Satellite-based Precise Point Positioning Method, System, Device and Storage Medium

Abstract

The present invention discloses a satellite-based precise point positioning method, system, device and storage medium, belonging to the technical field of precise point positioning, including: receiving Beidou observation data and PPP-B2b services; calculating the user ranging accuracy based on the parameters provided by PPP-B2b; eliminating the satellites exceeding the limit; constructing a stochastic model of satellite observations; performing the solution of precise point positioning based on the stochastic model and outputting the positioning result; wherein, the constructing of the stochastic model of satellite observations includes: setting the coefficients of the elevation angle model; calculating the noise amplification coefficient according to the ranging accuracy; setting the constraint conditions of the other satellite coefficients. This satellite-based precise point positioning method, system, device and storage medium utilize URA for quality control, provide a new method and guarantee for satellite observation quality control, and further improve the stability and reliability of positioning.

Description

Technical Field

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

[0002] Precise Point Positioning (PPP) refers to a method that only uses a single-station GNSS receiver to collect raw observation data, and uses precise satellite orbits and precise satellite clock differences provided by the International GNSS Services (IGS) or self-calculated to accurately correct or process various errors in the raw observation equations to obtain centimeter-to-decimeter-level high-precision single-point coordinates. Precise point positioning makes up for the deficiency of poor accuracy in standard single-point positioning. Without relying on relative positioning, it can also obtain high-precision positioning results, making it possible to achieve a globally unified GNSS high-precision positioning service. PPP has unique application values in fields such as satellite precise orbit determination, earthquake monitoring, crustal movement monitoring, tsunami monitoring, ocean development, precision agriculture, and autonomous driving.

[0003] With the development of Beidou-3 (BDS-3), in 2020, the interface control document for the precise point positioning service signal PPP-B2b was officially announced. Using the PPP-B2b signals of 3 GEO satellites (C59 - C61) in the nominal space constellation of Beidou-3 as the data broadcast channel, it provides free real-time PPP services for users in the near-earth area on the earth's surface and extending 1000 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 channels, showing broad application prospects.

[0004] Although there are currently various algorithms and technologies for precise point positioning using the PPP-B2b service, most of these algorithms are only limited to using the orbit and clock difference corrections provided by PPP-B2b to replace traditional precise ephemeris and clock differences, but they fail to deeply analyze and fully utilize the unique characteristics of the PPP-B2b service. In fact, in addition to the above-mentioned precise orbit / clock difference corrections, the PPP-B2b service also provides hardware delay (DCB) products and Signal in Space Ranging Error (SISRE). Among them, SISRE reflects the comprehensive influence of satellite position and clock difference on the user's ranging error, and has important guiding significance for the satellite weights in the positioning solution.

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

[0006] To sum up, the existing random models for precise point positioning have the following defects: (1) They do not make full use of the space signal ranging error provided by PPP-B2b; (2) For the triple-frequency and quadruple-frequency dual ionosphere-free combination solution modes, the equal-weight model is used for different combined observation values, which does not conform to the objective reality and further amplifies the influence of observation noise on the positioning accuracy. Therefore, it is necessary to develop a space-based precise point positioning method, system, device and storage medium to solve the existing problems. Summary of the Invention

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

[0008] To achieve the above purpose, the present invention provides the following technical solution: A space-based precise point positioning method, including:

[0009] Receiving Beidou observation data and PPP-B2b service;

[0010] Calculating the user ranging accuracy based on the parameters provided by the PPP-B2b service;

[0011] Eliminating the satellites beyond the limit;

[0012] Constructing a random model of satellite observation values;

[0013] Performing the solution of precise point positioning based on the random model and outputting the positioning result;

[0014] Among them, the construction of the random model of satellite observation values includes:

[0015] Setting the coefficients of the elevation angle model;

[0016] Calculating the noise amplification coefficient according to the ranging accuracy;

[0017] Setting the constraint conditions of other satellite coefficients.

[0018] Preferably, the random model for constructing satellite observations further includes:

[0019] Adjust the coefficients of the elevation angle model according to the frequencies of two ionosphere-free combinations.

[0020] Preferably, the satellites with excessive limits are excluded, including excluding satellites with a precision level ≥5.

[0021] Preferably, the setting of the coefficients of the elevation angle model includes:

[0022] In the coefficients of the elevation angle model, the formula is as follows:

[0023] ;

[0024] In the formula, represents the mean square error, respectively represent empirical values, represents the elevation angle, represents the coefficient related to URA.

[0025] Preferably, the calculation of the noise amplification coefficient according to the ranging accuracy includes: Select any satellite as the reference satellite, and the reference satellite coefficient is , and the coefficients of other satellites are: ; In the formula, represents the user ranging accuracy of the reference satellite, represents the user ranging accuracy of all satellites, =0 indicates the reference satellite, and the coefficient is exactly 1.

[0026] Preferably, the constraint conditions for setting the coefficients of other satellites include: Restrict the coefficient of other satellites to be between 0.3 and 3, and the calculation method is:

[0027] .

[0028] Preferably, the adjustment of the coefficients of the elevation angle model according to the frequencies of two ionosphere-free combinations includes: ; In the formula, represents the frequency of the ionosphere combination, where ; C is a constant, taking .

[0029] The present invention further provides a space-based precise point positioning system, including:

[0030] A receiving unit for receiving Beidou observation data and PPP-B2b services;

[0031] A calculation unit for calculating the user's ranging accuracy based on the parameters provided by the PPP-B2b service;

[0032] An elimination unit for eliminating satellites beyond the limit;

[0033] A random model construction unit for constructing a random model of satellite observations;

[0034] A solution unit for performing precise point positioning calculations based on the random model and outputting positioning results;

[0035] Wherein, the random model construction unit includes:

[0036] A coefficient module for the elevation angle model for setting the coefficients of the elevation angle model;

[0037] A noise amplification coefficient module for calculating the noise amplification coefficient according to the ranging accuracy;

[0038] A constraint condition module for setting the constraint conditions of other satellite coefficients.

[0039] The present invention further provides a satellite-based precise point positioning device, including:

[0040] A memory for storing non-temporary computer-readable instructions; and

[0041] A processor for running the computer-readable instructions, such that when the computer-readable instructions are executed by the processor, the satellite-based precise point positioning method according to the above is implemented.

[0042] The present invention further provides a storage medium for storing non-temporary computer-readable instructions, such that when the non-temporary computer-readable instructions are executed by a computer, the computer executes the satellite-based precise point positioning method according to the above.

[0043] The satellite-based precise point positioning method, system, device and storage medium use URA for quality control, providing a new method and guarantee for satellite observation quality control, and further improving the stability and reliability of positioning; using URA to refine the original elevation angle model, making the weight distribution of satellite observation values more reasonable, thereby improving the positioning accuracy, having good scalability, and can also be combined with other stochastic models such as the carrier-to-noise ratio model; this application innovatively discloses a segmented processing method, effectively avoiding unreasonable extreme values of the adjustment coefficient. If the segmented processing strategy is not adopted, it may lead to the weight of the pseudorange observation value of some satellites being abnormally higher than the carrier observation value of other satellites, which is obviously contrary to the actual situation; and further refining the ionosphere-free combination. As the observation frequency increases, there are also two combined observation values in the ionosphere-free combination, and the two combined observation values are obviously different. By refining the weight distribution, the positioning accuracy can be further improved, providing a reasonable weight distribution for the precise point positioning solution based on PPP-B2b service, and improving the positioning accuracy and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is the flowchart of the precise point positioning of the stochastic model of the present invention;

[0045] Figure 2 is the schematic diagram of the ZHD0 antenna, base station and B2b receiving board card settings in Embodiment 1 of the present invention;

[0046] Figure 3 is the schematic diagram of the visible satellite number and PDOP value of the ZHD0 base station of the present invention;

[0047] Figure 4 is the schematic diagram of the pseudo-dynamic positioning result in Embodiment 1 of the present invention;

[0048] Figure 5 is the static positioning result diagram in Embodiment 1 of the present invention;

[0049] Figure 6 is the test trajectory diagram in Embodiment 2 of the present invention;

[0050] Figure 7 is the positioning result error diagram in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0052] Example 1

[0053] The present invention provides a satellite-based precise point positioning method. In this embodiment, the data of base station ZHD0 of a certain university is collected for analyzing static and dynamic positioning results to verify the effectiveness of the present invention. The antenna and the base station are used to receive observation data, and the board is used to receive PPP-B2b services; the specific setting method is as Figure 2 shown in

[0054] Figure 3 For the number of visible satellites and PDOP value of ZHD0 base station, it mainly illustrates the data collection situation. Among them, NSAT: Number of Sats, the number of satellites;

[0055] PDOP: position dilution of precision, position dilution of precision;

[0056] PPP-B2b GPS: refers to the number of effective GPS satellites provided by the PPP-B2b service;

[0057] PPP-B2b BDS: refers to the number of effective BDS satellites provided by the PPP-B2b service;

[0058] RMS: Root Mean Square, root mean square of positioning error; where: 1 represents the conventional random model, and 2 represents the random model considering ranging accuracy.

[0059] Figure 4 For the positioning results using different random models. Whether it is dynamic positioning or 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 judge 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, there are significant fluctuations in the solution results of the conventional random model in the horizontal direction between 6:00 and 7:00, while the random model considering URA remains stable throughout the time period.

[0060] Example 2

[0061] The data quality control and random model in the precise point positioning solution process based on PPP-B2b service are optimized and improved. Figure 1 For the flowchart of precise point positioning using the random model considering URA. In practical applications, the implementation steps of this method are as follows:

[0062] Step 1: Use conventional GNSS equipment to receive Beidou observation data and PPP-B2b services;

[0063] Step 2: Calculate the user ranging accuracy based on the parameters provided by the PPP-B2b service;

[0064] Step 3: Use to eliminate satellites beyond the limit;

[0065] Step 4: After completing the satellite data screening, construct a stochastic model of satellite observations according to Formulas (5) and (6);

[0066] Step 5: On the basis of constructing the stochastic model, use methods such as filtering to perform precise point positioning calculations and output the positioning results.

[0067] The above Steps 3 and 4 are the technological innovation points of the present invention, and Steps 1, 2, and 5 are necessary steps to achieve the positioning function; by adding and improving Steps 3 and 4, the accuracy and stability of the final positioning results are improved.

[0068] As Figure 5 , Figure 6 , Figure 7 shown, a field dynamic experiment was carried out. Compared with the conventional stochastic model, the model proposed by the present invention maintains a high accuracy in the initial convergence stage. It can also be seen from the RMS value that the overall positioning accuracy after convergence is significantly better than that of the conventional stochastic model. In addition, there is an obvious mutation in the positioning error of the conventional stochastic model between 8:00 and 10:00, while the stochastic model proposed by the present invention remains stable during the same time period, demonstrating its advantages. Provide reasonable weight allocation for precise point positioning calculations based on PPP-B2b and improve the positioning accuracy and stability.

[0069] I. Signal reception

[0070] 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 perform calculations. This part is mainly completed independently by hardware devices, providing a data basis for subsequent positioning calculations.

[0071] II. Calculate the user ranging accuracy

[0072] The PPP-B2b service represents the space ranging error through the User Range Accuracy (URA). Therefore, the construction of this stochastic model is based on the calculation of URA. First, calculate URA.

[0073] In the PPP-B2b service, URA is divided into two parts: accuracy level ( ) and accuracy value ( ) and broadcast, and is calculated according to the following formula:

[0074] (1)

[0075] Among them, and both range from 0 to 7. When both are 0, it means that the URA is undefined or unknown and cannot be used; when both are 7, it means that URA > 5466.5 mm, indicating very poor ranging accuracy and being uncontrollable. It is not recommended to continue using this satellite for positioning.

[0076] III. Constructing a stochastic model using the user range accuracy index

[0077] The use of URA is divided into two aspects, one is data quality control, and the other is the adjustment of the stochastic model.

[0078] ① Evaluation of satellite availability. The applicable range given in the PPP - B2b official document is , however, according to the actual statistical results, when the of the satellite ≥ 5, it has already had an adverse impact on the positioning accuracy of PPP. Therefore, when the number of satellites is sufficient, satellites with ≥ 5 should be excluded.

[0079] ② Stochastic model adjustment

[0080] The stochastic model determines the weights of the observations of each satellite in positioning. Currently, most precise point positioning models use the elevation angle model as the stochastic model, as shown in Equation (2).

[0081] (2)

[0082] In the formula, represents the mean square error; is an empirical value, generally taking ; is the elevation angle.

[0083] It can be seen that Equation (2) is only related to the satellite elevation angle, which means that the observations of different satellites have the same weight at the same elevation angle. Obviously, this does not conform to the actual situation. The present invention will adjust the precise point positioning stochastic model based on URA.

[0084] In order to distinguish the differences between different satellites, the method of adding a coefficient before the elevation angle model is used, that is (3);

[0085] In the formula, is a coefficient related to URA.

[0086] The solution method is as follows: Select the first satellite as the reference satellite. Any satellite can be chosen. The coefficient of the reference satellite is (usually 1 can be taken), and the coefficients of other satellites are:

[0087] (4)

[0088] In the formula, represents the user range accuracy of the reference satellite, and represents the user range accuracy of all satellites.

[0089] It should be noted that since the URA has a small discrimination at present, there may be a situation where the ratio is too large or too small, resulting in an ill-conditioned matrix or the weight of the pseudorange observation value being abnormally greater than that of the carrier observation value. Therefore, a constraint condition must be added. The present invention adopts a segmented processing method to limit the coefficient of other satellites between 0.3 and 3. The final calculation method is as follows:

[0090] (5)

[0091] Formula (3) and formula (5) together constitute a stochastic model considering URA.

[0092] IV. Further refinement for the dual ionosphere-free combination

[0093] For the Beidou triple-frequency or quadruple-frequency ionosphere-free combination model, each satellite will have two combined observations. Among them, the triple-frequency is combined pairwise by reusing a certain frequency. Since these two combined observations have different wavelengths and amplified noises, there are theoretically differences in their observation qualities, and most research and experiments have also proven this.

[0094] The present invention adjusts the coefficients of the elevation angle model according to the frequencies of the two ionosphere-free combinations: the larger the frequency, the smaller the wavelength, and the smaller the theoretical mean square error of the observation value. Based on formula (3), further adjustment is made, and the following formula is obtained: (6)

[0095] In the formula, represents the frequency of the ionosphere-free combination, where ; C is a constant and can be taken as .

[0096] Formula (5) and formula (6) together constitute a stochastic model for precise point positioning with dual ionosphere-free combinations considering URA.

[0097] 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 orbits and clock differences of PPP-B2b products to replace the traditional ways of obtaining orbits and clock differences, without making full use of the characteristics of PPP-B2b products. Based on this background, the present invention establishes a stochastic model for precise point positioning considering the user range accuracy index.

[0098] The present invention further provides a satellite-based precise point positioning system, comprising:

[0099] a receiving unit, configured to receive BeiDou observation data and PPP-B2b service;

[0100] a calculation unit, configured to calculate the user range accuracy based on the parameters provided by the PPP-B2b service;

[0101] a rejection unit, configured to reject the satellites exceeding the limit;

[0102] a stochastic model construction unit, configured to construct a stochastic model of satellite observations;

[0103] a solution unit, configured to perform the solution of precise point positioning based on the stochastic model and output the positioning result;

[0104] wherein, the stochastic model construction unit includes:

[0105] a coefficient module of the elevation angle model, configured to set the coefficients of the elevation angle model;

[0106] a noise amplification coefficient module, configured to calculate the noise amplification coefficient according to the range accuracy;

[0107] a constraint condition module, configured to set the constraint conditions of other satellite coefficients.

[0108] The present invention further provides a satellite-based precise point positioning device, comprising:

[0109] a memory, configured to store non-transitory computer-readable instructions; and

[0110] a processor, configured 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 the above is implemented.

[0111] The present invention further provides a storage medium, configured to store non-transitory computer-readable instructions, and when the non-transitory computer-readable instructions are executed by a computer, the computer is enabled to execute the satellite-based precise point positioning method according to the above.

[0112] (1) Using URA for quality control provides a new method and guarantee for satellite observation quality control, and further improves the stability and reliability of positioning.

[0113] (2) The URA is used to refine the original elevation angle model, making the weight distribution of satellite observations more reasonable, thereby improving the positioning accuracy. It has good scalability and can also be combined with other stochastic models (such as the carrier-to-noise ratio model).

[0114] (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, it may lead to the weight of the pseudorange observations of some satellites being abnormally higher than the carrier observations of other satellites, which is obviously contrary to the actual situation.

[0115] (4) Further refinement is carried out for the ionosphere-free combination. As the observation frequency increases, there are also two combined observation values in the ionosphere-free combination, and there are obvious differences between the two combined observation values. By refining the weight distribution, the positioning accuracy can be further improved.

[0116] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0117] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0118] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions in the process Figure 1One or more processes and / or blocks Figure 1 The functions specified in one or more blocks.

[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 One or more processes and / or blocks Figure 1 The steps of the functions specified in one or more blocks.

[0120] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0121] 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 equivalent technologies, the present application is also intended to include these modifications and variations.

[0122] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within 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 the user ranging accuracy URA based on the parameters provided by the 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; The removing of satellites exceeding the limit includes: removing satellites with accuracy level URAclass ≥ 5; 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; 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 other satellites; The constraint conditions for setting other satellite coefficients include: coefficient Limited to between 0.3 and 3, the calculation method is: .

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. A satellite-based precise point positioning method according to claim 2, 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; C is a constant, .

4. A system based on the satellite-based precise point positioning method according to any one of claims 1 to 3, characterized in that: include: Receiving unit, used to receive Beidou observation data and PPP-B2b services; A calculation unit, configured to calculate a user ranging accuracy URA 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.

5. 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 3 is implemented.

6. 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 3.

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