Positioning method and device based on satellite hardware delay deviation correction

Abstract

The present invention discloses a positioning method and device based on satellite hardware delay deviation correction, which is used to solve the problem of inaccurate positioning due to satellite hardware delay deviation. This solution includes: obtaining positioning data received by a receiver from a satellite; determining a first ionospheric observation quantity and a second ionospheric observation quantity according to the positioning data; determining an error equation according to a preset ionospheric model and a preset center of gravity reference constraint; determining a random model according to a first variance factor corresponding to the first ionospheric observation quantity and a second variance factor corresponding to the second ionospheric observation quantity; determining the hardware delay deviation result of the satellite according to the random model and the error equation, and the hardware delay deviation result of the satellite is used to correct the positioning data to determine the positioning result of the receiver. This solution determines the satellite hardware delay deviation based on two ionospheric observation quantities, which can improve the accuracy of the determined deviation, and then accurately correct the positioning data to improve the positioning accuracy.

Description

Technical Field

[0001] The present invention relates to the field of satellite positioning, and in particular to a positioning method and device based on satellite hardware delay deviation correction. Background Art

[0002] In the field of satellite positioning, high-precision GNSS (Global Navigation Satellite System) precision positioning technology research, various deviation models have been finely modeled and corrected. Hardware delay bias (Differential hardware bias, DCB) exists in GNSS pseudorange observations, and is generally defined as the deviation between hardware delays of pseudorange observations of different frequencies or different types of pseudorange observations of the same frequency. This bias is widely present in GNSS satellites and receivers, and is an important cause of satellite positioning errors.

[0003] How to solve the problem of inaccurate positioning due to satellite hardware delay deviation is the technical problem to be solved by this application. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a positioning method and device based on satellite hardware delay deviation correction, so as to solve the problem of inaccurate positioning caused by satellite hardware delay deviation.

[0005] In a first aspect, a positioning method based on satellite hardware delay deviation correction is provided, comprising:

[0006] Acquire positioning data received by a receiver from a satellite, wherein the positioning data includes a satellite carrier and a pseudorange observation value;

[0007] Determining a first ionospheric observation amount and a second ionospheric observation amount according to the positioning data, wherein the first ionospheric observation amount and the second ionospheric observation amount are determined in different methods;

[0008] Determining error equations corresponding to the first ionospheric observation quantity and the second ionospheric observation quantity according to a preset ionospheric model and a preset center of gravity reference constraint;

[0009] Determining a random model according to a first variance factor corresponding to the first ionospheric observation and a second variance factor corresponding to the second ionospheric observation;

[0010] The hardware delay deviation result of the satellite is determined according to the random model and the error equation, and the hardware delay deviation result of the satellite is used to correct the positioning data to determine the positioning result of the receiver.

[0011] In a second aspect, a positioning device based on satellite hardware delay deviation correction is provided, comprising:

[0012] An acquisition module is used to acquire positioning data received by a receiver from a satellite, wherein the positioning data includes a satellite carrier and a pseudorange observation value;

[0013] A first determining module is configured to determine a first ionospheric observation amount and a second ionospheric observation amount according to the positioning data, wherein the first ionospheric observation amount and the second ionospheric observation amount are determined in different methods;

[0014] A second determination module determines an error equation corresponding to the first ionospheric observation quantity and the second ionospheric observation quantity according to a preset ionospheric model and a preset center of gravity reference constraint;

[0015] A third determination module determines a random model according to a first variance factor corresponding to the first ionospheric observation quantity and a second variance factor corresponding to the second ionospheric observation quantity;

[0016] The fourth determination module determines the hardware delay deviation result of the satellite according to the random model and the error equation, and the hardware delay deviation result of the satellite is used to correct the positioning data to determine the positioning result of the receiver.

[0017] According to a third aspect, an electronic device is provided. The electronic device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the method according to the first aspect are implemented.

[0018] According to a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method according to the first aspect are implemented.

[0019] In an embodiment of the present application, by acquiring positioning data received by a receiver from a satellite, the positioning data includes a satellite carrier and a pseudorange observation value; determining a first ionospheric observation and a second ionospheric observation according to the positioning data, wherein the first ionospheric observation and the second ionospheric observation are determined by different methods; determining the error equations corresponding to the first ionospheric observation and the second ionospheric observation according to a preset ionospheric model and a preset center of gravity reference constraint; determining a random model according to a first variance factor corresponding to the first ionospheric observation and a second variance factor corresponding to the second ionospheric observation; determining the hardware delay deviation result of the satellite according to the random model and the error equation, and the hardware delay deviation result of the satellite is used to correct the positioning data to determine the positioning result of the receiver. This scheme determines the satellite hardware delay deviation based on two ionospheric observations, which can improve the accuracy of the deviation, and then accurately correct the positioning data to improve the positioning accuracy. Among them, since the two ionospheric observations in this scheme are determined by different methods and have different accuracies, determining the hardware delay deviation through these two ionospheric observations of different accuracies can effectively increase the amount of data, thereby improving the accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0021] Figure 1 It is one of the flow charts of a positioning method based on satellite hardware delay deviation correction according to an embodiment of the present invention;

[0022] Figure 2 This is a second flow chart of a positioning method based on satellite hardware delay deviation correction according to an embodiment of the present invention;

[0023] Figure 3 This is a flowchart of a positioning method based on satellite hardware delay deviation correction according to an embodiment of the present invention;

[0024] Figure 4 This is a fourth flow chart of a positioning method based on satellite hardware delay deviation correction according to an embodiment of the present invention;

[0025] Figure 5 This is a flowchart of a positioning method based on satellite hardware delay deviation correction according to an embodiment of the present invention;

[0026] Figure 6 This is a sixth flow chart of a positioning method based on satellite hardware delay deviation correction according to an embodiment of the present invention;

[0027] Figure 7 It is a structural schematic diagram of a positioning device based on satellite hardware delay deviation correction according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The figure numbers in this application are only used to distinguish the various steps in the scheme, and are not used to limit the execution order of the various steps. The specific execution order is subject to the description in the specification.

[0029] In order to solve the problems existing in the prior art, the embodiment of the present application provides a positioning method based on satellite hardware delay deviation correction. The execution subject of the method can be a receiver or an electronic device connected to the receiver for communication. Figure 1 As shown, including:

[0030] S11: Acquire positioning data received by a receiver from a satellite, wherein the positioning data includes a satellite carrier and a pseudorange observation value.

[0031] During the positioning process, the receiver continuously receives positioning data sent by the satellite, which includes satellite carrier and pseudo-range observations. The positioning data can also be called observations or observation data. Among them, the satellite carrier refers to the original carrier signal sent by the satellite. The pseudo-range observations can be obtained through the pseudo-range measurement method. The pseudo-range measurement is a correlation technology between the pseudo-random code broadcast by the satellite and the receiver replica code during navigation and positioning. It can be used to measure the distance between the station and the satellite, including clock errors and atmospheric refraction delays.

[0032] The positioning data is sent by the satellite and received by the receiver. Various factors, including the hardware delay of the satellite equipment, the hardware delay of the receiver, and the ionosphere in the signal path, will affect the positioning data.

[0033] S12: Determine a first ionospheric observation quantity and a second ionospheric observation quantity according to the positioning data, wherein the first ionospheric observation quantity and the second ionospheric observation quantity are determined in different methods.

[0034] In this step, different ionospheric observations can be determined by a variety of methods. In this embodiment, the above-mentioned first ionospheric observation can be determined by the non-difference non-combined PPP (Uncombined Precise Point Positioning, UPPP) method, and the second ionospheric observation can be determined by the carrier phase smoothed pseudorange (Phased-Smoothed Range, P4) method. The accuracy of ionospheric observations determined by different methods is often different. Generally speaking, the first ionospheric observation determined by the non-difference non-combined PPP method is more accurate than the second ionospheric observation. The positioning accuracy of non-difference non-combined PPP is greatly affected by the satellite DCB. The solution provided in this application combines the ionospheric observations determined by two different methods to comprehensively perform corrected positioning, which can effectively improve the accuracy of corrected positioning.

[0035] S13: Determine error equations corresponding to the first ionospheric observation quantity and the second ionospheric observation quantity according to a preset ionospheric model and a preset center of gravity reference constraint.

[0036] Specifically, the preset ionosphere model is used to characterize the influence of the ionosphere on the traversing satellite positioning signal. Based on the ionosphere model, the deviation of the ionosphere observation quantity can be separated into the deviation caused by the ionosphere and the deviation caused by the hardware delay. Furthermore, the center of gravity reference constraint is used to further separate the deviation caused by the satellite and the deviation caused by the receiver from the deviation caused by the hardware delay.

[0037] In this step, the error equation is determined based on the preset ionosphere model and the preset center of gravity reference constraint. The preset ionosphere model can be used to separate the hardware delay bias from the ionosphere observation, and the center of gravity reference constraint can further separate the hardware delay bias of the satellite from the hardware delay bias. Therefore, the error equation generated in this step can improve the accuracy of the hardware delay bias of the satellite determined in the subsequent steps.

[0038] S14: Determine a random model according to a first variance factor corresponding to the first ionospheric observation quantity and a second variance factor corresponding to the second ionospheric observation quantity.

[0039] The first ionospheric observation quantity and the second ionospheric observation quantity are ionospheric observation quantities with different precisions determined by different methods. Wherein, when the precisions of the first ionospheric observation quantity and the second ionospheric observation quantity are different, the first variance factor and the second variance factor are also different. In this step, constructing a random model based on the variance factor corresponding to the ionospheric observation quantity can improve the quality of the constructed random model and further improve the accuracy of the subsequently determined satellite hardware delay bias.

[0040] S15: Determine the hardware delay deviation result of the satellite according to the random model and the error equation, and the hardware delay deviation result of the satellite is used to correct the positioning data to determine the positioning result of the receiver.

[0041] The satellite hardware delay deviation determined in the above step represents the deviation of the positioning data caused by the satellite hardware delay deviation. In this step, the pseudorange observation value in the positioning data can be corrected based on the above deviation result, and the receiver is positioned based on the positioning data containing the corrected pseudorange observation value to determine the positioning result of the receiver.

[0042] Optionally, the above step S15 includes:

[0043] A least squares estimation is performed based on the stochastic model and the error equation to determine a hardware delay bias result of the satellite.

[0044] The solution provided in the embodiment of the present application can obtain a high-precision satellite hardware delay deviation product, which can be used for GNSS satellite precise positioning and effectively improve the precision of satellite precise positioning. Through this solution, the positioning data received by the receiver from the satellite is obtained, and the positioning data includes satellite carrier and pseudorange observation values; the first ionospheric observation quantity and the second ionospheric observation quantity are determined according to the positioning data, and the determination methods of the first ionospheric observation quantity and the second ionospheric observation quantity are different; the error equation corresponding to the first ionospheric observation quantity and the second ionospheric observation quantity is determined according to the preset ionospheric model and the preset center of gravity reference constraint; the random model is determined according to the first variance factor corresponding to the first ionospheric observation quantity and the second variance factor corresponding to the second ionospheric observation quantity; the hardware delay deviation result of the satellite is determined according to the random model and the error equation, and the hardware delay deviation result of the satellite is used to correct the positioning data to determine the positioning result of the receiver. This solution determines the satellite hardware delay deviation based on two ionospheric observation quantities, which can improve the accuracy of the determined deviation, and then perform accurate correction on the positioning data to improve the positioning accuracy. Among them, since the two ionospheric observation quantities in this scheme are determined by different methods and have different accuracies, determining the hardware delay bias through these two ionospheric observation quantities with different accuracies can effectively increase the amount of data and thus improve accuracy.

[0045] Based on the solution provided in the above embodiment, optionally, Figure 2 As shown, the above step S12 includes:

[0046] S21: Determine the first ionospheric observation quantity according to the positioning data using a non-differential non-combined precise point positioning method based on the ephemeris and clock error of the satellite.

[0047] S22: Determine the second ionospheric observation quantity according to the positioning data using a carrier phase smoothed pseudorange method based on the position of the satellite.

[0048] It should be noted that the execution steps of steps S21 and S22 in the embodiment of the present application can be swapped, or can be executed simultaneously.

[0049] Among them, the carrier phase smoothed pseudorange method can be used for ionospheric modeling and obtain the hardware delay bias between the observation values ​​of each frequency. This method can more conveniently construct ionospheric observation values ​​and obtain hardware delay bias products through reasonable modeling, but the modeling process will be affected by the noise of pseudorange observation values, which is not conducive to improving the modeling accuracy, and is also affected by the satellite arc length and multipath error.

[0050] The satellite DCB and ionosphere model obtained by extracting the oblique ionosphere through non-differenced non-combined PPP and then modeling the ionosphere are more accurate than the carrier phase smoothed pseudorange method. However, since some satellites lack precise clock and orbit information, and the satellite DCB estimation strategy is still to impose a zero mean constraint on all satellite DCBs, the estimated DCB product will be significantly affected. Considering that the equivalent ionospheric observations obtained by non-differenced non-combined PPP and carrier smoothed pseudorange, which include the station-satellite oblique ionosphere and hardware delay bias, are highly unified in expression, this scheme effectively improves the estimation accuracy of satellite DCB by combining the two types of observations, thereby achieving data correction and precise positioning.

[0051] Below, this scheme is explained with reference to examples.

[0052] The positioning data in this application may specifically include the GNSS original observation equation, which can be expressed as (1):

[0053]

[0054] Wherein, j, r, i, and S represent the satellite pseudo random noise code (PRN), receiver, frequency number (e.g., i=1, 2), and system, respectively.

[0055] and represent pseudorange and carrier phase observations respectively.

[0056] Indicates the geometric distance from the receiver to the satellite.

[0057] and δt S,j are the receiver and satellite clock errors, respectively.

[0058] c is the speed of light in vacuum.

[0059] zw r and are the tropospheric delay and its projection function respectively.

[0060] Represents the ionospheric coefficient factor.

[0061] is the ionospheric parameter of the first frequency.

[0062] in, Indicates the electron content of the oblique ionosphere of the station satellite.

[0063] is the wavelength of the ith frequency.

[0064] and represent the pseudorange hardware delays at the satellite and receiver ends respectively.

[0065] and represent the phase hardware delays at the satellite and receiver ends respectively.

[0066] are the observation noises of pseudorange and carrier phase including their multipath errors.

[0067] Then, using precise satellite clock error and ephemeris products, the model correction values ​​are subtracted from the observed values ​​to obtain the error equation (2) for the satellite end absorbing the ionospheric hardware delay bias:

[0068]

[0069] In formula (2),

[0070]

[0071] Then, the carrier phase smoothed pseudorange expression is as follows (4):

[0072]

[0073] In practical applications, for ease of analysis, the carrier phase smoothed pseudorange observation can be multiplied by the coefficient Then the carrier phase smoothed pseudorange observation and the oblique ionospheric observation obtained by the non-difference non-combined PPP can be unified in form, as shown in the following equation (5):

[0074]

[0075] Among them, Method represents non-difference non-combined PPP or carrier phase smoothed pseudorange.

[0076] Based on the solution provided in the above embodiment, optionally, Figure 3 As shown, the above step S13 includes:

[0077] S31: constructing a single-station vertical ionosphere model based on a generalized trigonometric series model according to the parameters of the satellite and the receiver and a preset ionosphere model.

[0078] In practical applications, a set of model coefficients can be estimated based on a preset time period, for example, once every 2 hours, and the period length can be changed as needed. The estimated model coefficients are used to construct a single-station vertical ionosphere model based on the generalized trigonometric series model.

[0079] Specifically, the formula Ionosphere Through single-station ionosphere modeling, it is modeled, so that several modeling parameters to be solved are obtained, and and Forming the parameters to be requested Among them, a km 、b l 、c l Characterize the modeled parameters for ionospheric modeling.

[0080] S32: Determine error equations corresponding to the first ionospheric observation quantity and the second ionospheric observation quantity according to the single-station vertical ionospheric model and a preset center of gravity reference constraint.

[0081] In this step, the error equation is constructed: Observable quantity in and L uppp Indicates that two equivalent ionospheric observations are obtained through carrier phase smoothed pseudorange and non-difference non-combined PPP, namely the first ionospheric observation and the second ionospheric observation, which are specifically represented as a vector containing multiple observations. The following 0 means adding a "center of gravity reference constraint, i.e., 0 mean constraint" to separate and because and Linearly related, so it is necessary to add mandatory constraints to achieve separation, so as to separate the hardware delay deviation of the satellite and the hardware delay deviation of the receiver.

[0082] The error equation constructed in this embodiment can be expressed as:

[0083]

[0084] Based on the solution provided in the above embodiment, optionally, Figure 4 As shown, before the above step S31, it also includes:

[0085] S41: Determine the station-satellite oblique ionospheric electron content of the satellite according to the positioning data.

[0086] Among them, the oblique ionospheric electron content of the station is expressed as It can be determined based on the positioning data using the above-mentioned non-difference non-combined PPP method.

[0087] S42: Determine the vertical total electron content according to the projection function and the oblique ionospheric electron content of the station satellite.

[0088] In this step, the STEC is converted into the VTEC using a projection function.

[0089] S43: Constructing the preset ionosphere model according to the generalized trigonometric series model and the vertical total electron content.

[0090] In this example, the ionospheric delay is first separated from the hardware delay bias by using a preset ionospheric model, and then the satellite hardware delay bias and the receiver hardware delay bias are separated from the hardware delay bias by using a center of gravity reference constraint.

[0091] In this step, it is assumed that the total electron content of the ionosphere is concentrated in a thin shell with a height of H. Therefore, the projection function can be used to convert the slant total electron content (STEC) in the above positioning data into the vertical total electron content (VTEC). Then, the generalized trigonometric series model is introduced to model the vertical ionosphere of a single station as shown in formula (5):

[0092]

[0093] In the formula, represents the satellite zenith distance, and represents the geomagnetic latitude and solar longitude of the ionosphere pierce point (IPP), H ion is the height of the ionosphere thin layer, which is set to 506.7 km by default in this embodiment, R earth represents the average radius of the Earth, represents the geomagnetic latitude of the receiver, a km , b l and c l is the model coefficient. In this example, k max , m max and l max Set them to 2, 2 and 5 respectively.

[0094] Based on the above steps, substituting equation (5) into equation (2) and equation (3) respectively, it can be seen that the satellite and receiver DCBs are inseparable because of the same coefficients. In this scheme, based on the center of gravity reference constraint, assuming that the sum of the DCBs of all satellites is 0, the satellite and receiver DCBs are separated under this constraint:

[0095]

[0096] Through the solution provided in the embodiment of the present application, the separation of hardware delay deviation and ionospheric delay deviation, as well as the separation of satellite delay deviation and receiver hardware delay deviation can be achieved in combination with a preset ionosphere model and a preset center of gravity reference constraint.

[0097] Further, in the embodiment of the present application, non-difference non-combined PPP ionospheric observation and carrier phase smoothed pseudorange observation are applied, and there are the following differences between these two observations:

[0098] In terms of processing strategy, the PPP solution fixes the satellite position and clock error, uses power spectral density to model the changes in ionospheric observations, and the DCB benchmark is determined by the satellite ephemeris and clock error; the phase-smoothed pseudorange observations are completely determined by the original observations.

[0099] In terms of observation information utilization, the carrier phase smoothed pseudorange only uses the information of the current satellite when forming a single ionospheric observation, which is independent of other satellites; the non-differenced non-combined PPP is solved by filtering method, and the equivalent ionospheric observation value of a single satellite in a single epoch is the adjustment solution of all satellites in the current epoch, which is equivalent to the global observation. Therefore, the correlation between the two is further weakened.

[0100] Based on the difference between the above two different observations, the correlation between the two types of observations is weak and can be regarded as unrelated. Therefore, the covariance matrix of the combined model error equation and its observations is expressed as follows (8):

[0101]

[0102] In formula (8), ∑ 0 Set to 10 -6 The weighting of non-differenced non-combined PPP ionospheric observations or carrier phase smoothed pseudorange observations is generally based on the altitude angle weighting method:

[0103]

[0104] in Denotes the altitude angle of satellite j. Subsequently, an adjustment calculation is performed according to the random model to determine the adjustment result of the hardware delay bias of the satellite.

[0105] Based on the scheme provided by the above embodiment, the random model plays a vital role in the adjustment process. If the random model is not constructed reasonably, it will have a greater negative impact on the adjustment result. However, the variance-covariance matrix of the observation value set with equal weight ratio cannot objectively and truly reflect the accuracy difference of the above two observation values.

[0106] In view of the above problems, the solution provided by the embodiments of the present application is as follows: Figure 5 As shown, the above step S14 includes:

[0107] S51: determining an initial variance matrix according to a first initial variance factor corresponding to the first ionospheric observation quantity and a second initial variance factor corresponding to the second ionospheric observation quantity, wherein the initial variance matrix is ​​used to construct an initial random model;

[0108] S52: performing adjustment calculation on the initial random model to determine a first ionospheric observation value estimate and a second ionospheric observation value estimate;

[0109] S53: performing a posteriori estimation on the initial random model according to the first ionospheric observation value estimate and the second ionospheric observation value estimate using a residual vector;

[0110] S54: determining a first variance factor corresponding to the first ionospheric observation quantity and a second variance factor corresponding to the second ionospheric observation quantity according to the result of the a posteriori estimation;

[0111] S55: Constructing a random model according to a first variance factor corresponding to the first ionospheric observation quantity and a second variance factor corresponding to the second ionospheric observation quantity.

[0112] This scheme introduces the variance component estimation method, and iteratively adjusts the weight ratio of the two types of observations by using the posterior estimation method through the residual vector to construct a suitable random model. The idea of ​​this method is: first give the initial variance matrix of the two types of observations as shown in the following formula (10):

[0113]

[0114] Then, the adjustment calculation is performed to obtain the estimation of the two types of observations, and then the residual vector is used to perform a posterior estimation of the random model, and then the weights are re-determined, and the above process is iterated until the initial variance factors of the two types of observations reach Stop the iteration and finally get the estimation of two types of observation values.

[0115] In the solution provided in the embodiment of the present application, the non-differenced non-combined PPP equivalent ionospheric observations and the carrier phase smoothed pseudorange observations are first extracted, and then the function model and the random model of ionospheric modeling are constructed. In view of the problem that the weights of the two types of observations in the random model are difficult to determine, a variance component estimation method is proposed. The weights of the two types of observations are iteratively adjusted by using the posterior estimation method through the residual vector to construct a more reasonable random model. Finally, the center of gravity reference constraint is introduced to separate the satellite and the receiver DCB to determine the satellite hardware delay deviation result, thereby correcting the positioning data and determining the positioning result of the receiver, thereby improving the accuracy of the positioning result.

[0116] Based on the solution provided in the above embodiment, optionally, Figure 6 As shown, the above step S54 includes:

[0117] S61: if the ratio of the first initial variance factor to the second initial variance factor does not fall within a preset interval, determining a first weight matrix product of the first initial variance factor and the first ionospheric observation quantity and a second weight matrix product of the second initial variance factor and the second ionospheric observation quantity;

[0118] S62: performing least square adjustment on the first weight matrix product and the second weight matrix product to obtain a first residual vector and a second residual vector;

[0119] S63: Determine a first weighted residual sum of squares and a second weighted residual sum of squares according to the first residual vector and the second residual vector;

[0120] S64: determining a first iteration variance factor and a second iteration variance factor by using a variance component estimation method according to the first weighted residual sum of squares and the second weighted residual sum of squares;

[0121] S65: If the ratio of the first iteration variance factor to the second iteration variance factor falls within the preset interval, determining the first iteration variance factor as the first variance factor and determining the second iteration variance factor as the second variance factor.

[0122] In the solution provided in the embodiment of the present application, two initial variance factors are first set, and then the weight factors set by these two sets of variances are multiplied with the weight matrices of the two types of observations respectively, and then a weighted least squares adjustment is performed to obtain two sets of residual vectors, as shown in formula (11):

[0123]

[0124] Then construct two sets of weighted residual sum of squares, as shown in formula (12):

[0125]

[0126] This scheme can calculate a new set of variance factors based on w using the variance component estimation formula and Determine whether the ratio of the two falls within a preset interval (for example, the preset interval is greater than or equal to 0.99 and less than or equal to 1.01). If it falls within the preset interval, output the adjustment value; otherwise repeat the above process. Through the scheme provided by the embodiment of the present application, a better variance factor can be obtained through iterative calculation, so as to perform non-equal weight calculation for two different types of observations, determine the variance factor for the accuracy of the two types of observations, improve the accuracy of the determined random model, and then improve the accuracy of the determined satellite hardware delay deviation, and the final positioning accuracy.

[0127] In order to solve the problems existing in the prior art, the embodiment of the present application also provides a positioning device 70 based on satellite hardware delay deviation correction, such as Figure 7 As shown, including:

[0128] An acquisition module 71 is used to acquire positioning data received by a receiver from a satellite, wherein the positioning data includes a satellite carrier and a pseudorange observation value;

[0129] A first determining module 72 is configured to determine a first ionospheric observation amount and a second ionospheric observation amount according to the positioning data, wherein the first ionospheric observation amount and the second ionospheric observation amount are determined in different methods;

[0130] The second determination module 73 determines the error equation corresponding to the first ionospheric observation quantity and the second ionospheric observation quantity according to a preset ionospheric model and a preset center of gravity reference constraint;

[0131] A third determination module 74 determines a random model according to a first variance factor corresponding to the first ionospheric observation and a second variance factor corresponding to the second ionospheric observation;

[0132] The fourth determination module 75 determines the hardware delay deviation result of the satellite according to the random model and the error equation, and the hardware delay deviation result of the satellite is used to correct the positioning data to determine the positioning result of the receiver.

[0133] The device provided in the embodiment of the present application obtains the positioning data received by the receiver from the satellite, wherein the positioning data includes the satellite carrier and the pseudorange observation value; determines the first ionospheric observation amount and the second ionospheric observation amount according to the positioning data, and the determination methods of the first ionospheric observation amount and the second ionospheric observation amount are different; determines the error equation corresponding to the first ionospheric observation amount and the second ionospheric observation amount according to the preset ionospheric model and the preset center of gravity reference constraint; determines the random model according to the first variance factor corresponding to the first ionospheric observation amount and the second variance factor corresponding to the second ionospheric observation amount; determines the hardware delay deviation result of the satellite according to the random model and the error equation, and the hardware delay deviation result of the satellite is used to correct the positioning data to determine the positioning result of the receiver. This scheme determines the satellite hardware delay deviation based on two ionospheric observation amounts, which can improve the accuracy of determining the deviation, and then accurately correct the positioning data to improve the positioning accuracy. Among them, since the two ionospheric observation amounts in this scheme are determined by different methods and have different accuracies, determining the hardware delay deviation through these two ionospheric observation amounts of different accuracies can effectively increase the amount of data, thereby improving the accuracy.

[0134] Among them, the above modules in the device provided by the embodiment of the present application can also implement the method steps provided by the above method embodiment. Alternatively, the device provided by the embodiment of the present application can also include other modules in addition to the above modules to implement the method steps provided by the above method embodiment. And the device provided by the embodiment of the present application can achieve the technical effects that can be achieved by the above method embodiment.

[0135] Preferably, an embodiment of the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned positioning method embodiment based on satellite hardware delay deviation correction are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0136] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned positioning method embodiment based on satellite hardware delay deviation correction is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0137] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0138] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0139] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0141] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0142] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0143] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0144] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0145] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0146] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A positioning method based on satellite hardware delay deviation correction, characterized in that: include: Acquire positioning data received by a receiver from a satellite, wherein the positioning data includes a satellite carrier and a pseudorange observation value; Determining a first ionospheric observation amount and a second ionospheric observation amount according to the positioning data, wherein the first ionospheric observation amount and the second ionospheric observation amount are determined in different methods; Determining error equations corresponding to the first ionospheric observation quantity and the second ionospheric observation quantity according to a preset ionospheric model and a preset center of gravity reference constraint; Determining a random model according to a first variance factor corresponding to the first ionospheric observation and a second variance factor corresponding to the second ionospheric observation; The hardware delay deviation result of the satellite is determined according to the random model and the error equation, and the hardware delay deviation result of the satellite is used to correct the positioning data to determine the positioning result of the receiver.

2. The method according to claim 1, characterized in that Determining a first ionospheric observation amount and a second ionospheric observation amount according to the positioning data includes: Determine the first ionospheric observation according to the positioning data by using a non-differential non-combined precise point positioning method based on the ephemeris and clock error of the satellite; The second ionospheric observation quantity is determined according to the positioning data using a carrier phase smoothed pseudorange method based on the position of the satellite.

3. The method according to claim 1, characterized in that Determining error equations corresponding to the first ionospheric observation quantity and the second ionospheric observation quantity according to a preset ionospheric model and a preset center of gravity reference constraint includes: Constructing a single-station vertical ionosphere model based on a generalized trigonometric series model according to the parameters of the satellite and the receiver and a preset ionosphere model; The error equations corresponding to the first ionospheric observation quantity and the second ionospheric observation quantity are determined according to the single-station vertical ionospheric model and the preset center of gravity reference constraint.

4. The method according to claim 3, characterized in that Before constructing a single-station vertical ionosphere model based on a generalized trigonometric series model according to the parameters of the satellite and the receiver, the method further includes: Determine the station-satellite oblique ionospheric electron content of the satellite according to the positioning data; Determine the vertical total electron content based on the projection function and the oblique ionospheric electron content of the station satellite; The preset ionosphere model is constructed according to the generalized trigonometric series model and the vertical total electron content.

5. The method according to claim 3 or 4, characterized in that Determining a hardware delay deviation result of the satellite according to the random model and the error equation includes: A least squares estimation is performed based on the random model and the error equation to determine a hardware delay bias result for the satellite.

6. The method according to claim 5, characterized in that Determining a random model according to a first variance factor corresponding to the first ionospheric observation quantity and a second variance factor corresponding to the second ionospheric observation quantity includes: Determine an initial variance matrix according to a first initial variance factor corresponding to the first ionospheric observation and a second initial variance factor corresponding to the second ionospheric observation, wherein the initial variance matrix is ​​used to construct an initial random model; Performing an adjustment calculation on the initial stochastic model to determine a first ionospheric observation estimate and a second ionospheric observation estimate; Performing a posteriori estimation on the initial random model with a residual vector based on the first ionospheric observation estimate and the second ionospheric observation estimate; Determining a first variance factor corresponding to the first ionospheric observation and a second variance factor corresponding to the second ionospheric observation according to the result of the a posteriori estimation; A random model is constructed according to a first variance factor corresponding to the first ionospheric observation quantity and a second variance factor corresponding to the second ionospheric observation quantity.

7. The method according to claim 6, characterized in that Determining a first variance factor corresponding to the first ionospheric observation quantity and a second variance factor corresponding to the second ionospheric observation quantity according to the result of the a posteriori estimation includes: If the ratio of the first initial variance factor to the second initial variance factor does not fall within a preset interval, determining a first weight matrix product of the first initial variance factor and the first ionospheric observation quantity and a second weight matrix product of the second initial variance factor and the second ionospheric observation quantity; Performing a least square adjustment on the first weight matrix product and the second weight matrix product to obtain a first residual vector and a second residual vector; Determine a first weighted residual sum of squares and a second weighted residual sum of squares according to the first residual vector and the second residual vector; Determine a first iteration variance factor and a second iteration variance factor by using a variance component estimation method according to the first weighted residual sum of squares and the second weighted residual sum of squares; If the ratio of the first iteration variance factor to the second iteration variance factor falls within the preset interval, the first iteration variance factor is determined as the first variance factor and the second iteration variance factor is determined as the second variance factor.

8. A positioning device based on satellite hardware delay deviation correction, characterized in that: include: An acquisition module is used to acquire positioning data received by a receiver from a satellite, wherein the positioning data includes a satellite carrier and a pseudorange observation value; A first determining module is configured to determine a first ionospheric observation amount and a second ionospheric observation amount according to the positioning data, wherein the first ionospheric observation amount and the second ionospheric observation amount are determined in different methods; A second determination module determines an error equation corresponding to the first ionospheric observation quantity and the second ionospheric observation quantity according to a preset ionospheric model and a preset center of gravity reference constraint; A third determination module determines a random model according to a first variance factor corresponding to the first ionospheric observation quantity and a second variance factor corresponding to the second ionospheric observation quantity; The fourth determination module determines the hardware delay deviation result of the satellite according to the random model and the error equation, and the hardware delay deviation result of the satellite is used to correct the positioning data to determine the positioning result of the receiver.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the method according to any one of claims 1 to 7 when executed by the processor.

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

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