Navigation enhancement method and system based on cross check and data fusion

Abstract

The invention relates to the technical field of navigation satellites, and provides a navigation enhancement method and system based on cross check and data fusion. The method comprises the following steps: acquiring GNSS correction data of a plurality of GNSS analysis centers, wherein the GNSS correction data comprises satellite clock error correction data, satellite orbit correction data and satellite code deviation data; performing cross check and data fusion processing on the satellite clock error correction data, the satellite orbit correction data and the satellite code deviation correction data of the plurality of GNSS analysis centers; and carrying out integrity verification and precision reliability verification on the result of the cross check and the data fusion processing. According to the invention, by constructing a cross check mechanism, abnormal or incomplete correction information can be effectively identified and eliminated, and the quality of input data is ensured. And through intelligent fusion, not only is advantage complementation of information realized, but also continuity and stability of correction numbers are remarkably enhanced.

Description

Technical Field

[0001] The present invention generally relates to the field of navigation satellite technology. Specifically, the present invention relates to a navigation enhancement method and system based on cross-checking and data fusion. Background Art

[0002] Precise Point Positioning (PPP) is a high-precision global navigation satellite system (GNSS) positioning technology. Its core idea is to achieve high-precision position and time measurement on a single receiver. PPP technology eliminates the limitation of the need for a reference station in traditional relative positioning methods by utilizing high-precision satellite clock errors, orbital parameters, and ionospheric and tropospheric delay corrections. This technology is widely used in geodesy, geodynamic research, disaster monitoring, and high-precision navigation, and its positioning accuracy can reach centimeter level.

[0003] The Low-Satellite-Based Augmentation System (LSBAS) is a satellite-based navigation augmentation system that establishes a reference station network on the ground, calculates and sends correction information to user terminals in real time, thereby improving the accuracy and reliability of GNSS positioning. However, since the LSBAS system usually relies on a ground station network in a specific area, its coverage is limited, and the correction information may be abnormal or missing. This problem is particularly prominent in the ocean, remote areas or complex terrain conditions.

[0004] The GNSS data analysis center provides high-quality GNSS observation data, satellite clock errors, orbital parameters, atmospheric delay corrections and other products that can support a variety of high-precision positioning applications. However, the acquisition and processing of resources in different data analysis centers also face some challenges. On the one hand, due to the diversity of data sources and differences between different sites, how to ensure the consistency and reliability of data has become a key issue; on the other hand, with the continuous upgrading of GNSS systems and technological advances, how to efficiently process massive data and extract useful information from it has also become a technical problem that needs to be solved urgently. Summary of the invention

[0005] In order to at least partially solve the above problems in the prior art, the present invention proposes a navigation enhancement method based on cross-checking and data fusion, comprising the following steps:

[0006] Acquire GNSS correction data from multiple GNSS analysis centers, wherein the GNSS correction data includes satellite clock correction data, satellite orbit correction data, and satellite code deviation data;

[0007] Cross-check and fuse the satellite clock correction data, satellite orbit correction data and satellite code bias correction data from multiple GNSS analysis centers; and

[0008] The results of cross-checking and data fusion processing are checked for integrity and accuracy reliability.

[0009] In one embodiment of the present invention, obtaining GNSS correction data of multiple GNSS analysis centers includes:

[0010] Receive GNSS correction data from multiple GNSS analysis centers;

[0011] Decoding the GNSS correction data and extracting satellite orbit parameters, clock correction values ​​and ranging code deviations; and

[0012] The GNSS correction data is preprocessed, including storing the GNSS correction data and determining the data integrity rate.

[0013] In one embodiment of the present invention, cross-checking and data fusion processing of satellite clock correction data from multiple GNSS analysis centers includes the following steps:

[0014] Restore the satellite clock correction data, where the clock correction value Δk at the observation time t is obtained based on the quadratic three coefficients decoded from the clock correction number, and the clock correction Δt calculated by combining the broadcast ephemeris is obtained. b , and obtain the corrected clock error data Δx s , expressed as the following formula;

[0015] Δk=C0+C1×(t-t0)+C2×(t-t0)×2

[0016] Δx s =Δt b -Δk / Vclight

[0017] Among them, C0, C1, C2 represent the three coefficients of the satellite clock error polynomial, and Vclight represents the speed of light.

[0018] Perform data fusion processing on satellite clock correction data from multiple GNSS analysis centers; and

[0019] Cross-check satellite clock correction data from multiple GNSS analysis centers.

[0020] In one embodiment of the present invention, a Kalman filter is used to perform data fusion processing on satellite clock correction data of multiple GNSS analysis centers, which includes the following steps:

[0021] Determine the state vector X, which includes the system bias x1 of the GNSS analysis center clock error, the epoch bias x2, and the bias x of each satellite in the epoch s , expressed as the following formula:

[0022] X = [x1, x2, x s1 , x s2 ,...x sn ] T

[0023] Among them, x s1 、x s2 ,…x sn Represents the satellite bias from the first to the nth satellite in the epoch;

[0024] Set the initial state estimate and its covariance matrix;

[0025] A system model describing the evolution of satellite clock error is constructed, which can be expressed as follows:

[0026] X k =AX k-1 +W k

[0027] Among them, X k represents the current state vector, X k-1 represents the state vector at the previous moment, A represents the state transfer matrix, W k represents process noise;

[0028] The observation model is established and expressed as follows:

[0029] Z k =HX k-1 +V k

[0030] Among them, Z k represents the observation vector, H represents the observation matrix, V k represents the observation noise;

[0031] Input observation data input, where the satellite clock corrections from different GNSS analysis centers are input into the Kalman filter as observation data, where the clock correction observations of m satellites in the current epoch of n analysis centers are expressed as follows:

[0032] Z k =[z1s1,z1s2,z1s3,....z n s1,z n s2...z n s m ] T

[0033] Among them, zn represents the nth analysis center, S m Represents the observation value of the satellite numbered m;

[0034] Calculate the Kalman gain K based on the state estimation and observation data;

[0035] Update the state estimate and its covariance matrix, where the Kalman gain is used to update the state estimate and its covariance matrix P k , where the covariance matrix P k It is expressed as the following formula:

[0036] P k =(I-KH)P k|k-1

[0037] Among them, I represents the unit matrix, K represents the Kalman gain, H represents the observation matrix, P k|k-1 represents the forecast covariance matrix; and

[0038] Repeat the above steps until the preset convergence condition or the upper limit of the number of iterations is reached.

[0039] In one embodiment of the present invention, cross-checking satellite clock correction data of multiple GNSS analysis centers includes the following steps:

[0040] Conduct consistency checks on clock corrections from different GNSS analysis centers and remove data points that deviate from the average value;

[0041] Using multiple data processing models to process data, comparing the consistency of outputs of the data processing models, and selecting the data processing model with the highest consistency, wherein the multiple data processing models include a polynomial fitting model and a spline interpolation model; and

[0042] Identify and eliminate abnormal fluctuations through time series analysis methods.

[0043] In one embodiment of the present invention, cross-checking and data fusion processing of satellite orbit correction data of multiple GNSS analysis centers includes:

[0044] Recovering satellite orbit correction data, wherein corresponding satellite orbit parameters in the broadcast ephemeris are selected according to the age of the ephemeris data;

[0045] Cross-check satellite orbit correction data from multiple GNSS analysis centers, including:

[0046] Check whether the satellite orbit correction data of different GNSS analysis centers are consistent;

[0047] Use statistical methods to evaluate the consistency and reliability of satellite orbit correction data from different GNSS analysis centres; and

[0048] Detecting the stability and trend of satellite orbit correction data through time series analysis; and

[0049] The weighted average method is used to perform orbit fusion of satellite orbit correction data from multiple GNSS analysis centers, including:

[0050] Determine the weights of data sources for different GNSS analysis center orbits;

[0051] A weighted average calculation is performed, where the weighted average value y is expressed as follows:

[0052]

[0053] Among them, x i represents the orbit information data source of the i-th GNSS analysis center of the satellite, w i represents the weight corresponding to the orbit information data source of the i-th GNSS analysis center, where

[0054] The weights sum to 1; and

[0055] The accuracy and stability of the weighted average are analyzed, and the root mean square error and standard deviation of the weighted average are calculated to evaluate the quality of the orbit calculation results.

[0056] In one embodiment of the present invention, cross-checking and data fusion processing of satellite code bias correction data of multiple GNSS analysis centers includes:

[0057] Cross-check satellite code bias correction data from multiple GNSS analysis centers to determine the consistency and reliability of satellite code bias correction data from different sources; and

[0058] The satellite code bias correction data from multiple GNSS analysis centers are fused using the weighted average method.

[0059] The present invention also proposes a navigation enhancement system based on cross-checking and data fusion, comprising:

[0060] The data receiving module is configured to obtain GNSS correction data from multiple GNSS analysis centers, wherein the GNSS correction data includes satellite clock correction data, satellite orbit correction data and satellite code deviation data.

[0061] a cross-check and data fusion processing module, which is configured to perform cross-check and data fusion processing on satellite clock correction data, satellite orbit correction data and satellite code bias correction data of multiple GNSS analysis centers; and

[0062] The verification module is configured to perform integrity verification and accuracy reliability verification on the results of the cross-verification and data fusion processing.

[0063] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program executes the steps according to the method when executed by a processor.

[0064] The present invention also provides a computer system, comprising:

[0065] a processor configured to execute machine-executable instructions; and

[0066] A memory having machine executable instructions stored thereon, wherein the machine executable instructions, when executed by a processor, perform the steps according to the method

[0067] The present invention has at least the following beneficial effects: by constructing a cross-check mechanism for correction numbers provided by multiple GNSS analysis centers, the present invention can effectively identify and eliminate abnormal or incomplete correction information to ensure the quality of input data. Furthermore, the present invention uses a fusion (EKF, weighted average) algorithm to intelligently fuse screened GNSS correction products from different data sources, which not only achieves complementary information advantages, but also significantly enhances the continuity and stability of the correction numbers. The present invention not only provides a more solid data foundation for the application of PPP technology, but also reduces the risk of relying on a single data source, which is of great significance for improving the overall performance of low-orbit navigation enhanced GNSS positioning services. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] To further illustrate the advantages and features of the various embodiments of the present invention, a more detailed description of the various embodiments of the present invention will be presented with reference to the accompanying drawings. It will be understood that these drawings only depict typical embodiments of the present invention and are not to be considered as limiting the scope thereof. In the accompanying drawings, for clarity, the same or corresponding parts will be represented by the same or similar reference numerals.

[0069] Figure 1 A schematic diagram of a computer system implementing the system and / or method according to the present invention is shown.

[0070] Figure 2 A flowchart of a navigation enhancement method based on cross-checking and data fusion in one embodiment of the present invention is shown.

[0071] Figure 3 The error time series diagram of the PPP static simulation dynamic experiment performed according to an embodiment of the present invention is shown.

[0072] Figure 4A module schematic diagram of a navigation enhancement system based on cross-checking and data fusion in one embodiment of the present invention is shown. DETAILED DESCRIPTION

[0073] It should be noted that the components in the figures may be shown exaggeratedly for the sake of illustration and are not necessarily true to scale. In the figures, identical or functionally identical components are provided with the same reference numerals.

[0074] In the present invention, unless otherwise specified, "arranged on...", "arranged above..." and "arranged above..." do not exclude the existence of an intermediate between the two. In addition, "arranged on or above..." merely indicates the relative positional relationship between two components, and in certain cases, such as after reversing the product direction, it can also be converted into "arranged below or below...", and vice versa.

[0075] In the present invention, each embodiment is only intended to illustrate the aspects of the present invention and should not be construed as limiting.

[0076] In the present invention, unless otherwise specified, the quantifiers "a" or "an" do not exclude the presence of multiple elements.

[0077] It should also be noted that in the embodiments of the present invention, for the sake of clarity and simplicity, only a portion of the parts or components may be shown, but those of ordinary skill in the art will understand that under the teachings of the present invention, the required parts or components may be added according to the needs of the specific scenario. In addition, unless otherwise specified, the features in different embodiments of the present invention may be combined with each other. For example, a feature in the second embodiment may be used to replace a corresponding or functionally identical or similar feature in the first embodiment, and the resulting embodiment also falls within the disclosure scope or recorded scope of the present application.

[0078] It should also be noted that within the scope of the present invention, the terms "same", "equal", "equal to", etc. do not mean that the values ​​of the two are absolutely equal, but allow a certain reasonable error, that is, the terms also cover "substantially the same", "substantially equal", "substantially equal to". By analogy, in the present invention, the terms "perpendicular to", "parallel to", etc., which indicate directions, also cover the meanings of "substantially perpendicular to" and "substantially parallel to".

[0079] In the present application, the term "configuration" refers to the setting of the shape, structure, material and / or function of the target object to achieve the desired technical effect, wherein "configuration" includes a variety of alternative technical means for achieving the technical effect, which become obvious under the teaching of the present application.

[0080] In addition, the numbering of the steps of the methods of the present invention does not limit the execution order of the method steps. Unless otherwise specified, the method steps can be executed in different orders.

[0081] The present invention will be further described below in conjunction with specific embodiments with reference to the accompanying drawings.

[0082] Figure 1 FIG. 1 shows a computer system 100 for implementing the system and / or method according to the present invention. Unless otherwise specified, the method and / or system according to the present invention may be implemented in Figure 1 The present invention can be implemented in the computer system 100 shown in the figure to achieve the purpose of the present invention, or the present invention can be implemented in a distributed manner in multiple computer systems 100 according to the present invention through a network, such as a local area network or the Internet. The computer system 100 of the present invention can include various types of computer systems, such as handheld devices, laptop computers, personal digital assistants (PDAs), multi-processor systems, microprocessor-based or programmable consumer electronic devices, network PCs, minicomputers, mainframes, network servers, tablet computers, etc.

[0083] like Figure 1 As shown, the computer system 100 includes a processor 111, a system bus 101, a system memory 102, a video adapter 105, an audio adapter 107, a hard disk drive interface 109, an optical drive interface 113, a network interface 114, and a universal serial bus (USB) interface 112. The system bus 101 can be any of several types of bus structures, such as a memory bus or a memory controller, a peripheral bus, and a local bus using various types of bus architectures. The system bus 101 is used for communication between various bus devices. In addition to Figure 1In addition to the bus devices or interfaces shown in , other bus devices or interfaces are also conceivable. System memory 102 includes read-only memory (ROM) 103 and random access memory (RAM) 104, wherein ROM 103 can, for example, store basic input / output system (BIOS) data for basic routines for implementing information transmission at startup, and RAM 104 is used to provide the system with a running memory with a faster access speed. Computer system 100 also includes a hard disk drive 109 for reading and writing hard disk 110, an optical drive interface 113 for reading and writing optical media such as CD-ROM, etc. Hard disk 110 can, for example, store an operating system and application programs. The drive and its associated computer-readable medium provide non-volatile storage of computer-readable instructions, data structures, program modules and other data for computer system 100. Computer system 100 can also include a video adapter 105 for image processing and / or image output, which is used to connect output devices such as display 106. The computer system 100 may further include an audio adapter 107 for audio processing and / or audio output, which is used to connect output devices such as speakers 108. In addition, the computer system 100 may further include a network interface 114 for network connection, wherein the network interface 114 may be connected to the Internet 116 through a network device such as a router 115, wherein the connection may be wired or wireless. In addition, the computer system 100 may further include a universal serial bus interface (USB) 112 for connecting peripheral devices, wherein the peripheral devices include, for example, a keyboard 117, a mouse 118, and other peripheral devices, such as microphones, cameras, etc.

[0084] When the present invention Figure 1 When implemented on the computer system 100 shown, a cross-check mechanism for correction numbers provided by multiple GNSS analysis centers can be constructed to effectively identify and eliminate abnormal or incomplete correction information to ensure the quality of input data. Furthermore, the present invention uses a fusion (EKF, weighted average) algorithm to intelligently fuse the screened GNSS correction products from different data sources, which not only achieves complementary advantages of information, but also significantly enhances the continuity and stability of the correction numbers. The present invention not only provides a more solid data foundation for the application of PPP technology, but also reduces the risk of relying on a single data source, which is of great significance for improving the overall performance of low-orbit navigation enhanced GNSS positioning services.

[0085] Furthermore, the embodiments may be provided as a computer program product that may include one or more machine-readable media having machine-executable instructions stored thereon, which, when executed by one or more machines such as a computer, a computer network or other electronic device, may cause the one or more machines to perform operations according to the embodiments of the present invention. The machine-readable medium may include, but is not limited to, a floppy disk, an optical disk, a CD-ROM (compact disk read-only memory) and a magneto-optical disk, a ROM (read-only memory), a RAM (random access memory), an EPROM (erasable programmable read-only memory), an EEPROM (electrically erasable programmable read-only memory), a magnetic or optical card, a flash memory, or other types of media / machine-readable media suitable for storing machine-executable instructions.

[0086] In addition, various embodiments may be downloaded as a computer program product, where the program may be transmitted from a remote computer (e.g., a server) to a requesting computer (e.g., a client) via a communication link (e.g., a modem and / or a network connection) using one or more data signals implemented and / or modulated by a carrier wave or other propagation medium. Thus, the machine-readable medium used herein may include such a carrier wave, but this is not required.

[0087] In the present invention, each module of the system according to the present invention can be implemented using software, hardware, firmware or a combination thereof. When the module is implemented using software, the function of the module can be implemented by a computer program flow, for example, the module can be implemented by a code segment (such as a code segment of a language such as C, C++, etc.) stored in a storage device (such as a hard disk, a memory, etc.), wherein the corresponding function of the module can be implemented when the code segment is executed by a processor. When the module is implemented using hardware, the function of the module can be implemented by setting a corresponding hardware structure, for example, the function of the module can be implemented by hardware programming a programmable device such as a field programmable gate array (FPGA), or the function of the module can be implemented by designing an application-specific integrated circuit (ASIC) including electronic devices such as a plurality of transistors, resistors and capacitors. When the module is implemented using firmware, the function of the module can be written into a read-only memory such as an EPROM or EEPROM of the device in the form of a program code, and the corresponding function of the module can be implemented when the program code is executed by a processor. In addition, certain functions of the module may need to be implemented by separate hardware or by collaboration with the hardware, for example, the detection function is implemented by corresponding sensors (such as proximity sensors, acceleration sensors, gyroscopes, etc.), the signal transmission function is implemented by corresponding communication devices (such as Bluetooth devices, infrared communication devices, baseband communication devices, Wi-Fi communication devices, etc.), the output function is implemented by corresponding output devices (such as displays, speakers, etc.), and so on.

[0088] Figure 2A flowchart of a navigation enhancement method based on cross-checking and data fusion in one embodiment of the present invention is shown.

[0089] like Figure 2 As shown, the method comprises the following steps:

[0090] Step 201: Obtain data streams of multi-source GNSS correction products.

[0091] Step 202: Cross-check and data fusion processing is performed on the satellite clock correction information of multiple analysis centers.

[0092] Step 203: Cross-check and data fusion processing is performed on the satellite orbit correction information of multiple analysis centers.

[0093] Step 204: cross-check and perform data fusion processing on the satellite code bias correction information of multiple analysis centers.

[0094] Step 205: Perform integrity check and accuracy reliability check on the results of the cross-check and data fusion processing.

[0095] The following describes in detail each step of the method.

[0096] In step 201, a data stream of multi-source GNSS correction products is obtained, wherein data reception and transmission are performed at the hardware level, and data processing and correction calculation are performed at the software level.

[0097] The hardware adopts modular design and supports multiple data input and output methods, including but not limited to Network Real-time Transport Protocol (NTRIP), Serial Communication Interface (Serial), Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), ensuring efficient transmission and compatibility of data streams. For example, through the NTRIP protocol, correction information can be received from a designated GNSS analysis center, while serial ports, TCP or UDP can be used to exchange data with local or remote devices to achieve real-time update and sharing of data.

[0098] The software integrates functional modules such as multi-source data stream reception, decoding, and preprocessing. First, through multi-threading, the real-time correction data stream products of multiple GNSS analysis centers are synchronously received. The data stream is generally in rtcm or custom binary format. Based on rtcm or custom protocols, data decoding is performed to extract basic information such as satellite orbit parameters, clock correction, ranging code deviation (DCB), etc. Then, pre-processing operations such as storage and data integrity judgment are performed on the GNSS correction data.

[0099] In step 202, the satellite clock correction information of multiple analysis centers is cross-checked and data fusion processed, including:

[0100] S1. Recover satellite clock correction information. Based on the quadratic three coefficients obtained by decoding the clock correction number obtained in step 201, obtain the clock correction value Δk at the observation time t, and combine the clock correction Δt calculated by the broadcast ephemeris b , and obtain the corrected clock error information Δx s , expressed as the following formula;

[0101] Δk=C0+C1×(t-t0)+C2×(t-t0)×2

[0102] Δx s =Δt b -Δk / Vclight

[0103] Among them, C0, C1, C2 represent the three coefficients of the satellite clock error polynomial, and Vclight represents the speed of light.

[0104] S2. Comprehensively analyze the correction data from multiple GNSS analysis centers and perform data fusion.

[0105] Since different GNSS analysis centers independently estimate the satellite clock error based on their own data processing processes and algorithm models, there may be systematic deviations between the clock error corrections provided by different analysis centers. In order to improve the reliability and accuracy of satellite clock error corrections, it is necessary to conduct a comprehensive analysis of the correction data from multiple GNSS analysis centers and adopt effective data fusion technology.

[0106] In some embodiments, Kalman filtering can be used for data fusion processing. Kalman filtering is a recursive optimal estimation method, which is particularly suitable for processing dynamic system state estimation problems containing noise. In the fusion of satellite clock correction numbers, Kalman filtering can be implemented by the following steps:

[0107] S21, define the state vector X, which contains the system deviation x1 of the different analysis center clock errors at the current time, the epoch deviation x2, and the deviation x of each satellite in the epoch s For example, for a particular satellite, the state vector can be expressed as follows:

[0108] X = [x1, x2, xs1, x s2 ,...x sn ] T .

[0109] S22. Set the initial state estimate and its covariance matrix. Generally speaking, a more conservative initial value can be set based on historical data or experience.

[0110] S23. Construct a system model to describe the evolution of satellite clock error. Based on the stability of satellite clock error in a short period of time, the model can be assumed to be linear and expressed as follows:

[0111] X k =AX k-1 +W k

[0112] Among them, X k Indicates, X k-1 represents, A represents the state transfer matrix, W k Represents process noise. Further, an observation model is established, which is expressed as follows:

[0113] Z k =HX k-1 +V k

[0114] Among them, Z k represents the observation vector, H represents the observation matrix, V k represents the observation noise.

[0115] S24, input observation data input, wherein the satellite clock correction numbers from different GNSS analysis centers are input into the Kalman filter as observation data, wherein the clock correction number observation values ​​of m satellites in the current epoch of n analysis centers are expressed as the following formula:

[0116] Z k =[z1s1,z1s2,z1s3,....z n s1,z n s2...z n s m ] T

[0117] Among them, z n represents the nth analysis center, s m Represents the observation value of satellite numbered m.

[0118] S25, perform Kalman gain calculation, wherein the Kalman gain K is calculated based on the current state estimate and observation data, which determines the proportion of the new observation data to the state estimate correction. Different weights can be set for different analysis center clock correction observation values ​​based on experience.

[0119] S26, update the state estimate and covariance matrix, wherein the Kalman gain is used to update the state estimate and its covariance matrix P k , where the covariance matrix P k It is expressed as the following formula:

[0120] P k =(I-KH)Pk|k-1

[0121] Among them, I represents the unit matrix, K represents the Kalman gain, H represents the observation matrix, P k|k-1 represents the forecast covariance matrix.

[0122] S27. Repeat the above steps until a preset convergence condition or an upper limit of the number of iterations is reached.

[0123] The above Kalman filtering method can be used to obtain different satellite clock error deviation values ​​in different analysis centers, between epochs, and within epochs.

[0124] S3. Based on the data fusion processing using Kalman filtering, a cross-check strategy is combined to further improve the reliability of clock correction data, including:

[0125] S31. Check the consistency of clock corrections from different analysis centers, remove data points that are obviously deviated from the average value, and reduce the impact of outliers on the final result. In some embodiments, 0.2 m can be set as the removal threshold that deviates from the average.

[0126] S32. Use a variety of different models (such as polynomial fitting, spline interpolation, etc.) to process the same set of data separately, compare the consistency of the outputs of each model, and select the most stable and reliable model as the final result.

[0127] S33. Use time series analysis methods to identify and eliminate abnormal fluctuations caused by special events (such as solar storms, earthquakes, etc.) to ensure the smoothness of long-term trends.

[0128] In step 203, the satellite orbit correction information of multiple analysis centers is cross-checked and data fused, including:

[0129] S1. Restore satellite orbit correction information, wherein a corresponding set of satellite orbit parameters in the broadcast ephemeris is selected by analyzing the age of the ephemeris data (10D) provided in the central satellite orbit correction information, wherein different representations of the age of data of different satellite systems need to be considered. In some embodiments, the satellite antenna phase center APC or the satellite center of mass COM needs to be considered when restoring satellite orbit information. If the SSR correction value is relative to the satellite APC, the satellite antenna phase deviation correction needs to be applied to obtain the satellite center of mass position coordinates under ITRF. In some embodiments, when restoring satellite orbit information, the satellite orbit coordinate system (radial, tangential, normal) in the SSR orbit correction number needs to be converted to the Earth-centered Earth-fixed system O-XYZ.

[0130] S2. After the orbit information is restored, the orbit information of different analysis centers is cross-checked. The purpose of the cross-check is to verify and screen the data from different sources to ensure that the most reliable data is used. The specific steps are as follows:

[0131] S21. Check whether the data from different sources are consistent. For example, the orbit data from different systems at the same time should be consistent to a certain extent. If they are inconsistent, further analysis is needed, which may be due to errors in the system itself or errors introduced during data transmission.

[0132] S22. Use statistical methods (such as standard deviation, root mean square error, etc.) to evaluate the consistency and reliability of different data sources through post-hoc data. If a data source has a large deviation compared with other data sources, it may be necessary to exclude the data source.

[0133] S23. Use time series analysis to detect data stability and trends. For example, some data sources may perform better in certain time periods and worse in other time periods.

[0134] Through the above steps, empirical values ​​of different analysis centers and different satellite orbits can be obtained to determine the weight values ​​for subsequent fusion.

[0135] S4. Perform orbital fusion. In some embodiments, a weighted average method may be used to perform orbital fusion. The weighted average method is used to combine data from different sources into a more accurate result. The core idea is to assign different weights to the data according to its reliability, and then obtain the final result through weighted average, which may include the following steps:

[0136] S41. Determine the weight based on the reliability and accuracy of the data source. For example, if a data source has been relatively stable and accurate in the past, it can be given a higher weight. Common weight determination methods include subjective weighting based on experience and objective weighting based on statistical analysis.

[0137] S42. Perform weighted average calculation. Assuming that a certain satellite has three different GNSS analysis center orbit information data sources x1, x2, x3, and the corresponding weights are w1, w2, w3, respectively, then the weighted average value y can be expressed as follows:

[0138]

[0139] The sum of the weights is 1 to ensure the normalization of the results.

[0140] S43, after calculating the weighted average value, further analyzing its accuracy and stability. The quality of the orbit calculation result can be evaluated by calculating the root mean square error (RMSE), standard deviation and other indicators of the weighted average value.

[0141] In step 204, the satellite code bias (DCB) correction information of multiple analysis centers is cross-checked and data fused.

[0142] The DCB correction information provided by different GNSS analysis centers contains the DCB values ​​of different satellite frequencies and channels. The data format or information type of each analysis center may be different, but they all contain basic DCB correction information.

[0143] S1. Perform cross-checking to verify the consistency and reliability of DCB correction information from different sources. The specific steps are as follows:

[0144] S11, perform data consistency check, wherein the DCB correction information provided by different analysis centers is compared to check the differences between them. If the data of a certain analysis center is significantly different from the data of other centers, further analysis of the cause is required.

[0145] S12. Use statistical methods (such as standard deviation, root mean square error, etc.) to evaluate the consistency and reliability of different data sources. For example, calculate the standard deviation of DCB correction information from each center to determine which data sources are more reliable.

[0146] S13. Use time series analysis to detect data stability and trends. For example, some data sources may perform better in certain time periods and worse in other time periods.

[0147] S2. Data fusion of DCB correction information is performed by weighted average method, wherein different weights are assigned to different data sources to obtain the final result. The following steps are included:

[0148] S21. Determine the weights based on the reliability and accuracy of the data source. Common weight determination methods include subjective weighting methods based on experience and objective weighting methods based on statistical analysis.

[0149] S22, perform weighted average calculation, assuming that there are multiple GNSS analysis centers providing DCB correction information of a certain channel of a certain satellite, which are x1, x2, x3...x n , the corresponding weights are w1, w2, w3...w n , then the weighted average y can be expressed as follows:

[0150]

[0151] S23. Analyze the results. After calculating the weighted average, further analyze its accuracy and stability. The quality of the results can be evaluated by calculating the root mean square error (RMSE), standard deviation and other indicators of the weighted average.

[0152] In step 205, the results of the cross-check and data fusion processing are checked for integrity and accuracy reliability.

[0153] At present, the main GNSS high-precision positioning technologies are PPP (precision point positioning) and RTK (real-time kinematic positioning). RTK technology calculates the position of the mobile station in real time by differentially processing the carrier phase observations between a known base station and a mobile station (such as a drone, survey vehicle, etc.). The RTK system requires good communication between the base station and the mobile station, and usually uses radio or other wireless communication methods to transmit differential correction information. Its advantage is that it can be initialized quickly: RTK can complete the initialization process within a few seconds and obtain centimeter-level precision positioning results. Good real-time performance: It can provide continuous high-precision positioning services and is suitable for applications that require instant feedback. Strong anti-multipath capability: It reduces the impact of multipath effects through differential technology, especially in complex environments. It is currently widely used in the high-precision market. However, its disadvantage is that it relies on dense base stations: RTK requires available base stations nearby, which limits its scope of application. High cost: The cost of establishing and maintaining a base station network is relatively high. Limited coverage: The effective coverage of the base station is limited, and the accuracy will decrease after exceeding a certain distance.

[0154] PPP technology uses observation data from multiple GNSS constellations to achieve high-precision positioning by solving satellite orbit and clock information. Compared with RTK technology, its advantage is that it does not require a base station: PPP does not require a ground base station and can operate independently on a global scale. High precision: When stationary, PPP can achieve centimeter-level accuracy; it can also maintain sub-meter accuracy when moving. Flexibility: It is suitable for use in remote areas or places where it is impossible to establish a base station. However, the reliability of PPP technology is heavily dependent on the accuracy and integrity of global or regional precise orbit and clock products, which are often difficult for a single GNSS analysis center to provide.

[0155] The results of the GNSS cross-check and data fusion processing obtained in step 201 to step 205 can be further verified for their reliability from the perspective of the GNSS correction data integrity rate and PPP technology, including:

[0156] S1. Test the completeness of GNSS product data. Select the GNSS correction data received under the condition of one day of uninterrupted network, and calculate the completeness of the single analysis center and the integrated products of the five analysis centers. The completeness calculation formula is expressed as follows:

[0157]

[0158] According to the calculation results, the data integrity rate after fusion processing by multiple analysis centers is 99.72%, and the data integrity rate after processing by a single analysis center is 89.29%. That is to say, the use of the present invention can increase the integrity rate of GNSS service correction data by about 10%.

[0159] S2. Conduct PPP static simulation dynamic experiment, in which 3WKB measuring station can be tested, and GNSS products are selected for comparison during the uninterrupted period. Figure 3 The error time series diagram of the PPP static simulation dynamic experiment performed by an embodiment of the present invention is shown. The NEU direction positioning error RMS is shown in Table 1:

[0160] Table 1

[0161]

[0162]

[0163] That is to say, the positioning accuracy can be improved by more than 50% by adopting the present invention.

[0164] Figure 4 FIG. 1 shows a schematic diagram of a module of a navigation enhancement system based on cross-checking and data fusion in one embodiment of the present invention. Figure 4 As shown, the system includes a data receiving module 401 , a cross-check and data fusion processing module 402 , and a verification module 403 .

[0165] The data receiving module 402 is configured to obtain GNSS correction data from multiple GNSS analysis centers, wherein the GNSS correction data includes satellite clock correction data, satellite orbit correction data and satellite code deviation data.

[0166] The cross-check and data fusion processing module 402 includes a satellite clock correction data processing module 4021, a satellite orbit correction data processing module 4022 and a satellite code bias correction data 4023, which are configured to perform cross-check and data fusion processing on the satellite clock correction data, satellite orbit correction data and satellite code bias correction data of multiple GNSS analysis centers.

[0167] The verification module 403 is configured to perform integrity verification and accuracy reliability verification on the results of the cross-verification and data fusion processing.

[0168] The present invention proposes a navigation enhancement method and system combining cross-check and fusion technology, aiming to improve the reliability and accuracy of PPP technology when using satellite-based navigation to enhance correction data. Specifically, the present invention is divided into two parts:

[0169] First, a cross-check mechanism for corrections provided by multiple GNSS data analysis centers is established to strictly control the quality of input data. This mechanism uses the inherent consistency between correction information from different sources for comparison and analysis, thereby effectively identifying and eliminating abnormal or incomplete correction data. For example, statistical methods can be used to detect mutation points or trend changes in corrections, or machine learning algorithms can be used to identify data points that deviate from the normal distribution, thereby ensuring the quality of input data.

[0170] Furthermore, advanced filtering algorithms (such as extended Kalman filter EKF or weighted averaging) are used to intelligently fuse the screened GNSS correction products from different sources. This method can not only achieve complementary information advantages, improve the accuracy and stability of correction numbers, but also significantly reduce the risk of relying on a single data source. For example, by weighted averaging or nonlinear fusion of multi-source correction data, the advantages of each data source can be fully utilized, the error accumulation effect can be reduced, and the overall positioning performance can be improved.

[0171] The comprehensive solution proposed in the present invention not only provides a more solid data foundation for the application of PPP technology, but also significantly improves the overall performance of low-orbit navigation enhanced GNSS positioning services. Specifically, the present invention can greatly improve positioning accuracy and reliability by optimizing software algorithms and data processing procedures without increasing additional hardware costs. This has important practical significance and broad application prospects for promoting the development of high-precision positioning technology, especially in the fields of unmanned driving, precision agriculture, disaster emergency response, etc.

[0172] In summary, the present invention effectively solves the problems of anomalies and missing correction data faced when using PPP technology by introducing cross-checking and fusion technology. It not only improves positioning accuracy and stability, but also reduces the risk of dependence on a single data source, laying a solid technical foundation for the widespread application of GNSS navigation systems.

[0173] Although various embodiments of the present invention are described above, it should be understood that they are presented as examples only and not as limitations. It is obvious to those skilled in the relevant art that various combinations, deformations and changes can be made thereto without departing from the spirit and scope of the present invention. Therefore, the breadth and scope of the present invention disclosed herein should not be limited by the exemplary embodiments disclosed above, but should only be defined according to the attached claims and their equivalents.

Claims

1. A navigation enhancement method based on cross-checking and data fusion, characterized in that: The following steps are involved: Acquire GNSS correction data from multiple GNSS analysis centers, wherein the GNSS correction data includes satellite clock correction data, satellite orbit correction data, and satellite code deviation data; Cross-check and perform data fusion processing on satellite clock correction data, satellite orbit correction data and satellite code bias correction data from multiple GNSS analysis centers; as well as The results of cross-checking and data fusion processing are checked for integrity and accuracy reliability.

2. The navigation enhancement method based on cross-checking and data fusion according to claim 1, characterized in that: Access to GNSS correction data from multiple GNSS analysis centers includes: Receive GNSS correction data from multiple GNSS analysis centers; Decoding the GNSS correction data and extracting satellite orbit parameters, clock correction values ​​and ranging code deviations; and The GNSS correction data is preprocessed, including storing the GNSS correction data and determining the data integrity rate.

3. The navigation enhancement method based on cross-checking and data fusion according to claim 2, characterized in that: Cross-checking and data fusion processing of satellite clock correction data from multiple GNSS analysis centers includes the following steps: Restore the satellite clock correction data, where the clock correction value Δk at the observation time t is obtained based on the quadratic three coefficients decoded from the clock correction number, and the clock correction Δt calculated by combining the broadcast ephemeris is obtained. b , and obtain the corrected clock error data Δx s , expressed as the following formula; Δk=C0+C1×(t-t0)+C2×(t-t0)×2 Δx s =Δt b -Δk / Vclight Among them, C0, C1, C2 represent the three coefficients of the satellite clock error polynomial, and Vclight represents the speed of light. Perform data fusion processing on satellite clock correction data from multiple GNSS analysis centers; and Cross-check satellite clock correction data from multiple GNSS analysis centers.

4. The navigation enhancement method based on cross-checking and data fusion according to claim 3, characterized in that: The Kalman filter is used to perform data fusion processing on the satellite clock correction data of multiple GNSS analysis centers, which includes the following steps: Determine the state vector X, which includes the system bias x1 of the GNSS analysis center clock error, the epoch bias x2, and the bias x of each satellite in the epoch S , expressed as the following formula: X=[x1,x2,x s1 x s2 ,...x sn ] T Among them, x s1 、x s2 ,…x sn Represents the satellite bias from the first to the nth satellite in the epoch; Set the initial state estimate and its covariance matrix; A system model describing the evolution of satellite clock error is constructed, which can be expressed as follows: X k =AX k-1 +W k Among them, X k represents the current state vector, X k-1 represents the state vector at the previous moment, A represents the state transfer matrix, W k represents process noise; The observation model is established and expressed as follows: Z k =HX k-1 +V k Among them, Z k represents the observation vector, H represents the observation matrix, V k represents the observation noise; Input observation data input, where the satellite clock corrections from different GNSS analysis centers are input into the Kalman filter as observation data, where the clock correction observations of m satellites in the current epoch of n analysis centers are expressed as follows: WITH k =[z1s1,z1s2,z1s3,....z n s1,z n s2...z n s m ] T Among them, z n represents the nth analysis center, s m Represents the observation value of the satellite numbered m; Calculate the Kalman gain K based on the state estimation and observation data; Update the state estimate and its covariance matrix, where the Kalman gain is used to update the state estimate and its covariance matrix P k , where the covariance matrix P k It is expressed as the following formula: P k =(I-KH)P k|k-1 Among them, I represents the unit matrix, K represents the Kalman gain, H represents the observation matrix, P k|k-1 represents the forecast covariance matrix; and Repeat the above steps until the preset convergence condition or the upper limit of the number of iterations is reached.

5. The navigation enhancement method based on cross-checking and data fusion according to claim 3, characterized in that: Cross-checking satellite clock correction data from multiple GNSS analysis centers includes the following steps: Conduct consistency checks on clock corrections from different GNSS analysis centers and remove data points that deviate from the average value; Using multiple data processing models to process data, comparing the consistency of outputs of the data processing models, and selecting the data processing model with the highest consistency, wherein the multiple data processing models include a polynomial fitting model and a spline interpolation model; and Identify and eliminate abnormal fluctuations through time series analysis methods.

6. The navigation enhancement method based on cross-checking and data fusion according to claim 2, characterized in that: Cross-checking and data fusion processing of satellite orbit correction data from multiple GNSS analysis centers includes: Recovering satellite orbit correction data, wherein corresponding satellite orbit parameters in the broadcast ephemeris are selected according to the age of the ephemeris data; Cross-check satellite orbit correction data from multiple GNSS analysis centers, including: Check whether the satellite orbit correction data of different GNSS analysis centers are consistent; Use statistical methods to evaluate the consistency and reliability of satellite orbit correction data from different GNSS analysis centres; and Detecting the stability and trend of satellite orbit correction data through time series analysis; and The weighted average method is used to perform orbit fusion of satellite orbit correction data from multiple GNSS analysis centers, including: Determine the weights of data sources for different GNSS analysis center orbits; A weighted average calculation is performed, where the weighted average value y is expressed as follows: Among them, x i represents the orbit information data source of the i-th GNSS analysis center of the satellite, w i represents the weight corresponding to the orbit information data source of the i-th GNSS analysis center, where The weights sum to 1; and The accuracy and stability of the weighted average are analyzed, and the root mean square error and standard deviation of the weighted average are calculated to evaluate the quality of the orbit calculation results.

7. The navigation enhancement method based on cross-checking and data fusion according to claim 2, characterized in that: Cross-checking and data fusion processing of satellite code bias correction data from multiple GNSS analysis centers includes: Cross-check satellite code bias correction data from multiple GNSS analysis centers to determine the consistency and reliability of satellite code bias correction data from different sources; and The satellite code bias correction data from multiple GNSS analysis centers are fused using the weighted average method.

8. A navigation enhancement system based on cross-checking and data fusion, characterized in that: include: The data receiving module is configured to obtain GNSS correction data from multiple GNSS analysis centers, wherein the GNSS correction data includes satellite clock correction data, satellite orbit correction data and satellite code deviation data. A cross-check and data fusion processing module, which is configured to perform cross-check and data fusion processing on satellite clock correction data, satellite orbit correction data and satellite code bias correction data of multiple GNSS analysis centers; as well as The verification module is configured to perform integrity verification and accuracy reliability verification on the results of the cross-verification and data fusion processing.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program, when executed by a processor, performs the steps of the method according to any one of claims 1 to 7.

10. A computer system, characterized in that: include: a processor configured to execute machine-executable instructions; as well as A memory having machine executable instructions stored thereon, wherein the machine executable instructions, when executed by a processor, perform the steps of the method according to any one of claims 1 to 7.

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