Method and device for constructing random model considering satellite and station dependent precision dilution

Abstract

A method and apparatus for constructing a random model that considers satellite- and station-dependent dilutions of precision. The method comprises: selecting all available observation residuals in a sliding window during each observation period, calculating the dilution of precision for inter-station dependence based on pseudorange residuals and phase residuals, and calculating the dilution of precision for satellite dependence based on phase residuals; and constructing a time-varying random model that considers the dilutions of precision for inter-station dependence and satellite dependence. The time-varying random model constructed by the present invention considers the dilutions of precision for inter-station dependence and satellite dependence, thereby improving the accuracy of satellite clock error estimation.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation and positioning technology, and in particular to a method and device for constructing a random model that takes into account satellite and station dependent precision factors. Background Art

[0002] With the development of real-time global navigation satellite system products, real-time precise point positioning has become an important tool and has been widely used in many scientific and engineering fields. Real-time satellite clock error, one of the key products for achieving real-time precise point positioning on the user side, has attracted considerable attention in recent years. Currently, the International GNSS Service (IGS) publicly provides real-time satellite products, including satellite clock errors. The error in satellite clock errors predicted for a few minutes using the commonly used quadratic polynomial model is non-negligible; when predicting for 20 minutes, the error can exceed 0.2 nanoseconds. Predicting for an hour using complex wavelet neural network models can result in even greater errors. Satellite clock errors are typically estimated in real-time using a stream of observations from globally / regionally distributed reference stations to control the error in satellite clock error predictions. To meet the demand for high-precision clock error estimation, various strategies have been developed to increase data processing speed by leveraging the capabilities of high-performance computing devices and designing accelerated computation methods. However, relatively little research has been conducted on stochastic models for real-time satellite clock error estimation, including the expected value and dispersion of observation noise.

[0003] Similar to precise point positioning and relative positioning, observations are typically weighted based on satellite elevation angle or carrier-to-noise ratio. However, these methods cannot fully reflect the magnitude of observation noise. Although the observation stations used for satellite clock error estimation are typically equipped with geodetic receivers, the quality of observations varies from station to station due to differences in instrument design and observation environment. If these differences between observation stations are ignored, the accuracy of the estimated satellite clock error will be affected.

[0004] Although the GNSS observation function model has been significantly improved in recent years, there are still some unresolved systematic errors. Because different satellite systems are at different stages of development, these errors are particularly evident in GNSS data processing. In addition, the accuracy and stability of satellite orbits are updated every few hours and then used to predict subsequent satellite clock error estimates. In real-time satellite clock error estimation, the estimated satellite clock error can partially absorb the satellite orbit error, but the actual size varies with the size of the reference network and the type of satellite. The above errors should be optimally considered in the stochastic model, otherwise they will affect the data processing accuracy. Comparing the solutions of different weighting strategies in real-time precise single-point positioning, it is found that appropriately weighting the system based on the quality of satellite products can shorten the convergence time and improve coordinate reproducibility. However, due to the delay problem of reference satellite products, this method is not applicable in actual real-time analysis.

[0005] In summary, stochastic models play an important role in accurate GNSS data processing, but the existing traditional weighting strategy based on satellite elevation angle and carrier-to-noise ratio cannot fully represent the contribution of observations between each observation station and satellite in network data processing, resulting in low accuracy of real-time satellite clock error estimation. Summary of the Invention

[0006] The purpose of the present invention is to overcome the above-mentioned defects and problems existing in the prior art and to provide a method and device for constructing a random model that takes into account the precision factors of satellite and station dependence. The method takes into account the precision factors of observation station dependence and satellite dependence, thereby improving the accuracy of real-time satellite clock error estimation.

[0007] To achieve the above objectives, the technical solution of the present invention is: a method for constructing a random model that considers satellite and inter-station dependent precision dilution, comprising:

[0008] In each observation period, all available observation residuals are selected in the sliding window, and the inter-station dependent dilution of precision is calculated based on the pseudorange residuals and phase residuals, and the satellite dependent dilution of precision is calculated based on the phase residuals;

[0009] A time-varying random model is constructed that considers the inter-station dependent dilution of precision and the satellite dependent dilution of precision.

[0010] The time-varying random model is:

[0011]

[0012] σ ori =sσ0 / sin(ele);

[0013] Where, σ new is the standard deviation; w r is the dilution of precision between observation stations; w s is the satellite-dependent dilution of precision; σ ori is the standard deviation calculated by the original random model; σ0 is the standard deviation of the unprocessed phase observation in the zenith direction; ele is the satellite elevation angle; s is the ratio of the pseudorange to the phase observation error.

[0014] The calculation method of the dilution of precision of the inter-station dependence is:

[0015] Divide all pseudorange and phase observations by s / sin(ele) to obtain normalized observation residuals;

[0016] The root mean square error of each observation station is calculated using the normalized observation residuals associated with each observation station in the sliding window;

[0017] Calculate the overall root mean square error of all normalized observation residuals in the sliding window;

[0018] Calculate the precision factor Ratio of the observation site r :

[0019] Ratio r =RMS r / RMS all (STA);

[0020] Where, RMS r is the root mean square error; RMS all (SAT) is the overall root mean square error of all normalized observation residuals;

[0021] The dilution of precision of inter-observation station dependence is calculated based on the dilution of precision of observation station dependence.

[0022] The dilution of precision of the inter-station dependency is:

[0023]

[0024] When calculating the dilution of precision of inter-station dependence, the sliding window length is set to 1 day.

[0025] The satellite-dependent DOP is calculated as follows:

[0026] Calculate the root mean square error of the unnormalized phase residuals for the station-satellite pair

[0027]

[0028] Where, v i is the i-th unnormalized phase residual in the sliding window; n is the number of phase residuals in the sliding window;

[0029] Calculate the effect of satellite correlation errors on observations between station-satellite pairs

[0030]

[0031] Where, is the total root mean square error of all normalized phase residuals; ave(·) is the average value of the phase residuals in the sliding window;

[0032] Calculate the satellite-dependent dilution of precision w s :

[0033]

[0034] Where m is the number of observation stations that observed the satellite within the sliding window; obsnum r is the phase residual number of the observation station.

[0035] When calculating the satellite-dependent DOP, the sliding window length is set to 30 minutes.

[0036] A device for constructing a random model that takes into account satellite and inter-station dependent dilution of precision, the device being applied to the above-mentioned method, comprising:

[0037] The DOP calculation module is used to select all available observation residuals in the sliding window during each observation period, and calculate the inter-station dependent DOP based on the pseudorange residuals and phase residuals, and the satellite dependent DOP based on the phase residuals;

[0038] The stochastic model building module is used to build a time-varying stochastic model that takes into account the inter-station dependent dilution of precision and the satellite dependent dilution of precision.

[0039] A stochastic model building device considering satellite and inter-station dependent precision dilution, comprising a memory and a processor;

[0040] The memory is configured to store computer program code and transmit the computer program code to the processor;

[0041] The processor is configured to execute the method according to the instructions in the computer program code.

[0042] A computer-readable storage medium stores a computer program, which implements the above-mentioned method when executed by a processor.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention provides a method and apparatus for constructing a random model that considers satellite- and station-dependent dilutions of precision. For each observation period, the method selects all available observation residuals in a sliding window, calculates the dilution of precision for inter-station dependence based on pseudorange residuals and phase residuals, and calculates the dilution of precision for satellite dependence based on phase residuals, thereby constructing a time-varying random model that considers both inter-station-dependent and satellite-dependent dilutions of precision. By considering both inter-station-dependent and satellite-dependent dilutions of precision, the present invention improves the accuracy of satellite clock error estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 The present invention is a flowchart of a random model construction method that considers satellite and station-dependent precision factors.

[0046] Figure 2 The present invention is a structural block diagram of a random model construction device that takes into account satellite and station-dependent precision factors.

[0047] Figure 3 The present invention is a structural block diagram of a random model construction device that takes into account satellite and station-dependent precision factors. DETAILED DESCRIPTION

[0048] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] See also Figure 1 The present invention provides a method for constructing a random model that considers satellite and station-dependent precision dilution, comprising:

[0050] S1. In each observation period, all available observation residuals are selected in the sliding window, and the inter-station dependent DOP is calculated based on the pseudorange residuals and phase residuals, and the satellite dependent DOP is calculated based on the phase residuals.

[0051] S2. Construct a time-varying random model that considers the inter-station dependent dilution of precision and the satellite dependent dilution of precision.

[0052] Satellite clock error estimation is performed continuously, and all observation residuals are calculated after parameter estimation.

[0053] Furthermore, the time-varying random model is:

[0054]

[0055] σ ori =sσ0 / sin(ele);

[0056] Where, σ new is the standard deviation; w r is the dilution of precision between observation stations; w s is the satellite-dependent dilution of precision; σ ori is the standard deviation calculated by the original random model, which adopts a highly dependent weighting strategy; σ0 is the standard deviation of the unprocessed GPS / BDS phase observations in the zenith direction; ele is the satellite elevation angle; S is the ratio of the pseudorange to phase observation errors. Since the measurement accuracy of the phase is 1 / 100 of the pseudorange, for the pseudorange, s = 100, and for the phase, s = 1.

[0057] Furthermore, the calculation method of the dilution of precision of the inter-station dependence is:

[0058] Divide all pseudorange and phase observations by s / sin(ele) to obtain normalized observation residuals;

[0059] The root mean square error of each observation station is calculated using the normalized observation residuals associated with each observation station in the sliding window;

[0060] Calculate the overall root mean square error of all normalized observation residuals in the sliding window;

[0061] Calculate the precision factor Ratio of the observation site r :

[0062] Ratio r =RMS r / RMS all (STA);

[0063] Where, RMS r is the root mean square error; RMS all (SAT) is the overall root mean square error of all normalized observation residuals;

[0064] The dilution of precision of inter-observation station dependence is calculated based on the dilution of precision of observation station dependence.

[0065] Furthermore, the dilution of precision of the inter-station dependency is:

[0066]

[0067] Furthermore, since the precision factor of inter-station dependence is mainly determined by the quality of the instrument and the observation environment, the sliding window length is set to 1 day when calculating the precision factor of inter-station dependence.

[0068] Furthermore, the satellite-dependent dilution of precision is calculated as follows:

[0069] Calculate the root mean square error of the unnormalized phase residuals for the station-satellite pair

[0070]

[0071] Where, v i is the i-th unnormalized phase residual in the sliding window; n is the number of phase residuals in the sliding window;

[0072] Calculate the effect of satellite correlation errors on observations between station-satellite pairs

[0073]

[0074] Where, is the total root mean square error of all normalized phase residuals; ave(·) is the average value of the phase residuals in the sliding window;

[0075] Calculate the satellite-dependent dilution of precision w s :

[0076]

[0077] Where m is the number of observation stations that observed the satellite within the sliding window; obsnum r is the phase residual number of the observation station.

[0078] Furthermore, the satellite-dependent precision factor in the real-time satellite clock error estimation is mainly determined by the satellite orbit error, which changes every time the satellite orbit is updated. Therefore, when calculating the satellite-dependent precision factor, the sliding window length is set to 30 minutes.

[0079] To account for the accuracy differences among all satellites, a satellite-dependent Dilution of Precision (DOP) is calculated for each satellite separately. To reliably separate the inter-station-dependent DOP from the satellite-dependent DOP, a real-time GPS satellite clock error estimation process is performed. During this process, the satellite orbits are fixed to the post-processed GNSS orbits, and the inter-station-dependent DOP is calculated at each instant to assess its evolution over time. In practice, due to its stability, the inter-station-dependent DOP may be updated every few days in post-processing mode. GPS / BDS satellite clock errors are then estimated in real-time mode, along with the satellite-dependent DOP. It is beneficial to calculate the satellite-dependent DOP simultaneously with the satellite products during server-side data processing and provide them to users along with the orbit and clock products.

[0080] The present invention proposes an improved method for a time-varying random model that takes into account the inter-station and satellite-dependent dilution of precision. Compared with the traditional weighting strategy, the improved random model takes into account the differences between the observation accuracy of different observation stations and satellites, thereby improving the accuracy of real-time satellite clock error estimation. Data analysis based on 7 days of GPS / BDS observations shows that there are differences in the dilution of precision between different observation stations. In addition, the dilution of precision between satellites varies greatly and is closely related to the imported satellite orbits. When using GNSS orbits updated hourly, the accuracy of GPS, BDS IGSO satellites, and BDS MEO satellite clock errors is improved by 39.8%, 43.3%, and 28.5%, respectively.

[0081] See also Figure 2 A device for constructing a random model that considers satellite and inter-station dependent precision dilution is provided. The device is applied to the above-mentioned method for constructing a random model that considers satellite and inter-station dependent precision dilution. The device comprises:

[0082] The DOP calculation module is used to select all available observation residuals in the sliding window during each observation period, and calculate the inter-station dependent DOP based on the pseudorange residuals and phase residuals, and the satellite dependent DOP based on the phase residuals;

[0083] The stochastic model building module is used to build a time-varying stochastic model that takes into account the inter-station dependent dilution of precision and the satellite dependent dilution of precision.

[0084] See also Figure 3 ,The present invention also provides a random model construction device that considers satellite and inter-station dependent precision dilution, including a memory and a processor;

[0085] The memory is configured to store computer program code and transmit the computer program code to the processor;

[0086] The processor is configured to execute the above-mentioned random model construction method considering satellite and station-dependent precision factors according to the instructions in the computer program code.

[0087] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for constructing a random model that takes into account the satellite and station-dependent precision factors is implemented.

[0088] Generally speaking, computer instructions for implementing the method of the present invention may be carried by any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media may include any computer-readable media except for signals that are temporarily propagating.

[0089] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or components, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EKROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

[0090] Computer program code for performing the operations of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages, in particular, Python suitable for neural network computing and platform frameworks based on TensorFlow, PyTorch, etc. can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or to an external computer (for example, through the Internet using an Internet service provider).

[0091] The above-mentioned device and non-temporary computer-readable storage medium can be referred to the detailed description of a random model construction method considering satellite and station-dependent precision factors and its beneficial effects, which will not be repeated here.

[0092] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A random model construction method considering satellite and station dependent precision dilution, characterized by: include: In each observation period, all available observation residuals are selected in the sliding window, and the inter-station dependent dilution of precision is calculated based on the pseudorange residuals and phase residuals, and the satellite dependent dilution of precision is calculated based on the phase residuals; Construct a time-varying random model that considers the inter-station dependent dilution of precision and the satellite dependent dilution of precision; The time-varying random model is: ； ； Where, is the standard deviation; is the dilution of precision of the inter-station dependence; is the satellite-dependent Dilution of Precision; The standard deviation calculated for the original random model; is the standard deviation of the unprocessed phase observations in the zenith direction; is the satellite elevation angle; is the ratio of the pseudorange to phase observation errors.

2. The method for constructing a random model considering satellite and station-dependent precision dilution according to claim 1, characterized in that: The calculation method of the dilution of precision of the inter-station dependence is: Divide all pseudorange and phase observations by Get normalized observation residuals; The root mean square error of each observation station is calculated using the normalized observation residuals associated with each observation station in the sliding window; Calculate the overall root mean square error of all normalized observation residuals in the sliding window; Calculate the site-dependent dilution of precision : ； Where, is the root mean square error; is the overall root mean square error of all normalized observation residuals; The dilution of precision of inter-observation station dependence is calculated based on the dilution of precision of observation station dependence.

3. The method for constructing a random model considering satellite and station-dependent dilution of precision according to claim 2, characterized in that: The dilution of precision of the inter-station dependency is: 。 4. The method for constructing a random model considering satellite and station-dependent dilution of precision according to claim 2, characterized in that: When calculating the dilution of precision of inter-station dependence, the sliding window length is set to 1 day.

5. The method for constructing a random model considering satellite and station-dependent precision dilution according to claim 1, characterized in that: The satellite-dependent DOP is calculated as follows: Calculate the root mean square error of the unnormalized phase residuals for the station-satellite pair : ； Where, The first Unnormalized phase residuals; is the number of phase residuals in the sliding window; Calculate the effect of satellite correlation errors on observations between station-satellite pairs : ； Where, is the total root mean square error of all normalized phase residuals; is the average value of the phase residual in the sliding window; Calculate satellite-dependent Dilution of Precision : ； Where, is the number of observation stations that observed the satellite within the sliding window; is the phase residual number of the observation station.

6. The method for constructing a random model considering satellite and station-dependent dilution of precision according to claim 5, characterized in that: When calculating the satellite-dependent DOP, the sliding window length is set to 30 minutes.

7. A random model construction device considering satellite and station dependent precision dilution, characterized in that: The device is used to perform the method according to any one of claims 1 to 6, and the device comprises: The DOP calculation module is used to select all available observation residuals in the sliding window during each observation period, and calculate the inter-station dependent DOP based on the pseudorange residuals and phase residuals, and the satellite dependent DOP based on the phase residuals; The stochastic model building module is used to build a time-varying stochastic model that takes into account the inter-station dependent dilution of precision and the satellite dependent dilution of precision.

8. A random model construction device considering satellite and station dependent precision dilution, characterized in that: including memory and processor; The memory is configured to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method according to any one of claims 1 to 6 according to instructions in the computer program code.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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