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

Abstract

The invention discloses a GNSS diffraction error detection method based on signal-to-noise ratio change rate estimation. The method comprises the following steps: placing a receiver in an open environment, observing multi-day data, reading observation files obtained by Beidou / GNSS receivers of a base station and a monitoring station, performing pseudo-range single-point positioning calculation, obtaining elevation angles of satellites, and outputting signal-to-noise ratio sequences of the satellites; counting the change of the signal-to-noise ratio of each satellite along with the elevation angle of the satellite in an open environment; building a function model of the change of the satellite signal-to-noise ratio along with the satellite elevation angle; in an actual shielding environment, constructing a signal-to-noise ratio rate estimation model based on Kalman filtering; during real-time positioning, the calculated signal-to-noise ratio predicted value and the change rate thereof are compared with a result in an open environment, and whether a current observation value is a diffraction error is judged; according to the invention, the method achieves the real-time detection of the diffraction error of the carrier phase observation value during the Beidou / GNSS data processing, facilitates the accurate control of the data quality of the observation value, and improves the positioning precision of Beidou / GNSS.

Description

Technical Field

[0001] The present invention belongs to the field of satellite navigation and positioning, and particularly relates to high-precision navigation and positioning and deformation monitoring applications based on GNSS (Global Navigation Satellite System). Background Art

[0002] The Beidou / GNSS positioning technology is widely applied in fields such as geodetic surveying and navigation, engineering safety monitoring, and geological disaster monitoring. In an open environment, the Beidou / GNSS technology can achieve a positioning accuracy of up to millimeter level at most. The Beidou / GNSS technology uses electromagnetic waves for distance measurement. According to the electromagnetic wave theory, when electromagnetic waves encounter an obstacle, in addition to signal reflection causing multipath errors, signal diffraction will also occur, causing diffraction errors. The GNSS diffraction error refers to the diffraction effect that occurs when satellite signals pass through the edge or pore of an obstacle, causing the direct signal to bend and reach the receiver in the "shadow area" that cannot be reached by the direct path, thereby generating non-line-of-sight errors. The Beidou / GNSS carrier phase diffraction error can reach the decimeter level, far exceeding the theoretical limit of carrier multipath errors, and its time-varying characteristics are complex, having a great impact on the positioning accuracy. It is a bottleneck problem affecting the application of the Beidou / GNSS positioning technology in the field of high-precision navigation and positioning.

[0003] The Beidou / GNSS technology uses electromagnetic waves for distance measurement. According to the electromagnetic wave theory, when electromagnetic waves encounter an obstacle, signal reflection will occur, causing multipath errors, and signal diffraction will also occur, causing diffraction errors. The diffraction error magnitude has a certain correlation with the signal-to-noise ratio. When the diffraction effect occurs and becomes more and more serious, the Beidou / GNSS observation quality will decline accordingly, and the signal-to-noise ratio of the observed value will show a trend of decreasing. The diffraction error caused by occlusion can reach the centimeter or even decimeter level, which will make it difficult to fix the carrier phase ambiguity during Beidou / GNSS positioning, the positioning result accuracy will drop to the meter level, and the reliability will decrease extremely.

[0004] Therefore, in an occluded environment, how to effectively detect the diffraction error and adopt a certain method to eliminate or process it is the key problem for achieving high-precision positioning. Summary of the Invention

[0005] The object of the present invention is to provide a method for detecting GNSS diffraction errors based on the estimation of the signal-to-noise ratio change rate, so as to achieve the detection of GNSS diffraction errors and improve the GNSS positioning accuracy.

[0006] To achieve the above object, the technical solution of the present invention is as follows:

[0007] A method for detecting GNSS diffraction errors based on the estimation of the signal-to-noise ratio change rate, characterized in that the method includes:

[0008] Place the receiver in an open environment, observe data for multiple days, read the observation value files and broadcast ephemeris files obtained by the Beidou / GNSS receivers of the reference station and the monitoring station, perform pseudo-range single-point positioning calculations, obtain the elevation angles of each satellite, and output the signal-to-noise ratio sequences of each satellite;

[0009] Statistics of the signal-to-noise ratio of each satellite varying with the elevation angle of the satellite in an open environment;

[0010] Construction of the function model for the variation of the signal-to-noise ratio of the satellite with the elevation angle;

[0011] In an actual occlusion environment, based on the function model for the variation of the signal-to-noise ratio of the satellite with the elevation angle,

[0012] Construct a signal-to-noise ratio rate estimation model based on Kalman filtering;

[0013] During real-time positioning, compare the predicted signal-to-noise ratio value and its change rate calculated according to the signal-to-noise ratio rate estimation model based on Kalman filtering with the results in an open environment to determine whether the current observed value is a diffraction error.

[0014] Furthermore, the obtaining of the elevation angles of each satellite includes the following steps:

[0015] S11: Construct a pseudo-range single-point positioning function model, and the positioning model equation is

[0016] If (X, Y, Z) represents the station coordinates and (X s , Y s , Z s ) represents the satellite position, then the original observation equation can be expressed as

[0017]

[0018] In the formula, ρ represents the satellite-earth distance, δ A represents the atmospheric delay error, mainly including tropospheric and ionospheric delays, etc., δ dt and δ dT represent the receiver clock error and the satellite clock error respectively, and δ e is the pseudo-range noise;

[0019] S12: Perform a linearization operation on formula (1),

[0020]

[0021] In the formula, (X 0 , Y 0 , Z 0 ) represents the given initial station coordinates, and i represents the numbers of each satellite;

[0022] S13: Differentiate formula (2) among satellites to construct the inter-satellite single-difference observation. For each epoch, select the satellite with the highest satellite elevation angle in the visible satellite list as the reference satellite, and perform the difference operation between other satellites in the list and the reference satellite to establish the inter-satellite single-difference observation as shown in formula (3).

[0023]

[0024] Where:

[0025]

[0026] S14: Construct formula (3) into a matrix to establish the error equation

[0027] V = Aδ X + L (4)

[0028] In the formula, V is the residual vector; A is the design matrix; X is the state vector, which mainly includes the position parameters (X, Y, Z) of the receiver; L is the observation vector, that is, the pseudorange observation.

[0029] Where,

[0030]

[0031] S15: Use the least squares parameter estimation method to solve the coordinates of the station epoch by epoch and convert them into longitude and latitude

[0032] δ X = -(A T PA) -1 A T PL (6)

[0033] Then, the station coordinates are

[0034] (X, Y, Z) T = (X 0 , Y 0 , Z 0 ) T + (dX, dY, dZ) T (7)

[0035] Use the XYZ to BLH method to obtain the station longitude, latitude and geodetic height (B, L, H);

[0036] S16: Calculate the satellite positions (X s , Y s , Z s ) of each satellite at each epoch according to the broadcast ephemeris and the station coordinates (X, Y, Z) obtained by pseudorange positioning solution, and calculate the distance r between the two

[0037]

[0038] Calculate the satellite elevation angle at each epoch according to Equation (9).

[0039]

[0040] Furthermore, the statistics of the signal-to-noise ratio of each satellite varying with the satellite elevation angle in the open environment include the following steps:

[0041] S21: Extract the continuous arcs of each satellite, and use a sliding window to obtain the mean value and standard deviation of the signal-to-noise ratio sequence;

[0042]

[0043]

[0044] Where N is the window length, x is the signal-to-noise ratio sequence, i is the data number within the window, and k represents the k-th unit elevation angle;

[0045] S22: Calculate the mean value and standard deviation of the signal-to-noise ratio varying with the elevation angle rate within the S21 sliding window;

[0046]

[0047]

[0048] Furthermore, the construction of the function model of the satellite signal-to-noise ratio varying with the satellite elevation angle includes the following steps:

[0049] S31: Use an exponential function as the objective function, and take the mean value of the signal-to-noise ratio within each unit elevation angle window given by S22 as the observed value for modeling

[0050] y s = c 0 + c 1 × exp(-θ / θ 0 ) (14)

[0051] Where y s is the signal-to-noise ratio after modeling, c 0 , c 1 and θ 0 are model parameters, and θ is the elevation angle;

[0052] S32: Use the least squares parameter estimation method to solve the parameters in Equation (14) and construct the function model of the signal-to-noise ratio varying with the satellite elevation angle.

[0053] Furthermore, the construction of the signal-to-noise ratio rate estimation model based on Kalman filter includes the following steps:

[0054] S41: Construct the state equation of the signal-to-noise ratio value and rate parameter of each satellite,

[0055]

[0056] In the formula, x k is the parameter at time k, x k+1 is the parameter at time k+1, Δel is the change in altitude angle between the two times, and p x 、v x and a x They represent the signal-to-noise ratio value, signal-to-noise ratio change rate and acceleration at time k respectively;

[0057] S42: Determine the prediction noise covariance matrix Q,

[0058]

[0059] S43: Based on the function model of signal-to-noise ratio changing with satellite altitude angle in an open environment given by S32, the observation equation is constructed, and the Kalman filter estimation model is constructed in conjunction with S41.

[0060] L k =A k X k +V k

[0061] X k+1 =Φ k+1,k X k +W k (17)

[0062] Where k is the current epoch, L k is the observation vector, A k is the design matrix, X k is the state vector; V k is the residual vector, Φ k+1,k represents the coefficient matrix of the state transfer equation, that is, F in equation (14) k , W k is zero-mean and its covariance matrix is ​​Q k Normal white noise;

[0063] S44: Use the standard Kalman filter parameter estimation method to estimate the signal-to-noise ratio prediction value and its rate of change with altitude angle on an epoch-by-epoch basis.

[0064] Furthermore, the determining whether the current observation value is a diffraction error comprises the following steps:

[0065] S51: The signal-to-noise ratio change rate v obtained in S43 x (k) Compare with the signal-to-noise ratio rate at the corresponding unit altitude angle given by S23;

[0066] S52: If it indicates that the current observed value is an observed value without diffraction effect, where α is the amplification factor;

[0067] S53: If it indicates that the diffraction effect has occurred in the current observed value, and in data processing, the current observed value will not participate in data solution.

[0068] The beneficial effects of the present invention are as follows:

[0069] 1. By judging the change rate of the signal-to-noise ratio with the elevation angle, the present invention can effectively distinguish non-direct signals caused by diffraction effects, etc., and can realize real-time detection of diffraction errors of carrier phase observed values during Beidou / GNSS data processing, which helps to accurately control the quality of observed value data and improve the positioning accuracy of Beidou / GNSS.

[0070] 2. By collecting data in an open environment, the present invention constructs a model of the change of the signal-to-noise ratio with the elevation angle in an unobstructed state, which can be used as a reference to determine whether the observed value is affected by diffraction interference.

[0071] 3. The present invention constructs a Kalman filter model by using the elevation angle instead of time, and realizes accurate modeling and control of the change of the signal-to-noise ratio with the elevation angle.

[0072] 4. The method proposed by the present invention can be used in both real-time and post-positioning, and is applicable to various satellite positioning systems and positioning solution models (such as undifferenced and non-combined precise point positioning solution models, double-difference relative positioning solution models, etc.). BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is a flow chart of the method of the present invention.

[0074] Figure 2 is the station distribution and observation environment of the embodiment.

[0075] Figure 3 is the diffraction error and its signal-to-noise ratio in the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0076] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0077] Based on previous research, the present invention proposes a method for detecting GNSS diffraction errors based on the estimation of the signal-to-noise ratio (SNR) change rate. When the diffraction effect occurs and becomes more severe, the observation quality of Beidou / GNSS will decline, and the SNR of the observation values will show a trend of decreasing. By determining the correlation between the magnitude of the diffraction error and the SNR, the present invention proposes to detect the diffraction error according to the change rate of the SNR of the Beidou / GNSS carrier phase observation values.

[0078] The present invention first calibrates the receiver in an open environment to determine the function model, statistical information, and rate change range of the SNR of Beidou / GNSS observation values with respect to the satellite elevation angle. Then, it is placed in the actual measurement environment, and an SNR rate estimation model is designed. By comparing the rate of change of the SNR with respect to the elevation angle in the presence and absence of obstructions, the detection and processing of diffraction errors are realized. The specific steps are as follows:

[0079] S1: Place the receiver in an open environment and observe data for multiple days. Read the observation value files and broadcast ephemeris files obtained by the Beidou / GNSS receivers at the reference station and the monitoring station, perform pseudorange single-point positioning calculations to obtain the elevation angles of each satellite, and output the SNR sequences of each satellite. This step further includes:

[0080] S11: Construct a pseudorange single-point positioning function model, and the positioning model equation is

[0081] If (X, Y, Z) represents the station coordinates and (X s , Y s , Z s ) represents the satellite position, the original observation equation can be expressed as

[0082]

[0083] In the formula, ρ represents the satellite-earth distance, δ A represents the atmospheric delay error, mainly including tropospheric and ionospheric delays, etc., δ dt and δ dT represent the receiver clock error and the satellite clock error respectively, and δ e is the pseudorange noise.

[0084] S12: Perform a linearization operation on formula (1),

[0085]

[0086] In the formula, (X 0 , Y 0 , Z 0 ) represents the given initial station coordinates, and i represents the number of each satellite.

[0087] S13: Differentiate formula (2) among satellites to construct the inter-satellite single-difference observation value. Specifically, for each epoch, select the satellite with the highest satellite elevation angle in the visible satellite list as the reference satellite, and perform a difference operation between each other satellite in the list and the reference satellite to establish the inter-satellite single-difference observation value as shown in formula (3).

[0088]

[0089] Where:

[0090]

[0091] S14: Construct formula (3) into a matrix to establish the error equation

[0092] V = Aδ X + L (4)

[0093] In the formula, V is the residual vector; A is the design matrix; X is the state vector, which mainly includes the position parameters (X, Y, Z) of the receiver; L is the observation value vector, that is, the pseudorange observation value.

[0094] Where,[[]]

[0095]

[0096] S15: Use the least squares parameter estimation method to solve the coordinates of the measuring station epoch by epoch and convert them into longitude and latitude

[0097] δ X = -(A T PA) -1 A T PL (6)

[0098] Then, the coordinates of the measuring station are

[0099] (X, Y, Z) T = (X 0 , Y 0 , Z 0 ) T + (dX, dY, dZ) T (7)

[0100] Then, use the XYZ to BLH method to obtain the longitude, latitude and geodetic height (B, L, H) of the measuring station.

[0101] S16: Calculate the elevation angle of each satellite at each epoch according to the coordinates of each satellite provided by the measuring station coordinates and the broadcast ephemeris; specifically

[0102] Calculate the satellite position (X s , Y s , Z s) and the station coordinates (X, Y, Z) obtained by pseudorange positioning solution, calculate the distance r between the two.

[0103]

[0104] Then, calculate the satellite altitude angle at each epoch according to Equation (9).

[0105]

[0106] S2: Statistics of the signal-to-noise ratio of each satellite varying with the satellite altitude angle in an open environment; this step further includes

[0107] S21: Extract the continuous arc segments of each satellite, and use a sliding window to obtain the mean value and standard deviation of the signal-to-noise ratio sequence.

[0108]

[0109]

[0110] In the formula, N is the window length, which can be determined according to the number of epochs within a unit altitude angle, generally within the range of an integer unit angle, such as every 1 degree, 2 degrees, or 5 degrees of altitude angle range. x is the signal-to-noise ratio sequence, i is the data number within the window. k represents the kth unit altitude angle.

[0111] S22: Calculate the mean value and standard deviation of the signal-to-noise ratio varying with the altitude angle velocity within the S21 sliding window.

[0112]

[0113]

[0114] S3: Construction of the function model of the signal-to-noise ratio varying with the satellite altitude angle; this step further includes:

[0115] S31: Use an exponential function as the objective function, and use the mean value of the signal-to-noise ratio within each unit altitude angle window given by S22 as the observed value for modeling.

[0116] y s =c 0 +c 1 ×exp(-θ / θ 0 ) (14)

[0117] In the formula, y s is the signal-to-noise ratio after modeling, c 0 , c 1 and θ 0 are the model parameters, and θ is the altitude angle.

[0118] S32: Using the least squares parameter estimation method, solve the parameters in equation (14) and construct a function model of the signal-to-noise ratio with the satellite altitude angle.

[0119] S4: Under the actual occlusion environment, construct a signal-to-noise ratio rate estimation model based on Kalman filtering; this step further includes:

[0120] S41: During data processing, the state equations of the signal-to-noise ratio values ​​and rate parameters of each satellite are constructed.

[0121]

[0122] In the formula, x k is the parameter at time k, x k+1 is the parameter at time k+1, Δel is the change in altitude angle between the two times, and p x 、v x and a x They represent the signal-to-noise ratio value, signal-to-noise ratio change rate and acceleration at time k, i.e., the parameters to be estimated.

[0123] S42: Determine the prediction noise covariance matrix Q according to the noise statistics information of the signal-to-noise ratio,

[0124]

[0125] S43: Based on the function model of signal-to-noise ratio changing with satellite altitude angle in an open environment given by S32, the observation equation is constructed, and the Kalman filter estimation model is constructed in conjunction with S41.

[0126] L k =A k X k +V k

[0127] X k+1 =Φ k+1,k X k +W k (17)

[0128] Where k is the current epoch; L k is the observation value vector, i.e., the actual signal-to-noise ratio observation value under occlusion environment; A k is the design matrix; X k is the state vector, which includes the position parameters and ambiguity parameters; V k is the residual vector. k+1,k represents the coefficient matrix of the state transfer equation, that is, F in equation (14) k , W k is zero-mean and its covariance matrix is ​​Q k Normal white noise.

[0129] S44: Use the standard Kalman filter parameter estimation method to estimate the predicted SNR value and its rate of change with the elevation angle epoch by epoch.

[0130] S5: During real-time positioning, compare the predicted SNR value and its rate of change estimated in S4 with the results in an open environment to determine whether the current observation value is a diffraction error; this step further includes:

[0131] S51: Compare the SNR rate of change v x (k) obtained in S43 with the SNR rate at the corresponding unit elevation angle given in S23.

[0132] S52: If it indicates that the observation value is an observation value without diffraction effect, where α is the amplification factor, and generally an empirical value of 3 can be taken.

[0133] S53: If it indicates that the observation value has a diffraction effect, and in data processing, this observation value will not participate in data solution.

[0134] This embodiment is from a certain engineering safety monitoring system in actual operation. In the system, 1 reference station (JZ01) is set on the roof of the data center, and 2 monitoring stations (WY01, WY8) are respectively located at the monitoring points. Each station is equipped with a NET10 Plus receiver and a supporting antenna from Hexagon Geosystems. The receiver is set to receive observation data of the GPS / BDS-2 / BDS-3 / GALILEO satellite system, the sampling frequency is set to 5 s, and each 4 hours is a time period for storage and solution. The information of the two stations is as Figure 2 shown.

[0135] The test data used in this embodiment are the Beidou-2 / Beidou-3 / GPS / Galileo observation data from September 29, 2022 to October 8, 2022, with a sampling frequency of 5 s. Figure 2 are the pictures of the reference station and some monitoring stations. It can be seen that the reference station is blocked by buildings, and the monitoring stations are severely blocked by trees and slopes. Figure 3 gives a sequence of diffraction errors and their SNR. It can be seen that during the period of diffraction effect, the SNR sequence shows a sharp downward trend, and its rate of change with the elevation angle is significantly greater than that without diffraction. Therefore, the occurrence of diffraction effect can be effectively identified by estimating the rate of change of SNR, and diffraction error detection can be realized.

[0136] Finally, it should be noted that the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art. The above are only the preferred embodiments of the present invention and are not used 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 recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall 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 comprises: Place the receiver in an open environment, observe data for multiple days, read the observation value files and broadcast ephemeris files obtained by Beidou / GNSS receivers at the base station and monitoring station, perform pseudo-range single-point positioning calculations, obtain the elevation angles of each satellite, and output the signal-to-noise ratio sequence of each satellite; Statistics of the signal-to-noise ratio of each satellite in an open environment as it changes with the satellite altitude angle; The satellite signal-to-noise ratio as a function of the satellite altitude angle is constructed; In an actual occlusion environment, based on the satellite signal-to-noise ratio variation function model with the satellite altitude angle, a Kalman filter-based signal-to-noise ratio rate estimation model is constructed; During real-time positioning, the predicted signal-to-noise ratio value and its changing rate calculated by the signal-to-noise ratio rate estimation model based on Kalman filtering are compared with the results in an open environment to determine whether the current observation value is a diffraction error.

2. The GNSS diffraction error detection method based on signal-to-noise ratio change rate estimation according to claim 1, characterized in that: Described obtaining each satellite altitude angle comprises the following steps: S11: Construct a pseudo-range single-point positioning function model. The positioning model equation is: If (X, Y, Z) represents the coordinates of the measuring station, (X s ,Y s ,Z s ) represents the satellite position, then the original observation equation can be expressed as In the formula, ρ represents the satellite-to-ground distance, δ A Represents the atmospheric delay error, mainly including tropospheric and ionospheric delays, etc., δ dt and δ dT They represent the receiver clock error and satellite clock error respectively, δ e is the pseudorange noise; S12: Linearize formula (1). In the formula, (X0, Y0, Z0) represents the given initial coordinates of the station, and i represents the number of each satellite; S13: Differentiate formula (2) between satellites to construct inter-satellite single difference observations. For each epoch, select the satellite with the highest satellite elevation angle in the visible satellite list as the reference satellite. Differentiate the other satellites in the list with the reference satellite to establish inter-satellite single difference observations as shown in formula (3): in: S14: Construct equation (3) into a matrix to establish the error equation V=Aδ X +L(4)In the formula, V is the residual vector; A is the design matrix; X is the state vector, which mainly includes the position parameters (X, Y, Z) of the receiver; L is the observation value vector, that is, the pseudorange observation value; in, S15: Use the least squares parameter estimation method to solve the coordinates of the measuring station epoch by epoch and convert them into longitude and latitude d X =-(A T (PA) -1 A T PL (6) Then, the station coordinates are (X,Y,Z) T =(X0,Y0,Z0) T +(dX,dY,dZ) T (7) The XYZ to BLH method is used to obtain the station latitude and longitude and geodetic height (B, L, H); S16: Calculate the satellite position (X) of each satellite at each epoch based on the broadcast ephemeris s ,Y s ,Z s ) and the station coordinates (X, Y, Z) obtained by pseudo-range positioning, calculate the distance r between the two, According to formula (9), the satellite altitude angle of each epoch is calculated 3. The GNSS diffraction error detection method based on signal-to-noise ratio change rate estimation according to claim 1, characterized in that: The statistics of the signal-to-noise ratio of each satellite in the open environment changing with the satellite altitude angle include the following steps: S21: Extract the continuous arc segments of each satellite and use a sliding window to obtain the mean value and standard deviation of the signal-to-noise ratio sequence; Where N is the window length, x is the signal-to-noise ratio sequence, i is the data number in the window, and k represents the kth unit altitude angle; S22: Calculate the mean and standard deviation of the signal-to-noise ratio in the sliding window of S21 as it changes with the height angular velocity; 4. The GNSS diffraction error detection method based on signal-to-noise ratio change rate estimation according to claim 1, characterized in that: The satellite signal-to-noise ratio and satellite altitude angle variation function model construction comprises the following steps: S31: Use the exponential function as the objective function and the mean signal-to-noise ratio in each unit altitude angle window given by S22 as the observed value to build a model y s =c0+c1×exp(-θ / θ0) (14) In the formula, y s is the signal-to-noise ratio after modeling, c0, c1 and θ0 are model parameters, and θ is the altitude angle; S32: Using the least squares parameter estimation method, solve the parameters in equation (14) and construct a function model of the signal-to-noise ratio with the satellite altitude angle.

5. The GNSS diffraction error detection method based on signal-to-noise ratio change rate estimation according to claim 1, characterized in that: The construction of the signal-to-noise ratio rate estimation model based on Kalman filtering comprises the following steps: S41: Construct the state equation of the signal-to-noise ratio value and rate parameter of each satellite, In the formula, x k is the parameter at time k, x k+1 is the parameter at time k+1, Δel is the change in altitude angle between the two times, and p x 、v x and a x They represent the signal-to-noise ratio value, signal-to-noise ratio change rate and acceleration at time k respectively; S42: Determine the prediction noise covariance matrix Q, S43: Based on the function model of signal-to-noise ratio changing with satellite altitude angle in an open environment given by S32, the observation equation is constructed, and the Kalman filter estimation model is constructed in conjunction with S41. L k =A k X k +V k X k+1 =Φ k+1,k X k +W k (17) Where k is the current epoch, L k is the observation vector, A k is the design matrix, X k is the state vector; V k is the residual vector, φ k+1,k Represents the coefficient matrix of the state transfer equation, W k is zero-mean and its covariance matrix is ​​Q k Normal white noise; S44: Use the standard Kalman filter parameter estimation method to estimate the signal-to-noise ratio prediction value and its rate of change with altitude angle on an epoch-by-epoch basis.

6. 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 comprises the following steps: S51: The signal-to-noise ratio change rate v obtained in S43 x (k) Compare with the signal-to-noise ratio rate at the corresponding unit altitude angle given by S23; S52: If It means that the current observation value is the observation value without diffraction effect, where α is the magnification coefficient; S53: If This means that the current observation value has a diffraction effect, and in data processing, the current observation value will not participate in data solution.

Images