Navigation satellite positioning method and device and storage medium

Abstract

A positioning method and device of a navigation satellite and a storage medium, the method comprising: determining a common-view satellite set and a non-common-view satellite set based on observation data obtained by a rover station and a reference station, wherein the common-view satellite set is a set of a plurality of first satellites commonly observed by the rover station and the reference station, and the non-common-view satellite set is a set of at least one second satellite observed only by the rover station; determining a reference satellite from the common-view satellite set; determining double-difference observation values based on a plurality of first observation data of the plurality of first satellites and observation data of the reference satellite; determining single-difference pseudo-range observation values based on second observation data of the at least one second satellite and the observation data of the reference satellite, wherein the reference satellite is determined based on the common-view satellite set; constructing a Kalman filtering equation based on the double-difference observation values and the single-difference pseudo-range observation values, and determining a positioning result of the rover station. The application has the technical effect of improving the positioning accuracy of a global navigation satellite system.

Description

Technical Field

[0001] This disclosure relates to the field of navigation satellite positioning technology, and in particular to a navigation satellite positioning method, apparatus and storage medium. Background Technology

[0002] The Global Navigation Satellite System (GNSS) is a core infrastructure enabling global, all-weather, high-precision positioning and navigation, and is widely used in surveying and mapping engineering, intelligent transportation, precision agriculture, and consumer electronics. In applications requiring centimeter-level positioning accuracy, Real-time Kinematic (RTK) technology can calculate the rover's three-dimensional coordinates using carrier phase observations by combining observation data from both a base station and a rover. The high-precision positioning calculation process can utilize the Kalman Filter (KF) algorithm to eliminate common errors using satellite data jointly observed by the base station and the rover. With the rapid development of the Internet of Things (IoT) and autonomous driving technologies, application scenarios place higher demands on the continuity and availability of positioning algorithms in complex environments such as urban canyons and tree-lined roads. Therefore, improving the positioning efficiency and stability of GNSS in complex environments with limited observation conditions is a crucial issue. Summary of the Invention

[0003] In view of this, embodiments of the present disclosure provide a positioning method, apparatus, and storage medium for navigation satellites, aiming to improve the positioning efficiency and stability of global navigation satellite systems in complex environments with limited observation conditions. A first aspect provides a positioning method for navigation satellites, comprising: determining a common-view satellite set and a non-common-view satellite set based on observation data acquired by a rover and a base station, wherein the common-view satellite set is a set of multiple first satellites jointly observed by the rover and the base station, and the non-common-view satellite set is a set of at least one second satellite observed only by the rover; determining a reference satellite from the common-view satellite set; determining double-difference observations based on multiple first observation data of the multiple first satellites and observation data of the reference satellite; determining single-difference pseudorange observations based on second observation data of at least one second satellite and observation data of the reference satellite, wherein the reference satellite is determined based on the common-view satellite set; constructing a Kalman filter equation based on the double-difference observations and the single-difference pseudorange observations, and solving it to determine the positioning result of the rover.

[0004] The above satellite navigation and positioning method utilizes non-common-view satellites observed only by the rover station and combines double-difference observations to introduce the Kalman filter equation for solution. This breaks the limitation of existing RTK technology, which must rely on common-view satellites. In scenarios with severe obstruction, such as urban canyons, and insufficient common-view satellites, the introduction of single-difference data from non-common-view satellites can increase the number of satellites involved in the solution, optimize the spatial geometric distribution of satellites, and thus provide more observational constraints for Kalman filtering. This improves the positioning accuracy and reliability of carriers equipped with global navigation satellite systems in complex environments.

[0005] Optionally, determining the set of non-common-view satellites includes: acquiring observation data of at least one third satellite observed by the rover station, wherein the third satellite is not in the set of common-view satellites; screening and determining a candidate set of non-common-view satellites based on the observation data of at least one third satellite and a first observation constraint, wherein the first observation constraint includes one or all of a satellite elevation angle threshold and a first signal-to-noise ratio threshold; screening and determining the set of non-common-view satellites based on the candidate set of non-common-view satellites and a second observation constraint, wherein the second observation constraint includes one or all of a second signal-to-noise ratio threshold and a multipath error threshold; wherein the first signal-to-noise ratio threshold is less than or equal to the second signal-to-noise ratio threshold.

[0006] Optionally, based on multiple first observation data from multiple first satellites and observation data from a reference satellite, double-difference observation values ​​are determined, including: determining a first carrier station inter-station single difference based on the difference between the carrier phase observation data of the first satellite obtained by the rover station and the carrier phase observation data of the first satellite obtained by the base station; determining a second carrier station inter-station single difference based on the difference between the carrier phase observation data of the reference satellite obtained by the rover station and the carrier phase observation data of the reference satellite obtained by the base station; and determining double-difference carrier phase observation values ​​based on the difference between the first carrier station inter-station single difference and the second carrier station inter-station single difference.

[0007] Optionally, determining the double-difference observation value based on multiple first observation data from multiple first satellites and observation data from reference satellites further includes: determining a first pseudorange inter-station single difference based on the difference between pseudorange observation data of the first satellite acquired by the rover station and pseudorange observation data of the first satellite acquired by the base station; determining a second pseudorange inter-station single difference based on the difference between pseudorange observation data of the reference satellite acquired by the rover station and pseudorange observation data of the reference satellite acquired by the base station; and determining the double-difference pseudorange observation value based on the difference between the first pseudorange inter-station single difference and the second pseudorange inter-station single difference.

[0008] Optionally, based on second observation data from at least one second satellite and observation data from a reference satellite, a single-difference pseudorange observation value is determined, including: determining the single-difference pseudorange observation value based on the difference between the pseudorange observation data from the second satellite and the pseudorange observation data from the reference satellite.

[0009] Optionally, a Kalman filter equation is constructed based on double-difference observations and single-difference pseudorange observations, including: determining the double-difference geometric distance of the first satellite relative to the reference satellite and the single-difference geometric distance of the second satellite relative to the reference satellite based on the first position of the rover station, the second position of the base station, and the satellite positions of the first satellite, the second satellite, and the reference satellite; determining the single-difference pseudorange residual based on the difference between the single-difference pseudorange observation and the single-difference geometric distance; determining the double-difference carrier phase residual based on the difference between the double-difference carrier phase observation and the double-difference geometric distance; determining the double-difference pseudorange observation residual based on the difference between the double-difference pseudorange observation and the double-difference geometric distance; and constructing the Kalman filter equation based on the single-difference pseudorange residual, combined with the double-difference carrier phase residual and / or the double-difference pseudorange observation residual as the observation vector.

[0010] Optionally, the positioning result of the rover is determined by solving the following steps: updating the state vector of the Kalman filter equation based on the observation vector to obtain a floating-point solution, which includes floating-point values ​​of the rover's position parameters and floating-point values ​​of the double-difference integer ambiguity parameters; fixing the floating-point values ​​of the double-difference integer ambiguity parameters based on a preset ambiguity fixing algorithm; when the double-difference integer ambiguity parameter fixing solution is successful, correcting the floating-point values ​​of the position parameters based on the fixed values ​​of the double-difference integer ambiguity parameters obtained by the solution, determining the fixed solution of the rover's position parameters, and using it as the positioning result; when the double-difference integer ambiguity parameter fixing solution fails, the floating-point values ​​of the position parameters are used as the positioning result.

[0011] Optionally, the solution to determine the positioning result of the rover station also includes: comparing the single-difference pseudorange residual with a preset residual threshold; when the single-difference pseudorange residual is greater than or equal to the preset residual threshold, determining that the observation quality of the second satellite is abnormal, and removing the state parameters corresponding to the second satellite with abnormal observation quality from the state vector of the Kalman filter equation, or freezing the numerical update of the state parameters corresponding to the second satellite with abnormal observation quality in the Kalman filter equation.

[0012] Secondly, a positioning device for navigation satellites is provided, comprising: a non-common-view satellite identification unit, used to determine a common-view satellite set and a non-common-view satellite set based on observation data acquired by a rover and a base station, wherein the common-view satellite set is a set of multiple first satellites jointly observed by the rover and the base station, and the non-common-view satellite set is a set of at least one second satellite observed only by the rover; a reference satellite determination unit, used to determine a reference satellite from the common-view satellite set; a first determination unit, used to determine double-difference observation values ​​based on multiple first observation data of multiple first satellites and observation data of the reference satellite; a second determination unit, used to determine single-difference pseudorange observation values ​​based on second observation data of at least one second satellite and observation data of the reference satellite, wherein the reference satellite is determined based on the common-view satellite set; and a solution unit, used to construct a Kalman filter equation based on the double-difference observation values ​​and the single-difference pseudorange observation values, and solve it to determine the positioning result of the rover.

[0013] Thirdly, a computer-readable storage medium is provided, including instructions that, when read by a processor, execute, such as the positioning method for navigation satellites of the first aspect. Attached Figure Description

[0014] The accompanying drawings used in the description of the embodiments of this disclosure are briefly introduced below:

[0015] Figure 1 A flowchart illustrating a navigation satellite positioning method provided in some embodiments of this application is shown;

[0016] Figure 2 A schematic diagram of the structure of a navigation satellite positioning device provided in some embodiments of this application is shown. Detailed Implementation

[0017] To more clearly illustrate the technical solutions in the embodiments of this disclosure, examples of implementation methods of this disclosure will be described below with reference to the accompanying drawings. The accompanying drawings described below are merely some embodiments of this disclosure. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without creative effort. Adjustments and improvements made without departing from the concept of this disclosure are all within the protection scope of this disclosure.

[0018] To keep the drawings simple, each figure only schematically shows the parts relevant to the embodiment, and they do not represent the actual structure of the product. In addition, for the sake of clarity and ease of understanding, some figures only schematically show parts of components with the same structure or function, and there may actually be more or fewer components with the same structure or function.

[0019] In this disclosure, unless otherwise expressly specified and limited, ordinal numbers, such as “first”, “second”, etc., are used only to distinguish and describe related objects, and should not be construed as indicating or implying the relative importance or order between related objects; furthermore, they do not represent the quantity of related objects. “Multiple” includes two or more, and other quantifiers are similar. “ / ” is used to describe the relationship between related objects, indicating an “or” relationship between them. “And / or” is used to describe the relationship between related objects, including any combination relationship between them, such as “a and / or b” including: “a alone”, “b alone”, or “a and b”. “One or more” or “at least one” of multiple objects refers to any object or any combination of multiple objects, such as “one or more of a1, a2, a3” or “at least one of a1, a2, a3” including: “a1 alone”, “a2 alone”, “a3 alone”, “a1 and a2”, “a1 and a3”, “a2 and a3”, or “a1, a2 and a3”.

[0020] Global Navigation Satellite Systems (GNSS), as the core infrastructure of modern spatiotemporal information services, acquire observations by measuring satellite signal propagation time and carrier phase changes, and then calculate the position, velocity, and time information of the carrier. To achieve centimeter-level high-precision positioning, real-time dynamic differential (RTK) technology is typically employed to effectively eliminate satellite clock errors, receiver clock errors, and reduce common errors such as ionospheric and tropospheric delays. This allows the construction of a Kalman filter model incorporating double-difference carrier phase observations, utilizing the recursive nature of the filter to achieve convergence of state parameters and fixation of ambiguity. However, this classic double-difference RTK architecture strictly relies on the number and geometric distribution of common-view satellites, requiring both the rover and base station to simultaneously observe the same set of satellites to construct the double-difference equations. In complex application scenarios such as densely built-up urban areas, under overpasses, or forest obstructions, this architecture faces significant challenges. Due to the reduced number of visible satellites caused by obstruction, the number of common-view satellites between the rover and base station is often insufficient for calculation (e.g., less than four). In such cases, traditional RTK algorithms can only utilize limited common-view satellite data, making it difficult to pass the ambiguity fixation test, and may even lead to filter divergence or failure to locate. In view of this, this application provides a positioning method, device and storage medium for navigation satellites. By determining the common-view satellite set to construct a double-difference observation model, the method further filters and utilizes non-common-view satellites observed only by the rover station, and introduces the single-difference pseudorange residual and the double-difference residual into the Kalman filter equation. The geometric constraints provided by the non-common-view satellites are used to assist the filter convergence, thereby improving the accuracy and reliability of the rover station's positioning results.

[0021] The following description is in conjunction with the accompanying drawings:

[0022] Please refer to Figure 1This document illustrates a flowchart of a navigation satellite positioning method provided in some embodiments of this application. The navigation satellite positioning method includes at least the following steps:

[0023] S110: Based on the observation data obtained by the rover station and the base station, determine the common-view satellite set and the non-common-view satellite set, wherein the common-view satellite set is the set of multiple first satellites jointly observed by the rover station and the base station, and the non-common-view satellite set is the set of at least one second satellite observed only by the rover station.

[0024] S120: Identify reference satellites from the common-view satellite set;

[0025] S130: Determine double-difference observations based on multiple first observation data from multiple first satellites and observation data from reference satellites;

[0026] S140: Determine single-difference pseudorange observations based on second observation data from at least one second satellite and observation data from a reference satellite, wherein the reference satellite is determined based on a set of common-view satellites;

[0027] S150: Construct the Kalman filter equation based on double-difference observations and single-difference pseudorange observations, and solve it to determine the positioning result of the rover station.

[0028] In the embodiments of the above satellite navigation and positioning method, a rover station refers to a receiver whose location is yet to be determined and which is in motion or stationary. For example, this receiver can be installed on non-motorized vehicles, motorized vehicles, ships, aircraft (e.g., flying vehicles, drones), mobile terminals (e.g., mobile phones, wearable devices), non-mobile terminals (e.g., computers, controllers, servers), industrial robots, or home robots, etc., without specific limitations. A reference station refers to a reference receiver whose location is known and used to provide differential data, such as a physical reference station. The satellite navigation and positioning method of this application can be implemented using a GNSS chip or receiver, or other processors or electronic devices with satellite navigation information reception and processing capabilities, without specific limitations.

[0029] During the positioning process, the rover and base station track satellite signals from the Global Navigation Satellite System in real time and acquire observation data separately. When acquiring satellite signals, the data from the rover and base station can be matched. Due to differences in the observation environment—for example, the rover may be located near a tall building while the base station has a wide field of view—the lists of satellites observed by the two stations may not be entirely identical.

[0030] In this application, the first satellite refers to the satellite that is simultaneously locked by both the rover and the base station and for which valid observation data has been acquired. The set of these first satellites is the common-view satellite set. Correspondingly, in complex urban environments, the rover may observe some satellites that the base station has not observed, i.e., second satellites. The set of these second satellites is the non-common-view satellite set. In traditional RTK algorithms, these non-common-view satellites are usually directly discarded because double-difference cannot be constructed. However, in this application, these data will be retained and utilized.

[0031] The selection of a reference satellite is crucial for constructing the difference model. It can be the satellite with the highest elevation angle, best signal-to-noise ratio, or optimal geometric position among the commonly viewed satellites. This reference satellite will be used for subsequent difference calculations with other commonly viewed and non-common viewed satellites.

[0032] For a shared-view satellite ensemble, this application employs a double-difference model. This model utilizes first observation data from multiple first satellites and observation data from a reference satellite to perform inter-station differencing between the rover and the base station, and inter-satellite differencing between satellites, thereby determining the double-difference observations. Double-difference observations possess high accuracy because they simultaneously eliminate satellite clock errors, receiver clock errors, and most atmospheric delay errors (such as ionospheric and tropospheric delays).

[0033] For non-common-view satellite sets, the double-difference model mentioned above cannot be constructed due to the lack of corresponding observation data at the base station. In order to utilize this data, this application innovatively uses the aforementioned determined reference satellite as a bridge to calculate the difference between the second observation data of at least one second satellite and the observation data of the reference satellite to determine the single-difference pseudorange observation value. By performing single-difference processing with the reference satellite, the receiver clock error of the rover station can be effectively eliminated.

[0034] Furthermore, the two types of observations with different properties can be fused together. The double-difference observations and the single-difference pseudorange observations can be used together as observation inputs to construct the Kalman filter equation. Through the prediction and update steps of the Kalman filter algorithm, the state vector containing the three-dimensional coordinates of the rover can be calculated, thereby determining the positioning result of the rover.

[0035] The above satellite navigation and positioning method utilizes non-common-view satellites observed only by the rover station and combines double-difference observations to jointly introduce the Kalman filter equation for solution. This breaks the limitation of existing RTK technology, which must rely on common-view satellites. Especially in scenarios with severe obstruction, such as urban canyons, and insufficient common-view satellites, the introduction of single-difference data from non-common-view satellites can increase the number of satellites participating in the solution, optimize the spatial geometric distribution of satellites, and thus provide more observation constraints for Kalman filtering, improving the positioning accuracy and reliability of carriers equipped with global navigation satellite systems in complex environments.

[0036] In some embodiments of this application, determining the non-common-view satellite set includes: acquiring observation data of at least one third satellite observed by the rover, wherein the third satellite is not in the common-view satellite set; screening and determining a candidate set of non-common-view satellites based on the observation data of at least one third satellite and a first observation constraint, wherein the first observation constraint includes one or all of a satellite elevation angle threshold and a first signal-to-noise ratio threshold; screening and determining the non-common-view satellite set based on the candidate set of non-common-view satellites and a second observation constraint, wherein the second observation constraint includes one or all of a second signal-to-noise ratio threshold and a multipath error threshold; wherein the first signal-to-noise ratio threshold is less than or equal to the second signal-to-noise ratio threshold.

[0037] In the above embodiments, by comparing the observation lists of the rover and the base station, satellites that appear in the rover's observation list but not in the common-view satellite set are identified as third satellites. These third satellites failed to provide data due to factors such as base station obstruction or base station receiver sensitivity settings. At this point, a first-level screening is performed on the third satellites, using a first observation constraint to quickly eliminate obviously unusable satellites. For example, the first observation constraint could be a satellite elevation angle threshold (e.g., 5 or 10 degrees), thus eliminating satellites with extremely low elevation angles, as the signal paths of these satellites through the atmosphere are too long, atmospheric delay errors are difficult to correct with the model, and they are highly susceptible to ground obstruction. Alternatively, the first observation constraint could be a first signal-to-noise ratio threshold, thus eliminating satellites with extremely weak signals that are almost impossible to lock onto. The first observation constraint can be set selectively or simultaneously, thereby forming a non-common-view satellite candidate set of the remaining satellites after screening. Further, a second-level screening can be performed on the candidate set, using a second observation constraint, to select high-quality satellites from the candidates to participate in the final solution. The second observation constraint may include setting a second signal-to-noise ratio (SNR) threshold greater than the first SNR threshold to ensure that the noise level of the single-difference pseudorange observations is within a controllable range. It may also include setting a multipath error threshold. Multipath error can be estimated using a combination of code pseudorange and carrier phase. Since the single-difference model cannot effectively mitigate multipath effects like the double-difference model, satellites with multipath errors exceeding the multipath error threshold (e.g., 1 meter or 2 meters) can be eliminated. Through these steps, on the one hand, a lower first SNR threshold ensures a sufficient number of candidate non-common-view satellites, avoiding the accidental deletion of potentially usable satellites during the initial screening stage. On the other hand, a higher second SNR threshold and multipath constraints ensure high reliability of the single-difference observations introduced into the Kalman filter algorithm, preventing filter divergence or accuracy degradation due to the introduction of inferior non-common-view satellites.

[0038] In some embodiments of this application, determining double-difference observations based on multiple first observation data from multiple first satellites and observation data from a reference satellite includes: determining a first carrier-station inter-station single difference based on the difference between carrier-phase observation data of the first satellite obtained by the rover station and carrier-phase observation data of the first satellite obtained by the base station; determining a second carrier-station inter-station single difference based on the difference between carrier-phase observation data of the reference satellite obtained by the rover station and carrier-phase observation data of the reference satellite obtained by the base station; and determining double-difference carrier-phase observations based on the difference between the first carrier-station inter-station single difference and the second carrier-station inter-station single difference.

[0039] For the same satellite, whether it's a first satellite in the common viewing set or a selected reference satellite, its carrier phase observation data at the rover station and its observation data at the reference station can be acquired separately. Since the rover station and the reference station are observing the same satellite, the satellite clock bias and satellite orbit error contained in both are almost identical. By calculating the difference between the two station data, the first carrier inter-station single difference and the second carrier inter-station single difference can be obtained. At this point, the satellite clock bias term in the observation equation is effectively eliminated, but the remaining observation values ​​still contain the difference between the rover station receiver clock bias and the reference station receiver clock bias. Further, to eliminate the receiver clock bias remaining in the above steps, a reference satellite can be used as a reference. The first carrier inter-station single difference corresponding to each first satellite is subtracted from the second carrier inter-station single difference corresponding to the reference satellite. Since at the same time, all satellite inter-station single differences contain the same receiver clock bias term, this receiver clock bias term is completely canceled out. The final double-difference carrier phase observation is a pure observation that contains neither satellite clock error nor receiver clock error. Its main error sources are only multipath effect, measurement noise, and atmospheric residuals that have not been completely eliminated by the model, thus providing high-precision input data for satellite navigation and positioning.

[0040] In some embodiments of this application, determining double-difference observations based on multiple first observation data from multiple first satellites and observation data from a reference satellite further includes: determining a first pseudorange inter-station single difference based on the difference between pseudorange observation data of the first satellite acquired by the rover station and pseudorange observation data of the first satellite acquired by the base station; determining a second pseudorange inter-station single difference based on the difference between pseudorange observation data of the reference satellite acquired by the rover station and pseudorange observation data of the reference satellite acquired by the base station; and determining double-difference pseudorange observations based on the difference between the first pseudorange inter-station single difference and the second pseudorange inter-station single difference.

[0041] Similarly, since the rover and base station synchronously observe the same satellite, such as the first satellite or a reference satellite, the satellite clock bias and propagation errors through the ionosphere and troposphere contained in their data are mostly the same. By calculating the difference between the rover and base station data, the aforementioned common errors are effectively eliminated in the inter-station single difference. Furthermore, by calculating the difference between the first pseudorange inter-station single difference and the second pseudorange inter-station single difference, the double-difference pseudorange observation can be determined. In this inter-satellite differential process, the receiver clock bias terms (i.e., the difference between the rover clock bias and the base station clock bias) contained in the first and second pseudorange inter-station single differences are further canceled out. The finally determined double-difference pseudorange observation has the characteristic of no integer cycle ambiguity, which can provide stable absolute position constraints for Kalman filtering and assist in the resolution of carrier phase ambiguity.

[0042] In some embodiments of this application, determining a single-difference pseudorange observation value based on second observation data from at least one second satellite and observation data from a reference satellite includes: determining the single-difference pseudorange observation value based on the difference between the pseudorange observation data from the second satellite and the pseudorange observation data from the reference satellite.

[0043] In the same observation epoch, the rover receiver simultaneously tracks the second satellite in the non-common-view satellite set and the reference satellite in the common-view satellite set, and measures the pseudorange observation data of the second satellite and the reference satellite respectively. Since these two sets of pseudorange observation data are obtained by the rover receiver measuring different satellite signals at the same time, they contain completely consistent rover receiver clock bias components. Therefore, the difference between the pseudorange observation data of the second satellite and the pseudorange observation data of the reference satellite can be calculated. That is, by subtraction, the shared rover receiver clock bias components are mutually canceled. The single-difference pseudorange observation value determined in this way retains the geometric distance difference information of the second satellite relative to the reference satellite and the satellite clock bias difference information, establishing the relative observation relationship between the non-common-view satellite and the reference satellite, thus providing an effective observation constraint without receiver clock bias parameters for the subsequent construction of the Kalman filter equation. For example, the pseudorange observation value of the non-common-view satellite (the second satellite) can be processed by single-difference. The pseudorange observation value corresponding to the second satellite svi (where i can take the values ​​1, 2, ..., N) can be expressed by Equation 1:

[0044] Formula 1

[0045] in, This represents the pseudorange observation value of the i-th second satellite; The geometric distance from the i-th second satellite to the rover station can be calculated using the satellite position of the second satellite and the first position of the rover station. Indicates the clock bias of the rover receiver; Indicates satellite clock bias; Indicates tropospheric delay; Indicates ionospheric delay; c is the speed of light; Indicates pseudorange observation noise; where , , All of these can be corrected using a model. Therefore, the corrected pseudorange observations of the second satellite... This can be expressed by Formula 2:

[0046] Formula 2

[0047] in, The model correction residual is the corrected error, which is small in magnitude and can be included in the noise term. During the filtering process, it is adapted by adjusting the filtering variance.

[0048] Within the common-view satellite ensemble, select a reference satellite (svref) and determine its pseudorange observations. Combined with the pseudorange observations of the second satellite as described above. Calculating single-difference pseudorange observations: Since the receiver clock biases of corresponding observations from non-common-view satellites are the same, they are completely eliminated under single-difference conditions. Refer to Formula 3:

[0049] Formula 3

[0050] In some embodiments of this application, the Kalman filter equation is constructed based on double-difference observations and single-difference pseudorange observations, including: determining the double-difference geometric distance of the first satellite relative to the reference satellite and the single-difference geometric distance of the second satellite relative to the reference satellite based on the first position of the rover, the second position of the base station, and the satellite positions of the first satellite, the second satellite, and the reference satellite; determining the single-difference pseudorange residual based on the difference between the single-difference pseudorange observation and the single-difference geometric distance; determining the double-difference carrier phase residual based on the difference between the double-difference carrier phase observation and the double-difference geometric distance; determining the double-difference pseudorange observation residual based on the difference between the double-difference pseudorange observation and the double-difference geometric distance; and constructing the Kalman filter equation based on the single-difference pseudorange residual, combined with the double-difference carrier phase residual and / or the double-difference pseudorange observation residual as the observation vector.

[0051] In the above embodiments, the first position of the rover station can be obtained, such as the Kalman filter predicted position of the previous epoch or the approximate position calculated by single-point positioning in the current epoch, and the known precise coordinates of the base station, i.e., the second position, can be obtained. Simultaneously, the precise satellite positions of each satellite (first satellite, second satellite, and reference satellite) in the Earth coordinate system at the current moment are calculated based on the satellite ephemeris. Based on this, the theoretical geometric distance is calculated. For a common-view satellite set, the double-difference geometric distance can be calculated based on the first position, the second position, and the satellite positions of the first and reference satellites. This double-difference geometric distance represents the theoretically observable double-difference distance value when the rover station is assumed to be at the first position, i.e., it includes the double-difference combination of the geometric distances from the rover station to the satellite and from the base station to the satellite. Secondly, for a non-common-view satellite set, the single-difference geometric distance is calculated based on the first position and the satellite positions of the second and reference satellites, i.e., the theoretically observable distance difference between the second satellite and the reference satellite when the rover station is assumed to be at the first position. After obtaining the theoretical value, the difference between the single-difference pseudorange observation (actual measured value) and the aforementioned single-difference geometric distance (theoretical calculated value) can be calculated to determine the single-difference pseudorange residual. Simultaneously, the difference between the double-difference carrier phase observation and the double-difference geometric distance is calculated to obtain the double-difference carrier phase residual; and the difference between the double-difference pseudorange observation and the double-difference geometric distance is calculated to obtain the double-difference pseudorange observation residual. Finally, an observation vector can be constructed using these three residuals. This observation vector contains the deviation information of the rover's true position relative to the first position. After inputting this observation vector into the Kalman filter equation, the state correction is calculated through a measurement update step, thereby correcting the first position and obtaining the high-precision positioning result of the rover. For example, the second satellite's single-difference pseudorange is incorporated into the observation equation to update the state estimate, resulting in the Kalman filter equation (see Formula 4).

[0052] Formula 4

[0053] This represents the double-difference pseudorange observation residual or the double-difference carrier phase residual. This represents a double-difference observation cosine array. Represents the ambiguity observation matrix, and the parameters to be estimated. This indicates the floating-point solution location information. This represents the ambiguity parameter. This represents the single-difference pseudorange residual formed by the second satellite svi and the reference satellite, where i can take values ​​of 1, 2, ..., N, as shown in Formula 5:

[0054] Formula 5

[0055] To represent the single-difference pseudorange observation, refer to Formula 6:

[0056] Formula 6

[0057] In the above formula, , , Indicates direction cosine. , , ,

[0058] Among them, (X) si Y si Z si ) represents the location information of the i-th second satellite, (X pos Y pos Z pos () indicates the location information of the mobile station.

[0059] In some embodiments of this application, determining the positioning result of the rover includes: updating the state vector of the Kalman filter equation based on the observation vector to obtain a floating-point solution, which includes: floating-point values ​​of the rover's position parameters and floating-point values ​​of double-difference integer ambiguity parameters; fixing the floating-point values ​​of the double-difference integer ambiguity parameters based on a preset ambiguity fixing algorithm; when the double-difference integer ambiguity parameter fixing is successful, correcting the floating-point values ​​of the position parameters based on the fixed values ​​of the double-difference integer ambiguity parameters obtained from the fixing, determining the fixed solution of the rover's position parameters, and using it as the positioning result; when the double-difference integer ambiguity parameter fixing fails, the floating-point values ​​of the position parameters are used as the positioning result.

[0060] The above embodiments can update the state vector of the Kalman filter equation based on the constructed observation vector. By calculating the Kalman gain and mapping the observation residuals to the state space, a floating-point solution of the state vector is obtained. This floating-point solution mainly includes the floating-point values ​​of the rover's position parameters and the floating-point values ​​of the double-difference integer ambiguity parameters. At this time, the double-difference integer ambiguity parameters are still real numbers (i.e., floating-point numbers) and have not yet been constrained to the integer characteristics that they should physically possess. Affected by this, although the accuracy of the corresponding position parameters is better than that of single-point positioning, there is usually still a certain numerical fluctuation (e.g., decimeter-level error). In order to further eliminate errors and improve positioning accuracy, a preset ambiguity fixing algorithm, such as the least squares ambiguity decorrelation adjustment method LAMBDA, can be used to fix the floating-point values ​​of the double-difference integer ambiguity parameters. This solution process is essentially searching for the set of integer ambiguity combinations with the highest statistical probability within the range determined by the floating-point solution and its covariance matrix. If the double-difference integer ambiguity parameter fixing solution is successful, it indicates that a set of high-confidence double-difference integer ambiguity parameter fixing values ​​has been found. At this point, the difference between the fixed and floating-point values ​​can be used, combined with the covariance matrix of the Kalman filter, to numerically correct the floating-point position parameter, thereby determining the fixed solution for the rover's position parameters and outputting it as the final high-precision positioning result. Conversely, if the fixed solution for the double-difference integer ambiguity parameter fails due to adverse observation conditions, such as severe obstruction or multipath interference, the aforementioned floating-point position parameter value can be directly used as the current positioning result to ensure the continuity of positioning services. Because this application introduces single-difference pseudorange observations from non-common-view satellites into the filtering equation, optimizing the satellite geometric distribution, even when outputting a floating-point solution, the accuracy and stability of this floating-point solution are superior to traditional floating-point solutions that rely solely on common-view satellites.

[0061] In some embodiments of this application, the calculation of the positioning result of the rover station further includes: comparing the single-difference pseudorange residual with a preset residual threshold; when the single-difference pseudorange residual is greater than or equal to the preset residual threshold, determining that the observation quality of the second satellite is abnormal, and removing the state parameter corresponding to the second satellite with abnormal observation quality from the state vector of the Kalman filter equation, or freezing the numerical update of the state parameter corresponding to the second satellite with abnormal observation quality in the Kalman filter equation.

[0062] Since non-common-line-of-sight (NLS) satellites cannot be modeled using double-difference to eliminate most common errors, their observation data are more sensitive to multipath effects and ephemeris errors. To prevent low-quality single-difference observations from contaminating the state estimation of the Kalman filter, this application introduces a residual verification process. Specifically, during the Kalman filter update process, the single-difference pseudorange residual corresponding to each NLS satellite can be calculated and monitored in real time, and compared with a preset residual threshold. This preset residual threshold can be set based on the posterior variance or empirical model of the Kalman filter to define the confidence interval of the observations. When the single-difference pseudorange residual of a certain NLS satellite is detected to be greater than or equal to the preset residual threshold, the current observation quality of that NLS satellite is determined to be abnormal, indicating that its signal may be subject to severe non-line-of-sight propagation interference or have large measurement gross errors. For NLS satellites determined to have abnormal observation quality, isolation and protection measures can be taken, such as directly removing the state parameters corresponding to that NLS satellite from the state vector of the Kalman filter equation, causing it to completely exit the subsequent filtering solution, thereby cutting off the error propagation path. For example, the numerical updates of the state parameters corresponding to the second satellite in the Kalman filter equation can be frozen. That is, the correction of the specific state quantity is paused in the current filter epoch, and its value is kept unchanged until the residual of the satellite returns to the normal level.

[0063] The above embodiments can be applied in urban autonomous driving scenarios, such as when vehicles are traveling between tall buildings and the common-view satellites are frequently changing. This application can maximize the use of all visible satellites to maintain positioning continuity and accuracy. In the scenario of precise inspection by UAVs, when flying in areas with severe obstruction such as high-voltage lines and canyons, it can enhance positioning reliability and avoid positioning drift caused by temporary signal loss. In the scenario of portable device mapping, when there are partially obstructed environments such as under trees, it can speed up initialization and improve work efficiency.

[0064] Figure 2A schematic diagram of a navigation satellite positioning device provided in some embodiments of this application is shown. The navigation satellite positioning device 200 includes: a non-common-view satellite identification unit 210, used to determine a common-view satellite set and a non-common-view satellite set based on observation data acquired by a rover and a base station, wherein the common-view satellite set is a set of multiple first satellites jointly observed by the rover and the base station, and the non-common-view satellite set is a set of at least one second satellite observed only by the rover; a reference satellite determination unit 220, used to determine a reference satellite from the common-view satellite set; a first determination unit 230, used to determine double-difference observation values ​​based on multiple first observation data of multiple first satellites and observation data of the reference satellite; a second determination unit 240, used to determine single-difference pseudorange observation values ​​based on second observation data of at least one second satellite and observation data of the reference satellite, wherein the reference satellite is determined based on the common-view satellite set; and a solution unit 250, used to construct a Kalman filter equation based on the double-difference observation values ​​and the single-difference pseudorange observation values, and solve it to determine the positioning result of the rover.

[0065] It should be noted that, Figure 2 The specific implementation of the positioning device for navigation satellites can be described in the specific process and related description of the positioning method for navigation satellites mentioned above, and will not be repeated here.

[0066] The above division of units is merely a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these units can be implemented by a processor calling software; for example, a navigation satellite positioning device includes a processor coupled to memory, which stores instructions. The processor calls these stored instructions to implement any of the above navigation satellite positioning methods or to achieve the functions of each unit. The processor can be, for example, a general-purpose processor, such as a CPU, and the memory can be memory within a cross-platform acquisition device or memory outside of it. Alternatively, these units can be implemented as hardware circuits. The functions of some or all units can be achieved through the design of the hardware circuit, which can be understood as one or more processors. For example, this hardware circuit includes an Application-Specific Integrated Circuit (ASIC), which implements the functions of some or all units by designing the logical relationships between the components within the circuit. Furthermore, this hardware circuit can be implemented using a programmable logic device (PLD), which can include a large number of logic gates. The logical relationships between these logic gates are configured through a configuration file, thereby achieving the functions of some or all units. All units of the above cross-platform acquisition device can be implemented entirely through processor calling programs, or entirely through hardware circuits, or partially through processor calling programs with the remaining parts implemented through hardware circuits.

[0067] Based on the same technical concept, this application also provides a computer-readable storage medium including instructions, which, when read by a processor, execute the navigation satellite positioning method provided in the above embodiments.

[0068] In an exemplary embodiment, a computer device is provided, which may be a server. The computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores raw measurement data. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a navigation satellite positioning method.

[0069] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the navigation satellite positioning method in any of the above embodiments.

[0070] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0071] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0072] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A positioning method for navigation satellites, characterized in that, include: Based on the observation data obtained from the rover and the base station, a common-view satellite set and a non-common-view satellite set are determined. The common-view satellite set is a set of multiple first satellites jointly observed by the rover and the base station, and the non-common-view satellite set is a set of at least one second satellite observed only by the rover. The determination of the non-common-view satellite set includes: acquiring observation data of at least one third satellite observed by the rover station, wherein the third satellite is not in the common-view satellite set; screening and determining a candidate set of non-common-view satellites based on the observation data of at least one third satellite and a first observation constraint, wherein the first observation constraint includes one or all of a satellite elevation angle threshold and a first signal-to-noise ratio threshold; screening and determining the non-common-view satellite set based on the candidate set of non-common-view satellites and a second observation constraint, wherein the second observation constraint includes one or all of a second signal-to-noise ratio threshold and a multipath error threshold; wherein the first signal-to-noise ratio threshold is less than or equal to the second signal-to-noise ratio threshold; Determine reference satellites from the aforementioned set of shared satellites; Based on multiple first observation data from multiple first satellites and observation data from the reference satellite, double-difference observation values ​​are determined; Based on the second observation data of at least one of the second satellites and the observation data of the reference satellite, the single-difference pseudorange observation value is determined; Based on the double-difference observations and the single-difference pseudorange observations, a Kalman filter equation is constructed and solved to determine the positioning result of the rover station.

2. The positioning method for navigation satellites according to claim 1, characterized in that, The determination of double-difference observations based on multiple first observation data from multiple first satellites and observation data from the reference satellite includes: Based on the difference between the carrier phase observation data of the first satellite obtained by the rover station and the carrier phase observation data of the first satellite obtained by the base station, the first carrier station inter-difference is determined. Based on the difference between the carrier phase observation data of the reference satellite obtained by the rover station and the carrier phase observation data of the reference satellite obtained by the base station, a second inter-carrier station single difference is determined; Based on the difference between the first carrier station-to-station single difference and the second carrier station-to-station single difference, the double-difference carrier phase observation value is determined.

3. The positioning method for navigation satellites according to claim 2, characterized in that, The step of determining double-difference observations based on multiple first observation data from multiple first satellites and observation data from the reference satellite further includes: Based on the difference between the pseudorange observation data of the first satellite obtained by the rover station and the pseudorange observation data of the first satellite obtained by the base station, the first pseudorange inter-station single difference is determined. Based on the difference between the pseudorange observation data of the reference satellite obtained by the rover station and the pseudorange observation data of the reference satellite obtained by the base station, a second pseudorange inter-station single difference is determined. The double-difference pseudorange observation value is determined based on the difference between the first pseudorange inter-station single difference and the second pseudorange inter-station single difference.

4. The positioning method for navigation satellites according to claim 3, characterized in that, The determination of single-difference pseudorange observations based on second observation data from at least one second satellite and observation data from the reference satellite includes: The single-difference pseudorange observation value is determined based on the difference between the pseudorange observation data of the second satellite and the pseudorange observation data of the reference satellite.

5. The positioning method for navigation satellites according to claim 3, characterized in that, The construction of the Kalman filter equation based on the double-difference observations and the single-difference pseudorange observations includes: Based on the first position of the rover, the second position of the base station, and the satellite positions of the first satellite, the second satellite, and the reference satellite, the double-difference geometric distance of the first satellite relative to the reference satellite and the single-difference geometric distance of the second satellite relative to the reference satellite are determined. The single-difference pseudorange residual is determined based on the difference between the single-difference pseudorange observation and the single-difference geometric distance. The double-difference carrier phase residual is determined based on the difference between the double-difference carrier phase observation value and the double-difference geometric distance; The double-difference pseudorange observation residual is determined based on the difference between the double-difference pseudorange observation value and the double-difference geometric distance. Based on the single-difference pseudorange residual, and combined with the double-difference carrier phase residual and / or the double-difference pseudorange observation residual as the observation vector, the Kalman filter equation is constructed.

6. The positioning method for navigation satellites according to claim 5, characterized in that, The calculation to determine the positioning result of the rover includes: The state vector of the Kalman filter equation is measured and updated based on the observation vector to obtain a floating-point solution, which includes: the floating-point value of the position parameter of the rover and the floating-point value of the double-difference integer ambiguity parameter; The floating-point value of the double-difference integer ambiguity parameter is fixedly calculated based on a preset ambiguity fixing algorithm. When the double-difference integer ambiguity parameter is successfully fixed, the floating-point value of the position parameter is corrected based on the fixed value of the double-difference integer ambiguity parameter, and the fixed solution of the position parameter of the rover is determined and used as the positioning result. When the fixed solution of the double-difference integer ambiguity parameter fails, the floating-point value of the position parameter is used as the positioning result.

7. The positioning method for navigation satellites according to claim 5, characterized in that, The calculation to determine the positioning result of the rover station also includes: Compare the single-difference pseudorange residual with a preset residual threshold; When the single-difference pseudorange residual is greater than or equal to the preset residual threshold, the observation quality of the second satellite is determined to be abnormal, and the state parameter corresponding to the second satellite with abnormal observation quality is removed from the state vector of the Kalman filter equation, or the numerical update of the state parameter corresponding to the second satellite with abnormal observation quality in the Kalman filter equation is frozen.

8. A positioning device for navigation satellites, characterized in that, include: A non-common-view satellite identification unit is used to determine a common-view satellite set and a non-common-view satellite set based on observation data acquired by a rover and a base station. The common-view satellite set is a set of multiple first satellites jointly observed by the rover and the base station, and the non-common-view satellite set is a set of at least one second satellite observed only by the rover. Determining the non-common-view satellite set includes: acquiring observation data of at least one third satellite observed by the rover, wherein the third satellite is not in the common-view satellite set; filtering and determining a candidate set of non-common-view satellites based on the observation data of at least one third satellite and a first observation constraint, wherein the first observation constraint includes one or more of a satellite elevation angle threshold and a first signal-to-noise ratio threshold; filtering and determining the non-common-view satellite set based on the candidate set of non-common-view satellites and a second observation constraint, wherein the second observation constraint includes one or more of a second signal-to-noise ratio threshold and a multipath error threshold; wherein the first signal-to-noise ratio threshold is less than or equal to the second signal-to-noise ratio threshold. A reference satellite determination unit is used to determine a reference satellite from the set of commonly viewed satellites; The first determining unit is used to determine double-difference observation values ​​based on multiple first observation data from multiple first satellites and observation data from the reference satellite. The second determining unit is used to determine a single-difference pseudorange observation value based on the second observation data of at least one second satellite and the observation data of the reference satellite, wherein the reference satellite is determined based on the common-view satellite set; The solution unit is used to construct a Kalman filter equation based on the double-difference observations and the single-difference pseudorange observations, and solve it to determine the positioning result of the rover station.

9. A computer-readable storage medium, characterized in that, The instruction includes, when read by the processor, the positioning method for navigation satellites as described in any one of claims 1 to 7, which is then executed.

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