Satellite optical communication blind frequency offset estimation method and device based on extended phase difference, equipment and storage medium

Abstract

The invention discloses a satellite optical communication blind frequency offset estimation method, device and equipment based on extended phase difference, and a storage medium, and relates to the technical field of signal processing, and the method comprises the steps: obtaining an analog digital signal after digital-to-analog conversion during photoelectric detection; performing phase difference calculation and expansion processing according to the analog digital signal to obtain a phase difference sequence; performing anomaly detection and interpolation reconstruction according to the phase difference sequence to obtain a reconstructed phase difference sequence; and calculating an instantaneous frequency offset according to the reconstructed phase difference sequence, and carrying out noise smoothing processing on the instantaneous frequency offset to obtain a smoothed frequency offset estimation value. According to the invention, abnormal point detection and elimination are carried out on the phase difference sequence of the received signal, and the interpolation algorithm is utilized to reconstruct the continuous phase change track, so that the synchronization precision and link stability of the satellite optical communication system in a large-frequency-offset and high-dynamic scene are improved.

Description

Technical Field

[0001] This application relates to the technical field of signal processing, and in particular to a method, apparatus, device and storage medium for blind frequency offset estimation in satellite optical communication based on extended phase difference. Background Technology

[0002] Laser-based satellite communication systems, with their advantages of high transmission rates, high security, and miniaturized terminals, have become a key technology for the development of space communication networks. However, unlike terrestrial fiber optic communication, low Earth orbit satellites are in high-speed motion, which introduces significant Doppler frequency offsets into inter-satellite or satellite-to-ground laser links. These offsets can reach ±10 GHz and change at extremely rapid rates. This frequency offset severely degrades the signal-to-noise ratio of the received signal and causes serious interference to digital signal processing stages such as carrier phase recovery and symbol synchronization. It is a core challenge restricting the performance and reliability of high-speed coherent satellite optical communication links.

[0003] To address this problem, a layered, multi-level compensation strategy is commonly adopted: first, open-loop prediction based on satellite ephemeris and orbital parameters is used to complete preliminary compensation; then, coarse compensation is performed using optical front-end devices such as tunable lasers to suppress the residual frequency offset to within the range that the digital signal processing unit can handle; finally, fine compensation is performed in the digital domain using a frequency offset estimation algorithm. Blind estimation algorithms are favored because they do not incur additional spectral overhead. However, this strategy has significant limitations: first, it relies excessively on external prediction, resulting in poor robustness when orbital parameters are uncertain or frequency offset changes drastically; second, high-performance optical front-end devices are complex and costly to implement; and third, the digital compensation stage itself has limited capabilities, making it difficult to independently handle extremely large frequency offsets. Specifically, in blind frequency offset estimation algorithms for digital signal processing, the widely used algorithm is the M-power-based spectral estimation algorithm. This algorithm estimates the frequency offset by performing a nonlinear transformation on the received signal and calculating the spectrum, but its unambiguous estimation range is limited by the modulation order M. As satellite optical communication rates develop towards 100Gbps and above, although the increase in sampling rate theoretically makes it possible to directly process large frequency offsets in the digital domain, the inherent range bottleneck of the M-power algorithm makes it difficult to apply, severely limiting the system's acquisition and tracking capabilities.

[0004] Therefore, how to improve the accuracy and stability of frequency offset estimation under large frequency offset conditions is an urgent problem to be solved. Summary of the Invention

[0005] The main objective of this application is to provide a method, apparatus, device, and storage medium for blind frequency offset estimation in satellite optical communication based on extended phase difference, aiming to solve the technical problem of how to improve the accuracy and stability of frequency offset estimation under large frequency offset conditions.

[0006] To achieve the above objectives, this application proposes a blind frequency offset estimation method for satellite optical communication based on extended phase difference, the method comprising: During photoelectric detection, the analog-to-digital signal after digital-to-digital conversion is acquired; The phase difference sequence is obtained by performing phase difference calculation and expansion processing on the analog-digital signal. Anomaly detection and interpolation reconstruction are performed based on the phase difference sequence to obtain a reconstructed phase difference sequence. The instantaneous frequency offset is calculated based on the reconstructed phase difference sequence, and noise smoothing is applied to the instantaneous frequency offset to obtain a smoothed frequency offset estimate.

[0007] In one embodiment, the step of calculating and expanding the phase difference based on the analog-digital signal to obtain a phase difference sequence includes: The current sampling point and the delayed sampling point in the analog-digital signal are multiplied by their conjugates to obtain the differential signal; Calculate the phase of the differential signal to obtain the initial phase difference; The initial phase difference is subjected to phase expansion processing to obtain a continuous phase difference sequence.

[0008] In one embodiment, the step of performing anomaly detection and interpolation reconstruction based on the phase difference sequence to obtain a reconstructed phase difference sequence includes: The second-order difference is calculated point by point for the phase difference sequence to obtain the rate of change of the difference; When the absolute value of the differential change rate is greater than a preset dynamic threshold, the sampling points that exceed the preset dynamic threshold are marked as abnormal points and removed to obtain a normal point sample set. Interpolation reconstruction is performed on the abnormal points and the normal point sample set to obtain the reconstructed phase difference sequence.

[0009] In one embodiment, before the step of marking and removing sampling points exceeding the preset dynamic threshold as outliers when the absolute value of the differential change rate is greater than a preset dynamic threshold to obtain a normal point sample set, the method further includes: Obtain the current signal-to-noise ratio and the preset signal-to-noise ratio range; The dynamic threshold is adjusted based on the signal-to-noise ratio and the preset signal-to-noise ratio range to obtain the adjusted preset dynamic threshold.

[0010] In one embodiment, the step of performing interpolation reconstruction based on the sample sets of outliers and normal points to obtain a reconstructed phase difference sequence includes: Based on the set of normal point samples, locate the normal points adjacent to the abnormal points to obtain the first normal point and the second normal point; A linear interpolation path is constructed between the first normal point and the second normal point according to a preset interpolation strategy, and the interpolation phase is calculated point by point. The interpolated phase is written into the rejection region of the outlier to obtain the reconstructed phase difference sequence.

[0011] In one embodiment, the step of calculating the instantaneous frequency offset based on the reconstructed phase difference sequence and performing noise smoothing on the instantaneous frequency offset to obtain a smoothed frequency offset estimate includes: Obtain the current signal-to-noise ratio and determine the preset sliding window length based on the signal-to-noise ratio; The instantaneous frequency offset sequence is calculated based on each sampling point value in the reconstructed phase difference sequence and the corresponding delayed sampling interval; The instantaneous frequency offset sequence is averaged according to the sliding window length to obtain a smoothed frequency offset estimate.

[0012] In one embodiment, after the steps of calculating the instantaneous frequency offset based on the reconstructed phase difference sequence and performing noise smoothing on the instantaneous frequency offset to obtain a smoothed frequency offset estimate, the method further includes: A compensation phase signal is generated based on the frequency offset estimate; The analog-to-digital signal is phase-rotated and compensated based on the compensated phase signal to obtain the corrected target signal.

[0013] Furthermore, to achieve the above objectives, this application also proposes a satellite optical communication blind frequency offset estimation device based on extended phase difference, the device comprising: The signal conversion module is used to acquire analog-to-digital signals after digital-to-analog conversion during photoelectric detection. The phase difference extraction module is used to perform phase difference calculation and expansion processing based on the analog-digital signal to obtain a phase difference sequence. The filtering and interpolation reconstruction module is used to perform anomaly detection and interpolation reconstruction based on the phase difference sequence to obtain a reconstructed phase difference sequence; The frequency offset estimation module is used to calculate the instantaneous frequency offset based on the reconstructed phase difference sequence, and to perform noise smoothing on the instantaneous frequency offset to obtain a smoothed frequency offset estimate.

[0014] Furthermore, to achieve the above objectives, this application also proposes a satellite optical communication blind frequency offset estimation device based on extended phase difference. The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the satellite optical communication blind frequency offset estimation method based on extended phase difference as described above.

[0015] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the satellite optical communication blind frequency offset estimation method based on extended phase difference as described above.

[0016] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the satellite optical communication blind frequency offset estimation method based on extended phase difference as described above.

[0017] This application provides a blind frequency offset estimation method for satellite optical communication based on extended phase difference. The method includes: acquiring an analog-to-digital signal after digital-to-analog conversion during photoelectric detection; calculating and expanding the phase difference based on the analog-to-digital signal to obtain a phase difference sequence; performing anomaly detection and interpolation reconstruction based on the phase difference sequence to obtain a reconstructed phase difference sequence; calculating the instantaneous frequency offset based on the reconstructed phase difference sequence, and performing noise smoothing on the instantaneous frequency offset to obtain a smoothed frequency offset estimate. In summary, this application achieves instantaneous frequency offset estimation and smoothed output by detecting and eliminating anomalies in the phase difference sequence of the received signal and reconstructing a continuous phase change trajectory using an interpolation algorithm. This solves the problems of narrow estimation range and poor dynamic tracking capability caused by modulation order limitations in traditional algorithms, improving the synchronization accuracy and link stability of satellite optical communication systems in scenarios with large frequency offsets and high dynamics. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the first embodiment of the blind frequency offset estimation method for satellite optical communication based on extended phase difference in this application; Figure 2 This is a schematic diagram of the system execution process in one embodiment of the satellite optical communication blind frequency offset estimation method based on extended phase difference of this application; Figure 3 This is a flowchart illustrating the second embodiment of the blind frequency offset estimation method for satellite optical communication based on extended phase difference in this application; Figure 4 This is a flowchart illustrating the third embodiment of the blind frequency offset estimation method for satellite optical communication based on extended phase difference in this application; Figure 5 This is a schematic diagram of the ±10GHz frequency offset estimation and tracking results in one embodiment of the satellite optical communication blind frequency offset estimation method based on extended phase difference in this application. Figure 6 This is a schematic diagram of the module structure of the satellite optical communication blind frequency offset estimation device based on extended phase difference according to an embodiment of this application; Figure 7 This is a schematic diagram of the device structure of the hardware operating environment involved in the blind frequency offset estimation method for satellite optical communication based on extended phase difference in the embodiments of this application.

[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0023] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0024] The main solution of this application embodiment is as follows: when performing photoelectric detection, an analog-to-digital signal after digital-to-analog conversion is acquired; phase difference calculation and expansion processing are performed on the analog-to-digital signal to obtain a phase difference sequence; anomaly detection and interpolation reconstruction are performed on the phase difference sequence to obtain a reconstructed phase difference sequence; instantaneous frequency offset is calculated based on the reconstructed phase difference sequence, and noise smoothing processing is performed on the instantaneous frequency offset to obtain a smoothed frequency offset estimate.

[0025] Laser-based satellite communication systems, with their advantages of high transmission rates, high security, and miniaturized terminals, have become a key technology for the development of space communication networks. However, unlike terrestrial fiber optic communication, low Earth orbit satellites are in high-speed motion, which introduces significant Doppler frequency offsets into inter-satellite or satellite-to-ground laser links. These offsets can reach ±10 GHz and change at extremely rapid rates. This frequency offset severely degrades the signal-to-noise ratio of the received signal and causes serious interference to digital signal processing stages such as carrier phase recovery and symbol synchronization. It is a core challenge restricting the performance and reliability of high-speed coherent satellite optical communication links.

[0026] To address this problem, a layered, multi-level compensation strategy is commonly adopted: first, open-loop prediction based on satellite ephemeris and orbital parameters is used to complete preliminary compensation; then, coarse compensation is performed using optical front-end devices such as tunable lasers to suppress the residual frequency offset to within the range that the digital signal processing unit can handle; finally, fine compensation is performed in the digital domain using a frequency offset estimation algorithm. Blind estimation algorithms are favored because they do not incur additional spectral overhead. However, this strategy has significant limitations: first, it relies excessively on external prediction, resulting in poor robustness when orbital parameters are uncertain or frequency offset changes drastically; second, high-performance optical front-end devices are complex and costly to implement; and third, the digital compensation stage itself has limited capabilities, making it difficult to independently handle extremely large frequency offsets. Specifically, in blind frequency offset estimation algorithms for digital signal processing, the widely used algorithm is the M-power-based spectral estimation algorithm. This algorithm estimates the frequency offset by performing a nonlinear transformation on the received signal and calculating the spectrum, but its unambiguous estimation range is limited by the modulation order M. As satellite optical communication rates develop towards 100Gbps and above, although the increased sampling rate theoretically makes it possible to directly process large frequency offsets in the digital domain, the inherent range bottleneck of the M-power algorithm makes it difficult to apply, severely limiting the system's acquisition and tracking capabilities. Therefore, how to improve the accuracy and stability of frequency offset estimation under large frequency offset conditions is a problem that urgently needs to be solved.

[0027] It should be noted that the executing entity in this embodiment can be a satellite optical communication blind frequency offset estimation system based on extended phase difference, a computing service device with data processing, network communication, and program execution functions, or an electronic device capable of implementing the aforementioned satellite optical communication blind frequency offset estimation function based on extended phase difference. This embodiment does not specifically limit it in this way. The following uses a satellite optical communication blind frequency offset estimation system based on extended phase difference as an example to describe this embodiment and the following embodiments.

[0028] Based on this, embodiments of this application provide a method for estimating blind frequency offset in satellite optical communication based on extended phase difference, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the blind frequency offset estimation method for satellite optical communication based on extended phase difference in this application.

[0029] In this embodiment, the satellite optical communication blind frequency offset estimation method based on extended phase difference includes steps S10~S40: Step S10: During photoelectric detection, acquire the analog-to-digital signal after digital-to-analog conversion.

[0030] It should be noted that, in this embodiment, the system is composed of... Figure 2As shown, the system consists of units (modules) such as a photodetector, analog-to-digital converter, phase difference extraction, filtering and interpolation reconstruction, frequency offset estimation and smoothing, and frequency offset compensation. In this step, the system first converts the received optical signal into an analog electrical signal through the photodetector. Then, this analog electrical signal is sampled and quantized by the analog-to-digital converter, ultimately obtaining a discrete-time digital signal sequence. This is for use by the subsequent phase difference extraction module.

[0031] Among them, digital signal sequence As shown in Formula 1: (Formula 1) Where n is the sampling point index, which is an integer. This represents the symbol sent at the nth sampling time. This represents the phase generated by the accumulation of time-varying frequency offset. For noise, The sampling period.

[0032] Step S20: Perform phase difference calculation and expansion processing based on the analog-digital signal to obtain a phase difference sequence.

[0033] It should be noted that, due to the modulus of the directly calculated phase value... Due to the ambiguity of the signal, phase unrolling is necessary to obtain a continuous sequence of phase differences, laying the foundation for subsequent frequency offset estimation. That is, this step aims to extract the phase change, or phase difference, between adjacent sampling points from the received signal.

[0034] In one feasible implementation, step S20 specifically includes: Step S201: Perform conjugate multiplication on the current sampling point and the delayed sampling point in the analog-digital signal to obtain the differential signal.

[0035] It should be noted that in this step, the system will process the signal. Perform delay differential processing. Specifically, the current sampling point... Compared with its previous m-th sampling point Perform conjugate multiplication to obtain the difference signal. As shown in Formula 2: (Formula 2) in, express The conjugate complex number of , where m is the number of delayed samples, usually taken as 1.

[0036] Step S202: Calculate the phase of the differential signal to obtain the initial phase difference.

[0037] It should be noted that in this step, the system will calculate the aforementioned differential signal. The initial phase difference can be obtained by determining the argument. Specifically, as shown in Formula 3: (Formula 3) Step S203: Perform phase expansion processing on the initial phase difference to obtain a continuous phase difference sequence.

[0038] It should be noted that in this step, the system performs phase expansion processing on the initial phase difference to obtain a continuous, non-jumping phase difference sequence. Specifically, as shown in Formula 4: (Formula 4) Step S30: Perform anomaly detection and interpolation reconstruction based on the phase difference sequence to obtain the reconstructed phase difference sequence.

[0039] It should be noted that, in this step, due to the influence of noise and modulation symbol transitions, outliers may exist in the continuous phase difference sequence obtained in step S20. The system will identify and remove these outliers using a dynamic detection algorithm, and then reconstruct a smooth, continuous phase difference sequence using an interpolation algorithm. For example, in satellite optical communication, when the signal-to-noise ratio fluctuates drastically or the signal briefly fades, obvious spikes will appear in the phase difference sequence; this step can effectively filter out these interferences.

[0040] Step S40: Calculate the instantaneous frequency offset based on the reconstructed phase difference sequence, and perform noise smoothing on the instantaneous frequency offset to obtain a smoothed frequency offset estimate.

[0041] It should be noted that in this step, the system converts the reconstructed continuous phase difference sequence into an instantaneous frequency offset value by combining it with the system's sampling time interval. Subsequently, a moving average method is used to smooth this instantaneous frequency offset sequence to suppress the influence of random noise, ultimately outputting a stable and reliable frequency offset estimate. The length of the sliding window in the noise smoothing process can be dynamically adjusted according to the channel signal-to-noise ratio (SNR). A shorter window is used under high SNR conditions to maintain tracking agility, while a longer window is used under low SNR conditions to improve estimation accuracy.

[0042] This embodiment provides a blind frequency offset estimation method for satellite optical communication based on extended phase difference. The method includes: acquiring an analog-to-digital signal after digital-to-analog conversion during photoelectric detection; calculating and expanding the phase difference based on the analog-to-digital signal to obtain a phase difference sequence; performing anomaly detection and interpolation reconstruction based on the phase difference sequence to obtain a reconstructed phase difference sequence; calculating the instantaneous frequency offset based on the reconstructed phase difference sequence, and smoothing the instantaneous frequency offset to obtain a smoothed frequency offset estimate. In summary, this embodiment achieves instantaneous frequency offset estimation and smoothed output by detecting and removing anomalies in the phase difference sequence of the received signal and reconstructing the continuous phase change trajectory using an interpolation algorithm. This solves the problems of narrow estimation range and poor dynamic tracking capability caused by modulation order limitations in traditional algorithms, improving the synchronization accuracy and link stability of satellite optical communication systems in scenarios with large frequency offsets and high dynamics.

[0043] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the satellite optical communication blind frequency offset estimation method based on extended phase difference in this application. Step S30 specifically includes: Step S301: Calculate the second-order difference point by point for the phase difference sequence to obtain the rate of change of the difference.

[0044] It should be noted that in this step, the system calculates the second-order difference between each sampling point and its two preceding sampling points for the continuous phase difference sequence after phase expansion. Specifically, the second-order difference at the current point is obtained by subtracting twice the phase difference of the previous point from the current point's phase difference and then adding the phase difference of the two preceding points. This step effectively amplifies abrupt changes in the phase sequence because outliers (such as those caused by noise spikes or residual modulation symbol transitions) cause a significantly greater acceleration in phase change than the smooth changes caused by normal frequency offsets. Specifically, the rate of difference change... The calculation process is shown in Formula 5: (Formula 5) Step S302: When the absolute value of the differential change rate is greater than a preset dynamic threshold, the sampling points that exceed the preset dynamic threshold are marked as abnormal points and removed to obtain a normal point sample set.

[0045] It should be noted that in this step, the system will calculate the absolute value of the differential rate of change for each point. With a preset dynamic threshold The data is compared. If the absolute value of a point exceeds a threshold, that point and several of its neighbors are considered unreliable outliers and are removed from the valid data. All the remaining points constitute a set of normal data. Specifically, as shown in Formula 6: (Formula 6) Understandably, the purpose of this step is to achieve dynamic characteristic separation, separating the smooth phase trajectory that characterizes normal frequency offset changes from the abnormal abrupt changes caused by interference.

[0046] In one feasible implementation, prior to step S302, the method further includes: Step A10: Obtain the current signal-to-noise ratio and the preset signal-to-noise ratio range.

[0047] It should be noted that in this step, the system obtains the estimated signal-to-noise ratio (SNR) of the current communication link and calls a preset SNR range parameter. This preset SNR range is pre-configured in the system and is used to distinguish between high and low SNR operating conditions.

[0048] Step A20: Adjust the dynamic threshold according to the signal-to-noise ratio and the preset signal-to-noise ratio range to obtain the adjusted preset dynamic threshold.

[0049] It should be noted that in this step, the system dynamically adjusts the dynamic threshold used for anomaly detection based on the signal-to-noise ratio (SNR) range. Specifically, when in a high SNR range, the system lowers the dynamic threshold to improve the sensitivity to detect minor anomalies; when in a low SNR range, the dynamic threshold is increased to prevent large noise fluctuations from being misjudged as anomalies, thereby enhancing the algorithm's robustness under adverse channel conditions.

[0050] Step S303: Perform interpolation reconstruction processing based on the abnormal points and the normal point sample set to obtain the reconstructed phase difference sequence.

[0051] It should be noted that in this step, the system performs interpolation calculations on the positions of all marked as outliers based on the normal point sample set. That is, using the reliable phase trajectory formed by the normal points, the system repairs the phase continuity disrupted by the outliers through numerical interpolation methods, and finally outputs a complete and smooth phase difference sequence.

[0052] In one feasible implementation, step S303 specifically includes: Step B10: Locate the normal points adjacent to the abnormal points based on the normal point sample set to obtain the first normal point and the second normal point.

[0053] It should be noted that in this step, for each outlier location requiring interpolation, the system searches the normal point sample set to find the nearest normal point before (to the left) the outlier index position, denoted as the first normal point; simultaneously, it finds the nearest normal point after (to the right) the outlier index position, denoted as the second normal point. These two normal points provide a reliable reference benchmark for subsequent interpolation calculations. Specifically, the first and second normal points are shown in Equations 7 and 8, respectively: (Formula 7) (Formula 8) Step B20: Construct a linear interpolation path between the first normal point and the second normal point according to the preset interpolation strategy, and calculate the interpolation phase point by point.

[0054] It should be noted that the system can employ different interpolation schemes for interpolation reconstruction, including piecewise linear interpolation, spline interpolation, and Farrow structure interpolation. Considering real-time performance and low complexity, in this step, the system will use piecewise linear interpolation to calculate the interpolated phase point by point. Specifically, the phase difference sequence is reconstructed. The calculation process is shown in Formula 9: (Formula 9) Understandably, this method has the significant advantage of low computational complexity while ensuring interpolation smoothness, making it very suitable for real-time processing in DSPs or FPGAs.

[0055] Step B30: Write the interpolated phase into the removal region of the outlier to obtain the reconstructed phase difference sequence.

[0056] It should be noted that in this step, the system calculates the interpolated phase for all outliers and writes it one by one into the corresponding positions in the original phase difference sequence that were removed. Finally, a reconstructed phase difference sequence with the same length as the original sequence but with continuous and reliable phase values ​​at all sample points is generated.

[0057] In this embodiment, the system dynamically detects and removes abnormal jump points in the phase difference sequence and uses normal points for interpolation reconstruction, thereby achieving accurate recovery of continuous phase trajectories in high dynamic and large frequency offset scenarios. This solves the estimation inaccuracy problem caused by symbol jumps and noise in traditional methods, and significantly improves the accuracy, robustness and link stability of frequency offset estimation.

[0058] Based on the first and second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in embodiments one and two above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4 , Figure 4 This is a flowchart illustrating the third embodiment of the satellite optical communication blind frequency offset estimation method based on extended phase difference in this application. Step S40 specifically includes: Step S401: Obtain the current signal-to-noise ratio and determine the preset sliding window length based on the signal-to-noise ratio.

[0059] It should be noted that in this step, the system acquires the signal-to-noise ratio (SNR) measurement of the current communication link in real time and dynamically determines the sliding window length L for frequency offset smoothing based on the preset SNR-window length mapping relationship. Specifically, when the system is detected to be under high SNR conditions, a smaller window length (e.g., 25-301 samples) is selected; when under low SNR conditions, a larger window length (e.g., 301-501 samples) is selected. This adaptive mechanism effectively suppresses noise interference while maintaining the dynamic response speed of frequency offset tracking.

[0060] Step S402: Calculate the instantaneous frequency offset sequence based on the value of each sampling point in the reconstructed phase difference sequence and the corresponding delayed sampling interval.

[0061] It should be noted that in this step, the system will use the reconstructed phase difference sequence. As input, this sequence is continuous phase difference data obtained through the aforementioned steps (such as outlier removal and interpolation reconstruction), with each sampling point representing a phase difference value. When calculating the instantaneous frequency offset, for each sampling point value in the reconstructed phase difference sequence, it is divided by a constant factor related to the delay sampling interval and pi to obtain the corresponding instantaneous frequency offset value. The delay sampling interval refers to the delay amount in the delay differential processing used during phase difference extraction, typically expressed in units of sampling periods (e.g., a delay of one sampling period). This constant factor is determined by the delay amount, pi (π), and the sampling period. A point-by-point calculation generates an instantaneous frequency offset sequence, where each element corresponds to an estimated frequency offset value at a given sampling time.

[0062] (Formula 10) Step S403: Perform a moving average on the instantaneous frequency offset sequence according to the sliding window length to obtain a smoothed frequency offset estimate.

[0063] It should be noted that in this step, the system performs a moving average processing on the generated instantaneous frequency offset sequence based on the determined sliding window length L. The specific process of the moving average processing is as follows: Centered on the current sampling point, multiple instantaneous frequency offset values ​​within the sliding window length are taken (for example, if the window length is L, then L consecutive sampling points are taken), and the arithmetic mean of these values ​​is calculated as the smoothed frequency offset estimate for the current point. Then, the window slides point by point, repeating the above calculation until the entire instantaneous frequency offset sequence is processed, outputting the smoothed frequency offset estimate sequence. Specifically, the smoothed frequency offset estimate... The calculation process is shown in Formula 11: (Formula 11) Understandably, this step aims to suppress random noise and fluctuations in the instantaneous frequency offset sequence, improve the stability and accuracy of frequency offset estimation, and maintain the ability to track frequency offset changes. In satellite optical communication scenarios, since the rate of Doppler frequency offset change is much lower than the symbol rate, moving averages can effectively smooth high-frequency noise without affecting the response to low-frequency frequency offset changes. The method in this application can achieve tracking and estimation of Doppler frequency offsets with a sinusoidal variation of ±10 GHz. Specifically, its effect is as follows: Figure 5 As shown.

[0064] In one feasible implementation, after step S40, the method further includes: Step S50: Generate a compensated phase signal based on the frequency offset estimate.

[0065] It should be noted that in this step, the system will use the smoothed frequency offset estimate. As input, a numerically controlled oscillator (CNC) is driven to generate a compensation phase signal. The specific process is as follows: the CNC oscillator calculates the accumulated phase based on the frequency offset estimate, generating a time-varying phase signal that represents the phase rotation required to compensate for the frequency offset. For example, for each sampling point, the CNC oscillator multiplies the current frequency offset estimate by the sampling period and adds the phase values ​​from previous moments, thereby generating a continuous compensation phase signal. Specifically, the calculation process of the compensation phase signal is shown in Equation 12: (Formula 12) Step S60: Perform phase rotation compensation on the analog-digital signal according to the compensation phase signal to obtain the corrected target signal.

[0066] It should be noted that in this step, the system multiplies the generated compensated phase signal with the received signal after analog-to-digital conversion to perform phase rotation compensation. The specific calculation process is shown in Formula 13: (Formula 13) in, This represents the corrected target signal after frequency offset compensation at sampling time point n.

[0067] It is understandable that the compensated signal is the corrected target signal, and its frequency offset effect is significantly suppressed, which can be used in subsequent digital signal processing stages, such as carrier phase recovery, symbol synchronization and demodulation.

[0068] In this embodiment, an adaptive sliding window is used to smooth the instantaneous frequency offset estimation, and a numerically controlled oscillator is used to generate a compensation phase for real-time rotation correction. This achieves fast tracking and compensation of the wide-range, high-dynamic Doppler frequency offset in the entire digital domain, solving the problems of limited estimation range, slow dynamic response, and reliance on additional hardware in traditional methods. This significantly improves the synchronization accuracy and link stability of the satellite optical communication system.

[0069] This application also provides a satellite optical communication blind frequency offset estimation device based on extended phase difference. Please refer to [reference needed]. Figure 6 The satellite optical communication blind frequency offset estimation device based on extended phase difference includes: The signal conversion module 10 is used to acquire the analog-to-digital signal after digital-to-analog conversion during photoelectric detection. Phase difference extraction module 20 is used to perform phase difference calculation and expansion processing based on the analog-digital signal to obtain a phase difference sequence; The filtering and interpolation reconstruction module 30 is used to perform anomaly detection and interpolation reconstruction based on the phase difference sequence to obtain a reconstructed phase difference sequence; The frequency offset estimation module 40 is used to calculate the instantaneous frequency offset based on the reconstructed phase difference sequence, and to perform noise smoothing on the instantaneous frequency offset to obtain a smoothed frequency offset estimate.

[0070] The satellite optical communication blind frequency offset estimation device based on extended phase difference provided in this application employs the satellite optical communication blind frequency offset estimation method based on extended phase difference in the above embodiments, which can solve the technical problem of how to improve the accuracy and stability of frequency offset estimation under large frequency offset conditions. Compared with the prior art, the beneficial effects of the satellite optical communication blind frequency offset estimation device based on extended phase difference provided in this application are the same as those of the satellite optical communication blind frequency offset estimation method based on extended phase difference provided in the above embodiments, and other technical features in the satellite optical communication blind frequency offset estimation device based on extended phase difference are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0071] In one embodiment, the phase difference extraction module 20 is further configured to perform conjugate multiplication of the current sampling point and the delayed sampling point in the analog-digital signal to obtain a differential signal; calculate the phase of the differential signal to obtain an initial phase difference; and perform phase expansion processing on the initial phase difference to obtain a continuous phase difference sequence.

[0072] In one embodiment, the filtering and interpolation reconstruction module 30 is further configured to calculate the second-order difference point by point in the phase difference sequence to obtain the difference change rate; when the absolute value of the difference change rate is greater than a preset dynamic threshold, the sampling points exceeding the preset dynamic threshold are marked as abnormal points and removed to obtain a normal point sample set; and interpolation reconstruction processing is performed based on the abnormal points and the normal point sample set to obtain the reconstructed phase difference sequence.

[0073] In one embodiment, the filtering and interpolation reconstruction module 30 is further configured to obtain the current signal-to-noise ratio and a preset signal-to-noise ratio range; and adjust the dynamic threshold according to the signal-to-noise ratio and the preset signal-to-noise ratio range to obtain an adjusted preset dynamic threshold.

[0074] In one embodiment, the filtering and interpolation reconstruction module 30 is further configured to locate normal points adjacent to the abnormal points based on the normal point sample set, to obtain a first normal point and a second normal point; construct a linear interpolation path between the first normal point and the second normal point according to a preset interpolation strategy, and calculate the interpolation phase point by point; write the interpolation phase into the removal region of the abnormal point to obtain a reconstructed phase difference sequence.

[0075] In one embodiment, the frequency offset estimation module 40 is further configured to obtain the current signal-to-noise ratio and determine a preset sliding window length based on the signal-to-noise ratio; calculate an instantaneous frequency offset sequence according to each sampling point value in the reconstructed phase difference sequence and the corresponding delayed sampling interval; and perform a sliding average on the instantaneous frequency offset sequence according to the sliding window length to obtain a smoothed frequency offset estimate.

[0076] In one embodiment, the satellite optical communication blind frequency offset estimation device based on extended phase difference further includes a frequency offset compensation module 50, which is further configured to generate a compensation phase signal based on the frequency offset estimation value; and perform phase rotation compensation on the analog-digital signal based on the compensation phase signal to obtain the corrected target signal.

[0077] This application provides a satellite optical communication blind frequency offset estimation device based on extended phase difference. The satellite optical communication blind frequency offset estimation device based on extended phase difference includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the satellite optical communication blind frequency offset estimation method based on extended phase difference in the above embodiment 1.

[0078] The following is for reference. Figure 7This document illustrates a structural schematic diagram of a satellite optical communication blind frequency offset estimation device suitable for implementing embodiments of this application. The satellite optical communication blind frequency offset estimation device based on extended phase difference in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The satellite optical communication blind frequency offset estimation device based on extended phase difference shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0079] like Figure 7 As shown, the satellite optical communication blind frequency offset estimation device based on extended phase difference may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the satellite optical communication blind frequency offset estimation device based on extended phase difference. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, LCDs (Liquid Crystal Displays), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the satellite optical communication blind frequency offset estimation device based on extended phase difference to exchange data with other devices wirelessly or via wired communication. Although the figure shows a satellite optical communication blind frequency offset estimation device based on extended phase difference with various systems, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems can be implemented alternatively.

[0080] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0081] The satellite optical communication blind frequency offset estimation device based on extended phase difference provided in this application employs the satellite optical communication blind frequency offset estimation method based on extended phase difference in the above embodiments, which can solve the technical problem of how to improve the accuracy and stability of frequency offset estimation under large frequency offset conditions. Compared with the prior art, the beneficial effects of the satellite optical communication blind frequency offset estimation device based on extended phase difference provided in this application are the same as those of the satellite optical communication blind frequency offset estimation method based on extended phase difference provided in the above embodiments, and other technical features in this satellite optical communication blind frequency offset estimation device based on extended phase difference are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0082] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0083] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0084] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the satellite optical communication blind frequency offset estimation method based on extended phase difference in the above embodiments.

[0085] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0086] The aforementioned computer-readable storage medium may be included in a satellite optical communication blind frequency offset estimation device based on extended phase difference; or it may exist independently and not assembled into a satellite optical communication blind frequency offset estimation device based on extended phase difference.

[0087] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the satellite optical communication blind frequency offset estimation device based on extended phase difference, the satellite optical communication blind frequency offset estimation device based on extended phase difference performs the following actions: during photoelectric detection, it acquires an analog-to-digital signal after digital-to-analog conversion; it performs phase difference calculation and expansion processing on the analog-to-digital signal to obtain a phase difference sequence; it performs anomaly detection and interpolation reconstruction on the phase difference sequence to obtain a reconstructed phase difference sequence; it calculates the instantaneous frequency offset based on the reconstructed phase difference sequence and performs noise smoothing processing on the instantaneous frequency offset to obtain a smoothed frequency offset estimate.

[0088] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0089] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0090] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0091] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described blind frequency offset estimation method for satellite optical communication based on extended phase difference. This solves the technical problem of how to improve the accuracy and stability of frequency offset estimation under large frequency offset conditions. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the blind frequency offset estimation method for satellite optical communication based on extended phase difference provided in the above embodiments, and will not be repeated here.

[0092] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for estimating blind frequency offset in satellite optical communication based on extended phase difference.

[0093] The computer program product provided in this application can solve the technical problem of how to improve the accuracy and stability of frequency offset estimation under large frequency offset conditions. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the blind frequency offset estimation method for satellite optical communication based on extended phase difference provided in the above embodiments, and will not be repeated here.

[0094] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for estimating blind frequency offset in satellite optical communication based on extended phase difference, characterized in that, The method includes: During photoelectric detection, the analog-to-digital signal after digital-to-digital conversion is acquired; The phase difference sequence is obtained by performing phase difference calculation and expansion processing on the analog-digital signal. Anomaly detection and interpolation reconstruction are performed based on the phase difference sequence to obtain a reconstructed phase difference sequence. The instantaneous frequency offset is calculated based on the reconstructed phase difference sequence, and noise smoothing is applied to the instantaneous frequency offset to obtain a smoothed frequency offset estimate.

2. The method as described in claim 1, characterized in that, The step of calculating and expanding the phase difference based on the analog-digital signal to obtain the phase difference sequence includes: The current sampling point and the delayed sampling point in the analog-digital signal are multiplied by their conjugates to obtain the differential signal; Calculate the phase of the differential signal to obtain the initial phase difference; The initial phase difference is subjected to phase expansion processing to obtain a continuous phase difference sequence.

3. The method as described in claim 1, characterized in that, The step of performing anomaly detection and interpolation reconstruction based on the phase difference sequence to obtain the reconstructed phase difference sequence includes: The second-order difference is calculated point by point for the phase difference sequence to obtain the rate of change of the difference; When the absolute value of the differential change rate is greater than a preset dynamic threshold, the sampling points that exceed the preset dynamic threshold are marked as abnormal points and removed to obtain a normal point sample set. Interpolation reconstruction is performed on the abnormal points and the normal point sample set to obtain the reconstructed phase difference sequence.

4. The method as described in claim 3, characterized in that, Before the step of marking and removing sampling points exceeding the preset dynamic threshold as outliers when the absolute value of the differential rate of change is greater than the preset dynamic threshold, thus obtaining a normal point sample set, the method further includes: Obtain the current signal-to-noise ratio and the preset signal-to-noise ratio range; The dynamic threshold is adjusted based on the signal-to-noise ratio and the preset signal-to-noise ratio range to obtain the adjusted preset dynamic threshold.

5. The method as described in claim 3, characterized in that, The step of performing interpolation reconstruction based on the sample sets of outliers and normal points to obtain the reconstructed phase difference sequence includes: Based on the set of normal point samples, locate the normal points adjacent to the abnormal points to obtain the first normal point and the second normal point; A linear interpolation path is constructed between the first normal point and the second normal point according to a preset interpolation strategy, and the interpolation phase is calculated point by point. The interpolated phase is written into the rejection region of the outlier to obtain the reconstructed phase difference sequence.

6. The method as described in claim 1, characterized in that, The steps of calculating the instantaneous frequency offset based on the reconstructed phase difference sequence and performing noise smoothing on the instantaneous frequency offset to obtain a smoothed frequency offset estimate include: Obtain the current signal-to-noise ratio and determine the preset sliding window length based on the signal-to-noise ratio; The instantaneous frequency offset sequence is calculated based on each sampling point value in the reconstructed phase difference sequence and the corresponding delayed sampling interval; The instantaneous frequency offset sequence is averaged according to the sliding window length to obtain a smoothed frequency offset estimate.

7. The method as described in claim 1, characterized in that, After the steps of calculating the instantaneous frequency offset based on the reconstructed phase difference sequence and performing noise smoothing on the instantaneous frequency offset to obtain a smoothed frequency offset estimate, the method further includes: A compensation phase signal is generated based on the frequency offset estimate; The analog-to-digital signal is phase-rotated and compensated based on the compensated phase signal to obtain the corrected target signal.

8. A satellite optical communication blind frequency offset estimation device based on extended phase difference, characterized in that, The device includes: The signal conversion module is used to acquire analog-to-digital signals after digital-to-analog conversion during photoelectric detection. The phase difference extraction module is used to perform phase difference calculation and expansion processing based on the analog-digital signal to obtain a phase difference sequence. The filtering and interpolation reconstruction module is used to perform anomaly detection and interpolation reconstruction based on the phase difference sequence to obtain a reconstructed phase difference sequence; The frequency offset estimation module is used to calculate the instantaneous frequency offset based on the reconstructed phase difference sequence, and to perform noise smoothing on the instantaneous frequency offset to obtain a smoothed frequency offset estimate.

9. A satellite optical communication blind frequency offset estimation device based on extended phase difference, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the satellite optical communication blind frequency offset estimation method based on extended phase difference as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the satellite optical communication blind frequency offset estimation method based on extended phase difference as described in any one of claims 1 to 7.

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