A method for generating and evaluating low-orbit satellite Doppler simulation data

Abstract

This invention discloses a method for generating and evaluating Doppler simulation data for low-Earth orbit (LEO) satellites. By employing source-end discrete-time gating, the system is forced to directly block output when the time deviation exceeds 1 ms, fundamentally severing the error coupling path and ensuring the purity of the output data. In the inertial frame velocity synthesis, an explicit Earth rotation-related velocity correction term is introduced, enabling even stationary receivers to accurately reflect their motion in inertial space. An elevation-angle-weighted noise model is used to accurately simulate the physical phenomenon of sharp link deterioration in LEO satellites at low elevation angles, realistically reflecting the tracking and loss-of-lock behavior of receivers in complex geometric environments. Terminal physical consistency verification is introduced. By comparing the distance change rate calculated in the velocity domain with the distance rate of the position domain derivative in real time, a rigorous closed-loop feedback mechanism is constructed, which can automatically identify and eliminate abnormal data caused by ephemeris jumps or numerical divergences, significantly improving the engineering confidence of the simulation data.

Description

Technical Field

[0001] This invention belongs to the field of satellite navigation simulation and testing technology, specifically focusing on low-Earth orbit satellite scenarios, and provides a complete technical solution covering ephemeris generation, Doppler simulation, data verification and standardized output. Background Technology

[0002] Global Navigation Satellite System (GNSS), as a core technology for high-precision positioning services, can provide stable positioning accuracy in open, unobstructed environments and has been widely used in military reconnaissance, civilian navigation, and traffic management. However, the propagation of GNSS signals is easily constrained by the geographical environment. In obstructed or blocked environments such as urban canyons, underground spaces, and indoor spaces, signal propagation can be hindered, leading to positioning failures. Therefore, building a reliable backup positioning and navigation system has become an urgent need.

[0003] Low Earth Orbit (LEO) satellite systems, with their unique technological advantages of wide global coverage, high signal strength upon landing, and rapid geometric changes, have become a core backup option for GNSS. Since most LEO satellites do not broadcast navigation signals, existing LEO satellite positioning technologies primarily rely on Signal of Opportunity (SOP) positioning, achieving location by extracting the Doppler frequency offset of LEO satellites. However, the accurate acquisition of high-precision Doppler frequency offset measurements is limited by expensive high-precision spectrum analyzers. This limitation makes it difficult for measured data to meet the requirements of engineering applications. Therefore, developing Doppler frequency offset simulation technology suitable for large-constellation LEO satellite systems has become a key path to overcome this bottleneck.

[0004] Existing solution implementation method:

[0005] Currently, existing technologies related to Doppler frequency offset simulation for low-Earth orbit satellites exhibit several typical characteristics during implementation, as follows: 1. Regarding the source of ephemeris, existing schemes generally rely on TLE (two-line orbital elements) as input data and combine it with SGP4 (simplified conventional perturbation model) or SDP4 (simplified deep space perturbation model) for orbit prediction. Overall, the source of ephemeris is relatively singular and lacks the ability to be flexibly adapted.

[0006] 2. In the time processing stage, for computational efficiency reasons, ephemeris data are usually generated at relatively long time intervals. When the simulation scenario demands higher time resolution, the industry commonly uses mathematical interpolation between sparse ephemeris points to generate satellite position and velocity data at intermediate moments.

[0007] 3. Regarding the construction of the receiver velocity model, existing solutions often simply set the receiver's velocity vector to zero in static receiver simulations, while ignoring the influence of the Earth's rotation—a fixed point on the Earth's surface actually has a significant velocity component relative to the Earth's inertial frame.

[0008] 4. In the noise modeling stage, the additive white Gaussian noise (AWGN) model is the simplest and most commonly used choice in channel simulation. The noise power spectral density of this model is a constant value and is not related to the signal frequency or the geometric relationship of the propagation path.

[0009] 5. In terms of data output, the output formats of existing simulation data are diverse, mostly custom text or binary formats, lacking a universal data format that aligns with industry standards. This situation directly increases the difficulty of sharing simulation data and hinders its engineering applications.

[0010] In the existing engineering implementation of Doppler frequency offset simulation for low-Earth orbit satellites, there are several systemic technical defects, resulting in simulation accuracy and engineering applicability failing to meet actual needs. These defects are as follows: 1. Nonlinear Coupling of Interpolation Error and Radial Acceleration: Existing techniques achieve time matching between observations and ephemeris through ephemeris time interpolation or alignment to a fixed ephemeris sampling grid. However, in the high-dynamic scenarios of low-Earth orbit satellites, differences in time scale and step size between the observation time series and the ephemeris sampling grid can easily lead to time asynchrony. This time misalignment couples with radial acceleration, ultimately resulting in frequency offset error. Under typical geometric conditions, the error can reach 5-10Hz, and under unfavorable geometry, the error can even exceed 20Hz.

[0011] 2. Coordinate system mixing leads to dynamic projection bias: Existing schemes perform simple velocity difference calculations in the ECEF coordinate system, neglecting the consistency of transformation between the inertial frame and the Earth-fixed frame. Especially when dealing with static receivers, simply setting their velocity to zero fails to correctly calculate the tangential velocity component generated by the Earth's rotation in the inertial frame, resulting in systematic biases in the simulation results.

[0012] 3. Decoupling of noise modeling from geometric characteristics: Existing noise models do not consider the influence of geometric visibility observed by LEO satellites, and cannot accurately reflect the characteristics of link signal degradation in low elevation angle scenarios, resulting in insufficient fit between simulation data and actual scenarios.

[0013] 4. The system lacks a disaster recovery mechanism: the ephemeris source is singular, relying solely on the TLE+SGP4 model. Once the external data source is interrupted or the verification fails, the entire simulation link will be interrupted immediately.

[0014] 5. Existing technologies lack a self-checking mechanism for generated data, resulting in erroneous data being output to users.

[0015] To address the shortcomings of existing technologies, there is an urgent need for a Doppler simulation evaluation method for low-Earth orbit satellite scenarios. This method should provide a system-level solution that simultaneously ensures consistency across three dimensions: source time grid, kinematic solution benchmark, and link geometric characteristics. This would improve the reliability and engineering applicability of Doppler simulations under high dynamic conditions. Summary of the Invention

[0016] In view of this, the present invention provides a high-dynamic Doppler simulation method for low-Earth orbit satellites based on a unified spatiotemporal reference. By constructing a dual-path ephemeris-driven architecture, a discrete-time gating strategy, an inertial frame full-velocity vector synthesis model, and a geometrically inverse weighted noise model, it specifically addresses the shortcomings of existing technologies. The specific technical solution is as follows: A method for generating and evaluating low-Earth orbit satellite Doppler simulation data includes: Step 1: Obtain satellite orbital status based on ephemeris; Step 2: Before generating Doppler data, calculate the deviation between the original ephemeris timestamp and the observation request time. Only when the deviation is less than the preset synchronization tolerance threshold is it determined to be a valid synchronization epoch and allowed to proceed to subsequent calculations. Step 3: For effective synchronization epochs, establish a unified geocentric inertial frame solution framework. When calculating the receiver velocity vector, add the entrainment velocity term caused by the Earth's rotation to obtain the final receiver full velocity vector under the geocentric inertial frame ECI. Step 4: Calculate the ideal Doppler frequency offset based on the receiver's full velocity vector, and inject non-stationary noise that is inversely proportional to the satellite's line-of-sight elevation angle to generate the observed Doppler frequency offset; Step 5: Perform integrity verification on the generated data stream, calculate the residual between the observed Doppler frequency offset and the rate of change of the satellite-to-ground geometric distance, and if the residual exceeds the preset physical rationality threshold, then discard the corresponding epoch. Step 6: Standardize the packaging and output.

[0017] Preferably, in step one, when the external two-line orbital elements or precise ephemeris data are available, the SGP4 model or SDP4 model is used to obtain the satellite orbital state.

[0018] Preferably, in step one, when TLE data is detected to be missing, expired, or the SGP4 model solution diverges, the position vector of the satellite in the inertial frame is analytically extrapolated based on the two-body dynamics model using the Kepler six roots.

[0019] Preferably, in step four, the method for calculating the ideal Doppler frequency offset includes: Satellite-to-ground connection vector: ; Geometric distance between Earth and space: And the line-of-sight vector between stars and ground units: ; in, This indicates the satellite position in the ECI coordinate system calculated in step one; Indicates the receiver position in the ECI coordinate system; Relative velocity between Earth and space: ; Distance variation rate: The distance variation rate is obtained by projecting the relative velocity between the star and the ground onto the line of sight. ; Ideal Doppler frequency offset: Doppler frequency offset at each frequency point ,in For carrier frequency, The speed of light; in, This represents the satellite velocity in the ECI coordinate system calculated in step one; This represents the receiver's full velocity in the ECI coordinate system calculated in step three.

[0020] Preferably, in step four, the method for generating the observed Doppler frequency offset includes: Noise scaling factor: ; in, As the reference noise intensity, The elevation angle of the satellite relative to the receiver; The final generated Doppler frequency offset with noise: ,in For ideal Doppler frequency deviation, This is standard normally distributed noise.

[0021] Preferably, in step four, the method for generating the observed Doppler frequency offset includes: Elevation angle calculation: Using the unit line-of-sight vector The arcsine of the celestial component after transformation to a local ENU, i.e. Where e, n, and u are respectively In the ENU coordinate system, the east, north, and celestial components.

[0022] Preferably, step four also includes filtering abnormal data: The preset elevation angle threshold and preset satellite-to-ground distance threshold (>100000m) are used to delete observation data corresponding to elevation angles below the elevation angle threshold or satellite-to-ground distances below the satellite-to-ground distance threshold for the final generated observation Doppler frequency offset data.

[0023] Furthermore, step five also includes a full-process timing continuity check: First, extract the unique GPS time stamp for each epoch from the generated data stream, and then scan the time difference between adjacent valid epochs. ,like Equal to the preset sampling interval, determine the temporal continuity, if If the sampling interval is an integer multiple of the preset sampling interval, it indicates that there are epochs that are rejected by the system. The system will automatically record this as an interruption event and mark it in the output file of the subsequent step five.

[0024] The present invention has the following beneficial effects: 1. Eliminates non-physical oscillations caused by coupling: In low-orbit, high-dynamic scenarios, existing technologies can amplify microsecond-level interpolation errors through drastic acceleration, creating spurious Doppler frequency ripples. This invention, through source-end discrete-time gating, forces the system to directly block the output when the time deviation is >1ms, fundamentally severing the error coupling path and ensuring the purity of the output data.

[0025] 2. Correction of systematic frequency offset in static simulation: Traditional models often neglect the influence of Earth's rotation on static receivers. This invention introduces an explicit Earth rotation-related velocity correction term in the inertial frame velocity synthesis, enabling even a stationary receiver to accurately reflect its motion in inertial space.

[0026] 3. It realistically reproduces the non-stationary characteristics of the LEO channel: By adopting an elevation angle-weighted noise model, it accurately simulates the physical phenomenon of the rapid deterioration of the LEO satellite link at low elevation angles. Compared with the traditional constant variance model, it can more realistically reflect the tracking and loss-of-lock behavior of the receiver in complex geometric environments.

[0027] 4. Achieved end-to-end physical consistency assurance: Unlike the open-loop generation of existing technologies, this invention introduces terminal physical consistency verification. By comparing the distance change rate calculated in the velocity domain with the differential in the position domain in real time, a rigorous closed-loop feedback mechanism is constructed, which can automatically identify and eliminate abnormal data caused by ephemeris jumps or numerical divergences, significantly improving the engineering confidence of simulation data. Detailed Implementation

[0028] The present invention provides a method for generating and evaluating Doppler simulation data of low-Earth orbit satellites, which mainly includes the following steps: Step 1: Ephemeris Acquisition Steps: Obtain satellite orbital status using a dual-path ephemeris generation mechanism based on availability assessment.

[0029] Step 2, Source-end time gating step: Before generating Doppler data, calculate the deviation between the original ephemeris timestamp and the observation request time. Only when the deviation is less than the preset synchronization tolerance threshold (e.g., 1ms) is it determined to be a valid synchronization epoch and allowed to enter the subsequent calculation. Otherwise, trigger the blocking mechanism to directly terminate the generation of the current epoch, thereby avoiding the dynamic coupling error introduced by the interpolation algorithm. Step 3: Inertial Frame Velocity Composition: For the effective synchronous epoch, a unified geocentric inertial frame (ECI) solution framework is established. When calculating the receiver velocity vector, the entrainment velocity term caused by the Earth's rotation is explicitly superimposed. The calculation formula is as follows: Among them, the receiver's own velocity relative to the ground surface, i.e., the local velocity. The velocity is obtained by transforming the receiver's local ENU velocity through the rotation matrix P; Earth's rotational drag velocity. The calculations are based on the International Earth Rotation Service (IERS) standard angular velocity; subsequently, a coordinate transformation matrix is ​​used to project it onto the ECI coordinate system to obtain the final receiver full velocity vector in the geocentric inertial ECI system. .

[0030] Step 4, Geometric Coupling Noise Injection Step: Calculate the ideal Doppler frequency offset based on the inertial frame velocity, and inject non-stationary noise that is inversely proportional to the satellite's line-of-sight elevation angle to generate the observed Doppler frequency offset; Step 5, Terminal Physical Consistency Verification: Perform integrity verification on the generated data stream, calculate the residual between the observed Doppler frequency offset and the rate of change of the satellite-to-ground geometric distance, and if the residual exceeds the preset physical rationality threshold, then discard that epoch.

[0031] Step 6: Standardize the packaging and output.

[0032] The following is a detailed implementation process for each step: 1) Step one specifically includes: To address the issue of a single ephemeris source being unavailable in extreme scenarios, this step designs a primary-backup redundant ephemeris driving mechanism.

[0033] 1. Main Path: When external orbital elements or precise ephemeris data are available, the SGP4 (Simplified General Perturbations-4) model or the SDP4 model is preferred. Using TLE as input, orbital propagation is performed in the inertial reference frame, incorporating the effects of Earth's non-spherical perturbations and atmospheric drag, to output the satellite's position and velocity.

[0034] 2. Secondary Path: The system has a built-in status monitor that monitors the solution status of the primary path in real time. When TLE data is detected as missing, expired, or SGP4 solution diverges, it automatically and seamlessly switches to the secondary path. The secondary path is based on a two-body dynamics model and uses Kepler's six roots to perform analytical extrapolation of the satellite's position vector in the inertial frame. The core algorithm is as follows: (1) Mean angular velocity: ,in The gravitational constant (unit: ), The semi-major axis of the satellite orbit (unit: m); (2) Kepler equations: Where M is the horizontal anomaly angle, E is the eccentric anomaly angle, and e is the orbital eccentricity. (3) Relationship between true anterior angle and eccentric anterior angle: ,in It is a true near point angle; (4) Radial distance: ,in The connection vector between the satellite and the receiver is the magnitude (in meters). (5) Track plane position: ; (6) Position of the inertial frame: ,in Right ascension of the ascending node, It is the angle of inclination. Let the perigee argument be denoted by the rotation matrix, which is defined as: The rotation matrix around the x-axis is: , The rotation matrix around the z-axis is:

[0035] 2) Step two specifically includes: Before performing the Doppler solution, calculate the time of the current ephemeris data. Simulation request time deviation Set a threshold. ,when If the current epoch is deemed invalid, the calculation process for that epoch is terminated immediately, and no data is passed down. Only if... Only when it is determined to be a valid epoch can the process proceed to step three.

[0036] 3) Step three specifically includes: This step, while ensuring time alignment, performs motion state calculations under a unified benchmark to eliminate systematic frequency offsets introduced by reference frame differences.

[0037] 1. Coordinate mapping: To address the geometric errors caused by multi-frame mixed calculations, this invention constructs a unified transformation system based on the WGS-84 ellipsoid parameters.

[0038] The WGS-84 ellipsoid parameters are defined as follows: , The specific conversion formula is as follows: Transformation formula from latitude and longitude coordinate system (LLA) to Earth-centered Earth-fixed coordinate system (ECEF): (1) Radius of curvature of the zonal loop: ,in Geographic latitude (unit: radians); (2) ECEF coordinates: , , ,in h represents geographical longitude (in radians), and h represents elevation (in meters). ENU to ECEF conversion formula: Using a rotation matrix P with East, North, and Sky as orthogonal column bases, the local ENU motion of the receiver is transformed to the ECEF coordinate system. The rotation matrix is ​​defined as follows:

[0039] The matrix satisfies The vector transformation relationship is as follows: .

[0040] 2. Modified receiver full velocity vector synthesis: To address the shortcomings of existing technologies that neglect the Earth's rotation velocity term, this invention constructs a modified velocity model to calculate the receiver's full velocity vector in the geocentric inertial frame (ECI).

[0041] Earth's rotation speed calculation: The standard value of Earth's rotation angular velocity published by the International Earth Rotation and Retirement Service (IERS) is used. The calculation receiver's entrainment velocity in the inertial frame due to the Earth's rotation. Its explicit expression is , where x and y are the ECEF coordinate components of the receiver.

[0042] Total velocity synthesis: Following the principle of "local motion velocity + Earth's rotation velocity", the total velocity vector of the receiver in the ECEF coordinate system is calculated, i.e. For static receivers Its total velocity is contributed solely by the Earth's rotation, completely correcting the systematic bias of the traditional model. Finally, using the Earth's rotation coordinate transformation matrix, this total velocity vector is projected onto the ECI coordinate system to obtain the final receiver's total velocity vector in the geocentric inertial frame. : ,in ,in This refers to the Greenwich Mean Time corresponding to the observation epoch.

[0043] 4) Step four specifically includes: After velocity synthesis is completed, this step performs refined geometric relationship calculations based on a unified framework and injects observation noise strongly coupled with elevation angle.

[0044] 1. Geometric relation calculation: Satellite-to-ground connection vector: ; Geometric distance between Earth and space: And the line-of-sight vector between stars and ground units: ; Elevation angle calculation: Using the unit line-of-sight vector The arcsine of the celestial component after transformation to a local ENU, i.e. (Range: -90°~90°), where e, n, and u are respectively In the ENU coordinate system, the east, north, and celestial components, and when When this occurs, it is determined to be an invalid observation epoch; Azimuth calculation: Azimuth is defined as: (Range 0°~360°), where .

[0045] in, This indicates the satellite position in the ECI coordinate system calculated in step one; This indicates the receiver position in the ECI coordinate system, which is obtained by converting the ECEF position in step three to the ECI coordinate system. 2. Doppler frequency offset calculation: Relative velocity between Earth and space: ; Distance variation rate: The distance variation rate is obtained by projecting the relative velocity between the star and the ground onto the line of sight. ; Ideal Doppler frequency offset: Doppler frequency offset at each frequency point ,in For carrier frequency, The speed is the speed of light; the preferred dual-frequency combination is L1 frequency (1575.42 MHz) and L2 frequency (1227.60 MHz), or other dual-frequency combinations compatible with satellite navigation systems.

[0046] in, This represents the satellite velocity in the ECI coordinate system calculated in step one; This represents the receiver's total velocity in the ECI coordinate system calculated in step three. 3. Geometric coupling noise modeling and injection: To address the issues of existing technologies such as "noise being decoupled from geometric visibility and failing to reflect low elevation angle link degradation," a noise scaling model associated with elevation angle was designed.

[0047] Noise scaling factor: ; in, Reference noise level (in Hz). The elevation angle of the satellite relative to the receiver (in radians).

[0048] The final generated Doppler frequency offset with noise: ,in For ideal Doppler frequency deviation, This is standard normally distributed noise.

[0049] 4. Abnormal data filtering: With preset elevation angle thresholds (5°~10°) and preset satellite-to-ground distance thresholds (>100000m), for the Doppler frequency offset data of the final observations after injecting geometric coupling noise in step 3, the observation data corresponding to elevation angles lower than the elevation angle threshold or the observation data corresponding to satellite-to-ground distances lower than the satellite-to-ground distance threshold are deleted. In this way, abnormal data at low elevation angles and in close-range scenes are automatically filtered, further improving the reliability of simulation data.

[0050] 5) Step five specifically includes: This step is located at the end of data generation and serves as a quality gating step, performing dual verification of the physical consistency and temporal continuity of the noisy observation data stream generated in step four.

[0051] 1. Terminal physical consistency verification: This step constructs a self-consistent detection model for the "Doppler-distance change rate". The final Doppler observations generated in step four are then read. With geometric distance change rate Calculate the physical residuals of the two. , If the residual exceeds the preset physical rationality threshold (usually determined by the noise level), it is determined that there is a dynamic violation in the current epoch (which may be caused by ephemeris jumps or solution divergence), and the abnormal data is automatically marked and removed.

[0052] 2. End-to-end timing continuity verification First, extract the unique GPS time stamp (GPS Week + SOW) for each epoch from the generated data stream, and then scan the time difference between adjacent valid epochs. ,like Strictly equal to the preset sampling interval, determining the temporal continuity, if If the sampling interval is an integer multiple of the preset sampling interval, it indicates that there are epochs that were automatically removed by the system because they failed to pass the time gating in step two, or the elevation angle and distance filtering in step four, or the physical consistency check. The system will automatically record these as interruption events and mark them in the output file of the subsequent step five.

[0053] 6) Step six specifically includes: To address the shortcomings of existing technologies, such as the lack of a unified standard for output formats and poor engineering traceability, this step encapsulates the validated data into an industry-standard format.

[0054] 1. Format standard definition: The RINEX 3.04 standard format is used for encapsulation.

[0055] 2. File Structure: The output file header contains standard fields such as VERSION / TYPE, PGM / RUNBY / DATE, INTERVAL, and SYS / # / OBS TYPES. The observation epoch fields uniformly include GPS Week, GPS SOW, receiver ID, satellite ID, L1 / L2 dual-frequency Doppler frequency offset, elevation angle, azimuth angle, distance change rate, receiver ECEF coordinates / velocity, and interruption events.

[0056] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for generating and evaluating Doppler simulation data of low-Earth orbit satellites, characterized in that, include: Step 1: Obtain satellite orbital status based on ephemeris; Step 2: Before generating Doppler data, calculate the deviation between the original ephemeris timestamp and the observation request time. Only when the deviation is less than the preset synchronization tolerance threshold is it determined to be a valid synchronization epoch and allowed to proceed to subsequent calculations. Step 3: For effective synchronization epochs, establish a unified geocentric inertial frame solution framework. When calculating the receiver velocity vector, add the entrainment velocity term caused by the Earth's rotation to obtain the final receiver full velocity vector under the geocentric inertial frame ECI. Step 4: Calculate the ideal Doppler frequency offset based on the receiver's full velocity vector, and inject non-stationary noise that is inversely proportional to the satellite's line-of-sight elevation angle to generate the observed Doppler frequency offset; Step 5: Perform integrity verification on the generated data stream, calculate the residual between the observed Doppler frequency offset and the rate of change of the satellite-to-ground geometric distance, and if the residual exceeds the preset physical rationality threshold, then discard the corresponding epoch. Step 6: Standardize the packaging and output.

2. The method for generating and evaluating low-Earth orbit satellite Doppler simulation data as described in claim 1, characterized in that, In step one, when the external two rows of orbital elements or precise ephemeris data are available, the SGP4 model or SDP4 model is used to obtain the satellite orbital status.

3. A method for generating and evaluating low-Earth orbit satellite Doppler simulation data as described in claim 1 or 2, characterized in that, In step one, when TLE data is detected to be missing, expired, or the SGP4 model solution diverges, the position vector of the satellite in the inertial frame is analytically extrapolated based on the two-body dynamics model using the Kepler six roots.

4. The method for generating and evaluating low-Earth orbit satellite Doppler simulation data as described in claim 1, characterized in that, In step four, the method for calculating the ideal Doppler frequency offset includes: Satellite-to-ground connection vector: ; Geometric distance between Earth and space: And the line-of-sight vector between the star and the ground: ; in, This indicates the satellite position in the ECI coordinate system calculated in step one; Indicates the receiver position in the ECI coordinate system; Relative velocity between Earth and space: ; Distance variation rate: The distance variation rate is obtained by projecting the relative velocity between the star and the ground onto the line of sight. ; Ideal Doppler frequency offset: Doppler frequency offset at each frequency point ,in For carrier frequency, The speed of light; in, This represents the satellite velocity in the ECI coordinate system calculated in step one; This represents the receiver's full velocity in the ECI coordinate system calculated in step three.

5. The method for generating and evaluating low-Earth orbit satellite Doppler simulation data as described in claim 4, characterized in that, In step four, the method for generating the observed Doppler frequency offset includes: Noise scaling factor: ; in, As the reference noise intensity, The elevation angle of the satellite relative to the receiver; The final generated Doppler frequency offset with noise: ,in For ideal Doppler frequency deviation, This is standard normally distributed noise.

6. The method for generating and evaluating low-Earth orbit satellite Doppler simulation data as described in claim 5, characterized in that, In step four, the method for generating the observed Doppler frequency offset includes: Elevation angle calculation: Using the unit line-of-sight vector The arcsine of the celestial component after transformation to a local ENU, i.e. Where e, n, and u are respectively In the ENU coordinate system, the east, north, and celestial components.

7. The method for generating and evaluating low-Earth orbit satellite Doppler simulation data as described in claim 5, characterized in that, Step four also includes filtering abnormal data: The preset elevation angle threshold and preset satellite-to-ground distance threshold (>100000m) are used to delete observation data corresponding to elevation angles below the elevation angle threshold or satellite-to-ground distances below the satellite-to-ground distance threshold for the final generated observation Doppler frequency offset data.

8. The method for generating and evaluating low-Earth orbit satellite Doppler simulation data as described in claim 1, characterized in that, Step five also includes a full-process timing continuity check: First, extract the unique GPS time stamp for each epoch from the generated data stream, and then scan the time difference between adjacent valid epochs. ,like Equal to the preset sampling interval, determine the temporal continuity, if If the sampling interval is an integer multiple of the preset sampling interval, it indicates that there are epochs that are rejected by the system. The system will automatically record this as an interruption event and mark it in the output file of the subsequent step five.