GNSS diffractive error detection method based on signal-to-noise ratio change rate estimation

Abstract

The application discloses a GNSS diffraction error detection method based on signal-to-noise ratio change rate estimation, which comprises the following steps: placing a receiver in an open environment, observing multi-day data, reading observation files obtained by a Beidou / GNSS receiver of a reference station and a monitoring station, performing pseudorange single-point positioning calculation, obtaining satellite elevation angles, and outputting satellite signal-to-noise ratio sequences; statistics of satellite signal-to-noise ratio changes with satellite elevation angles in the open environment; construction of a satellite signal-to-noise ratio change function model with satellite elevation angles; in an actual shielding environment, a signal-to-noise ratio rate estimation model based on Kalman filtering is constructed; in real-time positioning, the calculated signal-to-noise ratio prediction value and its change rate are compared with the results in the open environment, and whether the current observation value is a diffraction error is judged; the application realizes real-time detection of carrier phase observation value diffraction errors in Beidou / GNSS data processing, is beneficial to accurate control of observation value data quality, and improves the positioning accuracy of Beidou / GNSS.

Description

Technical Field

[0001] This invention belongs to the field of satellite navigation and positioning, specifically involving high-precision navigation and positioning and deformation monitoring applications based on GNSS (Global Navigation Satellite System). Background Technology

[0002] BeiDou / GNSS positioning technology is widely used in fields such as geodesy and navigation, engineering safety monitoring, and geological disaster monitoring. In open environments, BeiDou / GNSS technology can achieve positioning accuracy down to the millimeter level. BeiDou / GNSS technology uses electromagnetic waves for distance measurement. According to electromagnetic wave theory, when electromagnetic waves encounter obstructions, in addition to signal reflection causing multipath errors, signal diffraction also occurs, causing diffraction errors. GNSS diffraction error refers to the diffraction effect that occurs when satellite signals pass through the edges or gaps of obstructions, causing the direct signal to bend and reach a receiver in the "shadow zone" that the direct path cannot reach, thus generating non-line-of-sight errors. BeiDou / GNSS carrier phase diffraction errors can reach the decimeter level, far exceeding the theoretical limit of carrier multipath errors, and have complex time-varying characteristics, significantly impacting positioning accuracy. This is a bottleneck problem affecting the application of BeiDou / GNSS positioning technology in high-precision navigation and positioning fields.

[0003] BeiDou / GNSS technology uses electromagnetic waves for distance measurement. According to electromagnetic wave theory, when electromagnetic waves encounter obstructions, signal reflection causes multipath errors, and signal diffraction causes diffraction errors. The magnitude of diffraction error is correlated with the signal-to-noise ratio (SNR). As the diffraction effect occurs and becomes more severe, the quality of BeiDou / GNSS observations deteriorates, and the SNR of the observed values ​​shows a downward trend. Diffraction errors caused by obstructions can reach the centimeter or even decimeter level, making it difficult to fix carrier phase ambiguity during BeiDou / GNSS positioning. This reduces positioning accuracy to the meter level and drastically decreases reliability.

[0004] Therefore, in obstructed environments, effectively detecting diffraction errors and using certain methods to eliminate or process them is a key issue in achieving high-precision positioning. Summary of the Invention

[0005] The purpose of this invention is to provide a GNSS diffraction error detection method based on signal-to-noise ratio change rate estimation, so as to realize GNSS diffraction error detection and improve GNSS positioning accuracy.

[0006] To achieve the above objectives, the technical solution of this invention is as follows: A GNSS diffraction error detection method based on signal-to-noise ratio change rate estimation, characterized in that the method includes: Place the receiver in an open environment, observe data for multiple days, read the observation files and broadcast ephemeris files obtained from the BeiDou / GNSS receivers of the base station and monitoring station, perform pseudorange single-point positioning calculations, obtain the elevation angle of each satellite, and output the signal-to-noise ratio sequence of each satellite. Statistics on the variation of signal-to-noise ratio of each satellite with the satellite elevation angle in open environments; The aforementioned model for the satellite signal-to-noise ratio as a function of satellite elevation angle is constructed; In actual obstruction environments, based on the aforementioned satellite signal-to-noise ratio variation function model with satellite elevation angle, a signal-to-noise ratio rate estimation model based on Kalman filtering is constructed. During real-time positioning, the predicted signal-to-noise ratio (SNR) value and its rate of change calculated based on the Kalman filter-based SNR rate estimation model are compared with the results in an open environment to determine whether the current observation value is a diffraction error.

[0007] Furthermore, obtaining the elevation angle of each satellite includes the following steps: S11: Construct a pseudorange single-point localization function model. The localization model equation is: like Indicates the coordinates of the survey station. Given the satellite's position, the original observation equation can be expressed as: (1) In the formula, Indicates the distance between the satellite and the ground. This represents atmospheric delay error, mainly including tropospheric and ionospheric delays. and These represent the receiver clock bias and the satellite clock bias, respectively. This is pseudorange noise; S12: Perform linearization on formula (1). (2) In the formula, This represents the initial coordinates of the given station, and i represents the satellite number; S13: Perform inter-satellite difference operations on formula (2) to construct inter-satellite single-difference observations. For each epoch, select the satellite with the highest elevation angle from the visible satellite list as the reference satellite. Perform inter-satellite difference operations on other satellites in the list with the reference satellite to establish inter-satellite single-difference observations as shown in formula (3). (3) in: ; S14: Construct equation (3) into a matrix and establish the error equation. (4) In the formula, The residual vector; For designing the matrix; This is a state vector, which mainly includes the receiver's position parameters. ; This is the vector of observations, i.e., pseudorange observations; in, (5) S15: The coordinates of the station are obtained by solving the least squares parameter estimation method epoch by epoch, and then converted into latitude and longitude. (6) Then, the coordinates of the station are (7) The latitude, longitude, and geodetic height of the station were obtained using the XYZ-to-BLH method. ; S16: Calculate the satellite positions at each epoch based on the broadcast ephemeris. Station coordinates obtained from pseudorange positioning calculation Find the distance r between the two. (8) The satellite elevation angle at each epoch is calculated using equation (9).

[0008] Furthermore, the statistical analysis of the signal-to-noise ratio of each satellite in the open environment as a function of satellite elevation angle includes the following steps: S21: Extract continuous arc segments from each satellite and use a sliding window to calculate the signal-to-noise ratio sequence mean and its standard deviation; (10) (11) In the formula, For window length, For signal-to-noise ratio sequences, Number the data within the window. This represents the k-th unit elevation angle; S22: Calculate the mean and standard deviation of the signal-to-noise ratio as a function of elevation angular velocity within the sliding window of S21; (12) (13).

[0009] Furthermore, the construction of the satellite signal-to-noise ratio variation function model with satellite elevation angle includes the following steps: S31: Using an exponential function as the objective function, and taking the mean signal-to-noise ratio within each unit elevation angle window given in S22 as the observed value, modeling is performed. (14) In the formula, The signal-to-noise ratio after modeling. , and For model parameters, The elevation angle; S32: Using the least squares parameter estimation method, solve the parameters in equation (14) to construct a function model of signal-to-noise ratio with satellite elevation angle.

[0010] Furthermore, the construction of the signal-to-noise ratio rate estimation model based on Kalman filtering includes the following steps: S41: Construct the state equations for the signal-to-noise ratio and rate parameters of each satellite. (15) In the formula, The parameters at time k, The parameters at time k+1, The change in elevation angle between two time points. , and These represent the signal-to-noise ratio (SNR), the rate of change of the SNR, and the acceleration at time k, respectively. S42: Determine the prediction noise covariance matrix Q. (16) S43: Based on the signal-to-noise ratio variation function with satellite elevation angle in open environments given in S32, construct the observation equation, and combine it with S41 to construct the Kalman filter estimation model. (17) In the formula, For the current epoch, For the observation vector, To design the matrix, It is a state vector; For the residual vector, The coefficient matrix represents the state transition equation. The mean is zero and the covariance matrices are respectively Normal white noise; S44: Using the standard Kalman filter parameter estimation method, the signal-to-noise ratio prediction value and its rate of change with elevation angle are estimated epoch by epoch.

[0011] Furthermore, determining whether the current observation value is a diffraction error includes the following steps: S51: The rate of change of signal-to-noise ratio obtained from S43 Compare with the signal-to-noise ratio rate given in S23 at the corresponding unit elevation angle; S52: If This indicates that the current observation value is the observation value without diffraction effect, where, This is the magnification factor; S53: If This indicates that the current observation has undergone diffraction, and in data processing, the current observation will not be included in the data solution.

[0012] The beneficial effects of this invention are: 1. This invention can effectively distinguish non-direct signals caused by diffraction effects by judging the rate of change of signal-to-noise ratio with elevation angle. It can realize real-time detection of diffraction error of carrier phase observation value during BeiDou / GNSS data processing, which helps to accurately control the quality of observation data and improve the positioning accuracy of BeiDou / GNSS.

[0013] 2. This invention constructs a signal-to-noise ratio variation model with elevation angle under unobstructed conditions by collecting data in an open environment, which can be used as a reference to determine whether the observed values ​​are affected by diffraction interference.

[0014] 3. This invention constructs a Kalman filter model by replacing time with elevation angle, thereby achieving accurate modeling and control of the signal-to-noise ratio as elevation angle changes.

[0015] 4. The method proposed in this invention can be used in both real-time and post-event positioning, and is applicable to various satellite positioning systems and positioning solution models (such as non-differential non-combined precise single-point positioning solution models, double-differential relative positioning solution models, etc.). Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention.

[0017] Figure 2 This describes the station distribution and observation environment in the example.

[0018] Figure 3 This refers to the diffraction error and its signal-to-noise ratio in the embodiment. Detailed Implementation

[0019] 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 and not intended to limit the scope of this application.

[0020] Based on previous research, this invention proposes a GNSS diffraction error detection method based on signal-to-noise ratio (SNR) change rate estimation. This method is based on the premise that as diffraction effects occur and become more severe, the quality of BeiDou / GNSS observations deteriorates, and the SNR of the observed values ​​exhibits a downward trend. By determining the correlation between the magnitude of diffraction error and the SNR, this invention proposes to detect diffraction errors based on the rate of change of the SNR of BeiDou / GNSS carrier phase observations.

[0021] This invention first calibrates the receiver in an open environment to determine the functional model, statistical information, and rate variation range of the signal-to-noise ratio (SNR) of BeiDou / GNSS observations as a function of satellite elevation angle. Then, under actual measurement conditions, a SNR rate estimation model is designed. By comparing the rate of SNR change with elevation angle under obstructed and unobstructed conditions, diffraction errors are detected and processed. The specific steps are as follows: S1: Place the receiver in an open environment and observe data for multiple days. Read the observation files and broadcast ephemeris files obtained from the BeiDou / GNSS receivers at the base station and monitoring station, perform pseudorange point positioning calculations, obtain the elevation angles of each satellite, and output the signal-to-noise ratio sequence for each satellite; this step further includes: S11: Construct a pseudorange single-point localization function model. The localization model equation is: like Indicates the coordinates of the survey station. Given the satellite's position, the original observation equation can be expressed as: (1) In the formula, Indicates the distance between the satellite and the ground. This represents atmospheric delay error, mainly including tropospheric and ionospheric delays. and These represent the receiver clock bias and the satellite clock bias, respectively. This is pseudorange noise.

[0022] S12: Perform linearization on formula (1). (2) In the formula, This represents the initial coordinates of the given station, and i represents the satellite number.

[0023] S13: Difference formula (2) between satellites to construct inter-satellite single-difference observations, specifically as follows: For each epoch, the satellite with the highest elevation angle in the visible satellite list is selected as the reference satellite. The other satellites in the list are differentially analyzed with the reference satellite to establish the inter-satellite single-difference observations as shown in Equation (3). (3) in: .

[0024] S14: Construct equation (3) into a matrix and establish the error equation. (4) In the formula, The residual vector; For designing the matrix; This is a state vector, which mainly includes the receiver's position parameters. ; This is the observation vector, i.e., the pseudorange observations.

[0025] in, (5) S15: The coordinates of the station are obtained by solving the least squares parameter estimation method epoch by epoch, and can be converted into latitude and longitude. (6) Then, the coordinates of the station are (7) Then, the XYZ to BLH method is used to obtain the station's latitude, longitude, and geodetic height. .

[0026] S16: Based on the station coordinates and the satellite coordinates provided by the broadcast ephemeris, calculate the elevation angle of each satellite at each epoch; specifically... The satellite positions at each epoch are calculated based on the broadcast ephemeris. Station coordinates obtained from pseudorange positioning calculation Find the distance r between the two. (8) Then, the satellite elevation angle for each epoch is calculated according to equation (9). S2: Statistical analysis of the signal-to-noise ratio of each satellite as a function of satellite elevation angle in open environments; this step further includes S21: Extract continuous arc segments from each satellite and use a sliding window to calculate the signal-to-noise ratio sequence mean and its standard deviation; (10) (11) In the formula, The window length can be determined based on the number of epochs within a unit elevation angle, and is generally an integer unit angle range, such as every 1 degree, 2 degrees, or 5 degrees of elevation angle range. For signal-to-noise ratio sequences, Number the data within the window. This represents the k-th unit elevation angle.

[0027] S22: Calculate the mean and standard deviation of the signal-to-noise ratio as a function of elevation angular velocity within the sliding window of S21; (12) (13) S3: Construction of the signal-to-noise ratio variation function model with satellite elevation angle; this step further includes: S31: Using an exponential function as the objective function, and taking the mean signal-to-noise ratio within each unit elevation angle window given in S22 as the observed value, modeling is performed. (14) In the formula, The signal-to-noise ratio after modeling. , and For model parameters, The elevation angle.

[0028] S32: Using the least squares parameter estimation method, solve the parameters in equation (14) to construct a function model of signal-to-noise ratio with satellite elevation angle.

[0029] S4: In actual occlusion environments, construct a signal-to-noise ratio rate estimation model based on Kalman filtering; this step further includes: S41: During data processing, construct the state equations for the signal-to-noise ratio and rate parameters of each satellite. (15) In the formula, The parameters at time k, The parameters at time k+1, The change in elevation angle between two time points. , and Let S and R represent the signal-to-noise ratio (SNR), the rate of change of SNR, and the acceleration at time k, respectively, which are the parameters to be estimated.

[0030] S42: Based on the noise statistics of the signal-to-noise ratio, determine the prediction noise covariance matrix Q. (16) S43: Based on the signal-to-noise ratio variation function with satellite elevation angle in open environments given in S32, construct the observation equation, and combine it with S41 to construct the Kalman filter estimation model. (17) In the formula, For the current epoch; This is the observation vector, which represents the actual signal-to-noise ratio observations under occlusion conditions; For designing the matrix; This is a state vector, which includes position parameters and ambiguity parameters; This is the residual vector. The coefficient matrix represents the state transition equation. The mean is zero and the covariance matrices are respectively Normal white noise.

[0031] S44: Using the standard Kalman filter parameter estimation method, the signal-to-noise ratio prediction value and its rate of change with elevation angle are estimated epoch by epoch.

[0032] S5: During real-time positioning, based on the predicted signal-to-noise ratio value and its rate of change estimated in S4, compare it with the results in an open environment to determine whether the current observation value is a diffraction error; this step further includes: S51: The rate of change of signal-to-noise ratio obtained from S43 Compare with the signal-to-noise ratio rate given in S23 at the corresponding unit elevation angle. S52: If This indicates that the observation value is the observation value without diffraction effect, where, This is the magnification factor, which can generally be taken as an empirical value of 3. S53: If This indicates that the observation has undergone diffraction, and in data processing, this observation will not be included in the data solution.

[0033] This embodiment is derived from a real-world engineering safety monitoring system. In the system, one base station (JZ01) is located on the roof of a data center building, and two monitoring stations (WY01, WY8) are located at monitoring points. Each station is equipped with a NET10 Plus receiver and matching antenna from UniStrong. The receiver is configured to receive observation data from GPS / BDS-2 / BDS-3 / GALILEO satellite systems, with a sampling frequency of 5 seconds, and storage and processing performed every 4 hours. Information from both stations is as follows: Figure 2 As shown.

[0034] The experimental data used in this embodiment are BeiDou-2 / BeiDou-3 / GPS / Galileo observation data from September 29, 2022 to October 8, 2022, with a sampling frequency of 5 seconds. Figure 2 These are images of the base station and some monitoring stations. It is evident that the base station is obstructed by buildings, while the monitoring stations are significantly obstructed by trees and slopes. Figure 3A sequence of diffraction error and its signal-to-noise ratio (SNR) is presented. It can be seen that during the period when the diffraction effect occurs, the SNR sequence exhibits a sharp decreasing trend, and its rate of change with elevation angle is significantly greater than that when diffraction does not occur. Therefore, the occurrence of diffraction effects can be effectively identified by estimating the rate of change of the SNR, thus enabling the detection of diffraction errors.

[0035] Finally, it should be noted that the contents not described in detail in this specification belong to the prior art known to those skilled in the art. The above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A GNSS diffraction error detection method based on signal-to-noise ratio change rate estimation, characterized in that... The method includes: Place the receiver in an open environment, observe data for multiple days, read the observation files and broadcast ephemeris files obtained from the BeiDou / GNSS receivers of the base station and monitoring station, perform pseudorange single-point positioning calculations, obtain the elevation angle of each satellite, and output the signal-to-noise ratio sequence of each satellite. Statistics on the variation of signal-to-noise ratio of each satellite with the satellite elevation angle in open environments; The aforementioned model for the satellite signal-to-noise ratio as a function of satellite elevation angle is constructed; In actual obstruction environments, based on the aforementioned satellite signal-to-noise ratio variation function model with satellite elevation angle, a signal-to-noise ratio rate estimation model based on Kalman filtering is constructed. During real-time positioning, the predicted signal-to-noise ratio (SNR) value and its rate of change calculated by the Kalman filter-based SNR rate estimation model are compared with the results in an open environment to determine whether the current observation value is a diffraction error. The construction of the signal-to-noise ratio rate estimation model based on Kalman filtering includes the following steps: S41: Construct the state equations for the signal-to-noise ratio and rate parameters of each satellite. （15） In the formula, The parameters at time k are... The parameters at time k+1, The change in elevation angle between two time points. , and These represent the signal-to-noise ratio (SNR), the rate of change of the SNR, and the acceleration at time k, respectively. S42: Determine the prediction noise covariance matrix Q. （16） S43: Based on the signal-to-noise ratio variation function with satellite elevation angle in open environments given in S32, construct the observation equation, and combine it with S41 to construct the Kalman filter estimation model. （17） In the formula, For the current epoch, For the observation vector, To design the matrix, It is a state vector; For the residual vector, The coefficient matrix represents the state transition equation. The mean is zero and the covariance matrices are respectively Normal white noise; S44: Using the standard Kalman filter parameter estimation method, the signal-to-noise ratio prediction value and its rate of change with elevation angle are estimated epoch by epoch.

2. The GNSS diffraction error detection method based on signal-to-noise ratio change rate estimation according to claim 1, characterized in that, Obtaining the elevation angle of each satellite includes the following steps: S11: Construct a pseudorange single-point localization function model. The localization model equation is: like Indicates the coordinates of the survey station. To represent the satellite position, the original observation equation can be expressed as: （1） In the formula, Indicates the distance between the satellite and the ground. This represents atmospheric delay error, mainly including tropospheric and ionospheric delays. and These represent the receiver clock bias and the satellite clock bias, respectively. This is pseudorange noise; S12: Perform linearization on formula (1). （2） In the formula, This represents the initial coordinates of the given station, and i represents the satellite number; S13: Perform inter-satellite difference operations on formula (2) to construct inter-satellite single-difference observations. For each epoch, select the satellite with the highest elevation angle from the visible satellite list as the reference satellite. Perform inter-satellite difference operations on other satellites in the list with the reference satellite to establish inter-satellite single-difference observations as shown in formula (3). （3） in: ； S14: Construct equation (3) into a matrix and establish the error equation. （4） In the formula, The residual vector; For designing the matrix; This is a state vector, which mainly includes the receiver's position parameters. ; This is the vector of observations, i.e., pseudorange observations; in, （5） S15: The coordinates of the station are obtained by solving the least squares parameter estimation method epoch by epoch, and then converted into latitude and longitude. （6） Then, the coordinates of the station are （7） The latitude, longitude, and geodetic height of the station were obtained using the XYZ-to-BLH method. ; S16: Calculate the satellite positions of each satellite at each epoch based on the broadcast ephemeris. Station coordinates obtained from pseudorange positioning calculation Find the distance r between the two. （8） The satellite elevation angle at each epoch is calculated using equation (9). （9）。 3. The GNSS diffraction error detection method based on signal-to-noise ratio change rate estimation according to claim 1, characterized in that, The statistical analysis of the signal-to-noise ratio of each satellite as a function of satellite elevation angle in open environments includes the following steps: S21: Extract continuous arc segments from each satellite and use a sliding window to calculate the signal-to-noise ratio sequence mean and its standard deviation; （10） （11） In the formula, For window length, For signal-to-noise ratio sequences, Number the data within the window. This represents the k-th unit elevation angle; S22: Calculate the mean and standard deviation of the signal-to-noise ratio as a function of elevation angular velocity within the sliding window of S21; （12） （13）。 4. The GNSS diffraction error detection method based on signal-to-noise ratio change rate estimation according to claim 1, characterized in that, The model for the satellite signal-to-noise ratio as a function of satellite elevation angle is constructed by the following steps: S31: Using an exponential function as the objective function, and taking the mean signal-to-noise ratio within each unit elevation angle window given in S22 as the observed value, modeling is performed. （14） In the formula, The signal-to-noise ratio after modeling. , and For model parameters, The elevation angle; S32: Using the least squares parameter estimation method, solve the parameters in equation (14) to construct a function model of signal-to-noise ratio with satellite elevation angle.

5. The GNSS diffraction error detection method based on signal-to-noise ratio change rate estimation according to claim 1, characterized in that, Determining whether the current observation value is a diffraction error includes the following steps: S51: The rate of change of signal-to-noise ratio obtained from S43 Compare with the signal-to-noise ratio rate given in S23 at the corresponding unit elevation angle; S52: If This indicates that the current observation value is the observation value without diffraction effect, where, This is the magnification factor. This represents the average signal-to-noise ratio as a function of elevation angular velocity within the sliding window. S53: If This indicates that the current observation has undergone diffraction, and in data processing, the current observation will not be included in the data solution. This represents the standard deviation of the signal-to-noise ratio as a function of elevation and angular velocity within the sliding window.

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