Precise point positioning ambiguity fixing method based on phase deviation precision factor

Abstract

The invention discloses a precise point positioning ambiguity fixing method based on a phase deviation precision factor, and belongs to the technical field of a global navigation satellite system, and the method comprises the steps: calculating the decimal cycle parts of ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity based on a GNSS reference station phase observation value; sequentially selecting observation stations according to the number of observation satellites, calculating a receiver end UPD value and a satellite end UPD value through iteration by using a phase deviation whole network resolving method and decimal cycles of the ultra-wide lane ambiguity, the wide lane ambiguity and the narrow lane ambiguity, and extracting a residual vector of phase deviation; calculating a UPD precision factor according to the residual vector of the phase deviation; and updating the ambiguity variance-covariance matrix of the user side by using the UPD precision factor, and fixing the ambiguity of the user side based on an LAMBDA algorithm. According to the invention, the problem of inconsistency between the ambiguity and the precision of the ambiguity variance-covariance matrix in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to a method for fixing ambiguity of precise single-point positioning based on phase deviation precision factor, and belongs to the technical field of global navigation satellite systems. Background Art

[0002] As a critical infrastructure providing national positioning, navigation, and timing services, the Global Navigation Satellite System (GNSS) has demonstrated widespread application value in diverse fields, including precision agriculture, intelligent transportation, and space environment monitoring. Traditional Precise Point Positioning (PPP) technology, using a single receiver combined with precise ephemeris and clock products, can theoretically achieve centimeter-level positioning accuracy. However, PPP suffers from a long convergence time, typically exceeding 30 minutes, severely limiting its application in real-time, high-precision positioning scenarios. To overcome this limitation, PPP-AR (Precise Point Positioning Ambiguity Resolution) technology has emerged. By restoring the integer nature of carrier phase ambiguities, PPP-AR significantly shortens convergence time and improves positioning accuracy. In particular, the least-squares ambiguity reduction and correlation adjustment (LAMBDA) method is widely used in GNSS ambiguity resolution. It relies on real ambiguities and their corresponding variance-covariance matrices to perform integer ambiguity searches. In recent years, PPP-AR has been widely adopted in surveying and mapping, geological disaster monitoring, and other fields, becoming a core tool for high-precision GNSS positioning.

[0003] Although PPP-AR technology has made significant progress in high-precision positioning, existing techniques still have some significant drawbacks. First, when using the LAMBDA method for PPP-AR, real ambiguities are typically obtained by deducting the phase bias from the PPP float solution, while the variance-covariance matrix uses the ambiguity variance-covariance information of the PPP float solution. However, due to phase bias estimation errors, there may be inconsistencies in the accuracy between the real ambiguities obtained after deducting the phase bias and the variance-covariance matrix of the PPP float solution. This inconsistency reduces the success rate of ambiguity fixation, especially in complex environments such as those with active ionospheric activity or areas with significant multipath effects. Second, with the growing demand for real-time applications, although the International GNSS Service (IGS) already provides real-time satellite orbit, clock, and phase bias products, promoting the development of real-time PPP-AR, the quality and latency of these real-time products significantly impact PPP-AR performance. The latency of these real-time products leads to the accumulation of satellite clock and orbit errors, further exacerbating the uncertainty of ambiguity fixation. Furthermore, existing methods fail to fully account for the uncertainty of UPD (uncalibrated phase delay) correction during user-side processing, which also reduces the success rate of ambiguity fixation, a problem that is particularly severe when real-time product quality is poor. In complex environments, the performance of traditional PPP-AR methods is further limited, and convergence time is significantly prolonged, making it difficult to meet the requirements of real-time centimeter-level positioning. Therefore, existing technologies still face significant bottlenecks in efficiency and robustness for high-precision real-time positioning. Summary of the Invention

[0004] The purpose of the present invention is to provide a precise single-point positioning ambiguity fixation method based on phase deviation precision factor. By introducing the phase deviation precision factor into the ambiguity variance-covariance matrix, the problem of inconsistency between the accuracy of the ambiguity and the ambiguity variance-covariance matrix existing in the prior art is solved, and the ambiguity fixation success rate and positioning robustness are improved in complex environments and real-time product delays.

[0005] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions:

[0006] The present invention provides a method for fixing ambiguity in precise point positioning based on phase deviation precision dilution, comprising:

[0007] Get the GNSS base station phase observation value on the server side;

[0008] Based on the phase observations of the GNSS base station, the fractional cycle part of the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity of the floating point ambiguity is calculated;

[0009] The measurement stations are selected in sequence according to the number of observed satellites. The phase deviation network-wide solution method and the fractional cycle part of the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity are used to iteratively calculate the receiver-side UPD values ​​of all receivers in the reference station network and the satellite-side UPD values ​​of all visible satellites, and then extract the residual vector of the phase deviation.

[0010] Calculate the UPD precision factor based on the residual vector of the phase deviation;

[0011] The UPD precision factor is used to update the ambiguity variance-covariance matrix of the user side, and the ambiguity of the user side is fixed based on the LAMBDA algorithm.

[0012] Furthermore, based on the phase observation values ​​of the GNSS reference station, the fractional cycle parts of the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity of the floating point ambiguity are calculated, including:

[0013] estimating floating-point ambiguities from the GNSS reference station phase observations based on an undifferenced, non-combined observation model;

[0014] The floating point ambiguity includes integer ambiguity, receiver-side and satellite-side phase deviation;

[0015] Performing linear combination processing on the floating-point ambiguities to transform them into ultra-wide lane ambiguities, wide lane ambiguities, and narrow lane ambiguities;

[0016] Performing rounding operations on the ultra-wide lane ambiguity, the wide lane ambiguity, and the narrow lane ambiguity respectively to obtain the integer parts of the ultra-wide lane ambiguity, the wide lane ambiguity, and the narrow lane ambiguity;

[0017] The fractional parts of the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity are obtained by subtracting the integer parts of the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity from the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity.

[0018] Furthermore, the stations are selected in sequence according to the number of observed satellites, and the UPD values ​​of all receivers in the reference station network and the UPD values ​​of all visible satellites are iteratively calculated using the phase deviation whole network solution method and the fractional cycle parts of the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity, including:

[0019] The receiver-side UPD value of the station with the largest number of observed satellites is set to 0, and the initial satellite-side UPD value is obtained by subtracting the receiver-side UPD value from the fractional cycle part of the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity;

[0020] Select the remaining stations in descending order of the number of observed satellites and repeat the following steps until all stations are processed:

[0021] For each known satellite at each station:

[0022] The UPD value at the receiver is obtained by subtracting the satellite-side UPD value from the fractional cycle parts of the ultra-wide lane ambiguity, the wide lane ambiguity, and the narrow lane ambiguity, and then taking the average value as the UPD value at the receiver;

[0023] For each unknown satellite at each station:

[0024] The satellite-side UPD value of the unknown satellite is obtained by subtracting the UPD value at the receiver from the fractional cycle part of the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity.

[0025] Performing a weighted average of the satellite-side UPD values ​​of the same satellite obtained by each measuring station to obtain the satellite-side UPD value of the satellite;

[0026] If the difference between the satellite-side UPD values ​​obtained in two adjacent iterations is less than a preset threshold, the loop is exited to obtain the receiver-side UPD values ​​of all receivers in the reference station network and the satellite-side UPD values ​​of all visible satellites.

[0027] Furthermore, the UPD value at the receiver side is expressed as:

[0028] ;

[0029] Where, Indicates the UPD value at the receiver end, that is, The UPD value of each receiver, Indicates the The total number of satellites that can be observed by a receiver, Indicates the satellites, represents the floating point ambiguity, represents the integer ambiguity, Indicates the UPD value of the satellite end, that is, The UPD value of the satellite.

[0030] Furthermore, the satellite UPD value is expressed as:

[0031] ;

[0032] Where, Indicates the UPD value of the satellite end, that is, UPD value of satellites Indicates that the first The total number of receivers for satellites, Indicates the A receiver, Indicates the Satellite No. The UPD value is calculated at each receiver. Indicates the satellites No. The UPD value calculated at each receiver The corresponding weight.

[0033] Furthermore, the residual vector of the phase deviation is distributed on each satellite, wherein the residual vector of the phase deviation is expressed as:

[0034] ;

[0035] Where, The residual vector representing the phase deviation, represents the total number of receivers in the base station network, Indicates the total number of visible satellites in the base station network, Indicates the The receiver observes The phase bias residuals of the satellites, Indicates the The receiver observes Floating point ambiguities for satellites, Indicates the The receiver observes The integer ambiguity of the satellites, express OK The identity matrix of the columns, represents the Kronecker product, express A column vector with 1 row and 1 column, express A column vector with 1 row and 1 column, express OK The identity matrix of the columns, represents the satellite-side UPD vector of all visible satellites in the reference station network, represents the receiver-side UPD vector of all receivers in the reference station network, Indicates transpose.

[0036] Furthermore, the UPD precision factor is calculated based on the residual vector of the phase deviation, including:

[0037] Calculate the RMS of all elements in the residual vector of the phase deviation to obtain the UPD precision factor;

[0038] The UPD precision factor is expressed as:

[0039] ;

[0040] Where, Indicates the The UPD precision of each satellite is Indicates that the first The total number of receivers for satellites, Indicates the A receiver, Indicates the The receiver observes The phase bias residuals of the satellites.

[0041] Furthermore, the UPD precision coefficient is used to update the ambiguity variance-covariance matrix of the user terminal, wherein the updated ambiguity variance-covariance matrix is ​​expressed as:

[0042] ;

[0043] Where, represents the updated user-side ambiguity variance-covariance matrix, represents the ambiguity variance-covariance matrix of the user side, where represents the ambiguity at the user end, represents the diagonal matrix consisting of UPD dilutions of precision, Indicates the total number of satellites that the receiver can observe.

[0044] Furthermore, the UPD precision factor is used to update the ambiguity variance-covariance matrix of the user end, and the ambiguity of the user end is fixed based on the LAMBDA algorithm, including:

[0045] Using the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity as ambiguities at the user end;

[0046] De-correlating the ambiguity of the user terminal and the updated ambiguity variance-covariance matrix using a reversible integer transformation matrix to obtain an ambiguity vector and a transformed ambiguity variance-covariance matrix;

[0047] performing ambiguity integer search and verification on the ambiguity vector and the converted ambiguity variance-covariance matrix to obtain an integer ambiguity solution;

[0048] The ambiguity at the user end is fixed according to the integer ambiguity solution.

[0049] Furthermore, the ambiguity vector and the transformed ambiguity variance-covariance matrix are respectively expressed as:

[0050] ;

[0051] Where, represents the ambiguity vector, represents the ambiguity at the user end, represents a reversible integer transformation matrix, represents the transformed ambiguity variance-covariance matrix, represents the ambiguity variance-covariance matrix of the user side, represents the transpose of the reversible integer transformation matrix, Indicates transpose.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. The present invention introduces the phase deviation precision factor and combines it with the phase observation value of the GNSS reference station to fix the ambiguity of precise single-point positioning. It can accurately calculate and update the ambiguity variance-covariance matrix of the user end, which not only improves the accuracy of ambiguity fixation, but also enhances the robustness in complex environments and real-time data delays, thereby significantly improving the overall performance and reliability of precise single-point positioning.

[0054] 2. The present invention selects measurement stations in sequence according to the number of observed satellites, and uses the phase deviation whole-network solution method combined with the fractional cycle part of the wide-lane and narrow-lane ambiguity for iterative calculation. It can accurately solve the receiver-side UPD values ​​of all receivers and the satellite-side UPD values ​​of all visible satellites. It not only takes into account the influence of the number of observed satellites and gives priority to measurement stations with rich observation data, but also continuously updates the receiver-side UPD values ​​and satellite-side UPD values ​​through a step-by-step approximation method until the convergence condition is reached. It significantly improves the accuracy and reliability of the estimation of the receiver-side UPD values ​​and the satellite-side UPD values, provides a solid foundation for subsequent ambiguity fixation, and thus improves the accuracy and efficiency of precise point positioning.

[0055] 3. The present invention updates the ambiguity variance-covariance matrix of the user end by combining the UPD precision factor, which greatly improves the search efficiency and success rate of the LAMBDA algorithm in the ambiguity fixation process, thereby accelerating the convergence speed while ensuring positioning accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 1 is a flow chart of a method for fixing ambiguity in precise point positioning based on phase deviation precision dilution provided by an embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of an example of calculating the convergence speed of a precise single-point positioning ambiguity fixation method based on phase deviation precision factor provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0059] The term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " generally indicates an "or" relationship between the related objects.

[0060] Example 1

[0061] like Figure 1 As shown, this embodiment introduces a method for fixing ambiguity in precise point positioning based on phase deviation precision dilution, which is characterized by comprising:

[0062] Step 1: Obtain the phase observation value of the GNSS base station on the server.

[0063] GNSS (Global Navigation Satellite System) base stations provide high-precision satellite observation data, including pseudorange and phase observations. The present invention calculates floating-point ambiguities by obtaining phase observations.

[0064] Step 2: Based on the phase observations of the GNSS base station, calculate the fractional cycle parts of the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity of the floating point ambiguity.

[0065] Floating ambiguities include integer-cycle ambiguities, receiver-side UPD values, and satellite-side UPD values. Widelane and narrowlane ambiguities are obtained by linearly combining floating-point ambiguities of different frequencies. Their sensitivities to ionospheric delay differ, so they can be used to separate and estimate phase deviations. The present invention estimates receiver-side and satellite-side UPD values ​​by calculating the fractional-cycle components of widelane and narrowlane ambiguities.

[0066] Step 3: Select the measurement stations in sequence according to the number of observed satellites, and use the phase deviation whole network solution method and the fractional cycle part of the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity to iteratively calculate the receiver-side UPD values ​​of all receivers in the reference station network and the satellite-side UPD values ​​of all visible satellites and extract the residual vector of the phase deviation.

[0067] The network-wide phase deviation solution method uses observation data from multiple stations and satellites to iteratively estimate receiver-side and satellite-side UPD values. Stations with a large number of observed satellites typically provide more observation information and are therefore prioritized. This iterative UPD calculation reduces the uncertainty of the ambiguity parameters. The extracted phase deviation residual vector is used to evaluate the accuracy of the UPD estimate.

[0068] Step 4: Calculate the UPD precision factor based on the residual vector of the phase deviation.

[0069] The UPD DPI reflects the uncertainty or accuracy of the UPD value estimate. The UPD DPI is calculated based on the statistical properties of the phase deviation residual vector, such as the root mean square error (RMSE). The present invention uses the UPD DPI to update the user-side ambiguity variance-covariance matrix, thereby improving the reliability and accuracy of ambiguity resolution.

[0070] Step 5: Use the UPD precision factor to update the user-side ambiguity variance-covariance matrix and fix the user-side ambiguity based on the LAMBDA algorithm.

[0071] The LAMBDA (Least-squares AMBiguity Decorrelation Adjustment) algorithm is an efficient method for integer ambiguity search. It reduces the correlation between ambiguity parameters through decorrelation, thereby improving search efficiency and success rate. The updated ambiguity variance-covariance matrix provided by the present invention provides more accurate statistical information for the LAMBDA algorithm, making the ambiguity fixation process more stable and reliable. Ultimately, the present invention achieves high-precision positioning by fixing the user-side ambiguity using the LAMBDA algorithm.

[0072] Example 2

[0073] Based on the same inventive concept as Example 1, this example introduces a method for fixing ambiguity in precise point positioning based on phase deviation precision dilution, including the following steps:

[0074] Step 1: Obtain the phase observation value of the GNSS base station on the server.

[0075] Step 2: Based on the phase observations of the GNSS reference station, calculate the fractional cycle parts of the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity of the floating point ambiguity.

[0076] In some embodiments, calculating fractional cycle portions of ultra-widelane ambiguities, widelane ambiguities, and narrowlane ambiguities of floating point ambiguities based on GNSS reference station phase observations includes:

[0077] Step 2.1: Estimate floating point ambiguities from the GNSS reference station phase observations based on an undifferenced, non-combined observation model;

[0078] In this embodiment, the floating point ambiguity includes the integer ambiguity, the phase deviation between the receiver and the satellite. The floating point ambiguity of each satellite is expressed as:

[0079] ;

[0080] Where, Indicates the Floating point ambiguities for satellites, represents the integer ambiguity, Indicates the UPD value at the receiver end, that is, The UPD value of each receiver, Indicates the UPD value of the satellite end, that is, The satellite-side UPD value of each satellite.

[0081] Step 2.2: Perform linear combination processing on the floating-point ambiguities and transform them into ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity.

[0082] In this embodiment, the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity are expressed as:

[0083] ;

[0084] Where, 、 and represent the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity respectively, 、 and Respectively, the frequencies are 、 and Previous The receiver observes The floating point ambiguity solution is satellite-hours, where 、 and Respectively represent the first signal frequency, second signal frequency, and third signal frequency transmitted by the corresponding satellite system:

[0085] The corresponding satellite systems may be the GPS system, the GLONASS system, the Galileo system, the BDS system, etc., wherein the specific values ​​of the first signal frequency, the second signal frequency, and the third signal frequency in these satellite systems are as follows:

[0086] GPS system: The first signal frequency is L1: 1575.42MHz, the second signal frequency is L2: 1227.60MHz, and the third signal frequency is L5: 1176.45MHz;

[0087] GLONASS system: The first signal frequency is L1: ~1602MHz, the second signal frequency is L2: ~1246MHz, and the third signal frequency is L3: 1202.025MHz;

[0088] Galileo system: The first signal frequency is E1: 1575.42 MHz, the second signal frequency is E5a: 1176.45 MHz, and the third signal frequency is E5b: 1207.14 MHz.

[0089] BDS system: The first signal frequency is B1: 1561.098MHz, the second signal frequency is B2: 1207.14MHz, and the third signal frequency is B3: 1268.52MHz.

[0090] This embodiment can effectively separate ambiguity information of different frequencies by performing linear combination processing on the floating-point ambiguities, thereby laying the foundation for subsequent phase deviation estimation.

[0091] Step 2.3: Perform rounding operations on the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity respectively to obtain the integer parts of the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity.

[0092] Step 2.4: Subtract the integer parts of the ultra-widelane ambiguity, widelane ambiguity, and narrowlane ambiguity from the ultra-widelane ambiguity, widelane ambiguity, and narrowlane ambiguity to obtain the fractional parts of the ultra-widelane ambiguity, widelane ambiguity, and narrowlane ambiguity.

[0093] Step 3: Select the measurement stations in sequence according to the number of observed satellites. Use the whole-network phase deviation solution method and the fractional cycle part of the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity to iteratively calculate the receiver-side UPD values ​​of all receivers in the reference station network and the satellite-side UPD values ​​of all visible satellites and extract the residual vector of the phase deviation.

[0094] In this embodiment, when the first The receiver observes When the data of 10 satellites are collected, the receiver-side UPD values ​​and satellite-side UPD values ​​of all satellites in all measurement stations are merged. The merged receiver-side UPD values ​​and satellite-side UPD values ​​are expressed as:

[0095] ;

[0096] Where, Indicates the The receiver observes The floating point ambiguity of the satellites, Indicates the The receiver observes The satellites get the integer ambiguity, express The identity matrix, express vector, express The identity matrix, express vector, ⊗ represents the Kronecker product, represents the phase deviation vector at the receiver end, represents the satellite phase deviation vector, Indicates transpose.

[0097] In this embodiment, since the UPD value at the receiver end and the UPD value at the satellite end are linearly correlated, resulting in a rank deficiency between the UPD value at the receiver end and the UPD value at the satellite end, this embodiment introduces an additional reference condition for constraint, which is expressed as:

[0098] ;

[0099] In some embodiments, stations are selected sequentially according to the number of observed satellites, and the receiver-side UPD values ​​of all receivers in the reference station network and the satellite-side UPD values ​​of all visible satellites are iteratively calculated using a phase deviation whole-network solution method and fractional cycle parts of ultra-wide lane ambiguities, wide lane ambiguities, and narrow lane ambiguities, including:

[0100] In some embodiments, since the solution of phase deviation depends on the rounding of ambiguity, and the ambiguity needs to eliminate the influence of phase deviation to have integer characteristics, the above-mentioned phase deviation processing requires multiple iterations to complete the phase deviation separation between the receiver and the satellite.

[0101] Step 3.1: Set the receiver-side UPD value of the station with the largest number of observed satellites to 0, and subtract the receiver-side UPD value from the fractional cycle part of the ultra-widelane ambiguity, widelane ambiguity, and narrowlane ambiguity to obtain the initial satellite-side UPD value;

[0102] Step 3.2: Select the remaining stations in descending order of the number of observed satellites and repeat the following steps until all stations are processed:

[0103] Step 3.2.1: For each known satellite at each station:

[0104] The UPD value at the receiver is obtained by subtracting the satellite-side UPD value from the fractional cycle parts of the ultra-wide lane ambiguity, the wide lane ambiguity, and the narrow lane ambiguity, and then taking the average value as the UPD value at the receiver;

[0105] In this embodiment, the UPD value at the receiver is expressed as:

[0106] ;

[0107] Where, Indicates the UPD value at the receiver end, that is, The UPD value of each receiver, Indicates the The total number of satellites that can be observed by a receiver, Indicates the satellites, represents the floating point ambiguity, represents the integer ambiguity, Indicates the UPD value of the satellite end, that is, The UPD value of the satellite.

[0108] Step 3.2.2: For each unknown satellite at each station:

[0109] The satellite-side UPD value of the unknown satellite is obtained by subtracting the UPD value at the receiver from the fractional cycle part of the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity.

[0110] Step 3.3: Perform weighted averaging on the satellite-side UPD values ​​of the same satellite obtained by each measuring station to obtain the satellite-side UPD value of the satellite;

[0111] In this embodiment, the satellite UPD value is expressed as:

[0112] ;

[0113] Where, Indicates the UPD value of the satellite end, that is, UPD value of satellites Indicates that the first The total number of receivers for satellites, Indicates the A receiver, Indicates the Satellite No. The UPD value is calculated at each receiver. Indicates the satellites No. The UPD value calculated at each receiver The corresponding weight.

[0114] Step 3.4: If the difference between the satellite-side UPD values ​​obtained in two consecutive iterations is less than the preset threshold, the loop is exited to obtain the receiver-side UPD values ​​of all receivers in the reference station network and the satellite-side UPD values ​​of all visible satellites.

[0115] In this embodiment, the difference between the satellite UPD values ​​obtained in two adjacent iterations is less than a preset threshold value, which is expressed as:

[0116] ;

[0117] Where, and Respectively represent Second and The difference in satellite UPD values ​​obtained in the iterations is Indicates the preset threshold.

[0118] In some embodiments, the residual vector of the phase deviation is distributed on each satellite, wherein the residual vector of the phase deviation is expressed as:

[0119] ;

[0120] Where, The residual vector representing the phase deviation, Indicates the total number of receivers in the base station network (station and receiver have the same meaning, so I think this is a better way to express it). Indicates the total number of visible satellites in the base station network, Indicates the The receiver observes The phase bias residuals of the satellites, Indicates the The receiver observes Floating point ambiguities for satellites, Indicates the The receiver observes The integer ambiguity of the satellites, express OK The identity matrix of the columns, represents the Kronecker product, express A column vector with 1 row and 1 column, express A column vector with 1 row and 1 column, express OK The identity matrix of the columns, represents the satellite-side UPD vector of all visible satellites in the reference station network, represents the receiver-side UPD vector of all receivers in the reference station network, Indicates transpose.

[0121] Step 4: Calculate the UPD precision factor based on the residual vector of the phase deviation.

[0122] In some embodiments, calculating the UPD precision factor based on the residual vector of the phase deviation includes:

[0123] Calculate the RMS of all elements in the residual vector of the phase deviation to obtain the UPD precision factor;

[0124] In this embodiment, the UPD precision factor is expressed as:

[0125] ;

[0126] Where, Indicates the The UPD precision of each satellite is Indicates that the first The total number of receivers for satellites, Indicates the A receiver, Indicates the The receiver observes The phase bias residuals of the satellites.

[0127] Step 5: Use the UPD precision factor to update the ambiguity variance-covariance matrix of the user end, and fix the ambiguity of the user end based on the LAMBDA algorithm.

[0128] In this embodiment, the UPD precision dilution is used to update the ambiguity variance-covariance matrix of the user terminal, wherein the updated ambiguity variance-covariance matrix is ​​expressed as:

[0129] ;

[0130] Where, represents the updated user-side ambiguity variance-covariance matrix, represents the ambiguity variance-covariance matrix of the user side, where represents the ambiguity at the user end, represents the diagonal matrix consisting of UPD dilutions of precision, Indicates the total number of satellites that the receiver can observe.

[0131] In some embodiments, the ambiguity variance-covariance matrix of the user terminal is updated using the UPD precision dilution, and the ambiguity of the user terminal is fixed based on the LAMBDA algorithm, including:

[0132] Step 5.1: Using the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity as the ambiguity of the user end;

[0133] Step 5.2: De-correlate the ambiguity of the user terminal and the updated ambiguity variance-covariance matrix using a reversible integer transformation matrix to obtain an ambiguity vector and a transformed ambiguity variance-covariance matrix;

[0134] Step 5.3: Performing ambiguity integer search and verification on the ambiguity vector and the converted ambiguity variance-covariance matrix to obtain an integer ambiguity solution;

[0135] Step 5.4: Fix the ambiguity at the user end according to the integer ambiguity solution.

[0136] In this embodiment, the ambiguity vector and the converted ambiguity variance-covariance matrix are respectively expressed as:

[0137] ;

[0138] Where, represents the ambiguity vector, represents the ambiguity at the user end, represents a reversible integer transformation matrix, represents the transformed ambiguity variance-covariance matrix, represents the ambiguity variance-covariance matrix of the user side, represents the transpose of the reversible integer transformation matrix, Indicates transpose.

[0139] like Figure 2 The convergence speed calculation example shown in the figure shows the influence of whether or not to introduce the phase deviation precision factor on the convergence time of the PPP-AR fixed solution under different delay conditions, and compares the convergence time in the active and inactive periods of the ionosphere. Figure 2 It can be seen that the present invention accelerates the convergence speed under different environments and different product delays.

[0140] Example 3

[0141] Based on the same inventive concept as other embodiments, this embodiment introduces a computer-readable storage medium on which computer instructions are stored. When the computer instructions are executed by a processor, the steps of the method of the above-mentioned embodiment 1 or 2 are implemented.

[0142] Example 4

[0143] Based on the same inventive concept as other embodiments, this embodiment introduces a computer program product, including computer instructions. When the computer instructions are executed by a processor, the steps of the method in the above-mentioned embodiment 1 or 2 are implemented.

[0144] In summary, the present invention introduces the phase deviation precision factor and combines the phase observation value of the GNSS reference station to fix the ambiguity of precise single-point positioning. It can accurately calculate and update the ambiguity variance-covariance matrix of the user end, which not only improves the accuracy of ambiguity fixation, but also enhances the robustness in complex environments and real-time data delays, thereby significantly improving the overall performance and reliability of precise single-point positioning.

[0145] The present invention selects measurement stations in sequence according to the number of observed satellites, and uses the phase deviation whole-network solution method combined with the fractional cycle part of wide-lane and narrow-lane ambiguities for iterative calculation. It can accurately solve the receiver-side UPD values ​​of all receivers and the satellite-side UPD values ​​of all visible satellites. It not only takes into account the influence of the number of observed satellites and gives priority to measurement stations with rich observation data, but also continuously updates the receiver-side UPD values ​​and satellite-side UPD values ​​through a step-by-step approximation method until the convergence condition is reached. It significantly improves the accuracy and reliability of the estimation of the receiver-side UPD values ​​and the satellite-side UPD values, provides a solid foundation for subsequent ambiguity fixation, and thus improves the accuracy and efficiency of precise point positioning.

[0146] The present invention updates the ambiguity variance-covariance matrix of the user end by combining the UPD precision factor, so that the search efficiency and success rate of the LAMBDA algorithm in the ambiguity fixing process are greatly improved, thereby accelerating the convergence speed while ensuring positioning accuracy.

[0147] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, 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 magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0148] 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 flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0151] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A method for fixing ambiguity in precise point positioning based on phase deviation precision dilution, characterized in that: include: Get the GNSS base station phase observation value on the server side; Based on the phase observations of the GNSS base station, the fractional cycle part of the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity of the floating point ambiguity is calculated; The measurement stations are selected in sequence according to the number of observed satellites. The phase deviation network-wide solution method and the fractional cycle part of the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity are used to iteratively calculate the receiver-side UPD values ​​of all receivers in the reference station network and the satellite-side UPD values ​​of all visible satellites, and then extract the residual vector of the phase deviation. Calculate the UPD precision factor based on the residual vector of the phase deviation; The UPD precision factor is used to update the ambiguity variance-covariance matrix of the user side, and the ambiguity of the user side is fixed based on the LAMBDA algorithm.

2. The method for fixing single point positioning ambiguity based on phase deviation precision dilution according to claim 1, characterized in that: Based on the GNSS base station phase observations, the fractional cycle part of the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity of the floating point ambiguity is calculated, including: estimating floating-point ambiguities from the GNSS reference station phase observations based on an undifferenced, non-combined observation model; The floating point ambiguity includes integer ambiguity, receiver-side and satellite-side phase deviation; Performing linear combination processing on the floating-point ambiguities to transform them into ultra-wide lane ambiguities, wide lane ambiguities, and narrow lane ambiguities; Performing rounding operations on the ultra-wide lane ambiguity, the wide lane ambiguity, and the narrow lane ambiguity respectively to obtain the integer parts of the ultra-wide lane ambiguity, the wide lane ambiguity, and the narrow lane ambiguity; The fractional parts of the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity are obtained by subtracting the integer parts of the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity from the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity.

3. The method for fixing single point positioning ambiguity based on phase deviation precision dilution according to claim 2, characterized in that: The measurement stations are selected in sequence according to the number of observed satellites. The UPD values ​​of all receivers in the reference station network and the satellite-side UPD values ​​of all visible satellites are calculated iteratively using the phase deviation whole-network solution method and the fractional cycle parts of the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity, including: The receiver-side UPD value of the station with the largest number of observed satellites is set to 0, and the initial satellite-side UPD value is obtained by subtracting the receiver-side UPD value from the fractional cycle part of the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity; Select the remaining stations in descending order of the number of observed satellites and repeat the following steps until all stations are processed: For each known satellite at each station: The UPD value at the receiver is obtained by subtracting the satellite-side UPD value from the fractional cycle parts of the ultra-wide lane ambiguity, the wide lane ambiguity, and the narrow lane ambiguity, and then taking the average value as the UPD value at the receiver; For each unknown satellite at each station: The satellite-side UPD value of the unknown satellite is obtained by subtracting the UPD value at the receiver from the fractional cycle part of the ultra-wide lane ambiguity, wide lane ambiguity, and narrow lane ambiguity. Performing a weighted average of the satellite-side UPD values ​​of the same satellite obtained by each measuring station to obtain the satellite-side UPD value of the satellite; If the difference between the satellite-side UPD values ​​obtained in two adjacent iterations is less than a preset threshold, the loop is exited to obtain the receiver-side UPD values ​​of all receivers in the reference station network and the satellite-side UPD values ​​of all visible satellites.

4. The method for fixing single point positioning ambiguity based on phase deviation precision dilution according to claim 3, characterized in that: The UPD value at the receiver end is expressed as: ； Where, Indicates the UPD value at the receiver end, that is, The UPD value of each receiver, Indicates the The total number of satellites that can be observed by a receiver, Indicates the satellites, represents the floating point ambiguity, represents the integer ambiguity, Indicates the UPD value of the satellite end, that is, The UPD value of the satellite.

5. The method for fixing single point positioning ambiguity based on phase deviation precision dilution according to claim 3, characterized in that: The satellite UPD value is expressed as: ； Where, Indicates the UPD value of the satellite end, that is, UPD value of satellites Indicates that the first The total number of receivers for satellites, Indicates the A receiver, Indicates the Satellite No. The UPD value is calculated at each receiver. Indicates the satellites No. The UPD value calculated at each receiver The corresponding weight.

6. The method for fixing single point positioning ambiguity based on phase deviation precision dilution according to claim 1, characterized in that: The residual vector of the phase deviation is distributed on each satellite, wherein the residual vector of the phase deviation is expressed as: ； Where, The residual vector representing the phase deviation, represents the total number of receivers in the base station network, Indicates the total number of visible satellites in the base station network, Indicates the The receiver observes The phase bias residuals of the satellites, Indicates the The receiver observes Floating point ambiguities for satellites, Indicates the The receiver observes The integer ambiguity of the satellites, express OK The identity matrix of the columns, represents the Kronecker product, express A column vector with 1 row and 1 column, express A column vector with 1 row and 1 column, express OK The identity matrix of the columns, represents the satellite-side UPD vector of all visible satellites in the reference station network, represents the receiver-side UPD vector of all receivers in the reference station network, Indicates transpose.

7. The method for fixing single point positioning ambiguity based on phase deviation precision dilution according to claim 1, characterized in that: Calculate the UPD precision factor based on the residual vector of the phase deviation, including: Calculate the RMS of all elements in the residual vector of the phase deviation to obtain the UPD precision factor; The UPD precision factor is expressed as: ； Where, Indicates the The UPD precision of each satellite is Indicates that the first The total number of receivers for satellites, Indicates the A receiver, Indicates the The receiver observes The phase bias residuals of the satellites.

8. The method for fixing single point positioning ambiguity based on phase deviation precision dilution according to claim 2, characterized in that: The UPD precision factor is used to update the ambiguity variance-covariance matrix of the user terminal, wherein the updated ambiguity variance-covariance matrix is ​​expressed as: ； Where, represents the updated user-side ambiguity variance-covariance matrix, represents the ambiguity variance-covariance matrix of the user side, where represents the ambiguity of the user side, represents the diagonal matrix consisting of UPD dilutions of precision, Indicates the total number of satellites that the receiver can observe.

9. The method for fixing single point positioning ambiguity based on phase deviation precision dilution according to claim 8, characterized in that: The UPD precision factor is used to update the user-side ambiguity variance-covariance matrix, and the ambiguity of the user-side is fixed based on the LAMBDA algorithm, including: Using the ultra-wide lane ambiguity, wide lane ambiguity and narrow lane ambiguity as ambiguities at the user end; De-correlating the ambiguity of the user terminal and the updated ambiguity variance-covariance matrix using a reversible integer transformation matrix to obtain an ambiguity vector and a transformed ambiguity variance-covariance matrix; performing ambiguity integer search and verification on the ambiguity vector and the converted ambiguity variance-covariance matrix to obtain an integer ambiguity solution; The ambiguity at the user end is fixed according to the integer ambiguity solution.

10. The method for fixing single point positioning ambiguity based on phase deviation precision dilution according to claim 9, characterized in that: The ambiguity vector and the transformed ambiguity variance-covariance matrix are respectively expressed as: ； Where, represents the ambiguity vector, represents the ambiguity of the user side, represents a reversible integer transformation matrix, represents the transformed ambiguity variance-covariance matrix, represents the ambiguity variance-covariance matrix of the user side, represents the transpose of the reversible integer transformation matrix, Indicates transpose.

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