Ionosphere data extraction method and device, storage medium and electronic equipment
By constructing phase observation equations and epoch difference models, eliminating gross errors and cycle slips, repairing clock slips, and using Kalman filtering to solve ionospheric data, the problem of real-time extraction of ionospheric data in environments without public networks was solved, achieving efficient and accurate data processing.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- STATE GRID LOCATION BASED SERVICE CO LTD
- Filing Date
- 2023-12-11
- Publication Date
- 2026-07-28
AI Technical Summary
Existing technologies struggle to extract ionospheric data in real time without a public network, requiring the introduction of high-precision correction data from outside satellite navigation systems and relying on network connectivity.
By acquiring pseudorange and phase observations from the satellite using a receiver, a phase observation equation is constructed. Combined with an epoch difference model, gross errors and cycle slips are eliminated, clock slips are repaired, and ionospheric data is solved using Kalman filtering.
Real-time extraction of ionospheric data was achieved in an environment without a public network, improving the convenience and accuracy of data processing.
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Figure CN117908053B_ABST
Abstract
Description
Technical Field
[0001] The embodiments of this application relate to the field of satellite communication technology, and in particular to a method, apparatus, storage medium and electronic device for extracting ionospheric data. Background Technology
[0002] Methods for extracting ionospheric data using observations from satellite navigation systems often require the introduction of high-precision correction data from outside the satellite navigation system, making ionospheric data processing very inconvenient. Furthermore, they often require access to real-time orbit or clock bias data via network, thus relying heavily on environments with public internet access; in environments without public internet access, real-time ionospheric data extraction is impossible. Summary of the Invention
[0003] In view of this, the purpose of this application is to provide a method, apparatus, storage medium and electronic device for extracting ionospheric data.
[0004] To achieve the above objectives, this application provides a method for extracting ionospheric data, including:
[0005] Using the receiver's position as the observation position, the receiver acquires observation data for each satellite. The observation data for each satellite includes the pseudorange and phase observations corresponding to that satellite, and the corrections and broadcast ephemeris for each satellite are also acquired.
[0006] For each phase observation, a phase observation equation is constructed for that phase observation, an epoch difference model is constructed based on the phase observation equation, and a first phase observation equation is constructed based on the epoch difference model.
[0007] Based on the first phase observation equation, a linear system of equations for the true error vector of the phase observation is constructed, and the constructed projection matrix is added to obtain the true error equation system. The quasi-observation norm minimum condition is added to the true error equation system to obtain the full-rank equation system. By solving the full-rank equation system, phase observations with gross errors and cycle slips are eliminated, and the clock slip of the receiver is determined and repaired to correct the corresponding phase observations.
[0008] Ionospheric data are obtained by solving the modified phase observations according to the recursive formula of the constructed Kalman filter.
[0009] Furthermore, for each phase observation, a phase observation equation is constructed regarding that phase observation, including:
[0010] The phase observation equation is constructed as shown below.
[0011]
[0012] Where S represents the satellite system, s represents the s-th satellite, k represents the receiver, and j represents the frequency number. This represents the phase observation between satellite s and receiver k at the j-th frequency. δt represents the geometric distance from satellite s to the antenna phase center of receiver k. k δt represents the receiver clock bias. s Indicates satellite clock bias, Indicates tropospheric delay, Indicates ionospheric delay, α j Represents the frequency ratio, and f j f1 represents the value of the j-th frequency, and f2 represents the value of the 1-th frequency. This represents the receiver's hardware delay at the j-th frequency. This represents the hardware delay of the satellite at the j-th frequency. This represents the phase deviation of the receiver at the j-th frequency. λ represents the phase deviation of the satellite at the j-th frequency. j N represents the carrier wavelength at the j-th frequency. j This represents the non-difference phase integer ambiguity at the j-th frequency. This represents a modelable error. Let represent the phase observation noise at the j-th frequency, and c represent the speed of light.
[0013] Further, an epochal difference model is constructed based on the phase observation equation, and a first phase observation equation is constructed based on the epochal difference model, including:
[0014] Based on the phase observation equation, the following epoch difference model between the two epochs is constructed.
[0015]
[0016] Where Δ represents the change between epoch t+1 and epoch t;
[0017] The epoch difference model Ignore, and After performing a Taylor expansion and retaining the first-order terms, we obtain the first-phase observation equation as shown below.
[0018]
[0019] in, Let Δξ be the unit vector from satellite s to receiver k. k Let k represent the receiver displacement vector to be estimated. Δcδt represents the change in distance between the station and the satellite caused by the satellite's motion. kΔcδt represents the change in receiver clock bias k. s This indicates the change in satellite k-clock bias.
[0020] Further, a system of linear equations for the true error vector of the phase observations is constructed based on the first phase observation equation, and the constructed projection matrix is added to obtain the true error equation system, including:
[0021] In response to the number of observed satellites exceeding a preset satellite number threshold, a truth vector representing the receiver displacement vector and receiver clock bias change is constructed based on all observed satellites.
[0022] Based on the first phase observation equation, a system of linear equations is constructed between the phase observations superimposed with the true error vector and the true value vector, as shown below.
[0023]
[0024] Where A represents the preset truth vector coefficient matrix, X represents the truth vector, and X = [Δξ] k Δcδt k ], where L represents the phase observation vector, Represents the true error vector of phase observations;
[0025] Construct the projection matrix as shown below.
[0026] R = IA(A) T PA) -1 A T P;
[0027] Where R represents the projection matrix, P represents the weight matrix of the phase observations, and I represents the identity matrix;
[0028] Multiplying both sides of the linear equations by the projection on the left, we obtain the following true error equations.
[0029]
[0030] Furthermore, by adding the quasi-observation norm minimum condition to the true error equation system, a full-rank equation system is obtained, including:
[0031] Construct the quasi-observation norm minimum condition as shown below.
[0032]
[0033] By applying the quasi-standard test method to the true error equation system and attaching the quasi-standard observation norm minimum condition, a full-rank equation system is obtained as shown below.
[0034]
[0035] in, P represents the coefficient matrix corresponding to the quasi-observations. k This represents the weight matrix corresponding to the proposed observation.
[0036] Furthermore, phase observations with gross errors and cycle slips are eliminated by solving the full-rank equation system, and clock slip correction is performed after determining the clock slip of the receiver, including:
[0037] Solving the full-rank system of equations yields the conjugate transpose of the true error vector, as shown below.
[0038]
[0039] If a clustering phenomenon occurs in response to the conjugate transpose of the true error vector, then it is determined that the corresponding phase observation has gross errors and / or cycle slips.
[0040] Determine the clock jump value using the formula shown below.
[0041]
[0042]
[0043] Where Js represents the clock jump value, Round represents the rounding function, and k2 represents the preset clock jump threshold;
[0044] The phase observations are corrected according to the following correction formula to correct clock skipping.
[0045]
[0046] Furthermore, following the recursive formula of the constructed Kalman filter, the ionospheric data are obtained using the corrected phase observations, including:
[0047] The corrected phase observations are input into the recursive formula of the robust Kalman filter shown below for iteration to predict ionospheric data.
[0048]
[0049] Where t represents time, X t，t-1 M represents the one-step prediction value of the ionospheric data. t，t-1 Let K represent the variance-covariance matrix of the predicted values in the first step. t Represents the gain matrix, Θ t，t-1 Let represent the one-step transition matrix from time t-1 to time t, Q represent the variance matrix of the noise sequence of the satellite system consisting of the satellite and receiver, and R represent the variance matrix of the observation noise of the satellite system consisting of the satellite and receiver. M represents the filtered estimate of ionospheric data. t This represents the variance-covariance matrix of the filtered estimate;
[0050] In response to the determination that the standardized residual of the pseudorange observation predicted at any time in the iteration is less than or equal to a preset first residual threshold, and the standardized residual of the phase observation is less than or equal to the first residual threshold, the filtered estimate of the ionospheric data predicted at the output time is provided.
[0051] Based on the same inventive concept, this application also provides an ionospheric data extraction device, including: a data acquisition module, a first data processing module, a second data processing module, and a settlement module;
[0052] The data acquisition module is configured to use the receiver's position as the observation position, and to enable the receiver to acquire the observation data of each satellite. The observation data of each satellite includes the pseudorange observation and phase observation corresponding to that satellite, and to acquire the correction number and broadcast ephemeris of each satellite.
[0053] The first data processing module is configured to, for each phase observation, construct a phase observation equation for that phase observation, construct an epoch difference model based on the phase observation equation, and construct a first phase observation equation based on the epoch difference model;
[0054] The second data processing module is configured to construct a linear system of equations for the true error vector of the phase observations based on the first phase observation equations, and add the constructed projection matrix to obtain the true error equation system. The true error equation system is then subjected to a minimum quasi-observation norm condition to obtain a full-rank equation system. By solving the full-rank equation system, phase observations with gross errors and cycle slips are eliminated, and the clock slip of the receiver is determined and repaired to correct the corresponding phase observations.
[0055] The solution module is configured to obtain ionospheric data by using the modified phase observations according to the recursive formula of the constructed Kalman filter.
[0056] Based on the same inventive concept, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the ionospheric data extraction method described in any of the above claims.
[0057] Based on the same inventive concept, this application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions for causing the computer to execute the ionospheric data extraction method described above.
[0058] As can be seen from the above, the ionospheric data extraction method, apparatus, storage medium, and electronic device provided in this application construct a phase observation equation based on the acquired pseudorange and phase observations. By combining it with an epoch difference model, a first phase observation equation is obtained, and a linear equation system is constructed accordingly. By introducing a projection matrix into the linear equation system, a true error equation is obtained. The full-rank equation system is determined by comprehensively considering the minimum condition of the quasi-observation norm in quasi-detection. Based on this, phase observations with gross errors and cycle slips are eliminated, and clock slips are repaired to obtain the repaired phase observations. Furthermore, robust Kalman filtering is used to solve the ionospheric data, and the consistency of the ambiguity parameter and its variance is considered in each iteration. Attached Figure Description
[0059] To more clearly illustrate the technical solutions in this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0060] Figure 1 This is a flowchart of the ionospheric data extraction method according to an embodiment of this application;
[0061] Figure 2 This is a schematic diagram of the structure of the ionospheric data extraction device according to an embodiment of this application;
[0062] Figure 3 This is a schematic diagram of the electronic device structure according to an embodiment of this application. Detailed Implementation
[0063] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.
[0064] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.
[0065] As described in the background section, the methods for extracting relevant ionospheric data are still insufficient to meet the needs of actual production.
[0066] In the process of developing this application, the applicant discovered that the main problem with the relevant methods for extracting ionospheric data is that the methods for extracting ionospheric data using observations from satellite navigation systems often require the introduction of high-precision correction data from outside the satellite navigation system, which makes the calculation of ionospheric data very inconvenient. At the same time, it is often necessary to access real-time orbit or clock bias data through the network. Therefore, it relies on an environment with a public network, and it is impossible to extract ionospheric data in real time in an environment without a public network.
[0067] Based on this, one or more embodiments of this application provide a method for extracting ionospheric data.
[0068] In the embodiments of this application, the BeiDou Navigation Satellite System can be used as a specific implementation scenario. The satellite navigation system can have one or more satellites, and the receiver can obtain the relevant data of each satellite, and at the same time receive the correction numbers of satellite orbit clock error and code deviation.
[0069] In this embodiment, when observing various data from the satellite, the observation position and the receiver position can be considered to be the same.
[0070] In this embodiment, based on the above-described implementation scenario, ionospheric data, such as ionospheric delay, is used as a parameter to be estimated in real time.
[0071] The embodiments of this application are described in detail below with reference to the accompanying drawings.
[0072] refer to Figure 1 An embodiment of the present application provides a method for extracting ionospheric data, comprising the following steps:
[0073] Step S101: Using the receiver's position as the observation position, the receiver acquires the observation data of each satellite. The observation data of each satellite includes the pseudorange observation and phase observation corresponding to that satellite, and the correction number and broadcast ephemeris of each satellite are acquired.
[0074] In the embodiments of this application, the receiver can acquire observation data from various satellites, wherein the observation data may specifically include phase observations (i.e., carrier phase observations) and pseudorange observations.
[0075] Furthermore, for observable satellites, the receiver can also receive the satellite's broadcast ephemeris, orbital clock corrections, and satellite code bias, and store this data in the corresponding structures.
[0076] Step S102: For each phase observation, construct a phase observation equation for that phase observation, construct an epoch difference model based on the phase observation equation, and construct a first phase observation equation based on the epoch difference model.
[0077] In the embodiments of this application, the phase observations obtained above can be preprocessed to control the data quality.
[0078] In this embodiment, data preprocessing may include gross error removal, cycle slip detection, receiver clock slip detection and repair, etc.
[0079] Specifically, based on the observed phase measurements, the following phase observation equation can be constructed:
[0080]
[0081] Where S represents the satellite system, s represents the s-th satellite, k represents the k-th receiver, and j represents the frequency number. This represents the phase observation between satellite s and receiver k at the j-th frequency. δt represents the geometric distance from satellite s to the antenna phase center of receiver k. k δt represents the receiver clock bias. s Indicates satellite clock bias, Indicates tropospheric delay, Indicates ionospheric delay, α j Represents the frequency ratio, and f j f1 represents the value of the j-th frequency, and f2 represents the value of the 1-th frequency. This represents the receiver's hardware delay at the j-th frequency. This represents the hardware delay of the satellite at the j-th frequency. This represents the phase deviation of the receiver at the j-th frequency. λ represents the phase deviation of the satellite at the j-th frequency. j N represents the carrier wavelength at the j-th frequency. j This represents the non-difference phase integer ambiguity at the j-th frequency. This represents a modelable error. Let represent the phase observation noise at the j-th frequency, and c represent the speed of light.
[0082] In this embodiment, the modelable errors include antenna phase center correction, antenna phase entanglement, relativistic effects, and tidal correction, etc., and it is assumed that the modelable errors have been corrected into the observations using a preset empirical model.
[0083] In this embodiment, for any two epochs, based on the phase observation equation constructed above, the following epoch difference model can be formed:
[0084]
[0085] Here, Δ represents the change between epoch t+1 and epoch t, which can be the increment between epochs.
[0086] Furthermore, for high-frequency data, due to the very short intervals, the epoch difference model described above... It can be ignored, there's no rush, and... Performing a Taylor expansion, and retaining only the first-order terms, it can be expressed as follows:
[0087]
[0088] Furthermore, the formula expressed above can be used as the first phase observation equation.
[0089] in, Let Δξ be the unit vector from satellite s to receiver k. k This represents the receiver displacement vector k to be estimated. Δcδt represents the change in distance between the station and the satellite caused by the satellite's motion. k Δcδt represents the change in receiver clock bias k. s This indicates the change in satellite k-clock bias.
[0090] Furthermore, the above and Δcδt s It can be calculated using precise orbital corrections and broadcast ephemeris.
[0091] As can be seen, the first observation equation in this embodiment specifically describes the relationship between the carrier phase high-frequency epoch differential observations and the changes in receiver displacement and receiver clock bias.
[0092] Step S103: Construct a linear system of equations for the true error vector of the phase observations based on the first phase observation equation, and add the constructed projection matrix to obtain the true error equation system. Add the quasi-observation norm minimum condition to the true error equation system to obtain the full-rank equation system. By solving the full-rank equation system, phase observations with gross errors and cycle slips are eliminated, and the clock slip of the receiver is determined and repaired to correct the corresponding phase observations.
[0093] In the embodiments of this application, when the number of observable satellites is sufficient, a linear equation system can be established based on the first observation equation constructed above, and a full-rank equation system in the quasi-calibration method can be constructed to process gross errors, cycle slips and clock slips.
[0094] Specifically, a threshold for the number of satellites can be preset, such as 4 satellites. When the number of observable satellites is greater than 4, a system of linear equations can be constructed.
[0095] The receiver displacement vector to be estimated and the receiver clock bias change can be combined to form the true value vector as shown below:
[0096] X=[Δξ k Δcδt k ]
[0097] Where X represents the truth vector.
[0098] Based on this, using the truth vector and the first phase observation equation described above, we can construct the following system of linear equations:
[0099]
[0100] Where A represents the preset truth vector coefficient matrix, and L represents the phase observation vector. This represents the true error vector of the phase observation.
[0101] It can be seen that the above linear equations specifically describe the relationship between the phase observations after the true error vector is obtained and the true value vector.
[0102] Furthermore, a projection matrix can be introduced into the linear equation system to obtain the true error equation system.
[0103] Specifically, construct the projection matrix as shown below:
[0104] R = IA(A) T PA) -1 A T P;
[0105] Where R represents the projection matrix, P represents the weight matrix of the phase observations, and I represents the identity matrix.
[0106] Based on this, we can multiply both sides of the equation of the above linear equation system by the projection matrix on the left to obtain the following true error equation system:
[0107]
[0108] Based on this set of true error equations, the data can be processed by a quasi-test method that adds the minimum condition of the quasi-observation norm.
[0109] Specifically, the following quasi-observation norm minimization condition is constructed:
[0110]
[0111] Based on this, the minimum condition of the quasi-observation norm is added to the above true error equations, resulting in the full-rank equations shown below.
[0112]
[0113] in, P represents the coefficient matrix corresponding to the quasi-observations. k This represents the weight matrix corresponding to the proposed observation.
[0114] Based on this, the above full-rank system of equations can be solved, and the following results can be obtained:
[0115]
[0116] in, This represents the conjugate transpose of the true error vector.
[0117] Furthermore, when When clustering occurs, for example, one or more The value of is more than most others. If the value of is large, it can be assumed that the corresponding phase observation vector L contains gross errors or cycle slips. Therefore, the phase observations corresponding to the phase observation vector L can be removed.
[0118] Furthermore, the following check formula can be constructed to detect the clock skipping of the receiver: |Ω t (s)|=|D s (t)-D s (t-1)-(λ·φ s (t)-λ·φ(t-1))|>k1=0.001·C
[0119] Among them, Ω t(s) represents the test value of satellite s at epoch t, D represents the pseudorange observation, λ represents the wavelength of the phase observation, and k1 represents the preset test threshold.
[0120] Based on this, for any epoch, if and only if the check quantities corresponding to all observable satellites satisfy the above check quantity formula, then the epoch can be considered to have millimeter-level cycle slips.
[0121] Based on this, the cycle jump value can be determined using the following formula:
[0122]
[0123]
[0124] Where Js represents the clock jump value in milliseconds, Round represents the rounding function, and k2 represents the preset clock jump threshold.
[0125] In this embodiment, a more lenient clock jump threshold setting can be considered more conducive to maintaining the continuity of data processing. For example, the clock jump threshold can be 0.05.
[0126] Therefore, in order to maintain the pseudorange clock error benchmark while taking into account the continuity of ambiguity parameters in the estimation, when a clock jump is detected, the corresponding phase observation can be corrected to fix the clock jump and ensure the consistency between the phase observation and the pseudorange observation.
[0127] Specifically, the phase observation can be corrected according to the following repair formula:
[0128]
[0129] in, This indicates the corrected phase observation.
[0130] Step S104: According to the recursive formula of the constructed Kalman filter, the ionospheric data is obtained by solving the modified phase observations.
[0131] In the embodiments of this application, based on the above-described modified phase observations, robust Kalman filtering can be used to solve the ionospheric data.
[0132] Specifically, the recursive formula for robust Kalman filtering can be constructed as follows:
[0133]
[0134] Where t represents time, X t，t-1 M represents a one-step prediction of ionospheric data. t，t-1K represents the variance-covariance matrix of the one-step predictions. t Represents the gain matrix, Θ t，t-1 Let represent the one-step transition matrix from time t-1 to time t, Q represent the variance matrix of the noise sequence of the satellite system consisting of the satellite and receiver, and R represent the variance matrix of the observation noise of the satellite system consisting of the satellite and receiver. M represents the filtered estimate of ionospheric data. t This represents the variance-covariance matrix of the filtered estimate.
[0135] In this embodiment, for ease of description, the time is represented by t. In conjunction with the aforementioned use of t to represent the epoch, t can refer to different epochs in this embodiment.
[0136] In this embodiment, ionospheric data may be ionospheric delay, etc.
[0137] Furthermore, the weighting of phase observations and pseudorange observations can be constructed as follows:
[0138]
[0139] Among them, W i The element l in the phase observation vector L represents i The corresponding rights, k represents the corresponding standardized residual. a and k b All are constants, and their respective values can be, for example: k a =1.0 to 1.5, k b =2.0 to 3.0, and k a As the first residual threshold, k b As the second residual threshold.
[0140] Based on this, according to the weighting described above, the filtered estimate of the ionospheric data for each iteration can be predicted, and the phase and pseudorange observations corresponding to the filtered estimate can be determined. The corresponding standardized residuals can then be used to process the pseudorange and / or phase observations.
[0141] Specifically, for phase observations and pseudorange observations, when the standardized residual corresponding to the phase observation is less than or equal to the first residual threshold, it is considered that no cycle slip has occurred, and there is no need to reset the ambiguity parameters or reduce the corresponding weights.
[0142] Furthermore, when the standardized residual corresponding to the pseudorange observation is also less than or equal to the first residual threshold, there is no need to reduce the weight or process the variance corresponding to the ambiguity parameter.
[0143] Based on this, the corresponding filtered estimate of the ionospheric data can be output.
[0144] In some other embodiments, when the standardized residual corresponding to the phase observation is greater than a first residual threshold, it is considered that a cycle slip has occurred and the ambiguity parameters need to be reset without reducing the corresponding weights.
[0145] Furthermore, when the standardized residual corresponding to the pseudorange observation is greater than the first residual threshold, in addition to reducing the corresponding weights, the variance corresponding to the ambiguity parameter can be appropriately increased. The specific amplification factor can be set based on experience, for example, it can be amplified by 2.5 times.
[0146] Based on this, after applying the aforementioned ambiguity parameters and weight reduction, another round of iterations can be performed.
[0147] As can be seen, the ionospheric data extraction method of this application constructs a phase observation equation based on the acquired pseudorange and phase observations. By combining it with an epoch difference model, a first phase observation equation is obtained, and a linear equation system is constructed accordingly. By introducing a projection matrix into the linear equation system, a true error equation is obtained. The full-rank equation system is determined by comprehensively considering the minimum condition of the quasi-observation norm in quasi-detection. Based on this, phase observations with gross errors and cycle slips are eliminated, and clock slips are repaired to obtain the repaired phase observations. Furthermore, robust Kalman filtering is used to solve the ionospheric data, and the consistency of the ambiguity parameter and its variance is considered in each iteration.
[0148] It should be noted that the method of the embodiments of this application can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of the embodiments of this application, and the multiple devices will interact with each other to complete the method described.
[0149] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0150] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, the embodiments of this application also provide an ionospheric data extraction device.
[0151] refer to Figure 2 The ionospheric data extraction device includes: a data acquisition module 201, a first data processing module 202, a second data processing module 203, and a settlement module 204;
[0152] The data acquisition module 201 is configured to use the receiver's position as the observation position, and to enable the receiver to acquire the observation data of each satellite. The observation data of each satellite includes the pseudorange observation and phase observation corresponding to the satellite, and to acquire the correction number and broadcast ephemeris of each satellite.
[0153] The first data processing module 202 is configured to, for each phase observation, construct a phase observation equation for that phase observation, construct an epoch difference model based on the phase observation equation, and construct a first phase observation equation based on the epoch difference model;
[0154] The second data processing module 203 is configured to construct a linear equation set of the true error vector of the phase observation based on the first phase observation equation, and add the constructed projection matrix to obtain the true error equation set. The true error equation set is then subjected to the quasi-observation norm minimum condition to obtain the full-rank equation set. The phase observations with gross errors and cycle slips are eliminated by solving the full-rank equation set, and the clock slip of the receiver is determined and repaired to correct the corresponding phase observations.
[0155] The solution module 204 is configured to obtain ionospheric data by using the modified phase observations according to the recursive formula of the constructed Kalman filter.
[0156] For ease of description, the above apparatus is described in terms of its functions, divided into various modules. Of course, in implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware.
[0157] The apparatus described above is used to implement the corresponding ionospheric data extraction method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.
[0158] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the ionospheric data extraction method as described in any of the above embodiments.
[0159] Figure 3This embodiment illustrates a more specific hardware structure of an electronic device, which may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.
[0160] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.
[0161] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this application are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.
[0162] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.
[0163] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).
[0164] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.
[0165] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this application, and not necessarily all the components shown in the figures.
[0166] The apparatus described above is used to implement the corresponding ionospheric data extraction method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.
[0167] Based on the same inventive concept, corresponding to the methods of any of the above embodiments, this application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the ionospheric data extraction method as described in any of the above embodiments.
[0168] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.
[0169] The computer instructions stored in the storage medium of the above embodiments are used to cause the computer to execute the ionospheric data extraction method as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.
[0170] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in detail for the sake of brevity.
[0171] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.
[0172] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.
[0173] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.
Claims
1. A method for extracting ionospheric data, characterized in that, include: Using the receiver's position as the observation position, the receiver acquires observation data for each satellite. The observation data for each satellite includes the pseudorange and phase observations corresponding to that satellite, and the corrections and broadcast ephemeris for each satellite are also acquired. For each phase observation, a phase observation equation is constructed for that phase observation, an epoch difference model is constructed based on the phase observation equation, and a first phase observation equation is constructed based on the epoch difference model. Based on the first phase observation equation, a linear system of equations for the true error vector of the phase observation is constructed, and the constructed projection matrix is added to obtain the true error equation system. The quasi-observation norm minimum condition is added to the true error equation system to obtain the full-rank equation system. By solving the full-rank equation system, phase observations with gross errors and cycle slips are eliminated, and the clock slip of the receiver is determined and repaired to correct the corresponding phase observations. Ionospheric data are obtained by solving the modified phase observations according to the recursive formula of the constructed Kalman filter. The process of constructing a system of linear equations for the true error vector of the phase observation based on the first phase observation equation, and adding the constructed projection matrix to obtain the true error equation system, includes: In response to the number of observed satellites exceeding a preset satellite number threshold, a truth vector representing the receiver displacement vector and receiver clock bias change is constructed based on all observed satellites. Based on the first phase observation equation, a system of linear equations is constructed between the phase observations superimposed with the true error vector and the true value vector, as shown below. ； Where A represents the preset truth vector coefficient matrix, X represents the truth vector, and , Let k represent the receiver displacement vector to be estimated. The clock bias of receiver k represents the change in clock bias, and L represents the phase observation vector. Represents the true error vector of phase observations; Construct the projection matrix as shown below. ； Where R represents the projection matrix, P represents the weight matrix of the phase observations, and I represents the identity matrix; Multiplying both sides of the linear equations by the projection matrix on the left, we obtain the true error equations shown below. ； The addition of the quasi-observation norm minimization condition to the true error equation system yields a full-rank equation system, including: Construct the quasi-observation norm minimum condition as shown below. ； By employing the quasi-calibration method and adding the quasi-observation norm minimum condition to the true error equation system, a full-rank equation system is obtained as shown below. ； in, This represents the coefficient matrix corresponding to the quasi-observations. This represents the weight matrix corresponding to the proposed observation.
2. The method according to claim 1, characterized in that, For each phase observation, the phase observation equation for that phase observation is constructed, including: The phase observation equation is constructed as shown below. ； Where S represents the satellite system, s represents the s-th satellite, k represents the receiver, and j represents the frequency number. This represents the phase observation between satellite s and receiver k at the j-th frequency. This represents the geometric distance from satellite s to the antenna phase center of receiver k. Indicates receiver clock bias. Indicates satellite clock bias, Indicates tropospheric delay, Indicates ionospheric delay, Represents the frequency ratio, and , This represents the value of the j-th frequency. This represents the value of the first frequency. This represents the phase deviation of the receiver at the j-th frequency. This represents the phase deviation of the satellite at the j-th frequency. This represents the carrier wavelength at the j-th frequency. This represents the non-difference phase integer ambiguity at the j-th frequency. This represents a modelable error. Let represent the phase observation noise at the j-th frequency, and c represent the speed of light.
3. An ionospheric data extraction device, characterized in that, include: The system comprises a data acquisition module, a first data processing module, a second data processing module, and a solution module. The data acquisition module is configured to use the receiver's position as the observation position, and to enable the receiver to acquire the observation data of each satellite. The observation data of each satellite includes the pseudorange observation and phase observation corresponding to that satellite, and to acquire the correction number and broadcast ephemeris of each satellite. The first data processing module is configured to, for each phase observation, construct a phase observation equation for that phase observation, construct an epoch difference model based on the phase observation equation, and construct a first phase observation equation based on the epoch difference model; The second data processing module is configured to construct a linear system of equations for the true error vector of the phase observations based on the first phase observation equations, and add the constructed projection matrix to obtain the true error equation system. The true error equation system is then subjected to a minimum quasi-observation norm condition to obtain a full-rank equation system. By solving the full-rank equation system, phase observations with gross errors and cycle slips are eliminated, and the clock slip of the receiver is determined and repaired to correct the corresponding phase observations. The solution module is configured to obtain ionospheric data by using the modified phase observations according to the recursive formula of the constructed Kalman filter. The process of constructing a system of linear equations for the true error vector of the phase observation based on the first phase observation equation, and adding the constructed projection matrix to obtain the true error equation system, includes: In response to the number of observed satellites exceeding a preset satellite number threshold, a truth vector representing the receiver displacement vector and receiver clock bias change is constructed based on all observed satellites. Based on the first phase observation equation, a system of linear equations is constructed between the phase observations superimposed with the true error vector and the true value vector, as shown below. ； Where A represents the preset truth vector coefficient matrix, X represents the truth vector, and , Let k represent the receiver displacement vector to be estimated. The clock bias of receiver k represents the change in clock bias, and L represents the phase observation vector. Represents the true error vector of phase observations; Construct the projection matrix as shown below. ； Where R represents the projection matrix, P represents the weight matrix of the phase observations, and I represents the identity matrix; Multiplying both sides of the linear equations by the projection matrix on the left, we obtain the true error equations shown below. ； The addition of the quasi-observation norm minimization condition to the true error equation system yields a full-rank equation system, including: Construct the quasi-observation norm minimum condition as shown below. ； By employing the quasi-calibration method and adding the quasi-observation norm minimum condition to the true error equation system, a full-rank equation system is obtained as shown below. ； in, This represents the coefficient matrix corresponding to the quasi-observations. This represents the weight matrix corresponding to the proposed observation.
4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 2.
5. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing the computer to perform the method according to any one of claims 1 to 2.