Positioning method and device and electronic equipment

Abstract

The invention discloses a positioning method and device and electronic equipment. The method comprises the following steps: acquiring first observation data acquired by a plurality of global navigation satellite system (GNSS) terminals in a service area; ionosphere modeling is carried out according to the first observation data and second observation data collected by a reference station in the service area, an ionosphere model is obtained, and the ionosphere model is used for representing the relation between ionosphere delay and the ground position in the service area; and positioning the GNSS terminal according to the predicted ionosphere delay amount and the ionosphere delay variation output by the ionosphere model. The technical problem that accurate ionosphere information cannot be provided for a user outside a coverage area of a reference station due to the fact that establishment of an atmosphere model of a PPP-RTK service system adopted in related technologies depends on the fixed reference station is solved.

Description

Technical Field

[0001] This application relates to the field of positioning technology, and more specifically, to a positioning method, device, and electronic device. Background Technology

[0002] In the field of satellite navigation and positioning, PPP-RTK (Precise Point Positioning Real-Time Dynamic) technology integrates the advantages of PPP (Precise Point Positioning) and RTK (Real-Time Dynamic Positioning), providing high-precision positioning possibilities for many industries such as intelligent transportation, surveying and mapping engineering, and disaster monitoring.

[0003] However, the atmospheric model of the PPP-RTK service system used in related technologies relies on fixed reference stations. With limited number and layout of reference stations, areas far from the reference stations will find it difficult to enjoy the positioning advantages brought by high-precision ionospheric correction. Especially in areas where reference stations are sparse or completely absent, accurate estimation of ionospheric delay becomes a problem, resulting in the inability to provide users with accurate ionospheric information outside the coverage area of ​​the reference stations.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides a positioning method, apparatus, and electronic device to at least solve the technical problem that the atmospheric model of the PPP-RTK service system used in related technologies depends on a fixed reference station, resulting in the inability to provide accurate ionospheric information to users outside the coverage area of ​​the reference station.

[0006] According to one aspect of the embodiments of this application, a positioning method is provided, comprising: acquiring first observation data collected by multiple Global Navigation Satellite System (GNSS) terminals in a service area, wherein the service area includes areas not covered by reference stations, the first observation data being used to solve for ionospheric delay variation, the ionospheric delay variation being used to characterize the changing trend of the ionospheric state over time; performing ionospheric modeling based on the first observation data and second observation data collected by reference stations in the service area to obtain an ionospheric model, wherein the ionospheric model is used to characterize the relationship between ionospheric delay and ground position in the service area; and positioning the GNSS terminals based on the predicted ionospheric delay and ionospheric delay variation output by the ionospheric model.

[0007] In some embodiments of this application, the ionospheric delay variation is solved in the following way: The difference between the carrier phase observation value at a first frequency point and the carrier phase observation value at a second frequency point in the first observation data is determined to obtain the first carrier phase observation value, wherein the first frequency point and the second frequency point are different frequency points of the same satellite at the same epoch, and the first carrier phase observation value is used to eliminate the error term related to geometric distance between the GNSS terminal and the satellite; A difference operation is performed on the first carrier phase observation values ​​corresponding to the first epoch and the second epoch respectively to obtain the second carrier phase observation value, wherein the second carrier phase observation value is used to eliminate the influence of ionospheric residuals between the GNSS terminal and the satellite, wherein the epoch interval between the first epoch and the second epoch is less than a preset threshold; and the ionospheric delay variation is determined based on the second carrier phase observation value.

[0008] In some embodiments of this application, after performing a differential operation on the first carrier phase observation values ​​corresponding to the first epoch and the second epoch respectively to obtain the second carrier phase observation value, the method further includes: if the absolute value of the second carrier phase observation value is greater than the cycle slip detection threshold, removing the observation data corresponding to the first epoch and the second epoch from the first observation data.

[0009] In some embodiments of this application, ionospheric modeling is performed based on first observation data and second observation data from a reference station in the service area to obtain an ionospheric model. This includes: dividing the service area into multiple grids, wherein each grid includes at least one of the following: a reference station and a GNSS terminal; mapping the data sets corresponding to the first and second observation data to the multiple grids to obtain grid observation data corresponding to each grid; and performing ionospheric modeling based on the grid observation data of each grid to obtain an ionospheric model corresponding to each grid, wherein the grid observation data includes at least one of the following: third observation data collected by a reference station in the grid and fourth observation data collected by a GNSS terminal in the grid.

[0010] In some embodiments of this application, ionospheric modeling is performed based on the grid observation data corresponding to each grid to obtain an ionospheric model corresponding to each grid, including: determining the first location information of the grid, wherein the first location information includes the longitude and latitude of the center location of the grid; determining the difference between the second location information of the reference station in the service area and the first location information; and based on the difference, performing polynomial fitting on the ionospheric delay using a two-dimensional second-order Taylor expansion to obtain an ionospheric model corresponding to each grid.

[0011] In some embodiments of this application, the GNSS terminal is located based on the predicted ionospheric delay and the change in ionospheric delay output by the ionospheric model, including: determining the ionospheric residual value corresponding to the ionospheric model, wherein the ionospheric residual value is used to characterize the difference between the predicted ionospheric delay and the measured ionospheric delay, and the measured ionospheric delay of the GNSS terminal is determined based on the change in ionospheric delay; and locating the GNSS terminal using the ionospheric model and the ionospheric residual value.

[0012] In some embodiments of this application, determining the ionospheric residual value corresponding to the ionospheric model includes: when using a reference station for ionospheric modeling, obtaining the first measured ionospheric delay calculated by the reference station; using the ionospheric model to determine the first predicted ionospheric delay corresponding to the reference station; and determining the first ionospheric residual value based on the first measured ionospheric delay and the first predicted ionospheric delay.

[0013] In some embodiments of this application, determining the ionospheric residual value corresponding to the ionospheric model includes: when using a GNSS terminal for ionospheric modeling, using the ionospheric model to determine a second predicted ionospheric delay at a first time corresponding to the GNSS terminal; determining the ionospheric delay change between the first time and the second time, and using the cumulative value of the ionospheric delay change and the second predicted ionospheric delay as the second measured ionospheric delay; using the ionospheric model to determine a third predicted ionospheric delay at a second time corresponding to the GNSS terminal; and determining the second ionospheric residual value based on the second measured ionospheric delay and the third predicted ionospheric delay.

[0014] In some embodiments of this application, each grid includes multiple subgrids, and each subgrid includes at least one of the following: a reference station and a GNSS terminal; locating the GNSS terminal based on the predicted ionospheric delay and ionospheric delay change output by the ionospheric model includes: obtaining all third ionospheric residual values ​​in each subgrid, wherein the third ionospheric residual values ​​include the ionospheric residual values ​​of the reference station and the ionospheric residual values ​​of the GNSS terminal in the subgrid; determining the fourth ionospheric residual values ​​corresponding to multiple vertices of the subgrid based on the third ionospheric residual values; and locating the GNSS terminal using the ionospheric model and the fourth ionospheric residual values.

[0015] According to another aspect of the embodiments of this application, a positioning device is also provided, comprising: an acquisition module, configured to acquire first observation data collected by multiple Global Navigation Satellite System (GNSS) terminals in a service area, wherein the service area includes areas not covered by reference stations, the first observation data being used to solve for ionospheric delay variation, the ionospheric delay variation being used to characterize the changing trend of the ionospheric state over time; a modeling module, configured to perform ionospheric modeling based on the first observation data and second observation data collected by reference stations in the service area to obtain an ionospheric model, wherein the ionospheric model is used to characterize the relationship between ionospheric delay and ground position in the service area; and a positioning module, configured to position the GNSS terminals based on the predicted ionospheric delay and ionospheric delay variation output by the ionospheric model.

[0016] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory and a processor, wherein the memory is used to store program instructions; and the processor is connected to the memory and used to execute the above-described positioning method.

[0017] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored computer program, wherein the device containing the non-volatile storage medium executes the above-described positioning method by running the computer program.

[0018] According to another aspect of the embodiments of this application, a computer program product is also provided, including computer instructions that, when executed by a processor, implement the above-described positioning method.

[0019] In this embodiment, an integrated processing approach is adopted. By comprehensively utilizing the first observation data provided by all GNSS terminals within the service area, including the coverage area and blind zone of the reference station, and combining it with the second observation data of the reference station, a comprehensive ionospheric model is obtained. This model accurately describes the relationship between ionospheric delay and geographical location. Then, by combining the ionospheric delay predicted by the model with the ionospheric delay change, the position of any GNSS terminal is accurately corrected. This achieves the goal of expanding the coverage of PPP-RTK services and improving positioning accuracy. Thus, it realizes the technical effect of providing high-precision ionospheric information and positioning services even in areas lacking reference stations. This solves the technical problem that the atmospheric model of the PPP-RTK service system used in related technologies depends on a fixed reference station, resulting in the inability to provide accurate ionospheric information to users outside the coverage area of ​​the reference station. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 This is a hardware structure block diagram of a computer terminal according to a positioning method according to an embodiment of this application;

[0022] Figure 2 This is a flowchart of a positioning method according to an embodiment of this application;

[0023] Figure 3 This is a schematic diagram of the overall process of a positioning method according to an embodiment of this application;

[0024] Figure 4 This is a schematic diagram of a positioning device according to an embodiment of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] To better understand the embodiments of this application, the technical terms involved in the embodiments of this application are explained below:

[0028] Precise Point Positioning (PPP) is a technique that uses Global Navigation Satellite System (GNSS) observation data, combined with precise satellite orbit and clock bias products, to determine the high-precision three-dimensional position of a single receiver. It can achieve meter-level or even sub-meter-level positioning accuracy without baseline calculation, but its convergence time is relatively long. In this embodiment, PPP technology is used to process crowdsourced GNSS data to improve the positioning accuracy of the PPP-RTK system, especially in areas lacking traditional reference station support.

[0029] Real-Time Kinematic (RTK) positioning is a high-precision GNSS positioning technology that achieves centimeter-level or even millimeter-level positioning accuracy through data exchange between a base station (reference station) and a rover. The base station provides differential corrections, which the rover uses for real-time calculations. In this embodiment, RTK is combined with PPP to form PPP-RTK, accelerating PPP convergence time and providing faster, higher-precision positioning services.

[0030] Global Navigation Satellite System (GNSS): A Global Navigation Satellite System is a combination of satellite systems that provide navigation and positioning services worldwide. In the embodiments of this application, the Global Navigation Satellite System provides a basic positioning data source and is the basis for the implementation of PPP and RTK technologies.

[0031] PPP-RTK (Precise Point Positioning Real-Time Kinematic): PPP-RTK is a positioning technology that combines the advantages of Precise Point Positioning (PPP) and Real-Time Kinematic (RTK). By providing mobile terminals with information such as precise satellite orbits, clock biases, and ionospheric delays, it enables users to achieve high-precision positioning in a short time. In the embodiments of this application, PPP-RTK is the core technology, aiming to improve the positioning service performance in areas not covered by reference stations by utilizing crowdsourced data, with particular emphasis on the ability to correct for ionospheric delays.

[0032] Ionospheric delay refers to the phenomenon where GNSS signals, when passing through the Earth's ionosphere, experience a slower signal propagation speed due to uneven electron density distribution, resulting in deviations in arrival time and path. This delay affects the accuracy of GNSS positioning. In this application embodiment, ionospheric delay is one of the main factors affecting positioning accuracy. This application embodiment proposes a method based on distributed GNSS terminal data, using geometrically distance-free carrier epoch differential technology to accurately calculate ionospheric delay, thereby improving overall positioning performance.

[0033] Crowdsourcing is a model in which businesses or organizations use the internet to outsource certain jobs or tasks to an undefined group of people, leveraging their knowledge, skills, or wisdom to complete specific tasks. In this application embodiment, the crowdsourcing model refers to collecting and utilizing observation data uploaded by various GNSS terminals (such as vehicle-mounted terminals, smartphones, drones, etc.) scattered across a wide area to assist in building a broader ionospheric model, thereby providing more accurate positioning services in areas with insufficient reference station coverage.

[0034] In satellite positioning technology, PPP-RTK combines the advantages of high-precision PPP positioning and fast convergence of RTK. However, atmospheric delay (ionospheric delay and tropospheric delay) is a key factor affecting its convergence time, positioning accuracy, and reliability, especially ionospheric delay. The PPP-RTK service system used in related technologies requires the construction of a large number of fixed reference stations (interval of 50-80 km) to ensure modeling accuracy, especially in complex terrain areas where the cost of station construction and operation and maintenance increases significantly. Furthermore, the data coverage of a limited number of reference stations is limited; in areas far from reference stations, the accuracy of atmospheric delay correction is significantly affected. Therefore, the service performance of the PPP-RTK service system also degrades in areas without reference stations.

[0035] To address the aforementioned technical problems, this application provides corresponding solutions, which are detailed below.

[0036] The positioning method embodiments provided in this application can be executed in a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware structure block diagram of a computer terminal for implementing a positioning method is shown. Figure 1As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission module 106 for communication functions connected via wired and / or wireless networks. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0037] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0038] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the positioning method in this embodiment. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the above-mentioned positioning method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 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.

[0039] The transmission module 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission module 106 includes a network interface controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission module 106 may be a radio frequency (RF) module, used for wireless communication with the Internet.

[0040] The display can be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0041] It should be noted here that, in some optional embodiments, the above... Figure 1 The computer terminal shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 1 This is only one instance of a specific particular instance, and is intended to illustrate the types of components that may exist in the aforementioned computer terminal.

[0042] In the above operating environment, this application provides a positioning method embodiment. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than that shown here.

[0043] Figure 2 This is a flowchart of a positioning method according to an embodiment of this application, such as... Figure 2 As shown, the method includes the following steps:

[0044] Step S202: Obtain first observation data collected by multiple GNSS terminals of global navigation satellite systems in the service area. The service area includes areas not covered by the reference station. The first observation data is used to solve for the ionospheric delay change, which is used to characterize the trend of ionospheric state change over time.

[0045] In step S202 above, the GNSS terminal is a device that receives and processes signals from the Global Navigation Satellite System, including but not limited to vehicle-mounted terminals, smartphones, and GNSS receivers for drones. The service area refers to the geographical area covered by the PPP-RTK service, including the area directly covered by the reference station and the area not covered by the reference station. The expansion of the service area is achieved by using data from crowdsourced GNSS terminals to construct the ionospheric model.

[0046] In some embodiments of this application, raw observation data (unprocessed observation values ​​directly received from satellites by GNSS terminals in the service area, including but not limited to pseudorange and carrier phase observation values) can be acquired from multiple GNSS terminals in the service area. The raw observation data is then preprocessed to obtain first observation data. Specifically, distributed GNSS terminals (such as vehicle-mounted terminals, smartphones, drones, etc.) can be used to collect GNSS pseudorange and carrier phase observation values ​​in real time. The GNSS terminals then transmit the crowdsourced data back to the PPP-RTK service platform via a 4G / 5G network. The PPP-RTK server performs real-time data filtering and processing on the received GNSS data.

[0047] It should be noted that preprocessing refers to a series of operations performed on the raw observation data to remove noise, errors, and biases, making it suitable for subsequent data analysis. The first observation data is obtained after preprocessing and can be used to calculate the ionospheric delay change. It is obtained by filtering and differential processing of the raw observation data, which can more accurately capture the dynamics of the ionosphere.

[0048] The ionospheric delay variation refers to the rate at which the influence of the ionosphere on the propagation time of GNSS signals changes over time. In some embodiments of this application, this parameter can be calculated using the geometrically distance-free carrier epoch difference method to construct a more accurate ionospheric model.

[0049] In some embodiments of this application, the ionospheric delay variation can be solved in the following way: The difference between the carrier phase observation value at a first frequency point and the carrier phase observation value at a second frequency point in the first observation data is determined to obtain the first carrier phase observation value, wherein the first frequency point and the second frequency point are different frequency points of the same satellite at the same epoch, and the first carrier phase observation value is used to eliminate the error term related to geometric distance between the GNSS terminal and the satellite; A difference operation is performed on the first carrier phase observation values ​​corresponding to the first epoch and the second epoch respectively to obtain the second carrier phase observation value, wherein the second carrier phase observation value is used to eliminate the influence of ionospheric residuals between the GNSS terminal and the satellite, wherein the epoch interval between the first epoch and the second epoch is less than a preset threshold; The ionospheric delay variation is determined based on the second carrier phase observation value.

[0050] It should be noted that the first carrier phase observation is obtained by subtracting the carrier phase observations of the same satellite at two different frequencies (the first frequency and the second frequency) within the same epoch. This difference helps eliminate errors related to geometric distance, such as satellite orbit errors, receiver clock errors, and tropospheric delay, thus focusing on the calculation of ionospheric delay. The second carrier phase observation is obtained by subtracting the first carrier phase observations from adjacent epochs. This method can further eliminate residual errors in the time dimension of ionospheric delay, improving the accuracy of the estimation of ionospheric delay variation. Furthermore, different frequencies within the same epoch refer to data observed by the receiver at different frequencies from the signal transmitted by the same satellite at the same time point (epoch). GNSS satellites typically use two or more different frequencies to transmit signals, such as the L1 frequency (1575.42 MHz) and L2 frequency (1227.60 MHz) in the GPS system. The signals transmitted at these frequencies carry different codes and information to improve positioning accuracy and reliability.

[0051] Specifically, a geometric free combination method can be adopted, that is, the difference between the carrier phase observation values ​​of the same satellite at different frequency points within the same epoch (corresponding to the formula (3) below) can be used to eliminate error terms related to geometric distance, such as satellite clock error, receiver clock error and tropospheric delay, so that the result mainly reflects the ionospheric delay.

[0052] Subsequently, the first carrier phase observations of the first epoch and the second epoch (epoch interval less than a preset threshold, such as 30s) are differentially analyzed again (corresponding to the following formula (5)). This step further improves the accuracy of the solution of the ionospheric delay change by eliminating the influence of the ionospheric residual between the two observations. In practical applications, this means that even if the ionospheric conditions change, such changes can be captured quickly and accurately, thereby updating the ionospheric model in a timely manner.

[0053] Based on the second carrier phase observation, the change in ionospheric delay can be calculated by using the mathematical relationship between frequency difference and carrier phase difference (as shown in formula (7) below). This step solves the uncertainty problem of ionospheric delay changing with time and provides an important basis for building a more accurate ionospheric model.

[0054] In some embodiments of this application, after performing a differential operation on the first carrier phase observation values ​​corresponding to the first epoch and the second epoch respectively to obtain the second carrier phase observation value, the following steps can also be performed: if the absolute value of the second carrier phase observation value is greater than the cycle slip detection threshold, the observation data corresponding to the first epoch and the second epoch are removed from the first observation data.

[0055] The cycle slip detection threshold is a preset numerical standard used to detect cycle slip phenomena in carrier phase observations, which are jumps in the integer counter caused by signal loss or poor signal quality. Observations exceeding this threshold are considered to contain cycle slips and need to be removed from the dataset.

[0056] To facilitate understanding of step S202 above, the following explanation is provided in conjunction with some specific embodiments. Specifically, the preprocessing can be implemented in the following ways:

[0057] (1) The PPP-RTK server needs to perform time synchronization and data integrity screening on the GNSS pseudorange observations and carrier phase observations uploaded by crowdsourcing users.

[0058] It should be noted that time synchronization ensures that the deviation between the crowdsourced data received by the PPP-RTK server and its own ionospheric modeling time reference is within an acceptable range. The time threshold can be set to 1 second for example. After time synchronization is satisfied, the data integrity is then screened. Considering the cost of GNSS antennas for crowdsourced users, the data integrity threshold can be set to 80% for example.

[0059] (2) The PPP-RTK server performs the first quality control on the screened data. For example, when the satellite elevation angle of the observation data is lower than 7° or the signal-to-noise ratio is lower than 30dB, the data is removed. Then, the pseudorange observations are removed for gross errors.

[0060] (3) Perform a second quality control on the remaining data, specifically:

[0061] GNSS data uploaded to the PPP-RTK service platform, including user data. Satellite observed In the pseudorange at the original frequency and carrier phase The observation equation for the observed values ​​(in meters) can be expressed as:

[0062] (1)

[0063] (2)

[0064] In the above formulas (1) and (2), the meanings of each parameter are as follows:

[0065] : The geometric distance between the receiver and the satellite, in meters;

[0066] Receiver clock error, in meters;

[0067] Satellite clock error, in meters;

[0068] : No. Ionospheric slack delay at each frequency, in meters;

[0069] : Tropospheric oblique delay, in meters;

[0070] : No. The receiver pseudorange hardware delay at a frequency, also known as the receiver pseudorange deviation, is measured in meters.

[0071] : No. The pseudorange hardware delay at a frequency is also called the satellite pseudorange offset, which is measured in meters.

[0072] : No. The wavelength of each frequency carrier phase observation, in meters;

[0073] : No. Integer ambiguity of a frequency carrier phase observation, in weeks;

[0074] The receiver phase hardware delay, also known as receiver phase deviation, is measured in weeks.

[0075] Satellite phase hardware delay, also known as satellite phase offset, is measured in weeks.

[0076] : No. Pseudorange measurement noise, pseudorange multipath error, and unmodeled error on pseudorange observations at various frequencies;

[0077] : No. Phase measurement noise, phase multipath error, and unmodeled error on each frequency phase observation.

[0078] Subsequently, the phase observation data at different frequency points of the same satellite observation at the same epoch are subtracted. The number of frequency points is not limited. For ease of understanding, the observation values ​​at the first and second frequency points are used as examples below. For clarity, the superscripts and subscripts of the formula letters are omitted as follows:

[0079]

[0080] (3)

[0081] In the above formula (3), The carrier phase value is a GF (Geometry-Free) combination. This represents the combined term of observation noise and multipath error. The meanings of the other expressions are as described above. This combination eliminates error terms related to geometric distance, such as satellite clock error, receiver clock error, and tropospheric delay. The combined term only includes ionospheric delay, integer ambiguity, observation noise, and multipath error.

[0082] in, and The following relationship exists:

[0083] (4)

[0084] In the above formula (4), and These are the frequencies of the first frequency point and the second frequency point, respectively.

[0085] By performing epoch-time difference on equation (3), we can obtain the following expression:

[0086] (5)

[0087] In other words, when the sampling rate of GNSS observations is less than or equal to 30s (as an example), the ionosphere can be considered to change relatively slowly in the time domain. Epoch difference can eliminate most of the influence of ionospheric residuals, and it can be considered that... Approximately equal to 0. Considering the characteristic that ambiguity remains constant throughout integer cycles, under the condition that the GNSS signal is continuously tracking and the ambiguity does not experience cycle slips, It is 0, so It only includes observation noise and multipath effect errors, and the carrier observations are less affected by multipath errors and observation noise. Therefore, an appropriate threshold (i.e., the cycle slip detection threshold) can be set for cycle slip detection. If the value exceeds the threshold, a cycle slip is considered to have occurred, and the GNSS observation for that epoch is discarded.

[0088] Once "clean" GNSS carrier phase observations are obtained, the epoch difference interval can be appropriately adjusted for carrier phase observations where cycle slips occur in continuous arc segments. (The time difference between two adjacent sets of observation data selected in the time series analysis), the high-precision ionospheric delay change can be obtained by using equation (5). For ease of description, the above preprocessed crowdsourced GNSS observation data will be referred to as non-reference station observation data (i.e., the first observation data).

[0089] It should be noted that the above preprocessing steps effectively eliminate error terms related to geometric distance (such as satellite clock error, receiver clock error, and tropospheric delay) through Geometry-Free (GF) combination and difference between previous and subsequent epochs. Furthermore, by setting a threshold for cycle slip detection, data points severely affected by multipath effects and observation noise are eliminated, thereby enabling the calculation of high-precision ionospheric delay variation.

[0090] In some embodiments of this application, based on the first observation data, PPP technology can also be used to obtain the precise coordinates of non-reference stations (i.e., GNSS terminals).

[0091] Step S204: Based on the first observation data and the second observation data collected by the reference station in the service area, ionospheric modeling is performed to obtain the ionospheric model, wherein the ionospheric model is used to characterize the relationship between ionospheric delay and ground position in the service area.

[0092] In step S204 above, ionospheric modeling is a process used to predict and describe the impact of the ionosphere on GNSS signal propagation. In this embodiment, it mainly utilizes first observation data (i.e., processed crowdsourced GNSS data) and second observation data (i.e., GNSS data from reference stations) to construct a model (ionospheric model) that accurately reflects the relationship between ionospheric delay and ground position within the service area, used to accurately predict the amount of ionospheric delay at a specific geographical location. It should be noted that the second observation data is data collected and processed by reference stations within the service area. This type of data is usually of higher quality and better stability, and is the main reference data for ionospheric modeling, that is, providing a benchmark for the ionospheric delay information of the GNSS terminal.

[0093] To address the challenge of constructing high-precision ionospheric models over vast areas with limited reference stations, ionospheric modeling can be performed on first observation data and second observation data from reference stations within the service area using the following method: The service area is divided into multiple grids, each grid including at least one of the following: a reference station and a GNSS terminal; the datasets corresponding to the first and second observation data are mapped to multiple grids to obtain grid observation data for each grid; ionospheric modeling is then performed based on the grid observation data for each grid, resulting in an ionospheric model for each grid, where the grid observation data includes at least one of the following: third observation data collected by reference stations within the grid and fourth observation data collected by GNSS terminals within the grid.

[0094] It should be noted that the third observation data is a further subdivision of the data from the reference station within each grid, that is, the observation data of the reference station within its own grid, which is used to refine the construction of the ionospheric model of that grid; the fourth observation data is similar to the third observation data, but for the crowdsourced GNSS terminals within the grid, it is the observation data of these terminals within their own grid, which is used to supplement the reference station data and enhance the coverage and robustness of the model.

[0095] Specifically, the service area can be divided into multiple grids, each with an ID number based on its location. This division method takes into account the spatial non-uniformity of the ionosphere, which is beneficial for constructing more accurate ionospheric models, especially in areas with complex terrain and uneven distribution of reference stations. Furthermore, finer-grained subdivisions can be made within larger grids, which are not limited here.

[0096] For the data within each grid (i.e., grid observation data), a polynomial model can be used to model the ionospheric delay. It should be noted that this polynomial model describes the variation in ionospheric delay based on the latitude and longitude difference between the grid center and the reference station or GNSS terminal. Specifically, the solution of the model coefficients depends on the quantity and quality of the observation data within the grid. The model parameters are optimized using the least squares method or Kalman filtering method to ensure the accuracy of the ionospheric model.

[0097] In some embodiments of this application, grid partitioning occurs in three scenarios: the grid contains only reference stations, the grid contains only non-reference stations (i.e., GNSS terminals), and the grid contains both reference and non-reference stations. Under these conditions, the ionospheric modeling methods will differ across different grids, specifically:

[0098] (1) Only reference stations exist in the grid.

[0099] In this case, ionospheric modeling can be performed directly using data from the reference station (i.e., the second observation data). Since the observation data from the reference station is usually of high quality, these data can be used first when modeling. For example, the difference between the latitude and longitude of the reference station and the center of the grid can be used to perform a two-dimensional second-order Taylor expansion on the slant delay of the single star ionosphere, and model it according to the following formula (6). Then, the polynomial coefficients can be solved by the least squares method or the Kalman filter method.

[0100] (2) Only non-reference stations exist in the grid.

[0101] When only non-reference station data is available within the grid, ionospheric modeling will rely on data uploaded from these terminals. For example, quality control of non-reference station data can be performed to ensure its validity and accuracy. This can also be done using a two-dimensional second-order Taylor expansion based on the latitude and longitude difference. However, considering the uncertainty of non-reference station data, additional error models or more robust statistical methods can be introduced, and then the least squares method or Kalman filtering method can be used to solve for the model parameters.

[0102] In some embodiments of this application, reference station data within a neighboring grid can also be used to assist in ionospheric modeling. This method is based on the assumption that ionospheric delay is spatially continuous and correlated, meaning that ionospheric conditions in adjacent regions are similar over a certain time scale. Specifically:

[0103] Identify the nearest neighboring grids, each containing a reference station. Collect ionospheric delay data from all reference stations within the selected neighboring grids; this data will serve as a reference baseline for grid ionospheric modeling. Using the data from the reference stations in the neighboring grids, combined with the location information of non-reference stations within the grid, perform spatial extrapolation or interpolation. It should be noted that when performing interpolation or extrapolation, the spatial correlation of ionospheric delay can also be considered. This means that the further a non-reference station is from the reference station, the greater the prediction error of its ionospheric delay may be. Therefore, a distance weighting factor can be introduced to reduce the influence of data from distant reference stations.

[0104] (3) The grid contains both reference stations and non-reference stations.

[0105] In this hybrid scenario, both reference station and non-reference station data are used for ionospheric modeling. Due to the different qualities of the two data sources, the differences in data quality need to be considered during modeling. For example, observational data from all reference and non-reference stations within the grid are collected and classified according to data quality; a two-dimensional second-order Taylor expansion is performed using the differences in latitude and longitude of the reference and non-reference stations relative to the grid center; when solving for the polynomial coefficients, weights can be assigned separately to the reference and non-reference station data, with reference station data being given a higher weight.

[0106] To address the spatial inhomogeneity and nonlinear variation of ionospheric delay, an ionospheric model corresponding to each grid can be constructed as follows: determine the first location information of the grid, which includes the longitude and latitude of the grid's center; determine the difference between the second location information of the reference station in the service area and the first location information; based on the difference, perform polynomial fitting on the ionospheric delay using a two-dimensional second-order Taylor expansion to obtain the ionospheric model corresponding to each grid.

[0107] It should be noted that the two-dimensional second-order Taylor expansion is used to approximate the ionospheric delay as a spatial function near the grid center. By expanding to the second derivative term, the spatial variation trend of the ionospheric delay can be described more accurately. This method can not only capture the ionospheric delay variation in a linear direction, but also approximately reflect nonlinear variations (such as curves or surfaces). The ionospheric model obtained from the above steps is constructed using a polynomial fitting method. It is a mathematical model describing the relationship between the ionospheric delay and the ground position within a specific grid. Each grid has its corresponding ionospheric model. These models are combined to cover the entire service area, providing atmospheric correction data for improving the accuracy of PPP-RTK positioning.

[0108] To facilitate understanding of the above process of ionosphere modeling, the following explanation will be provided with reference to some specific embodiments:

[0109] (1) First, the PPP-RTK server divides the ionospheric modeling area (i.e. the service area mentioned above) into grids.

[0110] Taking a 2.5°x2.5° latitude and longitude grid as an example, the grids are numbered with IDs. The first grid is denoted as IDi (i=1,2,3...n). Then, within grid IDi, smaller grids (i.e., subgrids) are divided with IDs. Here, a 0.25°x0.25° latitude and longitude grid is used as an example and numbered as idj (j=1,2,3...n).

[0111] (2) Set a radius threshold R, draw a circle with radius R at the center of the grid idj, and then record all reference stations and non-reference stations within the circle.

[0112] It should be noted that there are four possible scenarios: 1) There are only reference stations around idj; 2) There are only non-reference stations around idj; 3) There are both reference stations and non-reference stations around idj; 4) There are no stations around idj.

[0113] For the fourth case, continue the search by increasing the distance by 1.5 times (as an example) each time (that is, increase the search radius to 1.5R) until the first three cases occur.

[0114] (3) Polynomial modeling of the regional ionosphere is adopted, based on the latitude and longitude of the reference station. Relative to the latitude and longitude of each grid IDi center The difference ( , The two-dimensional second-order Taylor expansion of the ionospheric slant delay (i.e., ionospheric delay) of a single star is represented as follows:

[0115] (6)

[0116] In the above formula (6), Denotes the coefficients of the ionospheric slack delay polynomial, where This indicates items unrelated to the reference station. This indicates items related to the latitude and longitude of the reference station.

[0117] Subsequently, the least squares or Kalman filtering can be used to calculate the model parameters, i.e. the optimal set of polynomial coefficients, to construct a model that can accurately reflect the characteristics of the ionospheric slant delay distribution within the grid area. Through these model parameters, the PPP-RTK server can predict the ionospheric delay at any location (not limited to reference station and non-reference station locations).

[0118] Step S206: Position the GNSS terminal based on the predicted ionospheric delay and the change in ionospheric delay output by the ionospheric model.

[0119] In step S206 above, the ionospheric model includes grid division based on the service area and polynomial fitting results of the corresponding ionospheric delay and ground position relationship, which can predict the amount of ionospheric delay at a specific geographical location (i.e., predict the amount of ionospheric delay). The amount of ionospheric delay change refers to the amount that reflects the change of ionospheric delay over time, which is calculated by the second carrier phase observation value. It can help the positioning system to quickly update the ionospheric model in a short time to adapt to the rapid changes in the ionospheric state.

[0120] To effectively offset the impact of the ionosphere on positioning accuracy, GNSS terminals can be positioned using the following method: Ionospheric residual values ​​corresponding to the ionospheric model are determined, whereby the ionospheric residual values ​​characterize the difference between the predicted ionospheric delay and the measured ionospheric delay of the GNSS terminal, which is determined based on the ionospheric delay variation; the GNSS terminal is then positioned using the ionospheric model and the ionospheric residual values.

[0121] It should be noted that the predicted ionospheric delay is an estimated value based on the ionospheric model for the location of any GNSS terminal. It is calculated using model parameters and terminal location information and is used to compensate for the influence of the ionosphere on signal propagation speed. The ionospheric residual value is the difference between the measured and predicted ionospheric delay, revealing the deviation between the accuracy of the ionospheric model prediction and the actual situation. In PPP-RTK positioning, the ionospheric residual value can be used to further correct the positioning results and improve positioning accuracy.

[0122] The methods for predicting and measuring ionospheric delay, as well as the calculation of residual values, differ depending on the data source and the location scenario. For example:

[0123] (1) The reference station ionospheric modeling scheme focuses on using fixed reference station data for ionospheric modeling.

[0124] Reference stations typically have high-precision GNSS receivers and stable operating environments, which can provide high-quality observation data. This makes the calculation of the predicted ionospheric delay (first predicted ionospheric delay) and the measured ionospheric delay (first measured ionospheric delay) more accurate. This scheme is suitable for the initial stage of ionospheric modeling, i.e. when a stable and reliable model needs to be established.

[0125] (2) The crowdsourced GNSS terminal ionospheric modeling scheme utilizes a large amount of distributed GNSS terminal data, such as vehicle terminals, smartphones, drones, etc.

[0126] These terminals may move in different geographical locations and environments. Therefore, the acquisition of measured ionospheric delay and the calculation of predicted ionospheric delay need to take into account the continuity of time and the dynamic changes in terminal location. The second measured ionospheric delay is obtained by accumulating the second predicted ionospheric delay with the change in ionospheric delay. This method is more suitable for scenarios with rapid changes in terminal location and unstable ionospheric conditions, thus improving the real-time performance and adaptability of the model.

[0127] Specifically, for the reference station ionospheric modeling scheme, the ionospheric residual value corresponding to the ionospheric model can be determined in the following way: when using a reference station for ionospheric modeling, obtain the first measured ionospheric delay calculated by the reference station; use the ionospheric model to determine the first predicted ionospheric delay corresponding to the reference station; and determine the first ionospheric residual value based on the first measured ionospheric delay and the first predicted ionospheric delay.

[0128] It should be noted that the first measured ionospheric delay is the actual measured ionospheric delay obtained through precise processing and analysis based on the observation data of the reference station. In some embodiments of this application, the reference station collects observation data in real time through a GPS receiver, including pseudorange, carrier phase, etc., and then uses PPP technology and a specific ionospheric model to calculate the delay of the ionosphere on signal propagation (the first measured ionospheric delay).

[0129] When the service area is divided into grids, the predicted ionospheric delay (first predicted ionospheric delay) of each reference station location (relative to the grid center) can be calculated based on the ionospheric model. Specifically, the predicted ionospheric delay value of each reference station at the current time can be obtained through the polynomial model coefficients and the difference in latitude and longitude between the reference station and the grid center, so as to solve the problem of how to estimate the impact of the ionosphere on the reference station without direct measurement.

[0130] Finally, by comparing the first measured ionospheric delay with the first predicted ionospheric delay, the first ionospheric residual value can be calculated.

[0131] For the ionospheric modeling scheme of GNSS terminals, the ionospheric residual value corresponding to the ionospheric model can be determined in the following way: When using GNSS terminals for ionospheric modeling, the second predicted ionospheric delay at the first time corresponding to the GNSS terminal is determined using the ionospheric model; the change in ionospheric delay between the first and second times is determined, and the cumulative value of the change in ionospheric delay and the second predicted ionospheric delay is taken as the second measured ionospheric delay; the third predicted ionospheric delay at the second time corresponding to the GNSS terminal is determined using the ionospheric model; and the second ionospheric residual value is determined based on the second measured ionospheric delay and the third predicted ionospheric delay.

[0132] It should be noted that the second predicted ionospheric delay refers to the predicted value calculated based on the ionospheric model at the first time point, reflecting the model's estimate of the ionospheric delay at a specific time point. The second measured ionospheric delay is obtained by adding the change in ionospheric delay to the predicted ionospheric delay at the first time point, thus obtaining the actual ionospheric delay at the second time point. This value comprehensively reflects the true impact of the ionosphere at the second time point.

[0133] Specifically, at the first moment (such as the current moment), the predicted ionospheric delay value (the second predicted ionospheric delay) at that location can be calculated based on the constructed ionospheric model and the geographical location of the GNSS terminal. Subsequently, using the principle of difference between preceding and following epochs, the change in ionospheric delay between the first and second moments can be calculated using the above formula (5) to address the impact of dynamic changes in the ionosphere on positioning accuracy. By updating the ionospheric state in a timely manner, the adaptability and positioning accuracy of PPP-RTK in dynamic environments are improved.

[0134] At the second time point, the change in ionospheric delay from the first to the second time point is added to the predicted ionospheric delay at the first time point to obtain the second measured ionospheric delay at the second time point (i.e., the updated positional ionospheric delay). This method combines information from both model prediction and real-time observation, improving the accuracy of estimating the current state of ionospheric delay. Furthermore, at the second time point, the predicted ionospheric delay value for that time point is calculated again using the ionospheric model (the third predicted ionospheric delay).

[0135] Finally, the difference between the second measured ionospheric delay and the third predicted ionospheric delay at the second time point is calculated, which is the second ionospheric residual value.

[0136] In some embodiments of this application, in addition to dividing the service area into multiple grids, each grid can also be divided into multiple subgrids, each subgrid including at least one of the following: a reference station and a GNSS terminal. Based on this, the GNSS terminal can be located by: obtaining all third ionospheric residual values ​​in each subgrid, wherein the third ionospheric residual values ​​include the ionospheric residual values ​​of the reference station and the ionospheric residual values ​​of the GNSS terminal in the subgrid; determining the fourth ionospheric residual values ​​corresponding to multiple vertices of the subgrid based on the third ionospheric residual values; and locating the GNSS terminal using an ionospheric model and the fourth ionospheric residual values.

[0137] A grid is the basic unit for dividing a service area in a PPP-RTK positioning system, while a subgrid is a finer geographical division within the grid, used to improve the spatial resolution of ionospheric modeling. This division helps to capture the spatial variability of the ionosphere more accurately, which is especially important for improving the accuracy of PPP-RTK positioning in complex terrain or urban environments.

[0138] Specifically, ionospheric residual values ​​(i.e., the first and second ionospheric residual values ​​mentioned above) of all reference stations and GNSS terminals located within each subgrid can be collected. Within each subgrid, ionospheric residual values ​​at the four vertices of the subgrid are calculated using inverse distance weighted interpolation or other spatial interpolation techniques to form the fourth ionospheric residual value. This addresses the uncertainty and discontinuity of ionospheric spatial variations. Through interpolation, estimates of ionospheric influence can be provided at vertex locations without direct observation data, thus refining the model and achieving high-precision positioning within continuous areas. Subsequently, based on the constructed ionospheric model and the fourth ionospheric residual value, the positioning of GNSS terminals located within the subgrid is corrected. By combining global prediction and local residual information, the discrepancy between model prediction and actual observation is resolved, improving the accuracy and reliability of PPP-RTK positioning. Especially within the subgrid, the positioning results can be refined based on data from the nearest reference station and observations from neighboring GNSS terminals.

[0139] It should be noted that the multiple vertices of a subgrid include the four corner points on the boundary of each subgrid. These vertices are important reference points in spatial interpolation methods, used to calculate and estimate the ionospheric delay at various locations within the subgrid. Since the ionospheric delay within the grid may not be uniform but varies with spatial location, the ionospheric residual values ​​at the four vertices can be used to construct a spatial distribution function of the ionospheric delay through spatial interpolation techniques (such as inverse distance-weighted interpolation (IDW), Kriging interpolation, etc.). Thus, even for any point within the grid without direct observation data, its ionospheric delay can be estimated based on the ionospheric residual values ​​at the vertices, thereby providing more accurate ionospheric correction information for the GNSS terminal at that location.

[0140] To facilitate understanding of the above positioning process, the following explanation will be provided with reference to some specific embodiments. Based on the embodiments of the ionosphere modeling process described above, after obtaining the ionosphere model parameters, the following steps can also be performed:

[0141] (1) When using a reference station for ionospheric modeling, the reference station will calculate its own ionospheric delay, denoted as For non-reference stations, the ionospheric delay at time t can be obtained by using the calculated model coefficients at time t, and denoted as the ionospheric value. For t+ The ionosphere after time t ( (t>30s), the high-precision ionospheric delay change can be obtained using the above embodiments, and the expression is as follows:

[0142] (7)

[0143] For ease of subsequent expression, t+ will now be used uniformly. At epoch t, ​​the ionospheric delay output by both the reference station and the non-reference station is denoted as . It should be noted that the ionosphere calculated by non-reference stations is calculated by equation (7), while the ionosphere of reference stations is generally calculated by PPP.

[0144] (2) Calculate the ionospheric residual values ​​at the four vertices of grid idj. Based on the threshold R set in the above embodiment, calculate all ionospheric residual values ​​within this range. The expression is as follows:

[0145] (8)

[0146] Then, the ionospheric residuals at the four vertices of the grid idj are calculated using spatial interpolation, specifically employing an inverse distance weighted method. For the target grid point g, the estimated value is... for:

[0147] (9)

[0148] in, It is the ionospheric residual value from the k-th reference station or non-reference station; is the distance from the k-th reference station or non-reference station to the target grid point g; p is the power parameter (here, it is taken as 2), and n is the total number of reference stations and non-reference stations used for interpolation.

[0149] In some embodiments of this application, considering the difference in the quality of observations from reference stations and non-reference stations, a weight adjustment factor can be added when both reference stations and non-reference stations are present. The specific weight value is not limited here.

[0150] Subsequently, the PPP-RTK server can send the calculated ionospheric model coefficients, ionospheric residual values ​​of grid points, grid information, and satellite clock bias, orbit, and phase deviation information to the PPP-RTK user. Upon receiving the correction information from PPP-RTK, the user can then perform PPP-RTK high-precision positioning. Specifically:

[0151] After receiving the correction information sent by the PPP-RTK server, the user device (such as an in-vehicle navigation system or smartphone) performs location tracking through the following steps:

[0152] (1) Determine the location grid: The user-side calculation software first determines which grid the user's current location belongs to based on the received grid information.

[0153] (2) Ionospheric correction: Using the ionospheric model coefficients and the ionospheric residual values ​​at the user's grid location, the ionospheric delay correction value for the current location is calculated. By combining the model prediction and actual observation deviation, the most accurate ionospheric information is provided.

[0154] (3) Satellite status correction: The user terminal software will use the received satellite clock error, orbit and phase deviation information to correct the signal received from the satellite and eliminate errors in the signal propagation and reception process.

[0155] (4) PPP-RTK calculation: The user terminal software combines the received GNSS observation data (pseudorange, carrier phase) with the correction information (ionospheric delay correction value, satellite state correction value, etc.) and performs ambiguity fixing and positioning calculation through the PPP-RTK calculation algorithm to finally obtain a high-precision position result.

[0156] It should be noted that the ionospheric model coefficients are used by the user to predict and correct ionospheric delay based on their location. After receiving these coefficients, the user can calculate the estimated ionospheric delay of their current location using formula (6) based on their geographical location (longitude and latitude), thereby accurately compensating for the ionospheric effect in the positioning solution. For example, the user-side solution software will use the received ionospheric model coefficients, combined with its current latitude and longitude coordinates, to calculate the predicted ionospheric delay value corresponding to the location, and then subtract this delay value during the positioning solution process to obtain a more accurate positioning result.

[0157] The ionospheric residual values ​​of grid points reflect the deviation between model predictions and actual observations. By obtaining residual values ​​near the user's location through spatial interpolation methods, the deficiencies of the ionospheric model can be further corrected, improving positioning accuracy. For example, the user can use the ionospheric residual values ​​of grid points through spatial interpolation (such as the inverse distance weighting method) to estimate the ionospheric delay correction for its current location. This correction will be added to the ionospheric model prediction to update the ionospheric delay estimate, thereby improving the accuracy of positioning solutions.

[0158] Grid information, including grid ID, center latitude and longitude, and grid size, helps users determine their grid location, facilitating the accurate application of ionospheric model coefficients and residual values. Specifically, the user-side calculation software quickly locates the user's grid based on the received grid information, then uses the ionospheric model coefficients and corresponding residual values ​​to ensure that the most relevant and accurate ionospheric information is used during the location calculation.

[0159] Satellite clock bias, orbit, and phase deviation information are used to correct errors caused by changes in the satellite's own state (such as orbital deviation and clock bias fluctuations) during satellite signal propagation, as well as phase deviations during signal reception. Specifically, the user-end positioning calculation combines the received satellite state information, including satellite clock bias, orbital deviation, and phase deviation, to accurately correct the satellite signal propagation time, eliminating the impact of these errors on positioning and thus achieving more accurate positioning.

[0160] Through steps S202 to S206 above, an integrated processing approach is adopted. By comprehensively utilizing the first observation data provided by all GNSS terminals within the service area, including the coverage area and blind zone of the reference station, and combining it with the second observation data of the reference station, a comprehensive ionospheric model is obtained, which can accurately describe the relationship between ionospheric delay and geographical location. Then, by using the ionospheric delay predicted by the model and the ionospheric delay change, the position of any GNSS terminal is accurately corrected. This achieves the goal of expanding the coverage of PPP-RTK services and improving positioning accuracy. Thus, it realizes the technical effect of providing high-precision ionospheric information and positioning services in areas lacking reference stations. This solves the technical problem that the atmospheric model of the PPP-RTK service system used in related technologies depends on a fixed reference station, resulting in the inability to provide accurate ionospheric information to users outside the coverage area of ​​the reference station.

[0161] Figure 3 This is a schematic diagram of the overall process of a positioning method according to an embodiment of this application, as shown below. Figure 3 As shown, in some embodiments of this application, the following steps are included:

[0162] Step S302: Crowdsourced GNSS data collection (vehicle-mounted terminals, mobile phones, drones, and other equipment containing GNSS receiving modules receive GNSS observation data).

[0163] Step S304: Real-time uploading of crowdsourced GNSS data (uploading the collected GNSS data to the PPP-PTK service platform in real time via the Internet 4G / 5G).

[0164] Step S306: The PPP-PTK server filters and performs quality control on the crowdsourced GNSS data, and constructs epoch-differenced geometric phase-free observations to calculate high-precision ionospheric delay variation.

[0165] Step S308: The PPP-PTK server divides the ionosphere into regions IDi and grid idj within the region. Based on the set threshold, the reference station and non-reference stations are matched one by one with the grid idj within the region. Ionospheric modeling is performed using the reference station within region IDi. At time t: the ionospheric value calculated by the non-reference station related to grid idj is used as the initial value. At subsequent times, the high-precision delay change calculated in S306 plus the ionospheric value calculated by the model at time t is used as the ionospheric value of the subsequent non-reference station. Finally, the ionospheric residual value of each grid idj is calculated.

[0166] Step S310: The PPP-PTK server sends the satellite orbit, clock error, satellite phase deviation, satellite code deviation, ionospheric grid IDi ionospheric model coefficients, ionospheric residual values ​​of grid idj, and grid information to the user, who then performs PPP-PTK high-precision positioning.

[0167] It should be noted that the above Figure 3 For the specific implementation of each step, please refer to Figure 2 The various embodiments of the positioning method shown will not be described in detail here.

[0168] This application embodiment integrates crowdsourced GNSS observation data and uses a geometrically distance-free carrier epoch difference method to calculate high-precision ionospheric delay variables. Combined with an ionospheric model established by a reference station, a high-precision ionospheric model is established in areas not covered by the reference station. This enables the PPP-RTK server to provide users with high-precision ionospheric information in areas without a reference station but with crowdsourced GNSS observation data. Users can then achieve high-precision PPP-RTK positioning. This method can save the high cost of establishing reference stations required for PPP-RTK atmospheric modeling, and also improves the reliability and robustness of the PPP-RTK service system's positioning service in areas without reference stations.

[0169] Figure 4 This is a structural diagram of a positioning device according to an embodiment of this application, such as... Figure 4 As shown, the device includes:

[0170] The acquisition module 402 is used to acquire first observation data collected by multiple GNSS terminals of global navigation satellite systems in the service area, wherein the service area includes areas not covered by the reference station. The first observation data is used to solve for the ionospheric delay change, and the ionospheric delay change is used to characterize the trend of ionospheric state change over time.

[0171] Modeling module 404 is used to perform ionospheric modeling based on the first observation data and the second observation data collected by the reference station in the service area to obtain an ionospheric model, wherein the ionospheric model is used to characterize the relationship between ionospheric delay and ground position in the service area.

[0172] The positioning module 406 is used to locate the GNSS terminal based on the predicted ionospheric delay and the change in ionospheric delay output by the ionospheric model. It should be noted that... Figure 4 The positioning device shown is used to perform Figure 2 The positioning method shown, therefore Figure 2 The relevant explanations in the positioning method also apply to Figure 4 The positioning device shown will not be described in detail here.

[0173] This application also provides an electronic device, which includes a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute steps that implement the positioning methods in various embodiments of this application.

[0174] This application also provides a non-volatile storage medium including a stored computer program, wherein the device containing the non-volatile storage medium executes the steps of the positioning method in various embodiments of this application by running the computer program.

[0175] This application also provides a computer program product, including computer instructions that, when executed by a processor, implement the positioning method steps in various embodiments of this application.

[0176] This application also provides a computer program that, when executed by a processor, implements the positioning method steps in various embodiments of this application.

[0177] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0178] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0179] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0180] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0181] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0182] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0183] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A positioning method, characterized in that, include: First observation data collected by multiple GNSS terminals of Global Navigation Satellite System in the service area is acquired, wherein the service area includes areas not covered by the reference station, and the first observation data is used to solve for the ionospheric delay change, wherein the ionospheric delay change is used to characterize the trend of ionospheric state change over time; Ionospheric modeling is performed based on the first observation data and the second observation data collected by reference stations in the service area to obtain an ionospheric model, wherein the ionospheric model is used to characterize the relationship between ionospheric delay and ground position in the service area; The GNSS terminal is located based on the predicted ionospheric delay output by the ionospheric model and the change in ionospheric delay.

2. The method according to claim 1, characterized in that, The ionospheric delay change is determined in the following way: The difference between the carrier phase observation value of the first frequency point and the carrier phase observation value of the second frequency point in the first observation data is determined to obtain the first carrier phase observation value. The first frequency point and the second frequency point are different frequency points of the same satellite at the same epoch. The first carrier phase observation value is used to eliminate the error term related to geometric distance between the GNSS terminal and the satellite. The first carrier phase observation values ​​corresponding to the first epoch and the second epoch are differentially processed to obtain the second carrier phase observation value. The second carrier phase observation value is used to eliminate the influence of ionospheric residual between the GNSS terminal and the satellite. The epoch interval between the first epoch and the second epoch is less than a preset threshold. The amount of ionospheric delay change is determined based on the second carrier phase observation value.

3. The method according to claim 2, characterized in that, After performing a differential operation on the first carrier phase observations corresponding to the first epoch and the second epoch respectively to obtain the second carrier phase observation, the method further includes: if the absolute value of the second carrier phase observation is greater than the cycle slip detection threshold, removing the observation data corresponding to the first epoch and the second epoch from the first observation data.

4. The method according to claim 1, characterized in that, Ionospheric modeling is performed based on the first observation data and the second observation data from reference stations in the service area to obtain an ionospheric model, including: The service area is divided into multiple grids, wherein each grid includes at least one of the following: a reference station and a GNSS terminal; The data sets corresponding to the first observation data and the second observation data are mapped to the plurality of grids to obtain grid observation data corresponding to each grid. Ionospheric modeling is performed based on grid observation data for each grid to obtain an ionospheric model corresponding to each grid. The grid observation data includes at least one of the following: third observation data collected by reference stations in the grid, and fourth observation data collected by GNSS terminals in the grid.

5. The method according to claim 4, characterized in that, Ionospheric modeling is performed based on the grid observation data corresponding to each grid, resulting in an ionospheric model for each grid, including: Determine the first location information of the grid, wherein the first location information includes the longitude and latitude of the center location of the grid; Determine the difference between the second location information and the first location information of the reference station in the service area; Based on the difference, a two-dimensional second-order Taylor expansion is used to perform polynomial fitting on the ionospheric delay to obtain the ionospheric model corresponding to each grid.

6. The method according to claim 1, characterized in that, The GNSS terminal is located based on the predicted ionospheric delay output by the ionospheric model and the change in ionospheric delay, including: Determine the ionospheric residual value corresponding to the ionospheric model, wherein the ionospheric residual value is used to characterize the difference between the predicted ionospheric delay and the measured ionospheric delay of the ionospheric model, and the measured ionospheric delay of the GNSS terminal is determined based on the ionospheric delay change. The ionospheric model and the ionospheric residual values ​​are used to locate the GNSS terminal.

7. The method according to claim 6, characterized in that, Determining the ionospheric residual value corresponding to the ionospheric model includes: When using a reference station for ionospheric modeling, the first measured ionospheric delay calculated by the reference station is obtained; The ionospheric model is used to determine the first predicted ionospheric delay corresponding to the reference station; The first ionospheric residual value is determined based on the first measured ionospheric delay and the first predicted ionospheric delay.

8. The method according to claim 6, characterized in that, Determining the ionospheric residual value corresponding to the ionospheric model includes: When using the GNSS terminal for ionospheric modeling, the ionospheric model is used to determine the second predicted ionospheric delay at the first moment corresponding to the GNSS terminal. Determine the ionospheric delay change between the first time point and the second time point, and take the cumulative value of the ionospheric delay change and the second predicted ionospheric delay as the second measured ionospheric delay. The ionospheric model is used to determine the third predicted ionospheric delay corresponding to the second time moment of the GNSS terminal; The second ionospheric residual value is determined based on the second measured ionospheric delay and the third predicted ionospheric delay.

9. The method according to claim 4, characterized in that, Each of the grids includes multiple subgrids, and each subgrid includes at least one of the following: a reference station, a GNSS terminal; positioning the GNSS terminal based on the predicted ionospheric delay output by the ionospheric model and the change in ionospheric delay includes: Obtain all third ionospheric residual values ​​in each of the subgrids, wherein the third ionospheric residual values ​​include the ionospheric residual values ​​of the reference stations in the subgrids and the ionospheric residual values ​​of the GNSS terminals in the subgrids; The fourth ionospheric residual values ​​corresponding to multiple vertices of the subgrid are determined based on the third ionospheric residual value. The GNSS terminal is located using the ionospheric model and the fourth ionospheric residual value.

10. A positioning device, characterized in that, include: The acquisition module is used to acquire first observation data collected by multiple GNSS terminals of Global Navigation Satellite System in the service area, wherein the service area includes areas not covered by the reference station, and the first observation data is used to solve for the ionospheric delay change, wherein the ionospheric delay change is used to characterize the change trend of the ionospheric state over time. The modeling module is used to perform ionospheric modeling based on the first observation data and the second observation data collected by the reference station in the service area to obtain an ionospheric model, wherein the ionospheric model is used to characterize the relationship between ionospheric delay and ground position in the service area; The positioning module is used to locate the GNSS terminal based on the predicted ionospheric delay output by the ionospheric model and the change in ionospheric delay.

11. An electronic device, characterized in that, include: A memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute the positioning method according to any one of claims 1 to 9.

12. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored computer program, wherein the device containing the non-volatile storage medium executes the positioning method according to any one of claims 1 to 9 by running the computer program.

13. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the positioning method according to any one of claims 1 to 9.

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