;

[0048] Figure 3 It is a curve graph of the maximum single-frequency number sequence fitted in the signal jitter separation method provided by an embodiment of the present application;

[0049] Figure 4 It is a waveform graph of the input signal after sampling and quantization in the test of the method of the present application;

[0050] Figure 5 It is a spectrogram of the resampled signal fitted by the method of the present application and a spectrogram of the separated periodic jitter;

[0051] Figure 6 It is a module schematic diagram of the signal jitter separation device provided by an embodiment of the present application;

[0052] Reference numerals in the figure: 101 - processor, 102 - communication bus, 103 - network interface, 104 - user interface, 105 - memory. Detailed implementation manners

[0053] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] Referring to the appended Figure 1 appendix Figure 1 is a schematic structural diagram of an electronic device in the hardware operating environment involved in the embodiment solution of the present application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. Among them, the communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 104 may further include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WI-FI) interface). The memory 105 may optionally be a storage device independent of the foregoing processor 101. The memory 105 may be a high-speed random access memory (Random Access Memory, RAM) memory, or may be a stable non-volatile memory (Non-Volatile Memory, NVM), such as at least one disk memory; the processor 101 may be a general-purpose processor, including a central processor, a network processor, etc., and may also be a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0055] Those skilled in the art can understand that the structure shown in the appended Figure 1 does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or combine some components, or arrange different components.

[0056] As shown in the appended Figure 1 figure, the memory 105 as a storage medium may include an operating system, a network communication module, a user interface module, and a signal jitter separation device.

[0057] In the appended Figure 1In the electronic device shown, the network interface 103 is mainly used for data communication with a network server; the user interface 104 is mainly used for data interaction with a user; in this application, the processor 101 and the memory 105 can be arranged in the electronic device, and the electronic device calls the signal jitter separation device stored in the memory 105 through the processor 101 and executes the signal jitter separation method provided by the embodiments of this application.

[0058] Referring to the attached Figure 2 , based on the hardware device of the foregoing embodiment, an embodiment of this application provides a signal jitter separation method, which is applicable to separating the jitter components of a serial signal without crosstalk or bounded uncorrelated jitter, and includes the following steps:

[0059] S10: Perform clock recovery on the signal data to obtain a clock signal; wherein, the signal data is the quantization data of the serial signal.

[0060] In the specific implementation process, clock recovery can be processed by means such as a constant clock, a first-order phase-locked loop, a second-order phase-locked loop, etc., and the recovered clock signal is used for the next step of processing. Before this, it is necessary to convert the serial signal and quantize it into data that can be processed, that is: before performing clock recovery on the signal data to obtain a clock signal, the method further includes:

[0061] Quantize the serial signal to obtain signal data.

[0062] The analog serial signal can be converted from digital to analog by using a digital oscilloscope or a data acquisition card and then quantized into data that can be processed.

[0063] S20: Resample the signal data according to the time interval error data, jitter timestamp data, and clock signal of the signal data to obtain resampled signal data.

[0064] In the specific implementation process, linear resampling can be used for resampling. Specifically, resampling the signal data according to the time interval error data, jitter timestamp data, and clock signal of the signal data to obtain resampled signal data includes:

[0065] Using the time interval error data of the signal data as the amplitude and the jitter timestamp data as the time, resample the signal data at the sampling interval of the clock signal to obtain resampled signal data.

[0066] Perform time interval error analysis using the signal data of the foregoing steps, and obtain that the time interval error TIE data stream is , is the number of TIE elements; obtain the jitter timestamp data , is the number of elements. Taking is the amplitude, is the time. The data point is linearly resampled at the restored clock as the sampling interval, and the amplitude value sequence of the sampled data sequence is , is the number of Y elements.

[0067] S30: Fit the number of multi-tone signals in the amplitude value sequence of the resampled signal data to obtain a sequence of fitted single-frequency numbers.

[0068] In the specific implementation process, since the best fit is performed on the number of multi-tone signals in the amplitude value sequence of the resampled signal data, it is necessary to first fit the sequence of the possible number of multi-tone signals in the signal , that is, the sequence of fitted single-frequency numbers. For different sequences, a maximum number of multi-tone signals is allowed to be specified , if not specified, the maximum number of multi-tone signals .

[0069] In one embodiment, before fitting the number of multi-tone signals in the amplitude value sequence of the resampled signal data to obtain a sequence of fitted single-frequency numbers, the method further includes:

[0070] Determine the fitting method according to the value range of the maximum number of multi-tone signals;

[0071] Fitting the number of multi-tone signals in the amplitude value sequence of the resampled signal data to obtain a sequence of fitted single-frequency numbers includes:

[0072] According to the fitting method, fit the number of multi-tone signals in the amplitude value sequence of the resampled signal data to obtain a sequence of fitted single-frequency numbers.

[0073] In the specific implementation process, the sequence of fitted single-frequency numbers is calculated according to the value range of the maximum number of multi-tone signals . The fitting method is a combination of linear fitting and logarithmic fitting. Different intervals use different fitting methods. Specifically, determining the fitting method according to the value range of the maximum number of multi-tone signals includes:

[0074] According to the value of the maximum number of multi-tone signals within the first value range, determine that the fitting method is linear fitting;

[0075] According to the value of the maximum number of multi-tone signals within the second value range, determine that the fitting method is a combination of linear fitting and logarithmic fitting.

[0076] For different serial signals, the maximum number of single frequencies will be different, but generally it will not be greater than 100. Different fitting methods are adopted by dividing intervals. For example, 14 is selected as the demarcation point for interval division. The value range of L is from 1 to 14, that is, within the first value interval, linear fitting is adopted. From 15 to 100, that is, within the second value interval, a method combining linear and logarithmic fitting is adopted. According to the value interval of, there are the following three cases:

[0077] 1) When is

[0078] linear fitting is adopted, the length of is , and the single-frequency number sequence is determined by the following formula:

[0079]

[0080] 2) When is

[0081] the length of is , and the data consists of two parts. The first 14 points are the results of linear fitting, and the last points are obtained by logarithmic fitting. The single-frequency number sequence sequence values are determined by the following formula:

[0082]

[0083] 3) When is

[0084] the length of is 25, and the data consists of two parts. The first 14 points are the results of linear fitting, and the last 11 points are obtained by logarithmic fitting. The sequence values are determined by the following formula:

[0085]

[0086] When is, it conforms to the regulations in 3), and the curve of the maximum single-frequency number sequence fitted is as shown in Appendix Figure 3 .

[0087] S40: Fit the single-frequency number sequence based on the optimal model of the Bayesian information criterion to obtain the target single-frequency number.

[0088] In the specific implementation process, the calculation formula of the optimal model BIC based on the Bayesian information criterion is:

[0089]

[0090] In the above formula, is the maximum likelihood estimate value of the model on the data; is the number of free parameters in the model; is the number of samples. Since the BIC optimal model needs to compare multiple models and then select the model with the smallest BIC value, the single-frequency number sequence fitted in the foregoing steps is in the above formula. In this step, fitting continues to determine the target single-frequency number, that is, the optimal maximum single-frequency number.

[0091] In one embodiment, before obtaining the target single-frequency number by fitting the single-frequency number sequence based on the Bayesian information criterion optimal model, the method further includes:

[0092] Obtain the root mean square of random jitter according to the single-frequency number sequence;

[0093] Obtain the model likelihood function according to the sample quantity and the root mean square of random jitter;

[0094] Obtain the Bayesian information criterion optimal model according to the model calculation formula and the model likelihood function.

[0095] In the specific implementation process, due to the BIC optimal model fitting method, in the above formula, it is necessary to determine , , three parameters, where is the number of parameters in the model, and is obtained from one of the three calculation formulas for ; is an element in is the sample quantity, which is determined by the number of elements of after resampling; ; is the model likelihood function. The likelihood function in this application is defined as follows:

[0096]

[0097] In the above formula, is the root mean square value of random jitter after determining the maximum single-frequency number in is the number of samples. Therefore, is related to the maximum single-frequency number included in the data. Specifically, obtaining the root mean square of random jitter according to the single-frequency number sequence includes:

[0098] Adopt the method of extracting mixed single-frequency information to separate the single-frequency signals from the single-frequency number sequence to obtain single-frequency signals;

[0099] Obtain the root mean square of random jitter based on the time-domain data of the single-frequency signal.

[0100] Let the number of single frequencies be When there are It is determined by the following steps:

[0101] 1) Use the method of extracting mixed single-frequency information to Separate single-frequency signals. The time-domain data of the separated single-frequency signals are: DTA1, DTA2, ……, DTAk, and the length of each time-domain data is M;

[0102] 2) Obtain the time-domain data after separating single-frequency signals as :

[0103]

[0104] 3) Calculate the root mean square value of

[0105] ;

[0106] In the formula represents taking the root mean square value of the data sequence in the brackets.

[0107] According to the above method, determine the root mean square value and then combine it with the sample quantity That is, obtain the model likelihood function, and substitute it into the calculation formula of BIC to get the BIC model as:

[0108]

[0109] According to the fitted data sequence and the expression of the BIC model, perform the best single-frequency number fitting on the sequence according to the following steps:

[0110] The first step: Let , ;

[0111] The second step: Let , assume that the maximum number of single frequencies in the data sequence is , and separate single-frequency signals according to the previous method for single-frequency signals. Calculate the root mean square value of and obtain the The value, according to the expression of the BIC model, the maximum number of single frequencies obtained is at value.

[0112] Step 3: If , then let 、 , repeat Step 2; if , then proceed to Step 4.

[0113] Step 4: Arrange the values fitted in Step 2 in sequence to form an array, and find the index number of the position where the smallest element in is located as .

[0114] Step 5: Obtain the best number of single frequencies in the sequence :

[0115]

[0116] S50: Extract the mixed single-frequency information according to the target number of single frequencies, and separate the jitter information according to the extraction result.

[0117] In the specific implementation process, after determining the maximum number of single frequencies, the jitter information is separated based on the spectrum separation method. The separation results include periodic jitter and random jitter. Specifically, the mixed single-frequency information is extracted according to the target number of single frequencies, and the jitter information is separated according to the extraction result, including:

[0118] Extract the mixed single-frequency information according to the target number of single frequencies to obtain the target single-frequency signal;

[0119] Perform time-domain addition on the target single-frequency signal to obtain the addition result;

[0120] Separate the periodic jitter information according to the addition result;

[0121] Separate the random jitter information according to the root mean square of the subtraction result between the amplitude value sequence of the resampled signal data and the target single-frequency signal.

[0122] In the specific implementation process, according to the maximum number of single frequencies included in the data sequence fitted, each single-frequency signal is extracted by using the method of extracting mixed single-frequency information. The time-domain addition is performed on the extracted multiple single-frequency signals. The peak-to-peak value of the periodic jitter is the maximum value minus the minimum value of the addition result, and its root mean square value is the root mean square value of the periodic jitter; use the data sequence to subtract all the separated single-frequency signals, and the root mean square value of the subtraction result is the separated random jitter result.

[0123] In this embodiment, jitter separation is performed based on the spectrum separation method, the serial signal is quantized and clock recovery is performed, and then resampling is performed using the time interval error data, jitter timestamp data, and clock signal. Since the best fit is performed for the number of single frequencies, it is necessary to first fit the sequence of the number of multi-tone signals that may be included in the signal, and finally fit the optimal number of single frequencies. Since the Bayesian information criterion optimal model is used for fitting, both the goodness of fit of the model and the complexity of the model are considered, overfitting is avoided, the calculation accuracy is taken into account while ensuring faster calculation operation, and at the same time it is more flexible. After determining the target number of single frequencies, when extracting the mixed single-frequency information, the calculation time can be greatly reduced, the jitter information can be separated more accurately and quickly, and the effect of jitter separation in serial bus communication is improved.

[0124] The following further illustrates this application in combination with actual engineering tests:

[0125] (1)Test conditions

[0126] 1) The input signal is: a 100 MHz square wave clock signal;

[0127] 2) Jitter is added to the input signal: the mean of random jitter is 0 us, the standard deviation is 25 ps, and the random seed is 3; for periodic jitter 1, the frequency is 10 MHz, the amplitude is 200 ps, and the phase is 0°; for periodic jitter 2, the frequency is 20 MHz, the amplitude is 100 ps, and the phase is 0°.

[0128] 3) Sampling information: the sampling rate is 100 GSa / s, the sampling time is 40 us, and the waveform diagram of the input signal after sampling quantization is as shown in the appendix Figure 4 as follows.

[0129] (2)Test content

[0130] The following two tests are performed on this application:

[0131] 1) The first one is to separate the peak-to-peak value of the periodic jitter of the input signal through this application 、take the root mean square value to obtain the value of the periodic jitter, and take the root mean square of the subtraction result to obtain the random jitter .

[0132] The test results are 、 、 , and the jitter separation result is roughly the same as the input signal, indicating the correctness of the result.

[0133] The spectrogram of the resampled signal after fitting through this application and the spectrogram of the separated periodic jitter are as shown in the appendix Figure 5 as follows, appendix Figure 5The spectral component of the medium-period jitter is 2, indicating that the maximum number of optimal single frequencies after fitting by this application is 2, which matches the period jitter of the input signal, indicating the correctness of this application.

[0134] 2) The second is the time comparison between jitter separation of the input signal with a fixed maximum number of single frequencies and jitter separation by this application.

[0135] The calculation time of this application is: about 30 ms.

[0136] The calculation time with a fixed maximum number of 20 single frequencies: about 302 ms;

[0137] The calculation time with a fixed maximum number of 50 single frequencies: about 810 ms;

[0138] The calculation time with a fixed maximum number of 100 single frequencies: about 1588 ms;

[0139] The above comparison shows that: when there is no crosstalk or bounded uncorrelated jitter, in the case of using spectral separation to separate random jitter and periodic jitter, this application can greatly reduce the jitter separation time.

[0140] Referring to the attached Figure 6 , based on the same inventive concept as in the foregoing embodiments, the embodiment of this application also provides a signal jitter separation device, including:

[0141] A recovery module, which is used to perform clock recovery on signal data to obtain a clock signal; wherein, the signal data is the quantization data of a serial signal;

[0142] A resampling module, which is used to resample the signal data according to the time interval error data, jitter timestamp data of the signal data, and the clock signal to obtain resampled signal data;

[0143] A first fitting module, which is used to fit the number of multi-tone signals in the amplitude value sequence of the resampled signal data to obtain a fitted single-frequency number sequence;

[0144] A second fitting module, which is used to fit the fitted single-frequency number sequence based on the optimal model of the Bayesian information criterion to obtain the target single-frequency number;

[0145] A separation module, which is used to extract mixed single-frequency information according to the target single-frequency number and separate jitter information according to the extraction result.

[0146] Those skilled in the art should understand that the division of each module in the embodiments is only a division of logical functions. In actual applications, they can be fully or partially integrated into one or more actual carriers, and these modules can all be implemented in the form of software called by a processing unit, or all be implemented in the form of hardware, or be implemented in the form of a combination of software and hardware. It should be noted that each module in the signal jitter separation device in this embodiment corresponds one by one to each step in the signal jitter separation method in the foregoing embodiment. Therefore, the specific implementation manners of this embodiment can refer to the implementation manners of the foregoing signal jitter separation method, which will not be elaborated here.

[0147] Based on the same inventive concept as in the foregoing embodiments, an embodiment of the present application further provides a computer-readable storage medium storing a computer program, which when loaded and executed by a processor, implements the signal jitter separation method provided by the embodiments of the present application.

[0148] Based on the same inventive concept as in the foregoing embodiments, an embodiment of the present application further provides an electronic device, including a processor and a memory, where

[0149] the memory is used to store a computer program;

[0150] the processor is used to load and execute the computer program so that the electronic device executes the signal jitter separation method provided by the embodiments of the present application.

[0151] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disc, or CD-ROM; or may be various devices including one or any combination of the foregoing memories. The computer may be various computing devices including smart terminals and servers.

[0152] In some embodiments, the executable instructions may be in the form of a program, software, software module, script, or code, and may be written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including being deployed as an independent program or being deployed as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0153] As an example, the executable instructions may or may not correspond to files in the file system, and may be stored as part of a file that stores other programs or data. For example, they may be stored in one or more scripts in a Hyper Text Markup Language (HTML) document, stored in a single file dedicated to the program being discussed, or stored in multiple cooperating files (for example, files that store one or more modules, subroutines, or code portions).

[0154] As an example, the executable instructions may be deployed to execute on a computing device, or on multiple computing devices located at one location, or on multiple computing devices distributed at multiple locations and interconnected by a communication network.

[0155] It should be noted that in this document, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or system comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or system comprising that element.

[0156] The serial numbers of the embodiments of the present application above are only for description and do not represent the superiority or inferiority of the embodiments.

[0157] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as a read-only memory / random access memory, magnetic disk, optical disk), and includes several instructions for causing a multimedia terminal device (which may be a mobile phone, a computer, a television receiver, or a network device, etc.) to execute the methods described in the various embodiments of the present application.

[0158] In summary, the present application provides a signal jitter separation method, device, medium and equipment, the method comprising: performing clock recovery on signal data to obtain a clock signal; wherein the signal data is quantized data of a serial signal; resampling the signal data according to the time interval error data, jitter timestamp data and clock signal of the signal data to obtain resampled signal data; fitting the number of polyphonic signals in the amplitude value sequence of the resampled signal data to obtain a fitted single frequency number sequence; fitting the fitted single frequency number sequence based on the optimal model of the Bayesian information criterion to obtain a target single frequency number; extracting mixed single frequency information according to the target single frequency number, and separating the jitter information according to the extracted result. The present application performs jitter separation based on the spectrum separation method, quantizes the serial signal and performs clock recovery, and then resamples it with time interval error data, jitter timestamp data and clock signal. Since the best fit is to be made to the number of single frequencies, it is necessary to first fit a sequence of the number of polyphonic signals that may be contained in the signal, and finally fit the best number of single frequencies. Since the Bayesian information criterion optimal model is used for fitting, both the goodness of fit and the complexity of the model are considered to avoid overfitting, while ensuring that the calculation runs faster and the accuracy of the calculation is taken into account. At the same time, it is more flexible. After determining the target number of single frequencies, when extracting mixed single frequency information, the calculation time can be greatly reduced, and the jitter information can be separated more accurately and quickly, thereby improving the effect of jitter separation in serial bus communication.

[0159] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A signal jitter separation method, characterized in that: The following steps are involved: Performing clock recovery on the signal data to obtain a clock signal; wherein the signal data is quantized data of the serial signal; Resampling the signal data according to the time interval error data, the jitter timestamp data and the clock signal of the signal data to obtain resampled signal data; Fitting the number of polyphonic signals in the amplitude value sequence of the resampled signal data to obtain a fitted single frequency number sequence; Fitting the fitted single frequency number sequence based on the optimal model of the Bayesian information criterion to obtain the target single frequency number; Mixed single frequency information is extracted according to the target number of single frequencies, and jitter information is separated according to the extraction result.

2. The signal jitter separation method according to claim 1, characterized in that: The resampling of the signal data according to the time interval error data, the jitter timestamp data and the clock signal of the signal data to obtain the resampled signal data includes: The signal data is resampled according to the clock signal as a sampling interval, with the time interval error data of the signal data as the amplitude and the jitter timestamp data as the time, to obtain resampled signal data.

3. The signal jitter separation method according to claim 1, characterized in that: Before fitting the number of polyphonic signals in the amplitude value sequence of the resampled signal data to obtain a fitted single frequency number sequence, the method further includes: Determine the fitting method according to the value interval of the maximum number of multi-tone signals; The step of fitting the number of polyphonic signals in the amplitude value sequence of the resampled signal data to obtain a fitted single frequency number sequence comprises: According to the fitting method, the number of polyphonic signals in the amplitude value sequence of the resampled signal data is fitted to obtain a fitting single frequency number sequence.

4. The signal jitter separation method according to claim 3, characterized in that: The step of determining the fitting method according to the value interval of the maximum number of multi-tone signals includes: According to the value of the maximum number of multi-tone signals being within the first value interval, determining that the fitting method is linear fitting; According to the value of the maximum number of polyphonic signals being within the second value interval, it is determined that the fitting method is linear fitting and logarithmic fitting.

5. The signal jitter separation method according to claim 1, characterized in that: Before fitting the fitted single frequency number sequence based on the Bayesian information criterion optimal model to obtain the target single frequency number, the method further includes: According to the fitted single frequency number sequence, a random jitter root mean square is obtained; Obtaining a model likelihood function according to the number of samples and the root mean square of the random jitter; According to the model calculation formula and the model likelihood function, the Bayesian information criterion optimal model is obtained.

6. The signal jitter separation method according to claim 5, characterized in that: The step of obtaining a random jitter root mean square according to the fitted single frequency number sequence comprises: Using a mixed single-frequency information extraction method to separate the single-frequency signal of the fitted single-frequency number sequence to obtain a single-frequency signal; A random jitter root mean square is obtained according to the time domain data of the single frequency signal.

7. The signal jitter separation method according to claim 1, characterized in that: The extracting mixed single frequency information according to the target number of single frequencies and separating the jitter information according to the extracted result includes: Extract mixed single-frequency information according to the number of target single frequencies to obtain a target single-frequency signal; Performing time domain addition on the target single frequency signal to obtain an addition result; Separating periodic jitter information according to the addition result; Random jitter information is separated according to a root mean square of a subtraction result between the amplitude value sequence of the resampled signal data and the target single frequency signal.

8. A signal jitter separation device, characterized in that: include: A recovery module, the recovery module is used to perform clock recovery on the signal data to obtain a clock signal; wherein the signal data is quantized data of the serial signal; A resampling module, the resampling module is used to resample the signal data according to the time interval error data of the signal data, the jitter timestamp data and the clock signal to obtain resampled signal data; A first fitting module, the first fitting module is used to fit the number of multi-tone signals in the amplitude value sequence of the resampled signal data to obtain a fitted single frequency number sequence; A second fitting module, the second fitting module is used to fit the fitting single frequency number sequence based on the Bayesian information criterion optimal model to obtain a target single frequency number; A separation module is used to extract mixed single-frequency information according to the target number of single frequencies, and to separate jitter information according to the extraction result.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is loaded and executed by a processor, the signal jitter separation method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: comprising a processor and a memory, wherein: The memory is used to store computer programs; The processor is used to load and execute the computer program so that the electronic device executes the signal jitter separation method according to any one of claims 1 to 7.

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