Asynchronous optical sampling synchronization parameter estimation optimization method and system

Abstract

The invention provides an asynchronous optical sampling synchronization parameter estimation optimization method and system. The method comprises the following steps: acquiring optical sampling pulse data acquired by an asynchronous optical sampling system, extracting a peak sequence by adopting a peak extraction function, performing Fourier transform to obtain a complex frequency spectrum, and performing modulus processing to obtain an amplitude spectrum; calculating the number of harmonic waves based on the repetition frequency of the sampling light pulse source and the repetition frequency of the measured signal light, calculating aliasing beat frequency based on the number of harmonic waves, and determining a prediction index position based on the aliasing beat frequency; positioning the position of the prediction index in the amplitude spectrum, constructing a search window, and searching a maximum peak value in the search window; and determining a synchronization parameter based on the frequency value of the data point where the maximum peak value in the search window is located. According to the scheme, global optimization is avoided by constructing the search window, and the search process is limited in a narrow-band region near the predicted index position, so that the problem of misjudgment of a main spectrum peak in a full-frequency scanning method is avoided.

Description

An Optimization Method and System for Estimating Synchronization Parameters of Asynchronous Optical Sampling Technical Field

[0001] This invention relates to the fields of optical communication and photoelectric detection technology, and in particular to an asynchronous optical sampling synchronization parameter estimation and optimization method and system. Background Technology

[0002] With the rapid growth in demand for high-speed optical communication, ultra-wideband interconnection, and ultrafast optoelectronic testing, accurately capturing the waveforms of picosecond or even sub-picosecond high-speed optical signals under limited bandwidth conditions has become a crucial issue in the field of optoelectronic measurement. Asynchronous Optical Sampling (AOS) technology, due to its advantages such as high equivalent sampling rate, low cost, and elimination of the need for high-speed electronic samplers, is widely used in fiber optic communication testing, optical frequency comb measurement, ultrafast laser characterization, and high-speed pulse signal detection. The AOS system constructs two optical pulse sequences with slightly detuned repetition frequencies and utilizes the beat frequency principle to effectively downconvert the high-speed signal to a lower frequency, thereby reconstructing the high-speed waveform.

[0003] In existing technologies, the core of AOS lies in accurately estimating the aliasing beat frequency from the sampled pulse peak sequence and calculating the synchronization parameter S accordingly for waveform reconstruction. Common synchronization parameter estimation processes primarily rely on performing a Fast Fourier Transform (FFT) on the peak sequence and finding the spectral peak with the largest amplitude across the entire frequency range as the beat frequency location. However, in practical systems, due to light source noise, EDFA amplification noise, modulator nonlinearity, and O / E link frequency response limitations, the peak sequence often exhibits amplitude fluctuations, spurious peaks, and noise interference. This results in two significant drawbacks in traditional synchronization parameter acquisition methods based on full-domain FFT maximum search: first, global optimization is required in a high-dimensional spectrum, leading to high computational complexity and hindering real-time processing; second, multiple spurious peaks appear in the spectrum under noisy conditions, easily causing synchronization parameter shifts or even complete errors, resulting in subsequent waveform reconstruction failure. As asynchronous optical sampling systems are applied to higher beat frequencies, more complex signals, and more severe noise conditions, these problems become even more pronounced. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide an asynchronous optical sampling synchronization parameter estimation optimization method to eliminate or improve one or more defects existing in the prior art.

[0005] One aspect of the present invention provides an asynchronous optical sampling synchronization parameter estimation and optimization method, the method comprising the following steps: acquiring optical sampling pulse data collected by an asynchronous optical sampling system; extracting a peak sequence using a peak extraction function and performing a fast Fourier transform to obtain a complex spectrum; performing modulus-taking processing based on the complex spectrum to obtain an amplitude spectrum; calculating the harmonic order based on the repetition frequency of the sampling optical pulse source and the repetition frequency of the measured signal light; calculating the aliasing beat frequency based on the harmonic order; determining a prediction index position based on the aliasing beat frequency; locating the prediction index position in the amplitude spectrum and constructing a search window; searching for the maximum peak value in the search window; and determining synchronization parameters based on the frequency value of the data point where the maximum peak value is located in the search window.

[0006] The above scheme first constructs the amplitude spectrum and calculates the harmonic order and aliasing beat frequency. The prediction index position is determined by the aliasing beat frequency to perform fuzzy positioning of the synchronization parameters. The scheme further constructs a search window by the prediction index position and finally searches to obtain the maximum peak value in the search window and determines the synchronization parameters. On the one hand, this scheme avoids global optimization by constructing a search window. On the other hand, the search process of this scheme is strictly limited to a narrow band region near the prediction index position. Even in low signal-to-noise ratio or spectral pseudo-peak interference environments, it can still effectively avoid the problem of misjudging the main peak of the spectrum that is prone to occur in traditional full-frequency scanning methods, thereby maintaining the stability and reliability of the synchronization parameter estimation results.

[0007] In some embodiments of the present invention, in the step of determining the synchronization parameter based on the frequency value of the data point where the maximum peak is located in the search window, the frequency value of the data point where the maximum peak is located in the search window is used as the synchronization parameter, or the peak sequence and the data point where the maximum peak is located in the search window are used as inputs to a local spectrum refinement algorithm, and the optimized synchronization parameter is obtained through the local spectrum refinement algorithm.

[0008] In some embodiments of the present invention, the local spectrum refinement algorithm employs the Chirp Z-transform algorithm, the Zoom-FFT algorithm, or an interpolation-based frequency refinement algorithm.

[0009] In some embodiments of the present invention, in the step of performing modulo processing based on the complex spectrum to obtain the amplitude spectrum, the average value of each complex point in the complex spectrum is calculated, and the average value of the complex points is subtracted from the complex value of each complex point to obtain an updated complex spectrum. The updated complex spectrum is then subjected to modulo processing to obtain the amplitude spectrum.

[0010] In some embodiments of the present invention, in the step of calculating the harmonic order based on the repetition frequency of the sampling optical pulse source and the repetition frequency of the measured signal light, the harmonic order is calculated using the following formula: in, Indicates the harmonic order. This indicates the repetition frequency of the measured signal light. This indicates the repetition frequency of the sampling optical pulse source. Indicates rounding down.

[0011] In some embodiments of the present invention, in the step of calculating the aliasing beat frequency based on the harmonic order, the aliasing beat frequency is calculated using the following formula: in, This indicates the overlapping beat frequency.

[0012] In some embodiments of the present invention, in the step of determining the predicted index position based on the aliasing beat frequency, the frequency resolution is calculated based on the repetition frequency of the sampled light pulse source and the length of the peak sequence, and the predicted index position is calculated based on the frequency resolution.

[0013] In some embodiments of the present invention, in the step of calculating the frequency resolution based on the repetition frequency of the sampled optical pulse source and the length of the peak sequence, the frequency resolution is calculated using the following formula: In the step of calculating the predicted index position based on the frequency resolution, the predicted index position is calculated using the following formula: in, Indicates frequency resolution. Indicates the predicted index position. Indicates aliasing beat frequency. Indicates the length of the peak sequence. This indicates the repetition frequency of the sampling optical pulse source.

[0014] In some embodiments of the present invention, the step of locating the prediction index position in the amplitude spectrum and constructing a search window involves extending a distance of a preset tolerance coefficient from the prediction index position along the axis where the frequency is located in the amplitude spectrum in both the increasing and decreasing directions to construct the search window.

[0015] A second aspect of the present invention also provides an asynchronous optical sampling synchronization parameter estimation and optimization system, the system comprising a computer device including a processor and a memory, the memory storing computer instructions, the processor executing the computer instructions stored in the memory, and the system implementing the steps of the method described above when the computer instructions are executed by the processor.

[0016] A third aspect of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned asynchronous optical sampling synchronization parameter estimation optimization method.

[0017] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the text, or may be learned by practice of the invention. The objects and other advantages of the invention will become apparent from the description and the accompanying drawings.

[0018] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0019] The accompanying drawings, which are provided to further illustrate the invention and form part of this application, are not intended to limit the scope of the invention.

[0020] Figure 1 is a schematic diagram of one embodiment of the asynchronous optical sampling synchronization parameter estimation and optimization method of the present invention; Figure 2 is a schematic diagram of the overall processing flow of one embodiment of the present invention; Figure 3 is a schematic diagram of the amplitude spectrum of the present invention; Figure 4 is a schematic diagram of the system architecture of the experimental example of the present invention; Figure 5 is a schematic diagram of the digital signal processing flow of the present invention; Figure 6 is a schematic diagram comparing the processing results of the present invention with those of the prior art. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0022] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0023] In existing technologies, transforming traditional synchronization parameter estimation methods that rely on global search into local search methods that are more reliable, less sensitive to noise, and subject to physical constraints is a key technical requirement for improving the stability and real-time performance of AOS systems. Based on this, this solution develops an asynchronous optical sampling synchronization parameter estimation optimization method and system, which has significant engineering value and practical implications for improving the accuracy of high-speed optical signal recovery, enhancing the system's anti-interference capability, and realizing real-time digital signal processing.

[0024] This solution addresses the problems of synchronization parameter estimation in existing asynchronous optical sampling systems being susceptible to noise interference and spurious peaks leading to search errors, as well as the low efficiency and poor accuracy of traditional full-band search. It proposes an optimized method and system for asynchronous optical sampling synchronization parameter estimation.

[0025] As shown in Figure 1, this invention proposes an asynchronous optical sampling synchronization parameter estimation and optimization method. The method includes the following steps: Step S100, acquiring optical sampling pulse data collected by the asynchronous optical sampling system, extracting the peak sequence using a peak extraction function, performing a fast Fourier transform to obtain a complex spectrum, and performing modulo operation based on the complex spectrum to obtain an amplitude spectrum; In some embodiments of this invention, the peak sequence obtained after processing the optical sampling pulse data of the electrical signal acquired by the data acquisition card in the asynchronous optical sampling system using the peak extraction function is... , The length of the peak sequence is represented by a Fast Fourier Transform (FFT) to obtain a complex spectrum. The DC component is removed and the amplitude spectrum is obtained by taking the modulus. In specific implementation, the asynchronous optical sampling system includes a Mach-Zehnder modulator, a low-speed photodetector, and a data acquisition card connected in sequence. The input of the Mach-Zehnder modulator is connected to the signal light under test and the sampling light pulse source.

[0026] In the specific implementation process, the peak extraction function adopts the cubic difference algorithm.

[0027] In the specific implementation process, the step of removing the DC component is to calculate the average value of each complex point in the complex spectrum, and then subtract the average value of the complex point from the complex value of each complex point to obtain the updated complex spectrum. The updated complex spectrum is then subjected to modulo operation to obtain the amplitude spectrum.

[0028] Using the above scheme, peak detection is performed on the asynchronous sampling signal output by the data acquisition card, and continuous peaks are extracted. The peak amplitudes of the effective pulses constitute a peak sequence, and a window function can be selectively applied to the peak sequence to suppress spectral sidelobes, enhance the prominence of the main peak, and map the amplitude changes in the time domain to the frequency domain, thus preparing for the search of beat frequency components that reflect the time scaling ratio.

[0029] In the specific implementation process, after the FFT, a series of complex spectrum values ​​are obtained, where each complex point is represented as... These correspond to different frequency components; among them The component corresponding to frequency 0 is the average value of this series of peaks (DC component). This value might be very large, "flattening" other useful spectral peaks, so this DC component needs to be removed. The method is to subtract the average value from the sequence in the code, which is equivalent to removing the DC component. Next, we only care about "which frequency point has the highest energy," so for each complex spectral point... Calculate the modulus: The amplitude spectrum is obtained.

[0030] Step S200: Calculate the harmonic order based on the repetition frequency of the sampled optical pulse source and the repetition frequency of the measured signal light; calculate the aliasing beat frequency based on the harmonic order; and determine the prediction index position based on the aliasing beat frequency. In specific implementation, after performing a discrete Fourier transform on the peak sequence, the beat frequency corresponds to a certain index position in the discrete spectrum.

[0031] Step S300: Locate the prediction index position in the amplitude spectrum and construct a search window to search for the maximum peak value in the search window; Step S400: Determine the synchronization parameters based on the frequency value of the data point where the maximum peak value is located in the search window.

[0032] In practical implementation, the synchronization parameter is used to characterize the ratio between the equivalent sampling time base and the real time base of the measured signal in the asynchronous optical sampling system.

[0033] The above scheme first constructs the amplitude spectrum and calculates the harmonic order and aliasing beat frequency. The prediction index position is determined by the aliasing beat frequency to perform fuzzy positioning of the synchronization parameters. The scheme further constructs a search window by the prediction index position and finally searches to obtain the maximum peak value in the search window and determines the synchronization parameters. On the one hand, this scheme avoids global optimization by constructing a search window. On the other hand, the search process of this scheme is strictly limited to a narrow band region near the prediction index position. Even in low signal-to-noise ratio or spectral pseudo-peak interference environments, it can still effectively avoid the problem of misjudging the main peak of the spectrum that is prone to occur in traditional full-frequency scanning methods, thereby maintaining the stability and reliability of the synchronization parameter estimation results.

[0034] In some embodiments of the present invention, in the step of determining the synchronization parameter based on the frequency value of the data point where the maximum peak is located in the search window, the frequency value of the data point where the maximum peak is located in the search window is used as the synchronization parameter, or the peak sequence and the data point where the maximum peak is located in the search window are used as inputs to a local spectrum refinement algorithm, and the optimized synchronization parameter is obtained through the local spectrum refinement algorithm.

[0035] In the specific implementation process, if the scaling factor between the asynchronous sampling time base and the real time base is calculated by the beat frequency, the frequency value of the data point where the maximum peak value is located in the search window is used as the synchronization parameter.

[0036] In the specific implementation process, the frequency value of the data point where the maximum peak value is located in the search window refers to the initial estimate of the beat frequency position obtained through local search; the optimized synchronization parameter obtained by the local spectrum refinement algorithm refers to the high-precision beat frequency parameter obtained by using the local spectrum refinement algorithm near the coarse estimate; both can be used as synchronization parameters and can be used for time base reconstruction and waveform restoration. This step utilizes the physical hardware parameters of the system as prior knowledge to determine the theoretically correct center frequency position of the beat frequency signal.

[0037] In some embodiments of the present invention, the local spectrum refinement algorithm may employ the Chirp Z-transform algorithm, the Zoom-FFT algorithm, or an interpolation-based frequency refinement algorithm.

[0038] Using the above scheme, the frequency value of the data point where the maximum peak value is located in the search window is a coarse estimate of the synchronization parameter. This coarse estimate serves as the initial value, and a local spectrum refinement algorithm is used in its vicinity to further refine it, obtaining the precise synchronization parameter. The local spectrum refinement algorithm can perform local refinement analysis near the frequency point of interest, thereby breaking through the frequency resolution limitation of FFT and obtaining extremely high-precision synchronization parameters.

[0039] In some embodiments of the present invention, in the step of performing modulo processing based on the complex spectrum to obtain the amplitude spectrum, the average value of each complex point in the complex spectrum is calculated, and the average value of the complex points is subtracted from the complex value of each complex point to obtain an updated complex spectrum. The updated complex spectrum is then subjected to modulo processing to obtain the amplitude spectrum.

[0040] In some embodiments of the present invention, in the step of calculating the harmonic order based on the repetition frequency of the sampling optical pulse source and the repetition frequency of the measured signal light, the harmonic order is calculated using the following formula: in, Indicates the harmonic order. This indicates the repetition frequency of the measured signal light. This indicates the repetition frequency of the sampling optical pulse source. Indicates rounding down.

[0041] In some embodiments of the present invention, in the step of calculating the aliasing beat frequency based on the harmonic order, the aliasing beat frequency is calculated using the following formula: in, This indicates the overlapping beat frequency.

[0042] In some embodiments of the present invention, in the step of determining the predicted index position based on the aliasing beat frequency, the frequency resolution is calculated based on the repetition frequency of the sampled light pulse source and the length of the peak sequence, and the predicted index position is calculated based on the frequency resolution.

[0043] In some embodiments of the present invention, in the step of calculating the frequency resolution based on the repetition frequency of the sampled optical pulse source and the length of the peak sequence, the frequency resolution is calculated using the following formula: In the step of calculating the predicted index position based on the frequency resolution, the predicted index position is calculated using the following formula: in, Indicates frequency resolution. Indicates the predicted index position. Indicates aliasing beat frequency. Indicates the length of the peak sequence. This indicates the repetition frequency of the sampling optical pulse source.

[0044] In some embodiments of the present invention, the step of locating the prediction index position in the amplitude spectrum and constructing a search window involves extending a distance of a preset tolerance coefficient from the prediction index position along the axis where the frequency is located in the amplitude spectrum in both the increasing and decreasing directions to construct the search window.

[0045] In the specific implementation process, a local search interval is constructed based on the theoretical beat frequency index and the preset relative search tolerance.

[0046] Based on the predicted index position, a local search interval is set, and a search window is constructed in the following form: in, Indicates the predicted index position. The tolerance coefficient represents the relative deviation allowed in the search. The smaller the value of , the narrower the local search window and the fewer search points, but the higher the accuracy requirement for the theoretical prediction; tolerance coefficient The larger the value of , the larger the local search window becomes, and the more computationally intensive it becomes. As shown in Figure 3, the yellow area represents the constructed local search window, centered on the theoretical value. The purpose of setting this window is to limit the search space and shield against noise peaks and other aliasing components outside the window.

[0047] Specifically, The value range is 0.01 to 0.10, that is, 1% to 10% of the frequency index range is reserved on both sides of the theoretical predicted value of the synchronization parameter for the construction of the search window.

[0048] In some embodiments of the present invention, the step of locating the prediction index position in the amplitude spectrum and constructing a search window further includes comparing the interval of the search window with the length of the spectrum array, performing boundary protection, ensuring that the search interval falls entirely within the effective spectrum range, and preventing the index from going out of bounds.

[0049] As shown in Figure 2, the overall steps of this scheme can be represented as follows: Step S1, the peak sequence obtained by processing the sampled electrical signal acquired by the data acquisition card through the peak extraction function. Step S1: Perform FFT to obtain the complex spectrum, remove the DC component, and take the modulus to obtain the amplitude spectrum; Step S2: Calculate the integer harmonic relationship between the repetition frequency of the sampled light and the repetition frequency of the measured signal light based on the physical relationship of asynchronous optical sampling, and derive the aliasing beat frequency; Step S3: Map the continuous domain beat frequency position to the theoretical index position in the discrete spectrum based on the sampling length and frequency resolution; Step S4: Construct a local search interval around the theoretical beat frequency index according to the preset relative search tolerance; Step S5: Compare the interval with the length of the spectrum array, perform boundary protection, and ensure that the search interval falls completely within the effective spectrum range; Step S6: Perform a local maximum search on the amplitude spectrum to obtain the maximum amplitude and its local index within the interval, convert the local index into a global index, and obtain a rough estimate of the synchronization parameters. Step S7: Using the coarse estimate of the synchronization parameters obtained in step S6 as the initial value, further refine it in the vicinity using a local spectrum refinement algorithm to obtain the accurate synchronization parameters.

[0050] Experimental Example: As shown in Figure 4, the optical path in the experimental example includes two light sources: a 19.96 MHz pulsed laser and a 23.37 MHz pulsed laser, a Mach-Zehnder modulator, a pulse broadening module, and high-speed and low-speed photodetectors for driving and detection, respectively. The asynchronous optical sampling system consists of a Mach-Zehnder modulator, a low-speed photodetector, and a data acquisition card connected in sequence. The 19.96 MHz pulsed laser serves as the source of the measured signal light, and its output pulse light is directly injected into the signal arm of the Mach-Zehnder modulator. The 23.37 MHz pulsed laser serves as the sampling light pulse source; its output is converted into an electrical signal by the high-speed photodetector and amplified by an RF amplifier before driving the Mach-Zehnder modulator to achieve asynchronous optical sampling of the measured signal light. The modulated light pulse is broadened by the pulse broadening module, received by the low-speed photodetector, and converted into a low-speed analog electrical signal. Among them, the bandwidth design of high-speed photodetectors is used to meet the detection requirements of the original pulse frequency components of the measured signal, while low-speed photodetectors rely on the time broadening effect introduced by asynchronous sampling to complete the equivalent detection with only MHz-level bandwidth, thereby significantly reducing the system's dependence on expensive high-speed devices.

[0051] The circuit processing section mainly involves the modulated optical pulse being processed by a pulse broadening module, received by a low-speed photodetector, and converted into a low-speed analog electrical signal. This signal is then digitally acquired by a data acquisition card and finally sent to a digital signal processing unit (DSP) for algorithm processing.

[0052] The digital signal processing flow is the processing flow of this method, as shown in Figure 5, including the following steps: 1. Acquiring sampling data: Receive the raw asynchronous optical sampling pulse data output by the data acquisition card as input data for digital signal processing; 2. Peak extraction: Perform peak extraction processing on the sampling data to extract the amplitude peaks from the continuous sampling pulses, obtaining a peak sequence; 3. Coarse estimation of synchronization parameters: Process the peak sequence, construct a local search interval based on the physical prior knowledge of asynchronous optical sampling, and search within this interval to determine the frequency value of the data point where the maximum peak is located in the search window as a coarse estimate of the synchronization parameters. 4. Local Spectrum Refinement Algorithm: Based on the aforementioned coarse estimate... Based on this, a local spectrum refinement algorithm is executed to refine the search around the coarse estimate and calculate the precise frequency and phase deviation; 5. Eye diagram acquisition: Optimized synchronization parameters are obtained through the local spectrum refinement algorithm, and the original sampled data is reconstructed, rearranged, and superimposed to form a clear signal eye diagram; 6. Vienna filtering: Vienna filtering is applied to the acquired eye diagram signal to suppress noise interference to the greatest extent and optimize the signal-to-noise ratio; 7. Waveform sorting: The filtered data is sorted and reassembled according to the timing logic to recover the original high-speed measured signal waveform.

[0053] Compared with existing technologies and this solution, the results are shown in Figure 6. Figure 6(a) shows the waveform recovery result when the coarse estimate of the synchronization parameters obtained by the traditional full-band search is directly used for time base reconstruction. It can be seen that the signal amplitude has severe jitter, the waveform outline is blurred, and there is a lot of noise, indicating that the inaccurate estimation of the synchronization parameters leads to the time base reconstruction error.

[0054] Figure 6(b) shows the waveform recovery result after time base reconstruction using the precise values ​​of the synchronization parameters obtained by this scheme. It can be seen that the recovered pulse waveform is very smooth and clear, and the signal-to-noise ratio is significantly improved, verifying the superiority of the method of this invention in asynchronous optical sampling waveform recovery.

[0055] Through the above structure and processing flow, this embodiment demonstrates that this solution can achieve high-precision synchronization and waveform recovery of high-speed narrow pulse signals while ensuring a simple hardware structure and a low sampling rate.

[0056] In summary, this invention achieves fast and robust synchronous parameter estimation by combining physical layer parameters to calculate prior positions and using local window constraints to limit the search range, making it particularly suitable for high-precision asynchronous optical sampling measurements.

[0057] Compared with the prior art, the present invention has the following beneficial effects: 1. By introducing the theoretical position of the synchronization parameter based on physical prior constraints and constructing a local search window near the position, the present invention can significantly compress the search space and reduce the amount of computation while ensuring the estimation accuracy, thereby greatly improving the computational efficiency of the synchronization parameter estimation.

[0058] 2. This scheme accurately predicts the theoretical beat frequency position, and the search process is strictly limited to a narrow band region near the theoretical prediction position. Even in environments with low signal-to-noise ratio or spectral pseudo-peak interference, it can effectively avoid the problem of misjudging the main peak of the spectrum that is prone to occur in traditional full-frequency scanning methods, thereby maintaining the stability and reliability of the synchronization parameter estimation results.

[0059] 3. The synchronization parameter estimation and waveform reconstruction process of this solution is entirely based on digital signal processing, eliminating the need for complex hardware synchronization circuits such as external phase-locked loops and voltage-controlled oscillators, and also avoiding reliance on expensive testing equipment such as high-speed real-time oscilloscopes. With just a general-purpose data acquisition card and a programmable processing platform, real-time or near-real-time estimation of synchronization parameters and narrow-pulse waveform recovery can be achieved, significantly simplifying the system structure, reducing implementation costs, and facilitating integration and promotion in engineering applications.

[0060] This invention also provides an asynchronous optical sampling synchronization parameter estimation and optimization system. The system includes a computer device, which includes a processor and a memory. The memory stores computer instructions, and the processor executes the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps of the method described above.

[0061] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the aforementioned asynchronous optical sampling synchronization parameter estimation optimization method. The computer-readable storage medium can be a tangible storage medium, such as random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disks, removable storage disks, CD-ROMs, or any other form of storage medium known in the art.

[0062] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the desired tasks. The programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave.

[0063] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0064] In this invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations of the embodiments of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for estimating and optimizing asynchronous optical sampling synchronization parameters, characterized in that, The method includes the following steps: acquiring optical sampling pulse data collected by an asynchronous optical sampling system; extracting peak sequences using a peak extraction function and performing a fast Fourier transform to obtain a complex spectrum; performing modulus-taking processing based on the complex spectrum to obtain an amplitude spectrum; calculating harmonic orders based on the repetition frequency of the sampling optical pulse source and the repetition frequency of the measured signal light; calculating aliasing beat frequencies based on the harmonic orders; determining prediction index positions based on the aliasing beat frequencies; locating the prediction index positions in the amplitude spectrum and constructing a search window; searching for the maximum peak value in the search window; and determining synchronization parameters based on the frequency value of the data point where the maximum peak value is located in the search window.

2. The asynchronous optical sampling synchronization parameter estimation and optimization method according to claim 1, characterized in that, In the step of determining the synchronization parameter based on the frequency value of the data point where the maximum peak is located in the search window, the frequency value of the data point where the maximum peak is located in the search window is used as the synchronization parameter, or the peak sequence and the data point where the maximum peak is located in the search window are used as inputs to the local spectrum refinement algorithm, and the optimized synchronization parameter is obtained through the local spectrum refinement algorithm.

3. The asynchronous optical sampling synchronization parameter estimation and optimization method according to claim 2, characterized in that, The local spectrum refinement algorithm employs the Chirp Z-transform algorithm, the Zoom-FFT algorithm, or an interpolation-based frequency refinement algorithm.

4. The asynchronous optical sampling synchronization parameter estimation and optimization method according to claim 1, characterized in that, In the step of performing modulo operation on the complex spectrum to obtain the amplitude spectrum, the average value of each complex point in the complex spectrum is calculated, and the average value of the complex points is subtracted from the complex value of each complex point to obtain the updated complex spectrum. The updated complex spectrum is then subjected to modulo operation to obtain the amplitude spectrum.

5. The asynchronous optical sampling synchronization parameter estimation and optimization method according to claim 1, characterized in that, In the step of calculating the harmonic order based on the repetition frequency of the sampled optical pulse source and the repetition frequency of the measured signal light, the harmonic order is calculated using the following formula: in, Indicates the harmonic order. This indicates the repetition frequency of the measured signal light. This indicates the repetition frequency of the sampling optical pulse source. Indicates rounding down.

6. The asynchronous optical sampling synchronization parameter estimation and optimization method according to claim 5, characterized in that, In the step of calculating the aliasing beat frequency based on the harmonic order, the aliasing beat frequency is calculated using the following formula: in, This indicates the overlapping beat frequency.

7. The asynchronous optical sampling synchronization parameter estimation and optimization method according to any one of claims 1 to 6, characterized in that, In the step of determining the predicted index position based on the aliasing beat frequency, the frequency resolution is calculated based on the repetition frequency of the sampled light pulse source and the length of the peak sequence, and the predicted index position is calculated based on the frequency resolution.

8. The asynchronous optical sampling synchronization parameter estimation and optimization method according to claim 7, characterized in that, In the step of calculating the frequency resolution based on the repetition frequency and peak sequence length of the sampled optical pulse source, the frequency resolution is calculated using the following formula: In the step of calculating the predicted index position based on the frequency resolution, the predicted index position is calculated using the following formula: in, Indicates frequency resolution. Indicates the predicted index position. Indicates aliasing beat frequency. Indicates the length of the peak sequence. This indicates the repetition frequency of the sampling optical pulse source.

9. The asynchronous optical sampling synchronization parameter estimation and optimization method according to claim 1, characterized in that, The step of locating the prediction index position in the amplitude spectrum and constructing a search window involves extending a distance of a preset tolerance coefficient from the prediction index position along the axis where the frequency is located in the amplitude spectrum in both the increasing and decreasing directions, thereby constructing the search window.

10. An asynchronous optical sampling synchronization parameter estimation and optimization system, characterized in that, The system includes a computer device, which includes a processor and a memory. The memory stores computer instructions, and the processor executes the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps of the method as described in any one of claims 1 to 9.

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