A real-time kinematic positioning method, apparatus, electronic device, and storage medium

Abstract

The embodiment of the application discloses a real-time dynamic positioning method and device, electronic equipment and storage medium, belongs to the satellite positioning technical field, mainly includes obtaining satellite observation data and calculating the approximate position of the user end according to the satellite observation data; upload the approximate position of the user end to the server, and obtain the virtual observation data and the enhancement information determined based on the approximate position from the server, the enhancement information includes: reference station multipath error accuracy, satellite orbit residual error accuracy, troposphere and ionosphere atmosphere model correction accuracy, and troposphere and ionosphere atmosphere activity at the approximate position; and, according to the virtual observation data, the enhancement information and the satellite observation data, the accurate position information of the user end is obtained by real-time dynamic positioning calculation. The embodiment of the application can effectively improve the positioning accuracy of the user, especially under the condition that the measurement environment is relatively poor.

Description

Technical Field

[0001] This invention relates to the field of satellite positioning technology, and in particular to a real-time dynamic positioning method, device, electronic device, and storage medium. Background Technology

[0002] The accuracy of real-time dynamic positioning calculations in existing technologies depends on the level of data processing at the server end, i.e., the accuracy level of the calculated virtual observations. Virtual observations typically contain differential data from multiple different satellites. Due to the different positions of each satellite, the various errors affecting the signal propagation to the service area vary greatly, mainly including tropospheric, ionospheric, and multipath errors. These error terms differ by several decimeters to tens of meters. Therefore, the accuracy of the differential data calculated by different satellites at the server end after modeling and other calculations will inevitably differ. If users use satellite differential data with lower accuracy obtained from server processing under many adverse observation conditions as data with normal accuracy, the positioning results will be significantly affected. Summary of the Invention

[0003] This invention provides a real-time dynamic positioning method that obtains augmented information from the server and uses the augmented information for calculation during the solution process, which can effectively improve the accuracy of user positioning, especially under harsh measurement conditions.

[0004] In a first aspect, embodiments of the present invention provide a real-time dynamic positioning method, comprising: including:

[0005] The system acquires satellite observation data and calculates the approximate location of the user terminal based on the data. It then uploads this approximate location to a server and retrieves virtual observation data and augmentation information from the server, based on the approximate location. The augmentation information includes: the accuracy of the base station's multipath error, the accuracy of the satellite orbit residual error, the accuracy of corrections to the tropospheric and ionospheric atmospheric models, and the tropospheric and ionospheric atmospheric activity at the approximate location. Finally, it performs real-time dynamic positioning calculations based on the virtual observation data, augmentation information, and satellite observation data to obtain the accurate location information of the user terminal.

[0006] Secondly, embodiments of the present invention provide a real-time dynamic positioning device, comprising: an approximate position acquisition module, used to acquire satellite observation data and calculate the approximate position of the user terminal based on the satellite observation data; a virtual observation data acquisition module, used to upload the approximate position of the user terminal to a server and acquire virtual observation data and augmentation information determined based on the approximate position from the server, the augmentation information including: the accuracy of the multipath error of the reference station, the accuracy of the satellite orbit residual error, the accuracy of the tropospheric and ionospheric atmospheric model corrections, and the tropospheric and ionospheric atmospheric activity at the approximate position; and a positioning calculation module, used to perform real-time dynamic positioning calculation based on the virtual observation data, the augmentation information, and the satellite observation data to obtain the accurate position information of the user terminal.

[0007] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement any of the real-time dynamic positioning methods described in the embodiments of the present invention.

[0008] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the real-time dynamic positioning methods described in any of the embodiments of the present invention.

[0009] In this embodiment of the invention, by obtaining enhanced information from the server and using the enhanced information for calculation during the calculation process, the accuracy of user positioning can be effectively improved, especially under harsh measurement conditions. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating a real-time dynamic positioning method provided in an embodiment of the present invention;

[0011] Figure 2 This is another flowchart illustrating the real-time dynamic positioning method provided in this embodiment of the invention;

[0012] Figure 3 This is another flowchart illustrating the real-time dynamic positioning method provided in this embodiment of the invention;

[0013] Figure 4 This is a schematic diagram of the real-time dynamic positioning results before and after adjusting the positioning solution process using augmented information in the real-time dynamic positioning method provided in this embodiment of the invention;

[0014] Figure 5 This is a schematic diagram of the real-time dynamic positioning device provided in an embodiment of the present invention;

[0015] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0016] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0017] Traditional real-time dynamic positioning (RTK) services, also known as continuously operating reference station satellite positioning services, operate on the following basic principle:

[0018] Current network RTK positioning technology can achieve ambiguity fixation within seconds, obtaining high-precision positioning coordinates with an accuracy of approximately 2-4 cm. However, this depends entirely on the level of data processing at the network RTK server, specifically the accuracy of the generated Virtual Observation Set (VRS). VRS typically contains differential data from multiple different satellites. Due to the varying positions of each satellite, the signals are affected by numerous errors as they propagate to the service area, including tropospheric, ionospheric, and multipath errors, with differences ranging from a few decimeters to tens of meters. Therefore, the accuracy of the differential data calculated from different satellites at the server inevitably differs. However, traditional network RTK does not currently transmit the satellite accuracy differences in the differential data to the user. This leads to significant impacts on positioning results under harsh observation conditions, where users may use lower-accuracy satellite differential data as normal-accuracy data. Positioning accuracy could drop to a few decimeters, and in the worst case, RTK filtering may fail to converge or fix ambiguity.

[0019] Figure 1 This is a flowchart illustrating a real-time dynamic positioning method provided in an embodiment of the present invention. This method can be executed by a real-time dynamic positioning method device provided in this embodiment, which can be implemented in software and / or hardware. In a specific embodiment, the device can be integrated into an electronic device, such as a computer or server. The following embodiments will illustrate this using the integration of the device into an electronic device as an example. (Reference) Figure 1 The method may specifically include the following steps:

[0020] Step 101: Obtain satellite observation data and calculate the approximate location of the user terminal based on the satellite observation data. This enables the server to obtain virtual observation data and augmentation information based on the approximate location of the user terminal, and then perform further positioning calculations to obtain the accurate location information of the user terminal.

[0021] Specifically, the aforementioned satellite observation data includes observation data from multiple GNSS satellites. In practical applications, it includes observation data from four or more GNSS satellites.

[0022] Specifically, in satellite positioning, the user-end GNSS receiver first receives ephemeris data and measurement signals transmitted by GNSS satellites. After receiving these signals, the GNSS receiver calculates the distance between the receiver and the satellite based on the time interval between the transmitted and received signals. However, due to unavoidable clock errors between the satellite clock and the receiver clock, and the influence of atmospheric refraction and other factors during signal propagation, the distance directly measured by this method is not the true distance between the satellite and the ground receiver, but only an approximate distance. Once the receiver calculates its approximate distance to four or more satellites, its approximate position relative to the Earth's surface is determined.

[0023] Step 102: Upload the approximate location of the user terminal to the server, and obtain virtual observation data and augmentation information based on the approximate location from the server. The augmentation information includes: the accuracy of the multipath error of the reference station, the accuracy of the satellite orbit residual error, the accuracy of the correction of the tropospheric and ionospheric atmospheric models, and the tropospheric and ionospheric atmospheric activity at the approximate location. This will help to obtain the accurate location information of the user terminal by performing real-time dynamic positioning calculation based on the virtual observation data and augmentation information.

[0024] Specifically, existing network-based real-time dynamic positioning (RTK) services first establish numerous GNSS reference stations within the service area. These reference stations transmit the real-time data they receive back to the server via the network. Upon receiving this data, the server's data processing center uses this real-time data to model the atmosphere within the area. When operating, rover users first upload their approximate location to the server center via the network and then request differential data (VRS) based on their approximate location in real time using the RTCM protocol. After receiving the VRS data in the RTCM protocol format, users can combine it with their own observation data to perform RTK filtering and calculation, thereby obtaining high-precision coordinates at their measurement points.

[0025] In existing technologies, the user end only obtains virtual observation data from the server for filtering and calculation, and does not obtain information on satellite accuracy differences, including the accuracy of multipath error of the reference station, the accuracy of satellite orbit residual error, the accuracy of tropospheric and ionospheric atmospheric model corrections, and the tropospheric and ionospheric atmospheric activity at the approximate location. As a result, if the user uses the poorly accurate satellite differential data as the data with normal accuracy, the positioning results will be greatly affected.

[0026] In this embodiment of the invention, the user terminal not only acquires virtual observation data, but also acquires enhanced information including the accuracy of multipath error of the reference station, the accuracy of satellite orbit residual error, the accuracy of tropospheric and ionospheric atmospheric model corrections, and satellite accuracy difference information on the tropospheric and ionospheric atmospheric activity at approximate locations. This allows for the use of the acquired enhanced information to adjust the filtering and solving process, thereby obtaining more accurate positioning.

[0027] In optional specific embodiments of the present invention, the enhanced information can be obtained by extending the existing RTCM message broadcasting method, or it can be obtained in a custom format, such as using BeiDou short message service.

[0028] Specifically, the calculation of the aforementioned augmented information is performed on the server side. There are many ways to calculate the augmented information data, and this proposal does not limit this; the following are just examples of reference methods.

[0029] Satellite orbital residual error accuracy calculate:

[0030] Because RTK uses double-difference observations for calculation, most of the orbital error can be eliminated. However, as the distance between the user and the master station corresponding to the VRS increases, the residual orbital error will gradually increase. When the accuracy reaches mm or higher, it will affect the accuracy of RTK positioning. This proposal... The calculation method is as follows:

[0031]

[0032] In the formula, and These represent satellite positions calculated from ultra-fast orbits and broadcast ephemeris, respectively. The distance between the user's device and the main website. It is a scaling factor, which is usually taken as 1.0e-8.

[0033] pseudorange multipath error accuracy calculate:

[0034] The satellite signals received by the base station may be refracted signals, and the error introduced in this case is usually called multipath error. Multipath error has a very small impact on phase and is usually negligible, but it can affect pseudorange by several decimeters to meters. Since the coordinates of the base station are known, when the server uses real-time precise point positioning (PPP) to process the data, all errors can converge quickly, and the tropospheric delay can be obtained. ionospheric delay Receiver clock bias After considering various errors, the pseudorange multipath accuracy is... It can be represented as:

[0035]

[0036] Where r represents the user ID, i represents the satellite ID, and P represents the pseudorange observation. Indicates the geometric distance between the station and the satellite. Represents the speed of light. For receiver clock bias, To ensure precise satellite clock bias, under default pseudorange measurement noise... In the absence of known information, this item can be considered as absorption at... In the middle, they can be broadcast together as enhanced information to users.

[0037] Atmospheric model correction accuracy calculate:

[0038] Because different vendors' network RTK services may employ different atmospheric modeling methods, the methods for calculating the accuracy of atmospheric corrections will also differ. Generally, we assume the following network RTK atmospheric model:

[0039] ,

[0040] In the formula, Let the left matrix be the residual matrix of the equation. For the power formation, The coefficient matrix of the equation (which can generally be obtained based on the approximate location of the user and the location of the base station). For the corresponding ionospheric and tropospheric model parameters, Given the server-side baseline corresponding to the ionospheric and tropospheric estimation results, then, The calculation can be expressed as follows:

[0041]

[0042] in, The mean square error of the model. The variance of the model parameters, This refers to the distance between the user's device and the main website.

[0043] Atmospheric activity at user location calculate:

[0044] Based on the above formula for the network RTK atmospheric model, we can calculate... The atmospheric correction at the user's location at any given time is as follows:

[0045]

[0046] in, Indicates tropospheric delay, Indicates ionospheric delay, and These are the model coefficients calculated based on the user's location. Similarly, we can obtain... The atmospheric correction at the user's location at any given time is as follows:

[0047]

[0048] So, at the user location It can be represented as:

[0049] .

[0050] Step 103: Real-time dynamic positioning calculation is performed based on virtual observation data, augmented information, and satellite observation data to obtain accurate location information for the user terminal. The augmented information can be used to adjust the real-time dynamic positioning calculation process, thereby improving positioning accuracy.

[0051] Specifically, mobile station users When performing RTK positioning calculations using VRS virtual observations, the double-difference observation equation can be expressed as:

[0052]

[0053]

[0054] In the formula: r is the user ID, For phase observations, These are pseudorange observations. Indicates the geometric distance between the station and the satellite. Indicates tropospheric delay, Indicates ionospheric delay, For carrier wavelength, Indicates ambiguity. For multipath and other error terms, For phase measurement noise, For pseudorange measurement noise, The variance of the observation equation is represented by the superscript. , Number the satellite. This is a double difference operator. In the above formula, , , and the user's precise location coordinates ( ) are the parameters to be estimated, where ( ) included In RTK positioning, unknown parameters are typically estimated using Kalman filtering.

[0055] In an optional specific embodiment of the present invention, the process of performing real-time dynamic positioning calculation based on virtual observation data, augmentation information and satellite observation data includes: obtaining an observation model by performing real-time dynamic positioning calculation using a Kalman filter based on virtual observation data and satellite observation data, and adjusting the observation model using augmentation information.

[0056] Optionally, the process of adjusting the observation model using enhanced information includes: adjusting the phase observation equation in the observation model using the accuracy of corrections to the tropospheric and ionospheric atmospheric models, as well as the accuracy of satellite orbit residual errors.

[0057] Optionally, the process of adjusting the observation model using enhanced information includes: adjusting the pseudorange observation equation in the observation model using the accuracy of corrections to the tropospheric and ionospheric atmospheric models, the accuracy of satellite orbit residual errors, and the accuracy of multipath errors at the reference station.

[0058] Specifically, the adjustment of the observation model mainly involves the adjustment of the weights. Assuming the variances of the original phase and pseudorange observation equations are respectively... and Then the adjusted variances are as follows:

[0059] Adjusted phase observation equation:

[0060] Adjusted pseudorange observation equation

[0061] Among them, R orbit For satellite orbit residual error accuracy; R iono For the accuracy of ionospheric correction; R trop For the accuracy of tropospheric correction; R MP This refers to the accuracy of pseudorange multipath error.

[0062] In an optional specific embodiment of the invention, the process of performing real-time dynamic positioning calculation based on virtual observation data, augmentation information, and satellite observation data includes: obtaining a stochastic model by performing real-time dynamic positioning calculation using a Kalman filter based on virtual observation data and satellite observation data, and adjusting the stochastic model using the atmospheric activity of the troposphere and ionosphere at the approximate location.

[0063] Specifically, stochastic models are mainly used during parameter state updates. Assume the stochastic model corresponding to the parameters in the original state update process is:

[0064]

[0065] The adjusted stochastic model can then be represented as:

[0066]

[0067] In an optional specific embodiment of the present invention, the process of performing real-time dynamic positioning calculation based on virtual observation data, augmentation information, and satellite observation data includes: obtaining the Kalman state update covariance matrix and the Kalman filter gain matrix by performing real-time dynamic positioning calculation using a Kalman filter based on the virtual observation data and satellite observation data; and adjusting the Kalman state update covariance matrix and the Kalman filter gain matrix using augmentation information.

[0068] Optionally, the process of adjusting the Kalman state update covariance matrix using enhanced information includes adjusting the Kalman state update covariance matrix using the tropospheric and ionospheric atmospheric activity at the user's location.

[0069] Optionally, the process of adjusting the Kalman filter gain matrix using enhanced information includes adjusting the Kalman filter gain matrix using the accuracy of corrections to the tropospheric and ionospheric atmospheric models, as well as the accuracy of satellite orbit residual errors.

[0070] Optionally, the Kalman filter gain matrix can be adjusted using the accuracy of corrections to the tropospheric and ionospheric atmospheric models, the accuracy of satellite orbit residual errors, and the accuracy of multipath errors at the reference station.

[0071] Specifically, suppose that at time t the observation equation can be rewritten in the following form:

[0072] ,

[0073] In the formula, , , , This represents the residual of the equation at time t, with other symbols remaining the same as before.

[0074] During RTK positioning filtering, the Kalman state update covariance matrix can be adjusted as follows:

[0075]

[0076] In the formula, This is the variance matrix from the previous time step. To verify the forward deviation at the current moment, This is the state transition matrix. The Kalman filter gain matrix can be adjusted as follows:

[0077]

[0078] or

[0079]

[0080] The following further describes the real-time dynamic positioning method in another embodiment. In this specific embodiment, the satellite observation data includes satellite observation data from multiple satellites, the virtual observation data includes virtual observation data from multiple satellites, and the augmentation information includes augmentation information from multiple satellites. That is, as follows... Figure 2 As shown, Figure 1 Step 103 may include the following steps:

[0081] Step 1031: Identify some satellites among multiple satellites as valid satellites based on the augmentation information; and Step 1032: Perform real-time dynamic positioning calculation using virtual observation data of the valid satellites, augmentation information, and satellite observation data.

[0082] Or, such as Figure 3 As shown, Figure 1 Step 103 may include the following steps:

[0083] Step 1033: Obtain the solution weight of each satellite among multiple satellites based on the augmentation information; and Step 1034: Perform real-time dynamic positioning solution using the virtual observation data, augmentation information, and satellite observation data of the corresponding satellite based on the solution weight of each satellite.

[0084] Specifically, under many harsh observation conditions, if users use satellite differential data with lower accuracy as data with normal accuracy, the positioning results will be greatly affected. The positioning accuracy may drop to a few decimeters, and in the worst case, RTK filtering may even fail to converge or fix the ambiguity. This invention, based on augmented information, can selectively use different satellites during RTK calculation, discarding satellites with lower accuracy, or assigning higher weights to satellites with higher accuracy and lower weights to satellites with lower accuracy, in order to achieve the fastest convergence speed.

[0085] In this embodiment of the invention, after adjusting the real-time dynamic positioning solution process using enhanced information, the positioning results are greatly improved. For example... Figure 4 As shown in the real-time dynamic positioning results, before the addition of augmentation information, the ambiguity could not be fixed for nearly one hour during the period from UTC12:00 to UTC14:00 (the circled points in the figure indicate unfixed points, and the output is a floating-point solution), and the positioning error was relatively large. After the addition of augmentation information, the ambiguity could be fixed for almost the entire period (the unfixed points basically disappeared, and the output is a fixed solution). In addition, during the period from UTC12:00 to UTC14:00 when the atmospheric environment was severely turbulent, the positioning error of RTK was significantly improved, with only a few time points showing an error exceeding 0.5m, and the vast majority of results being within 0.1m.

[0086] Therefore, it is evident that adding enhanced information can significantly improve the service performance of network RTK, enabling users within the region to obtain higher and more stable positioning accuracy.

[0087] Figure 5 This is a structural diagram of a real-time dynamic positioning device provided in an embodiment of the present invention. This device is suitable for executing the real-time dynamic positioning method provided in an embodiment of the present invention. Figure 5 As shown, the device may specifically include:

[0088] The approximate location acquisition module 501 is used to acquire satellite observation data and calculate the approximate location of the user terminal based on the satellite observation data. This enables the server to obtain virtual observation data and augmentation information based on the approximate location of the user terminal, and then perform further positioning calculations to obtain the accurate location information of the user terminal.

[0089] The virtual observation data acquisition module 502 is used to upload the approximate location of the user terminal to the server and obtain virtual observation data and augmentation information determined based on the approximate location from the server. The augmentation information includes: the accuracy of the multipath error of the reference station, the accuracy of the satellite orbit residual error, the accuracy of the correction of the tropospheric and ionospheric atmospheric models, and the tropospheric and ionospheric atmospheric activity at the approximate location. This can facilitate the subsequent real-time dynamic positioning calculation based on the virtual observation data and augmentation information to obtain the accurate location information of the user terminal.

[0090] The positioning calculation module 503 is used to perform real-time dynamic positioning calculation based on virtual observation data, augmentation information, and satellite observation data to obtain accurate location information for the user. It can utilize augmentation information to adjust the real-time dynamic positioning calculation process, thereby improving positioning accuracy.

[0091] Specifically, existing network-based real-time dynamic positioning (RTK) services first establish numerous GNSS reference stations within the service area. These reference stations transmit the real-time data they receive back to the server via the network. Upon receiving this data, the server's data processing center uses this real-time data to model the atmosphere within the area. When operating, rover users first upload their approximate location to the server center via the network and then request differential data (VRS) based on their approximate location in real time using the RTCM protocol. After receiving the VRS data in the RTCM protocol format, users can combine it with their own observation data to perform RTK filtering and calculation, thereby obtaining high-precision coordinates at their measurement points.

[0092] In existing technologies, the user end only obtains virtual observation data from the server for filtering and calculation, and does not obtain information on satellite accuracy differences, including the accuracy of multipath error of the reference station, the accuracy of satellite orbit residual error, the accuracy of tropospheric and ionospheric atmospheric model corrections, and the tropospheric and ionospheric atmospheric activity at the approximate location. As a result, if the user uses the poorly accurate satellite differential data as the data with normal accuracy, the positioning results will be greatly affected.

[0093] The device of this invention not only acquires virtual observation data, but also acquires enhanced information including the accuracy of multipath error of the reference station, the accuracy of satellite orbit residual error, the accuracy of tropospheric and ionospheric atmospheric model corrections, and the satellite accuracy difference information of tropospheric and ionospheric atmospheric activity at approximate locations. The enhanced information is used in the calculation process, which can effectively improve the accuracy of user positioning, especially under harsh measurement environment conditions.

[0094] The real-time dynamic positioning device provided in this embodiment of the invention can be used to execute the real-time dynamic positioning method provided in any embodiment of the invention, and will not be described in detail here.

[0095] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0096] This invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the real-time dynamic positioning method provided in any of the above embodiments.

[0097] This invention also provides a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the real-time dynamic positioning method provided in any of the above embodiments.

[0098] The following is for reference. Figure 6 It shows a schematic diagram of the structure of a computer system 600 suitable for implementing an electronic device according to embodiments of the present invention. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0099] like Figure 6As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 602 or programs loaded from storage section 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the system 600. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0100] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0101] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs the functions defined above in the system of this invention.

[0102] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0103] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0104] The modules and / or units described in the embodiments of the present invention can be implemented in software or hardware. The described modules and / or units can also be housed in a processor; for example, a processor can be described as including: an approximate location acquisition module, a virtual observation data acquisition module, and a location calculation module.

[0105] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist alone and not assembled into the device.

[0106] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method of real-time kinematic positioning, characterized in that The method comprises: acquiring satellite observation data and calculating an approximate position of a user terminal based on the satellite observation data; uploading the approximate position of the user terminal to a server and obtaining, from the server, virtual observation data determined based on the approximate position and enhancement information, the enhancement information including: reference station multipath error accuracy, satellite orbit residual error accuracy, troposphere and ionosphere atmosphere model correction accuracy, and troposphere and ionosphere atmosphere activity at the approximate position; and performing real-time dynamic positioning calculation based on the virtual observation data, the enhancement information, and the satellite observation data to obtain accurate position information of the user terminal; the process of performing real-time dynamic positioning calculation based on the virtual observation data, the enhancement information, and the satellite observation data comprises: performing real-time dynamic positioning calculation based on the virtual observation data and the satellite observation data to obtain an observation model using a Kalman filter, and adjusting the observation model using the enhancement information; and performing real-time dynamic positioning calculation based on the virtual observation data and the satellite observation data to obtain a random model using a Kalman filter, and adjusting the random model using the troposphere and ionosphere atmosphere activity at the approximate position; the process of adjusting the observation model using the enhancement information comprises: adjusting a phase observation equation in the observation model using the troposphere and ionosphere atmosphere model correction accuracy and the satellite orbit residual error accuracy; and adjusting a pseudo-range observation equation in the observation model using the troposphere and ionosphere atmosphere model correction accuracy, the satellite orbit residual error accuracy, and the reference station multipath error accuracy.

2. The real-time kinematic positioning method of claim 1, wherein, The satellite observation data include satellite observation data of a plurality of satellites, the virtual observation data include virtual observation data of the plurality of satellites, and the enhancement information includes enhancement information of the plurality of satellites; the process of performing real-time dynamic positioning calculation based on the virtual observation data, the enhancement information, and the satellite observation data comprises: determining some of the plurality of satellites as valid satellites based on the enhancement information, and performing real-time dynamic positioning calculation using the virtual observation data, the enhancement information, and the satellite observation data of the valid satellites; or obtaining a calculation weight of each of the plurality of satellites based on the enhancement information, and performing real-time dynamic positioning calculation using the virtual observation data, the enhancement information, and the satellite observation data of the corresponding satellite based on the calculation weight of the corresponding satellite.

3. The real-time dynamic positioning method according to claim 1 or 2, wherein the process of performing real-time dynamic positioning calculation based on the virtual observation data, the enhancement information, and the satellite observation data comprises: performing real-time dynamic positioning calculation based on the virtual observation data and the satellite observation data to obtain a Kalman state update covariance matrix and a Kalman filter gain matrix using a Kalman filter; and The Kalman state update covariance matrix and the Kalman filter gain matrix are adjusted using the enhanced information.

4. The real-time kinematic positioning method of claim 3, wherein, The process of adjusting the Kalman state update covariance matrix using the enhanced information comprises adjusting the Kalman state update covariance matrix using troposphere and ionosphere atmospheric activity at the user position; The process of adjusting the Kalman filter gain matrix using the enhanced information comprises adjusting the Kalman filter gain matrix using the troposphere and ionosphere atmospheric model correction accuracy and the satellite orbit residual error accuracy; and The Kalman filter gain matrix is adjusted using the troposphere and ionosphere atmospheric model correction accuracy, the satellite orbit residual error accuracy, and the reference station multipath error accuracy.

5. A real-time kinematic positioning device, characterized in that Comprise: An approximate position obtaining module, configured to obtain satellite observation data and calculate an approximate position of a user end according to the satellite observation data; A virtual observation data obtaining module, configured to upload the approximate position of the user end to a server, and obtain virtual observation data and enhanced information determined based on the approximate position from the server, the enhanced information comprising reference station multipath error accuracy, satellite orbit residual error accuracy, troposphere and ionosphere atmospheric model correction accuracy, and troposphere and ionosphere atmospheric activity at the approximate position; and A positioning solution module, configured to perform real-time kinematic positioning solution according to the virtual observation data, the enhanced information, and the satellite observation data to obtain accurate position information of the user end; The positioning solution module is specifically configured to perform real-time kinematic positioning solution using a Kalman filter to obtain an observation model according to the virtual observation data and the satellite observation data, and adjust the observation model using the enhanced information; and The positioning solution module is specifically configured to perform real-time kinematic positioning solution using a Kalman filter to obtain a random model according to the virtual observation data and the satellite observation data, and adjust the random model using troposphere and ionosphere atmospheric activity at the approximate position; The positioning solution module is specifically configured to adjust a phase observation equation in the observation model using the troposphere and ionosphere atmospheric model correction accuracy and the satellite orbit residual error accuracy; and The positioning solution module is specifically configured to adjust a pseudo-range observation equation in the observation model using the troposphere and ionosphere atmospheric model correction accuracy, the satellite orbit residual error accuracy, and the reference station multipath error accuracy.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the real-time kinematic positioning method of any one of claims 1 to 4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the real-time kinematic positioning method of any one of claims 1 to 4.

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