Satellite navigation positioning signal-to-noise ratio weighting method, system, device and storage medium

Abstract

The embodiment of the application provides a satellite navigation positioning signal-to-noise ratio weighting method, system, device and storage medium, and belongs to the technical field of satellite navigation positioning. The scheme constructs a satellite signal-to-noise ratio reconstruction model in an open environment, inputs the satellite elevation angle into the corresponding satellite signal-to-noise ratio reconstruction model, obtains the reconstructed signal-to-noise ratio interval of satellite observation data in the open environment, determines the satellite weight according to the observation signal-to-noise ratio and the reconstructed signal-to-noise ratio interval, and performs positioning calculation according to the satellite weight to obtain positioning information. When facing a shielding environment, the signal-to-noise ratio of satellite observation values is analyzed based on the signal-to-noise ratio, so that the importance of satellite observation data with less diffraction error is highlighted during positioning calculation, the quality of satellite observation data is improved, the influence of diffraction error is suppressed, the accuracy of calculation results is improved, the precision and reliability of GNSS positioning are improved, and dynamic millimeter-level positioning is realized.

Description

Technical Field

[0001] This application relates to the field of satellite navigation and positioning technology, and in particular to a satellite navigation and positioning signal-to-noise ratio weighting method, system, device and storage medium. Background Technology

[0002] Global Navigation Satellite System (GNSS) is widely used in deformation monitoring and navigation positioning due to its strong real-time performance, all-weather and 24-hour capability, high measurement accuracy, and high degree of automation. It has become an important means for benchmark construction, civil engineering and water conservancy safety monitoring, and geological disaster monitoring.

[0003] However, in practical applications, on the one hand, the selection of base station sites is limited by unsolvable problems such as terrain or land acquisition, making it difficult to find an ideal open environment, resulting in a reduction in the number of visible satellites; on the other hand, monitoring stations are usually located in environments with high building and vegetation obstruction, making the monitoring environment more severe. The diffraction error caused by obstruction can reach the centimeter or even decimeter level, and multipath effects and signal diffraction will occur. At the same time, general elevation angle or signal-to-noise ratio random models will fail, further reducing the quality of the calculated observation values, seriously affecting the calculation results of real-time dynamic positioning under obstructed environments, and damaging the accuracy and reliability of GNSS positioning. Summary of the Invention

[0004] The main objective of this application is to propose a satellite navigation and positioning signal-to-noise ratio (SNR) weighting method, system, device, and storage medium. The aim is to perform SNR analysis based on satellite observations in obstructed environments, thereby highlighting the importance of satellite observation data with minimal diffraction errors during positioning calculations. This improves the quality of satellite observation data, suppresses the impact of diffraction errors, and enhances the accuracy of the calculation results, ultimately improving the precision and reliability of GNSS positioning and achieving dynamic millimeter-level positioning.

[0005] To achieve the above objectives, one aspect of this application proposes a satellite navigation and positioning signal-to-noise ratio weighting method, the method comprising:

[0006] The system collects the current first satellite elevation angle and first observation signal-to-noise ratio in the obstructed environment satellite system;

[0007] Input the first satellite elevation angle into the satellite signal-to-noise ratio reconstruction model to obtain the reconstructed signal-to-noise ratio range of satellite observation data in open environment;

[0008] Satellite weights are determined based on the first observed signal-to-noise ratio and the reconstructed signal-to-noise ratio interval, and positioning information is obtained by performing positioning calculations based on the satellite weights.

[0009] The satellite signal-to-noise ratio reconstruction model is obtained through the following steps:

[0010] Obtain the second observation signal-to-noise ratio sequence corresponding to different second satellite elevation angles in open environment and the satellite elevation angle signal-to-noise ratio relationship function in open environment;

[0011] By inputting different elevation angles of the second satellite into the elevation angle signal-to-noise ratio relationship function, a first fitted signal-to-noise ratio sequence is obtained;

[0012] The signal-to-noise ratio difference sequence is determined based on the second observed signal-to-noise ratio sequence and the first fitted signal-to-noise ratio sequence;

[0013] The mean and standard deviation of satellite differences are determined based on the signal-to-noise ratio difference sequence.

[0014] The satellite signal-to-noise ratio reconstruction model is determined based on the elevation angle signal-to-noise ratio relationship function, the mean of the satellite differences, and the standard deviation of the satellite differences.

[0015] In some embodiments, the second observation signal-to-noise ratio sequence corresponding to different second satellite elevation angles is obtained through the following steps:

[0016] Obtain current observation files and broadcast ephemeris files from open environment satellite systems;

[0017] Data processing is performed on the observation file and the broadcast ephemeris file to obtain the second satellite elevation angle and the second observation signal-to-noise ratio;

[0018] The second observation signal-to-noise ratio sequence is generated by taking the second observation signal-to-noise ratio corresponding to different second satellite elevation angles in order of the magnitude of the second satellite elevation angle.

[0019] In some embodiments, the elevation angle signal-to-noise ratio relationship function of the satellite in an open environment is obtained through the following steps:

[0020] The elevation angle and the second observation signal-to-noise ratio sequence are fitted with a function to obtain the elevation angle signal-to-noise ratio relationship function.

[0021] In some embodiments, the step of fitting a function to the second satellite elevation angle and the second observation signal-to-noise ratio sequence to obtain the elevation angle signal-to-noise ratio relationship function includes the following steps:

[0022] The first parameter is determined by fitting a function to the second satellite elevation angle and the second observation signal-to-noise ratio sequence using the least squares method.

[0023] The first fitting function is obtained based on the first parameters;

[0024] By inputting different elevation angles of the second satellite into the first fitting function, a second fitted signal-to-noise ratio sequence is obtained;

[0025] The signal-to-noise ratio weights of the second observed signal-to-noise ratio are determined based on the second fitted signal-to-noise ratio sequence;

[0026] Based on the signal-to-noise ratio weights, the weighted least squares method is used to fit a function to the second satellite elevation angle and the second observation signal-to-noise ratio sequence to determine the second parameter.

[0027] The elevation angle signal-to-noise ratio relationship function is obtained based on the second parameter.

[0028] In some embodiments, determining the signal-to-noise ratio weights of the second observed signal-to-noise ratio based on the second fitted signal-to-noise ratio sequence includes the following steps:

[0029] Determine the number of multiple second observation signal-to-noise ratios corresponding to preset elevation angle intervals in the second observation signal-to-noise ratio sequence;

[0030] Based on a preset signal-to-noise ratio (SNR) weighting rule, the SNR weight of the second observed SNR is determined according to the quantity, the second observed SNR, and the second fitted SNR sequence. The SNR weighting rule is as follows:

[0031]

[0032] Where k is the value of the satellite elevation angle, N represents the signal-to-noise ratio weight for the i-th second observation within the satellite elevation angle range [k, k+1]. k,k+1 SNR is the number of second observation signal-to-noise ratios within the satellite elevation angle range [k, k+1]. i Let be the signal-to-noise ratio of the i-th second observation within the satellite elevation angle range [k, k+1]. Let be the second fitted signal-to-noise ratio at the satellite elevation angle [k, k+1].

[0033] In some embodiments, the expression for the reconstructed signal-to-noise ratio range is:

[0034]

[0035] in, and These are the upper and lower limits of the reconstructed signal-to-noise ratio range, respectively, f2 G (EL) is the signal-to-noise ratio value obtained by fitting the elevation angle of the first satellite using the elevation angle signal-to-noise ratio relationship function. The mean of satellite differences. This represents the standard deviation of the satellite difference.

[0036] In some embodiments, the expression for the satellite weight is:

[0037]

[0038] Where SNR is the signal-to-noise ratio of the first observation, w is the satellite weight, and SNR is the signal-to-noise ratio of the second observation. max (EL) represents the upper limit of the reconstructed signal-to-noise ratio range, and SNR min (EL) represents the lower limit of the reconstructed signal-to-noise ratio range, and EL is the elevation angle of the first satellite. The variance determined for the stochastic model of elevation angle. This is the variance determined by adding the standard deviation of satellite differences to the variance determined by the stochastic elevation angle model.

[0039] To achieve the above objectives, another aspect of this application proposes a satellite navigation and positioning signal-to-noise ratio weighting system, the system comprising:

[0040] The first module is used to collect the current first satellite elevation angle and first observation signal-to-noise ratio in the satellite system under obstruction environment;

[0041] The second module is used to input the first satellite elevation angle into the satellite signal-to-noise ratio reconstruction model to obtain the reconstructed signal-to-noise ratio range of satellite observation data in open environment;

[0042] The third module is used to determine satellite weights based on the first observed signal-to-noise ratio and the reconstructed signal-to-noise ratio interval, and to perform positioning calculations based on the satellite weights to obtain positioning information.

[0043] The fourth module is used to obtain the second observation signal-to-noise ratio sequence corresponding to different second satellite elevation angles in open environment and the satellite elevation angle signal-to-noise ratio relationship function in open environment;

[0044] The fifth module is used to input different elevation angles of the second satellite into the elevation angle signal-to-noise ratio relationship function to obtain a first fitted signal-to-noise ratio sequence;

[0045] The sixth module is used to determine the signal-to-noise ratio difference sequence based on the second observed signal-to-noise ratio sequence and the first fitted signal-to-noise ratio sequence;

[0046] The seventh module is used to determine the mean of satellite differences and the standard deviation of satellite differences based on the signal-to-noise ratio difference sequence;

[0047] The eighth module is used to determine the satellite signal-to-noise ratio reconstruction model based on the elevation angle signal-to-noise ratio relationship function, the mean of the satellite difference, and the standard deviation of the satellite difference.

[0048] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0049] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0050] The embodiments of this application include at least the following beneficial effects: This application provides a satellite navigation and positioning signal-to-noise ratio (SNR) weighting method, system, device, and storage medium. This scheme constructs a satellite SNR reconstruction model in an open environment, inputs the satellite elevation angle into the corresponding satellite SNR reconstruction model, obtains the reconstructed SNR interval of satellite observation data in an open environment, determines the satellite weights based on the observed SNR and the reconstructed SNR interval, and performs positioning calculations based on the satellite weights to obtain positioning information. It can perform SNR analysis based on satellite observation values ​​when facing obstructed environments, thereby highlighting the importance of satellite observation data with less diffraction error during positioning calculations, improving the quality of satellite observation data, suppressing the influence of diffraction error, and improving the accuracy of the calculation results, thereby improving the accuracy and reliability of GNSS positioning and achieving dynamic millimeter-level positioning. Attached Figure Description

[0051] Figure 1 This is a flowchart of the satellite navigation and positioning signal-to-noise ratio weighting method provided in the embodiments of this application;

[0052] Figure 2 This is a flowchart illustrating the reconstructed observation signal-to-noise ratio range provided in the embodiments of this application;

[0053] Figure 3 This is a flowchart of a satellite navigation and positioning signal-to-noise ratio weighting method provided in another embodiment of this application;

[0054] Figure 4(a) is a schematic diagram of the GPS exponential function fitting effect provided in the embodiments of this application;

[0055] Figure 4(b) is a schematic diagram of the fitting effect of the GAL exponential function provided in the embodiment of this application;

[0056] Figure 4(c) is a schematic diagram of the fitting effect of the BDS-IGSO exponential function provided in the embodiment of this application;

[0057] Figure 4(d) is a schematic diagram of the fitting effect of the BDS-MEO exponential function provided in the embodiment of this application;

[0058] Figure 5(a) is a schematic diagram of the effect of the elevation angle model solution data provided in the embodiment of this application;

[0059] Figure 5(b) is a schematic diagram of the effect of the signal-to-noise ratio model solution data provided in the embodiment of this application;

[0060] Figure 5(c) is a schematic diagram of the effect of the signal-to-noise ratio reconstruction model solving data provided in the embodiment of this application;

[0061] Figure 6 This is a schematic diagram of the structure of the satellite navigation and positioning signal-to-noise ratio weighting system provided in the embodiments of this application;

[0062] Figure 7 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0064] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0065] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0067] In GNSS deformation monitoring and navigation positioning applications, diffraction errors caused by occlusion can reach the centimeter or even decimeter level. When such errors are too large, the prediction results of traditional stochastic elevation angle models or stochastic signal-to-noise ratio models deviate significantly from the actual situation, leading to model failure. During GNSS data processing, large errors in observations can make it difficult or incorrect to fix ambiguities in real-time dynamic positioning, severely affecting the ambiguity fixation rate and positioning accuracy. This has become a bottleneck restricting the widespread application of GNSS in high-precision navigation positioning, civil engineering safety monitoring, and geological disaster detection.

[0068] In view of this, this application provides a satellite navigation and positioning signal-to-noise ratio (SNR) weighting method, system, device, and storage medium. This scheme constructs a satellite SNR reconstruction model in an open environment, inputs the satellite elevation angle into the corresponding SNR reconstruction model, obtains the reconstructed SNR interval of satellite observation data in the open environment, determines satellite weights based on the observed SNR and the reconstructed SNR interval, and performs positioning calculations based on these satellite weights to obtain positioning information. This allows for SNR analysis based on satellite observation values ​​even in obstructed environments, thereby highlighting the importance of satellite observation data with low diffraction errors during positioning calculations, improving the quality of satellite observation data, suppressing the influence of diffraction errors, and increasing the accuracy of the calculation results. This ultimately improves the accuracy and reliability of GNSS positioning, achieving dynamic millimeter-level positioning.

[0069] The satellite navigation and positioning signal-to-noise ratio (SNR) weighting method provided in this application relates to the field of satellite navigation and positioning technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the satellite navigation and positioning SNR weighting method, but is not limited to the above forms.

[0070] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0071] Figure 1 This is an optional flowchart of the satellite navigation and positioning signal-to-noise ratio weighting method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S103.

[0072] Step S101: Collect the current first satellite elevation angle and first observation signal-to-noise ratio in the satellite system under the obstruction environment;

[0073] Step S102: Input the first satellite elevation angle into the satellite signal-to-noise ratio reconstruction model to obtain the reconstructed signal-to-noise ratio range of satellite observation data in open environment;

[0074] Step S103: Determine the satellite weights based on the first observation signal-to-noise ratio and the reconstructed signal-to-noise ratio interval, and perform positioning calculations based on the satellite weights to obtain positioning information;

[0075] The acquisition of the satellite signal-to-noise ratio reconstruction model may include, but is not limited to, steps S201 to S205:

[0076] Step S201: Obtain the second observation signal-to-noise ratio sequence corresponding to different second satellite elevation angles in open environment and the satellite elevation angle signal-to-noise ratio relationship function in open environment;

[0077] Step S202: Input different second satellite elevation angles into the elevation angle signal-to-noise ratio relationship function to obtain the first fitted signal-to-noise ratio sequence;

[0078] Step S203: Determine the signal-to-noise ratio difference sequence based on the second observed signal-to-noise ratio sequence and the first fitted signal-to-noise ratio sequence;

[0079] Step S204: Determine the mean and standard deviation of satellite differences based on the signal-to-noise ratio difference sequence;

[0080] Step S205: Determine the satellite signal-to-noise ratio reconstruction model based on the elevation angle signal-to-noise ratio relationship function, the mean of satellite differences, and the standard deviation of satellite differences.

[0081] In some specific embodiments, an open environment refers to a situation where, when a satellite is operating in space, there are no ground buildings or natural obstacles in its signal transmission path that could affect signal transmission. In such an environment, the satellite can transmit signals to the Earth's surface with minimal interference. An obstructed environment, on the other hand, refers to a situation where, when a satellite is operating in space, there are ground buildings or natural obstacles in its signal transmission path that significantly affect signal transmission. This type of environment is usually more complex, and when the signal passes through the edge of an obstruction, diffraction errors occur, resulting in signal diffraction and limiting the satellite's ability to transmit signals. To achieve precise positioning in complex observation environments, this application's embodiments first establish a satellite signal-to-noise ratio reconstruction model in an open environment.

[0082] Specifically, in this embodiment of the application, the observation data of each satellite in the satellite system obtained by the receivers of the base station and the monitoring station are read in an unobstructed open environment, and the elevation angle (i.e., the second satellite elevation angle) and signal-to-noise ratio (i.e., the second observation signal-to-noise ratio) of each satellite are calculated and output based on the observation data.

[0083] Reference Figure 2 The second observation signal-to-noise ratios (SNRs) of each satellite at each epoch are sorted according to the magnitude of the second satellite elevation angle, generating a second observation SNR sequence (i.e., SNR sequence) for each satellite. GNSS satellites are then divided according to their respective systems, obtaining the SNR sequence for each system. Since the number of SNR observations within each elevation angle varies, and anomalies may occur, based on the SNR sequences of each system, after fitting the relationship between SNR and elevation angle using ordinary least squares, a second fitting is performed using weighted least squares to apply weights to each SNR observation value, obtaining the elevation angle SNR relationship function (i.e., the fitting function).

[0084] By inputting different second satellite elevation angles into the elevation angle signal-to-noise ratio (SNR) relationship function, a first fitted SNR sequence is obtained. Statistical analysis is performed on the second observed SNR sequence and the first fitted SNR sequence for each satellite. The SNR difference sequence (i.e., the difference sequence) is obtained by subtracting the first fitted SNR sequence from the second observed SNR sequence. The mean and standard deviation of the SNR difference sequence are used as the mean and standard deviation of the satellite difference, which serve as the basis for reconstructing the SNR interval and restoring the observed SNR value of the satellite.

[0085] The satellite signal-to-noise ratio (SNR) reconstruction model is determined based on the elevation angle SNR relationship function, the mean of satellite differences, and the standard deviation of satellite differences. This model is stored in each satellite so that it can be used to reconstruct the SNR range in open environments during real-time dynamic positioning.

[0086] During real-time dynamic GNSS positioning, the acquired observation data is affected by highly obstructed environments and contains significant diffraction errors. Therefore, during the calculation process, it is necessary to evaluate the data based on the signal-to-noise ratio (SNR). A higher SNR indicates better data quality. The satellite SNR reconstruction model constructed in this application can reconstruct the SNR range of the satellite in open environments during real-time dynamic positioning to determine evaluation indicators.

[0087] First, in a highly obstructed environment, the observation data of each satellite in the satellite system, obtained from the receivers of the base station and monitoring station, is read. Based on the observation data, the elevation angle (i.e., the first satellite elevation angle) and the observed signal-to-noise ratio (i.e., the first observed signal-to-noise ratio) of each satellite are calculated and output. The first satellite elevation angle is input into the elevation angle signal-to-noise ratio relationship function in the satellite signal-to-noise ratio reconstruction model to obtain the fitted signal-to-noise ratio of the current satellite elevation angle in an open environment. Based on the fitted signal-to-noise ratio superimposed with the mean of satellite differences, the upper and lower limits of the signal-to-noise ratio in an open environment are obtained by adding or subtracting the standard deviation of the satellite differences, thus determining the reliable reconstructed signal-to-noise ratio range.

[0088] The quality of satellite observation data is evaluated based on the reconstructed signal-to-noise ratio (SNR) range and the first observation SNR. A weighting strategy is implemented based on the degree of influence from diffraction errors, assigning weights to the satellite observation data to emphasize the importance of data with fewer diffraction errors. This controls the extent to which satellite observation data participates in the data processing. Positioning information is then obtained from the weighted satellite observation data.

[0089] This application embodiment constructs a satellite signal-to-noise ratio (SNR) reconstruction model in an open environment. The satellite elevation angle is input into the corresponding SNR reconstruction model to obtain the reconstructed SNR interval of satellite observation data in the open environment. Satellite weights are determined based on the observed SNR and the reconstructed SNR interval, and positioning information is obtained through positioning calculation based on these weights. This enables SNR analysis of satellite observations in heavily obstructed environments, highlighting the importance of satellite observation data with low diffraction errors during positioning calculations. This improves the quality of satellite observation data, suppresses the impact of diffraction errors, and enhances the accuracy of the calculation results, thereby improving the precision and reliability of GNSS positioning and achieving dynamic millimeter-level positioning.

[0090] In some embodiments, obtaining the second observation signal-to-noise ratio sequence corresponding to different second satellite elevation angles may include, but is not limited to, steps S301 to S302:

[0091] Step S301: Obtain the current observation file and broadcast ephemeris file from the open environment satellite system;

[0092] Step S302: Perform data processing on the observation file and the broadcast ephemeris file to obtain the first satellite elevation angle and the first observation signal-to-noise ratio;

[0093] Step S303: Generate a second observation signal-to-noise ratio sequence by taking the second observation signal-to-noise ratios corresponding to different second satellite elevation angles in order of the magnitude of the second satellite elevation angles.

[0094] In some specific embodiments, firstly, in an unobstructed open environment, the observation files obtained from the GNSS receivers of the base station and monitoring station, as well as the broadcast ephemeris file, are read. Then, the pseudorange point positioning algorithm is used to solve the data, and the signal-to-noise ratio and elevation angle of each satellite are output. The pseudorange non-differenced observation equation is as follows:

[0095]

[0096] Where the superscript i represents the satellite number, the subscripts m and p represent the frequency number and receiver respectively, P is the original pseudorange observation value, ρ represents the geometric distance from the satellite to the receiver, c is the speed of light in a vacuum, and dt p dt i These represent the clock biases of the receiver and the satellite, respectively, with T and I representing the tropospheric and ionospheric delays, respectively. denoted as the pseudorange hardware delay at the receiver and satellite respectively, and e represents the pseudorange observation noise, which includes multipath effects and diffraction error information.

[0097] Converting formula (1) into matrix form, we get the following formula:

[0098] L k =A k X k +V k (2)

[0099] Where k is the current epoch, L k A is the satellite observation vector, i.e., pseudorange and carrier phase observations. k To design the matrix, X k V is a state vector containing position parameters and clock error parameters. k This is the residual vector.

[0100] The least squares solution to formula (2) is:

[0101]

[0102] The receiver's position coordinates (accuracy, latitude, and altitude) are obtained from the least squares solution. The second elevation angle of the satellite relative to the receiver is calculated using geometric relationships based on the receiver's position coordinates and the satellite's position coordinates. The receiver obtains the second observation signal-to-noise ratio by measuring the signal and noise.

[0103] Extract the second elevation angle and corresponding second observation signal-to-noise ratio of each satellite in the GNSS. Sort the corresponding second observation signal-to-noise ratios according to the order of the second satellite elevation angles to generate a second observation signal-to-noise ratio sequence.

[0104] The embodiments of this application use a pseudorange single-point positioning algorithm to output the elevation angle and signal-to-noise ratio of the monitoring station in an open environment, which simplifies the solution of satellite observation data.

[0105] In some embodiments, obtaining the elevation angle signal-to-noise ratio relationship function of a satellite in an open environment may include, but is not limited to, step S401:

[0106] Step S401: Perform function fitting on the second satellite elevation angle and the second observation signal-to-noise ratio sequence to obtain the elevation angle signal-to-noise ratio relationship function.

[0107] In step S401 of some embodiments, since the elevation angles and signal-to-noise ratios of satellites from different systems or types in GNSS vary significantly, the satellites are initially divided according to their system: Global Positioning System (GPS), Galileo Navigation Satellite System (GAL), and BeiDou Navigation Satellite System (BDS). The BeiDou satellites are further divided into BeiDou-GEO (geostationary orbit satellites), BeiDou-IGSO (inclined geosynchronous orbit satellites), and BeiDou-MEO (earth orbit satellites). Since the elevation angle of BDS-GEO satellites remains constant, no modeling is required. The GNSS satellites are then divided into four groups: A, B, C, and D, based on GPS, GAL, BDS-IGSO, and BDS-MEO. To predict the signal-to-noise ratio at a given elevation angle, a function fitting is performed on the second satellite elevation angle and second observed signal-to-noise ratio sequences for the four groups of satellite systems to obtain the elevation angle signal-to-noise ratio relationship function for each group.

[0108] The embodiments of this application divide GNSS according to the system to which the satellites belong, and determine the fitting function of the system based on the elevation angle and signal-to-noise ratio of all satellites in each group. This eliminates the need to establish parameters for each elevation angle, thus simplifying the model.

[0109] In some embodiments, step S401 may include, but is not limited to, steps S501 to S506:

[0110] Step S501: Using the least squares method, perform function fitting on the second satellite elevation angle and the second observation signal-to-noise ratio sequence to determine the first parameter;

[0111] Step S502: Obtain the first fitting function based on the first parameters;

[0112] Step S503: Input different second satellite elevation angles into the first fitting function to obtain the first fitted signal-to-noise ratio sequence;

[0113] Step S504: Determine the signal-to-noise ratio weight of the second observed signal-to-noise ratio based on the first fitted signal-to-noise ratio sequence;

[0114] Step S505: Based on the signal-to-noise ratio weight, the weighted least squares method is used to perform function fitting on the second satellite elevation angle and the second observation signal-to-noise ratio sequence to determine the second parameter;

[0115] Step S506: Obtain the elevation angle signal-to-noise ratio relationship function based on the second parameter.

[0116] In some specific embodiments, the ordinary least squares method is first used to fit functions to the four groups of satellites to determine the first parameter.

[0117] The function to be fitted, f(EL), is as follows:

[0118] f(EL)=a0exp(-EL / b0)+c0 (4)

[0119] Where EL is the satellite elevation angle, and a0, b0, and c0 are the parameters to be fitted.

[0120] The function to be fitted is nonlinear. The Jacobian matrix is ​​obtained by taking the first-order term of a Taylor expansion.

[0121]

[0122] The results are obtained through iteration:

[0123] X first =(H T H)H T z = [a1 b1 c1] T (6)

[0124] Where H is the design matrix, z is the signal-to-noise ratio (SNR) observation, and a 11 b1 and c1 are the first parameters.

[0125] The first fitting function is obtained based on the first parameters obtained from the solution:

[0126] f1 G (EL)=a1exp(-EL / b1)+c1 (7)

[0127] Where f1 G This represents the fitting result (i.e., the second fitted signal-to-noise ratio sequence) of the G(A, B, C, D) group calculated according to the first fitting function.

[0128] The first fitting step aims to derive a preliminary functional relationship between elevation angle and signal-to-noise ratio (SNR). The second fitting step considers weighting the SNR of each second observation. The more SNR observations within each degree of elevation angle, the greater the SNR weight. Similarly, the closer the SNR of the second observation is to the result of the first fitting step, the greater the SNR weight.

[0129] The weighted array W is:

[0130]

[0131] Repeat the least squares function fitting steps to fit the second satellite elevation angle and the second observation signal-to-noise ratio sequence, and obtain the results through iteration:

[0132] x second =(H T WH) -1 H T Wz = [a2 b2 c2] T (9)

[0133] Where a2, b2, and c2 are the second parameters;

[0134] The elevation angle signal-to-noise ratio relationship function is obtained based on the second parameter:

[0135]

[0136] in This represents the fitting result (i.e., the first fitted signal-to-noise ratio sequence) of the G(A, B, C, D) group calculated based on the elevation angle signal-to-noise ratio relationship function.

[0137] This application embodiment initially fits the functional relationship between elevation angle and signal-to-noise ratio (SNR). Then, it weights the second observation SNR based on the number of SNR observations within each elevation angle and fits the functional relationship again to improve the accuracy of the fitting function. This yields the elevation angle SNR relationship function for each group, which is used to construct a more accurate SNR interval and evaluate the quality of satellite observation data.

[0138] In some embodiments, step S504 may include, but is not limited to, steps S5601 to S602:

[0139] Step S601: Determine the number of multiple second observation signal-to-noise ratios corresponding to the preset elevation angle interval in the second observation signal-to-noise ratio sequence;

[0140] Step S602: Based on the preset signal-to-noise ratio weighting rules, determine the signal-to-noise ratio weight of the second observation signal-to-noise ratio according to the quantity, the second observation signal-to-noise ratio, and the first fitted signal-to-noise ratio sequence.

[0141] In some specific embodiments, since the number of signal-to-noise ratio (SNR) observations within each satellite elevation angle is different, and SNR values ​​may be abnormal, the number of second observation SNRs corresponding to all second satellite elevation angles within each degree of satellite elevation angle is counted. Based on a preset SNR weighting rule, the more SNR observations within each degree of elevation angle, the greater the SNR weight; the closer the second observation SNR is to the first fitting result, the greater the SNR weight. The SNR weight for each second observation SNR is then determined. The SNR weighting rule is as follows:

[0142]

[0143] Where k is the satellite elevation angle value (k is an integer), N represents the signal-to-noise ratio weight for the i-th second observation within the satellite elevation angle range [k, k+1]. k,k+1 SNR is the number of second observation signal-to-noise ratios within the satellite elevation angle range [k, k+1]. i Let be the signal-to-noise ratio of the i-th second observation within the satellite elevation angle range [k, k+1]. Let be the second fitted signal-to-noise ratio at the satellite elevation angle [k, k+1].

[0144] This application's embodiments, by considering the signal-to-noise ratio distribution within each degree of satellite elevation angle and anomalies in the observation data, assign greater weight to the signal-to-noise ratio of observations with a large number of data points and observation values ​​close to the fitted values, thereby improving the reliability and accuracy of data processing and analysis.

[0145] In step S102 of some embodiments, the standard deviation of satellite differences stored for each satellite is first checked. When the standard deviation of satellite differences is greater than the empirical threshold standard deviation, it is considered that the signal-to-noise ratio sequence of this satellite fluctuates greatly and is not suitable for modeling. When the standard deviation of satellite differences is less than the empirical threshold standard deviation, since 95% of the error will be contained within twice the standard deviation, the upper and lower limits of the signal-to-noise ratio interval of each satellite in the open environment can be reconstructed based on the elevation angle signal-to-noise ratio relationship function of each group, the mean of satellite differences for each satellite, and the standard deviation of satellite differences, as shown in the following formula:

[0146]

[0147] in, and These represent the upper and lower limits of the reconstructed signal-to-noise ratio range, respectively. The third fitted signal-to-noise ratio is obtained by fitting the current satellite elevation angle to the elevation signal-to-noise ratio relationship function. The mean of satellite differences. This represents the standard deviation of the satellite difference.

[0148] It is understandable that when reconstructing the upper and lower limits of the signal-to-noise ratio range of each satellite in an open environment based on the elevation angle signal-to-noise ratio relationship function of each group, the mean of satellite differences for each satellite, and the standard deviation of satellite differences, 2 times the standard deviation is only an example. The corresponding standard deviation multiple can be determined according to the actual positioning accuracy requirements. The upper and lower limits can be determined using the same standard deviation multiple or different standard deviation multiples.

[0149] For example, the first satellite elevation angle is input into the elevation angle signal-to-noise ratio relationship function in the satellite signal-to-noise ratio reconstruction model, and the third fitted signal-to-noise ratio is obtained according to formula (10).

[0150] Satellite difference mean Standard deviation of satellite difference The satellite signal-to-noise ratio reconstruction model can be obtained through the following steps:

[0151] Calculate the signal-to-noise ratio (SNR) difference sequence. Discretize the fitting result of the elevation angle SNR relationship function. You can take the median value of each degree of satellite elevation angle as the independent variable and input it into the elevation angle SNR relationship function to obtain the fitted SNR sequence between 10 degrees and 80 degrees of elevation angle.

[0152]

[0153] The average signal-to-noise ratio (SNR) of all second observations for a single satellite within each second elevation angle is taken as the SNR observation value for that degree.

[0154]

[0155] in, This represents the SNR of satellites in group G with prn=i within the elevation angle interval (k, k+1). j Let J be the signal-to-noise ratio of the j-th second observation within each degree of elevation angle.

[0156] According to formula (12), the average signal-to-noise ratio sequence of the second observation from 10 degrees to 80 degrees for each group of satellites is obtained.

[0157]

[0158] The second observation signal-to-noise ratio mean sequence Subtract the fitted signal-to-noise ratio sequence The signal-to-noise ratio (SNR) difference sequence is obtained, and the mean SNR difference is calculated from the SNR difference sequence. Standard deviation of satellite difference

[0159]

[0160]

[0161] in, With the same dimension And each element is

[0162] The embodiments of this application predict satellite observation data collected under high obstruction conditions based on the mean and standard deviation of satellite differences for each satellite, and reconstruct the signal-to-noise ratio range of each satellite. This can provide a reference standard for evaluating the data of each satellite under high obstruction conditions and help improve the quality of satellite observation data.

[0163] In step S103 of some embodiments, it can be determined whether the first observation signal-to-noise ratio is within the reconstructed signal-to-noise ratio range, evaluate the quality of satellite observation data acquired in real-time dynamic positioning, and allocate satellite weights according to the degree of influence of diffraction error on the quality of satellite observation data.

[0164] For example, the first observed signal-to-noise ratio can be divided into three cases based on the reconstructed signal-to-noise ratio range: the first observed signal-to-noise ratio is less than the lower limit of the reconstructed signal-to-noise ratio range, the first observed signal-to-noise ratio is greater than the upper limit of the reconstructed signal-to-noise ratio range, and the first observed signal-to-noise ratio is within the reconstructed signal-to-noise ratio range.

[0165] Since diffraction errors in high-obstruction environments can reach the centimeter or even decimeter level, when the signal-to-noise ratio of the first observation is less than the lower limit of the reconstructed signal-to-noise ratio range, it is considered that the satellite observation data is affected by the high-obstruction environment and has a large diffraction error, resulting in poor data quality. In the solution process, this data is removed to reduce the impact of diffraction error on real-time dynamic positioning.

[0166] When the signal-to-noise ratio of the first observation is greater than the upper limit of the signal-to-noise ratio, it is considered that the satellite observation data has no diffraction error or the diffraction error is small, and the weighting can be performed according to the variance determined by the high-angle stochastic model.

[0167] When the first observation signal-to-noise ratio is within the reconstructed signal-to-noise ratio range, the satellite observation data is affected by the high-obstruction environment and has a certain diffraction error. However, the diffraction error is not large. Instead of directly eliminating it, a control term can be added to the variance determined by the high-angle random model to control the satellite weight allocation. This control term is used to represent the degree of influence of diffraction error on satellite observation data. It increases as the first observation signal-to-noise ratio decreases and the standard deviation of satellite difference increases, thereby controlling the reduction of satellite weight.

[0168] The weighting rules are as follows:

[0169]

[0170] Where SNR is the signal-to-noise ratio of the first observation, w is the satellite weight, and SNR is the signal-to-noise ratio of the second observation. max (EL) represents the upper limit of the reconstructed signal-to-noise ratio range, and SNR min (EL) represents the lower limit of the reconstructed signal-to-noise ratio range, and EL is the first satellite elevation angle;

[0171]

[0172]

[0173] Let be the variance determined by the stochastic model of elevation angle, and let a and b be the model parameters. This is the variance determined by adding the standard deviation of satellite differences to the variance determined by the stochastic elevation angle model.

[0174] This application embodiment evaluates the quality of satellite observation data by reconstructing the signal-to-noise ratio range, allocates weights according to the degree of influence of high obstruction on the data, removes data with large diffraction errors, and adds a diffraction error control term to adjust the weights when the diffraction error is not large, thereby controlling the degree to which the data participates in the solution, effectively improving the accuracy and reliability of the data, reducing the impact of diffraction errors on real-time dynamic positioning, and improving GNSS positioning accuracy.

[0175] The following is a detailed description and explanation of the solutions in the embodiments of the present invention, using specific application examples:

[0176] See Figure 3 This application embodiment uses a GNSS positioning system consisting of a monitoring station and a rover station. The base station is deployed on the roof of a building, and the rover station is deployed on the roof of another building 103 meters away. The rover station is significantly obstructed by surrounding buildings and trees. Both test stations are equipped with multi-frequency, multi-mode GNSS receivers and antennas. The BeiDou Navigation Satellite System is further divided into the BeiDou-2 and BeiDou-3 satellite navigation systems based on satellite launch times, both of which include BDS-IGSO and BDS-MEO. The receivers are configured to receive observation data from GPS, BDS-2, BDS-3, and GAL satellite systems. The sampling frequency is set to 5 seconds, with storage and processing performed every 4 hours. LoRa wireless network transmission is used, and GNSS data management software is responsible for receiving and storing the data.

[0177] According to steps S301 to S303, in an unobstructed open environment, the pseudorange single-point positioning algorithm is used to solve the data, outputting the signal-to-noise ratio (SNR) time series and elevation angle time series for each satellite. Each time point has two values: elevation angle and SNR. Then, using the elevation angle as the x-axis and the SNR as the y-axis, the elevation angle SNR sequence for each satellite is obtained; see reference. Figures 4(a) to 4(d) According to steps S501 to S506, since the fitting trends of the elevation angle signal-to-noise ratio of satellites of the same system or type are similar, the satellites in the GNSS system are grouped according to system or type. The weighted least squares method is used to fit the elevation angle signal-to-noise ratio relationship of each system using an exponential function. The least squares fitting is performed according to the elevation angle signal-to-noise ratio sequence of each group to obtain the fitting function of each group. According to steps S202 to S205, since the signal-to-noise ratio of a single satellite is different in each elevation angle, in order to facilitate calculation, the average value of the satellite signal-to-noise ratio sequence is obtained by taking the average value in each elevation angle using equation (14). In equation (13), the fitting function is also calculated according to the midpoint of each elevation angle to obtain the corresponding fitting signal-to-noise ratio sequence. The difference between the signal-to-noise ratio sequence of each satellite and the fitting signal-to-noise ratio sequence is statistically analyzed to obtain the mean and variance of each satellite based on the fitting function. The difference between each satellite and the fitting function is calculated, and the mean and standard deviation of the difference sequence are used as the basis for restoring the signal-to-noise ratio of the satellite in the open environment during the solution. See Figure 2 According to step S102, when solving the problem in an obstructed environment, if a satellite is encountered, the positioning algorithm has already calculated the elevation angle of this satellite. Since the elevation angle is known, the signal-to-noise ratio (SNR) of this satellite under the assumption of no obstruction in the current environment can be reconstructed using the fitting function, mean, and standard deviation. During the solution process, based on the fitting function, each satellite uses the stored mean and variance to reconstruct its fitted SNR value in real time. The fitted SNR value of the satellite in an open environment is reconstructed by adding the mean to the fitting function and adding or subtracting three times the standard deviation. According to step S103, the observed SNR values ​​of the satellite are compared with the fitted SNR values, and weights are assigned to the satellite observation data according to the weighting strategy.

[0178] See Figures 5(a) to 5(c)Three sets of satellite observation data under high obstruction environments were collected and input into elevation angle model, signal-to-noise ratio model, and signal-to-noise ratio reconstruction model for calculation. Figure 5(a) shows the coordinate time series obtained by using the elevation angle model. Due to the amplification of diffraction errors during the calculation process, the calculated coordinates may show large jumps or be discarded due to unreliability, resulting in discontinuity in the coordinate time series at certain epochs. Figure 5(b) shows the coordinate time series obtained by using the signal-to-noise ratio model, which also shows large jumps or discontinuities at certain epochs. Figure 5(c) shows the coordinate time series obtained by using the signal-to-noise ratio reconstruction model provided in this application. This coordinate time series is continuous and relatively stable at certain epochs. Ambiguity fixation rate is one of the indicators describing the positioning accuracy of a satellite navigation system. It is reflected in the figure as the continuity of the time coordinate series. If the series continuity is good, it indicates that the ambiguity fixation rate is higher and the positioning accuracy is better. Through analysis... Figures 5(a) to 5(c) The continuity of the coordinate time series shows that the ambiguity fixation rate using the elevation angle model is 62.9%, the ambiguity fixation rate using the signal-to-noise ratio model is 57.3%, and the ambiguity fixation rate using the signal-to-noise ratio reconstruction model is 97.1%. The ambiguity fixation rate determined by the signal-to-noise ratio reconstruction model can achieve millimeter-level accuracy positioning, meeting the application requirements of high-precision positioning and deformation monitoring.

[0179] See Figure 6 This application also provides a satellite navigation and positioning signal-to-noise ratio (SNR) weighting system, which can implement the above-mentioned satellite navigation and positioning SNR weighting method. The system includes:

[0180] The first module is used to collect the current first satellite elevation angle and first observation signal-to-noise ratio in the satellite system under obstruction environment;

[0181] The second module is used to input the first satellite elevation angle into the satellite signal-to-noise ratio reconstruction model to obtain the reconstructed signal-to-noise ratio range of satellite observation data in open environments;

[0182] The third module is used to determine the satellite weights based on the first observation signal-to-noise ratio and the reconstructed signal-to-noise ratio interval, and to perform positioning calculations based on the satellite weights to obtain positioning information.

[0183] The fourth module is used to obtain the second observation signal-to-noise ratio sequence corresponding to different second satellite elevation angles in open environment and the satellite elevation angle signal-to-noise ratio relationship function in open environment;

[0184] The fifth module is used to input different second satellite elevation angles into the elevation angle signal-to-noise ratio relationship function to obtain the first fitted signal-to-noise ratio sequence;

[0185] The sixth module is used to determine the signal-to-noise ratio difference sequence based on the second observed signal-to-noise ratio sequence and the first fitted signal-to-noise ratio sequence;

[0186] The seventh module is used to determine the mean and standard deviation of satellite differences based on the signal-to-noise ratio difference sequence.

[0187] The eighth module is used to determine the satellite signal-to-noise ratio reconstruction model based on the elevation angle signal-to-noise ratio relationship function, the mean of satellite differences, and the standard deviation of satellite differences.

[0188] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0189] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described satellite navigation positioning signal-to-noise ratio weighting method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0190] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0191] See Figure 7 , Figure 7 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0192] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0193] The memory 902 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 using the satellite navigation and positioning signal-to-noise ratio weighting method of the embodiments of this application.

[0194] The input / output interface 903 is used to implement information input and output;

[0195] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0196] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0197] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0198] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described satellite navigation and positioning signal-to-noise ratio weighting method.

[0199] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0200] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0201] The satellite navigation and positioning signal-to-noise ratio (SNR) weighting method, system, electronic device, and storage medium provided in this application embodiment construct a satellite SNR reconstruction model in an open environment. By inputting the satellite elevation angle into the corresponding SNR reconstruction model, the reconstructed SNR interval of satellite observation data in the open environment is obtained. Satellite weights are determined based on the observed SNR and the reconstructed SNR interval, and positioning information is obtained through positioning calculation based on these weights. This allows for SNR analysis of satellite observation values ​​in obstructed environments, thereby highlighting the importance of satellite observation data with low diffraction errors during positioning calculations. This improves the quality of satellite observation data, suppresses the influence of diffraction errors, and enhances the accuracy of the calculation results, ultimately improving the precision and reliability of GNSS positioning and achieving dynamic millimeter-level positioning.

[0202] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0203] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0204] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0205] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0206] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0207] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A signal-to-noise ratio weighting method for satellite navigation and positioning, characterized in that, The method includes the following steps: The system collects the current first satellite elevation angle and first observation signal-to-noise ratio in the obstructed environment satellite system; Input the first satellite elevation angle into the satellite signal-to-noise ratio reconstruction model to obtain the reconstructed signal-to-noise ratio range of satellite observation data in open environment; Satellite weights are determined based on the first observed signal-to-noise ratio and the reconstructed signal-to-noise ratio interval, and positioning information is obtained by performing positioning calculations based on the satellite weights. The satellite signal-to-noise ratio reconstruction model is obtained through the following steps: Obtain the second observation signal-to-noise ratio sequence corresponding to different second satellite elevation angles in open environment and the satellite elevation angle signal-to-noise ratio relationship function in open environment; By inputting different elevation angles of the second satellite into the elevation angle signal-to-noise ratio relationship function, a first fitted signal-to-noise ratio sequence is obtained; The signal-to-noise ratio difference sequence is determined based on the second observed signal-to-noise ratio sequence and the first fitted signal-to-noise ratio sequence; The mean and standard deviation of satellite differences are determined based on the signal-to-noise ratio difference sequence. The satellite signal-to-noise ratio reconstruction model is determined based on the elevation angle signal-to-noise ratio relationship function, the mean of the satellite differences, and the standard deviation of the satellite differences.

2. The method according to claim 1, characterized in that, The second observation signal-to-noise ratio sequence corresponding to different second satellite elevation angles is obtained through the following steps: Obtain current observation files and broadcast ephemeris files from open environment satellite systems; Data processing is performed on the observation file and the broadcast ephemeris file to obtain the second satellite elevation angle and the second observation signal-to-noise ratio; The second observation signal-to-noise ratio sequence is generated by taking the second observation signal-to-noise ratio corresponding to different second satellite elevation angles in order of the magnitude of the second satellite elevation angle.

3. The method according to claim 1, characterized in that, The satellite's elevation angle signal-to-noise ratio function in open environments is obtained through the following steps: The elevation angle and the second observation signal-to-noise ratio sequence are fitted with a function to obtain the elevation angle signal-to-noise ratio relationship function.

4. The method according to claim 3, characterized in that, The step of fitting a function to the second satellite elevation angle and the second observation signal-to-noise ratio sequence to obtain the elevation angle signal-to-noise ratio relationship function includes the following steps: The first parameter is determined by fitting a function to the second satellite elevation angle and the second observation signal-to-noise ratio sequence using the least squares method. The first fitting function is obtained based on the first parameters; By inputting different elevation angles of the second satellite into the first fitting function, a second fitted signal-to-noise ratio sequence is obtained; The signal-to-noise ratio weights of the second observed signal-to-noise ratio are determined based on the second fitted signal-to-noise ratio sequence; Based on the signal-to-noise ratio weights, the weighted least squares method is used to fit a function to the second satellite elevation angle and the second observation signal-to-noise ratio sequence to determine the second parameter. The elevation angle signal-to-noise ratio relationship function is obtained based on the second parameter.

5. The method according to claim 4, characterized in that, The step of determining the signal-to-noise ratio weight of the second observed signal-to-noise ratio based on the second fitted signal-to-noise ratio sequence includes the following steps: Determine the number of multiple second observation signal-to-noise ratios corresponding to preset elevation angle intervals in the second observation signal-to-noise ratio sequence; Based on a preset signal-to-noise ratio (SNR) weighting rule, the SNR weight of the second observed SNR is determined according to the quantity, the second observed SNR, and the second fitted SNR sequence. The SNR weighting rule is as follows: Where k is the value of the satellite elevation angle, N represents the signal-to-noise ratio weight for the i-th second observation within the satellite elevation angle range [k, k+1]. k,k+1 SNR is the number of second observation signal-to-noise ratios within the satellite elevation angle range [k, k+1]. i Let be the signal-to-noise ratio of the i-th second observation within the satellite elevation angle range [k, k+1]. Let be the second fitted signal-to-noise ratio at the satellite elevation angle [k, k+1].

6. The method according to claim 1, characterized in that, The expression for the reconstructed signal-to-noise ratio interval is: in, and These represent the upper and lower limits of the reconstructed signal-to-noise ratio range, respectively. The signal-to-noise ratio (SNR) value is obtained by fitting the elevation angle of the first satellite to the elevation angle SNR function. The mean of satellite differences. This represents the standard deviation of the satellite difference.

7. The method according to claim 1, characterized in that, The expression for the satellite weight is: Where SNR is the signal-to-noise ratio of the first observation, w is the satellite weight, and SNR is the signal-to-noise ratio of the second observation. max (EL) represents the upper limit of the reconstructed signal-to-noise ratio range, and SNR min (EL) represents the lower limit of the reconstructed signal-to-noise ratio range, and EL is the elevation angle of the first satellite. The variance determined for the stochastic model of elevation angle. The variance is determined by adding the standard deviation of satellite differences to the variance determined by the stochastic model of elevation angle.

8. A satellite navigation and positioning signal-to-noise ratio weighting system, characterized in that, The system includes: The first module is used to collect the current first satellite elevation angle and first observation signal-to-noise ratio in the satellite system under obstruction environment; The second module is used to input the first satellite elevation angle into the satellite signal-to-noise ratio reconstruction model to obtain the reconstructed signal-to-noise ratio range of satellite observation data in open environment; The third module is used to determine satellite weights based on the first observed signal-to-noise ratio and the reconstructed signal-to-noise ratio interval, and to perform positioning calculations based on the satellite weights to obtain positioning information. The fourth module is used to obtain the second observation signal-to-noise ratio sequence corresponding to different second satellite elevation angles in open environment and the satellite elevation angle signal-to-noise ratio relationship function in open environment; The fifth module is used to input different elevation angles of the second satellite into the elevation angle signal-to-noise ratio relationship function to obtain a first fitted signal-to-noise ratio sequence; The sixth module is used to determine the signal-to-noise ratio difference sequence based on the second observed signal-to-noise ratio sequence and the first fitted signal-to-noise ratio sequence; The seventh module is used to determine the mean of satellite differences and the standard deviation of satellite differences based on the signal-to-noise ratio difference sequence; The eighth module is used to determine the satellite signal-to-noise ratio reconstruction model based on the elevation angle signal-to-noise ratio relationship function, the mean of the satellite difference, and the standard deviation of the satellite difference.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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