Track jump handling methods, electronic equipment and computer program products

Abstract

This invention discloses an orbital jump processing method, electronic device, and computer program product. The orbital jump processing method includes: determining a first mean square error in fitting the current orbit of the satellite to a first orbit, and a second mean square error in fitting the current orbit to a second orbit; the first orbit is the orbit preceding the current orbit of the satellite, and the second orbit is a predicted orbit obtained by batch processing based on the current orbit; determining the value of the orbital smoothing weight matrix in a preset first observation equation based on the first mean square error and the second mean square error; the orbital smoothing weight matrix characterizes the degree of dependence on the current orbit or the first orbit during orbit fitting; and determining the orbital value used for orbit updating based on the first observation equation.

Description

Technical Field

[0001] This invention relates to the field of transmission technology, and in particular to a method for handling track jumps, electronic equipment, and computer program products. Background Technology

[0002] Batch forecasting is a commonly used method for real-time precise satellite orbit determination. Under normal stress conditions, satellite motion changes smoothly, and its motion state can be described by a high-precision dynamic model. Batch-processed orbit prediction methods can obtain relatively accurate real-time orbits. However, when the satellite's stress changes, such as entering the Earth's shadow or undergoing abnormal motion conditions like orbital maneuvers, the accuracy of the dynamic model decreases significantly, leading to a reduction in the accuracy of the predicted orbit. Furthermore, the periodic updates of batch-processed orbits can cause significant orbital jumps near the update epoch. The real-time orbits obtained by this method also contain arc segment splicing with a certain update cycle, resulting in discontinuities between adjacent update arc segments, severely affecting the accuracy and stability of the orbit determination method. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a track jump processing method, electronic device, and computer program product that can accurately measure link delay.

[0004] The technical solution of this invention is implemented as follows:

[0005] On one hand, embodiments of the present invention provide a method for handling orbital jumps, the method comprising:

[0006] Determine the first mean square error of fitting the current orbit of the satellite to the first orbit, and the second mean square error of fitting the current orbit to the second orbit; the first orbit is the orbit preceding the current orbit of the satellite, and the second orbit is the predicted orbit obtained by batch processing based on the current orbit;

[0007] Based on the first mean square error and the second mean square error, the value of the orbit smoothing weight matrix in the preset first observation equation is determined; the orbit smoothing weight matrix characterizes the degree of dependence on the current orbit or the first orbit when fitting the orbit.

[0008] The orbit values ​​used for orbit updates are determined based on the first observation equation.

[0009] In the above scheme, determining the first intermediate error of fitting the current orbit of the satellite to the first orbit, and the second intermediate error of fitting the current orbit to the second orbit, includes:

[0010] Based on the second observation equation, the first residual of the orbital observations of the current orbit and the first orbit at multiple epochs is determined, and the second residual of the orbital observations of the current orbit and the second orbit at multiple epochs is determined.

[0011] The first mean error is determined based on the first residual of multiple epochs, and the second mean error is determined based on the second residual of multiple epochs.

[0012] In the above scheme, the method further includes:

[0013] The second observation equation is constructed using the satellite positions at the same epoch in adjacent orbits as the observation values.

[0014] In the above scheme, the method further includes:

[0015] Based on the orbital smoothing weight matrix and the second observation equation, the first observation equation is constructed.

[0016] In the above scheme, determining the first residual of the orbital observations of the current orbit and the first orbit at multiple epochs based on the second observation equation, and determining the second residual of the orbital observations of the current orbit and the second orbit at multiple epochs, includes:

[0017] The second observation equation is solved using the least squares algorithm to obtain a first estimate of the similarity transformation parameters and a second estimate of the satellite's position correction at the reference epoch; the second observation equation includes the similarity transformation parameters and the position correction at the reference epoch.

[0018] Based on the first estimate and the second estimate, the first residual and the second residual for multiple epochs are determined.

[0019] In the above scheme, determining the orbit value for orbit update based on the first observation equation includes:

[0020] The first observation equation is solved using the least squares algorithm to obtain a third estimate of the similarity transformation parameters and a fourth estimate of the satellite's position correction at the reference epoch; the first observation equation includes the similarity transformation parameters and the position correction at the reference epoch.

[0021] Based on the third and fourth estimates, the position correction for the orbital deviation in the first epoch of the second orbit is determined.

[0022] Based on the position correction of the orbital deviation at the first epoch in the second orbit, and the orbital value at the first epoch in the second orbit, the orbital value used for orbital update is determined.

[0023] In the above scheme, determining the position correction amount of the orbital deviation in the first epoch of the second orbit based on the third estimate and the fourth estimate includes:

[0024] Based on the third and fourth estimates, the position correction of the orbital deviation of the reference epoch in the second orbit is determined;

[0025] Based on the position correction of the orbital deviation at the reference epoch in the second orbit and the position correction of the orbital deviation at the last epoch in the second orbit, the position correction of the orbital deviation at the first epoch in the second orbit is determined; the position correction of the orbital deviation at the last epoch in the second orbit is constrained to 0.

[0026] In the above scheme, the orbital smoothing weight matrix is:

[0027]

[0028] Where p = l·s, l is a preset empirical coefficient, and s is a preset variance threshold. This is the first type of error. Let t be the second mean error. e For any epoch, (0 is the reference epoch, t) i Characterizing the second orbital, t i-1 Characterizing the current orbit, t i-2 Characterize the first orbit.

[0029] On the other hand, embodiments of the present invention provide an electronic device including a processor and a memory interconnected thereto, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the steps of the orbital jump processing method provided in the first aspect of the present invention.

[0030] On the other hand, embodiments of the present invention provide a computer-readable storage medium, comprising: the computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the orbital jump processing method provided in the first aspect of the present invention.

[0031] On the other hand, this application also provides a computer program product, including a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of the above-described orbital jump processing method.

[0032] This embodiment of the application determines a first mean square error in fitting the satellite's current orbit to a first orbit, and a second mean square error in fitting the current orbit to a second orbit. The first orbit is the previous orbit of the satellite's current orbit, and the second orbit is the predicted orbit obtained through batch processing based on the current orbit. Based on the first and second mean square errors, the value of the orbit smoothing weight matrix in the preset first observation equation is determined. The orbit smoothing weight matrix characterizes the degree of dependence on the current orbit or the first orbit during orbit fitting. Based on the first observation equation, the orbit value used for orbit updating is determined. This embodiment of the application determines the first mean square error in fitting the satellite's current orbit to the previous orbit, and the second mean square error in fitting the current orbit to the predicted orbit. Based on the first and second mean square errors, the value of the orbit smoothing weight matrix in the first observation equation is determined. The smoothness of the satellite orbit is adjusted based on the value of the orbit smoothing weight matrix, achieving real-time orbit smoothing. This effectively solves the problem of satellite orbit jumps, improves the stability and accuracy of precise satellite orbit determination, and avoids the problem of large orbit jumps in the preceding and following arc segments when the batch-processed predicted orbit is in the case of satellite entering or leaving the Earth's shadow or having few observations, thus severely reducing orbit accuracy. This achieves adaptive robust real-time orbit smoothing. Attached Figure Description

[0033] Figure 1 This is a schematic diagram illustrating the implementation process of a trajectory jump processing method provided in an embodiment of the present invention;

[0034] Figure 2 This is a schematic diagram of a track jump processing device provided in an embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Global Navigation Satellite System (GNSS) real-time precise positioning services have been widely applied in various fields of social production and scientific research, including precision agriculture, surveying and remote sensing, autonomous driving, space weather monitoring, and disaster monitoring. Stable and reliable real-time high-precision orbit products are one of the important foundations for wide-area, real-time, and high-precision positioning services.

[0038] Currently, high-precision GNSS real-time orbit determination technology mainly includes two methods: batch prediction and real-time filtering. Most commonly used real-time precise orbit products for navigation satellites are obtained through dynamic model prediction. This involves using hourly updated observation files, employing a post-processing batch model to calculate satellite position, velocity, and dynamic parameters, and then performing orbit integration to obtain the predicted orbit. The "optimal" estimate of the orbital state at a given epoch is determined using all observation data; this is the so-called batch algorithm orbit determination. Real-time filtering orbit determination methods, on the other hand, calculate and update orbital state parameters epoch-by-epoch based on real-time observation data to obtain real-time orbit products. It can improve the real-time orbit accuracy and reliability during periods of satellite dynamic model anomalies by adjusting process noise and dynamically adjusting the weighting relationship between geometric and dynamic information, among other stochastic model compensation methods. However, its system stability and orbit determination error are relatively large, requiring a long time (ten to tens of hours) to achieve centimeter-level convergence in filtered orbit accuracy, and it still struggles to handle centimeter-level real-time orbit jumps under dynamic anomalies. Therefore, batch prediction is currently the more commonly used method for handling real-time precise orbit jumps in navigation satellites.

[0039] Batch-processed orbit prediction is characterized by stability and high orbit accuracy. Under normal stress conditions, satellite motion changes smoothly, and its motion state can be described by a high-precision dynamic model. Batch-processed orbit prediction methods can obtain highly accurate real-time orbit products. However, once the satellite's stress conditions change abnormally, such as entering the Earth's shadow or undergoing orbital maneuvers, the accuracy of the dynamic model decreases significantly, leading to a significant reduction in the accuracy of the predicted orbit, or even rendering it unusable. Furthermore, the periodic updates of batch-processed orbits (1 hour, 3 hours, 6 hours, etc.) can cause significant orbital jumps near the update epoch. The real-time orbits obtained by this method also suffer from arc segment splicing within a certain update cycle, resulting in discontinuities between adjacent update arcs, severely impacting the accuracy and stability of the orbit determination method.

[0040] To address the shortcomings of the aforementioned related technologies, embodiments of the present invention provide an orbital jump processing method that can improve the stability and accuracy of satellite precise orbit determination. To illustrate the technical solution described in this invention, specific embodiments are provided below.

[0041] Figure 1 This is a schematic diagram illustrating the implementation flow of a track jump processing method provided in an embodiment of the present invention. The execution subject of the track jump processing method is an electronic device. (Reference) Figure 1 The methods for handling orbital jumps include:

[0042] S101, determine the first mean square error of fitting the current orbit of the satellite to the first orbit, and the second mean square error of fitting the current orbit to the second orbit; the first orbit is the orbit preceding the current orbit of the satellite, and the second orbit is the predicted orbit obtained by batch processing based on the current orbit.

[0043] It should be understood that the second orbit is a predicted orbit obtained using batch processing techniques in related technologies, and the satellite has not yet been connected to the second orbit.

[0044] This application refers to the interval between two adjacent batch orbit updates as the update time, which varies from 6 hours, 3 hours, to 1 hour. The two batch orbits are also referred to as adjacent arc segments. Typically, batch orbits are calculated over 40-48 hours and predicted 24 hours later. For an initial epoch of t... i-2 The arc segment, with an arc length of t for the track jump processing section. arc The batch processing time is Δt. slv The orbit is updated every Δt hours, and the real-time orbit period available to the user is [t]. i-2 +Δt,t i-2 +2Δt]. For the (i-1)th orbit update, the real-time orbit can be either the (i-2)th or (i-1)th orbit. Due to geometric and dynamic factors, the two are not the same, resulting in orbital jumps.

[0045] The mean square error is a numerical standard for measuring the accuracy of observations. It is the square root of the ratio of the sum of the squares of the deviations between the observed values ​​and the true values ​​to the number of observations (n). The magnitude of the mean square error reflects the accuracy of the set of observations.

[0046] Here, the mean error of the two orbit fittings refers to the median of the orbit residuals after the orbits are fitted according to dynamics. The mean error can be calculated from the residuals of the corresponding orbit observations.

[0047] For example, determining the batch processing track before and after the i-th update. and Although for these two sets of orbits, any identical epoch t e The satellite positions should be equal, but due to orbital dynamics and geometric observation errors, there is a difference ΔO(t) between them. e ):

[0048]

[0049] In the formula and Orbits and In t e The satellite positions of the epoch.

[0050] Batch tracks before and after the i-th update and Satellite positions at the same epoch t and Construct orbit fitting equations for pseudo-observations:

[0051]

[0052] In the formula, x represents the parameter to be estimated, which includes similarity transformation parameters H such as translation, rotation, and scaling, as well as the satellite orbit correction dO(t) for this epoch. Considering that dO(t) can be obtained through the state transition matrix Φ(t,t... i0 ) and reference epoch t i0 satellite position correction dO(t) i0 )get:

[0053] dO(t)=Φ(t,t i0 )dO(t i0 (3)

[0054] Therefore, equation (2) can be expressed in the following form:

[0055]

[0056]

[0057] in For satellite in orbit The positions of the X, Y, and Z directions at the epoch t.

[0058] Equation (4) can be directly solved using the least squares algorithm to obtain the similarity transformation parameters H and the satellite's position at reference epoch t. i0 satellite position correction dO(t) i0 The estimated value of ) and Equal weights can be used when solving least squares problems.

[0059] Substituting this into equation (4), we can obtain the residual of the corresponding orbital observation at any time:

[0060]

[0061] Based on the residual v(t) of orbital observations at multiple time points e The mean error σ of the orbit fitting can be calculated using the following formula. AB :

[0062]

[0063] Where N is the number of observations.

[0064] The orbit can be calculated using the method described above. and The standard error of the fit, if the current trajectory is Then the σ calculated above AB The second mean error can be calculated similarly to the first mean error.

[0065] S102, based on the first mean square error and the second mean square error, determine the value of the orbit smoothing weight matrix in the preset first observation equation; the orbit smoothing weight matrix characterizes the degree of dependence on the current orbit or the first orbit when fitting the orbit.

[0066] In related technologies, when the reference time is selected as the orbital jump time t u Then the estimated similarity transformation parameters are used. and The track can be calculated using the following formula. and orbit At time t u jump

[0067]

[0068] Add it to orbit Chinese u The satellite positions at subsequent moments can then be used to establish real-time connections between adjacent orbits.

[0069] Related technologies force the latter segment of the track to be Connect to the previous track Above. Affected by dynamic model and geometric observation errors, especially when the orbit is in or out of the Earth's shadow and when there are few observations, the orbital jumps in the preceding and following arc segments are relatively large.

[0070] It is evident that forced connections severely degrade real-time track accuracy; therefore, it is necessary to set appropriate adaptive factors to dynamically control the degree of real-time track smoothing. The track smoothing weight matrix P(t) consists of adaptive factors p. e The error can be calculated based on the mean square error of the above orbit fitting.

[0071] The aforementioned orbit smoothing weight matrix controls the degree of dependence on adjacent arc segments (the current orbit and the first orbit) during the smoothing process of the current arc segment. When the mean square error (second mean square error) of the fitting of the current arc segment is relatively large, the smoothed orbit is mainly calculated based on the previous arc segment; otherwise, the current arc segment information is mainly used for fitting and smoothing. Here, arc segment refers to orbit.

[0072] For example, different values ​​of the orbital smoothing weight matrix can be set according to the magnitudes of the first and second intermediate errors, and the corresponding relationships can be pre-stored in the database. When needed, the corresponding values ​​of the orbital smoothing weight matrix can be read according to the magnitudes of the first and second intermediate errors.

[0073] In one embodiment, the orbital smoothing weight matrix is:

[0074]

[0075] Where p = l·s, l is a preset empirical coefficient, and s is a preset variance threshold. This is the first type of error. The second mean error, t e For any time t i0 For reference time, t i Characterizing the second orbital, t i-1 Characterizing the current orbit, t i-2 Characterizes the first orbital.

[0076] l is an empirical coefficient used to adjust the weights of observations at different fitting epochs t in equation (2), and can be set to 1–5; s is the orbital variance threshold for adjacent arc segments, which can be set to 3 for GPS and GLONASS, and to 5 for BDS and Galileo. e This allows control over the dependence on adjacent arc segments during the smoothing process of the current arc segment. When the mean square error of the current arc segment's fit... When the second mean square error is relatively large, the smoothed trajectory is mainly calculated based on the previous arc segment; otherwise, the current arc segment information is mainly used for fitting and smoothing.

[0077] S103, determine the orbit value for orbit update based on the first observation equation.

[0078] For example, the orbital smoothing weight matrix is ​​denoted as P(t). e If ), then the first observation equation can be expressed as:

[0079]

[0080] Among them, the value of the orbit smoothing weight matrix can dynamically control the degree of real-time orbit smoothing, avoiding large orbit jumps in the preceding and following arc segments when the batch-processed predicted orbit is in situations such as satellite entering or leaving the Earth's shadow or when there are few observations.

[0081] Based on the first observation equation, the orbit values ​​used for orbit updates are re-predicted and applied to subsequent orbit updates to achieve real-time orbit smoothing.

[0082] Here, batch processing, a technique used in related technologies, can be used to predict the trajectory. The difference is that this application sets a weight parameter to control the smoothness of the trajectory and avoid large jumps in the trajectory between the preceding and following arc segments.

[0083] This embodiment of the application determines a first mean square error in fitting the satellite's current orbit to a first orbit, and a second mean square error in fitting the current orbit to a second orbit. The first orbit is the previous orbit of the satellite's current orbit, and the second orbit is the predicted orbit obtained through batch processing based on the current orbit. Based on the first and second mean square errors, the value of the orbit smoothing weight matrix in the preset first observation equation is determined. The orbit smoothing weight matrix characterizes the degree of dependence on the current orbit or the first orbit during orbit fitting. Based on the first observation equation, the orbit value used for orbit updating is determined. This embodiment of the application determines the first mean square error in fitting the satellite's current orbit to the previous orbit, and the second mean square error in fitting the current orbit to the predicted orbit. Based on the first and second mean square errors, the value of the orbit smoothing weight matrix in the first observation equation is determined. The smoothness of the satellite orbit is adjusted based on the value of the orbit smoothing weight matrix, achieving real-time orbit smoothing. This effectively solves the problem of satellite orbit jumps, improves the stability and accuracy of precise satellite orbit determination, and avoids the problem of large orbit jumps in the preceding and following arc segments when the batch-processed predicted orbit is in the case of satellite entering or leaving the Earth's shadow or having few observations, thus severely reducing orbit accuracy. This achieves adaptive robust real-time orbit smoothing.

[0084] In one embodiment, determining the first mean square error of fitting the current orbit of the satellite to the first orbit, and the second mean square error of fitting the current orbit to the second orbit, includes:

[0085] Based on the second observation equation, the first residual of the orbital observations of the current orbit and the first orbit at multiple epochs is determined, and the second residual of the orbital observations of the current orbit and the second orbit at multiple epochs is determined.

[0086] The first mean error is determined based on the first residual of multiple epochs, and the second mean error is determined based on the second residual of multiple epochs.

[0087] Prior to this, the method also included:

[0088] The second observation equation is constructed using the satellite positions at the same epoch in adjacent orbits as the observation values.

[0089] For example, the batch tracks before and after the i-th update and Satellite positions at the same epoch t and Construct a second observation equation for the pseudo-observations:

[0090]

[0091] For example, For the current orbit, This is the second track.

[0092] In the formula, x represents the parameter to be estimated, which includes similarity transformation parameters H such as translation, rotation, and scaling, as well as the satellite orbit correction dO(t) for this epoch. Considering that dO(t) can be obtained through the state transition matrix Φ(t,t... i0 ) and reference epoch t i0 satellite position correction dO(t) i0 )get:

[0093] dO(t)=Φ(t,t i0 )dO(t i0 )

[0094] Therefore, the second observation equation can be expressed in the following form:

[0095]

[0096]

[0097] in For satellite in orbit The positions of the X, Y, and Z directions at the epoch t.

[0098] In one embodiment, determining the first residual of the orbital observations of the current orbit and the first orbit at multiple epochs based on the second observation equation, and determining the second residual of the orbital observations of the current orbit and the second orbit at multiple epochs, includes:

[0099] The second observation equation is solved using the least squares algorithm to obtain a first estimate of the similarity transformation parameters and a second estimate of the satellite's position correction at the reference epoch; the second observation equation includes the similarity transformation parameters and the position correction at the reference epoch.

[0100] Based on the first estimate and the second estimate, the first residual and the second residual for multiple epochs are determined.

[0101] The second observation equation can be directly solved using the least squares algorithm to obtain the similarity transformation parameters H and the satellite's position at reference epoch t. i0 satellite position correction dO(t) i0 The estimated value of ) and Equal weights can be used when solving least squares problems.

[0102] Substituting this into equation (4), we can obtain the residuals of the corresponding orbital observations for any epoch:

[0103]

[0104] Based on the orbital observation residuals v(t) from multiple epochs e The mean error σ of the orbit fitting can be calculated using the following formula. AB :

[0105]

[0106] Where N is the number of observations.

[0107] Based on the above method, the first and second mean square errors can be calculated.

[0108] In one embodiment, the method further includes:

[0109] Based on the orbital smoothing weight matrix and the second observation equation, the first observation equation is constructed.

[0110] The orbital smoothing weight matrix is ​​added as a weighting parameter to the second observation equation, thus obtaining the weighted first observation equation.

[0111] In the above embodiments, the second observation equation is expressed in the following form:

[0112]

[0113] The orbital smoothing weight matrix is ​​denoted as P(t). e If ), then the first observation equation can be expressed as:

[0114]

[0115] In one embodiment, the orbital smoothing weight matrix is:

[0116]

[0117] Where p = l·s, l is a preset empirical coefficient, and s is a preset variance threshold. This is the first type of error. The second mean error, t e For any epoch, t i0 For reference epoch. t i Characterizing the second orbital, t i-1 Characterizing the current orbit, t i-2 Characterize the first orbit.

[0118] The aforementioned orbit smoothing weight matrix can control the degree of dependence on adjacent orbits (the current orbit and the first orbit) during the smoothing process. When the mean square error (second mean square error) of the current orbit fitting is relatively large, the smoothed orbit is mainly calculated based on the previous orbit; otherwise, the current orbit information is mainly used for fitting and smoothing.

[0119] In one embodiment, determining the orbit value for orbit update based on the first observation equation includes:

[0120] The first observation equation is solved using the least squares algorithm to obtain a third estimate of the similarity transformation parameters and a fourth estimate of the satellite's position correction at the reference epoch; the first observation equation includes the similarity transformation parameters and the position correction at the reference epoch.

[0121] Based on the third and fourth estimates, the position correction for the orbital deviation in the first epoch of the second orbit is determined.

[0122] Based on the position correction of the orbital deviation at the first epoch in the second orbit, and the orbital value at the first epoch in the second orbit, the orbital value used for orbital update is determined.

[0123] After using least squares adjustment, the similarity transformation parameters H and the satellite at the reference time t can be obtained. i0 satellite position correction dO(t) i0 The latest estimate of the orbit can be obtained using the above equation (4). Chinese e Correction for orbital deviation at epoch It can be used for orbit updates, and its components are: The corrected orbital values ​​can be calculated using the following formula:

[0124]

[0125] Among them, t e For the first epoch, This is the position correction for the orbital deviation in the first epoch of the second orbit. This represents the orbital value at the first epoch in the second orbit. The original orbital value in the second orbit. Based on the correction amount for track deviation For the original orbital value Make corrections to obtain the corrected orbital values. The corrected orbit values ​​are applied to subsequent updated orbits to achieve real-time orbit smoothing.

[0126] In one embodiment, determining the position correction amount of the orbital deviation at the first epoch in the second orbit based on the third estimate and the fourth estimate includes:

[0127] Based on the third and fourth estimates, the position correction of the orbital deviation of the reference epoch in the second orbit is determined;

[0128] Based on the position correction of the orbital deviation at the reference epoch in the second orbit and the position correction of the orbital deviation at the last epoch in the second orbit, the position correction of the orbital deviation at the first epoch in the second orbit is determined; the position correction of the orbital deviation at the last epoch in the second orbit is constrained to 0.

[0129] Based on the third and fourth estimates, the position correction of the orbital deviation at the reference epoch is calculated according to the above equation (4). The last epoch t i1 The correction constraint at a certain point is 0, at which point the prior deviation correction of the current arc segment at any time is... It can be represented as:

[0130]

[0131]

[0132]

[0133] in, In t i1 The positional component of the epoch is Using the above formula It can retrieve track values ​​that users can use for track updates.

[0134] This application's embodiments introduce transformation parameters when fitting adjacent arc segments using a dynamic model to obtain the median of the orbit residuals, representing the fitting accuracy. Based on this, an orbit smoothing weight matrix is ​​constructed to adjust the smoothing weights, achieving real-time orbit smoothing. This avoids the problem of large orbit jumps between arc segments during batch-processed predictions, which severely reduces orbit accuracy due to satellite entry / exit from Earth's shadow or limited observations. This achieves adaptive robust real-time orbit smoothing, effectively solving the orbit jump problem and improving the stability and accuracy of precise satellite orbit determination. Real-time orbit smoothing is achieved by estimating the jumps between adjacent arc segments at the orbit update time and applying them to subsequent orbit updates.

[0135] This application provides high-precision orbit data by correcting errors in GNSS satellite orbit prediction by improving the accuracy of GNSS satellite orbit determination. In satellite-based augmentation services, the precise orbit is broadcast to the user terminal as one of the precision corrections. Since satellite orbit errors have been largely eliminated, positioning accuracy can be significantly improved.

[0136] The technical solutions proposed in this application can be applied to future new products with satellite-based augmentation positioning. Satellite-based augmentation positioning provides high-precision positioning services for fields such as intelligent driving, drones, marine construction, and surveying and mapping, and has a broad market prospect and commercial value.

[0137] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0138] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0139] It should be noted that the technical solutions described in the embodiments of the present invention can be combined arbitrarily without conflict.

[0140] In addition, in the embodiments of the present invention, "first," "second," etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0141] refer to Figure 2 , Figure 2 This is a schematic diagram of a track jump processing device provided in an embodiment of the present invention, as shown below. Figure 2 As shown, the device includes:

[0142] The first determining module is used to determine the first intermediate error of fitting the current orbit of the satellite to the first orbit, and the second intermediate error of fitting the current orbit to the second orbit; the first orbit is the orbit preceding the current orbit of the satellite, and the second orbit is the predicted orbit obtained by batch processing based on the current orbit;

[0143] The second determining module is used to determine the value of the orbit smoothing weight matrix in the preset first observation equation based on the first mean square error and the second mean square error; the orbit smoothing weight matrix characterizes the degree of dependence on the current orbit or the first orbit when fitting the orbit.

[0144] The third determining module is used to determine the orbit value for orbit update based on the first observation equation.

[0145] In one embodiment, the first determining module is specifically used for:

[0146] Based on the second observation equation, the first residual of the orbital observations of the current orbit and the first orbit at multiple epochs is determined, and the second residual of the orbital observations of the current orbit and the second orbit at multiple epochs is determined.

[0147] The first mean error is determined based on the first residual of multiple epochs, and the second mean error is determined based on the second residual of multiple epochs.

[0148] In one embodiment, the device further includes:

[0149] The first construction module is used to construct the second observation equation using the satellite positions at the same epoch in adjacent orbits as observation values.

[0150] In one embodiment, the device further includes:

[0151] The second construction module is used to construct the first observation equation based on the orbital smoothing weight matrix and the second observation equation.

[0152] In one embodiment, the first determining module is specifically used to: solve the second observation equation based on the least squares algorithm to obtain a first estimated value of the similarity transformation parameters and a second estimated value of the position correction of the satellite at the reference epoch; the second observation equation includes the similarity transformation parameters and the position correction of the reference epoch;

[0153] Based on the first estimate and the second estimate, the first residual and the second residual for multiple epochs are determined.

[0154] In one embodiment, the third determining module is specifically used for:

[0155] The first observation equation is solved using the least squares algorithm to obtain a third estimate of the similarity transformation parameters and a fourth estimate of the satellite's position correction at the reference epoch; the first observation equation includes the similarity transformation parameters and the position correction at the reference epoch.

[0156] Based on the third and fourth estimates, the position correction for the orbital deviation in the first epoch of the second orbit is determined.

[0157] Based on the position correction of the orbital deviation at the first epoch in the second orbit, and the orbital value at the first epoch in the second orbit, the orbital value used for orbital update is determined.

[0158] In one embodiment, the third determining module is specifically used for:

[0159] Based on the third and fourth estimates, the position correction of the orbital deviation of the reference epoch in the second orbit is determined;

[0160] Based on the position correction of the orbital deviation at the reference epoch in the second orbit and the position correction of the orbital deviation at the last epoch in the second orbit, the position correction of the orbital deviation at the first epoch in the second orbit is determined; the position correction of the orbital deviation at the last epoch in the second orbit is constrained to 0.

[0161] In one embodiment, the orbital smoothing weight matrix is:

[0162]

[0163] Where p = l·s, l is a preset empirical coefficient, and s is a preset variance threshold. This is the first type of error. Let t be the second mean error. e For any epoch, t i0 For reference epoch, t i Characterizing the second orbital, t i-1 Characterizing the current orbit, t i-2 Characterize the first orbit.

[0164] In practical applications, the first determining module, the second determining module, and the third determining module can be implemented by processors in electronic devices, such as central processing units (CPUs), digital signal processors (DSPs), microcontroller units (MCUs), or field-programmable gate arrays (FPGAs).

[0165] It should be noted that the track change processing device provided in the above embodiments is only illustrated by the division of the above modules when performing track change processing. In actual applications, the above processing can be assigned to different modules as needed, that is, the internal structure of the device can be divided into different modules to complete all or part of the processing described above. In addition, the track change processing device and the track change processing method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0166] The aforementioned orbital jump processing device can be in the form of an image file. After execution, the image file can run as a container or virtual machine to implement the orbital jump processing method described in this application. However, it is not limited to the image file format; any software that can implement the orbital jump processing method described in this application is within the scope of protection of this application.

[0167] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiments of this application, the embodiments of this application also provide an electronic device. Figure 3 This is a schematic diagram of the hardware structure of the electronic device according to an embodiment of this application, as shown below. Figure 3 As shown, the electronic device includes:

[0168] The communication interface 301 enables information exchange with other devices, such as network devices.

[0169] The processor 302 is connected to the communication interface 301 to enable information interaction with other devices and, when running a computer program, executes the methods provided by one or more technical solutions on the electronic device side. The computer program is stored in the memory 303.

[0170] Of course, in practical applications, the various components in an electronic device are coupled together through a bus system 304. It can be understood that the bus system 304 is used to realize the connection and communication between these components. In addition to the data bus, the bus system also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, in... Figure 3 The general designated all buses as Bus System 304.

[0171] The memory 303 in this embodiment is used to store various types of data to support the operation of the electronic device. Examples of such data include any computer program used to operate on the electronic device.

[0172] It is understood that memory 303 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable types of memories.

[0173] The methods disclosed in the embodiments of this application can be applied to or implemented by processor 302. Processor 302 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor 302 may be a general-purpose processor, DSP, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly manifested as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, which is located in memory. The processor reads the program in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0174] Optionally, when the processor executes the program, it implements the corresponding processes implemented by the electronic device in the various methods of the embodiments of this application. For the sake of brevity, these will not be described in detail here.

[0175] In an exemplary embodiment, this application also provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, such as a first memory storing a computer program, which can be executed by a processor of an electronic device to complete the steps described in the aforementioned method. The computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM.

[0176] In an exemplary embodiment, this application also provides a computer program product, including a computer program that can be executed by a processor 302 of an electronic device to complete the steps described in the track jump processing method of this application embodiment.

[0177] In the several embodiments provided in this application, it should be understood that the disclosed apparatus, electronic devices, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components may be combined, or integrated into another system, or some features may be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0178] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0179] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0180] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0181] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0182] It should be noted that the technical solutions described in the embodiments of this application can be combined arbitrarily without conflict.

[0183] In addition, in this application example, terms such as "first" and "second" are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0184] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for handling orbital jumps, characterized in that, The method includes: Determine the first mean square error of fitting the current orbit of the satellite to the first orbit, and the second mean square error of fitting the current orbit to the second orbit; the first orbit is the orbit preceding the current orbit of the satellite, and the second orbit is the predicted orbit obtained by batch processing based on the current orbit; Based on the first mean square error and the second mean square error, the value of the orbit smoothing weight matrix in the preset first observation equation is determined; the orbit smoothing weight matrix characterizes the degree of dependence on the current orbit or the first orbit when fitting the orbit. The orbit values ​​used for orbit updates are determined based on the first observation equation.

2. The method according to claim 1, characterized in that, The determination of the first mean square error of fitting the current orbit of the satellite to the first orbit, and the second mean square error of fitting the current orbit to the second orbit, includes: Based on the second observation equation, the first residual of the orbital observations of the current orbit and the first orbit at multiple epochs is determined, and the second residual of the orbital observations of the current orbit and the second orbit at multiple epochs is determined. The first mean error is determined based on the first residual of multiple epochs, and the second mean error is determined based on the second residual of multiple epochs.

3. The method according to claim 2, characterized in that, The method further includes: The second observation equation is constructed using the satellite positions at the same epoch in adjacent orbits as the observation values.

4. The method according to claim 3, characterized in that, The method further includes: Based on the orbital smoothing weight matrix and the second observation equation, the first observation equation is constructed.

5. The method according to claim 2, characterized in that, The determination of the first residual of the orbital observations of the current orbit and the first orbit at multiple epochs based on the second observation equation, and the determination of the second residual of the orbital observations of the current orbit and the second orbit at multiple epochs, include: The second observation equation is solved using the least squares algorithm to obtain a first estimate of the similarity transformation parameters and a second estimate of the satellite's position correction at the reference epoch; the second observation equation includes the similarity transformation parameters and the position correction at the reference epoch. Based on the first estimate and the second estimate, the first residual and the second residual for multiple epochs are determined.

6. The method according to claim 1, characterized in that, The determination of orbit values ​​for orbit updates based on the first observation equation includes: The first observation equation is solved using the least squares algorithm to obtain a third estimate of the similarity transformation parameters and a fourth estimate of the satellite's position correction at the reference epoch; the first observation equation includes the similarity transformation parameters and the position correction at the reference epoch. Based on the third and fourth estimates, the position correction for the orbital deviation in the first epoch of the second orbit is determined. Based on the position correction of the orbital deviation at the first epoch in the second orbit, and the orbital value at the first epoch in the second orbit, the orbital value used for orbital update is determined.

7. The method according to claim 6, characterized in that, The determination of the position correction amount for the orbital deviation in the first epoch of the second orbit based on the third and fourth estimates includes: Based on the third and fourth estimates, the position correction of the orbital deviation of the reference epoch in the second orbit is determined; Based on the position correction of the orbital deviation at the reference epoch in the second orbit and the position correction of the orbital deviation at the last epoch in the second orbit, the position correction of the orbital deviation at the first epoch in the second orbit is determined; the position correction of the orbital deviation at the last epoch in the second orbit is constrained to 0.

8. The method according to claim 1, characterized in that, The orbital smoothing weight matrix is: Where p = l·s, l is a preset empirical coefficient, and s is a preset variance threshold. This is the first type of error. Let t be the second mean error. e For any epoch, t i0 For reference epoch, t i Characterizing the second orbital, t i-1 Characterizing the current orbit, t i-2 Characterize the first orbit.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the orbital jump processing method as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the orbital jump processing method as described in any one of claims 1 to 8.

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