Ionospheric integrity parameter estimation method and system

Abstract

The application discloses an ionosphere integrity parameter estimation method, comprising the following steps: obtaining historical observation data of a ground-based navigation satellite system receiver, extracting vertical ionosphere delay of a piercing point according to the historical observation data, and calculating a local ionosphere space activity index; modeling each vertical ionosphere delay with a neural network, respectively, and calculating modeling residuals of all vertical ionosphere delays; grouping the modeling residuals according to the size of the local ionosphere space activity index, and respectively fitting the relationship of the space activity index, the envelope standard deviation and the envelope deviation to obtain the integrity parameter value of the ionosphere. The application can also predict and adjust the activity degree of the ionosphere in different regions and time periods, and further optimize the correction effect of GNSS signals.

Description

Technical Field

[0001] This invention relates to the field of GNSS satellite navigation systems and enhancement technology, specifically to a method and system for estimating ionospheric integrity parameters. Background Technology

[0002] In GNSS (Navigation Satellite System) integrity monitoring, monitoring various factors that cause anomalies in observations is fundamental and crucial. Among these, the ionosphere is a significant factor and a challenging issue, attracting long-standing attention from academia. In civil aviation integrity monitoring schemes, SBAS calculates the ionospheric delay error limit at each IGP (Ionospheric Grid Point), i.e., the ionospheric delay integrity parameter GIVE, to encapsulate the ionospheric delay error. Users then use bilinear interpolation to calculate their own location's UIVE based on the received GIVE, further calculating the protection level for integrity monitoring.

[0003] When WAAS achieves Initial Operational Capability (IOC), it employs the GIVE (ionospheric integrity parameter) algorithm, which is paired with plane fitting. A static chi-square factor inflation algorithm is used to inflate the variance of the grid points calculated from the plane fitting, thereby calculating the GIVE. As noise characteristic analysis deepens, to further reduce the GIVE size and improve system availability while maintaining integrity, WAAS spatial statistical analysis utilizes an exponential variogram to model model noise at different distance intervals. The Kriging algorithm is used to calculate the ionospheric delay and variance of the grid points, and a dynamic chi-square factor inflation algorithm is used to verify ionospheric anomalies and calculate the GIVE parameter. However, although the Kriging-based GIVE algorithm performs well in the CONUS region, existing Kriging interpolation models, parameter selections, and conclusions are based on ionospheric characteristics under CONUS in the mid-to-high latitude region of the United States. Since the ionosphere exhibits significant regional variations, this model may not be applicable to other regions. In addition, the ionospheric spatial variation gradient is significant and the frequency of ionospheric anomalies is higher in low-latitude regions, thus traditional models have lower applicability in low-latitude regions. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that when there are obvious regional variations in the ionosphere, the traditional model is not applicable to the changing regions, resulting in low applicability and low continuity of the traditional model.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a method for estimating ionospheric integrity parameters, comprising:

[0008] Historical observation data from the ground-based navigation satellite system receiver is acquired, and the vertical ionospheric delay at the puncture point is extracted based on the historical observation data to calculate the local ionospheric spatial activity index.

[0009] Each of the vertical ionospheric delays is modeled using a neural network, and the modeling residuals for all the vertical ionospheric delays are calculated.

[0010] The modeling residuals are grouped according to the magnitude of the local ionospheric spatial activity index, and the relationships between the spatial activity index, envelope standard deviation, and envelope bias are fitted respectively to obtain the ionospheric integrity parameter values.

[0011] As a preferred embodiment of the ionospheric integrity parameter estimation method described in this invention, wherein:

[0012] The process of acquiring historical observation data from the ground-based navigation satellite system receiver, extracting the vertical ionospheric delay at the puncture point based on the historical observation data, and calculating the local ionospheric spatial activity index includes calculating the spatial activity index at the puncture point using the IPP search method.

[0013] Select a spatial point as the center and search for the surrounding IPP within the fitting radius. Set the initial value of the fitting radius to the minimum fitting radius.

[0014] If the number of IPPs is less than the target number of points, increase the fitting radius until the number of IPPs reaches the target number of points or the fitting radius reaches the maximum fitting radius.

[0015] As a preferred embodiment of the ionospheric integrity parameter estimation method described in this invention, wherein:

[0016] The process involves modeling each vertical ionospheric delay using a neural network and calculating the modeling residuals for all vertical ionospheric delays, including...

[0017] The latitude and longitude of all puncture points at each time point are used as input to the neural network model, and the corresponding vertical electron content is used as output. The neural network is trained, and after the neural network is trained, the vertical ionospheric delay of the IPP is estimated by the constructed neural network. The true value of the IPP ionospheric delay is subtracted to obtain the modeling residual of the ionospheric delay.

[0018] As a preferred embodiment of the ionospheric integrity parameter estimation method described in this invention, wherein:

[0019] The modeling residuals are grouped according to the magnitude of the local ionospheric spatial activity index, and the relationships between the spatial activity index, envelope standard deviation, and envelope bias are fitted respectively, including:

[0020] The fitting function is expressed as:

[0021] σ ovbd =0.8455·σ LISAI 0.6497 #

[0022] b ovbd =0.0748·σ LISAI 1.4211 #

[0023] Where, σ LISAI The calculation is based on the spatial activity index, σ. ovbd b is the envelope standard deviation. ovbd This is the envelope bias.

[0024] As a preferred embodiment of the ionospheric integrity parameter estimation method described in this invention, wherein:

[0025] Envelope standard deviation and envelope bias, including,

[0026] Based on the spatial activity index at the puncture point calculated using the IPP search method, the obtained spatial activity index is substituted into the fitting function to calculate the envelope standard deviation and envelope bias at the grid points.

[0027] As a preferred embodiment of the ionospheric integrity parameter estimation method described in this invention, wherein:

[0028] It also includes,

[0029] The spatial activity index at the puncture point is calculated using the IPP search method.

[0030] If the number of IPP points found is less than the set threshold, the grid point is unavailable and cannot be used to calculate the ionospheric delay.

[0031] If the number of IPP points found exceeds the set threshold, the grid points can be used to calculate the ionospheric delay.

[0032] As a preferred embodiment of the ionospheric integrity parameter estimation method described in this invention, wherein:

[0033] The modeling residuals are grouped according to the magnitude of the local ionospheric spatial activity index, and the relationships between the spatial activity index, envelope standard deviation, and envelope bias are fitted to obtain the ionospheric integrity parameter values. These ionospheric integrity parameter values ​​include...

[0034] GIVE = q(IR)·σint,IGP +b int,IGP

[0035] Where IR is the integrity risk probability, q(.) is the quantile function, and σ int,IGP b is the envelope standard deviation. int,IGP This is the envelope bias.

[0036] In a second aspect, the present invention provides a system for estimating ionospheric integrity parameters, comprising:

[0037] The extraction module acquires historical observation data from the ground-based navigation satellite system receiver, extracts the vertical ionospheric delay at the puncture point based on the historical observation data, and calculates the local ionospheric spatial activity index.

[0038] The modeling module models each of the vertical ionospheric delays using a neural network and calculates the modeling residuals for all the vertical ionospheric delays.

[0039] The fitting module groups the modeling residuals according to the magnitude of the local ionospheric spatial activity index, and fits the relationship between the spatial activity index, the envelope standard deviation, and the envelope bias to obtain the ionospheric integrity parameter values.

[0040] Thirdly, the present invention provides a computing device, comprising:

[0041] Memory and processor;

[0042] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the ionospheric integrity parameter estimation method.

[0043] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the ionospheric integrity parameter estimation method.

[0044] The beneficial effects of this invention are as follows: This invention can reflect the intensity and changing trend of ionospheric disturbances, effectively improving the continuity and availability of GNSS services, enabling reliable positioning services even in harsh environments. This invention can also predict and adjust the ionospheric activity levels for different regions and time periods, further optimizing the correction effect of GNSS signals. Attached Figure Description

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0046] Figure 1 This invention provides an overall flowchart of a method for measuring ionospheric integrity parameters.

[0047] Figure 2 The GIVE values ​​calculated by different GIVE algorithms in an ionospheric integrity parameter method provided by the present invention;

[0048] Figure 3 The GIVE envelope effect of different GIVE algorithms in the ionospheric integrity parameter method provided by this invention;

[0049] Figure 4 Stanford plots generated by different GIVE algorithms in an ionospheric integrity parameter method provided by this invention. Detailed Implementation

[0050] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0051] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0052] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0053] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0054] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0055] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0056] Example 1

[0057] Reference Figure 1 As an embodiment of the present invention, a method for estimating ionospheric integrity parameters is provided, comprising:

[0058] S1: Obtain historical observation data from the ground-based navigation satellite system receiver, extract the vertical ionospheric delay at the puncture point based on the historical observation data, and calculate the local ionospheric spatial activity index;

[0059] Furthermore, the IPP search algorithm was used to calculate the local ionospheric spatial activity index at the IPP.

[0060] It should be noted that the IPP search algorithm is based on measurement data between the receiver and the satellite. It requires the collection of GNSS signal measurement data, including the signal strength and phase delay received by the receiver. Based on the received signal data, the propagation delay of the signal in the ionosphere can be obtained using GNSS receiver processing technology. This delay can be obtained through phase difference measurement or pseudorange measurement. Further analysis of the extracted ionospheric delay, through statistical and filtering methods, can yield the statistical characteristics and trends of the ionospheric delay, thus obtaining a local ionospheric spatial activity index that reflects the activity level of the ionosphere, including the intensity and rate of change of ionospheric disturbances.

[0061] Using the IPP search algorithm for parameter estimation methods based on the local ionospheric spatial activity index can improve the accuracy, continuity, and availability of positioning and navigation systems, helping to optimize system performance and provide reliable positioning services.

[0062] S2: Model each of the vertical ionospheric delays using a neural network, and calculate the modeling residuals for all the vertical ionospheric delays;

[0063] Furthermore, neural networks are used to model the delay of each vertical ionosphere separately;

[0064] Furthermore, the modeling residuals for all vertical ionospheric delays are calculated.

[0065] It should be noted that the data required for training is collected using a GNSS receiver and measurement equipment. The data includes input samples with known ionospheric delay and corresponding output labels. The data is preprocessed. For vertical ionospheric delay modeling, commonly used neural network structures such as multilayer perceptron (MLP) or convolutional neural network (CNN) can be selected. The data is divided into training set, validation set and test set. The neural network is trained using the training set. The network parameters are updated through backpropagation algorithm and optimizer to minimize the error between the predicted output and the true label.

[0066] It should be noted that the smaller the calculated modeling residual, the higher the modeling accuracy and the better the model reliability. By calculating the modeling residuals of all vertical ionospheric delays, the modeling effect of the neural network can be evaluated, defects in the model can be identified and improved, and the accuracy and reliability of ionospheric delay modeling can be enhanced.

[0067] S3: The modeling residuals are grouped according to the magnitude of the local ionospheric spatial activity index, and the relationship between the spatial activity index, the envelope standard deviation, and the envelope bias is fitted respectively to obtain the ionospheric integrity parameter value.

[0068] Furthermore, the relationship between the spatial activity index, envelope standard deviation, and envelope bias is fitted.

[0069] It should be noted that the envelope standard deviation and envelope bias of each set of residuals are calculated using the two-step Gaussian envelope method. The relationship between the spatial activity index, envelope standard deviation and envelope bias is fitted using a fitting function. This can be used to predict ionospheric delay error and provide an assessment of the degree of influence of each independent variable on ionospheric delay error.

[0070] The IPP search method calculates the spatial activity index at the puncture point. The threshold value varies depending on different environmental conditions and application requirements. According to the requirements of this invention, the optimal threshold value is set to 5. If the number of IPP points searched is less than 5, the grid point is unusable and cannot be used to calculate the ionospheric delay.

[0071] It should also be noted that the calculation of the ionospheric integrity parameter value needs to take into account the interpolation error of the grid. Therefore, it is necessary to calculate the integrity parameter value of 9 points spaced 1.25° around the grid point, and take the maximum value as the final integrity parameter value of the grid point.

[0072] The above is an illustrative scheme of an ionospheric integrity parameter estimation method according to this embodiment. It should be noted that the technical solution of the ionospheric integrity parameter estimation device is based on the same concept as the ionospheric integrity parameter estimation method described above. Details not described in detail in the technical solution of the ionospheric integrity parameter estimation device in this embodiment can be found in the description of the ionospheric integrity parameter estimation method described above.

[0073] The apparatus for estimating ionospheric integrity parameters in this embodiment includes:

[0074] The extraction module acquires historical observation data from the ground-based navigation satellite system receiver, extracts the vertical ionospheric delay at the puncture point based on the historical observation data, and calculates the local ionospheric spatial activity index.

[0075] The modeling module models each of the vertical ionospheric delays using a neural network and calculates the modeling residuals for all the vertical ionospheric delays.

[0076] The fitting module groups the modeling residuals according to the magnitude of the local ionospheric spatial activity index, and fits the relationship between the spatial activity index, the envelope standard deviation, and the envelope bias to obtain the ionospheric integrity parameter values.

[0077] This embodiment also provides a computing device suitable for estimating ionospheric integrity parameters, including:

[0078] The system includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes these instructions to implement the ionospheric integrity parameter estimation method proposed in the above embodiments.

[0079] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for estimating ionospheric integrity parameters as proposed in the above embodiments.

[0080] The storage medium proposed in this embodiment and the method for estimating ionospheric integrity parameters proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0081] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0082] Example 2

[0083] Refer to Table 1, Figure 2-4 As an embodiment of the present invention, a method for estimating ionospheric integrity parameters is provided to verify and illustrate the technical effects of the method.

[0084] Table 1 Statistical values ​​of envelope results for different GIVE algorithms

[0085]

[0086] As shown in Table 1, the GIVE algorithm proposed in this invention can satisfy the corresponding envelope requirements under different integrity probabilities, and the GIVE value is more compact than that of WAAS-Kriging. Whether it is the envelope rate or the average value, the local ionospheric spatial activity index of this invention is superior to that based on Kriging. By using the local ionospheric spatial activity index, the spatial variation law of ionospheric delay can be described more accurately, and higher modeling accuracy can be obtained.

[0087] Depend on Figure 2 It can be seen that the GIVE values ​​calculated by this invention during calm and active ionospheric moments, compared with WAAS's Kriging-based GIVE algorithm, show that when based on the local ionospheric spatial activity index, the GIVE values ​​are lower than those of WAAS's Kriging-based GIVE values ​​during both calm and active moments. This indicates that the ionosphere has a relatively small impact on GNSS signals, resulting in higher navigation and positioning accuracy.

[0088] Depend on Figure 3 It can be seen that the GIVE value calculated by this invention is more closely related to the GIVE value of WAAS-Kriging, whether in calm or active times, and more accurately reflects the time delay changes in the ionosphere. Moreover, the spatial response of the ionospheric residual is more timely and accurate in active times.

[0089] Depend on Figure 4 As can be seen from the Stanford plot comparing the present invention with WAAS's Kriging-based GIVE algorithm, when IR = 1e-3, the normal information ratio of the present invention is as high as 99.997%, which is higher than that of the Kriging-based algorithm, indicating that the present invention has higher accuracy and a lower misinformation ratio than the Kriging-based algorithm. When IR = 1e-6, the normal information ratio of the present invention reaches 100.000%, with zero error information. It can be seen that the present invention has higher responsiveness and greater commercial value.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for estimating ionospheric integrity parameters, characterized in that, include: Historical observation data from the ground-based navigation satellite system receiver is acquired, and the vertical ionospheric delay at the puncture point is extracted based on the historical observation data to calculate the local ionospheric spatial activity index. Each of the vertical ionospheric delays is modeled using a neural network, and the modeling residuals for all the vertical ionospheric delays are calculated. The modeling residuals are grouped according to the magnitude of the local ionospheric spatial activity index, and the relationships between the spatial activity index, envelope standard deviation, and envelope bias are fitted respectively to obtain the ionospheric integrity parameter values. The process of acquiring historical observation data from the ground-based navigation satellite system receiver, extracting the vertical ionospheric delay at the puncture point based on the historical observation data, and calculating the local ionospheric spatial activity index includes calculating the spatial activity index at the puncture point using the IPP search method. Select a spatial point as the center and search for the surrounding IPP within the fitting radius. Set the initial value of the fitting radius to the minimum fitting radius. If the number of IPPs is less than the target number of points, increase the fitting radius until the number of IPPs reaches the target number of points or the fitting radius reaches the maximum fitting radius. The standard deviation of the vertical ionospheric delay of the IPP within the search range is calculated as an indicator of spatial activity.

2. The ionospheric integrity parameter estimation method as described in claim 1, characterized in that, The vertical ionospheric delay is modeled separately using a neural network, and the modeling residuals for all vertical ionospheric delays are calculated. include, The latitude and longitude of all puncture points at each time point are used as input to the neural network model, and the corresponding vertical electron content is used as output. The neural network is trained, and after the neural network is trained, the vertical ionospheric delay of the IPP is estimated by the constructed neural network. The true value of the IPP ionospheric delay is subtracted to obtain the modeling residual of the ionospheric delay.

3. The ionospheric integrity parameter estimation method as described in claim 2, characterized in that, The modeling residuals are grouped according to the magnitude of the local ionospheric spatial activity index, and the relationships between the spatial activity index, envelope standard deviation, and envelope bias are fitted respectively, including: The fitting function is expressed as: in, The calculation is based on spatial activity indicators. The envelope standard deviation, This is the envelope bias.

4. The ionospheric integrity parameter estimation method as described in claim 3, characterized in that, Envelope standard deviation and envelope bias, including, Substituting the obtained spatial activity index into the fitting function, the envelope standard deviation and envelope bias at the grid points are calculated.

5. The ionospheric integrity parameter estimation method as described in claim 4, characterized in that, It also includes, If the number of IPP points found is less than the set threshold, the grid point is unavailable and cannot be used to calculate the ionospheric delay. If the number of IPP points found exceeds the set threshold, the grid points can be used to calculate the ionospheric delay.

6. The ionospheric integrity parameter estimation method as described in claim 5, characterized in that, The modeling residuals are grouped according to the magnitude of the local ionospheric spatial activity index, and the relationships between the spatial activity index, envelope standard deviation, and envelope bias are fitted to obtain the ionospheric integrity parameter values. These ionospheric integrity parameter values ​​include... in, For the probability of integrity risk For quantile value functions, The envelope standard deviation, This is the envelope bias.

7. A system for estimating ionospheric integrity parameters, comprising applying the ionospheric integrity parameter estimation method as described in any one of claims 1-6, characterized in that, include: The extraction module acquires historical observation data from the ground-based navigation satellite system receiver, extracts the vertical ionospheric delay at the puncture point based on the historical observation data, and calculates the local ionospheric spatial activity index. The modeling module models each of the vertical ionospheric delays using a neural network and calculates the modeling residuals for all the vertical ionospheric delays. The fitting module groups the modeling residuals according to the magnitude of the local ionospheric spatial activity index, and fits the relationship between the spatial activity index, the envelope standard deviation, and the envelope bias to obtain the ionospheric integrity parameter values.

8. An electronic device, characterized in that, The device includes: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method described in any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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