Ionized layer monitoring method and device using crowdsourcing GNSS data, equipment and medium

Abstract

An ionosphere monitoring method, apparatus and device using crowdsourcing GNSS data, and a medium, the method comprising: acquiring inter-station single-difference ionosphere oblique delay between a crowdsourcing GNSS user and a static reference station, and calculating to obtain absolute ionosphere oblique delay of the crowdsourcing GNSS user; the total electron content in the sight direction of the crowdsourcing GNSS user is obtained through the absolute ionospheric oblique delay of the crowdsourcing GNSS user; and by taking the absolute ionospheric oblique delay at the static reference station as a reference, calculating the absolute ionospheric oblique delay of the crowdsourcing GNSS user group, and generating a line-of-sight direction total electron content distribution diagram of the crowdsourcing GNSS user group so as to monitor the ionosphere. The space coverage of the static reference station is enhanced through the dynamic GNSS user group, the spatial resolution of ionosphere discrete sampling information is improved, meanwhile, the static reference station is used for providing a high-precision reference for dynamic GNSS user group ionosphere modeling, and then a complementary enhancement mechanism of GNSS dynamic and static observation data is formed. And finally, the spatial resolution and precision of ionosphere monitoring are synergistically improved.

Description

Technical Field

[0001] The present invention relates to the field of ionosphere monitoring technology, and in particular to an ionosphere monitoring method, device, equipment and medium using crowdsourced GNSS data. Background Art

[0002] As a crucial component of the Sun-Earth space, the ionosphere's precise monitoring directly impacts the performance of GNSS navigation and positioning services, supporting applications such as the low-altitude economy and space weather monitoring. With the widespread adoption and performance improvements of BeiDou / GNSS terminals, the widespread and densely distributed crowdsourced GNSS data presents unprecedented opportunities for ionospheric monitoring research. However, traditional ionospheric monitoring methods rely on continuous observation of phase data in the time domain, making it difficult to fully tap the potential of crowdsourced GNSS users to extract high-precision ionospheric information within short observation windows.

[0003] High-precision, high-spatial-resolution ionospheric delay correction information plays a vital role in ensuring the accuracy and efficiency of applications such as GNSS positioning, timing, and ionospheric space weather monitoring. In recent years, with the surge in demand for high-precision spatiotemporal information in emerging sectors such as the low-altitude economy and intelligent driving, and the widespread adoption of Beidou / GNSS terminals, massive crowdsourced GNSS observation data has created new opportunities and challenges for building refined ionospheric models. On the one hand, collaborative processing of crowdsourced GNSS data can provide more widely distributed, higher-spatial-resolution discrete sampling of ionospheric delay, further improving GNSS precision positioning and ionospheric monitoring performance. On the other hand, different crowdsourced GNSS user groups exhibit significant differences in hardware performance, behavioral patterns, spatial distribution, and data quality. The utilization of crowdsourced GNSS data will raise many new scientific questions that require further in-depth research. my country's Beidou system is currently in a critical period of high-quality development and large-scale application, placing even higher and more pressing demands on the accuracy and spatial resolution of ionospheric delay corrections. In this context, in view of the uneven quality of crowdsourced GNSS data observations, we have expanded the ionospheric delay solution and modeling methods based on crowdsourced GNSS data, and combined it with the existing static GNSS station network data to achieve high-quality ionospheric delay error correction services. This not only meets the ubiquitous application requirements of Beidou / GNSS precise positioning technology, but is also an important technical means to improve the performance of ionospheric monitoring in the future.

[0004] The core challenge in efficiently and accurately utilizing crowdsourced GNSS data lies in how to effectively ensure the accuracy and reliability of the ionospheric information obtained by single-station users. The current mainstream GNSS ionospheric delay extraction methods, such as PPP and CCL technologies, are designed with algorithms mainly for static reference station networks with excellent observation quality. There are two major limitations in their implementation: first, their parameter convergence process relies on long-term continuous phase observations. This characteristic makes it difficult to fully utilize high-value, short-term, high-quality observation data in dynamic scenarios; second, the ionospheric slant delay estimated by these methods often has systematic biases, making it difficult for users to establish reliable indicators to determine whether the obtained ionospheric information is accurate and reliable. Due to the quality volatility and spatiotemporal heterogeneity of crowdsourced GNSS data, traditional methods find it difficult to efficiently integrate and utilize intermittent, high-quality crowdsourced observation data, making it difficult to meet the needs of fine-grained ionospheric monitoring. Summary of the Invention

[0005] The purpose of the present invention 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 crowdsourced GNSS data, so as to form a complementary enhancement mechanism for GNSS dynamic and static observation data, and to synergistically improve the spatial resolution and accuracy of ionospheric monitoring.

[0006] To achieve the above objectives, the technical solution of the present invention is:

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

[0008] Obtain the inter-station single-difference ionospheric slant delay between the crowdsourced GNSS user and the static reference station, and calculate the absolute ionospheric slant delay of the crowdsourced GNSS user; obtain the total electron content in the line of sight of the crowdsourced GNSS user through the absolute ionospheric slant delay of the crowdsourced GNSS user;

[0009] Taking the absolute ionospheric slant delay at the static reference station as the benchmark, the absolute ionospheric slant delay of the crowdsourced GNSS user group is calculated, and the total electron content distribution map in the line of sight direction of the crowdsourced GNSS user group is generated to monitor the ionosphere.

[0010] Absolute ionospheric slant delay of the crowdsourced GNSS users for:

[0011]

[0012] Where, represents the absolute ionospheric slant delay at the static reference station; represents the inter-station single-difference ionospheric slant delay between the crowdsourced GNSS user and the static base station.

[0013] Obtaining the total electron content in the line of sight of the crowdsourced GNSS user by using the absolute ionospheric slant delay of the crowdsourced GNSS user includes:

[0014]

[0015] Where A is the conversion constant between the total electron number unit and length; STEC is the total electron content in the line of sight; unit is the unit of measurement; m is the meter; TECu is the unit of total electron number; f1 and f2 are the two frequencies of the carrier phase, respectively.

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

[0017] Obtain biased ionospheric slant delay estimates at a static reference station;

[0018] The total ionospheric vertical electron concentration 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 ionospheric vertical electron concentration.

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

[0020]

[0021] Where, represents the biased estimator at the static reference station; represents the absolute ionospheric slant delay at the static reference station; c represents the speed of light; Δb sat Indicates the satellite's differential code deviation; Δb r represents the differential code deviation of the receiver; ε represents the measurement noise of the total electron content of the ionosphere and the differential code deviation.

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

[0023]

[0024] Where STEC represents the total electron content in the line of sight direction; Indicates the ionospheric puncture point The total vertical electron concentration at ; M(z) represents the projection function; λ and z represent longitude, latitude, and altitude, respectively; unit represents a unit of measurement; TECu represents a total electron unit; A represents a conversion constant between a total electron unit and length; m represents a meter; and f1 and f2 represent two frequencies of the carrier phase, respectively.

[0025] The obtaining of the inter-station single-difference ionospheric slant delay between the crowdsourced GNSS user and the static reference station includes:

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

[0027] In a second aspect, the present invention provides an ionospheric monitoring device using crowdsourced GNSS data, which is applied to the above-mentioned method, and includes:

[0028] The STEC acquisition module is used to obtain the inter-station single-difference ionospheric slant delay between the crowdsourced GNSS user and the static reference station, and calculate the absolute ionospheric slant delay of the crowdsourced GNSS user. The total electron content in the line of sight of the crowdsourced GNSS user is obtained from the absolute ionospheric slant delay of the crowdsourced GNSS user.

[0029] The STEC distribution map acquisition module is used to calculate the absolute ionospheric slant delay of the crowdsourced GNSS user group based on the absolute ionospheric slant delay at the static reference station, and generate the total electron content distribution map in the line of sight direction of the crowdsourced GNSS user group to monitor the ionosphere.

[0030] In a third aspect, the present invention provides an ionospheric monitoring device utilizing crowdsourced GNSS data, comprising a memory and a processor;

[0031] The memory is configured to store computer program code and transmit the computer program code to the processor;

[0032] The processor is configured to execute the method described above according to the instructions in the computer program code.

[0033] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the method described above when executed by a processor.

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

[0035] The present invention provides an ionospheric monitoring method, device, equipment and medium using crowdsourced GNSS data. The method first obtains the inter-station single-difference ionospheric slant delay between the crowdsourced GNSS user and the static reference station, and calculates the absolute ionospheric slant delay of the crowdsourced GNSS user; then, the total electron content in the line of sight of the crowdsourced GNSS user is obtained through the absolute ionospheric slant delay of the crowdsourced GNSS user; then, the absolute ionospheric slant delay at the static reference station is used as a benchmark to calculate the absolute ionospheric slant delay of the crowdsourced GNSS user group, and generate a distribution map of the total electron content in the line of sight of the crowdsourced GNSS user group, thereby providing total electron content information in the line of sight 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, and uses the static reference station to provide a high-precision benchmark for ionospheric modeling of the dynamic GNSS user group, thereby forming a complementary enhancement mechanism for GNSS dynamic and static observation data, and ultimately collaboratively improving the spatial resolution and accuracy of ionospheric monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of an ionospheric monitoring method using crowdsourced GNSS data provided by the present invention.

[0037] Figure 2 This is a European station network distribution map provided by an embodiment of the present invention.

[0038] Figure 3 This is a structural block diagram of an ionosphere monitoring device using crowdsourced GNSS data provided by the present invention.

[0039] Figure 4 This is a structural block diagram of an ionospheric monitoring device using crowdsourced GNSS data provided by the present invention. DETAILED DESCRIPTION

[0040] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] See also Figure 1 The present invention provides an ionospheric monitoring method using crowdsourced GNSS data, comprising:

[0042] S1. Obtain the inter-station single-difference ionospheric slant delay between the crowdsourced GNSS user and the static reference station, and calculate the absolute ionospheric slant delay of the crowdsourced GNSS user; obtain the total electron content in the line of sight of the crowdsourced GNSS user through the absolute ionospheric slant delay of the crowdsourced GNSS user;

[0043] S2. Using the absolute ionospheric slant delay at the static reference station as a benchmark, calculate the absolute ionospheric slant delay of the crowdsourced GNSS user group and generate a total electron content distribution map in the line of sight of the crowdsourced GNSS user group to monitor the ionosphere.

[0044] The present invention is based on unbiased inter-station single-difference ionospheric delay, and expands crowdsourced RTK from precise positioning applications to the field of ionospheric monitoring. Specifically, through crowdsourced RTK technology, high-precision unbiased inter-station single-difference ionospheric delay is extracted, and a crowdsourced ionospheric delay correction distribution map is constructed. Then, the absolute ionospheric slant delay separated by the static reference station using the traditional ionospheric modeling method is used as a benchmark to calculate the absolute amount of ionospheric slant delay of the dynamic user group, and construct a STEC distribution map within the crowdsourced RTK service range, thereby providing STEC information with higher spatial resolution for fine ionospheric monitoring. This method can efficiently utilize the short-term high-quality observation data of crowdsourced GNSS users, enhance the spatial coverage of static reference stations through dynamic user groups, improve the spatial resolution of ionospheric discrete sampling information, and use static reference stations to provide high-precision benchmarks for ionospheric modeling of dynamic user groups, thereby forming a complementary enhancement mechanism for GNSS dynamic / static observation data, and ultimately synergistically improving the spatial resolution and accuracy of ionospheric monitoring.

[0045] Furthermore, the absolute ionospheric slant delay of the crowdsourced GNSS user for:

[0046]

[0047] Where, represents the absolute ionospheric slant delay at the static reference station; represents the inter-station single-difference ionospheric slant delay between the crowdsourced GNSS user and the static base station.

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

[0049] Furthermore, obtaining the total electron content in the line of sight direction of the crowdsourced GNSS user by using the absolute ionospheric slant delay of the crowdsourced GNSS user includes:

[0050]

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

[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 a static reference station;

[0054] The total ionospheric vertical electron concentration 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 ionospheric vertical electron concentration.

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

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

[0057]

[0058] Where, represents the biased estimator at the static reference station; represents the absolute ionospheric slant delay at the static reference station; c represents the speed of light; Δb sat Indicates the satellite's differential code deviation; Δb r represents the differential code deviation of the receiver; ε represents the measurement noise of the total electron content of the ionosphere and the differential code deviation.

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

[0060]

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

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

[0063] For crowdsourced GNSS user data, a crowdsourced RTK networking method is adopted, that is, a triangulation network is constructed through dual static reference stations and each crowdsourced GNSS user. The ambiguity closure error test within the triangulation network is used to accurately obtain the centimeter-level accurate and unbiased inter-station single-difference ionospheric slant delay between the crowdsourced GNSS user and the static reference station.

[0064] The present invention constructs a crowdsourced ionospheric delay correction map (CIM) based on crowdsourced RTK technology, utilizes its unbiased estimation characteristics, and integrates the STEC information of static reference stations to establish a STEC distribution map within the crowdsourced RTK service range, which is named CIM-PLUS, to achieve spatial high-resolution ionospheric monitoring with collaborative enhancement of dynamic / static data. This solution breaks through the traditional ionospheric monitoring method's reliance on the time domain phase continuity of single-station users, and fully utilizes the wide distribution and dense coverage characteristics of crowdsourced users in space to improve the performance of ionospheric monitoring. Simulation experiments show that: in the scenario of 100 crowdsourced GNSS users, the present invention can achieve a theoretical gain improvement of two orders of magnitude; static reference network experiments verify that the present invention has an accuracy level comparable to that of traditional methods; and in the dynamic monitoring scenario, the present invention expands point-to-point monitoring to point-to-surface monitoring, achieving an order of magnitude improvement in spatial resolution.

[0065] The construction of CIM-PLUS not only provides a unified framework for the efficient utilization of GNSS crowdsourced data, but also supports the coordinated processing of dynamic and static GNSS observation data, thereby extracting reliable, high-precision ionospheric delay correction information. This not only meets the needs of detailed ionospheric monitoring, but also promotes the ubiquitous application of satellite precision positioning technology, addressing the lack of appeal of existing GNSS precision positioning technology to the general public. Table 1 lists the attributes and characteristics of CIM and CIM-PLUS.

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

[0067] project CIM CIM-PLUS Composition elements Unbiased estimator Unbiased estimator Accuracy Centimeter level Decimeter to meter level use Centimeter-level location services Ionosphere monitoring VS. Traditional Solutions Many discrete sampling points Transformation from point to surface

[0068] The present invention proposes a method for ionospheric monitoring using crowdsourced GNSS data that is widely distributed, densely covered, but has uneven observation data quality. This method fully utilizes the unbiased estimation characteristics of the single-difference ionosphere between stations and expands the differential space CIM constructed by crowdsourced RTK technology to the non-difference space CIM-PLUS. Its advantage is that it expands ionospheric monitoring from point-to-point to point-to-plane, thereby improving the spatial resolution of STEC monitoring by multiple orders of magnitude. By constructing two ionospheric delay correction maps, CIM and CIM-PLUS, it provides support for ionospheric monitoring for the ubiquitous application of satellite precision positioning technology, and will also enhance the user experience of satellite precision positioning technology for the general public.

[0069] The present invention uses a classic tomographic method to invert the three-dimensional electron density. This method divides the ionosphere in the inversion area into several pixels. The electrons in each pixel are assumed to be uniformly distributed. The STEC calculation formula on the dynamic GNSS user-satellite connection is:

[0070] y=Ax+e;

[0071] Where y is the column vector of ionospheric STEC observations along the GNSS signal propagation path; A is the vector of intercepts of GNSS rays in the corresponding pixel; e is the observation error vector; and x is the electron density at the center of each pixel.

[0072] The electron density information of the tomographic region is iteratively calculated using the additive algebraic reconstruction method. The basic idea is to split the overall inversion problem into a series of subproblems and use the observation equation corresponding to each GNSS signal to locally correct the current estimate. The calculation formula is as follows:

[0073]

[0074] Where, is the ionospheric electron density value at the kth iteration; A ij is the coefficient vector of the i-th 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 for each iteration. Each projection data is updated in turn, gradually approaching the global solution.

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

[0076] Bias=Ne Ionosonde -Ne CIT ;

[0077]

[0078] In the formula, Ne Ionosonde It represents the ionospheric electron density (IED) (hmF2 or less) measured by the altimeter; Ne CIT represents the IED obtained by computerized ionospheric tomography (CIT) of different schemes; i represents the statistic.

[0079] This example simulates and generates 10,000 crowdsourced GNSS users randomly distributed along the road network based on the spatial distribution of the CMONOC reference station network and the transportation road network in a certain province and its surrounding areas. Visual analysis shows that the density of both the transportation road network and the crowdsourced GNSS users exhibits significant spatial clustering, with their density values ​​increasing by several orders of magnitude compared to the reference station network. It is worth noting that as user density increases, the spatial resolution of the ionospheric delay correction service driven by massive crowdsourced data also increases accordingly. This self-organizing characteristic based on crowd-sensed data enables dynamic matching of user needs with the quality of the ionospheric delay correction service, thereby effectively improving the accuracy of regional ionospheric modeling and better supporting centimeter-level ubiquitous positioning and fine ionospheric monitoring for mass users.

[0080] Further simulation experiments were conducted to verify the feasibility of using GNSS dynamic users to enhance the three-dimensional ionospheric electron density model and thus enhance the ionospheric monitoring performance, and the gain effect on the geometric distribution of observation rays was analyzed. The simulation results show that: (1) After adding dynamic users, the number and coverage of observation rays used for ionospheric tomography inversion of electron density increased significantly. In particular, after adding 100 dynamic users, the theoretical gain of observation rays increased by two orders of magnitude compared with a single user. (2) The increase in the number of observation rays can increase the proportion of grids with observation information. Within 1 hour, the observation information of 100 dynamic users can cover more than 60.60% of the three-dimensional grids, which can effectively improve the ill-posed problem in the ionospheric tomography inversion process and further provide discrete ionospheric information data source support for the construction of high-precision and high-resolution regional ionospheric electron density models. (3) After adding dynamic users, the number of grids with observation information at different heights showed different levels of increase, and with the increase of observation time, the number of grids with observation information in the upper part of the tomography increased more significantly.

[0081] This example verifies the effectiveness of the proposed method through a static reference station network experiment and compares it with the traditional ionospheric modeling method designed for static station networks. The experiment selects 28 static observation stations in the European region to form a coverage network of approximately 900km×800km. The observation period is: May 10, 2024, the entire day. Its spatial distribution is similar to the position of the ionospheric digital altimeter. Figure 2 As shown in Table 2, there are two methods for static data processing:

[0082] Option 1 (traditional method) is to independently model all 28 static base stations based on the IGGDCB algorithm. The STEC of each station is separated through the modeling process. Then, the STEC of all stations is used for three-dimensional tomography to finally construct the ionospheric electron density model.

[0083] In the second approach (the method proposed in this paper), the CTAB base station adjacent to the ionospheric altimeter is used as the reference station, and its STEC is obtained through single-station modeling. The remaining 27 observation stations perform baseline calculations with this base station to obtain inter-station single-difference ionospheric slant delays. Using the absolute slant delay of the CTAB station as the reference, the absolute slant delay observations at each station are calculated. Finally, 3D tomographic modeling is completed based on the discretely distributed absolute slant delay data.

[0084] Table 2 Static data processing strategy

[0085] plan Absolute skew delay calculation method Option 1 Single-station modeling was performed for each of the 28 stations Option 2 1 station single station modeling, remaining baseline solution, relative + absolute

[0086] The results of the mutual difference of absolute ionospheric slant delay extracted by the two schemes at the GELL station are as follows: the standard deviation (STD) of the mutual difference is 1.02m during the magnetic quiet period and increases to 2.34m during the magnetic storm period. Although the statistical results show that the absolute slant delay difference between the method proposed in the present invention and the traditional method is significant (far exceeding the 0.1TECU claimed by the traditional modeling method, i.e., centimeter-level accuracy), the difference is due to the inherent error when separating the slant delay in the traditional single-station modeling, rather than the systematic deviation of the differential ionospheric parameters between stations. It should be emphasized that the differential ionospheric parameters between stations under the crowdsourced RTK framework have passed the triangulation ambiguity closure error test, and its centimeter-level accuracy ensures that the absolute slant delay errors calculated by the two schemes are actually at the same order of magnitude. The mutual difference value can be used as a new standard for evaluating the external coincidence accuracy of traditional ionospheric modeling. The traditional internal coincidence accuracy assessment only reflects the internal consistency of the modeling process and has the defect of false high precision characterization; while the mutual difference index proposed in this embodiment can truly reflect the error level of the absolute ionospheric slant delay after the separation of single-station modeling.

[0087] See also Figure 3 The present invention further provides an ionosphere monitoring device using crowdsourced GNSS data, which is applied to the above-mentioned ionosphere monitoring method using crowdsourced GNSS data. The device includes:

[0088] The STEC acquisition module is used to obtain the inter-station single-difference ionospheric slant delay between the crowdsourced GNSS user and the static reference station, and calculate the absolute ionospheric slant delay of the crowdsourced GNSS user. The total electron content in the line of sight of the crowdsourced GNSS user is obtained from the absolute ionospheric slant delay of the crowdsourced GNSS user.

[0089] The STEC distribution map acquisition module is used to calculate the absolute ionospheric slant delay of the crowdsourced GNSS user group based on the absolute ionospheric slant delay at the static reference station, and generate the total electron content distribution map in the line of sight direction of the crowdsourced GNSS user group to monitor the ionosphere.

[0090] See also Figure 4 ,The present invention also provides an ionospheric monitoring device using crowdsourced GNSS data, comprising a memory and a processor;

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

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

[0093] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the above-mentioned ionosphere monitoring method using crowdsourced GNSS data.

[0094] Generally speaking, computer instructions for implementing the method of the present invention may be carried by any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media except for signals that are temporarily propagating.

[0095] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or components, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

[0096] Computer program code for performing the operations of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, 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 to an external computer (for example, through the Internet using an Internet service provider).

[0097] The above-mentioned device and non-transitory computer-readable storage medium can be referred to the detailed description of an ionospheric monitoring method using crowdsourced GNSS data and its beneficial effects, which will not be repeated here.

[0098] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for ionospheric monitoring using crowdsourced GNSS data, characterized in that: include: Obtain the inter-station single-difference ionospheric slant delay between the crowdsourced GNSS user and the static reference station, and calculate the absolute ionospheric slant delay of the crowdsourced GNSS user; The total electron content in the line of sight of the crowdsourced GNSS user is obtained by the absolute ionospheric slant delay of the crowdsourced GNSS user; Taking the absolute ionospheric slant delay at the static reference station as the benchmark, the absolute ionospheric slant delay of the crowdsourced GNSS user group is calculated, and the total electron content distribution map in the line of sight direction of the crowdsourced GNSS user group is generated to monitor the ionosphere.

2. The ionospheric monitoring method using crowdsourced GNSS data according to claim 1, characterized in that: Absolute ionospheric slant delay of the crowdsourced GNSS users for: Where, represents the absolute ionospheric slant delay at the static reference station; represents the inter-station single-difference ionospheric slant delay between the crowdsourced GNSS user and the static base station.

3. The ionospheric monitoring method using crowdsourced GNSS data according to claim 2, characterized in that: Obtaining the total electron content in the line of sight of the crowdsourced GNSS user by using the absolute ionospheric slant delay of the crowdsourced GNSS user includes: Where A is the conversion constant between the total electron number unit and length; STEC is the total electron content in the line of sight; unit is the unit of measurement; m is the meter; TECu is the unit of total electron number; f1 and f2 are the two frequencies of the carrier phase, respectively.

4. The ionospheric monitoring method using crowdsourced GNSS data according to claim 1, characterized in that: The absolute ionospheric slant delay at the static reference station is obtained by the following method: Obtain biased ionospheric slant delay estimates at a static reference station; The total ionospheric vertical electron concentration 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 ionospheric vertical electron concentration.

5. The ionospheric monitoring method using crowdsourced GNSS data according to claim 4, characterized in that: The biased ionospheric slant delay estimate at the static reference station is obtained by the following formula: Where, represents the biased estimator at the static reference station; represents the absolute ionospheric slant delay at the static reference station; c represents the speed of light; Δb sat Indicates the satellite's differential code deviation; Δb r represents the differential code deviation of the receiver; ε represents the measurement noise of the total electron content of the ionosphere and the differential code deviation.

6. The ionospheric monitoring method using crowdsourced GNSS data according to claim 5, characterized in that: The absolute ionospheric slant delay at the static reference station is obtained by the following formula: Where STEC represents the total electron content in the line of sight direction; Indicates the ionospheric puncture point ) is the total vertical electron concentration at ; M(z) represents the projection function; λ and z represent longitude, latitude, and altitude, respectively; unit represents a unit of measurement; TECu represents a total electron unit; A represents a conversion constant between a total electron unit and length; m represents a meter; and f1 and f2 represent two frequencies of the carrier phase, respectively.

7. The ionospheric monitoring method using crowdsourced GNSS data according to claim 2, characterized in that: The obtaining of the inter-station single-difference ionospheric slant delay between the crowdsourced GNSS user and the static reference station includes: A triangulation network is constructed by dual static reference stations and each crowdsourced GNSS user, and the inter-station single-difference ionospheric slant delay between the crowdsourced GNSS user and the static reference station is obtained by using the ambiguity closure error test within the triangulation network.

8. An ionospheric monitoring device using crowdsourced GNSS data, characterized in that: The device is applied to the method according to any one of claims 1 to 7, and the device comprises: The STEC acquisition module is used to obtain the inter-station single-difference ionospheric slant delay between the crowdsourced GNSS user and the static reference station, and calculate the absolute ionospheric slant delay of the crowdsourced GNSS user. The total electron content in the line of sight of the crowdsourced GNSS user is obtained from the absolute ionospheric slant delay of the crowdsourced GNSS user. The STEC distribution map acquisition module is used to calculate the absolute ionospheric slant delay of the crowdsourced GNSS user group based on the absolute ionospheric slant delay at the static reference station, and generate the total electron content distribution map in the line of sight direction of the crowdsourced GNSS user group to monitor the ionosphere.

9. An ionospheric monitoring device using crowdsourced GNSS data, characterized in that: including memory and 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 any one of claims 1 to 7 according to instructions in the computer program code.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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