Star-based precision single-point positioning method, system and device and storage medium

Abstract

The invention discloses a satellite-based precise point positioning method, system and device and a storage medium, and belongs to the technical field of precise point positioning, and the method comprises the steps: receiving Beidou observation data and PPP-B2b service; based on the parameters provided by the PPP-B2b, calculating the distance measurement precision of the user; removing overrun satellites; constructing a random model of satellite observation values; calculating precise point positioning based on the random model, and outputting a positioning result; wherein the step of constructing the random model of the satellite observation value comprises the steps of setting a coefficient of an elevation angle model; calculating a noise amplification coefficient according to the ranging precision; and setting constraint conditions of the other satellite coefficients. According to the satellite-based precision single-point positioning method, system and device and the storage medium, URA is used for quality control, a new method and guarantee are provided for satellite observation quality control, and the stability and reliability of positioning are further improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of precise single-point positioning, and in particular relates to a satellite-based precise single-point positioning method, system, device and storage medium. Background Art

[0002] Precise Point Positioning (PPP) refers to a method that uses only a single-station GNSS receiver to collect raw observation data, and uses the precise satellite orbits and precise satellite clock errors provided by the International GNSS Services (IGS) or solved by itself to accurately correct or process the errors in the original observation equation to obtain high-precision single-point coordinates at the centimeter to decimeter level. Precise point positioning makes up for the shortcomings of the poor accuracy of standard single-point positioning, and can obtain high-precision positioning results without relying on relative positioning, making it possible to achieve globally unified GNSS high-precision positioning services. PPP has unique application value in the fields of satellite precision orbit determination, earthquake monitoring, crustal movement monitoring, tsunami monitoring, marine development, precision agriculture and autonomous driving.

[0003] With the development of BeiDou-3 (BDS-3), the PPP-B2b interface control file of the precise point positioning service signal was officially announced in 2020. The PPP-B2b signals of the three GEO satellites (C59-C61) in the BeiDou-3 nominal space constellation are used as data broadcast channels to provide free real-time PPP services for users on the earth's surface and in the near-Earth area extending 1,000 kilometers into the air. The PPP-B2b service broadcasts information to users based on satellites, which means that users can achieve real-time precise point positioning without relying on other information acquisition methods, showing broad application prospects.

[0004] Although there are many algorithms and technologies for precise point positioning using PPP-B2b services, most of these algorithms are limited to using the orbit and clock corrections provided by PPP-B2b to replace the traditional precise ephemeris and clock errors, but fail to deeply analyze and fully utilize the unique characteristics of PPP-B2b services. In fact, in addition to the above-mentioned precise orbit / clock corrections, PPP-B2b services also provide hardware delay (DCB) products and signal in space ranging error (SISRE). Among them, SISRE reflects the comprehensive impact of satellite position and clock error on user ranging error, which has important guiding significance for satellite weights in positioning solutions.

[0005] In addition, the ionosphere-free combination has the advantages of short convergence time and good positioning stability because there are fewer parameters to be estimated. At this stage, it is widely used in precise point positioning based on PPP-B2b. For the dual-frequency ionosphere-free combination, each satellite finally participates in the solution of only one combined observation value, which is generally given by empirical values. However, with the increase in observation frequency, most equipment already supports observation data of at least 3 frequencies of BDS-3 and GPS. At this time, using the ionosphere-free combination, each satellite in the observation equation will provide 2 combined observation values. Since the ionosphere-free combination will amplify the observation noise, the noise between the two combined observation values ​​will produce a greater difference. Continuing to use the equal-weight model is obviously not in line with the objective reality.

[0006] In summary, the currently available precise point positioning random models have the following defects: (1) they do not fully utilize the spatial signal ranging error provided by PPP-B2b; (2) for the three-frequency and four-frequency dual-ionization-free combination solution modes, the equal-weight model is used for different combination observation values, which does not conform to the objective reality and further amplifies the influence of observation noise on positioning accuracy. Therefore, it is necessary to develop a satellite-based precise point positioning method, system, device and storage medium to solve the existing problems. Summary of the invention

[0007] The object of the present invention is to provide a satellite-based precise point positioning method, system, device and storage medium to solve the above-mentioned problems.

[0008] To achieve the above object, the present invention provides the following technical solution: a satellite-based precise point positioning method, comprising: Receive BeiDou observation data and PPP-B2b services; Calculate user ranging accuracy based on parameters provided by PPP-B2b service; Eliminate satellites that exceed the limit; Construct stochastic models of satellite observations; Solving the precise point positioning based on the random model and outputting the positioning result; Wherein, the stochastic model of satellite observation values ​​is constructed including: Set the coefficients of the altitude angle model; Calculate the noise amplification factor based on the ranging accuracy; Set constraints on other satellite coefficients.

[0009] Preferably, the step of constructing a random model of satellite observations further comprises: The coefficients of the altitude angle model are adjusted according to the frequencies of the two ionosphere-free combinations.

[0010] Preferably, the step of removing satellites exceeding the limit includes: removing satellites with a precision level ≥5 satellites.

[0011] Preferably, the coefficients of setting the altitude angle model include: The coefficients of the altitude angle model are given by the following formula: ; In the formula, represents the mean error, Represent the experience value, represents the altitude angle, represents the coefficient associated with URA.

[0012] Preferably, the calculation of the noise amplification factor according to the ranging accuracy includes: selecting any satellite as a reference satellite, and the reference satellite coefficient is , other satellites The coefficients are: ; In the formula, Indicates the user ranging accuracy of the reference star, represents the user ranging accuracy of all satellites, =0 indicates the reference satellite, and the coefficient is exactly 1.

[0013] Preferably, the constraint conditions for setting other satellite coefficients include: setting other satellites coefficient Limited to between 0.3 and 3, the calculation method is: .

[0014] Preferably, adjusting the coefficients of the altitude angle model according to the frequencies of the two ionosphere-free combinations comprises: ; In the formula, represents the frequency of the ionospheric combination, where ; C is a constant, take .

[0015] The present invention further provides a satellite-based precise point positioning system, comprising: Receiving unit, used to receive Beidou observation data and PPP-B2b services; A calculation unit, configured to calculate user ranging accuracy based on parameters provided by the PPP-B2b service; A rejection unit is used to reject satellites that exceed the limit; A random model building unit, used to build a random model of satellite observations; A solving unit, used for solving precise point positioning based on the random model and outputting a positioning result; Wherein, the random model building unit comprises: The coefficient module of the altitude angle model is used to set the coefficient of the altitude angle model; Noise amplification factor module, used to calculate the noise amplification factor according to the ranging accuracy; The constraint module is used to set the constraints of other satellite coefficients.

[0016] The present invention further provides a satellite-based precise single-point positioning device, comprising: a memory for storing non-transitory computer-readable instructions; and A processor is used to run the computer-readable instructions, so that when the computer-readable instructions are executed by the processor, the satellite-based precise point positioning method is implemented.

[0017] The present invention further provides a storage medium for storing non-transitory computer-readable instructions. When the non-transitory computer-readable instructions are executed by a computer, the computer is enabled to execute the above-mentioned satellite-based precise point positioning method.

[0018] The satellite-based precise single-point positioning method, system, device and storage medium use URA for quality control, provide a new method and guarantee for satellite observation quality control, and further improve the stability and reliability of positioning; use URA to refine the original altitude angle model, so that the weight distribution of satellite observation values ​​is more reasonable, thereby improving the positioning accuracy, with good scalability, and can also be combined with other random models such as carrier-to-noise ratio models; the application innovatively discloses a segmented processing method, which effectively avoids unreasonable extreme values ​​of the adjustment coefficient. If the segmented processing strategy is not adopted, the weights of the pseudorange observation values ​​of some satellites may be abnormally higher than the carrier observation values ​​of other satellites, which is obviously contrary to the actual situation; and further refinement is performed for the ionosphere-free combination. As the observation frequency increases, the ionosphere-free combination also has two combined observation values, and the two combined observation values ​​are obviously different. By refining the weight distribution, the positioning accuracy can be further improved, and a reasonable weight distribution is provided for the precise single-point positioning solution based on the PPP-B2b service, thereby improving the positioning accuracy and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the precise point positioning of the random model of the present invention; Figure 2 This is a schematic diagram of the configuration of the ZHD0 antenna, base station and B2b receiving board in Example 1 of the present invention; Figure 3 This is a schematic diagram of the number of visible satellites and PDOP value of the ZHD0 base station of the present invention; Figure 4 This is a schematic diagram of pseudo dynamic positioning results in Example 1 of the present invention; Figure 5 This is a static positioning result diagram in Example 1 of the present invention; Figure 6This is the test trajectory diagram in Example 2 of the present invention;

[0020] Figure 7 This is a positioning result error diagram in Example 2 of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Example 1 The present invention provides a satellite-based precise single-point positioning method. This embodiment collects ZHD0 data of a university base station to analyze static and dynamic positioning results to verify the effectiveness of the present invention. The antenna and base station are used to receive observation data, and the board is used to receive PPP-B2b services; the specific setting method is as follows Figure 2 As shown in; Figure 3 It is the number of visible satellites and PDOP value of ZHD0 base station, which mainly describes the data collection situation. Among them, NSAT: Number of Sats, the number of satellites; PDOP: position dilution of precision, position precision factor; PPP-B2b GPS: refers to the number of valid GPS satellites provided by the PPP-B2b service; PPP-B2b BDS: refers to the number of effective BDS satellites provided by PPP-B2b service; RMS: Root Mean Square, root mean square of positioning error; where: 1 represents the conventional random model, and 2 represents the random model that takes into account the ranging accuracy.

[0023] Figure 4 The positioning results using different random models. For both dynamic and static positioning, the positioning accuracy is significantly improved after estimating URA, and the convergence time is also shortened to a certain extent. Whether using URA to determine satellite availability or reducing the weight of satellites with larger URA, the continuity and stability of positioning can be improved. In the pseudo-dynamic positioning results, the solution results of the conventional random model between 6:00 and 7:00 show significant fluctuations in the plane direction, while the random model taking URA into account remains stable throughout the entire time period.

[0024] Example 2 The data quality control and stochastic model in the precise point positioning solution process based on PPP-B2b service have been optimized and improved. Figure 1 Flowchart for precise point positioning using a random model taking into account URA. In practical applications, the implementation steps of this method are as follows: Step 1: Use conventional GNSS equipment to receive BeiDou observation data and PPP-B2b services; Step 2: Calculate the user ranging accuracy based on the parameters provided by the PPP-B2b service; Step 3: Use Eliminate satellites that exceed the limit; Step 4: After completing the satellite data screening, a random model of satellite observations is constructed according to formula (5) and formula (6); Step 5: Based on the construction of the random model, use filtering and other methods to solve the precise single-point positioning and output the positioning results.

[0025] The above steps 3 and 4 are the technical innovations of the present invention, and steps 1, 2 and 5 are necessary steps to realize the positioning function; by adding and improving steps 3 and 4, the accuracy and stability of the final positioning result are improved.

[0026] like Figure 5 , Figure 6 , Figure 7 As shown, a field dynamic experiment was conducted. Compared with the conventional random model, the model proposed in the present invention maintains a higher accuracy in the initial convergence stage. It can also be seen from the RMS value that the overall positioning accuracy after convergence is also significantly better than the conventional random model. In addition, the positioning error of the conventional random model showed an obvious mutation between 8:00 and 10:00, while the random model proposed in the present invention remained stable in the same time period, proving its advantages. Provide reasonable weight distribution for the precise single-point positioning solution based on PPP-B2b, and improve positioning accuracy and stability.

[0027] 1. Signal Reception Use common satellite navigation equipment to receive Beidou multi-frequency observation data, PPP-B2b signals, and broadcast ephemeris CNAV1 in real time, and decode the data. Receiving signals of any two or more frequencies can be solved. This part is mainly completed independently by hardware equipment, providing a data basis for subsequent positioning solutions.

[0028] 2. Calculate the user's ranging accuracy The PPP-B2b service uses the User Range Accuracy (URA) to represent the spatial ranging error. Therefore, the construction of this random model is based on URA, and the URA is calculated first.

[0029] In the PPP-B2b service, URA is divided into accuracy levels ( ) and precision values ​​( ) and is calculated according to the following formula: (1) in, and The value range of is 0~7. When both are 0, it means that URA is undefined or unknown and cannot be used; when both are 7, it means that URA>5466.5 mm, which means that the ranging accuracy is very poor and uncontrollable. It is not recommended to continue using this satellite for positioning.

[0030] 3. Constructing a random model using the user ranging accuracy index The use of URA is divided into two aspects: one is data quality control, and the other is the adjustment of the random model.

[0031] ① Satellite availability assessment. The scope of application given in the PPP-B2b official document is However, according to actual statistical results, when the satellite When ≥5, it has an adverse effect on the positioning accuracy of PPP. Therefore, when there are enough satellites, ≥5 satellites.

[0032] ② Random model adjustment The random model determines the weight of each satellite observation in positioning. Currently, most precision single-point models use the altitude angle model as the random model, as shown in formula (2).

[0033] (2) In the formula, Indicates mean error; It is an empirical value, usually taken as ; is the altitude angle.

[0034] It can be seen that formula (2) is only related to the satellite altitude angle, which means that the observations of different satellites have the same weight at the same altitude angle, which is obviously not in line with the actual situation. The present invention adjusts the precise single-point random model based on URA.

[0035] In order to distinguish the differences between different satellites, the method of adding coefficients before the altitude angle model is used, that is, (3); In the formula, is a coefficient related to URA.

[0036] The solution is as follows: Select the first satellite as the reference satellite, any satellite can be used, and the reference satellite coefficient is (Usually 1 is sufficient), other satellites The coefficients are: (4) In the formula, Indicates the user ranging accuracy of the reference star, Indicates the user ranging accuracy of all satellites.

[0037] It should be noted that due to the low differentiation of URA at present, If the ratio is too large or too small, the matrix will be ill-conditioned or the weight of the pseudo-range observation value will be abnormally greater than the carrier observation value. Therefore, constraints must be added. coefficient Limited to between 0.3 and 3, the final calculation method is as follows: (5) Formula (3) and formula (5) together constitute a random model that takes URA into account.

[0038] 4. Further refinement of the double ionosphere-free combination For the BeiDou triple-frequency or quad-frequency ionosphere-free combination model, each satellite will have two combined observations, where the three frequencies are combined in pairs by reusing a certain frequency. The observation quality of these two combined observations is theoretically different due to different wavelengths and different amplified noise, which is also proven by most studies and experiments.

[0039] The present invention adjusts the coefficients of the altitude angle model according to the frequency of the two ionosphere-free combinations: the larger the frequency, the smaller the wavelength, and the smaller the theoretical error of the observation value. Further adjustment is made on the basis of formula (3), and the following formula is obtained: (6) In the formula, represents the frequency of the ionospheric combination, where ; C is a constant, which can be taken as .

[0040] Formula (5) and formula (6) together constitute a random model of dual ionosphere-free combined precise point positioning taking URA into account.

[0041] The official operation of PPP-B2b service provides a new opportunity for precise point positioning. However, the existing ionosphere-free combined precise point positioning only uses the orbit and clock error of PPP-B2b products instead of the traditional orbit and clock error acquisition method, which does not fully utilize the characteristics of PPP-B2b products. Based on this background, the present invention establishes a random model for precise point positioning that takes into account the user ranging accuracy index.

[0042] The present invention further provides a satellite-based precise point positioning system, comprising: Receiving unit, used to receive Beidou observation data and PPP-B2b services; A calculation unit, configured to calculate user ranging accuracy based on parameters provided by the PPP-B2b service; A rejection unit is used to reject satellites that exceed the limit; A random model building unit, used to build a random model of satellite observations; A solving unit, used for solving precise point positioning based on the random model and outputting a positioning result; Wherein, the random model building unit comprises: The coefficient module of the altitude angle model is used to set the coefficient of the altitude angle model; Noise amplification factor module, used to calculate the noise amplification factor according to the ranging accuracy; The constraint module is used to set the constraints of other satellite coefficients.

[0043] The present invention further provides a satellite-based precise single-point positioning device, comprising: a memory for storing non-transitory computer-readable instructions; and A processor is used to run the computer-readable instructions, so that when the computer-readable instructions are executed by the processor, the satellite-based precise point positioning method is implemented.

[0044] The present invention further provides a storage medium for storing non-transitory computer-readable instructions. When the non-transitory computer-readable instructions are executed by a computer, the computer is enabled to execute the above-mentioned satellite-based precise point positioning method.

[0045] (1) Using URA for quality control provides a new method and guarantee for satellite observation quality control, further improving the stability and reliability of positioning.

[0046] (2) The original elevation angle model is refined using URA to make the weight distribution of satellite observation values ​​more reasonable, thereby improving positioning accuracy. It has good scalability and can also be combined with other random models (such as the carrier-to-noise ratio model).

[0047] (3) The present invention innovatively proposes a segmented processing method to effectively avoid unreasonable extreme values ​​of the adjustment coefficient. If the segmented processing strategy is not adopted, the weights of the pseudorange observations of some satellites may be abnormally higher than the carrier observations of other satellites, which is obviously contrary to the actual situation.

[0048] (4) Further refinement of the ionosphere-free combination. With the increase of observation frequency, the ionosphere-free combination also has two combined observation values, and there is an obvious difference between the two combined observation values. By refining the weight allocation, the positioning accuracy can be further improved.

[0049] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0050] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0051] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0052] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0053] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0054] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0055] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A satellite-based precise point positioning method, characterized in that: include: Receive BeiDou observation data and PPP-B2b services; Calculate user ranging accuracy based on parameters provided by PPP-B2b service; Eliminate satellites that exceed the limit; Construct stochastic models of satellite observations; Solving the precise point positioning based on the random model and outputting the positioning result; Wherein, the stochastic model of satellite observation values ​​is constructed including: Set the coefficients of the altitude angle model; Calculate the noise amplification factor based on the ranging accuracy; Set constraints on other satellite coefficients.

2. A satellite-based precise point positioning method according to claim 1, characterized in that: The stochastic model for constructing satellite observations also includes: The coefficients of the altitude angle model are adjusted according to the frequencies of the two ionosphere-free combinations.

3. The satellite-based precise point positioning method according to claim 1, characterized in that: The removing of satellites exceeding the limit includes: removing satellites with accuracy level URAclass≥5.

4. The satellite-based precise point positioning method according to claim 1, characterized in that: The coefficients of the altitude angle model are as follows: The coefficient of the altitude angle model is as follows: ； In the formula, represents the mean error, Represent the experience value, represents the altitude angle, represents the coefficient associated with URA.

5. A satellite-based precise point positioning method according to claim 4, characterized in that: The method of calculating the noise amplification factor according to the ranging accuracy includes: selecting any satellite as a reference satellite, and the reference satellite coefficient is , other satellites The coefficients are: ; In the formula, Indicates the user ranging accuracy of the reference star, Indicates the user ranging accuracy of all satellites.

6. A satellite-based precise point positioning method according to claim 5, characterized in that: The constraint conditions for setting other satellite coefficients include: coefficient Limited to between 0.3 and 3, the calculation method is: 。 7. A satellite-based precise point positioning method according to claim 6, characterized in that: The step of adjusting the coefficients of the altitude angle model according to the frequencies of the two ionosphere-free combinations comprises: ; In the formula, represents the frequency of the ionospheric combination, where ; C is a constant, take .

8. A satellite-based precise point positioning system, characterized in that: include: Receiving unit, used to receive Beidou observation data and PPP-B2b services; A calculation unit, configured to calculate user ranging accuracy based on parameters provided by the PPP-B2b service; A rejection unit is used to reject satellites that exceed the limit; A random model building unit, used to build a random model of satellite observations; A solving unit, used for solving precise point positioning based on the random model and outputting a positioning result; Wherein, the random model building unit comprises: The coefficient module of the altitude angle model is used to set the coefficient of the altitude angle model; Noise amplification factor module, used to calculate the noise amplification factor according to the ranging accuracy; The constraint module is used to set the constraints of other satellite coefficients.

9. A satellite-based precise point positioning device, comprising: a memory for storing non-transitory computer-readable instructions; as well as A processor is used to run the computer-readable instructions so that when the computer-readable instructions are executed by the processor, the satellite-based precise point positioning method according to any one of claims 1 to 7 is implemented.

10. A storage medium for storing non-transitory computer-readable instructions, which, when executed by a computer, enables the computer to execute the satellite-based precise point positioning method according to any one of claims 1 to 7.

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