Signal jitter separation method and device, medium and equipment

Abstract

The invention discloses a signal jitter separation method and device, a medium and equipment, and relates to the technical field of digital signal processing. According to the method, jitter separation is carried out on the basis of a frequency spectrum separation mode, serial signals are quantized, clock recovery is carried out, and then resampling is carried out according to time interval error data, jitter timestamp data and clock signals; as the Bayesian information criterion optimal model is adopted for fitting, the goodness of fit of the model is considered, the complexity of the model is also considered, the over-fitting is avoided, and the accuracy of the fitting is improved. Compared with the prior art, the jitter separation method has the advantages that the jitter information can be separated more accurately and quickly, the jitter separation effect in serial bus communication is improved, calculation accuracy is taken into account while faster calculation operation is ensured, the jitter separation method is more flexible, and after the number of target single frequencies is determined, the jitter information can be separated more accurately and quickly when mixed single frequency information is extracted.

Description

Technical Field

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

[0002] In serial bus communication, as the data communication rate continues to increase, jitter is one of the main reasons that affect high-speed serial link data transmission. In practice, jitter is caused by many reasons, so the components of jitter are also diverse. It is necessary to analyze the signal jitter efficiently and accurately, trace the source, and use the jitter analysis results to take measures 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 the causes of its formation, jitter can be divided into random jitter and deterministic jitter, among which 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 separation of random jitter and periodic jitter can be carried out by spectrum separation. After resampling the jitter sequence, the spectrum peak is modeled as a sinusoidal curve and the sum of the identified sinusoidal curves is returned to calculate the periodic jitter. The specific steps are: clock recovery and obtaining jitter timestamps; time interval error TIE calculation; resampling jitter timestamps and TIE; extracting mixed single-frequency information from the resampled data according to the input amplitude threshold and the maximum number of single frequencies. After extracting the mixed single-frequency signal, take the range of the exported time signal to obtain the peak-to-peak value of the periodic jitter. , take the RMS value of the derived time signal to get the period jitter The random jitter is obtained by subtracting the derived time signal from the resampled time signal and taking the root mean square. .

[0004] When performing mixed single-frequency information extraction in the above method, the developer manually specifies the maximum number of single frequencies, which will affect the extraction time. The larger the maximum number of single frequencies, the longer the calculation time, sometimes reaching more than seconds, 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, the maximum number of single frequencies will be different for different serial signals. This artificial determination method seriously affects the effect of jitter separation. Summary of the invention

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

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows: In a first aspect, an embodiment of the present application provides a signal jitter separation method, comprising the following steps: Performing clock recovery on the signal data to obtain a clock signal; wherein the signal data is quantized data of the serial signal; Resample 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 multi-tone signals in the amplitude value sequence of the resampled signal data to obtain a fitted single frequency number sequence; Based on the optimal model of Bayesian information criterion, the fitting single frequency number sequence is fitted to obtain the target single frequency number; Mixed single frequency information is extracted according to the number of target single frequencies, and jitter information is separated according to the extracted result.

[0007] In a possible implementation manner of the first aspect, 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 the resampled signal data includes: The signal data is resampled according to the clock signal as the 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 the resampled signal data.

[0008] 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 the fitted single frequency number sequence, the method further includes: Determine a fitting method according to a value interval of the maximum number of multi-tone signals; 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, including: According to the fitting method, the number of multi-tone signals in the amplitude value sequence of the resampled signal data is fitted to obtain a fitting single frequency number sequence.

[0009] In a possible implementation of the first aspect, determining a fitting method according to a value interval of a 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 multi-tone signals being within the second value interval, it is determined that the fitting method is linear fitting and logarithmic fitting.

[0010] In a possible implementation of the first aspect, before fitting the fitted single frequency number sequence based on the Bayesian information criterion optimal model and obtaining the target single frequency number, the method further includes: According to the fitted single frequency number sequence, the random jitter root mean square is obtained; Obtain the model likelihood function based on the number of samples and the root mean square of random jitter; According to the model calculation formula and model likelihood function, the optimal model of Bayesian information criterion is obtained.

[0011] In a possible implementation manner of the first aspect, obtaining a random jitter root mean square according to a fitted single frequency number sequence includes: The method of extracting mixed single-frequency information is used to separate the single-frequency signal of the fitted single-frequency number sequence to obtain the single-frequency signal; The random jitter RMS is obtained based on the time domain data of the single frequency signal.

[0012] In a possible implementation manner of the first aspect, extracting mixed single-frequency information according to the number of target single frequencies, and separating jitter information according to the extraction result includes: Extract mixed single-frequency information according to the number of target single frequencies to obtain target single-frequency signals; Performing time domain addition on the target single frequency signal to obtain an addition result; Separate the periodic jitter information according to the addition result; The random jitter information is separated 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.

[0013] In a second aspect, an embodiment of the present application provides a signal jitter separation device, including: 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, the jitter timestamp data and the clock signal of the signal data 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 fitting single frequency number sequence; The second fitting module is used to fit the fitting single frequency number sequence based on the Bayesian information criterion optimal model to obtain the target single frequency number; The separation module is used to extract mixed single-frequency information according to the number of target single frequencies, and to separate jitter information according to the extraction result.

[0014] In a third aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, the signal jitter separation method provided in any one of the first aspects above is implemented.

[0015] In a fourth aspect, an embodiment of the present application provides an electronic device, including a processor and a memory, wherein: The memory is used to store computer programs; The processor is used to load and execute a computer program so that the electronic device executes the signal jitter separation method provided in any one of the first aspects above.

[0016] Compared with the prior art, the beneficial effects of this application are: The embodiments of the present application propose a signal jitter separation method, device, medium and equipment, which include: 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 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 a Bayesian information criterion optimal model 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the structure of an electronic device of a hardware operating environment involved in an embodiment of the present application; Figure 2 A schematic diagram of a flow chart of a signal jitter separation method provided in an embodiment of the present application; Figure 3 A curve diagram of the maximum number of single frequencies fitted in the signal jitter separation method provided in an embodiment of the present application; Figure 4 In order to test the method of the present application, the waveform diagram of the input signal after sampling and quantization; Figure 5 The resampled spectrum diagram after fitting by the method of the present application and the spectrum diagram of the separated periodic jitter; Figure 6 A schematic diagram of a module of a signal jitter separation device provided in an embodiment of the present application; Markings in the figure: 101 - processor, 102 - communication bus, 103 - network interface, 104 - user interface, 105 - memory. DETAILED DESCRIPTION

[0018] 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.

[0019] Refer to the attached Figure 1 , attached Figure 1 This is a schematic diagram of the structure of an electronic device of the hardware operating environment involved in the embodiment 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), an input unit such as a keyboard (Keyboard), and the user interface 104 may also 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 (WIreless-FIdelity, WI-FI) interface). The memory 105 may optionally be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) memory, or it may be a stable non-volatile memory (NVM), such as at least one disk storage. The processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component.

[0020] Those skilled in the art will appreciate that Figure 1 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0021] As attached Figure 1 As shown, 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.

[0022] In the attached Figure 1In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in the present application can be set 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 in the embodiment of the present application.

[0023] Refer to the attached Figure 2 Based on the hardware device of the aforementioned embodiment, the embodiment of the present application provides a signal jitter separation method, which is applicable to the separation of jitter components of a serial signal without crosstalk or bounded uncorrelated jitter, and includes the following steps: S10: Perform clock recovery on the signal data to obtain a clock signal; wherein the signal data is quantized data of the serial signal.

[0024] In the specific implementation process, clock recovery can be processed by constant clock, first-order phase-locked loop, second-order phase-locked loop, etc., and the recovered clock signal is used for the next step of processing. Before this, the serial signal needs to be converted and quantized into processable data, that is, the signal data is clock recovered. Before obtaining the clock signal, the method also includes: Quantize the serial signal to obtain signal data.

[0025] The analog serial signal can be converted from digital to analog using a digital oscilloscope or a data acquisition card, and then quantified into processable data.

[0026] S20: resample 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.

[0027] In a specific implementation process, resampling may adopt linear resampling. Specifically, according to the time interval error data, jitter timestamp data and clock signal of the signal data, the signal data is resampled to obtain resampled signal data, including: The signal data is resampled according to the clock signal as the 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 the resampled signal data.

[0028] The signal data in the previous step is used to analyze the time interval error, and the time interval error TIE data stream is obtained as follows: , is the number of TIE elements; get the jitter timestamp data , for The number of elements. is the amplitude, is time, and the data point is linearly resampled according to the recovered clock as the sampling interval. The amplitude value sequence of the sampled data sequence is , is the number of Y elements.

[0029] S30: 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.

[0030] In the specific implementation process, since the best fit is performed on the number of polyphonic signals in the amplitude value sequence of the resampled signal data, it is necessary to first fit the sequence of the number of polyphonic signals that may be contained in the signal. , that is, fitting the single frequency number sequence. Sequence, allows to specify a maximum number of multi-tone signals If not specified, the maximum number of multi-tone signals .

[0031] In one embodiment, before fitting the number of multi-tone signals in the amplitude value sequence of the resampled signal data to obtain the fitted single frequency number sequence, the method further includes: Determine a fitting method according to a value interval of the maximum number of multi-tone signals; 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, including: According to the fitting method, the number of multi-tone signals in the amplitude value sequence of the resampled signal data is fitted to obtain a fitting single frequency number sequence.

[0032] In the specific implementation process, the fitting single frequency number sequence is based on the maximum number of multi-tone signals. The value interval of is calculated, and the fitting method adopts a combination of linear fitting and logarithmic fitting. Different fitting methods are used for different intervals. Specifically, the fitting method is determined according to the value interval of the maximum number of multi-tone signals, including: 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 multi-tone signals being within the second value interval, it is determined that the fitting method is linear fitting and logarithmic fitting.

[0033] For different serial signals, the maximum number of single frequencies will be different, but generally not greater than 100. Different fitting methods are used by dividing the intervals. For example, 14 is selected as the dividing point for interval division. The value range of L is 1-14, that is, in the first value range, linear fitting is used. In the range of 15-100, that is, in the second value range, a combination of linear and logarithmic fitting is used. according to The value range of is calculated, that is, there are three cases: 1) When hour Using linear fitting, The length is , fitting single frequency number series Determined by the following formula:

[0034] 2) When hour The length is The data consists of two parts. The first 14 points are the linear fitting results. The points are obtained by logarithmic fitting. Fitting single frequency number sequence The sequence value is determined by the following formula:

[0035] 3) When hour 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 value is determined by the following formula:

[0036] when When the maximum number of single frequencies is met, the sequence is The curve is as shown in the attached Figure 3 shown.

[0037] S40: Fitting the fitting single frequency number sequence based on the optimal model of the Bayesian information criterion to obtain the target single frequency number.

[0038] In the specific implementation process, the calculation formula of the optimal model BIC based on the Bayesian Information Criterion is:

[0039] In the above formula is the maximum likelihood estimate 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 requires comparison of multiple models and then selects the model with the minimum BIC value, the single frequency number sequence fitted in the above steps is the , this step continues the fitting to determine the target number of single frequencies, that is, the optimal maximum number of single frequencies.

[0040] In one embodiment, 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, the random jitter root mean square is obtained; Obtain the model likelihood function based on the number of samples and the root mean square of random jitter; According to the model calculation formula and model likelihood function, the optimal model of Bayesian information criterion is obtained.

[0041] In the specific implementation process, since the BIC optimal model fitting method is adopted, in the above formula, it is necessary to determine , , Three parameters, among which is the number of parameters in the model, which is for The three calculation formulas are obtained An element in is the sample size, after resampling, The number of elements Sure; is the model likelihood function, and the likelihood function in this application is defined as follows:

[0042] In the above formula for The RMS value of random jitter after determining the maximum number of single frequencies, is the number of samples. and The maximum number of single frequencies contained in the data is related. Specifically, the random jitter RMS is obtained based on the fitted single frequency number sequence, including: The method of extracting mixed single-frequency information is used to separate the single-frequency signal of the fitted single-frequency number sequence to obtain the single-frequency signal; The random jitter RMS is obtained based on the time domain data of the single frequency signal.

[0043] Assume the number of single frequencies is Hours, Determined by the following steps: 1) Using the method of extracting mixed single frequency information conduct Single frequency signal separation, separation The time domain data of the single frequency signals are: DTA1, DTA2, ..., DTAk, and the length of each time domain data is M; 2) Separate The time domain data after a single frequency signal is :

[0044] 3) Calculation The root mean square value of

[0045] ; In the formula It means taking the RMS value of the data sequence in brackets.

[0046] According to the above method, determine the RMS value Post-combination sample size That is, we get the model likelihood function, and substitute it into the BIC calculation formula to get the BIC model:

[0047] According to the fitting The expression of the data series and the BIC model is as follows: The sequence performs the best single frequency number fitting: Step 1: Order , ; Step 2: Order ,set up The maximum number of single frequencies in the data sequence is , according to the previous conduct Single frequency signal separation, according to The root mean square value of The calculation formula under this assumption is According to the expression of the BIC model, the maximum number of single frequencies is of value.

[0048] Step 3: If , then let , , repeat the second step; if , then proceed to step 4.

[0049] Step 4: Fit the second step The values ​​are composed in order array, and find The index number of the position of the smallest element in is .

[0050] Step 5: Get The optimal number of single frequencies in the sequence :

[0051] S50: extracting mixed single frequency information according to the number of target single frequencies, and separating jitter information according to the extraction result.

[0052] In the specific implementation process, after the maximum number of single frequencies is determined, the jitter information is separated based on the spectrum separation method. The separation result includes periodic jitter and random jitter. Specifically, 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: Extract mixed single-frequency information according to the number of target single frequencies to obtain target single-frequency signals; Performing time domain addition on the target single frequency signal to obtain an addition result; Separate the periodic jitter information according to the addition result; The random jitter information is separated 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.

[0053] In the specific implementation process, according to the fitting The maximum number of single frequencies contained in the data sequence is extracted by using the mixed single frequency information extraction method to extract each single frequency signal, and the extracted multiple single frequency signals are added in the time domain. The maximum value minus the minimum value of the addition result is the peak-to-peak value of the periodic jitter, and the root mean square value is the root mean square value of the periodic jitter; All separated single frequency signals are subtracted from the data sequence, and the RMS value of the subtraction result is the separated random jitter result.

[0054] In this embodiment, jitter separation is performed based on a spectrum separation method, the serial signal is quantized and clock recovery is performed, and then the time interval error data, jitter timestamp data and clock signal are resampled. Since the best fit is performed on 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.

[0055] The following is a further explanation of this application in combination with actual engineering tests: (1) Test conditions 1) Input signal: 100MHz square wave clock signal; 2) Add jitter to the input signal: random jitter mean 0us, standard deviation 25ps, random seed 3; period jitter 1 frequency 10MHz, amplitude 200ps, phase 0°; period jitter 2 frequency 20MHz, amplitude 100ps, phase 0°; 3) Sampling information: sampling rate 100GSa / s, sampling time 40us, the waveform of the input signal after sampling and quantization is as shown in the attached figure. Figure 4 shown.

[0056] (2) Test content The following two tests are performed on this application: 1) The first is to separate the peak-to-peak value of the periodic jitter of the input signal through this application , take the RMS value to get the period jitter The value and the subtraction result are taken to get the root mean square to get the random jitter .

[0057] The test results are , , ,The jitter separation result is roughly the same as the input signal, indicating the correctness of the result.

[0058] The resampled spectrum after fitting by this application and the spectrum of the separated periodic jitter are shown in the attached figure. Figure 5 As shown, attached Figure 5 The medium-periodic jitter spectrum component is 2, indicating that the maximum number of optimal single frequencies after fitting by the present application is 2, which matches the periodic jitter of the input signal, indicating the correctness of the present application.

[0059] 2) The second type is to compare the time of the input signal undergoing jitter separation after a fixed maximum number of single frequencies and the time of the jitter separation after the present application.

[0060] The calculation time of this application is: about 30ms.

[0061] The maximum number of fixed single frequencies is 20. Computation time: about 302ms; The maximum number of fixed single frequencies is 50. Computation time: about 810ms; The maximum number of fixed single frequencies is 100. Calculation time: about 1588ms; The above comparison shows that when there is no crosstalk or bounded uncorrelated jitter, the jitter separation time can be greatly reduced by using the present application when the spectrum separation method is used to separate random jitter and periodic jitter.

[0062] Refer to the attached Figure 6 Based on the same inventive concept as in the above-mentioned embodiment, the embodiment of the present application further provides a signal jitter separation device, comprising: 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, the jitter timestamp data and the clock signal of the signal data 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 fitting single frequency number sequence; The second fitting module is used to fit the fitting single frequency number sequence based on the Bayesian information criterion optimal model to obtain the target single frequency number; The separation module is used to extract mixed single-frequency information according to the number of target single frequencies, and to separate jitter information according to the extraction result.

[0063] Those skilled in the art should understand that the division of the various modules in the embodiment is merely a division of logical functions, and in actual application, all or part of them can be integrated into one or more actual carriers, and these modules can all be implemented in the form of software called through a processing unit, or all in the form of hardware, or 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-to-one to each step in the signal jitter separation method in the aforementioned embodiment. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned signal jitter separation method, which will not be repeated here.

[0064] Based on the same inventive concept as in the aforementioned embodiment, an embodiment of the present application further provides a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, the signal jitter separation method provided in the embodiment of the present application is implemented.

[0065] Based on the same inventive concept as in the above-mentioned embodiment, an embodiment of the present application further provides an electronic device, including a processor and a memory, wherein: The memory is used to store computer programs; The processor is used to load and execute a computer program so that the electronic device executes the signal jitter separation method provided in the embodiment of the present application.

[0066] 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 disk, or CD-ROM; or various devices including one or any combination of the above memories. The computer may be various computing devices including intelligent terminals and servers.

[0067] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, 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 as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0068] As an example, executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file storing other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0069] By way of example, executable instructions may be deployed to be executed on one computing device, or on multiple computing devices located at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0070] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0071] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0072] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a multimedia terminal device (which can be a mobile phone, a computer, a television receiver, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0073] 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.

[0074] 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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