Ionospheric monitoring method and device using crowd-sourced gnss data, equipment and medium

Abstract

An ionosphere monitoring method, device and equipment and medium using crowd-sourced GNSS data, the method comprising: obtaining inter-station single-difference ionosphere slant delay at crowd-sourced GNSS users and static reference stations, and calculating absolute ionosphere slant delay of the crowd-sourced GNSS users; obtaining line-of-sight direction total electron content of the crowd-sourced GNSS users through the absolute ionosphere slant delay of the crowd-sourced GNSS users; taking absolute ionosphere slant delay at the static reference stations as a reference, calculating absolute ionosphere slant delay of the crowd-sourced GNSS user group, and generating line-of-sight direction total electron content distribution map of the crowd-sourced GNSS user group to monitor the ionosphere. The application enhances the spatial coverage of the static reference station through the dynamic GNSS user group, improves the spatial resolution of the ionosphere discrete sampling information, and uses the static reference station to provide high-precision reference for ionosphere modeling of the dynamic GNSS user group, thereby forming a complementary enhancement mechanism of GNSS dynamic and static observation data, and finally cooperatively improving the spatial resolution and accuracy of ionosphere monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ionospheric monitoring, and in particular to an ionospheric monitoring method and device using crowdsourcing GNSS data, equipment and medium. BACKGROUND

[0002] The ionosphere, as an important part of the space between the earth and the sun, its fine monitoring directly affects the performance of GNSS navigation and positioning services, and provides support for low-altitude economy, space weather monitoring and other applications. With the large-scale popularization and performance improvement of Beidou / GNSS terminals, the widely distributed crowdsourcing GNSS data provides an unprecedented opportunity for ionospheric monitoring research. However, the traditional ionospheric monitoring method relies on the continuous observation of phase data in the time domain, and it is difficult to fully tap the potential of crowdsourcing GNSS users to extract high-precision ionospheric information in a short observation window.

[0003] High-precision, high-spatial-resolution ionospheric delay correction information plays an important role in ensuring the accuracy and efficiency of GNSS positioning, timing, and ionospheric space weather monitoring applications. In recent years, with the rapid growth of demand for high-precision spatio-temporal information in emerging fields such as low-altitude economy and intelligent driving, and the large-scale popularization of Beidou / GNSS terminals, the massive crowdsourcing GNSS observation data has brought new opportunities and challenges for the construction of fine ionospheric models. On the one hand, the collaborative processing of crowdsourcing GNSS data can provide more widely distributed and higher spatial resolution ionospheric delay discrete sampling information, which will further improve the performance of GNSS precise positioning and ionospheric monitoring; on the other hand, different crowdsourcing GNSS user groups have significant differences in hardware performance, behavior patterns, spatial distribution, data quality, and many other aspects, and the use of crowdsourcing GNSS data will generate many new scientific problems that need further research. At present, China's Beidou system is in a critical period of high-quality development and large-scale application, and higher and more urgent demands are put forward for ionospheric delay correction accuracy and spatial resolution. Under this background, in view of the uneven observation quality of crowdsourcing GNSS data, the ionospheric delay solving and modeling method based on crowdsourcing GNSS data is expanded, and combined with the existing static GNSS station network data, a high-quality ionospheric delay error correction service is realized, which not only meets the demand for the widespread application of Beidou / GNSS precise positioning technology, but also is an important technical means to improve the performance of ionospheric monitoring in the future.

[0004] The core challenge of efficient and accurate utilization of crowdsourcing GNSS data is how to effectively ensure the accuracy and reliability of ionospheric information obtained by single-station users. Current mainstream GNSS ionospheric delay extraction methods, such as PPP and CCL, are mainly designed for static reference station networks with good observation quality. However, there are two major limitations in the implementation process: first, the parameter convergence process relies on long-term continuous phase observations. This characteristic makes it difficult to fully utilize high-quality short-term observation data in dynamic scenarios; second, the estimated ionospheric slant delay often has systematic bias, making it difficult for users to establish reliable indicators to determine whether the obtained ionospheric information is accurate and reliable. Due to the quality fluctuations and spatio-temporal heterogeneity of crowdsourcing GNSS data, traditional methods are difficult to efficiently integrate and utilize intermittent high-quality crowdsourcing observation data, thus failing to meet the needs of ionospheric fine monitoring. SUMMARY

[0005] The purpose of the present application is to overcome the above-mentioned defects and problems existing in the prior art, and to provide an ionospheric monitoring method, device, equipment and medium using crowdsourcing GNSS data, forming a complementary enhancement mechanism for GNSS dynamic and static observation data, and synergistically improving the spatial resolution and accuracy of ionospheric monitoring.

[0006] To achieve the above purpose, the technical solution of the present application is:

[0007] In a first aspect, the present application provides an ionospheric monitoring method using crowdsourcing GNSS data, comprising:

[0008] obtaining the inter-station single-difference ionospheric slant delay at the crowdsourcing GNSS user and the static reference station, and calculating the absolute ionospheric slant delay of the crowdsourcing GNSS user; obtaining the total electron content in the line-of-sight direction of the crowdsourcing GNSS user from the absolute ionospheric slant delay of the crowdsourcing GNSS user;

[0009] using the absolute ionospheric slant delay at the static reference station as a reference, calculating the absolute ionospheric slant delay of the crowdsourcing GNSS user group, and generating the total electron content distribution map in the line-of-sight direction of the crowdsourcing GNSS user group to monitor the ionosphere.

[0010] The absolute ionospheric slant delay of the crowdsourcing GNSS user is for:

[0011]

[0012] wherein, represents the absolute ionospheric slant delay at the static reference station; represents the inter-station single-difference ionospheric slant delay at the crowdsourcing GNSS user and the static reference station.

[0013] The line-of-sight total electron content of the crowd-sourced GNSS user is obtained by crowd-sourcing absolute ionospheric slant delays of the crowd-sourced GNSS user, comprising:

[0014]

[0015] In the formula, A represents the conversion constant of total electron number unit and length; STEC represents the line-of-sight total electron content; unit represents the measurement unit; m represents meter; TECu represents the total electron number unit; f1 and f2 respectively represent two frequencies of carrier phase.

[0016] The absolute ionospheric slant delay at the static reference station is obtained by the following method:

[0017] An estimated biased ionospheric slant delay at the static reference station is obtained.

[0018] The ionospheric vertical electron concentration total content of the static reference station is separated from the differential code bias, and the absolute ionospheric slant delay at the static reference station is calculated based on the ionospheric vertical electron concentration total content.

[0019] The estimated biased ionospheric slant delay at the static reference station is obtained by the following formula:

[0020]

[0021] In the formula, represents the estimated biased quantity at the static reference station; represents the absolute ionospheric slant delay at the static reference station; c represents the speed of light; Δb sat represents the differential code bias of the satellite; Δb r represents the differential code bias of the receiver; ε represents the measurement noise of the ionospheric total electron content and the differential code bias.

[0022] The absolute ionospheric slant delay at the static reference station is obtained by the following formula:

[0023]

[0024] In the formula, STEC represents the line-of-sight total electron content; represents the vertical electron concentration total content at the ionospheric piercing point ; M(z) represents the projection function; λ and z respectively represent longitude, latitude and elevation angle; unit represents the measurement unit; TECu represents the total electron number unit; A represents the conversion constant of total electron number unit and length; m represents meter; f1 and f2 respectively represent two frequencies of carrier phase.

[0025] The inter-station single-difference ionospheric slant delay at the crowd-sourced GNSS user and the static reference station is obtained, comprising:

[0026] The triangulation network is constructed by the double static reference stations and each crowdsourcing GNSS user, and the inter-station single-difference ionospheric slant delay between the crowdsourcing GNSS user and the static reference station is obtained by using the ambiguity closure error test in the triangulation network.

[0027] In the second aspect, the application provides an ionospheric monitoring device using crowdsourcing GNSS data, which is applied to the method described above, and the device comprises:

[0028] The STEC obtaining module is used for obtaining the inter-station single-difference ionospheric slant delay between the crowdsourcing GNSS user and the static reference station, and calculating the absolute ionospheric slant delay of the crowdsourcing GNSS user; and the line-of-sight direction total electron content of the crowdsourcing GNSS user is obtained by the absolute ionospheric slant delay of the crowdsourcing GNSS user.

[0029] The STEC distribution map obtaining module is used for taking the absolute ionospheric slant delay at the static reference station as a reference, calculating the absolute ionospheric slant delay of the crowdsourcing GNSS user group, and generating the line-of-sight direction total electron content distribution map of the crowdsourcing GNSS user group, so as to monitor the ionosphere.

[0030] In the third aspect, the application provides an ionospheric monitoring device using crowdsourcing GNSS data, which comprises a memory and a processor.

[0031] The memory is used for storing computer program codes and transmitting the computer program codes to the processor.

[0032] The processor is used for executing the method described above according to the instructions in the computer program codes.

[0033] In the fourth aspect, the application provides a computer readable storage medium, and the computer readable storage medium stores computer programs, and the computer programs are executed by the processor to realize the method described above.

[0034] Compared with the prior art, the application has the following beneficial effects:

[0035] In the ionospheric monitoring method, device, equipment and medium using crowd-sourced GNSS data provided by the application, the inter-station single-difference ionospheric slant delay at the crowd-sourced GNSS user and the static reference station is first obtained, and the absolute ionospheric slant delay of the crowd-sourced GNSS user is calculated; then the total electron content in the line-of-sight direction of the crowd-sourced GNSS user is obtained through the absolute ionospheric slant delay of the crowd-sourced GNSS user; then the absolute ionospheric slant delay of the crowd-sourced GNSS user group is calculated by taking the absolute ionospheric slant delay at the static reference station as a reference, and the total electron content distribution map in the line-of-sight direction of the crowd-sourced GNSS user group is generated, so as to provide the total electron content information in the line-of-sight direction with higher spatial resolution for fine ionospheric monitoring. The method enhances the spatial coverage of the static reference station through the dynamic GNSS user group, improves the spatial resolution of the ionospheric discrete sampling information, simultaneously provides a high-precision reference for the ionospheric modeling of the dynamic GNSS user group by the static reference station, and then forms a complementary enhancement mechanism of the GNSS dynamic and static observation data, and finally cooperatively improves the spatial resolution and accuracy of the ionospheric monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 FIG. 1 is a flowchart of the ionospheric monitoring method using crowd-sourced GNSS data provided by the application.

[0037] Figure 2 FIG. 2 is a European station network distribution map provided by the embodiment of the application.

[0038] Figure 3 FIG. 3 is a structural block diagram of the ionospheric monitoring device using crowd-sourced GNSS data provided by the application.

[0039] Figure 4 FIG. 4 is a structural block diagram of the ionospheric monitoring equipment using crowd-sourced GNSS data provided by the application. DETAILED DESCRIPTION

[0040] The application will be further described in detail in combination with the description of the accompanying drawings and specific embodiments.

[0041] Referring to Figure 1 The application provides an ionospheric monitoring method using crowd-sourced GNSS data, comprising:

[0042] S1, obtaining the inter-station single-difference ionospheric slant delay at the crowd-sourced GNSS user and the static reference station, and calculating the absolute ionospheric slant delay of the crowd-sourced GNSS user; obtaining the total electron content in the line-of-sight direction of the crowd-sourced GNSS user through the absolute ionospheric slant delay of the crowd-sourced GNSS user;

[0043] S2, taking the absolute ionospheric slant delay at the static reference station as a reference, calculating the absolute ionospheric slant delay of the crowd-sourced GNSS user group, and generating a line-of-sight total electron content distribution map of the crowd-sourced GNSS user group to monitor the ionosphere.

[0044] The present application is based on the unbiased inter-station single-difference ionosphere, and extends the crowd-sourced RTK from precise positioning application to ionosphere monitoring field. Specifically, high-precision unbiased inter-station single-difference ionosphere delay is extracted by crowd-sourced RTK technology, and a crowd-sourced ionosphere delay correction distribution map is constructed. Then, the absolute ionospheric slant delay separated by the traditional ionosphere modeling method at the static reference station is taken as a reference to calculate the absolute ionospheric slant delay of the dynamic user group, and a STEC distribution map within the crowd-sourced RTK service range is constructed, thereby providing higher spatial resolution STEC information for fine ionosphere monitoring. This method can efficiently utilize the short-time high-quality observation data of the crowd-sourced GNSS user, enhance the spatial coverage of the static reference station by the dynamic user group, improve the spatial resolution of the discrete sampling information of the ionosphere, and provide high-precision reference for the ionosphere modeling of the dynamic user group by the static reference station, thereby forming a complementary enhancement mechanism of GNSS dynamic / static observation data, and finally synergistically improving the spatial resolution and accuracy of ionosphere monitoring.

[0045] Further, the absolute ionospheric slant delay of the crowd-sourced GNSS user is :

[0046]

[0047] In the formula, represents the absolute ionospheric slant delay at the static reference station; represents the inter-station single-difference ionospheric slant delay at the crowd-sourced GNSS user and the static reference station.

[0048] According to the error propagation law, the accuracy of the absolute ionospheric slant delay of the crowd-sourced GNSS user will be slightly lower than the accuracy of the absolute ionospheric slant delay at the static reference station .

[0049] Further, the line-of-sight total electron content of the crowd-sourced GNSS user obtained by the absolute ionospheric slant delay of the crowd-sourced GNSS user comprises:

[0050]

[0051] In the formula, A represents the conversion constant of total electron number unit and length, which is related to the signal frequency; STEC represents the line-of-sight total electron content; unit represents the unit of measurement; m represents meter; TECu represents the total electron number unit; f1 and f2 respectively represent two frequencies of the carrier phase.

[0052] Furthermore, the absolute ionospheric slant delay at the static reference station is obtained by the following method:

[0053] Obtain biased ionospheric slant delay estimates at static reference stations;

[0054] The total vertical electron concentration of the ionosphere and the differential code deviation of the static reference station are separated, and the absolute ionospheric slant delay at the static reference station is calculated based on the total vertical electron concentration of the ionosphere.

[0055] Specifically, a single-station ionospheric modeling method is used to separate the total vertical electron concentration and differential code deviation of the ionospheric at the static reference station. Based on the obtained total vertical electron concentration, the total electron concentration and absolute ionospheric slant delay of each satellite at the static reference station can be obtained by using a projection function.

[0056] Furthermore, the biased ionospheric slant delay estimate at the static reference station is obtained by the following formula:

[0057]

[0058] In the formula, This represents the biased estimator at the static reference station; The absolute ionospheric slack delay is represented at the static reference station; c represents the speed of light; Δb sat Indicates the differential code offset of the satellite; Δb r ε represents the differential code bias of the receiver; ε represents the measurement noise of the total electron content of the ionosphere and the differential code bias.

[0059] Furthermore, the absolute ionospheric slant delay at the static reference station is obtained by the following formula:

[0060]

[0061] In the formula, STEC represents the total electron content in the line of sight direction; Indicates the ionospheric puncture point The total vertical electron concentration at a given location; M(z) represents the projection function; λ and z represent longitude, latitude, and altitude angle, respectively; unit represents the unit of measurement; TECu represents the unit of total electron count; A represents the conversion constant between the unit of total electron count and length; m represents the meter; f1 and f2 represent the two frequencies of the carrier phase, respectively.

[0062] Furthermore, obtaining the inter-station single-difference ionospheric skew delay between the crowdsourced GNSS user and the static reference station includes:

[0063] For crowdsourcing GNSS user data, the networking mode in crowdsourcing RTK is adopted, that is, a triangular network is constructed by each crowdsourcing GNSS user and two static reference stations, and the centimeter-level precise and unbiased ionospheric delay between the crowdsourcing GNSS user and the static reference station is obtained by using the ambiguity closure error test in the triangular network.

[0064] The present application constructs a crowdsourcing ionospheric delay correction map (CIM) based on the crowdsourcing RTK technology, uses the unbiased estimation characteristic of the CIM, and establishes a STEC distribution map in the crowdsourcing RTK service range by fusing the STEC information of the static reference station, which is named as CIM-PLUS, to realize the spatial high-resolution ionospheric monitoring with dynamic and static data collaborative enhancement. The scheme breaks through the dependence of the traditional ionospheric monitoring method on the time domain phase continuity of a single station user, fully utilizes the extensive distribution and dense coverage characteristics of the crowdsourcing users in space, and improves the performance of ionospheric monitoring. Simulation experiments show that, in the scenario of 100 crowdsourcing GNSS users, the present application can obtain a two-order theoretical gain improvement; the static reference network experiment verifies that the present application has a precision level comparable to that of the traditional method; and in the dynamic monitoring scenario, the present application expands the point-to-point monitoring to point-to-surface monitoring, and the spatial resolution is improved by an order of magnitude.

[0065] The construction of the CIM-PLUS not only provides a unified framework for the efficient utilization of GNSS crowdsourcing data, supports the collaborative processing of dynamic and static GNSS observation data, and extracts reliable high-precision ionospheric delay correction information, but also meets the demand of ionospheric fine monitoring, reversely promotes the ubiquitous application of satellite precise positioning technology, and solves the problem of insufficient attraction of the existing GNSS precise positioning technology in popularization among the general public. Table 1 lists the attribute characteristics of the CIM and the CIM-PLUS.

[0066] Table 1 Attribute characteristics of the CIM and the CIM-PLUS

[0067] Item CIM CIM-PLUS Element Unbiased estimate Unbiased estimate Accuracy Centimeter level Decimeter to meter level Use Centimeter level position service Ionosphere monitoring VS. traditional scheme Discrete sampling points Point to surface transformation

[0068] The present application proposes a method for ionospheric monitoring by using the crowdsourcing GNSS data which is widely distributed and densely covered but has uneven observation data quality. The method fully utilizes the unbiased estimation characteristic of the inter-station single-difference ionosphere, expands the differential space CIM constructed by the crowdsourcing RTK technology to the non-difference space CIM-PLUS, and has the advantage of expanding the ionospheric monitoring from point-to-point to point-to-surface, so that the spatial resolution of the STEC monitoring is improved by multiple orders of magnitude. By constructing the CIM and the CIM-PLUS two kinds of ionospheric delay correction maps, the ubiquitous application of satellite precise positioning technology is supported, and the use experience of the general public for the satellite precise positioning technology is also improved.

[0069] The present application adopts a classic tomography method to inverse three-dimensional electron density, which divides the ionosphere of the inversion area into a plurality of pixels, and the electrons in each pixel are considered to be uniformly distributed, and the STEC calculation formula on the dynamic GNSS user-to-satellite connection line is as follows:

[0070] y = Ax + e;

[0071] In the formula, y is a column vector composed of ionospheric STEC observation values on the GNSS signal propagation path; A is a vector composed of the intercepts of the GNSS rays in the corresponding pixels; e is an observation error vector; and x is the electron density of each pixel center.

[0072] The additive algebraic reconstruction method is used to iteratively calculate the electron density information of the tomography area, and the basic idea is to divide the overall inversion problem into a series of sub-problems, and use the observation equation corresponding to each GNSS signal to locally correct the current estimate, and the calculation formula is as follows:

[0073]

[0074] In the formula, is the ionospheric electron density value at the kth iteration; A ij is the coefficient vector of the ith projection; y i is the corresponding SETC observation data; x j is the electron density of the jth grid; j is the grid number; m is the total number of grids; and λ is the relaxation factor of each iteration step. Each projection data is updated in turn, so as to gradually approach the global solution.

[0075] The present application evaluates the tomography inversion result by means of the ionospheric altimeter measured electron density profile, and uses bias (Bias) and root mean square (RMS) to quantitatively compare the accuracy of different tomography results, and the calculation formula is as follows:

[0076] Bias = Ne Ionosonde -Ne CIT ;

[0077]

[0078] In the formula, Ne Ionosonde represents the ionospheric electron density (IED) (below hmF2) measured by the altimeter; Ne CIT represents the IED obtained by different ionospheric tomography (CIT); and i represents the statistical quantity.

[0079] According to the spatial distribution of the CMONOC reference station network and the traffic road network in a certain province and its surrounding areas, 10,000 crowd-sourced GNSS users randomly distributed along the road network are simulated. Visualization analysis shows that the traffic road network and the crowd-sourced GNSS user density both exhibit significant spatial clustering, and the density value is improved by several orders of magnitude compared with the reference station network. Notably, as the user density increases, the spatial resolution of the ionospheric delay correction service driven by massive crowd-sourced data also increases accordingly. This self-organizing feature based on crowd-sensing data enables dynamic matching of user demand and ionospheric delay correction service quality, thereby effectively improving the regional ionospheric modeling accuracy and better supporting the centimeter-level ubiquitous positioning and ionospheric fine monitoring of the general public.

[0080] Further simulation experiments verify the feasibility of enhancing the GNSS dynamic user three-dimensional ionospheric electron density model and improving the ionospheric monitoring performance, and analyze the gain effect of the observation ray geometric distribution. The simulation results show that: (1) After increasing the dynamic users, the number and coverage of the observation rays used for ionospheric tomographic inversion of electron density increase significantly, especially after increasing 100 dynamic users, the theoretical gain of the observation rays is two orders of magnitude higher than that of a single user. (2) The increase in the number of observation rays can improve the proportion of observation information grids. Within 1 hour, the observation information of 100 dynamic users can cover more than 60.60% of the three-dimensional grid, which can effectively improve the ill-posed problem in the ionospheric tomographic inversion process and further provide discrete ionospheric information data source support for constructing high-precision and high-resolution regional ionospheric electron density models. (3) After increasing the dynamic users, the number of grids with observation information at different altitudes shows different levels of increasing trend, and with the increase of observation time, the number of grids with observation information in the upper region of the tomography increases more significantly.

[0081] The embodiment verifies the effectiveness of the method by a static reference station network experiment, and compares it with the traditional ionospheric modeling method designed for a static station network. The experiment selects 28 static observation stations in the European region to form a coverage network of about 900km×800km, and the observation period is: all day on May 10, 2024, and the spatial distribution and ionospheric digital altimeter positions are as shown in Figure 2 As shown in Table 2, two methods are used for static data processing:

[0082] Scheme one (traditional method), based on the IGGDCB algorithm, independently models all 28 static base stations, separates the STEC of each station through the modeling process, and then uses the STEC of all stations for three-dimensional tomography to finally construct the ionospheric electron density model.

[0083] Scheme two (the method proposed in the application), taking the CTAB base station adjacent to the ionospheric altimeter as the reference station, obtaining its STEC through single station modeling. The rest 27 observation stations obtain the inter-station single-difference ionospheric slant delay through baseline solution with the base station. Taking the absolute slant delay of the CTAB station as the reference, the absolute slant delay observation value of each station is solved, and finally the three-dimensional tomographic modeling is completed based on the absolute slant delay data of discrete distribution.

[0084] Table 2 static data processing strategy

[0085] Scheme Absolute slant delay calculation method Scheme one 28 stations respectively single station modeling Scheme two 1 station single station modeling, the remaining baseline solution, relative + absolute

[0086] The absolute ionospheric slant delay difference results of the two schemes of the GELL station: the standard deviation (STD) of the magnetic static period is 1.02 m, and the magnetic storm period increases to 2.34 m. Although the statistical results show that the absolute slant delay difference between the method proposed in the application and the traditional method is significant (far more than the 0.1 TECU claimed by the traditional modeling method, i.e. centimeter level accuracy), the difference is caused by the inherent error of the traditional single station modeling separation slant delay, not the systematic deviation of the inter-station differential ionospheric parameters. It needs to be emphasized that the inter-station differential ionospheric parameters under the crowdsourcing RTK framework have been verified by the triangular network ambiguity closure difference, and the centimeter level accuracy ensures that the absolute slant delay error calculated by the two schemes is actually in the same order of magnitude, and the difference value can be used as a new standard for evaluating the external compliance accuracy of the traditional ionospheric modeling. The traditional internal compliance accuracy evaluation only reflects the internal consistency of the modeling process, and there is a defect in the virtual high accuracy characterization. The difference index proposed in this embodiment can truly reflect the error level of the absolute ionospheric slant delay after single station modeling separation.

[0087] Referring to Figure 3 The application further provides an ionospheric monitoring device using crowdsourcing GNSS data, which is applied to the ionospheric monitoring method using crowdsourcing GNSS data.

[0088] The STEC acquisition module is used for acquiring the inter-station single-difference ionospheric slant delay of the crowdsourcing GNSS user and the static reference station, and calculating the absolute ionospheric slant delay of the crowdsourcing GNSS user; and the line-of-sight direction total electron content of the crowdsourcing GNSS user is obtained through the absolute ionospheric slant delay of the crowdsourcing GNSS user.

[0089] The STEC distribution map acquisition module is used for taking the absolute ionospheric slant delay of the static reference station as the reference, calculating the absolute ionospheric slant delay of the crowdsourcing GNSS user group, generating the line-of-sight direction total electron content distribution map of the crowdsourcing GNSS user group, and monitoring the ionosphere.

[0090] Referring to Figure 4 The application further provides an ionospheric monitoring device using crowdsourcing GNSS data, which comprises a memory and a processor.

[0091] the memory, configured to store the computer program code and transmit the computer program code to the processor;

[0092] the processor, configured to execute the above-mentioned ionospheric monitoring method using crowdsourced GNSS data according to instructions in the computer program code.

[0093] The application further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the above-mentioned ionospheric monitoring method using crowdsourced GNSS data.

[0094] Generally, the computer instructions used to implement the method of the present application can be carried by any combination of one or more computer readable storage media. The non-transitory computer readable storage medium can include any computer readable medium except a transitory propagating signal itself.

[0095] The computer readable storage medium may, for example, be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the above. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, the computer readable storage medium can be any tangible medium that contains or stores a program used by or in connection with an instruction execution system, apparatus, or device.

[0096] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages, particularly Python language and platform frameworks based on TensorFlow, PyTorch, etc. suitable for neural network computing. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0097] The above apparatus and non-transitory computer readable storage medium can refer to the specific description of the ionospheric monitoring method using crowd-sourced GNSS data and its advantages, which will not be repeated here.

[0098] Although the embodiments of the present application have been shown and described above, it should be understood by those skilled in the art that the above embodiments are exemplary and cannot be interpreted as a limitation of the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for ionospheric monitoring using crowdsourced GNSS data, characterized in that, The method comprises: obtaining inter-station single-difference ionospheric slant delays between crowd-sourced GNSS users and static reference stations, and calculating absolute ionospheric slant delays of the crowd-sourced GNSS users; obtaining total electron content in the line-of-sight direction of the crowd-sourced GNSS users according to the absolute ionospheric slant delays of the crowd-sourced GNSS users; calculating absolute ionospheric slant delays of the crowd-sourced GNSS users according to absolute ionospheric slant delays at the static reference stations, and generating a total electron content distribution map in the line-of-sight direction of the crowd-sourced GNSS users to monitor the ionosphere; the absolute ionospheric slant delays at the static reference stations are obtained by the following method: obtaining biased ionospheric slant delay estimates at the static reference stations: ； wherein denotes the biased estimator at the static reference station; denotes the absolute ionospheric slant delay at the static reference station; denotes the speed of light; denotes the satellite's differential code bias; denotes the receiver's differential code bias; denotes the measurement noise of the ionospheric total electron content and the differential code bias; separating ionospheric vertical electron density total content and differential code bias at the static reference stations, and calculating absolute ionospheric slant delays at the static reference stations based on the ionospheric vertical electron density total content: ； ； ； where represents the total electron content along the line of sight direction; represents the total content of the vertical electron density at the ionospheric piercing point ; represents the projection function; , and represent the longitude, latitude and height angle, respectively; represents the unit of measurement; represents the unit of total electron number; represents the conversion constant of the unit of total electron number and length; represents the meter; and represent two frequencies of the carrier phase, respectively. 2.The ionospheric monitoring method using crowd-sourced GNSS data according to claim 1, wherein, The absolute ionospheric slant delay of the crowd-sourced GNSS users Is: ； wherein represents the absolute ionospheric slant delay at the static reference station; represents the inter-station single-difference ionospheric slant delay at the crowd-sourced GNSS users and the static reference station. 3.The ionospheric monitoring method using crowd-sourced GNSS data according to claim 2, wherein, the total electron content in the line-of-sight direction of the crowd-sourced GNSS users is obtained according to the absolute ionospheric slant delays of the crowd-sourced GNSS users, and comprises: ； ； wherein denotes the conversion constant from total electron units to length; denotes the total electron content in the line of sight direction; denotes the unit of measurement; denotes the meter; denotes the total electron units; and denote the two frequencies of the carrier phase, respectively. 4.The ionospheric monitoring method using crowd-sourced GNSS data according to claim 2, wherein, the inter-station single-difference ionospheric slant delays between the crowd-sourced GNSS users and the static reference stations are obtained, and comprises: a triangular network is constructed by the double static reference stations and each crowd-sourced GNSS user, and the inter-station single-difference ionospheric slant delays between the crowd-sourced GNSS users and the static reference stations are obtained by using ambiguity closure difference test in the triangular network.

5. An ionospheric monitoring apparatus using crowd-sourced GNSS data, characterized by, The device is applied to the method of any one of claims 1-4, and the device comprises: an STEC obtaining module, configured to obtain inter-station single-difference ionospheric slant delays between crowd-sourced GNSS users and static reference stations, and calculate absolute ionospheric slant delays of the crowd-sourced GNSS users; and obtain total electron content in the line-of-sight direction of the crowd-sourced GNSS users according to the absolute ionospheric slant delays of the crowd-sourced GNSS users; an STEC distribution map obtaining module, configured to calculate absolute ionospheric slant delays of the crowd-sourced GNSS users according to absolute ionospheric slant delays at the static reference stations as a reference, and generate a total electron content distribution map in the line-of-sight direction of the crowd-sourced GNSS users to monitor the ionosphere.

6. An ionospheric monitoring device using crowd-sourced GNSS data, characterized in that, comprises a memory and a processor; the memory is configured to store computer program code and transmit the computer program code to the processor; the processor is configured to execute the method according to the instructions in the computer program code.

7. A computer readable storage medium characterized in that, The computer program is stored on the computer readable storage medium, and the computer program is executed by the processor to implement the method according to any one of claims 1-4.

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