Simulation training methods, systems, electronic devices and storage media

Abstract

This application proposes a simulation training method, system, electronic device, and storage medium, which includes: acquiring a preset geographic mapping model, fixed virtual geographic coordinates of a virtual reference station, and satellite ephemeris data; real-time acquisition of physical coordinate data fed back by the mobile platform; converting the physical coordinate data according to the geographic mapping model to obtain the virtual geographic coordinates of the mobile station; calculating the first satellite observation data using the fixed virtual geographic coordinates based on a unified time reference and precise satellite ephemeris data, and calculating the second satellite observation data using the virtual geographic coordinates of the mobile station; generating differential correction data based on the first satellite observation data; acquiring the positioning result output by the receiver of the tested mobile station; calculating the deviation index between the positioning result and the virtual geographic coordinates of the mobile station, and generating a training evaluation report.

Description

Technical Field

[0001] This application relates to the technical field of simulation training methods, and more particularly to simulation training methods, systems, electronic devices, and storage media. Background Technology

[0002] Real-time dynamic relative positioning (RTK) is a high-precision measurement based on carrier phase differential. Its basic principle is to use a base station for observation and transmit the observation data or differential correction data to the rover station through radio transmission equipment to achieve high-precision positioning measurement.

[0003] Currently, the use of RTK high-precision satellite positioning technology for teaching and equipment testing faces the problem of being highly dependent on the real field environment. Summary of the Invention

[0004] This application provides a simulation training method, system, electronic device, and storage medium to solve the problems existing in related technologies. The technical solution is as follows: Firstly, embodiments of this application provide a simulation training method, including: The system acquires a preset geographic mapping model, fixed virtual geographic coordinates of the virtual base station, and satellite ephemeris data. It also collects physical coordinate data fed back by the mobile platform in real time. The physical coordinate data represents the real-time position of the mobile station receiver under test in physical space. The physical coordinate data is transformed according to the geographic mapping model to obtain the virtual geographic coordinates of the mobile station; Based on a unified time base and precise satellite ephemeris data, the first satellite observation data is obtained by using fixed virtual geographic coordinates, and the second satellite observation data is obtained by using the virtual geographic coordinates of the rover station. Based on the first satellite observation data, differential correction data is generated and sent to the receiver of the tested mobile station via a data link. The receiver of the tested mobile station combines the received radio frequency signal of the corresponding second satellite observation data to perform differential positioning calculation and output the positioning result. Obtain the positioning results output by the receiver of the tested mobile station; Calculate the deviation index between the positioning result and the virtual geographic coordinates of the mobile station, and generate a training evaluation report.

[0005] Secondly, embodiments of this application provide a simulation training method system, including: The first acquisition module is used to acquire the preset geographic mapping model, the fixed virtual geographic coordinates of the virtual base station and satellite ephemeris data, and to collect the physical coordinate data fed back by the mobile platform in real time. The physical coordinate data represents the real-time position of the mobile station receiver under test in physical space. The first module is used to transform physical coordinate data according to the geographic mapping model to obtain the virtual geographic coordinates of the mobile station; The second module is used to obtain the first satellite observation data based on a unified time base and precise satellite ephemeris data, using fixed virtual geographic coordinates, and to obtain the second satellite observation data using the virtual geographic coordinates of the rover station. The first generation module is used to generate differential correction data based on the first satellite observation data, and send the differential correction data to the mobile station receiver under test through the data link, so that the mobile station receiver under test can combine the received radio frequency signal of the corresponding second satellite observation data to perform differential positioning calculation and output the positioning result. The second acquisition module is used to acquire the positioning results output by the receiver of the tested mobile station; The second generation module is used to calculate the deviation index between the positioning result and the virtual geographic coordinates of the mobile station, and generate a training evaluation report.

[0006] Thirdly, embodiments of this application provide an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to execute the above-described simulation training method.

[0007] Fourthly, embodiments of this application provide a computer-readable storage medium that stores computer instructions, which, when executed on a computer, allow the methods in any of the above embodiments to be performed.

[0008] The advantages or beneficial effects of the above technical solutions include at least the following: In this embodiment, the simulation training method converts the collected physical coordinates into virtual coordinates in real time through a geographic mapping model, logically decoupling the physical space from the test scenario. This allows for the derivation and reproduction of a wide-area field operation scenario within a limited indoor space. Based on a unified time reference, the method performs parallel calculations of satellite observation data from a fixed virtual reference station and a rover station. This strict spatiotemporal synchronization mechanism ensures that the two generated data streams have the geometric correlation required for double-difference operations in the mathematical model, enabling the receiver under test to effectively eliminate common errors and successfully achieve RTK fixed calculations in the simulation environment. Furthermore, the generated virtual coordinates are used as absolute true values ​​and compared with the positioning results calculated by the receiver based on differential data, establishing an objective, accurate, and closed-loop evaluation system that does not require expensive external measurement equipment. By constructing a real-time mapping closed loop between physical motion and virtual space, and using a mathematical model to convert finite physical displacements into wide-area virtual trajectories, the dependence of traditional training on the scale of the physical site is overcome. Based on a unified spatiotemporal reference, parallel driving of dual-channel signal generation ensures strict synchronization of observation data between the virtual reference station and the rover station in geometric phase, thus successfully constructing the mathematical correlation required for RTK fixed solution in an indoor simulation environment. Combined with real radio frequency radiation and differential data injection, full-element hardware-in-the-loop verification is achieved, and by utilizing the virtual coordinate truth value naturally mastered by the simulation system, the problem of difficulty in obtaining positioning references in dynamic testing is solved, ultimately achieving low-cost, high-precision, and repeatable standardized training evaluation.

[0009] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of this application will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0011] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in this application and should not be construed as limiting the scope of this application.

[0012] Figure 1 This is a flowchart of a simulation training method according to an embodiment of this application.

[0013] Figure 2 This is a block diagram of an electronic device according to an embodiment of the present application. Detailed Implementation

[0015] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of this application. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0016] Currently, teaching and equipment testing of high-precision satellite positioning technologies such as RTK (Real-Time Kinematics) face the challenge of heavily relying on real-world field environments. This method is susceptible to interference from various uncontrollable factors such as weather, satellite visibility, geographical obstruction, and radio conditions, resulting in non-repeatable training processes, inaccurate quantification of results, low efficiency, and safety risks. While commercial high-precision GNSS signal simulators can generate high-quality signals, they are expensive, complex to operate, and typically only simulate signals received by the terminal. They cannot construct a complete RTK working scenario including base station deployment, differential data link establishment, and rover dynamic response, leading to poor teaching intuitiveness and unsuitability for large-scale, standardized engineering training.

[0017] Therefore, the industry urgently needs a solution that can deeply integrate the spatial geometry and working principle of RTK systems with the operation of physical equipment, and realize full-element, quantifiable, and highly repeatable training in a safe, controllable, and low-cost indoor environment.

[0018] Figure 1 A flowchart illustrating a simulation training method according to an embodiment of this application is shown. Figures 1-2 As shown, the simulation training method may include: S110: Acquire the preset geographic mapping model, the fixed virtual geographic coordinates of the virtual base station, and satellite ephemeris data; collect the physical coordinate data fed back by the mobile platform in real time; the physical coordinate data represents the real-time position of the tested mobile station receiver in physical space. S120: Transform the physical coordinate data according to the geographic mapping model to obtain the virtual geographic coordinates of the mobile station; S130: Based on a unified time reference and precise satellite ephemeris data, the first satellite observation data is obtained by calculating using fixed virtual geographic coordinates, and the second satellite observation data is obtained by calculating using the virtual geographic coordinates of the rover station. S140: Based on the first satellite observation data, generate differential correction data and send the differential correction data to the receiver of the tested mobile station via the data link, so that the receiver of the tested mobile station can combine the received radio frequency signal of the corresponding second satellite observation data to perform differential positioning calculation and output the positioning result; S150: Obtain the positioning result output by the receiver of the tested mobile station; S160: Calculate the deviation index between the positioning result and the virtual geographic coordinates of the mobile station, and generate a training evaluation report.

[0019] In this embodiment, the simulation training method converts the collected physical coordinates into virtual coordinates in real time through a geographic mapping model, logically decoupling the physical space from the test scenario. This allows for the derivation and reproduction of a wide-area field operation scenario within a limited indoor space. Based on a unified time reference, the method performs parallel calculations of satellite observation data from a fixed virtual reference station and a rover station. This strict spatiotemporal synchronization mechanism ensures that the two generated data streams have the geometric correlation required for double-difference operations in the mathematical model, enabling the receiver under test to effectively eliminate common errors and successfully achieve RTK fixed calculations in the simulation environment. Furthermore, the generated virtual coordinates are used as absolute true values ​​and compared with the positioning results calculated by the receiver based on differential data, establishing an objective, accurate, and closed-loop evaluation system that does not require expensive external measurement equipment. By constructing a real-time mapping closed loop between physical motion and virtual space, and using a mathematical model to convert finite physical displacements into wide-area virtual trajectories, the dependence of traditional training on the scale of the physical site is overcome. Based on a unified spatiotemporal reference, parallel driving of dual-channel signal generation ensures strict synchronization of observation data between the virtual reference station and the rover station in geometric phase, thus successfully constructing the mathematical correlation required for RTK fixed-resolution calculations in an indoor simulation environment. Combined with real radio frequency radiation and differential data injection, full-element hardware-in-the-loop verification is achieved. Furthermore, by utilizing the virtual coordinate truth naturally possessed by the simulation system, the difficulty in obtaining positioning references in dynamic testing is solved, ultimately achieving low-cost, high-precision, and repeatable standardized training and evaluation. It can complete the entire process of standardized training and testing, from equipment deployment, parameter setting, data link establishment to dynamic positioning calculations and accuracy evaluation, without relying on real satellite signals and field environments.

[0020] By constructing a high-precision geographic sandbox and dynamically mapped RTK spoofing test environment indoors, test equipment equipped with real RTK receivers can simulate performing tasks in a real three-dimensional world. This enables end-to-end, high-fidelity, and large-scale teaching verification of high-precision positioning equipment deployment, parameter setting, data link establishment, dynamic positioning calculation, and equipment function evaluation.

[0021] like Figure 2 As shown, the precision electromechanical and truth reference subsystem is the physical basis for providing the truth value of spatial position, and uses industrial-grade precision motion control technology to generate a traceable positioning reference.

[0022] High-rigidity 3D moving platform: Utilizing a bridge or gantry structure, it consists of orthogonally constructed X, Y, and Z axis linear motion modules. Complete. The X and Y axis modules in the horizontal plane simulate planar motion trajectories, while the Z-axis module simulates elevation changes. An RTK mobile station antenna is installed at the end, enabling high-precision movement of the RTK mobile station. Each axis is driven by an AC servo motor, coupled with a precision ball screw (repeatability not exceeding ±0.1mm) or a high-precision synchronous belt for transmission, ensuring smooth low-speed operation and high-speed response. The guide and load-bearing components support the high-precision movement of the mobile platform and RTK mobile station, employing high-rigidity linear guides to ensure no significant deflection or deformation at the end of the movement under full load.

[0023] An absolute linear encoder with a resolution of 0.1 mm or higher is installed on each motion axis. The physical coordinates (x, y, z) of the platform end point read in real time by the encoder are the universally accepted and indisputable "true spatial position" of the entire system. A multi-axis motion control card is used to receive trajectory commands from the central host and read encoder feedback in a closed loop, enabling precise planning and control of the platform's speed and acceleration.

[0024] The equipment carrier terminal is used to rigidly mount the antenna of the RTK mobile station under test. It is necessary to ensure that the phase center of the antenna coincides with the mechanical center of the platform end to eliminate leverage error.

[0025] Coordinate mapping engine: Establishes a rigorous mathematical transformation model between the physical coordinate system of the sandbox and the real-world geographic coordinate system. The system is calibrated in one go using at least three known geographic coordinate calibration points, achieving sub-millimeter accuracy in the transformation model. The specific implementation method is as follows.

[0026] The geographic mapping and scene management subsystem uses a high-performance industrial computer as its platform and runs central control software.

[0027] Core hardware: It features a multi-core CPU, a high-performance GPU to support real-time 3D trajectory rendering, and multiple high-speed network interfaces and a synchronization clock card (such as PXIe-6674T) to receive encoder data and distribute precise synchronization clock signals to other subsystems.

[0028] The full-element RTK signal generation and radiation subsystem serves as the signal simulation and calculation unit: it employs a dedicated server equipped with a software-defined radio (SDR) board (such as the USRP X410). This board features multi-channel independent transmit and receive capabilities and incorporates a highly stable OCXO clock source.

[0029] The radio frequency radiation unit includes: Base station signal radiator: A fixed, low-gain omnidirectional antenna precisely aligned with the preset base station installation position in the southwest corner of the sand table to ensure uniform and stable signal field strength at that location.

[0030] Mobile station signal radiator: A miniaturized flat panel antenna is directly installed below the aforementioned quick-switch interface board and moves synchronously with the end of the platform to achieve close-fitting directional radiation of the signal to the mobile station antenna.

[0031] The integrated monitoring and quantitative evaluation subsystem includes a large touch screen displaying the control interface, 3D scene, and real-time data comparison curves.

[0032] 1. Hardware preparation and coordinate system definition Calibration equipment: a high-precision total station (or laser tracker) with an angle measurement accuracy of not less than 1″ and a distance measurement accuracy of not less than ±(0.5mm + 1ppm).

[0033] Calibration tools: a set (no less than 3) of cooperative targets (such as prisms or target balls) that can be accurately aimed by the total station and can be precisely installed at the end of the mobile platform and at specific locations on the sand table.

[0034] Definition of the inherent coordinate system: The physical coordinate system of the sand table {S} - (Xs, Ys, Zs): This coordinate system is fixed on the mechanical base of the 3D moving platform. Typically, the X-axis is the direction of platform movement, the Y-axis is the direction of movement, and the Zs axis is perpendicular to the horizontal plane of the sand table and pointing upwards. The platform encoder reads the end-effector coordinates (x...). enc , y enc , z enc This refers to the coordinate values ​​in this coordinate system, i.e., the true value of the physical location.

[0035] The real-world geographic coordinate system {G} -(Lat, Lon, H) adopts a nationally or internationally recognized map projection coordinate system, such as the WGS-84 latitude, longitude, and height coordinate system, or a Cartesian coordinate system (E, N, U) (east, north, elevation) after Gauss-Kruger projection. This is the coordinate space of the simulated virtual training scenario.

[0036] In step S110, a preset geographic mapping model, fixed virtual geographic coordinates of the virtual reference station, and satellite ephemeris data are acquired. Physical coordinate data fed back by the mobile platform are collected in real time. The physical coordinate data represents the real-time position of the mobile station receiver under test in physical space.

[0037] In this embodiment, preset satellite ephemeris data is acquired. This data typically originates from precise ephemeris files (such as .sp3 format) published by the International GNSS Service (IGS) or pre-recorded broadcast ephemeris, which contains the precise orbital parameters and satellite clock bias coefficients of navigation satellites such as GPS, BDS, and Galileo during the simulation period. This data constitutes the spatial reference for the satellite constellation in the simulation environment.

[0038] Simultaneously, fixed virtual geographic coordinates are set for the virtual base station at the software level. These coordinates are a fixed point set according to the needs of practical training (e.g., the longitude, latitude, and elevation of a field surveying control point). In the subsequent differential processing flow, these coordinates will serve as the reference origin for generating RTK base station observation data.

[0039] To address the reliance of traditional training on physical space, this system utilizes a geographic mapping model to transform limited physical experimental spaces into expansive virtual workspaces. In practice, a calibration procedure is executed: at least three non-collinear feature points are selected as calibration points within the travel range of the physical mobile platform. The physical coordinates of these points in the physical coordinate system are obtained using high-precision physical measurement methods (such as total station measurement or reading platform encoder values). Simultaneously, the corresponding real geographic coordinates (usually converted to spatial rectangular coordinates in a geocentric coordinate system) are specified on the virtual training map. Subsequently, the least squares method is used to adjust the physical coordinates and the real geographic coordinates, solving for the mathematical model parameters describing the transformation relationship between the two spaces. This geographic mapping model is specifically represented as a three-dimensional spatial transformation relationship including translation, rotation, and scaling transformations, and its mathematical expression logic is as follows:

[0040] The physical meaning of the above parameters is explained as follows: (Virtual geographic coordinates): refers to the position vector of the mobile station receiver in the virtual world after mapping calculation. (Physical coordinate data): refers to the position vector of the mobile platform in physical space, which is collected in real time. (Zero-point offset correction): Used to eliminate systematic deviations between the origin of the physical coordinate system and the measurement zero point. R (Rotation matrix): Used to correct the angular deviation between the orientation of the physical experimental platform and the north direction of the virtual map. T (Translation vector): Used to translate the origin of the physical coordinate system to a specified starting position on the virtual map. λ (Scale factor / Software scale): This is a key parameter in this embodiment. It can be calculated through calibration or directly specified as a preset software scale factor. For example, when λ=500, a tiny movement of 1 centimeter on the physical platform will be mapped to a distance of 5 meters on the virtual map. This feature allows the system to fully simulate a long-baseline RTK operation scenario spanning several kilometers in a small indoor space.

[0041] During system operation, the system establishes real-time communication with the precision electromechanical platform carrying the rover receiver under test. Encoder values ​​fed back from the rover platform's servo system are acquired at a high-frequency sampling rate (e.g., 10Hz or 100Hz) to generate physical coordinate data. This data accurately characterizes the real-time position and attitude of the rover receiver under test within the physical laboratory space.

[0042] In step S120, the physical coordinate data is transformed according to the geographic mapping model to obtain the virtual geographic coordinates of the mobile station.

[0043] In this embodiment, raw physical coordinate data is received from the underlying controller of the mobile platform. Since the raw data typically carries an inherent bias in the mechanical origin and may be in encoder pulse counts, the system first performs zero-point offset correction and unit standardization. Specifically, the processor subtracts a pre-calibrated zero-point offset vector from the raw data and multiplies the result by a pulse-distance conversion factor to obtain standard physical coordinates (denoted as Pstd) expressed in standard length units (e.g., meters). These coordinates accurately reflect the geometric position of the receiver under test relative to the physical laboratory reference origin.

[0044] Using the established 3D spatial transformation formula, standard physical coordinates are mapped to the virtual geospatial coordinate system. This step mainly aligns the coordinate systems and does not involve scaling the range of motion. The standard physical coordinates Pstd are substituted into the transformation formula. This calculation process can be expressed as:

[0045] Where: R (rotation parameter): is a rotation matrix used to eliminate the angular deviation between the physical platform coordinate axes and the virtual map coordinate axes (such as the NE-G coordinate system); T (translation parameter): is a translation vector used to align the origin of the physical coordinate system to the starting anchor point in the virtual map; This refers to the calculated initial virtual geographic coordinates. If the physical platform moves 1 meter, the corresponding initial virtual coordinates will only change by 1 meter.

[0046] To achieve the effect of "running a large trajectory on a small sand table," a crucial scaling step is performed. The software scale factor (denoted as λ, e.g., λ=1000) set for the current training task is obtained. This factor is then used to scale the planar displacement components in the initial virtual geographic coordinates. Specifically, the planar displacement vector of the initial virtual coordinates relative to the starting point at the current moment is calculated, and this vector is multiplied by the scale factor λ to obtain the magnified virtual displacement.

[0047] For the elevation component, this embodiment provides two processing modes: one is to apply the same scale scaling to simulate large elevation changes; the other is to query the corresponding terrain elevation (DEM) in the digital map based on the virtual displacement to achieve the effect of the receiver moving closely to the virtual ground surface. After the above scaling and elevation matching processing, the system calculates the final virtual geographic coordinates of the mobile station.

[0048] To meet the input requirements of the subsequent satellite signal simulation engine, the spatial rectangular coordinates calculated above are converted into geodetic coordinate system data (i.e., longitude L, latitude B, and geodetic height H).

[0049] Through dynamic transformation, the tiny centimeter-level mechanical movements on the physical platform are successfully converted into real-time field trajectories spanning several kilometers in the virtual world, providing spatial location parameters with "long baseline" characteristics for generation, thus fully meeting the testing requirements of RTK systems for long-distance differential operations.

[0050] In step S140, differential correction data is generated based on the first satellite observation data. The differential correction data is then sent to the mobile station receiver under test via a data link, so that the mobile station receiver under test can perform differential positioning calculation by combining the received radio frequency signal of the corresponding second satellite observation data and output the positioning result.

[0051] In this embodiment, to meet the data integrity requirements of the RTK algorithm, the "first satellite observation data" is converted into a binary stream that can be recognized by the receiver according to internationally accepted standards (such as the RTCM SC-104 standard). This process includes key parameter extraction and message construction steps: First, the observation components are analyzed and extracted. Carrier phase and pseudorange observations for each visible satellite at the current simulation epoch are extracted from the first satellite observation data. These observations must retain complete phase geometry features to support subsequent integer ambiguity searches.

[0052] Secondly, the base station coordinate parameter message needs to be constructed. RTK positioning is essentially relative positioning, that is, determining the relative position of the rover station with respect to the base station. Therefore, it is necessary to call the fixed virtual geographic coordinates (usually precise three-dimensional coordinates in the ECEF coordinate system) of the preset virtual base station and encapsulate them into a base station parameter message frame (e.g., RTCM 1005 / 1006 frame). If this information is missing, the receiver under test will not be able to establish a reference.

[0053] Next, differential observation messages are constructed. The extracted carrier phase and pseudorange data are classified according to the satellite constellation and encapsulated into high-precision observation message frames (e.g., RTCM MSM series frames).

[0054] Finally, strict timing alignment is performed. This ensures that the time tag encapsulated in the differential correction data is synchronized with the simulation time of the RF signal generated by the simulation engine at the microsecond level, ultimately generating differential correction data containing complete baseline solution information.

[0055] The generated differential correction data is transmitted in real time to the receiver of the tested mobile station via a simulated data link. In practice, the differential data input of the receiver is connected using a physical interface (such as an RS-232 serial port or an Ethernet port). This simulates a data radio or network CORS server in field operations, continuously pushing a data stream at a preset baud rate or network protocol (such as NTRIP protocol).

[0056] At this time, the receiver of the tested mobile station is in dual-input mode: First path (RF end): The receiver receives the RF signal corresponding to the observation data from the second satellite through the antenna port. This signal carries the dynamic position information of the rover on the virtual trajectory.

[0057] The second channel (data end): The receiver receives the aforementioned differential correction data through the communication port. This data carries the static position information and synchronous observations of the virtual base station.

[0058] The processor inside the receiver of the tested rover station combines these two inputs to perform standard differential positioning calculations: the receiver uses the fixed virtual geographic coordinates in the differential correction data as a reference origin, and performs a double difference operation on its own carrier phase observations and the carrier phase observations in the differential data. Since both data sources originate from the same set of precise ephemeris and the same simulated clock, and have undergone strict geometric synchronization, the double difference operation can effectively eliminate common errors such as satellite clock errors, orbital errors, and atmospheric delays. Based on this, the receiver uses an ambiguity search algorithm to fix integer ambiguities, thereby calculating the centimeter-level baseline vector of the rover station relative to the virtual reference station, and finally superimposing the reference origin coordinates to output high-precision positioning results (such as latitude and longitude data in NMEA-0183 format).

[0059] This embodiment successfully reproduced the complete RTK operation logic of base station-data link-rover station in an indoor physical environment, which not only verified the receiver's algorithm performance, but also its ability to parse differential data protocols and process communication links.

[0060] In step S150, the positioning result output by the receiver of the tested mobile station is obtained.

[0061] After the mobile station receiver under test receives the radio frequency signal and differential correction data and executes its internal algorithm, the positioning result output by the mobile station receiver under test is obtained in real time through the physical interface. The specific implementation process is as follows: To achieve non-invasive monitoring of the target object, the system is equipped with a universal physical data acquisition interface. Depending on the hardware specifications of the receiver under test, it connects to the receiver's positioning output port via an RS-232 / RS-422 serial communication interface, a USB virtual serial port, or an Ethernet interface. This physical connection acts as the system's stethoscope, receiving all navigation information calculated by the receiver under test in real time without interfering with its normal positioning operations.

[0062] The receiver under test typically outputs positioning data at a specific frequency (e.g., 1Hz to 20Hz). To accommodate different brands and models of equipment, the acquisition module integrates a multi-protocol parsing engine.

[0063] For standard formats: Supports parsing the NMEA-0183 protocol developed by the International Maritime Electronics Association. Real-time data stream capture, locking the initial statements, and performing XOR checksum verification to ensure data validity.

[0064] For proprietary formats: It also supports parsing the binary proprietary protocols of mainstream GNSS receiver manufacturers (such as NovAtelOEM series logs, U-blox UBX protocol, etc.) to obtain higher frequency or lower-level observation information.

[0065] The verified data frames are decoded to extract the key parameters that constitute the "location result." These parameters include not only location information but also quality status information, specifically including: Measurement coordinate vectors: resolved latitude, longitude, and ellipsoidal height. These are measured values ​​calculated by the receiver based on the simulated signal, representing the receiver's "perceived" location.

[0066] RTK Solution Status: This is the core metric for evaluating this training exercise. Taking the NMEA GPGGA statement as an example, extract the "GPS Quality Indicator" field: If the value is "1", it indicates single point positioning, meaning that the differential link may not be connected; If the value is "2" or "5", it represents a floating-point solution, indicating that the receiver is converging but the ambiguity has not yet been fixed. If the value is "4", it indicates that the solution is fixed, meaning that the receiver has successfully fixed the integer ambiguity and achieved centimeter-level accuracy.

[0067] GNSS Time: The current calculated time output by the receiver (such as UTC time or GPS week and second).

[0068] To ensure the accuracy of subsequent evaluations, time synchronization and alignment were performed simultaneously with the acquisition of the positioning results. Satellite time tags from the positioning results were read and matched with the simulation timeline running within the system. The generated virtual ground truth values ​​at the same moment corresponded strictly in the time dimension, thus eliminating systematic evaluation errors caused by data transmission delays or processing time, and providing a reliable data foundation for the final accuracy calculation.

[0069] In step S160, the deviation index between the positioning result and the virtual geographic coordinates of the mobile station is calculated, and a training evaluation report is generated.

[0070] In this embodiment, after the system obtains the positioning results fed back by the receiver under test in real time and simultaneously possesses the true value of the virtual geographic coordinates corresponding to that moment, it quantifies the dynamic performance of the device under test through rigorous mathematical comparison and outputs an objective evaluation conclusion.

[0071] To achieve meter-level or even millimeter-level error calculation, it is essential to first unify the reference framework between the "positioning results (measured values)" and the "virtual geographic coordinates of the mobile station (true values)." Since the original data for both is typically geodetic coordinates (WGS-84 latitude and longitude), the system employs a station-centric tangent plane projection algorithm (i.e., ENU coordinate system transformation) to convert the geodetic coordinates into Cartesian coordinates with the center of the virtual training scenario or virtual reference station as the origin. During the specific transformation process, the system calculates the eastward deviation (ΔE), northward deviation (ΔN), and celestial deviation (ΔU) of the measured values ​​relative to the true values. If there is a physical deviation between the installation position of the tested receiver antenna on the physical platform and the center point of the platform feedback, an antenna phase center deviation correction vector is pre-loaded and subtracted from the measured values ​​to ensure that the compared geometric objects strictly coincide in space.

[0072] Based on the projected coordinate components, multi-dimensional deviation indices are calculated to quantitatively evaluate RTK positioning accuracy: 2D plane error: , used to assess horizontal positioning ability; 3D spatial error: Used to evaluate comprehensive spatial positioning capabilities; Statistical Root Mean Square Error (RMS): The RMS value is calculated by statistically analyzing the error sequence throughout the entire training process or over a specific period. This indicator is an internationally accepted standard for judging the accuracy of RTK receivers.

[0073] In addition to geometric accuracy, dynamic metrics reflecting the robustness of the RTK algorithm are calculated by combining step-by-step solution status indicators: Fix Rate: The calculation formula is as follows:

[0074] in, To calculate the number of epochs with the state marked "RTK Fixed", This represents the total number of samples. This metric directly reflects the operational availability of the receiver in a simulated dynamic environment.

[0075] First Fixed Time (TTFF / Convergence Time): The system automatically retrieves the time axis and records the time difference from the moment the differential data link is established (tstart) to the moment when the positioning accuracy first converges to a preset threshold (e.g., horizontal error <0.05m) and the state changes to Fixed (tfix).

[0076] Based on the above calculation results, a pre-built report generation engine is used to automatically compile and output a training evaluation report. The report is presented in PDF or HTML format and includes: Panoramic trajectory review chart: The "true trajectory (green)" and "measured trajectory (red)" are overlaid on the virtual map base, which intuitively shows the trajectory following accuracy of the receiver under dynamic conditions such as cornering and speed change; Error time series graph: With time as the horizontal axis, the error fluctuation curves in the three directions of E, N, and U are plotted to help users analyze the correlation between error and specific training actions (such as sharp turns); Comprehensive scoring sheet: Based on the preset teaching syllabus standards (e.g., RMS < 3cm and fixation rate > 95% is "excellent"), the practical training score is automatically calculated and a "pass / fail" judgment is given.

[0077] This embodiment completes the final closed loop from "physical simulation" to "data analysis," solving the technical problems of difficulty in obtaining true values, reliance on human experience for evaluation, and inability to quantify results in traditional field training, and providing a standardized testing tool for RTK teaching and research.

[0078] In one embodiment of this application, obtaining a preset geographic mapping model includes: Obtain the physical measurement coordinates of at least three calibration points in the physical space coordinate system, and the real geographic coordinates of at least three calibration points in the virtual geospatial coordinate system; Construct a three-dimensional spatial transformation relationship that includes translation parameters, rotation parameters, and scale factors; The least squares method is used to perform adjustment calculations on physical measurement coordinates and actual geographic coordinates to obtain the optimal transformation parameters in the three-dimensional spatial transformation formula. The three-dimensional spatial transformation relationship containing the optimal transformation parameters is determined as the geographic mapping model.

[0079] To faithfully reproduce a wide-area field environment within a limited physical indoor space, this embodiment requires establishing a rigorous mathematical mapping between the physical and virtual spaces during the initialization phase. Through multi-point calibration and rigorous adjustment calculations, installation errors and measurement noise are eliminated, obtaining the physical coordinates of at least three calibration points in the physical space coordinate system, and the corresponding real geographic coordinates of at least three calibration points in the virtual geographic space coordinate system. The operator drives the physical mobile platform to several pre-set control points within the sandbox (more than three are recommended for redundant observation). The coordinate values ​​of each point in the local laboratory coordinate system are collected by sensors and denoted as physical coordinate vectors.

[0080] Operators specify corresponding geographical locations in the virtual training scenario. Since latitude and longitude (in degrees) cannot be directly transformed linearly, the latitude and longitude of these points are converted to rectangular coordinates in meters (e.g., ECEF coordinates), denoted as virtual coordinate vectors:

[0081] To describe the spatial mapping relationship between the physical sandbox and the virtual globe, a three-dimensional spatial transformation formula is constructed, incorporating translation parameters, rotation parameters, and scale factors. This embodiment employs a high-precision spatial similarity transformation model, whose mathematical expression is defined as:

[0082] Where: K (scale factor): represents the magnification factor of physical distance mapped to virtual distance. This is key to achieving full-element simulation using a "geographic sandbox," enabling a physical displacement of a few meters to drive trajectory changes of several kilometers in the virtual scene. R (rotation parameter matrix): consists of three tiny rotation angles around the X, Y, and Z axes. The resulting orthogonal matrix is ​​used to correct the directional deviation between the physical sand table coordinate axes and the northeast coordinate axis of the virtual map. [ΔX,ΔY,ΔZ]T (translation parameters): used to align the origin positions of the two coordinate systems.

[0083] Given the random errors present in the measurement process, this embodiment does not use a simple three-point solution, but instead uses the least squares method to perform adjustment calculations on the physical measurement coordinates and the actual geographic coordinates.

[0084] An overdetermined system of equations (more equations than unknown parameters) is established using all collected calibration point pairs. Based on the least squares principle, an optimal set of transformation parameters (i.e., optimal translation, rotation angle, and scaling ratio) is found to minimize the sum of squared residuals of all calibration points after transformation.

[0085] This method effectively filters out single-point measurement errors, ensuring that the generated model has uniform high accuracy throughout the entire training area and avoiding RTK positioning jumps caused by local deformation. Finally, the three-dimensional spatial transformation formula containing the optimal transformation parameters is determined as the geographic mapping model. The physical coordinates of the receiver under test are acquired in real time and directly substituted into the geographic mapping model for calculation, thereby outputting high-precision virtual geographic coordinates (true values) within milliseconds. This provides an accurate spatial reference for the subsequent parallel generation of satellite radio frequency signals, fully meeting the stringent requirements of the RTK system for centimeter-level testing environments.

[0086] In one embodiment of this application, converting physical coordinate data according to a geographic mapping model to obtain the virtual geographic coordinates of the mobile station includes: Zero-point offset correction and unit standardization are performed on the physical coordinate data to obtain standard physical coordinates; Substituting the standard physical coordinates into the geographic mapping model, the initial virtual geographic coordinates are obtained by multiplying the standard physical coordinates by the scale factor, the rotation parameter along each coordinate axis, and adding the corresponding translation parameter. Obtain the software scale factor set for the current training scenario; The planar displacement components in the initial virtual geographic coordinates are scaled using a software scale factor to obtain the virtual geographic coordinates of the mobile station.

[0087] In this embodiment, to resolve the contradiction between the limited physical space and the vast virtual testing scenario, raw data reported by the underlying hardware is received, and the physical coordinate data undergoes zero-point offset correction and unit standardization to obtain standard physical coordinates. Since the mechanical zero point of physical moving mechanisms (such as slide rails, robotic arms, or omnidirectional vehicles) often does not coincide with the geometric center or corner point defined by the sand table, a preset mechanical offset vector is used. Subtracting the offset vector from the original coordinates eliminates the fixed deviation caused by the equipment installation position. Non-standard units of sensor output (such as encoder pulse count, millimeters) are converted to the unified International System of Units (SI) (meters) using unit conversion factors. This yields the standard physical coordinates describing the precise geometric position of the mobile station relative to the physical origin of the sand table, denoted as... .

[0088] The standard physical coordinates are substituted into the geographic mapping model. Specifically, the following linear transformation operation is performed: the standard physical coordinates are multiplied by a scale factor, the rotation parameter along each coordinate axis, and the corresponding translation parameter are added to calculate the initial virtual geographic coordinates. The calculation formula is:

[0089] in, It is the fundamental scale factor in the model, used to correct for small deviations in the physical measurement scale; It is a rotation matrix used to rotate the orientation of the physical coordinate system (such as the laboratory X-axis) to be consistent with the virtual geographic coordinate system (such as the north-facing N-axis); This is a translation vector used to align the physical origin to a reference origin in virtual space. The resulting initial virtual geographic coordinates are... This represents the precise projection of a physical location into the virtual world at a 1:1 scale.

[0090] In order to achieve the testing objective of small space and large scene, the software scale factor set in the current training scene is obtained.

[0091] The factor K is specified by the user when configuring training cases (e.g., setting K=1000). It determines how many meters of travel in the virtual world will be represented by 1 meter displacement on the physical sandbox. This enables seamless switching from indoor precision parking scenarios (low scale) to intercity logistics transportation scenarios (high scale) solely through software parameters, without adjusting hardware facilities.

[0092] The planar displacement components in the initial virtual geographic coordinates are scaled using a software scale factor to obtain the virtual geographic coordinates of the mobile station. The horizontal components (i.e., East and North components) and vertical components (i.e., Up component) in the initial virtual geographic coordinates are identified.

[0093] For the planar displacement component, its change relative to the virtual starting point is multiplied by the software scale factor K, thereby stretching the movement trajectory in the horizontal direction and constructing a virtual motion path with a long baseline.

[0094] For the vertical component, to conform to the actual movement patterns of vehicles or pedestrians on the ground and avoid drastic elevation changes due to excessive scaling (such as a vertical drop of hundreds of meters), the vertical component is usually kept unscaled or uses an independent, small scaling ratio. The final synthesized coordinates are the virtual geographic coordinates (true values) of the mobile station. These coordinates are then sent to the satellite signal simulation module to calculate the pseudorange and carrier phase from the satellite to the receiver, thereby simulating the illusion of the mobile station moving at high speed in a vast open space at the radio frequency level, realizing indoor closed-loop training of all elements of RTK.

[0095] In one embodiment of this application, the first satellite observation data includes a first pseudorange observation value and a first carrier phase observation value, and the second satellite observation data includes a second pseudorange observation value and a second carrier phase observation value. Based on a unified time reference and precise satellite ephemeris data, the first satellite observation data is obtained by calculation using fixed virtual geographic coordinates, and the second satellite observation data is obtained by calculation using rover station virtual geographic coordinates, including: At the current simulation moment, based on the analysis of precise satellite ephemeris data, the spatial position vectors and satellite clock errors of all visible satellites are obtained; The first geometric distance is obtained by calculating the geometric distance between the spatial position vector of the visible satellite and the fixed virtual geographic coordinates; The second geometric distance is obtained by calculating the geometric distance between the spatial position vector of the visible satellite and the virtual geographic coordinates of the rover station; Based on the tropospheric delay model value, ionospheric delay model value and receiver clock error model value corresponding to the first geometric distance superposition, the first pseudorange observation value in the first satellite observation data is generated. Based on the tropospheric delay model value, ionospheric delay model value and receiver clock error model value corresponding to the superposition of the second geometric distance, the second pseudorange observation value in the second satellite observation data is generated. Based on the first geometric distance superimposed with integer ambiguity parameters and carrier phase noise, the first carrier phase observation value in the first satellite observation data is generated. The second carrier phase observation value in the second satellite observation data is generated by superimposing the integer ambiguity parameter and carrier phase noise based on the second geometric distance.

[0096] In this embodiment, to provide a realistic RTK testing environment, two sets of logically related but numerically independent observation data (i.e., base station data and rover station data) need to be generated in parallel within the simulation system. At the current simulation time t (controlled by the system master clock), the ephemeris calculation module is invoked. Based on the preset precise satellite ephemeris data (including orbital elements and perturbation parameters), the spatial position vectors of all visible satellites within the field of view at this moment are calculated. And the satellite clock difference δtsat of the satellite atomic clock relative to the system standard time. This ensures that all analog signals originate from the same physical constellation configuration.

[0097] Calculate the first geometric distance: using fixed virtual base station coordinates The geometric distance Rbase from the base station to the satellite is obtained by performing Euclidean distance calculation with the satellite position vector.

[0098] Calculate the second geometric distance: using the virtual geographic coordinates of the mobile station mapped in real time from the physical sandbox in the previous steps. The geometric distance Rrover from the rover station to the satellite is obtained by performing calculations with the same satellite position vector.

[0099] Here, since the Rrover changes in real time with the movement of the physical sand table, it carries the dynamic trajectory information of the object being measured.

[0100] To simulate the actual receiver measurement process, a physical error model is superimposed on the geometric distance: First pseudorange observation generation: For the base station, the system calculates tropospheric delay model values ​​(simulating path elongation caused by atmospheric refraction) and ionospheric delay model values ​​(simulating group delay caused by ionospheric electrons) based on its virtual position and satellite elevation angle. Simultaneously, a preset receiver clock bias model value is introduced (converting time deviation by multiplying the speed of light c to distance). Finally, the result is obtained using the formula... Generate the first pseudorange.

[0101] Second pseudorange observation generation: For a rover, the corresponding tropospheric and ionospheric delays are calculated based on its dynamic position, and the rover's independent receiver clock error is superimposed to generate the second pseudorange ρrover.

[0102] To support high-precision RTK calculations, superimposed carrier phase observations are generated, producing phase observations with millimeter-level precision. The first carrier phase observation is generated as follows: Based on the first geometric distance Rbase, it is first divided by the carrier wavelength λ to convert it into cycles. Then, a preset integer ambiguity parameter (i.e., the integer number N that cannot be directly measured) is superimposed. Specifically, when superimposing atmospheric errors, physical characteristics are considered to cause the ionospheric effect to lead the phase (with a sign opposite to the pseudorange). Finally, carrier phase noise conforming to a Gaussian distribution is superimposed.

[0103] Second carrier phase observation value generation: Similarly, based on the second geometric distance Rrover, the integer ambiguity, atmospheric delay and phase noise unique to the rover station are superimposed to generate the second carrier phase observation value.

[0104] This embodiment successfully constructed two sets of observation data, including common errors (such as ephemeris errors and most atmospheric errors) and independent errors (such as multipath effects and receiver noise). This enabled the tested RTK receiver to eliminate common terms through the double difference algorithm, thereby calculating the precise position of the rover station relative to the base station, perfectly replicating the mathematical environment of real RTK operations.

[0105] In one embodiment of this application, generating differential correction data based on first satellite observation data includes: Extract the first carrier phase observation value and the first pseudorange observation value corresponding to each visible satellite from the first satellite observation data; Call the fixed virtual geographic coordinates of the virtual base station to construct the base station coordinate parameter message; Differential observation messages are constructed by classifying carrier phase observations and pseudorange observations according to satellite systems. The base station coordinate parameter messages and differential observation messages are time-series aligned and encapsulated to generate differential correction data containing complete baseline solution information.

[0106] In this embodiment, in order for the mobile station receiver under test to acquire reference information from the base station in an indoor simulation environment as if it were outdoors, thereby achieving RTK differential positioning, it is necessary to construct and output a differential data stream that conforms to industry standards in the digital domain.

[0107] First, the first satellite observation data (i.e., simulation data from the virtual reference station) generated by the parallel computing module is processed. The steps of extracting the first carrier phase observation value and the first pseudorange observation value corresponding to each visible satellite from the first satellite observation data are performed. The data structure of all visible satellites at the current simulation time is traversed. For each satellite, all supported frequency points are identified (such as GPS L1 / L2 / L5, BeiDou B1I / B3I, etc.), and the corresponding two core physical quantities are extracted respectively. Pseudorange observations: These represent the ranging code measurement results from the satellite to the base station, including errors such as clock bias and atmospheric delay. Carrier phase observation: Represents the phase measurement results of the satellite signal carrier, with millimeter-level measurement resolution.

[0108] During this process, satellite health status indicators are read, and satellite data marked as "unhealthy" or in a "lost" state is automatically filtered out. To support the rover station in baseline vector calculation, the steps of calling the fixed virtual geographic coordinates of the virtual reference station and constructing the reference station coordinate parameter message are performed. The virtual reference station coordinates (e.g., the known point coordinates (X0, Y0, Z0) of a virtual test field) are read from the pre-existing configuration file. Based on these coordinates, according to the RTCM (Radio Technical Commission for Maritime Services) standard protocol, a coordinate parameter message (such as Message Type 1005) containing the location of the reference point of the reference station antenna is assembled. The purpose of this message is to inform the receiver under test of the spatial origin of the differential correction, which is an indispensable geometric reference in RTK calculation.

[0109] To meet the testing requirements of all elements and multiple constellations, the process involves classifying carrier phase observations and pseudorange observations according to satellite systems and constructing differential observation messages. Multiple independent message construction buffers are established, each corresponding to a different Global Navigation Satellite System (GNSS): for GPS satellites, their observations are packaged into GPS-specific observation messages (e.g., MSM4 / MSM7 format); for BDS satellites, their observations are packaged into BeiDou-specific observation messages. During the construction process, the extracted floating-point observation data undergoes high-precision quantization encoding, retaining sufficient significant bits to maintain millimeter-level phase accuracy, and the data is mapped to the various data segments specified in the protocol.

[0110] To ensure the real-time performance and convergence of the differential calculation, the base station coordinate parameter messages and differential observation messages are time-aligned and encapsulated to generate differential correction data containing complete baseline solution information.

[0111] Time alignment: Obtain the current global simulation time and convert it to a standard GNSS timestamp. Force this same timestamp to be written into the header of all generated coordinate message frames and observation message frames. This operation ensures that the spatial position of the base station is strictly locked in the time dimension with the observation data at that moment, preventing the receiver from refusing to solve the problem due to time asynchrony.

[0112] Encapsulation generation: The aligned message frames are concatenated in sequence, and their respective check and error correction codes (CRC) are calculated. Finally, they are encapsulated into a continuous binary data stream, i.e., differential correction data.

[0113] This data stream contains all the elements required for the receiver to perform RTK calculations, namely complete baseline calculation information: the coordinates of the base station and the observations of the base station. After receiving this data, the device under test can combine it with its own radio frequency observations to establish a double-difference equation, achieving centimeter-level high-precision positioning training.

[0114] In one embodiment of this application, the method further includes: In response to the virtual movement trajectory drawn by the user on the virtual map interface, obtain the coordinates of key points of the virtual movement trajectory; By utilizing the inverse transformation relationship of the geographic mapping model, virtual motion trajectories are converted into physical motion commands in physical space; Physical motion commands are sent to the motion controller to drive the mobile platform to move the receiver of the tested mobile station according to the physical motion commands, thereby generating real-time collected physical coordinate data.

[0115] In this embodiment, to meet the requirement of repeatable verification of specific trajectories (such as standard runways and complex overpass paths) in RTK testing, the bidirectional reversibility of the mathematical model is utilized to achieve reverse control from the virtual map to the physical sandbox. This process strictly corresponds to the steps of trajectory acquisition, inverse coordinate transformation, and instruction execution as described in the claims, and is implemented as follows: In response to user actions on the Virtual Map UI of the host computer software, users can use drawing tools to draw the desired test path (i.e., virtual motion trajectory) on the loaded electronic map.

[0116] The key point coordinates of the virtual motion trajectory are obtained. Specifically, interpolation sampling is performed on the continuous geometric curve drawn by the user. The sampling density can be dynamically adjusted according to the curvature of the trajectory (e.g., sparse sampling on straight sections and dense sampling on curves). After sampling, a series of ordered key point coordinate sequences are obtained. These coordinates are typically defined in the absolute geographic coordinate system of the virtual scene (such as WGS-84 latitude and longitude or projected plane coordinates).

[0117] To drive the physical devices to reproduce the trajectory, the corresponding path on the physical sandbox must be calculated. This involves using the inverse transformation relationship of the geographic mapping model to convert the virtual motion trajectory into physical motion commands in the physical space. This is based on the aforementioned established "physical-virtual" forward mapping model. The inverse transformation algorithm is pre-configured in the control module.

[0118] Inverse coordinate calculation: for each virtual key point Through the formula:

[0119] Solve for the coordinates of the corresponding physical target point. Here, T is the translation vector. It is the reciprocal of the scaling factor (for example, if the positive direction is magnified by 100 times, then it is reduced by 100 times here). It is an inverse rotation matrix.

[0120] Based on the calculated sequence of physical target points and the user-defined virtual driving speed, a physical motion command containing the velocity vector and target position is generated. For example, if the user sets the virtual vehicle speed to 72 km / h (20 m / s) and the scaling ratio to 1:100, the generated command will control the physical car to travel at a constant speed of 0.2 m / s.

[0121] Commands are sent to the onboard motion controller of the mobile platform via a low-latency wireless link. The controller uses a built-in path tracking algorithm (such as Pure Pursuit or PID control) to precisely drive the motor, moving the mobile platform carrying the receiver of the tested mobile station through the calculated physical target points on the physical sand table one by one.

[0122] As the mobile platform moves physically, the positioning system deployed indoors (such as UWB tags or infrared reflective dots) senses the platform's position changes in real time, generating real-time physical coordinate data. This creates a closed loop through reverse control: the user's planning on the virtual map drives the movement of the physical entity, which is then captured by sensors, re-mapped to generate virtual coordinates, and finally converted into radio frequency signals fed back to the receiver under test. This mechanism ensures a high degree of automation and accurate trajectory reproduction during testing, completely eliminating random errors caused by manual remote control operation.

[0123] In one embodiment of this application, the method further includes: The first satellite observation data is modulated into a first digital intermediate frequency signal, and the second satellite observation data is modulated into a second digital intermediate frequency signal; The first digital intermediate frequency signal and the second digital intermediate frequency signal are up-converted to the target satellite navigation frequency point to generate the first radio frequency signal and the second radio frequency signal, respectively. Through a physically isolated transmission channel, the first radio frequency signal is radiated directionally to the physical location corresponding to the virtual reference station, and the second radio frequency signal is radiated directionally to the antenna location of the receiver of the mobile station under test.

[0124] In this embodiment, to reproduce a wide-area outdoor RTK operating environment within a limited indoor physical space, a dual-path signal generation link with high electromagnetic isolation needs to be constructed. This link simulates the satellite signals received by the base station and the rover station respectively, ensuring that they do not interfere with each other.

[0125] High-performance signal generation boards (such as FPGA-based SDR platforms) are used to process baseband signals in the digital domain.

[0126] Generation of the first and second digital intermediate frequency (IF) signals: Based on the GNSS Interface Control Document (ICD), standard ranging codes (such as C / A codes) and navigation messages are generated. Subsequently, observation data is used as control parameters: pseudorange observations are used to control the transmission timing of the ranging code (Code Phase); carrier phase observations and Doppler shift are used to control the phase and frequency of the carrier. The aforementioned baseband signals are then loaded onto the IF carrier through digital quadrature modulation (IQ Modulation) to form the digital intermediate frequency (IF) signal. At this point, the signal still appears as a discrete digital sampling sequence.

[0127] In order to drive the actual receiver antenna, the first digital intermediate frequency signal and the second digital intermediate frequency signal are up-converted to the target satellite navigation frequency, respectively, to generate the first radio frequency signal and the second radio frequency signal.

[0128] Digital-to-analog converter (DAC): Converts a discrete digital intermediate frequency sequence into a continuous analog intermediate frequency voltage waveform.

[0129] Up-conversion: This involves using a mixer to mix the analog intermediate frequency (IF) signal with the local oscillator (LO) signal. According to the formula:

[0130] in, (Target Radio Frequency): This refers to the target satellite navigation frequency, i.e., the carrier center frequency of the final generated first or second radio frequency signal. For example, if simulating the GPS L1 band, the value of this parameter is 1575.42MHz. This is the physical frequency that the receiver antenna of the tested mobile station can sense and process.

[0131] (Local Oscillator Frequency): This refers to the frequency of the reference high-frequency signal generated by the local oscillator in the RF front-end hardware circuit. This frequency is a fixed value, obtained by multiplying the clock source of the hardware (such as a crystal oscillator or atomic clock), and is used to boost the signal to a higher frequency band.

[0132] (Intermediate Frequency): This refers to the analog center frequency of the first or second digital intermediate frequency signal after digital-to-analog conversion. This parameter represents the carrier position of the digital signal after baseband processing.

[0133] The frequency is converted to a mixer. The intermediate frequency analog signal and the frequency are The local oscillator signals are multiplied in the time domain (which is represented as convolution in the frequency domain) to produce a sum of frequencies (i.e., the upper sideband signal). For example, to generate a 1575.42MHz GPS signal ( If the local oscillator frequency of the system hardware ( If the frequency is set to 1500MHz, the digital generation module must set the center frequency of the digital intermediate frequency signal to 1500MHz. The precise setting is 75.42MHz. This is achieved through strict control of the right side of the formula. and This ensures the generation It accurately hits the navigation frequency of the target satellite with extremely small frequency error (usually in the Hertz range), thus meeting the stringent requirements of RTK for carrier frequency accuracy.

[0134] To ensure the accuracy of RTK differential calculation, the RF channels generating the first RF signal (base station side) and the second RF signal (mobile station side) share the same high-precision reference clock. This ensures that the relative clock drift between the two signals can be eliminated, making carrier phase differential calculation possible.

[0135] The first radio frequency signal is radiated directionally to the physical location corresponding to the virtual reference station through a physically isolated transmission channel, and the second radio frequency signal is radiated directionally to the antenna location of the receiver of the mobile station under test, specifically as follows: Physically isolated transmission channels refer to non-overlapping signal propagation paths constructed using electromagnetic shielding materials, directional antenna technology, or wired transmission media.

[0136] Directional radiation to the mobile station under test: For the mobile station under test moving on the physical sand table, the simulated dynamic environmental signal (second radio frequency signal) is precisely covered within the effective receiving range of the mobile station antenna by using a beamforming antenna or a servo transmitter probe, so as to minimize the signal spillover into the surrounding space.

[0137] Directional radiation to the virtual reference station location: For the reference receiver acting as the reference station (or the RF input port used for closed-loop self-test of the system), the static environmental signal (first RF signal) is directly transmitted to its RF port through a shielded cable or microwave anechoic box.

[0138] This ensures that the receiver of the tested mobile station only receives signals with its own motion characteristics, and not signals from the base station. This simulates the electromagnetic environment of two locations tens of kilometers apart within a few meters of physical space, providing rigorous physical layer support for full-element RTK training.

[0139] Secondly, embodiments of this application provide a simulation training method system, including: The first acquisition module is used to acquire the preset geographic mapping model, the fixed virtual geographic coordinates of the virtual base station and satellite ephemeris data, and to collect the physical coordinate data fed back by the mobile platform in real time. The physical coordinate data represents the real-time position of the mobile station receiver under test in physical space. The first module is used to transform physical coordinate data according to the geographic mapping model to obtain the virtual geographic coordinates of the mobile station; The second module is used to obtain the first satellite observation data based on a unified time base and precise satellite ephemeris data, using fixed virtual geographic coordinates, and to obtain the second satellite observation data using the virtual geographic coordinates of the rover station. The first generation module is used to generate differential correction data based on the first satellite observation data, and send the differential correction data to the mobile station receiver under test through the data link, so that the mobile station receiver under test can combine the received radio frequency signal of the corresponding second satellite observation data to perform differential positioning calculation and output the positioning result. The second acquisition module is used to acquire the positioning results output by the receiver of the tested mobile station; The second generation module is used to calculate the deviation index between the positioning result and the virtual geographic coordinates of the mobile station, and generate a training evaluation report.

[0140] In this embodiment, the simulation training method converts the collected physical coordinates into virtual coordinates in real time through a geographic mapping model, logically decoupling the physical space from the test scenario. This allows for the derivation and reproduction of a wide-area field operation scenario within a limited indoor space. Based on a unified time reference, the method performs parallel calculations of satellite observation data from a fixed virtual reference station and a rover station. This strict spatiotemporal synchronization mechanism ensures that the two generated data streams have the geometric correlation required for double-difference operations in the mathematical model, enabling the receiver under test to effectively eliminate common errors and successfully achieve RTK fixed calculations in the simulation environment. Furthermore, the generated virtual coordinates are used as absolute true values ​​and compared with the positioning results calculated by the receiver based on differential data, establishing an objective, accurate, and closed-loop evaluation system that does not require expensive external measurement equipment. By constructing a real-time mapping closed loop between physical motion and virtual space, and using a mathematical model to convert finite physical displacements into wide-area virtual trajectories, the dependence of traditional training on the scale of the physical site is overcome. Based on a unified spatiotemporal reference, parallel driving of dual-channel signal generation ensures strict synchronization of observation data between the virtual reference station and the rover station in geometric phase, thus successfully constructing the mathematical correlation required for RTK fixed solution in an indoor simulation environment. Combined with real radio frequency radiation and differential data injection, full-element hardware-in-the-loop verification is achieved, and by utilizing the virtual coordinate truth value naturally mastered by the simulation system, the problem of difficulty in obtaining positioning references in dynamic testing is solved, ultimately achieving low-cost, high-precision, and repeatable standardized training evaluation.

[0141] The functions of each module in each device in the embodiments of this application can be found in the corresponding descriptions in the above methods, and will not be repeated here.

[0142] Figure 2 A structural block diagram of an electronic device according to an embodiment of this application is shown. Figure 2 As shown, the electronic device includes a memory 410 and a processor 420. The memory 410 stores instructions that can be executed on the processor 420. When the processor 420 executes the instructions, it implements the simulation training method in the above embodiments. The number of memories 410 and processors 420 can be one or more. This electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0143] The electronic device may also include a communication interface 430 for communicating with external devices and exchanging data. The devices are interconnected using different buses and can be mounted on a common motherboard or otherwise as needed. The processor 420 can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). The bus can be divided into address buses, data buses, control buses, etc. For ease of illustration, Figure 2 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0144] Optionally, in a specific implementation, if the memory 410, processor 420 and communication interface 430 are integrated on a single chip, the memory 410, processor 420 and communication interface 430 can communicate with each other through an internal interface.

[0145] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0146] This application provides a computer-readable storage medium (such as the memory 410 described above) that stores computer instructions that, when executed by a processor, implement the methods provided in this application.

[0147] Optionally, memory 410 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device, etc. Furthermore, memory 410 may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 410 may optionally include memory remotely located relative to processor 420, and these remote memories can be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0148] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A simulation training method, characterized in that, include: The system acquires a preset geographic mapping model, fixed virtual geographic coordinates of the virtual reference station, and satellite ephemeris data. It also collects physical coordinate data fed back by the mobile platform in real time. The physical coordinate data represents the real-time position of the mobile station receiver under test in physical space. The physical coordinate data is transformed according to the geographic mapping model to obtain the virtual geographic coordinates of the mobile station; Based on a unified time reference and the precise satellite ephemeris data, the first satellite observation data is obtained by calculating using the fixed virtual geographic coordinates, and the second satellite observation data is obtained by calculating using the virtual geographic coordinates of the mobile station. Based on the first satellite observation data, differential correction data is generated, and the differential correction data is sent to the mobile station receiver under test via a data link, so that the mobile station receiver under test can perform differential positioning calculation by combining the received radio frequency signal corresponding to the second satellite observation data, and output the positioning result. Obtain the positioning result output by the receiver of the tested mobile station; Calculate the deviation index between the positioning result and the virtual geographic coordinates of the mobile station, and generate a training evaluation report.

2. The method according to claim 1, characterized in that, The process of obtaining the preset geographic mapping model includes: Obtain the physical measurement coordinates of at least three calibration points in the physical space coordinate system, and the real geographic coordinates of the at least three calibration points in the virtual geographic space coordinate system; Construct a three-dimensional spatial transformation relationship that includes translation parameters, rotation parameters, and scale factors; The least squares method is used to perform adjustment calculations on the physical measurement coordinates and the actual geographic coordinates to obtain the optimal transformation parameters in the three-dimensional spatial transformation formula. The three-dimensional spatial transformation relationship containing the optimal transformation parameters is determined as the geographic mapping model.

3. The method according to claim 2, characterized in that, The step of converting the physical coordinate data according to the geographic mapping model to obtain the virtual geographic coordinates of the mobile station includes: The physical coordinate data is subjected to zero-point offset correction and unit standardization to obtain standard physical coordinates; Substituting the standard physical coordinates into the geographic mapping model, the initial virtual geographic coordinates are obtained by multiplying the standard physical coordinates by a scale factor, a rotation parameter along each coordinate axis, and adding the corresponding translation parameter. Obtain the software scale factor set for the current training scenario; The planar displacement components in the initial virtual geographic coordinates are scaled using the software scale factor to obtain the virtual geographic coordinates of the mobile station.

4. The method according to claim 3, characterized in that, The first satellite observation data includes a first pseudorange observation and a first carrier phase observation; the second satellite observation data includes a second pseudorange observation and a second carrier phase observation. The calculation of the first satellite observation data using the fixed virtual geographic coordinates based on a unified time reference and the precise satellite ephemeris data, and the calculation of the second satellite observation data using the rover's virtual geographic coordinates, includes: At the current simulation moment, based on the analysis of the precise satellite ephemeris data, the spatial position vectors and satellite clock biases of all visible satellites are obtained; The geometric distance between the spatial position vector of the visible satellite and the fixed virtual geographic coordinates is calculated to obtain the first geometric distance; The geometric distance between the spatial position vector of the visible satellite and the virtual geographic coordinates of the mobile station is calculated to obtain the second geometric distance; Based on the tropospheric delay model value, ionospheric delay model value and receiver clock error model value corresponding to the first geometric distance superposition, the first pseudorange observation value in the first satellite observation data is generated. Based on the tropospheric delay model value, ionospheric delay model value and receiver clock error model value corresponding to the superposition of the second geometric distance, the second pseudorange observation value in the second satellite observation data is generated. Based on the first geometric distance superimposed with integer ambiguity parameters and carrier phase noise, the first carrier phase observation value in the first satellite observation data is generated. The second carrier phase observation value in the second satellite observation data is generated based on the second geometric distance superimposed with integer ambiguity parameters and carrier phase noise.

5. The method according to claim 4, characterized in that, The step of generating differential correction data based on the first satellite observation data includes: Extract the first carrier phase observation value and the first pseudorange observation value corresponding to each visible satellite from the first satellite observation data; The fixed virtual geographic coordinates of the virtual base station are invoked to construct the base station coordinate parameter message; The carrier phase observations and pseudorange observations are classified according to the satellite system to construct differential observation messages; The reference station coordinate parameter message and the differential observation message are time-series aligned and encapsulated to generate differential correction data containing complete baseline solution information.

6. The method according to claim 5, characterized in that, The method further includes: In response to the virtual motion trajectory drawn by the user on the virtual map interface, the coordinates of key points of the virtual motion trajectory are obtained; Using the inverse transformation relationship of the geographic mapping model, the virtual motion trajectory is converted into physical motion commands in physical space; The physical motion command is sent to the motion controller to drive the mobile platform to move the tested mobile station receiver according to the physical motion command, thereby generating the real-time acquired physical coordinate data.

7. The method according to claim 6, characterized in that, The method further includes: The first satellite observation data is modulated into a first digital intermediate frequency signal, and the second satellite observation data is modulated into a second digital intermediate frequency signal; The first digital intermediate frequency signal and the second digital intermediate frequency signal are up-converted to the target satellite navigation frequency point to generate the first radio frequency signal and the second radio frequency signal; The first radio frequency signal is radiated directionally to the physical location corresponding to the virtual reference station through a physically isolated transmission channel, and the second radio frequency signal is radiated directionally to the antenna location of the receiver of the mobile station under test.

8. A simulation training method system, characterized in that, include: The first acquisition module is used to acquire a preset geographic mapping model, fixed virtual geographic coordinates of the virtual reference station, and satellite ephemeris data, and to collect physical coordinate data fed back by the mobile platform in real time. The physical coordinate data represents the real-time position of the mobile station receiver under test in physical space. The first obtaining module is used to transform the physical coordinate data according to the geographic mapping model to obtain the virtual geographic coordinates of the mobile station; The second obtaining module is used to calculate the first satellite observation data based on a unified time reference and the precise satellite ephemeris data, using the fixed virtual geographic coordinates, and to calculate the second satellite observation data using the mobile station virtual geographic coordinates. The first generation module is used to generate differential correction data based on the first satellite observation data, and send the differential correction data to the mobile station receiver under test through a data link, so that the mobile station receiver under test can perform differential positioning calculation by combining the received radio frequency signal corresponding to the second satellite observation data, and output the positioning result. The second acquisition module is used to acquire the positioning result output by the receiver of the tested mobile station; The second generation module is used to calculate the deviation index between the positioning result and the virtual geographic coordinates of the mobile station, and generate a training evaluation report.

9. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method as described in any one of claims 1-7.

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