GNSS positioning method, system, terminal and medium based on signal-to-noise ratio arc segment analysis

Abstract

The present invention discloses a GNSS positioning method, system, terminal and medium based on signal-to-noise ratio arc segment analysis, and relates to the field of satellite navigation positioning. The method comprises: dividing the third observation data at equal intervals according to the lengths of the start and end epochs of the third observation data to obtain a plurality of first observation segments, and dividing the fourth observation data at equal intervals according to the lengths of the start and end epochs of the fourth observation data to obtain a plurality of second observation segments; respectively calculating the reference amount of each first observation segment and each second observation segment, respectively eliminating the observation segments that do not meet the reference amount threshold in the first observation segment and the second observation segment, and obtaining the third observation segment and the fourth observation segment; aligning the epochs of the third observation segment and the fourth observation segment to obtain the common arc segment of the satellite, and using the least square method to estimate the observation data of the common arc segment to obtain positioning parameters; performing integer ambiguity search and fixation processing on the positioning parameters to obtain the GNSS positioning result.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation and positioning, and more specifically, to a GNSS positioning method, system, terminal and medium based on signal-to-noise ratio arc segment analysis. Background Art

[0002] The Global Navigation Satellite System (GNSS) is a navigation technology based on satellite signals. It provides users with global position, speed and time information through a series of satellites orbiting the Earth. The positioning accuracy of the GNSS can reach millimeter level, which is the basis for urban navigation positioning and deformation monitoring applications.

[0003] Satellite signals from the Beidou satellite navigation system, GPS system, etc. are essentially electromagnetic wave signals. In a signal-blocking environment, satellite signals are prone to signal reflection, diffraction and other effects, causing ranging errors such as multipath and diffraction, affecting positioning accuracy and reliability. Summary of the invention

[0004] The purpose of the present invention is to provide a GNSS positioning method, system, terminal and medium based on signal-to-noise ratio arc segment analysis, which solves the problem of insufficient GNSS positioning accuracy and reliability due to ranging errors caused by environmental factors blocking satellite signals.

[0005] A first aspect of the present invention provides a GNSS positioning method based on signal-to-noise ratio arc segment analysis, the method comprising:

[0006] Acquire first observation data of the reference station and second observation data of the monitoring station;

[0007] Process the first observation data and the second observation data respectively to obtain third observation data and fourth observation data;

[0008] Dividing the third observation data at equal intervals according to the lengths of the start and end epochs of the third observation data to obtain a plurality of first observation segments, and dividing the fourth observation data at equal intervals according to the lengths of the start and end epochs of the fourth observation data to obtain a plurality of second observation segments;

[0009] Calculate the reference quantity of each first observation segment and each second observation segment respectively, and eliminate the observation segments that do not meet the reference quantity threshold in the plurality of first observation segments and the plurality of second observation segments respectively, to obtain the third observation segment and the fourth observation segment;

[0010] The epochs of the third observation segment and the fourth observation segment are aligned to obtain the common arc segment of the satellites, and the observation data of the common arc segment are estimated by the least square method to obtain the positioning parameters;

[0011] The integer ambiguity of the positioning parameters is searched and fixed to obtain the GNSS positioning result.

[0012] In one implementation, the first observation data and the second observation data are processed respectively to obtain the third observation data and the fourth observation data, specifically:

[0013] Get the ephemeris data sent by the satellite;

[0014] Calculate a first altitude angle of each epoch of the satellite according to the first observation data and the ephemeris data, and calculate a second altitude angle of each epoch of the satellite according to the second observation data and the ephemeris data;

[0015] The observation values ​​of each epoch whose first altitude angle is lower than the cut-off altitude angle are removed from the first observation data to obtain the third observation data, and the observation values ​​of each epoch whose second altitude angle is lower than the cut-off altitude angle are removed from the second observation data to obtain the fourth observation data.

[0016] In one implementation, the first observation data and ephemeris data are estimated using a pseudo-range single point positioning algorithm and a least square method to calculate the first altitude angle of each epoch of the satellite;

[0017] The second observation data and ephemeris data are estimated using the pseudo-range single-point positioning algorithm and the least squares method to calculate the second altitude angle of each epoch of the satellite.

[0018] In one implementation, the reference quantity includes epoch integrity, signal-to-noise ratio mean, and signal-to-noise ratio standard deviation;

[0019] Observation segments that do not meet the reference quantity threshold are eliminated from the multiple first observation segments and the multiple second observation segments respectively to obtain the third observation segment and the fourth observation segment. Specifically, according to the pre-configured epoch completeness threshold, signal-to-noise ratio mean threshold and signal-to-noise ratio standard deviation threshold, observation segments that do not meet any of the three types of completeness threshold, signal-to-noise ratio mean threshold and standard deviation threshold are eliminated from the multiple first observation segments and the multiple second observation segments to obtain the third observation segment and the fourth observation segment.

[0020] In one implementation, the least square method is used to estimate the observation data of the common arc segment to obtain the positioning parameters, which are specifically:

[0021] Double difference operation is performed on the carrier phase observation values ​​and pseudorange observation values ​​of the reference station and the monitoring station in the shared arc segment to calculate the double difference observation values ​​of each epoch;

[0022] The double difference observations are superimposed in the normal equation to construct the superimposed normal equation, and the superimposed normal equation is solved by the least square method to obtain the positioning parameters; wherein the positioning parameters include the floating point ambiguity parameters and the variance information of the floating point ambiguity parameters.

[0023] In one implementation, the expression for calculating the double difference observation value of each epoch is:

[0024] ;in, is the double difference operator, is the wavelength of the carrier phase observation value of the fth frequency, p is the base station, q For monitoring stations, i and j All represent satellite numbers. is the double-difference pseudorange observation value between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency, is the double difference satellite-to-ground distance between the i-th satellite and the j-th satellite and between the reference station p and the monitoring station q, is the double-difference pseudorange residual between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency, is the double-difference carrier phase residual between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency, is the double-difference carrier phase ambiguity parameter between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency, is the double difference carrier phase observation value between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency.

[0025] In one implementation, integer ambiguity search and fixation processing is performed on positioning parameters to obtain a GNSS positioning result, including:

[0026] The variance information of the floating-point ambiguity parameters is sorted from small to large, and the floating-point ambiguity parameters and the variance information are substituted into the integer criterion of ambiguity fixation for calculation. If the calculation result satisfies the integer criterion, the ambiguity parameters are fixed;

[0027] The fixed ambiguity parameters are substituted into the normal equation after superposition, and the GNSS positioning result is solved using the least squares method.

[0028] A second aspect of the present invention provides a GNSS positioning system based on signal-to-noise ratio arc segment analysis, the system comprising:

[0029] A data acquisition module, used to acquire first observation data of the reference station and second observation data of the monitoring station;

[0030] A data processing module, used to process the first observation data and the second observation data respectively to obtain third observation data and fourth observation data;

[0031] A segmentation module is used to divide the third observation data into equal intervals according to the lengths of the start and end epochs of the third observation data to obtain a plurality of first observation segments, and to divide the fourth observation data into equal intervals according to the lengths of the start and end epochs of the fourth observation data to obtain a plurality of second observation segments;

[0032] A segment screening module is used to calculate the reference quantity of each first observation segment and each second observation segment respectively, and to remove the observation segments that do not meet the reference quantity threshold value from the first observation segments and the second observation segments respectively, so as to obtain the third observation segment and the fourth observation segment;

[0033] A positioning parameter estimation module is used to align the epochs of the third observation segment and the fourth observation segment to obtain the common arc segment of the satellite, and use the least square method to estimate the observation data of the common arc segment to obtain the positioning parameters;

[0034] The positioning parameter processing module is used to search and fix the integer ambiguity of the positioning parameters to obtain the GNSS positioning result.

[0035] According to a third aspect of the present invention, a computer terminal is provided, comprising a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of a GNSS positioning method based on signal-to-noise ratio arc segment analysis as provided in the first aspect of the present invention are implemented.

[0036] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a GNSS positioning method based on signal-to-noise ratio arc segment analysis as provided in the first aspect of the present invention is implemented.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] According to the strong correlation between the observation value and the signal-to-noise ratio of the satellite navigation system, the present invention proposes a data processing strategy for performing arc segment preprocessing on the signal-to-noise ratio and the observation value of each satellite received by the base station and the monitoring station, realizes the selection of the optimal observation segment, and then constructs a least square parameter estimation method based on the superposition of normal equations to obtain the positioning parameters. Finally, the positioning parameters are searched and fixed for the integer ambiguity, so as to realize the GNSS precise positioning in an obstructed environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0040] Figure 1A schematic diagram of a flow chart of a GNSS positioning method based on signal-to-noise ratio arc segment analysis provided by an embodiment of the present invention;

[0041] Figure 2 A flowchart of double-difference relative positioning and ambiguity fixation provided by an embodiment of the present invention;

[0042] Figure 3 A principle block diagram of a GNSS positioning system based on signal-to-noise ratio arc segment analysis provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0044] It should be noted that the terms "include" or "may include" used in various embodiments of the present application indicate the presence of the function, operation or element applied for, and do not limit the addition of one or more functions, operations or elements. In addition, as used in various embodiments of the present application, the terms "include", "have" and their cognates are only intended to indicate specific features, numbers, steps, operations, elements, components or a combination of the foregoing items, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components or a combination of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or a combination of the foregoing items.

[0045] It should be understood that, in addition, terms such as "first" and "second" are only used for descriptive purposes and should not be understood to indicate or imply relative importance or implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0046] Satellite signals from the Beidou satellite navigation system, GPS system, etc. are essentially electromagnetic wave signals. In a signal-blocking environment, satellite signals are prone to signal reflection, diffraction and other effects, causing ranging errors such as multipath and diffraction, affecting positioning accuracy and reliability.

[0047] However, in urban navigation positioning and deformation monitoring applications, satellite signal obstruction environments are common, which can easily cause ranging errors, thus reducing the accuracy and reliability of positioning.

[0048] In order to solve the defects of the related art, the embodiment of the present invention proposes a GNSS positioning method based on signal-to-noise ratio arc segment analysis. According to the strong correlation between the observation value and the signal-to-noise ratio of the satellite navigation system, the present invention proposes a data processing strategy for arc segment preprocessing of the signal-to-noise ratio and the observation value of each satellite received by the base station and the monitoring station, realizes the selection of the optimal observation segment, and then constructs a least squares parameter estimation method based on the superposition of normal equations to obtain the positioning parameters. Finally, the positioning parameters are searched and fixed for the integer ambiguity to realize GNSS precise positioning in an occluded environment. At the same time, the GNSS positioning method proposed in the embodiment of the present invention is applicable to non-difference non-combined precise single-point positioning solution model, double-difference relative positioning solution model, etc.

[0049] Please refer to Figure 1 , Figure 1 A flow chart of a GNSS positioning method based on signal-to-noise ratio arc segment analysis provided by an embodiment of the present invention is as follows: Figure 1 As shown, the method includes:

[0050] S101, obtaining first observation data of a reference station and second observation data of a monitoring station.

[0051] In this embodiment, both the base station and the monitoring station are equipped with GNSS receivers to receive satellite signals. This is common knowledge and no unnecessary explanation is given in this embodiment. After the GNSS receiver receives the observation data, it is stored in the observation file value file. The observation file is a file used to store the observation data of the Global Navigation Satellite System (GNSS). These data are usually received and recorded by the receiver. For each satellite, its pseudorange observation value and phase observation value and other information are recorded. In addition, some GNSS receivers also output additional information such as Doppler and signal-to-noise ratio.

[0052] S102, processing the first observation data and the second observation data respectively to obtain third observation data and fourth observation data.

[0053] In this embodiment, the processing is as follows:

[0054] S1021, obtaining ephemeris data sent by the satellite.

[0055] In this embodiment, the ephemeris data is determined and provided by the ground control part of the global positioning system (GNSS). The ephemeris data is a set of parameters used to describe the orbital motion of the satellite, such as time parameters, Kepler orbit parameters and orbital perturbation parameters, etc. These parameters can be used to calculate the speed of the satellite at any position and its motion trajectory. For example, in a satellite navigation system, a GNSS receiver can calculate the precise position of the satellite by receiving the ephemeris data, thereby achieving high-precision positioning and navigation services.

[0056] S1022, calculating a first altitude angle of each epoch of the satellite according to the first observation data and the ephemeris data, and calculating a second altitude angle of each epoch of the satellite according to the second observation data and the ephemeris data.

[0057] In this embodiment, for the first observation data and ephemeris data received by the reference station, and the first observation data and ephemeris data received by the monitoring station, this embodiment uses a pseudo-range single-point positioning algorithm and a least squares method to process them in sequence, and the first altitude angle and the second altitude angle of each epoch of the satellite can be calculated. It can be understood that the satellite altitude angle refers to the angle of the satellite sensor relative to the ground. Therefore, the first altitude angle refers to the angle of the satellite relative to the ground reference station, and the second altitude angle refers to the angle of the satellite relative to the ground monitoring station. It should be noted that, generally speaking, the observation data received by the reference station does not need to be processed, but the method of this embodiment is applied to the application fields of urban precise positioning and deformation monitoring. Therefore, the observation data received by the reference station will also be affected by the occlusion of the city floors, resulting in the unavailability of some time series observation values.

[0058] It should be noted that the pseudo-range single-point positioning algorithm mainly uses the principle of resection of spatial distance. A GNSS receiver simultaneously receives the position coordinates of more than four satellites and the distance (pseudo-range) between the satellite and the GNSS receiver, and then uses the principle of resection to calculate the three-dimensional coordinates of the receiver. If the number of satellites observed by the GNSS receiver is more than four, the least squares method is used for adjustment calculation to solve for more accurate GNSS receiver coordinates, or the position coordinates of the reference station. Generally speaking, the reference station and the monitoring station will observe more than four satellites. Therefore, this embodiment uses the pseudo-range single-point positioning algorithm and the least squares method to process the observation data and ephemeris data in turn.

[0059] Specifically, this embodiment is illustrated by taking the second observation data and ephemeris data received by the monitoring station as an example. Since the reference station also has the same processing principle, a repeated description is not made here. The process of estimating the second observation data and ephemeris data using the pseudo-range single point positioning algorithm and the least squares method to calculate the second elevation angle of each epoch of the satellite is as follows:

[0060] The pseudo-range single point positioning algorithm is used to construct a pseudo-range single point positioning model. Specifically, is the satellite coordinate, s is the satellite number, is the monitoring station coordinate, then the expression of the pseudo-range single point positioning model is:

[0061] (1), where S is the distance from the satellite to the monitoring station, is the atmospheric delay including tropospheric and ionospheric delays, and are the GNSS receiver and satellite clock errors of the monitoring station, is the observation noise.

[0062] The pseudo-range single point positioning model is linearized to obtain the linearized pseudo-range single point positioning model, which is expressed as follows: (2) Among them, i Indicates i satellites, is the initial coordinate of the monitoring station, is the initial value of the straight-line distance between satellite i and the measuring station, , , are the three-dimensional rectangular coordinates of the i-th satellite, where the subscript s represents the position of the satellite.

[0063] Furthermore, the expression shown in formula (2) is subjected to single difference operation to obtain the calculation model for calculating the inter-satellite single difference observation value. The specific implementation is as follows: First, the satellite with the highest satellite elevation angle in the field of view is selected as the reference satellite. j , and then the observation values ​​of other satellites are differentially processed with the observation values ​​of the reference satellite to calculate the inter-satellite single difference observation value. At the same time, the satellite clock error can be calculated using the ephemeris data.

[0064] The expression for calculating the inter-satellite single difference observation is as follows: (3); among which, , is the initial value of the straight-line distance between satellite j and the measuring station, , , are the three-dimensional rectangular coordinates of the j-th satellite, where the subscript s represents the position of the satellite.

[0065] The computational model for calculating intersatellite single-difference observations is written as a matrix, namely (4), where V is the residual vector; A is the design matrix; X is the state vector, which includes the position parameters and ambiguity parameters; L is the observation value vector, is the parameter vector to be estimated, namely the pseudorange and carrier phase observation values.

[0066] in, (5), , the superscripts [1,2,…,c] in the design matrix and observation vector represent the epoch number; is the position parameter to be estimated.

[0067] The least squares method is used to estimate the parameters of the matrix and solve the station coordinates and longitude and latitude of the monitoring station; specifically,

[0068] (6).

[0069] The station coordinates of the monitoring station are: (7). At the same time, the latitude, longitude and geodetic height information of the monitoring station can also be output ( Lon, Lat, H ).

[0070] Then, the satellite position of each satellite is calculated according to the ephemeris data, and then the distance between the satellite and the monitoring station is calculated by combining the station coordinates and the satellite position. Specifically, calculating the satellite position of each satellite according to the ephemeris data is a conventional technical means in the technical field, so this embodiment will not be redundantly described.

[0071] The expression for calculating the distance between the satellite and the monitoring station is: (8).

[0072] Combining the calculation results of equations (1) to (8) above, the second elevation angle of each satellite is calculated according to equation (9), and its expression is: (9).

[0073] S1023, removing the observation values ​​of each epoch whose first elevation angle is lower than the cut-off elevation angle from the first observation data to obtain third observation data, and removing the observation values ​​of each epoch whose second elevation angle is lower than the cut-off elevation angle from the second observation data to obtain fourth observation data.

[0074] In this embodiment, the cut-off altitude angle is a shielding altitude angle set in the positioning measurement to shield the influence of obstructions (such as buildings, trees, etc.) and multipath effects. Satellites below this angle in the airspace of view will not be tracked. The setting of the cut-off altitude angle should be adjusted according to the openness of the positioning measurement environment. The more open the environment is, the smaller the cut-off altitude angle can be; the worse the environment is and the more obstacles there are around, the cut-off altitude angle should be appropriately increased.

[0075] For the epochs where the first altitude angle and the second altitude angle respectively belong, the observation values ​​observed by the reference station and the monitoring station in each epoch are respectively eliminated from the first observation data and the second observation data.

[0076] S103, dividing the third observation data at equal intervals according to the lengths of the start and end epochs of the third observation data to obtain a plurality of first observation segments, and dividing the fourth observation data at equal intervals according to the lengths of the start and end epochs of the fourth observation data to obtain a plurality of second observation segments.

[0077] In this embodiment, an epoch refers to the time interval between two adjacent observation data points. In this embodiment, the number of intervals is set to n, and N first observation segments and second observation segments are obtained, that is, , a Indicates aa first observation segment or a second observation segment.

[0078] S104, respectively calculating the reference amount of each first observation segment and each second observation segment, respectively eliminating the observation segments that do not meet the reference amount threshold in the plurality of first observation segments and the plurality of second observation segments, to obtain the third observation segment and the fourth observation segment.

[0079] In this embodiment, a reference quantity is calculated for each observation segment. Specifically, the reference quantity includes epoch completeness, signal-to-noise ratio mean and signal-to-noise ratio standard deviation, that is, the epoch completeness, signal-to-noise ratio mean and signal-to-noise ratio standard deviation of each observation segment are calculated.

[0080] Specifically, the calculation formula for the completeness of the observation segment is: (10); among which, is the total number of epochs in the observation segment of the ith satellite, is the actual number of epochs in the observation segment of the ith satellite, C a Indicates the completeness of the a-th observation segment.

[0081] The calculation formula for the mean signal-to-noise ratio of the observation segment is: (11); among them, represents the signal-to-noise ratio value of the i-th epoch, Represents the mean signal-to-noise ratio of the a-th observation segment.

[0082] The calculation formula for the standard deviation of the signal-to-noise ratio of the observation segment is: (12), where Indicates the a The mean signal-to-noise ratio of the observation segment, Represents the standard deviation of the signal-to-noise ratio of the a-th observation segment.

[0083] More specifically, according to the pre-configured epoch completeness threshold, signal-to-noise ratio mean threshold and signal-to-noise ratio standard deviation threshold, observation segments that do not reach any of the completeness threshold, signal-to-noise ratio mean threshold and signal-to-noise ratio standard deviation threshold are eliminated from multiple first observation segments and multiple second observation segments to obtain third observation segments and fourth observation segments.

[0084] Specifically, according to the set epoch completeness threshold, signal-to-noise ratio mean threshold and signal-to-noise ratio standard deviation threshold, the observation value segments that do not meet the standards are eliminated; the epoch completeness threshold, signal-to-noise ratio mean threshold and signal-to-noise ratio standard deviation threshold can be formulated according to actual conditions. For example, in this embodiment, the epoch completeness threshold is set to 80%, the signal-to-noise ratio mean threshold is set to 38, and the signal-to-noise ratio standard deviation threshold is set to 3. If one of the three reference quantity indicators in each observation segment is not met, the observation segment is directly eliminated.

[0085] It can be seen that the statistical analysis strategy of epochs and signal-to-noise ratios provided in this embodiment effectively ensures the quality of observation data in the subsequent positioning solution process.

[0086] S105, aligning the epochs of the third observation segment and the fourth observation segment to obtain the common arc segment of the satellites, and using the least square method to estimate the observation data of the common arc segment to obtain the positioning parameters.

[0087] In this embodiment, the epoch alignment method is a conventional technical means and will not be described in detail. Obtaining the common arc segment for receiving satellite observation data by the reference station and the monitoring station ensures the continuity of subsequent positioning solutions, facilitates the determination of floating point ambiguity parameters, and avoids the cumbersome cycle slip detection process.

[0088] like Figure 2 As shown in , the carrier phase observations and pseudorange observations of the reference station and the monitoring station in the shared arc segment are double-differenced to calculate the double-difference observations of each epoch. Specifically, the double-difference operation is performed as follows: In each epoch field of view, the satellite with the highest satellite elevation angle and the longest observation arc segment is selected as the reference satellite. For the carrier phase observations and pseudorange observations of the reference station and the monitoring station, the equation for calculating the double-difference observations is constructed, as shown in equation (13): (13); among them, is the double difference operator, is the wavelength of the carrier phase observation value of the fth frequency, p is the base station, q For monitoring stations, i and j All represent satellite numbers. is the double-difference pseudorange observation value between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency, is the double difference satellite-to-ground distance between the i-th satellite and the j-th satellite and between the reference station p and the monitoring station q, is the double-difference pseudorange residual between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency, is the double-difference carrier phase residual between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency, is the double-difference carrier phase ambiguity parameter between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency, is the double difference carrier phase observation value between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency.

[0089] The double difference observations are superimposed in the normal equation to construct the superimposed normal equation, and the superimposed normal equation is solved by the least square method to obtain the positioning parameters; wherein the positioning parameters include the floating point ambiguity parameters and the variance information of the floating point ambiguity parameters.

[0090] Specifically, Equation (13) is written in matrix form, the least squares solution equation is constructed, and the double-difference observations of each epoch are superimposed in the normal equation, where the matrix form is (14), where is the residual vector, is the coefficient matrix of coordinate parameters, is the coefficient matrix of the fuzzy parameters, is a column vector of observed values ​​minus calculated values, is the weight matrix, is the coordinate parameter vector, is the ambiguity parameter vector. If the total number of epochs in the data is n , the number of fuzzy parameters is m ,but

[0091] for , for dimensional matrix, for dimensional matrix, for Column vector of .

[0092] Among them, there are three coordinate component parameters and their coefficients for the pseudorange and carrier phase observations of each epoch; if there is no ambiguity parameter in the pseudorange observation, the position element of the corresponding matrix is ​​0; if there is an ambiguity parameter in the carrier phase observation, the corresponding position element of the ambiguity parameter is the wavelength , Indicates f The wavelength of a frequency.

[0093] After superimposing the double-difference observations of each satellite at each epoch according to formula (14), the superimposed normal equation is constructed, as shown in formula (15): (15), where is the coefficient matrix of coordinate parameters, is the coefficient matrix of the fuzzy parameters, is a column vector of observed values ​​minus calculated values, is the weight matrix, T represents the transpose of the matrix, is the coordinate parameter vector, is the fuzziness parameter vector.

[0094] Formula (15) can be further expressed as: (16), where Nxx Indicates A T PT, N xb Indicates A T PB, N bb Indicates B T PB, w x Indicates A T PI, w b Indicates B T PI. It should be noted that formula (16) is an abbreviation of the matrix, so the characters in this part have no actual meaning and are equivalent to the corresponding content in formula (15).

[0095] The least squares solution (16) is used to output the floating point ambiguity parameters and their variance information, namely:

[0096] (17). Among them, Indicates that the vector contains the floating-point solutions of each ambiguity parameter, represents the location parameters of the monitoring station, is the variance matrix of the location parameters, The diagonal elements of are the variances of the ambiguity parameters, is the covariance matrix of the location parameters and the ambiguity parameters.

[0097] This embodiment adopts the least squares parameter estimation strategy of superposition of normal equations, which is beneficial to the control of parameter quantity during program design and the process operation of data processing, avoiding the occurrence of problems such as memory overflow, thereby improving the processing efficiency of the terminal device output positioning parameters, where the terminal device can be a server, a computer, etc.

[0098] S106, performing integer ambiguity search and fixation processing on the positioning parameters to obtain a GNSS positioning result.

[0099] In this embodiment, the variance information of the floating-point ambiguity parameters is sorted from small to large, and the floating-point ambiguity parameters and the variance information are substituted into the integer criterion of ambiguity fixation for calculation. If the calculation result meets the integer criterion, the ambiguity parameters are fixed; the fixed ambiguity parameters are substituted into the normal equation after superposition, and the GNSS positioning result is solved using the least squares method.

[0100] Specifically, if Figure 2 As shown, the standard deviation information of the floating-point ambiguity parameters obtained in step S105 is sorted from small to large, and the floating-point ambiguity parameters and their standard deviations are brought into the following integer criterion for fixing ambiguity. If the criterion is met, the ambiguity is fixed.

[0101] The expression of the integer criterion for fixed ambiguity is: (18), where k is the candidate ambiguity value, is the confidence level, is the mean error of the floating point ambiguity, is the nearest integer, erf is the complementary error function, is the probability closest to an integer, and n is the integer closest to the floating point ambiguity. The confidence level is generally set to 0.1 (empirical value), that is, when the criterion exceeds 99.9%, the ambiguity is considered to be reliably fixed.

[0102] Substitute the fixed ambiguity value into the normal equation of equation (15) and set the weight of the weight matrix to 10 9 ;

[0103] The least squares solution is performed on equation (15) again. If there are still ambiguities that cannot be fixed, the search and fixation steps described above are repeated until all ambiguities can be fixed. Finally, the GNSS precise positioning result after the ambiguities are fixed is output.

[0104] Please refer to Figure 3 , Figure 3 A principle block diagram of a GNSS positioning system based on signal-to-noise ratio arc segment analysis provided by an embodiment of the present invention, such as Figure 3 As shown, the system includes:

[0105] A data acquisition module 310 is used to acquire first observation data of a reference station and second observation data of a monitoring station;

[0106] A data processing module 320, used to process the first observation data and the second observation data respectively to obtain third observation data and fourth observation data;

[0107] The segmentation module 330 is used to divide the third observation data into equal intervals according to the lengths of the start and end epochs of the third observation data to obtain a plurality of first observation segments, and to divide the fourth observation data into equal intervals according to the lengths of the start and end epochs of the fourth observation data to obtain a plurality of second observation segments;

[0108] The segment screening module 340 is used to calculate the reference amount of each first observation segment and each second observation segment respectively, and to remove the observation segments that do not meet the reference amount threshold from the first observation segments and the second observation segments respectively, so as to obtain the third observation segment and the fourth observation segment;

[0109] The positioning parameter estimation module 350 is used to align the epochs of the third observation segment and the fourth observation segment to obtain the common arc segment of the satellites, and estimate the observation data of the common arc segment using the least square method to obtain the positioning parameters;

[0110] The positioning parameter processing module 360 ​​is used to perform integer ambiguity search and fixation processing on the positioning parameters to obtain the GNSS positioning result.

[0111] Accordingly, the GNSS positioning system based on signal-to-noise ratio arc segment analysis provided in this embodiment proposes a data processing strategy for performing arc segment preprocessing on the signal-to-noise ratios and observation values ​​of each satellite received by the base station and the monitoring station, based on the fact that the observation values ​​of the satellite navigation system have a strong correlation with the signal-to-noise ratio. This achieves the selection of the optimal observation segment, and then constructs a least squares parameter estimation method based on the superposition of normal equations to obtain positioning parameters. Finally, the positioning parameters are searched and fixed for integer ambiguity to achieve GNSS precise positioning in an obstructed environment.

[0112] The embodiment of the present invention also provides a computer terminal, which includes a processor, a memory, a communication interface and at least one communication bus for connecting the processor, the memory and the communication interface. The memory includes but is not limited to a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (PROM) or a portable CD-ROM, and the memory is used for related instructions and data.

[0113] The communication interface is used to receive and send data. The processor may be one or more CPUs. When the processor is a CPU, the CPU may be a single-core CPU or a multi-core CPU. The processor in the computer terminal is used to read one or more programs stored in the memory and perform the following operations: obtain the first observation data of the reference station and the second observation data of the monitoring station; process the first observation data and the second observation data respectively to obtain the third observation data and the fourth observation data; divide the third observation data into equal intervals according to the length of the start and end epochs of the third observation data to obtain a plurality of first observation segments, and divide the fourth observation data into equal intervals according to the length of the start and end epochs of the fourth observation data to obtain a plurality of second observation segments; calculate the reference quantity of each first observation segment and each second observation segment respectively, and eliminate the observation segments that do not meet the reference quantity threshold in the plurality of first observation segments and the plurality of second observation segments respectively to obtain the third observation segment and the fourth observation segment; align the epochs of the third observation segment and the fourth observation segment to obtain the common arc segment of the satellite, and use the least square method to estimate the observation data of the common arc segment to obtain the positioning parameters; perform integer ambiguity search and fixation processing on the positioning parameters to obtain the GNSS positioning result.

[0114] It should be noted that the specific implementation of each operation can be Figure 1 The corresponding description of the method embodiment shown, the terminal can be used to execute a GNSS positioning method based on signal-to-noise ratio arc segment analysis of the above method embodiment of the present application, which will not be described in detail here.

[0115] In an embodiment of the present invention, a computer-readable storage medium is also provided, and the computer-readable storage medium is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, and the storage space stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of a GNSS positioning method based on signal-to-noise ratio arc segment analysis in the above embodiment. Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0116] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A GNSS positioning method based on signal-to-noise ratio arc segment analysis, characterized in that: Methods include: Acquire first observation data of the reference station and second observation data of the monitoring station; Process the first observation data and the second observation data respectively to obtain third observation data and fourth observation data; Dividing the third observation data at equal intervals according to the lengths of the start and end epochs of the third observation data to obtain a plurality of first observation segments, and dividing the fourth observation data at equal intervals according to the lengths of the start and end epochs of the fourth observation data to obtain a plurality of second observation segments; Calculate the reference quantity of each first observation segment and each second observation segment respectively, and remove the observation segments that do not meet the reference quantity threshold from the multiple first observation segments and the multiple second observation segments respectively, to obtain the third observation segment and the fourth observation segment; wherein the reference quantity includes epoch completeness, signal-to-noise ratio mean and signal-to-noise ratio standard deviation; remove the observation segments that do not meet the reference quantity threshold from the multiple first observation segments and the multiple second observation segments respectively, to obtain the third observation segment and the fourth observation segment, specifically: according to the pre-configured epoch completeness threshold, signal-to-noise ratio mean threshold and signal-to-noise ratio standard deviation threshold, remove the observation segments that do not meet any of the three types of completeness threshold, signal-to-noise ratio mean threshold and signal-to-noise ratio standard deviation threshold from the multiple first observation segments and the multiple second observation segments, to obtain the third observation segment and the fourth observation segment; The epochs of the third observation segment and the fourth observation segment are aligned to obtain the common arc segment of the satellites, and the observation data of the common arc segment are estimated by the least square method to obtain the positioning parameters; The integer ambiguity of the positioning parameters is searched and fixed to obtain the GNSS positioning result.

2. A GNSS positioning method based on signal-to-noise ratio arc segment analysis according to claim 1, characterized in that: The first observation data and the second observation data are processed respectively to obtain the third observation data and the fourth observation data, specifically: Get the ephemeris data sent by the satellite; Calculate a first altitude angle of each epoch of the satellite according to the first observation data and the ephemeris data, and calculate a second altitude angle of each epoch of the satellite according to the second observation data and the ephemeris data; The observation values ​​of each epoch whose first altitude angle is lower than the cut-off altitude angle are removed from the first observation data to obtain the third observation data, and the observation values ​​of each epoch whose second altitude angle is lower than the cut-off altitude angle are removed from the second observation data to obtain the fourth observation data.

3. The GNSS positioning method based on signal-to-noise ratio arc segment analysis according to claim 2, characterized in that: The first observation data and ephemeris data are estimated using a pseudo-range single point positioning algorithm and a least square method to calculate the first altitude angle of each epoch of the satellite; The second observation data and ephemeris data are estimated using the pseudo-range single-point positioning algorithm and the least squares method to calculate the second altitude angle of each epoch of the satellite.

4. The GNSS positioning method based on signal-to-noise ratio arc segment analysis according to claim 1, characterized in that: The least square method is used to estimate the observation data of the common arc segment to obtain the positioning parameters, which are as follows: Double difference operation is performed on the carrier phase observation values ​​and pseudorange observation values ​​of the reference station and the monitoring station in the shared arc segment to calculate the double difference observation values ​​of each epoch; The double difference observations are superimposed in the normal equation to construct the superimposed normal equation, and the superimposed normal equation is solved by the least square method to obtain the positioning parameters; wherein the positioning parameters include the floating point ambiguity parameters and the variance information of the floating point ambiguity parameters.

5. The GNSS positioning method based on signal-to-noise ratio arc segment analysis according to claim 4, characterized in that: The expression for calculating the double difference observation value of each epoch is: ;in, is the double difference operator, is the wavelength of the carrier phase observation value of the fth frequency, p is the base station, q For monitoring stations, i and j All represent satellite numbers. is the double-difference pseudorange observation value between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency, is the double difference satellite-to-ground distance between the i-th satellite and the j-th satellite and between the reference station p and the monitoring station q, is the double-difference pseudorange residual between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency, is the double-difference carrier phase residual between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency, is the double-difference carrier phase ambiguity parameter between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency, is the double difference carrier phase observation value between the i-th satellite and the j-th satellite and the reference station p and the monitoring station q at the f-th frequency.

6. The GNSS positioning method based on signal-to-noise ratio arc segment analysis according to claim 5, characterized in that: Perform integer ambiguity search and fixation on the positioning parameters to obtain the GNSS positioning results, including: The variance information of the floating-point ambiguity parameters is sorted from small to large, and the floating-point ambiguity parameters and the variance information are substituted into the integer criterion of ambiguity fixation for calculation. If the calculation result satisfies the integer criterion, the ambiguity parameters are fixed; The fixed ambiguity parameters are substituted into the normal equation after superposition, and the GNSS positioning result is solved using the least squares method.

7. A GNSS positioning system based on signal-to-noise ratio arc segment analysis, used to execute a GNSS positioning method based on signal-to-noise ratio arc segment analysis as claimed in any one of claims 1 to 6, characterized in that: The system includes: A data acquisition module, used to acquire first observation data of the reference station and second observation data of the monitoring station; A data processing module, used to process the first observation data and the second observation data respectively to obtain third observation data and fourth observation data; A segmentation module is used to divide the third observation data into equal intervals according to the lengths of the start and end epochs of the third observation data to obtain a plurality of first observation segments, and to divide the fourth observation data into equal intervals according to the lengths of the start and end epochs of the fourth observation data to obtain a plurality of second observation segments; A segment screening module is used to calculate the reference quantity of each first observation segment and each second observation segment respectively, and to remove the observation segments that do not meet the reference quantity threshold from the multiple first observation segments and the multiple second observation segments respectively, so as to obtain the third observation segment and the fourth observation segment; wherein the reference quantity includes epoch completeness, signal-to-noise ratio mean value and signal-to-noise ratio standard deviation; and to remove the observation segments that do not meet the reference quantity threshold from the multiple first observation segments and the multiple second observation segments respectively, so as to obtain the third observation segment and the fourth observation segment, specifically: according to the pre-configured epoch completeness threshold, signal-to-noise ratio mean threshold and signal-to-noise ratio standard deviation threshold, the observation segments that do not meet any of the three types of completeness threshold, signal-to-noise ratio mean threshold and signal-to-noise ratio standard deviation threshold are removed from the multiple first observation segments and the multiple second observation segments to obtain the third observation segment and the fourth observation segment; A positioning parameter estimation module is used to align the epochs of the third observation segment and the fourth observation segment to obtain the common arc segment of the satellite, and use the least square method to estimate the observation data of the common arc segment to obtain the positioning parameters; The positioning parameter processing module is used to search and fix the integer ambiguity of the positioning parameters to obtain the GNSS positioning result.

8. A computer terminal, characterized in that: The computer terminal includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, the steps of a GNSS positioning method based on signal-to-noise ratio arc segment analysis as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the GNSS positioning method based on signal-to-noise ratio arc segment analysis as described in any one of claims 1 to 6 is implemented.

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