A high dynamic satellite-ground channel simulation and delay calibration method and simulation system

Abstract

This disclosure relates to the field of wireless communication technology, and provides a high-dynamic satellite-to-ground channel simulation and delay calibration method and system to address the problem that existing simulation systems cannot generate high-density, long-duration, and continuous simulation data. The method includes: acquiring large-interval discrete trajectory data and transforming it into equally spaced, high-density, continuously time-varying satellite-to-ground transmission parameters through piecewise polynomial fitting; employing a fully digital dynamic interpolation reconstruction architecture to decompose the total transmission delay data into integer and fractional delay components for delay adjustment; multiplexing a third-order Lagrange polynomial interpolation algorithm to perform arbitrary sampling rate conversion to complete code Doppler simulation; synchronously acquiring the original signal and the simulation signal, extracting the device's inherent delay components to obtain delay deviation values, and completing closed-loop dynamic correction; constructing a unified FPGA fully digital real-time processing architecture, synchronously superimposing multi-dimensional space-air-ground channel propagation characteristics under a unified time base, and outputting a radio frequency simulation signal to complete channel simulation and delay calibration.

Description

Technical Field

[0001] This disclosure relates to the field of wireless communication technology, and in particular to a high dynamic satellite-to-ground channel simulation and delay calibration method and simulation system. Background Technology

[0002] In the research and development and testing of satellite communication terminals, it is necessary to rely on laboratory channel simulation equipment to reproduce the real satellite-to-ground wireless transmission channel environment. The core of the simulation system needs to achieve wide-range, high-precision, and high-dynamic transmission delay simulation of broadband signals, while overcoming key technical challenges such as uplink and downlink delay synchronization and real-time calculation of simulation parameters for satellite-to-ground distance and relative motion velocity. The continuous motion of satellites in orbit causes an inherent asymmetry in the uplink and downlink transmission delays of the satellite-to-ground link. If precise synchronization and matching of uplink and downlink delays cannot be achieved, the realism of the channel simulation scenario and the credibility of equivalent tests will be significantly reduced. In high-dynamic satellite communication scenarios, the accuracy of delay simulation is highly dependent on the sampling density of satellite-to-ground distance and relative velocity simulation parameters. However, conventional orbit simulation data sources have sparse sampling intervals and low parameter update rates, making it difficult to support high-precision, continuous delay extrapolation simulations.

[0003] Currently, dynamic time delay simulation of satellite-to-ground channels mainly includes two types: equal-interval sampling-non-equal-interval reconstruction and dynamic interpolation reconstruction. Among them, the equal-interval sampling-non-equal-interval reconstruction method transforms the dynamic time delay simulation of broadband signals into a sampling clock delay control problem. By sampling and storing the signal at equal intervals, a non-equal-interval reconstruction clock is generated according to the satellite-to-ground time delay law, driving the non-uniform output of the sampled data and obtaining the simulated signal through filtering and reconstruction. However, the equal-interval sampling-non-equal-interval reconstruction method relies on analog circuits to generate Doppler clocks and complete frequency conversion reconstruction. The analog link is prone to introducing spurious and image frequency interference, which leads to the degradation of clock phase and frequency accuracy, limiting the accuracy of time delay simulation. The system structure is complex, the hardware cost is high, and functional expansion requires additional hardware resources, resulting in poor scalability.

[0004] The dynamic interpolation reconstruction method is a fully digital implementation scheme. It buffers the sampled signal, dynamically selects the sampled data based on the real-time satellite-to-ground time delay, performs fractional-interval interpolation, reconstructs the non-equal interval sequence, and outputs the filter at equal intervals to realize channel time delay simulation. It has a simple architecture, low hardware implementation difficulty, and good flexibility and integration.

[0005] However, existing channel delay simulations generally use a buffer playback mechanism, which can only loop a fixed short-term fading coefficient sequence. This cannot generate high-density, long-term, continuous simulation data that conforms to the statistical characteristics of real channels, resulting in distorted terminal test statistical results and insufficient test reliability. At the same time, existing simulation equipment has a complex structure, cannot take into account both large-scale delay and ultra-high delay accuracy, has low sampling rate conversion efficiency, and lacks system versatility and scalability, making it difficult to meet the engineering application requirements of broadband, high-precision, and high-dynamic satellite-to-ground channel simulation. Summary of the Invention

[0006] The purpose of this disclosure is to address the technical problems of existing channel delay simulation systems, such as their inability to generate high-density, long-duration, continuous simulation data that conforms to the statistical characteristics of real channels, low sampling rate conversion efficiency, and insufficient system versatility and scalability. This disclosure provides a high-dynamic satellite-to-ground channel simulation and delay calibration method and system, thereby overcoming, to at least to some extent, one or more problems caused by the limitations and defects of related technologies.

[0007] The design concept of this invention is as follows: Based on a cubic dynamic motion time polynomial design combined with a distance piecewise polynomial fitting scheme, the large-interval discrete orbit data is segmented and processed. The motion equation of each segment is fitted by the least squares method, and then a multi-level accumulator is used for recursive calculation to transform the millisecond-level raw data into small-interval, high-density continuous satellite-to-ground distance and velocity parameters. A fully digital dynamic interpolation reconstruction method is selected as the core delay architecture, which splits the delay into integer delay and fractional delay to meet the simulation requirements of ultra-large delay ranges. The fractional delay architecture of the multiplexed transmission delay module realizes arbitrary sampling rate conversion. Combined with the physical principles of symbol stretching and compression, the sampling rate is dynamically adjusted to accurately reproduce the code phase drift phenomenon caused by code Doppler. An online calibration scheme for inherent delay is designed to separate the pure inherent delay component of the equipment. Then, a cross-correlation combined with area centroid algorithm is used to calculate the delay deviation in real time and dynamically issue compensation to complete the delay correction, realizing synchronous simulation of channel characteristics of integrated air-space-ground communication.

[0008] According to a first aspect of the present disclosure, a high-dynamic satellite-to-ground channel simulation and delay calibration method is provided, comprising: The original large-interval discrete trajectory data characterizing the relative motion between the satellite and the ground is acquired. The large-interval discrete trajectory data is segmented by piecewise polynomial fitting. The least squares method and multi-level accumulator recursive operation are used to transform the large-interval discrete trajectory data into equal-interval, high-density, continuously time-varying satellite-to-ground transmission parameters. The satellite-to-ground transmission parameters are mapped to total transmission delay data. A fully digital dynamic interpolation reconstruction architecture is adopted to split the total transmission delay data into integer delay components and fractional delay components. The integer delay components are coarsely adjusted by reading from a large-capacity cache, and the fractional delay components are finely adjusted by using a third-order Lagrange polynomial interpolation algorithm to complete the high-precision dynamic total delay data reconstruction. Based on the aforementioned satellite-to-ground transmission parameters, code-Doppler data is generated, and the third-order Lagrange polynomial interpolation algorithm is reused to perform arbitrary sampling rate conversion, simultaneously completing time delay fine-tuning and code-Doppler simulation. The system synchronously acquires the original signal at the input end of the system link and the simulated signal at the output end, removes multiple types of external interference components, and extracts the pure inherent delay component of the equipment; it uses a cross-correlation algorithm combined with an area centroid algorithm to obtain the delay deviation value, and dynamically issues compensation to complete the closed-loop dynamic correction of the total delay data; A unified FPGA-based all-digital real-time processing architecture is constructed to synchronously superimpose multi-dimensional space-ground channel propagation characteristics under a unified time base, generate and output radio frequency simulation signals, and complete high-dynamic space-ground channel simulation and delay calibration.

[0009] Furthermore, the process for generating the satellite-to-ground transmission parameters is as follows: Obtain the original large-interval discrete trajectory data that characterizes the relative motion between the star and the ground, and perform piecewise polynomial fitting on the large-interval discrete trajectory data based on the cubic dynamic motion time polynomial to divide the complete orbit time axis into several continuous sub-intervals. For each sub-interval, the least squares method is used for fitting, and the coefficients of the cubic motion equations corresponding to each sub-interval are solved to obtain the piecewise continuous distance motion equations. Taking the first derivative of the distance motion equation yields a piecewise continuous velocity motion equation; Based on the segmented continuous distance motion equation, the original input large-interval discrete trajectory data, i.e. millisecond-level discrete distance data, is transformed into equally spaced high-density continuous satellite-to-ground distance parameters and velocity parameters through multi-level accumulator recursive operation.

[0010] Furthermore, the cubic dynamic motion time polynomial satisfies the following equation, taking... As a fitting time period: in, This is the initial time. R(t) represents the initial distance of the segment; R(t) represents the distance between the satellite and the ground at time t; v(t) represents the radial velocity between the satellite and the ground at time t. 'a' represents the initial velocity of the segment; 'a' represents the initial acceleration. To accelerate.

[0011] Furthermore, the high-precision dynamic total delay reconstruction process is as follows: The high-density continuous satellite-to-ground distance parameters are mapped to a total transmission delay value; A fully digital dynamic interpolation reconstruction architecture is adopted to split the total transmission delay data into integer delay components and fractional delay components; Coarse delay adjustment of integer delay components is performed by controlling the address difference read from a large-capacity SDRAM cache. The fractional delay components are finely tuned using a third-order Lagrange polynomial interpolation algorithm. The third-order Lagrange polynomial interpolation algorithm is formed by combining the Farrow structure and the third-order Lagrange fractional delay filter. The coarse adjustment of the integer delay component and the fine adjustment of the fractional delay component are cascaded to complete the high-precision dynamic total delay reconstruction.

[0012] Furthermore, the third-order Lagrange polynomial interpolation algorithm satisfies the following equation: in: The fractional time delay component has a value range of 1. ; The input sampling sequence is a third-order Lagrange filter. To output the interpolated sequence, The reference sampling point index for the input discrete sequence. This is the index of the sampling points for the output discrete sequence.

[0013] Furthermore, the code-Doppler simulation process is as follows: The sampling rate transformation coefficient is calculated in real time based on the radial velocity of the aforementioned satellite-to-ground transmission parameters; The Farrow structure of the third-order Lagrange polynomial interpolation algorithm and the third-order Lagrange fractional delay filter are reused to perform arbitrary sampling rate conversion, and time delay fine-tuning and code Doppler simulation are completed simultaneously. The sampling rate conversion process satisfies the following equation: in, The sampling period of the input discrete signal is the time interval between adjacent sampling points in the input sampling sequence of the system's digital processing link. The sampling period of the output discrete signal after sampling rate conversion is the time interval between adjacent sampling points in the sampling sequence output by the sampling rate conversion module. For the input discrete signal at time... The sampled values; To output the discrete signal at time... The sampled values; This represents the impulse response of the interpolation filter; For the input sequence sampling point index, / This is the index of the sampling points for the output sequence.

[0014] Furthermore, the total latency data closed-loop dynamic correction process is as follows: During system operation, the raw signals at the input end of the system link and the simulated signals at the output end are collected synchronously. The original input signal and the simulated output signal are time-domain aligned, and external interference components are removed one by one. The external interference components include at least the initial delay, dynamic transmission delay and local oscillator Doppler. The pure inherent delay component of the device is extracted. The cross-correlation algorithm combined with the area centroid algorithm is used to calculate the separated pure intrinsic delay components and obtain the delay deviation value at the current moment in real time. The delay deviation value is dynamically sent to the total delay data reconstruction link for compensation superposition, thereby completing the closed-loop dynamic correction of the total delay data.

[0015] Furthermore, a unified FPGA all-digital real-time processing architecture is constructed to coherently superimpose and synchronously simulate multi-dimensional channel effects, including at least transmission delay, Doppler frequency shift, code Doppler, Gaussian noise, ionospheric dispersion, tropospheric refraction, and multipath fading, under the same clock reference. The processed digital signal is then converted into an RF simulation signal output through a multi-band RF link. The multi-band RF link is a dual-band RF processing link that natively integrates the Sub-6G and Ka bands, thereby completing high-dynamic satellite-to-ground channel simulation and delay calibration.

[0016] Furthermore, the Gaussian noise is digitally generated using an m-sequence combined with the Box-Muller algorithm, and then combined with multi-level filtering and power adjustment to generate a noise signal, which is then superimposed on the RF simulation signal.

[0017] According to a second aspect of the present disclosure, a high dynamic satellite-to-ground channel simulation and delay calibration system is provided, comprising: a parameter generation module, a transmission delay adjustment module, a code Doppler fusion processing module, an inherent delay calibration module, a Gaussian noise simulation module, and a radio frequency transceiver processing module; The parameter generation module is used to acquire large-interval discrete trajectory data characterizing the relative motion between the satellite and the ground, and to convert the large-interval discrete trajectory data into equally spaced, high-density, continuously time-varying satellite-to-ground transmission parameters. The input terminal of the transmission delay adjustment module is connected to the output terminal of the parameter generation module, and is used to receive the satellite-to-ground transmission parameters and map them into total transmission delay data; The transmission delay adjustment module includes cascaded integer delay adjustment units and fractional delay adjustment units: The integer delay adjustment unit is used to perform coarse delay adjustment on the integer delay components split from the total transmission delay data. The fractional delay adjustment unit is used to fine-tune the delay of the fractional delay components divided from the total transmission delay data; the integer delay adjustment unit and the fractional delay adjustment unit are cascaded and work together to complete the reconstruction of the total delay data and output the signal after delay reconstruction; The code-Doppler fusion processing module is connected to the parameter generation module and the time delay dynamic reconstruction module, respectively. It is used to generate code-Doppler frequency shift data according to the radial velocity output by the parameter generation module, and reuse the third-order Lagrange polynomial interpolation algorithm of the fractional time delay adjustment unit to simultaneously complete arbitrary sampling rate conversion, thereby realizing the functional integration of time delay adjustment and code-Doppler simulation. The signal acquisition end of the inherent delay calibration module is connected to the input signal node and the output signal node of the system link, respectively. It is used to synchronously acquire the original signal at the input end and the simulated signal at the output end of the system link, remove multiple external interference components, extract the pure inherent delay component of the device, calculate the delay deviation value in real time through the cross-correlation algorithm in the module combined with the area centroid algorithm module, and dynamically send the compensation amount to the transmission delay adjustment module in a closed-loop feedback manner to complete the real-time closed-loop correction of the total delay data. The Gaussian noise simulation module is used to realize broadband Gaussian white noise simulation, as well as colored noise and impulse noise extension; The radio frequency transceiver processing module is used to construct a unified FPGA all-digital real-time processing architecture, synchronously superimpose multi-dimensional space-ground channel propagation characteristics under a unified time base, generate and output radio frequency simulation signals, and complete high-dynamic space-ground channel simulation and delay calibration.

[0018] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: This invention provides a high-dynamic satellite-to-ground channel simulation and delay calibration method. It uses piecewise polynomial fitting combined with least squares and polynomial difference recursive operations to interpolate sparse discrete orbit data. It generates equally spaced, high-density, continuous satellite-to-ground transmission parameters by pure addition recursion using a multi-level accumulator. It does not require solving high-order polynomials point by point in real time, which greatly reduces the real-time computing resource overhead of the system. It can stably output high-time-resolution time-varying distance and velocity parameters, and is suitable for the high-precision simulation requirements of high-dynamic satellite scenarios. A fully digital dynamic interpolation reconstruction architecture is adopted to achieve delay adjustment of integer and fractional delay components; the same filtering hardware is reused to synchronously realize delay control, sampling rate conversion and code Doppler simulation, resulting in high hardware resource utilization; a full-link online closed-loop delay self-calibration mechanism is set up to synchronously acquire the input original signal and the simulation output signal, accurately solve the inherent delay deviation after eliminating external interference and dynamically compensate it, without the need for offline calibration, thus ensuring the long-term accuracy of simulation delay. Under a unified FPGA all-digital real-time processing architecture, multi-dimensional air-space-ground channel propagation characteristics are synchronously superimposed, and dual-band RF links are integrated. A single device completes multi-band integrated channel synchronous simulation, which greatly improves the overall computing efficiency and latency accuracy, ensures the long-term stability and reliability of the system, has strong multi-scenario adaptability, and has high applicability and practicality. Attached Figure Description

[0019] Figure 1 A flowchart illustrating a high-dynamic satellite-to-ground channel simulation and delay calibration method according to an exemplary embodiment of this disclosure is shown. Figure 2 This diagram illustrates a Farrow structure filter system block diagram in a high dynamic satellite-to-ground channel simulation and delay calibration system according to an exemplary embodiment of this disclosure. Figure 3 This diagram illustrates a block diagram of a third-order Lagrange polynomial interpolation filter system with the Farrow structure in an exemplary embodiment of this disclosure. Figure 4 This diagram illustrates a code-Doppler analog system block diagram in an exemplary embodiment of this disclosure. Figure 5 This diagram illustrates a piecewise polynomial fitting block diagram for orbital parameters in an exemplary embodiment of this disclosure. Figure 6 A block diagram of a Gaussian white noise simulation system in an exemplary embodiment of this disclosure is shown. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0021] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0022] This example implementation provides a high-dynamic satellite-to-ground channel simulation and delay calibration method, which, as follows: Figure 1 As shown, the method may include: Step S101: Obtain the original large-interval discrete trajectory data characterizing the relative motion between the satellite and the ground. Perform piecewise polynomial fitting on the large-interval discrete trajectory data. Use the least squares method and multi-level accumulator recursive operation to transform the large-interval discrete trajectory data into equal-interval, high-density, continuously time-varying satellite-to-ground transmission parameters. Step S102: Map the satellite-to-ground transmission parameters to total transmission delay data. Adopt a fully digital dynamic interpolation reconstruction architecture to split the total transmission delay data into integer delay components and fractional delay components. Perform coarse delay adjustment on the integer delay components by reading from a large-capacity cache, and fine delay adjustment on the fractional delay components by using a third-order Lagrange polynomial interpolation algorithm to complete the high-precision dynamic total delay data reconstruction. Step S103: Generate code Doppler data based on satellite-to-ground transmission parameters, reuse the third-order Lagrange polynomial interpolation algorithm to perform arbitrary sampling rate conversion, and simultaneously complete time delay fine-tuning and code Doppler simulation; Step S104: Synchronously acquire the original signal at the input end of the system link and the simulated signal at the output end, remove multiple types of external interference components, and extract the pure inherent delay component of the equipment; use the cross-correlation algorithm combined with the area centroid algorithm to obtain the delay deviation value, and dynamically issue the compensation amount to complete the closed-loop dynamic correction of the total delay data; Step S105: Construct a unified FPGA all-digital real-time processing architecture, synchronously superimpose multi-dimensional space-ground channel propagation characteristics under a unified time base, generate and output RF simulation signals, and complete high-dynamic space-ground channel simulation and delay calibration.

[0023] The above method employs a piecewise polynomial fitting approach combined with least squares and polynomial difference recursive operations to interpolate sparse discrete orbit data. It utilizes a multi-level accumulator with pure addition recursion to generate equally spaced, high-density, continuous satellite-to-ground transmission parameters. This eliminates the need for point-by-point real-time solving of high-order polynomials, significantly reducing real-time computational resource overhead and enabling stable output of high-time-resolution time-varying distance and velocity parameters, thus meeting the high-precision simulation requirements of highly dynamic satellite scenarios. Furthermore, it utilizes a fully digital dynamic interpolation and reconstruction architecture to adjust the delay of both integer and fractional delay components. The same filtering hardware is reused to synchronously implement delay control, sampling rate conversion, and code... Doppler simulation offers high hardware resource utilization; a full-link online closed-loop delay self-calibration mechanism is implemented, synchronously acquiring the original input signal and the simulation output signal, accurately solving for inherent delay deviations and dynamically compensating after eliminating external interference, eliminating the need for offline calibration, and ensuring long-term simulation delay accuracy; multi-dimensional air-space-ground channel propagation characteristics are synchronously superimposed under a unified FPGA all-digital real-time processing architecture, integrating dual-band RF links, and a single device completes multi-band integrated channel synchronous simulation, significantly improving overall computational efficiency and delay accuracy, ensuring long-term system stability and reliability, strong adaptability to multiple scenarios, and high applicability and practicality.

[0024] Below, we will refer to Figure 1 The steps of the method described above in this example embodiment will be explained in more detail.

[0025] In step S101, the original large-interval discrete trajectory data characterizing the relative motion between the satellite and the ground is acquired. The large-interval discrete trajectory data is segmented by piecewise polynomial fitting. The least squares method and multi-level accumulator recursive operation are used to transform the large-interval discrete trajectory data into equally spaced, high-density, continuously time-varying satellite-to-ground transmission parameters.

[0026] For example, large-interval discrete trajectory data are externally input satellite orbits, spacecraft ballistic data, etc., with sampling time intervals typically reaching the millisecond level and data points sparse. This step strictly follows the physical laws of celestial motion to ensure the linkage and matching of distance, velocity, and acceleration parameters, which can effectively ensure the continuity and high accuracy of high-dynamic satellite channel simulation from the data source level.

[0027] Further, optionally, in one embodiment, step S101 may include the following sub-steps: Step S1011: Obtain the original large-interval discrete trajectory data representing the relative motion between the star and the ground, and perform piecewise polynomial fitting on the large-interval discrete trajectory data based on the cubic dynamic motion time polynomial to divide the complete trajectory time axis into several continuous sub-intervals. Step S1012: For each sub-interval, the least squares method is used for fitting, and the coefficients of the cubic motion equations corresponding to each sub-interval are solved to obtain the piecewise continuous distance motion equations. Step S1013: Take the first derivative of the distance motion equation to obtain the piecewise continuous velocity motion equation; Step S1014: Based on the piecewise continuous distance motion equation, the original input large-interval discrete trajectory data, i.e. millisecond-level discrete distance data, is transformed into equally spaced high-density continuous satellite-to-ground distance parameters and velocity parameters through multi-level accumulator recursive operation.

[0028] In one embodiment, the distance piecewise polynomial is fitted based on the cubic dynamic motion time polynomial, the large-interval discrete orbit data is segmented, the motion equation of each segment is fitted by the least squares method, and then the millisecond-level raw data is transformed into high-density continuous satellite-to-ground distance parameters and velocity parameters with a 20ns interval by using a multi-level accumulator recursive operation.

[0029] For example, the original input millisecond-level discrete distance data is external input satellite orbit, spacecraft ballistic data, etc., which are all large-interval discrete trajectory data. The sampling time interval is usually at the millisecond level, and the data points are sparse.

[0030] Among these methods, the use of cubic dynamic motion time polynomials for distance piecewise fitting, combined with the least squares method to solve the motion equations, can effectively improve the fitting accuracy of orbit data and provide accurate basic data for subsequent time delay simulation. By processing large-interval discrete data in segments, the computational complexity is reduced and the data processing efficiency is improved, which can meet the real-time processing requirements. By using multi-level accumulator recursive operations, the millisecond-level raw data is transformed into high-density continuous satellite-to-ground distance and velocity parameters at certain intervals, which can provide accurate orbit data in real time and support high-dynamic satellite-to-ground channel simulation.

[0031] Furthermore, optionally, in one embodiment, the cubic dynamic motion time polynomial satisfies the following equation, taking... As a fitting time period: in, This is the initial time. R(t) represents the initial distance of the segment; R(t) represents the distance between the satellite and the ground at time t; v(t) represents the radial velocity between the satellite and the ground at time t. 'a' represents the initial velocity of the segment; 'a' represents the initial acceleration. To accelerate.

[0032] In step S102, the satellite-to-ground transmission parameters are mapped to total transmission delay data. A fully digital dynamic interpolation reconstruction architecture is adopted to split the total transmission delay data into integer delay components and fractional delay components. The integer delay components are coarsely adjusted by reading from a large-capacity cache, and the fractional delay components are finely adjusted by using a third-order Lagrange polynomial interpolation algorithm to complete the high-precision dynamic total delay data reconstruction.

[0033] In one embodiment, step S102 employs a fully digital dynamic interpolation reconstruction architecture that utilizes a Farrow structure fractional delay filter to achieve fractional delay, combined with a large-capacity buffer to achieve overall delay. The overall delay is split into an integer part and a fractional part for collaborative implementation. For the fractional delay, a third-order Lagrange polynomial is used to design the Farrow filter. This structure does not require reloading the filter coefficients when changing the delay parameters; only the fractional delay value needs to be adjusted, resulting in high hardware operating efficiency. The integer delay and fractional delay belong to the same continuous control process under the same clock domain. No phase reset or data loss is introduced during the switching instant, ensuring the phase continuity of the output signal during large dynamic delay changes.

[0034] Further, optionally, in one embodiment, step S102 may include the following sub-steps: Step S1021: Map the high-density continuous satellite-to-ground distance parameters to the total transmission delay value; Step S1022: Using a fully digital dynamic interpolation reconstruction architecture, the total transmission delay data is split into integer delay components and fractional delay components; Step S1023: Perform coarse delay adjustment on integer delay components by controlling the address difference read from the large-capacity SDRAM cache; Step S1024: Fine-tune the time delay of the fractional time delay component using a third-order Lagrange polynomial interpolation algorithm; Step S1025: The third-order Lagrange polynomial interpolation algorithm adopts the Farrow structure and the third-order Lagrange fractional delay filter to form the third-order Lagrange polynomial interpolation algorithm; Step S1026: Cascade the coarse adjustment of the integer delay component and the fine adjustment of the fractional delay component to complete the high-precision dynamic total delay reconstruction.

[0035] In one embodiment, the third-order Lagrange polynomial interpolation algorithm satisfies the following equation: in: The fractional time delay component has a value range of 1. ; The input sampling sequence is a third-order Lagrange filter. To output the interpolated sequence, The reference sampling point index for the input discrete sequence. This is the index of the sampling points for the output discrete sequence.

[0036] Among them, the fully digital dynamic interpolation reconstruction method can effectively improve the accuracy of time delay simulation, avoid the problems of spurious interference and image frequency interference introduced by analog circuits, and ensure the accuracy of clock phase and frequency. The time delay is split into integer time delay and fractional time delay and implemented separately, which can meet the simulation requirements of ultra-large time delay range. It is suitable for various space-to-ground communication scenarios, and the function expansion does not require additional hardware resources, and has strong scalability.

[0037] In one embodiment, a fully digital dynamic interpolation reconstruction method is selected as the core delay architecture. The total transmission delay data is split into two parts: an integer delay component and a fractional delay component. The integer delay component relies on the address difference read from a large-capacity SDRAM cache to achieve a large-range delay of up to 1 second. The fractional delay component uses a Farrow structure combined with a third-order Lagrange fractional delay filter to form a third-order Lagrange polynomial interpolation algorithm to achieve fine adjustment. The minimum step of the fractional delay can reach 0.048ns, the overall delay accuracy is better than 0.1ns, and the multipath relative delay step is less than 1ns.

[0038] Among them, the all-digital dynamic interpolation and reconstruction architecture can effectively avoid spurious and phase errors caused by analog devices, and while achieving an ultra-large delay range, it can ensure the delay control accuracy within the entire bandwidth, and fully adapt to the delay simulation requirements of Ka and Sub-6G dual-band broadband satellite channels.

[0039] In step S103, code Doppler data is generated based on satellite-to-ground transmission parameters, and the third-order Lagrange polynomial interpolation algorithm is reused to perform arbitrary sampling rate conversion, while simultaneously completing time delay fine-tuning and code Doppler simulation. Among them, by dynamically adjusting the sampling rate to reproduce the code phase drift phenomenon caused by code Doppler, the code Doppler effect in the satellite-to-ground channel can be realistically simulated, improving the realism of the channel simulation; the fractional delay architecture of the multiplexing transmission delay module realizes arbitrary sampling rate conversion without the need for additional hardware resources, reducing implementation complexity and saving costs; combined with the physical principles of symbol stretching and compression, it can adapt to different code Doppler frequencies and is suitable for various high-dynamic satellite-to-ground communication scenarios.

[0040] Further, optionally, in one embodiment, step S103 may include the following sub-steps: Step 1031: Calculate the sampling rate transformation coefficient in real time based on the radial velocity of the satellite-to-ground transmission parameters; Step 1032: Reuse the Farrow structure of the third-order Lagrange polynomial interpolation algorithm and the third-order Lagrange fractional delay filter to perform arbitrary sampling rate conversion, and simultaneously complete the time delay fine-tuning and code Doppler simulation.

[0041] In one embodiment, the sampling rate conversion process satisfies the following equation: in, The sampling period of the input discrete signal is the time interval between adjacent sampling points in the input sampling sequence of the system's digital processing link. The sampling period of the output discrete signal after sampling rate conversion is the time interval between adjacent sampling points in the sampling sequence output by the sampling rate conversion module. For the input discrete signal at time... The sampled values; To output the discrete signal at time... The sampled values; This represents the impulse response of the interpolation filter; For the input sequence sampling point index, / This is the index of the sampling points for the output sequence.

[0042] It is important to understand that the relative motion between the satellite and ground platforms not only generates carrier Doppler frequency shift but also causes baseband symbol stretching or compression, i.e., code Doppler. Code Doppler leads to pseudo-code correlation peak drift, severely affecting the acquisition and tracking performance of satellite receivers. Combining the physical principles of symbol stretching and compression, the sampling rate is dynamically adjusted to accurately reproduce the code phase drift phenomenon caused by code Doppler. At the same time, with the onboard large-capacity SDRAM data buffer, it supports ultra-long-term continuous signal buffering and playback. The arbitrary sampling rate conversion operation on which the code Doppler effect simulation depends is completed by the existing Farrow structure and third-order Lagrange fractional delay filter in the fractional delay module, realizing simultaneous delay fine-tuning and code Doppler simulation. With the large-capacity data buffer, the signal stretching and compression are completed without the need for additional independent interpolation or decimation cascade circuits, greatly reducing hardware resource overhead and significantly improving hardware resource utilization efficiency. This further improves the real-time computing efficiency of the FPGA, enabling stable code Doppler simulation across the entire Doppler range and meeting the testing requirements of highly dynamic and long-duration satellite equipment.

[0043] In step S104, the original signal at the input end of the system link and the simulated signal at the output end are acquired synchronously, multiple external interference components are removed, and the pure inherent delay component of the equipment is extracted; the delay deviation value is obtained by using the cross-correlation algorithm combined with the area centroid algorithm, and the compensation amount is dynamically issued to complete the closed-loop dynamic correction of the total delay data. Among them, the cross-correlation combined with the area centroid algorithm is used to calculate the delay deviation in real time, which can effectively improve the delay estimation accuracy and achieve high-precision online calibration; the pure inherent delay component of the device is separated in real time and the compensation amount is dynamically issued to complete the delay correction, ensuring the real-time performance and accuracy of the channel simulation. No additional calibration equipment or complicated operation procedures are required, which can adapt to different environmental conditions and improve the reliability and stability of the system.

[0044] Further, optionally, in one embodiment, step S104 may include the following sub-steps: Step S1041: During system operation, synchronously acquire the original signal at the input end of the system link and the simulated signal at the output end; Step S1042: Time-domain alignment of the acquired input raw signal and output simulation signal, and removal of external interference components one by one. External interference components include at least initial delay, dynamic transmission delay and local oscillator Doppler. Extract the pure inherent delay component of the device. Among them, external interference components such as initial delay, dynamic transmission delay, and local oscillator Doppler are eliminated one by one; Step S1043: Use the cross-correlation algorithm combined with the area centroid algorithm to calculate the separated pure intrinsic delay components and obtain the delay deviation value at the current moment in real time; Step S1044: Dynamically send the delay deviation value to the total delay data reconstruction link for compensation superposition, and complete the closed-loop dynamic correction of the total delay data.

[0045] It is important to understand that, due to the influence of ambient temperature changes, nonlinearity of simulation devices, and circuit routing during long-term operation, simulators will experience slowly changing inherent time delays. Therefore, an online calibration scheme for inherent delays is designed to complete real-time error compensation under normal simulation conditions without requiring system shutdown for calibration. The calibration principle is as follows: the original signal at the input end of the system link and the simulation signal at the output end are collected synchronously, and the three types of dynamic interference components—initial delay, continuous dynamic delay, and local oscillator Doppler—are eliminated in sequence. The remaining signal deviation is the inherent delay of the device. Then, the time delay difference is calculated using a cross-correlation algorithm combined with an area centroid algorithm, and the compensation parameters are fed back to the time delay module to form a closed-loop calibration.

[0046] In step S105, a unified FPGA all-digital real-time processing architecture is constructed, and multi-dimensional space-ground channel propagation characteristics are synchronously superimposed under a unified time base to generate and output radio frequency simulation signals, thereby completing high-dynamic space-ground channel simulation and delay calibration.

[0047] Specifically, by constructing a unified FPGA all-digital real-time processing architecture and integrating dual-band RF processing links, dual-band signal transmission and reception and processing are completed; transmission delay, Doppler, code Doppler, Gaussian noise, ionospheric dispersion, tropospheric refraction, and various fading models are integrated into the same real-time computing engine to achieve synchronous simulation of air-space-ground channel characteristics.

[0048] Further, optionally, in one embodiment, step S105 may include the following sub-steps: Step S1051: Construct a unified FPGA all-digital real-time processing architecture to coherently superimpose and synchronously simulate multi-dimensional channel effects, including at least transmission delay, Doppler frequency shift, code Doppler, Gaussian noise, ionospheric dispersion, tropospheric refraction and multipath fading, under the same clock reference. Step S1052: Use m-sequence combined with Box-Muller algorithm to digitally generate noise sequence, and use multi-stage filtering and power adjustment to generate noise signal, and superimpose the generated noise signal into the RF simulation signal; Step S1053: The processed digital signal is converted into an RF simulation signal output through a multi-band RF link. The multi-band RF link is a dual-band RF processing link that natively integrates the Sub-6G band and the Ka band, thereby completing the high dynamic satellite-to-ground channel simulation and delay calibration.

[0049] In one embodiment, the Doppler frequency shift calculation formula is as follows: In the formula: For carrier Doppler frequency shift; The center frequency of the carrier. denoted as ω, where ω is the radial relative velocity between the Earth and the planet; c is the speed of light in a vacuum.

[0050] Simultaneously, based on Kepler's equations and spherical trigonometric formulas, the orbital parameters were iteratively calculated to solve key parameters such as the angle of apogee, true angle of apogee, and Earth-satellite distance.

[0051] Specifically, by employing a unified FPGA-based all-digital real-time processing architecture, synchronous superposition of at least integrated transmission delay, carrier Doppler, code Doppler, ionospheric dispersion, tropospheric refraction, and multiple fading models under a unified time base enables synchronized simulation of integrated space-air-ground channel characteristics, improving the realism and reliability of channel simulation. Simultaneously, using m-sequences combined with the Box-Muller algorithm to generate broadband noise, coupled with multi-level filtering and power adjustment circuits, achieves Gaussian white noise simulation with bandwidths exceeding 400MHz. This can be extended to simulate colored noise and impulse noise. A single computation link synchronously completes the joint simulation of all spatial channel characteristics. The various technical solutions work together to effectively improve the overall system performance, meeting the engineering application requirements for broadband, high-precision, and high-dynamic space-ground channel simulation.

[0052] Meanwhile, this embodiment also proposes a high dynamic satellite-to-ground channel simulation and delay calibration system to implement the above method, including a parameter generation module, a transmission delay adjustment module, a code Doppler fusion processing module, an inherent delay calibration module, a Gaussian noise simulation module, and a radio frequency transceiver processing module; The parameter generation module is used to acquire large-interval discrete trajectory data characterizing the relative motion between satellite and ground, and to convert the large-interval discrete trajectory data into equally spaced, high-density, continuously time-varying satellite-to-ground transmission parameters. The input of the transmission delay adjustment module is connected to the output of the parameter generation module, and is used to receive satellite-to-ground transmission parameters and map them into total transmission delay data; The transmission delay adjustment module includes cascaded integer delay adjustment units and fractional delay adjustment units: The integer delay adjustment unit is used to perform coarse delay adjustment on the integer delay components that are split from the total transmission delay data. The fractional delay adjustment unit is used to fine-tune the delay of the fractional delay components divided from the total transmission delay data; the integer delay adjustment unit and the fractional delay adjustment unit are cascaded and work together to complete the reconstruction of the total delay data and output the signal after delay reconstruction. The code-Doppler fusion processing module is connected to the parameter generation module and the time delay dynamic reconstruction module respectively. It is used to generate code-Doppler frequency shift data based on the radial velocity output by the parameter generation module, and reuse the third-order Lagrange polynomial interpolation algorithm of the fractional time delay adjustment unit to simultaneously complete arbitrary sampling rate conversion, thereby realizing the functional fusion of time delay adjustment and code-Doppler simulation. The signal acquisition end of the inherent delay calibration module is connected to the input signal node and the output signal node of the system link respectively. It is used to synchronously acquire the original signal at the input end of the system link and the simulated signal at the output end, remove multiple external interference components, extract the pure inherent delay component of the device, calculate the delay deviation value in real time through the cross-correlation algorithm in the module combined with the area centroid algorithm module, and dynamically send the compensation amount to the transmission delay adjustment module in a closed-loop feedback manner to complete the real-time closed-loop correction of the total delay data. The Gaussian noise simulation module is used to implement broadband Gaussian white noise simulation, as well as colored noise and impulse noise extension; The radio frequency transceiver processing module is used to build a unified FPGA all-digital real-time processing architecture, synchronously superimpose multi-dimensional space-ground channel propagation characteristics under a unified time base, generate and output radio frequency simulation signals, and complete high-dynamic space-ground channel simulation and delay calibration.

[0053] Below, we will refer to Figures 2 to 6 The various modules of the system described in this example embodiment will be explained in more detail.

[0054] In one embodiment, the system achieves piecewise polynomial fitting of distance through the following steps; In one embodiment, such as Figure 5 As shown, the original large-interval discrete trajectory data of the satellite orbit is sampled at an interval of 1ms, and 3ms is used as a fitting segment to balance fitting accuracy and computational load. The least squares method is used to solve the polynomial coefficients for each segment of discrete trajectory data to obtain piecewise continuous distance motion equations and piecewise continuous velocity motion equations. This step uses double-precision floating-point arithmetic, which can effectively avoid quantization errors and improve calculation accuracy. A three-level accumulator recursive operation is used to generate equally spaced, high-density, continuous satellite-to-ground distance and velocity parameters by recursively calculating point by point according to the system's 20ns clock cycle. The accuracy of satellite-to-ground distance recursion is better than The accuracy of the speed recursion theory is better than The actual working accuracy is no less than The real-time distance and velocity parameters are synchronously sent to the transmission delay, code Doppler, and carrier Doppler modules to achieve parameter linkage across all modules.

[0055] During the fitting process, distance, velocity, and acceleration are derived from the same set of polynomial coefficients, ensuring that the motion parameters of each order are reasonable. The three-level accumulator recursive structure can output point by point without storing a large number of fitting coefficients, which is suitable for FPGA real-time pipeline processing. The algorithm can be adapted to various satellite motion laws such as uniform speed, uniform acceleration, variable acceleration, and sinusoidal fluctuation.

[0056] In one embodiment, transmission delay simulation is achieved by combining the following steps with the system; In one embodiment, the radio frequency input signal is sequentially mixed, converted into an ADC analog-to-digital converter, and then digitally down-converted to a complex baseband signal before being sent to a large-capacity SDRAM cache. The system is equipped with 8GB of high-speed SDRAM memory in a single channel, which operates at 250MHz and 16bit rate. It only requires 500MB to store a 1s long signal, which can meet the requirement of a maximum 1s transmission delay. Integer delay can be adjusted by controlling the difference between the read and write addresses of the cache. like Figure 2 and Figure 3 As shown, the baseband signal is read from the buffer and fed into the Farrow third-order Lagrange filter. The fractional delay is represented by 12-bit data, and the minimum delay step can reach 48.828125ps. Combined with the integer delay, the overall system delay accuracy is better than 2ns. The relative delay of multipath signals is achieved by combining FIFO buffer and fractional delay unit, with a maximum multipath relative delay of 1500ns and a step of less than 1ns. The baseband signal, after time delay adjustment, is digitally up-converted, DAC digital-to-analog conversion, and RF mixing to output an RF signal. This enables continuously adjustable time delay, which can be precisely adapted to the scenarios of large time delay and high dynamic time delay changes in low-orbit satellites. Integer time delay and fractional time delay belong to the same continuous control process under the same clock domain. No phase reset or data loss is introduced at the moment of switching, ensuring the phase continuity of the output signal during large dynamic time delay changes.

[0057] In one embodiment, code Doppler simulation is achieved by combining the system with the following steps; In one embodiment, such as Figure 4 As shown, a single-channel 8GB SDRAM can meet the long-term code Doppler data caching requirements. Taking a satellite altitude of 10355km and a maximum Doppler frequency of 2MHz as an example, the single-channel storage requirement is about 2.9GB, which is more than enough hardware resources. Based on the satellite orbit module's output of the satellite-to-ground radial velocity, the sampling rate conversion coefficient and the fractional delay parameter of the Farrow filter are calculated in real time. The Farrow filter is used to perform interpolation / decimation operations, thereby extending or compressing the symbol signal and simulating the code Doppler effect. This structure does not require frequent loading of filter coefficients and meets the real-time requirements of high dynamic scenarios. The system is equipped with a frequency divider and an NCO numerically controlled oscillator to dynamically adjust the fractional delay update rate to match changes in satellite motion speed; The code Doppler and carrier Doppler work in tandem, with an overall Doppler range of ≥±1.6MHz and a frequency change rate of ≥15KHz / s.

[0058] The arbitrary sampling rate conversion operation relied upon by code Doppler simulation is completed by the existing Farrow structure in the delay module. Combined with a large-capacity data buffer, it completes the signal extension and compression without the need for additional independent interpolation or decimation cascade circuits, significantly improving the efficiency of hardware resource utilization.

[0059] In one embodiment, the inherent delay online calibration is achieved by combining the system with the following steps; In one embodiment, input and output baseband signals of a specified duration are synchronously acquired within the same clock domain to ensure a unified timing reference. The first step is to eliminate the large initial time delay within the segment; the second step is to use a dynamic interpolation algorithm to offset the continuous time delay caused by the satellite-to-ground motion; the third step is to multiply the local oscillator Doppler signal to eliminate signal distortion caused by frequency offset. After the dynamic component cancellation is completed, the two signals retain only the inherent time delay. The time delay difference is calculated using a cross-correlation algorithm, and the results are optimized by combining the area centroid algorithm to reduce the calculation error caused by noise interference. The calibration compensation value is sent to the transmission delay module in real time to correct the fractional delay parameter; The calibration process is executed continuously and periodically, without interrupting the simulation task.

[0060] This calibration process does not rely on offline data acquisition or static compensation tables before the equipment leaves the factory. All compensation amounts are derived from real-time correlation calculations under the current conditions, and delay deviation correction can be completed within the feedback cycle, enabling the equipment to continuously offset delay drift caused by temperature drift and device nonlinearity throughout its service life. Simulation verification shows that the maximum root mean square error of this online calibration scheme is better than 20 ps, ​​and it can stabilize the equipment's delay accuracy over a long period of time.

[0061] In one embodiment, satellite orbital motion simulation is achieved by combining the following steps with the system; The parameter input mode for satellite orbit motion simulation supports two configuration methods: one is to manually input the six major orbital elements of the satellite, such as inclination, right ascension of the ascending node, argument of perigee, orbital eccentricity, semi-major axis, and time of perigee passage; the other is to import trajectory files generated by software such as STK to achieve linkage simulation. The asymptotic angle is solved based on the Kepler iterative algorithm, and the geometric distance between the satellite and the ground, radial velocity, and azimuth angle are calculated by combining the spherical trigonometric formula. The raw distance and velocity data are sent to the piecewise polynomial fitting module, and the carrier Doppler frequency shift is calculated based on the radial velocity and sent to the radio frequency and baseband processing unit. It is compatible with low-Earth orbit, medium-Earth orbit, and geostationary satellites, and also supports user-defined free motion modes, which can simulate non-orbital moving targets such as aircraft.

[0062] In one embodiment, additive white Gaussian noise is prevalent in spatial channels, such as Figure 6 As shown, this embodiment adopts a digital generation scheme of m-sequence + Box-Muller algorithm, combined with multi-level filtering and power adjustment circuit, to realize broadband Gaussian white noise simulation with bandwidth ≥400MHz, while supporting colored noise and impulse noise extension.

[0063] set up The Box-Muller transform is performed on two independent sets of Gaussian random numbers, which are mutually independent uniform random numbers in the interval (0, 1). The transformation satisfies the following formula: in, This is the first set of uniformly distributed random numbers generated based on an m-sequence pseudo-random number generator, with values ​​ranging from 1 to 2. ; This is the second set of uniformly distributed random numbers generated based on an m-sequence pseudo-random number generator, with values ​​ranging from... ,and and They are independent of each other; and These are two sets of independent standard Gaussian random numbers obtained through the Box-Muller transformation.

[0064] In one embodiment, a multi-stage linear feedback shift register is used to generate an m-sequence. The sequence length can be configured to be 24~64 bits. The m-sequence has a long period and excellent statistical properties, and can be used as a uniform random number source. The Box-Muller algorithm is used to convert uniformly distributed random numbers into standard Gaussian distributed random numbers. Bandwidth control is achieved through an FIR filter and a multiphase DUC, with a noise bandwidth ≥400MHz. At the same time, parameters can be configured to generate colored noise such as pink and blue. The noise signal amplitude can be adjusted according to user configuration to achieve flexible settings of carrier-to-noise ratio and signal-to-noise ratio; The generated noise signal is superimposed on the main channel baseband / RF signal, and both pulse and continuous output modes are supported. The all-digital generation method can effectively avoid the amplitude unevenness and noise figure drift introduced by analog noise sources. The noise power accuracy and spectrum flatness are not affected by changes in ambient temperature.

[0065] In one embodiment, the satellite orbital motion simulation also calculates the satellite's real-time position and radial velocity based on the satellite's six orbital elements, orbital parameters, or external trajectory files, and calculates the carrier Doppler frequency shift using the Doppler formula. It supports both orbital motion and free motion modes, and can also be integrated with third-party simulation software such as STK.

[0066] In one embodiment, the overall joint workflow of an FPGA-based system is as follows: System initialization: After the equipment is powered on, it completes self-test and initialization of the VPX chassis, FPGA, ADC, DAC, Ka / Sub-6G frequency converter, and clock module. The host computer loads the simulation task, track parameters, and channel configuration. Orbit calculation: The satellite orbit motion module parses parameters / trajectory files and outputs raw satellite-to-ground distance and velocity data; Parameter encryption: The distance piecewise polynomial fitting module performs piecewise fitting and recursion on sparse orbital data to generate high-density continuous parameters; Channel master simulation: The transmission delay module, code Doppler module, and carrier Doppler module complete signal modulation based on real-time parameters; Noise superposition: The Gaussian white noise module generates noise with a specified bandwidth and power and superimposes it onto the main signal; RF output: The processed baseband signal is output externally after digital up-conversion, DA conversion, and RF frequency conversion; Online calibration: The inherent delay online calibration module runs periodically to compensate for hardware delay deviations in real time; Task complete: After the simulation is complete, stop signal output and save the configuration file and simulation log.

[0067] The all-digital generation method in this embodiment effectively avoids amplitude unevenness and noise figure drift introduced by analog noise sources, and the noise power accuracy and spectrum flatness are not affected by changes in ambient temperature.

[0068] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0069] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A high-dynamic satellite-to-ground channel simulation and delay calibration method, characterized in that, include: The original large-interval discrete trajectory data characterizing the relative motion between the satellite and the ground is acquired. The large-interval discrete trajectory data is segmented by piecewise polynomial fitting. The least squares method and multi-level accumulator recursive operation are used to transform the large-interval discrete trajectory data into equal-interval, high-density, continuously time-varying satellite-to-ground transmission parameters. The satellite-to-ground transmission parameters are mapped to total transmission delay data. A fully digital dynamic interpolation reconstruction architecture is adopted to split the total transmission delay data into integer delay components and fractional delay components. The integer delay components are coarsely adjusted by reading from a large-capacity cache, and the fractional delay components are finely adjusted by using a third-order Lagrange polynomial interpolation algorithm to complete the high-precision dynamic total delay data reconstruction. Based on the aforementioned satellite-to-ground transmission parameters, code-Doppler data is generated, and the third-order Lagrange polynomial interpolation algorithm is reused to perform arbitrary sampling rate conversion, simultaneously completing time delay fine-tuning and code-Doppler simulation. The system synchronously acquires the original signal at the input end of the system link and the simulated signal at the output end, removes multiple types of external interference components, and extracts the pure inherent delay component of the equipment. The delay deviation value is obtained by combining the cross-correlation algorithm with the area centroid algorithm, and the compensation amount is dynamically issued to complete the closed-loop dynamic correction of the total delay data. A unified FPGA-based all-digital real-time processing architecture is constructed to synchronously superimpose multi-dimensional space-ground channel propagation characteristics under a unified time base, generate and output radio frequency simulation signals, and complete high-dynamic space-ground channel simulation and delay calibration.

2. The high-dynamic satellite-to-ground channel simulation and delay calibration method according to claim 1, characterized in that, The process for generating the satellite-to-ground transmission parameters is as follows: Obtain the original large-interval discrete trajectory data that characterizes the relative motion between the star and the ground, and perform piecewise polynomial fitting on the large-interval discrete trajectory data based on the cubic dynamic motion time polynomial to divide the complete orbit time axis into several continuous sub-intervals. For each sub-interval, the least squares method is used for fitting, and the coefficients of the cubic motion equations corresponding to each sub-interval are solved to obtain the piecewise continuous distance motion equations. Taking the first derivative of the distance motion equation yields a piecewise continuous velocity motion equation; Based on the segmented continuous distance motion equation, the original input large-interval discrete trajectory data, i.e. millisecond-level discrete distance data, is transformed into equally spaced high-density continuous satellite-to-ground distance parameters and velocity parameters through multi-level accumulator recursive operation.

3. The high dynamic satellite-to-ground channel simulation and delay calibration method according to claim 2, characterized in that: The cubic dynamic motion time polynomial satisfies the following equation, taking... As a fitting time period: in, This is the initial time. R(t) represents the initial distance of the segment; R(t) represents the distance between the satellite and the ground at time t; v(t) represents the radial velocity between the satellite and the ground at time t. 'a' represents the initial velocity of the segment; 'a' represents the initial acceleration. To accelerate.

4. The high dynamic satellite-to-ground channel simulation and delay calibration method according to claim 3, characterized in that, The high-precision dynamic total time delay reconstruction process is as follows: The high-density continuous satellite-to-ground distance parameters are mapped to a total transmission delay value; A fully digital dynamic interpolation reconstruction architecture is adopted to split the total transmission delay data into integer delay components and fractional delay components; Coarse delay adjustment of integer delay components is performed by controlling the address difference read from a large-capacity SDRAM cache. The fractional delay components are finely tuned using a third-order Lagrange polynomial interpolation algorithm. The third-order Lagrange polynomial interpolation algorithm is formed by combining the Farrow structure and the third-order Lagrange fractional delay filter. The coarse adjustment of the integer delay component and the fine adjustment of the fractional delay component are cascaded to complete the high-precision dynamic total delay reconstruction.

5. The high dynamic satellite-to-ground channel simulation and delay calibration method according to claim 4, characterized in that: The third-order Lagrange polynomial interpolation algorithm satisfies the following equation: in: The fractional time delay component has a value range of 1. ; The input sampling sequence is a third-order Lagrange filter. To output the interpolated sequence, The reference sampling point index for the input discrete sequence. This is the index of the sampling points for the output discrete sequence.

6. The high dynamic satellite-to-ground channel simulation and delay calibration method according to claim 5, characterized in that, The code-Doppler simulation process is as follows: The sampling rate transformation coefficient is calculated in real time based on the radial velocity of the aforementioned satellite-to-ground transmission parameters; The Farrow structure of the third-order Lagrange polynomial interpolation algorithm and the third-order Lagrange fractional delay filter are reused to perform arbitrary sampling rate conversion, and time delay fine-tuning and code Doppler simulation are completed simultaneously. The sampling rate conversion process satisfies the following equation: in, The sampling period of the input discrete signal. The sampling period of the output discrete signal after sampling rate conversion. For the input discrete signal at time... The sampled values; To output the discrete signal at time... The sampled values; This represents the impulse response of the interpolation filter; For the input sequence sampling point index, / This is the index of the sampling points for the output sequence.

7. The high dynamic satellite-to-ground channel simulation and delay calibration method according to claim 6, characterized in that, The closed-loop dynamic correction process for the total delay data is as follows: During system operation, the raw signals at the input end of the system link and the simulated signals at the output end are collected synchronously. The original input signal and the simulated output signal are time-domain aligned, and external interference components are removed one by one. The external interference components include at least the initial delay, dynamic transmission delay and local oscillator Doppler. The pure inherent delay component of the device is extracted. The cross-correlation algorithm combined with the area centroid algorithm is used to calculate the separated pure intrinsic delay components and obtain the delay deviation value at the current moment in real time. The delay deviation value is dynamically sent to the total delay data reconstruction link for compensation superposition, thereby completing the closed-loop dynamic correction of the total delay data.

8. The high dynamic satellite-to-ground channel simulation and delay calibration method according to claim 7, characterized in that: A unified FPGA all-digital real-time processing architecture is constructed to coherently superimpose and synchronously simulate multi-dimensional channel effects, including at least transmission delay, Doppler frequency shift, code Doppler, Gaussian noise, ionospheric dispersion, tropospheric refraction and multipath fading, under the same clock reference. The processed digital signal is then converted into an RF simulation signal output through a multi-band RF link. The multi-band RF link is a dual-band RF processing link that natively integrates the Sub-6G and Ka bands, thereby completing high-dynamic satellite-to-ground channel simulation and delay calibration.

9. The high dynamic satellite-to-ground channel simulation and delay calibration method according to claim 8, characterized in that: The Gaussian noise is generated digitally using an m-sequence combined with the Box-Muller algorithm. This noise sequence is then combined with multi-level filtering and power adjustment to generate a noise signal, which is then superimposed onto the RF simulation signal.

10. A high-dynamic satellite-to-ground channel simulation and delay calibration system, characterized in that, include: The module includes a parameter generation module, a transmission delay adjustment module, a code Doppler fusion processing module, an inherent delay calibration module, a Gaussian noise simulation module, and an RF transceiver processing module. The parameter generation module is used to acquire large-interval discrete trajectory data characterizing the relative motion between the satellite and the ground, and to convert the large-interval discrete trajectory data into equally spaced, high-density, continuously time-varying satellite-to-ground transmission parameters. The input terminal of the transmission delay adjustment module is connected to the output terminal of the parameter generation module, and is used to receive the satellite-to-ground transmission parameters and map them into total transmission delay data; The transmission delay adjustment module includes cascaded integer delay adjustment units and fractional delay adjustment units: The integer delay adjustment unit is used to perform coarse delay adjustment on the integer delay components split from the total transmission delay data. The fractional delay adjustment unit is used to fine-tune the delay of the fractional delay components divided from the total transmission delay data. The integer delay adjustment unit and the fractional delay adjustment unit are cascaded together to complete the total delay data reconstruction and output the signal after delay reconstruction. The code-Doppler fusion processing module is connected to the parameter generation module and the time delay dynamic reconstruction module, respectively. It is used to generate code-Doppler frequency shift data according to the radial velocity output by the parameter generation module, and reuse the third-order Lagrange polynomial interpolation algorithm of the fractional time delay adjustment unit to simultaneously complete arbitrary sampling rate conversion, thereby realizing the functional integration of time delay adjustment and code-Doppler simulation. The signal acquisition end of the inherent delay calibration module is connected to the input signal node and the output signal node of the system link, respectively. It is used to synchronously acquire the original signal at the input end and the simulated signal at the output end of the system link, remove multiple external interference components, extract the pure inherent delay component of the device, calculate the delay deviation value in real time through the cross-correlation algorithm in the module combined with the area centroid algorithm module, and dynamically send the compensation amount to the transmission delay adjustment module in a closed-loop feedback manner to complete the real-time closed-loop correction of the total delay data. The Gaussian noise simulation module is used to realize broadband Gaussian white noise simulation, as well as colored noise and impulse noise extension; The radio frequency transceiver processing module is used to construct a unified FPGA all-digital real-time processing architecture, synchronously superimpose multi-dimensional space-ground channel propagation characteristics under a unified time base, generate and output radio frequency simulation signals, and complete high-dynamic space-ground channel simulation and delay calibration.

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