A method for detecting cycle slip in beidou slope monitoring

Abstract

The application provides a Beidou side slope monitoring cycle slip detection method and relates to the technical field of Beidou satellite monitoring.In the application, IMU observation data is used to assist cycle slip detection, IMU observation values are used for displacement detection of a Beidou monitoring station, when no movement of a monitoring point is detected, a double difference method is used for preliminary cycle slip detection, when movement of the monitoring point is detected, a kalman filtering algorithm is used for cycle slip detection, and the cycle slip detection steps are simplified; a double difference observation equation in carrier phase difference resolution calculation is used for simple cycle slip judgment, when suspected cycle slip occurs, an improvement is made on the basis of an MW algorithm, a satellite elevation angle model of different orbit types is added, and a mean error of the elevation angle and the MW combination value is added in Kalman filtering, so that the detection success rate of the Beidou carrier phase cycle slip can be effectively improved, and the stability and precision of the monitoring result are improved.

Description

Technical Field

[0001] This invention relates to the field of BeiDou satellite monitoring technology, specifically to a method for detecting cycle slip in BeiDou slope monitoring. Background Technology

[0002] The BeiDou Navigation Satellite System is a navigation and positioning system independently developed by my country that provides high-precision location information around the clock. With the gradual improvement of the BeiDou system, its positioning accuracy has continuously increased, and BeiDou high-precision positioning technology has been increasingly applied to deformation monitoring during construction and maintenance phases in various engineering fields. When a BeiDou receiver receives satellite signals, if the antenna is blocked by obstacles or interfered with, a short-term loss of lock-on occurs, causing a jump in the integer value of the phase observation, known as a cycle slip. Cycle slips disrupt the carrier phase observation values ​​in the BeiDou observation data, making it impossible to correctly fix ambiguities. During BeiDou geological disaster monitoring, due to limitations of the on-site environment, ionospheric interference, high-voltage lines, and tree obstructions severely affect BeiDou data, significantly impacting the accuracy and stability of monitoring results due to cycle slips. Furthermore, my country's BeiDou satellites are divided into three orbit types: geostationary orbit, inclined geosynchronous orbit, and medium Earth orbit, and the impact of cycle slips varies for each orbit type.

[0003] To address the aforementioned problems, this invention provides a cycle slip detection method for BeiDou slope monitoring, which can effectively detect cycle slip and improve the stability and accuracy of monitoring results. Summary of the Invention

[0004] The purpose of this invention is to provide a cycle slip detection method for BeiDou slope monitoring, and the specific technical solution is as follows:

[0005] A cycle slip detection method for BeiDou slope monitoring includes the following steps:

[0006] Step S1: Initialization; Specifically: Set the initialization time; During the initialization process, the BeiDou receiver and IMU collect data respectively; Perform carrier phase difference calculation on the data from the monitoring station and the base station to obtain the initial coordinates (X) of the monitoring station within the corresponding time period. r0 Y r0 Z r0 ) and the initial coordinates (x) measured by the IMU imu y imu , z imu );

[0007] Step S2, displacement monitoring; specifically: set a threshold; retrieve the k-th result after IMU initialization. judge Does it exceed the threshold? If it exceeds the threshold, the monitoring point is suspected of displacement; if it does not exceed the threshold, the monitoring point is not considered to have moved.

[0008] Step S3: Based on the displacement monitoring results in Step S2, if it is determined that the monitoring point is suspected of having undergone displacement, proceed to Step S5; if it is determined that the monitoring point has not undergone displacement, proceed to Step S4.

[0009] Step S4: Perform a preliminary cycle slip judgment based on the double difference method. If no cycle slip is determined to have occurred, perform ambiguity fixing and coordinate calculation (among which, ambiguity fixing and coordinate calculation are common techniques in the BeiDou calculation process) to obtain the BeiDou slope monitoring results. If a cycle slip is suspected to have occurred, proceed to step S5.

[0010] Step S5: Use the Kalman filter algorithm to detect cycle slips. When no cycle slip is detected, dynamically adjust the Kalman gain according to the satellite elevation angle, and fix the ambiguity and solve the coordinates to obtain the BeiDou slope monitoring results. When a cycle slip is detected, set the Kalman gain to 0 so that the current cycle slip value does not affect the subsequent filtering effect, and proceed to step S6.

[0011] Step S6: When a cycle slip is detected at this epoch, firstly, polynomial fitting is performed using the clean carrier observations within the smoothing window. Then, the predicted carrier observations for the cycle slip epoch are extrapolated based on the polynomial and compared with the actual observations. The residual terms are rounded using the integer characteristics of the cycle slip to obtain the cycle slip value. Finally, the cycle slip observations are repaired, and the repaired observations are used to recalculate to obtain the BeiDou slope monitoring results.

[0012] Furthermore, the double-difference method is used for preliminary judgment of cycle slips. The specific steps are as follows:

[0013] Step A1: Establish the observation equation, as shown in Equation 1):

[0014]

[0015] Where: i represents the i-th satellite, j represents the j-th satellite, b represents the base station, r represents the monitoring station, and λ represents the wavelength. For base station b and monitoring station r, the double-difference observations of satellites i and j are given. The direction vector difference between the monitoring station and satellites i and j is calculated and is a known quantity. The difference between the distances from the monitoring station to satellites i and j. The difference in distance from the base station to satellites i and j is calculated and is a known quantity; (ΔX) r (t), ΔY r (t), ΔZ r (t) represents the coordinates of the monitoring station at time t (X). r (t), Y r (t), Z r (t) and initial coordinates (X)r0 Y r0 X r0 The difference between (ΔX) and (ΔX) under normal, no displacement conditions. r (t), ΔY r (t), ΔZ r (t) is a constant; The integer ambiguity difference between satellite j and satellite i at time t of the base station and monitoring station is a constant value when no cycle slip occurs;

[0016] Step A2: Calculate the double-difference ambiguity and residuals: Using 1 minute of observation data, construct the observation equations according to formula 1). Assuming the receiver sampling interval is s seconds, there are 60 double-difference equations in 1 minute. Calculate the ambiguity and residuals using the adjustment least squares algorithm. (ΔX r (t), ΔY r (t), ΔZ r (t)) and residual σ(t);

[0017] If the residual σ(t) > 4Δσ, then a cycle slip is suspected, and the Kalman filtering algorithm is used to further detect the cycle slip; if σ(t) ≤ 4Δσ, then no cycle slip is determined, and ambiguity fixing and coordinate calculation are performed; where Δσ is the standard error of σ(t).

[0018] Furthermore, the Kalman filter algorithm is used to detect cycle slips. The specific steps are as follows:

[0019] Step B1: Calculate the width ambiguity of the monitoring points using the MW combined algorithm;

[0020] The MW combination algorithm is obtained by combining dual-frequency carrier phase observations and dual-frequency pseudorange observations. Its mathematical model is as follows:

[0021]

[0022] In Equation 2), These are the carrier phase observations for L1 and L2, respectively; P1 and P2 are pseudorange observations; λ1 and λ2 are the band wavelengths; N mw For wide-lane ambiguity; where L1 and L2 refer to the observation data of the two frequency bands output by the dual-frequency Beidou receiver;

[0023] Step B2: Detect cycle slips using an adaptive Kalman filter model;

[0024] Step B3: Detect cycle slips using the predicted residuals in the Kalman filter model;

[0025] Step B4: When a cycle slip is detected, set J... k =0, so that the current cycle slip value does not affect the subsequent filtering effect;

[0026] When it is determined that there is no cycle slip, J k =J k β, β are the filtering coefficients. The Kalman gain is dynamically adjusted according to the satellite elevation angle, and ambiguity is fixed and coordinates are solved to obtain the BeiDou slope monitoring results.

[0027]

[0028]

[0029] In equations 3) and 4), J k δ is the Kalman gain, α is the satellite elevation angle; w θ represents the standard error of the predicted residuals within the sliding window. w V is the magnification factor. (k / k-1) To predict residuals.

[0030] Furthermore, step B2, which uses an adaptive Kalman filter model to detect cycle slips, specifically includes the following steps:

[0031] Step B2.1, Assume N mw The system state at time k is N mw (k) represents the width ambiguity at time k, and N′ mw (k) represents the corresponding rate of change, N″ mw (k) represents the corresponding changing acceleration;

[0032] The system state equation is expressed as:

[0033] S k =AS k-1 +ΓΩ k-1 5);

[0034] In equation 5), A is the transition matrix. Ω is the coefficient matrix of the dynamic noise. k-1 This refers to the system's dynamic noise.

[0035] The observation equation is:

[0036] L k =BS k +dr k 6);

[0037] In Equation 6), B = [1 0] is the observation coefficient matrix; dr k To observe noise;

[0038] Step B2.2, the recursive formula for Kalman filtering is as follows:

[0039] The system state predictions and variance matrix are as follows:

[0040] S (k / k-1) =AS k-1 +ΓΩ (k-1) 7);

[0041] In Equation 7), S (k / k-1) This is the one-step prediction value of the state vector;

[0042] D(S (k / k-1) )=AD(S (k-1) A T +ΓD(Ω (k-1) )Γ T 8);

[0043] In equation 8), D(S) (k / k-1) D(Ω) is the one-step prediction of the variance matrix of the state vector. k-1 ) represents the dynamic noise variance matrix;

[0044] The optimal valuation and variance matrix are:

[0045] V (k / k-1) =L k -BS( k / k-1)) 9);

[0046] In equation 9), V (k / k-1) To predict the residuals, L k The monitoring results at time k;

[0047]

[0048] In Equation 10), This is the optimal estimate of the state vector;

[0049]

[0050] In Equation 11), J is the variance matrix for the optimal estimate. k For Kalman gain; where,

[0051] J k =D(S) (k / k-1) B T [D(r k )+BD(S (k / k-1) B T ] -1 12);

[0052] In equation 12), D(r) k ) for t k The observation noise variance matrix at each time step.

[0053] Furthermore, step B3, which utilizes the predicted residual to detect cycle slips in the Kalman filter model, specifically includes the following steps:

[0054] Step B3.1: Calculate the initial value of the wide-lane ambiguity and the initial variance matrix of the wide-lane ambiguity;

[0055] Step B3.2: Take the average value of the first 100 wide-lane ambiguities in the initial value of wide-lane ambiguity, and take the standard error of wide-lane ambiguity as the initial variance matrix of wide-lane ambiguity;

[0056] Step B3.3, Cycle slip judgment:

[0057] For GEO satellites, the original 4-fold error determination principle is adopted;

[0058] For IGSO and MEO satellites, an amplification factor θ is added to the original four times the mean square error. w ,

[0059] θ w =exp(-α / 20)+1 13); where α is the satellite elevation angle;

[0060] The judgment principle is as shown in Equation 14):

[0061] |V (k / k-1) |≥4δ w θ w 14);

[0062] When |V (k / k-1) |≥4δ w θ w Then, a cycle slip is determined to have occurred; where δ w This represents the standard error of the predicted residuals within the sliding window.

[0063] Furthermore, when a cycle slip is detected at this epoch, a polynomial fitting is first performed using the clean carrier observations within the smoothing window. Then, the predicted carrier observations for the cycle slip epoch are extrapolated based on the polynomial and compared with the actual observations. The residual terms are rounded using the integer characteristics of the cycle slip to obtain the cycle slip value. Finally, the cycle slip observations are repaired to obtain the BeiDou slope monitoring results.

[0064]

[0065] Where Δslip is the cycle slip number, L′ i For the extrapolated carrier observation, L i These are actual observed values.

[0066] The application of the technical solution of the present invention has the following beneficial effects:

[0067] (1) This invention provides a cycle slip detection method for Beidou slope monitoring. It uses IMU observation data to assist in cycle slip detection, uses IMU observation values ​​to monitor the displacement of Beidou monitoring stations, and uses the double difference method to perform preliminary cycle slip detection when no movement of the monitoring point is detected. When movement of the monitoring point is detected, the Kalman filtering algorithm is used to perform cycle slip detection, which simplifies the cycle slip detection steps.

[0068] (2) In this invention, the double-difference observation equation in the carrier phase difference decomposition is used to make simple cycle slip judgment. When a cycle slip is suspected to occur, the MW algorithm is improved by adding satellite elevation angle models of different orbit types and adding the mean error of the elevation angle and MW combination value in the Kalman filter. This can effectively improve the detection success rate of Beidou carrier phase cycle slip and improve the stability and accuracy of monitoring results.

[0069] (3) In this invention, for IGSO and MEO satellites, the orbital changes are relatively large, so an amplification factor θ is added to the original 4 times error. w We developed decision-making rules using the exp function and adopted different methods for satellites in different orbits to improve the accuracy of cycle slip detection.

[0070] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the figures. Attached Figure Description

[0071] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0072] Figure 1 This is a flowchart of IMU-assisted cycle slip detection;

[0073] Figure 2 This is a flowchart of the Kalman filter algorithm for detecting cycle slips;

[0074] Figure 3 This is a schematic diagram of the simulated data test results. Detailed Implementation

[0075] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways as defined and covered by the claims.

[0076] Example:

[0077] This embodiment provides a cycle slip detection method for BeiDou slope monitoring. See [link to relevant documentation]. Figure 1 The specific steps are as follows:

[0078] Step S1: Initialization; Specifically: Set the initialization time; During the initialization process, the BeiDou receiver and IMU collect data respectively; Perform carrier phase difference calculation on the data from the monitoring station and the base station to obtain the initial coordinates (X) of the monitoring station within the corresponding time period. r0 Y r0 Z r0 ) and the initial coordinates (x) measured by the IMU imu y imu , z imu );

[0079] Step S2, displacement monitoring; specifically: set a threshold; retrieve the k-th result after IMU initialization. judge Does it exceed the threshold? If it exceeds the threshold, the monitoring point is suspected of displacement; if it does not exceed the threshold, the monitoring point is not considered to have moved.

[0080] Step S3: Based on the displacement monitoring results in Step S2, if it is determined that the monitoring point is suspected of having undergone displacement, proceed to Step S5; if it is determined that the monitoring point has not undergone displacement, proceed to Step S4.

[0081] Step S4: Perform a preliminary cycle slip judgment based on the double difference method. If no cycle slip is determined to have occurred, perform ambiguity fixing and coordinate solving (ambiguity fixing and coordinate solving are common techniques in passive solution processes) to obtain the BeiDou slope monitoring results. If a cycle slip is suspected to have occurred, proceed to step S5.

[0082] Step S5: Use the Kalman filter algorithm to detect cycle slips. When no cycle slips are detected, since the Kalman filter detects cycle slips based on the wide lane ambiguity, and the accuracy of the wide lane ambiguity is related to the satellite elevation angle, the Kalman gain is dynamically adjusted according to the satellite elevation angle, and the ambiguity is fixed and the coordinates are solved to obtain the BeiDou slope monitoring results. When a cycle slip is detected, the Kalman gain is set to 0 so that the current cycle slip value does not affect the subsequent filtering effect, and proceed to step S6.

[0083] Step S6: When a cycle slip is detected at this epoch, firstly, polynomial fitting is performed using the clean carrier observations within the smoothing window. Then, the predicted carrier observations for the cycle slip epoch are extrapolated based on the polynomial and compared with the actual observations. The residual terms are rounded using the integer characteristics of the cycle slip to obtain the cycle slip value. Finally, the cycle slip observations are repaired, and the repaired observations are used to recalculate to obtain the BeiDou slope monitoring results.

[0084] The following are the specific steps for using the double-difference method to make a preliminary judgment on cycle slips:

[0085] Step A1: Establish the observation equation, as shown in Equation 1):

[0086]

[0087] Where: i represents the i-th satellite, j represents the j-th satellite, b represents the base station, r represents the monitoring station, and λ represents the wavelength. For base station b and monitoring station r, the double-difference observations of satellites i and j are given. The direction vector difference between the monitoring station and satellites i and j is calculated and is a known quantity. The difference between the distances from the monitoring station to satellites i and j. The difference in distance from the base station to satellites i and j is calculated and is a known quantity; (ΔX) r (t), ΔY r (t), ΔZ r (t) represents the coordinates of the monitoring station at time t (X). r (t), Y r (t), Z r (t) and initial coordinates (X) r0 Y r0 Z r0 The difference between (ΔX) and (ΔX) under normal, no displacement conditions. r (t), ΔY r (t), ΔZ r (t) is a constant; The integer ambiguity difference between satellite j and satellite i at time t of the base station and monitoring station is a constant value when no cycle slip occurs;

[0088] Step A2: Calculate the double-difference ambiguity and residuals: Using 1 minute of observation data, construct the observation equations according to formula 1). Assuming the receiver sampling interval is s seconds, there are 60 double-difference equations in 1 minute. Calculate the ambiguity and residuals using the adjustment least squares algorithm. (ΔX r (t), ΔY r (t), ΔZ r (t)) and residual σ(t);

[0089] If the residual σ(t) > 4Δσ, then a cycle slip is suspected, and the Kalman filtering algorithm is used to further detect the cycle slip; if σ(t) ≤ 4Δσ, then no cycle slip is determined, and ambiguity fixing and coordinate calculation are performed; where Δσ is the standard error of σ(t).

[0090] See Figure 2 The Kalman filter algorithm is used to detect cycle slips. The specific steps are as follows:

[0091] Step B1: Calculate the width ambiguity of the monitoring points using the MW combined algorithm;

[0092] The MW combination algorithm is obtained by combining dual-frequency carrier phase observations and dual-frequency pseudorange observations. Its mathematical model is as follows:

[0093]

[0094] In Equation 2), These are the carrier phase observations for L1 and L2, respectively; P1 and P2 are pseudorange observations; λ1 and λ2 are the band wavelengths; N mw For wide-lane ambiguity; where L1 and L2 refer to the observation data of the two frequency bands output by the dual-frequency Beidou receiver;

[0095] Step B2: Detect cycle slips using an adaptive Kalman filter model; specifically:

[0096] Step B2.1, Assume N mw The system state at time k is N mw (k) represents the width ambiguity at time k, and N′ mw (k) represents the corresponding rate of change, N″ mw (k) represents the corresponding changing acceleration;

[0097] The system state equation is expressed as:

[0098] S k =AS k-1 +ΓΩ k-1 5);

[0099] In equation 5), A is the transition matrix. Ω is the coefficient matrix of the dynamic noise. k-1 This refers to the system's dynamic noise.

[0100] The observation equation is:

[0101] L k =BS k +dr k 6);

[0102] In Equation 6), B = [1 0] is the observation coefficient matrix; dr k To observe noise;

[0103] Step B2.2, the recursive formula for Kalman filtering is as follows:

[0104] The system state predictions and variance matrix are as follows:

[0105] S (k / k-1) =AS k-1 +TΩ (k-1) 7);

[0106] In Equation 7), S (k / k-1)This is the one-step prediction value of the state vector;

[0107] D(S (k / k-1) )=AD(S (k-1) A T +ΓD(Ω (k-1) )Γ T 8);

[0108] In equation 8), D(S) (k / k-1) D(Ω) is the one-step prediction of the variance matrix of the state vector. k-1 ) represents the dynamic noise variance matrix;

[0109] The optimal valuation and variance matrix are:

[0110] V (k / k-1) =L k -BS (k / k-1)) 9);

[0111] In equation 9), V (k / k-1) To predict the residuals, L k The monitoring results at time k;

[0112]

[0113] In Equation 10), This is the optimal estimate of the state vector;

[0114]

[0115] In Equation 11), J is the variance matrix for the optimal estimate. k For Kalman gain; where,

[0116] J k =D(S) (k / k-1)) B T [D(r k )+BD(S (k / k-1) B T ] -1 12);

[0117] In equation 12), D(r) k ) for t k The observation noise variance matrix at each time step.

[0118] Step B3: Detect cycle slips using the prediction residuals in the Kalman filter model; specifically:

[0119] Step B3.1: Calculate the initial value of the wide-lane ambiguity and the initial variance matrix of the wide-lane ambiguity;

[0120] Step B3.2: Take the average value of the first 100 wide-lane ambiguities in the initial value of wide-lane ambiguity, and take the standard error of wide-lane ambiguity as the initial variance matrix of wide-lane ambiguity;

[0121] Step B3.3, Cycle slip judgment:

[0122] For GEO satellites, the original 4-fold error determination principle is adopted;

[0123] For IGSO and MEO satellites, an amplification factor θ is added to the original four times the mean square error. w ,

[0124] θ w =exp(-α / 20)+1 13);

[0125] Where α is the satellite elevation angle;

[0126] The judgment principle is as shown in Equation 14):

[0127] |V( k / k-1) |≥4δ w θ w 14);

[0128] When |V (k / k-1) |≥4δ w θ w Then, a cycle slip is determined to have occurred; where δ w The mean square error of the predicted residuals within the sliding window;

[0129] Step B4: When a cycle slip is detected, set J... k =0, so that the current cycle slip value does not affect the subsequent filtering effect;

[0130] When it is determined that there is no cycle slip, J k =J k β, β are the filtering coefficients. The Kalman gain is dynamically adjusted according to the satellite elevation angle, and ambiguity is fixed and coordinates are solved to obtain the BeiDou slope monitoring results.

[0131]

[0132]

[0133] When a cycle slip is detected at this epoch, a polynomial fitting is first performed using the clean carrier observations within the smoothing window. Then, the predicted carrier observations for the cycle slip epoch are extrapolated based on the polynomial and compared with the actual observations. The residual terms are rounded using the integer characteristics of the cycle slip to obtain the cycle slip value. Finally, the cycle slip observations are repaired to obtain the BeiDou slope monitoring results.

[0134]

[0135] Where Δslip is the cycle slip number, L′ i For the extrapolated carrier observation, L i These are actual observed values.

[0136] To verify the effectiveness of the cycle slip detection method of this invention, data from a BeiDou receiver was used for testing. The receiver sampling interval was 1 second, and data was collected for 2 hours, resulting in a total of 2*60*60 = 7200 epoch data points. Cycle slips of different sizes were manually added to the L1 and L2 frequency observation data for testing. The specific steps are as follows:

[0137] (1) Initialization: Obtain the initial coordinates of the Beidou monitoring station and the initial position measured by the IMU.

[0138] (2) Calculate the double-difference ambiguity and residuals using the double-difference method. Calculate the double-difference residuals of the C06 satellite based on the initial coordinates of the BeiDou monitoring station obtained in step (1). There are 60 double-difference equations per minute. Calculate the ambiguity and residuals using the least squares algorithm according to formula 1). (ΔX r (t), ΔY r (t), ΔZ r (t) and residual σ(t); if residual σ(t) > 4Δσ, then it is judged that a cycle slip is suspected, and the Kalman filtering algorithm is used to further detect cycle slips; if σ(t) ≤ 4Δσ, then it is judged that no cycle slip has occurred, and the next step of ambiguity fixing and coordinate solution is performed; where Δσ is the standard error of σ(t).

[0139] (3) Cycle slip detection was performed using the Kalman filtering algorithm. The detection results are as follows: Figure 3 As shown in the figure, the data points are MW values, the solid black line in the middle is the Kalman filter value, and the black dashed lines above and below are the upper and lower limits of MW values ​​calculated according to formulas 11), 12), 13), and 14) of this embodiment; the data points circled are the detected cycle slips.

[0140] In this data experiment, artificially added cycle slips were effectively detected. The specific values ​​of added cycle slips and the detection results are shown in Table 1.

[0141] Table 1. Results of Triple Jump Detection

[0142]

[0143] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A cycle slip detection method for BeiDou slope monitoring, characterized in that, Includes the following steps: Step S1: Initialization; Specifically: Set the initialization time; During the initialization process, the BeiDou receiver and IMU collect data respectively; Perform carrier phase difference calculation on the data from the monitoring station and the base station to obtain the initial coordinates (X) of the monitoring station within the corresponding time period. r0 Y r0 Z r0 ) and the initial coordinates (x) measured by the IMU imu y imu , z imu ); Step S2, displacement monitoring; specifically: set a threshold; retrieve the k-th result after IMU initialization. judge Does it exceed the threshold? If the threshold is exceeded, the monitoring point is suspected of having shifted. If the threshold is not exceeded, it is determined that the monitoring point has not been displaced; Step S3: Based on the displacement monitoring results in Step S2, if it is determined that the monitoring point is suspected of having undergone displacement, proceed to Step S5; if it is determined that the monitoring point has not undergone displacement, proceed to Step S4. Step S4: Perform a preliminary cycle slip judgment based on the double difference method. If no cycle slip is determined to have occurred, perform ambiguity fixing and coordinate calculation to obtain the BeiDou slope monitoring results. If a cycle slip is suspected to have occurred, proceed to step S5. Step S5: Use the Kalman filter algorithm to detect cycle slips. When no cycle slip is detected, dynamically adjust the Kalman gain according to the satellite elevation angle, and fix the ambiguity and solve the coordinates to obtain the BeiDou slope monitoring results. When a cycle slip is detected, set the Kalman gain to 0 so that the current cycle slip value does not affect the subsequent filtering effect, and proceed to step S6. Step S6: When a cycle slip is detected at this epoch, firstly, polynomial fitting is performed using the clean carrier observations within the smoothing window. Then, the predicted carrier observations for the cycle slip epoch are extrapolated based on the polynomial and compared with the actual observations. The residual terms are rounded using the integer characteristics of the cycle slip to obtain the cycle slip value. Finally, the cycle slip observations are repaired, and the repaired observations are used to recalculate to obtain the BeiDou slope monitoring results.

2. The cycle slip detection method for BeiDou slope monitoring according to claim 1, characterized in that, The following are the specific steps for using the double-difference method to make a preliminary judgment on cycle slips: Step A1: Establish the observation equation, as shown in Equation 1): Where: i represents the i-th satellite, j represents the j-th satellite, b represents the base station, r represents the monitoring station, and λ represents the wavelength. For base station b and monitoring station r, the double-difference observations of satellites i and j are given. The direction vector difference between the monitoring station and satellites i and j is calculated and is a known quantity. The difference between the distances from the monitoring station to satellites i and j. The difference between the distances from the base station to satellites i and j is calculated and is a known quantity. (ΔX r (t), ΔY r (t), ΔZ r (t) represents the coordinates of the monitoring station at time t (X). r (t), Y r (t), Z r (t) and initial coordinates (X) r0 Y r0 Z r0 The difference between (ΔX) and (ΔX) under normal, no displacement conditions. r (t), ΔY r (t), ΔZ r (t) is a constant; The integer ambiguity difference between satellite j and satellite i at time t of the base station and monitoring station is a constant value when no cycle slip occurs; Step A2: Calculate the double-difference ambiguity and residuals: Using 1 minute of observation data, construct the observation equations according to formula 1). Assuming the receiver sampling interval is s seconds, there are 60 double-difference equations in 1 minute. Calculate the ambiguity and residuals using the adjustment least squares algorithm. (ΔX r (t), ΔY r (t), ΔZ r (t)) and residual σ(t); If the residual σ(t) > 4Δσ, then a cycle slip is suspected, and the Kalman filtering algorithm is used to further detect the cycle slip; if σ(t) ≤ 4Δσ, then no cycle slip is determined, and ambiguity fixing and coordinate calculation are performed; where Δσ is the standard error of σ(t).

3. The cycle slip detection method for BeiDou slope monitoring according to claim 1, characterized in that, The Kalman filter algorithm is used to detect cycle slips. The specific steps are as follows: Step B1: Calculate the width ambiguity of the monitoring points using the MW combined algorithm; The MW combination algorithm is obtained by combining dual-frequency carrier phase observations and dual-frequency pseudorange observations. Its mathematical model is as follows: In Equation 2), These are the carrier phase observations for L1 and L2, respectively; P1 and P2 are pseudorange observations; λ1 and λ2 are the band wavelengths; N mw For wide-lane ambiguity; where L1 and L2 refer to the observation data of the two frequency bands output by the dual-frequency Beidou receiver; Step B2: Detect cycle slips using an adaptive Kalman filter model; Step B3: Detect cycle slips using the predicted residuals in the Kalman filter model; Step B4: When a cycle slip is detected, set J... k =0, so that the current cycle slip value does not affect the subsequent filtering effect; When it is determined that there is no cycle slip, J k =J k β, β are the filtering coefficients. The Kalman gain is dynamically adjusted according to the satellite elevation angle, and ambiguity is fixed and coordinates are solved to obtain the BeiDou slope monitoring results. In equations 3) and 4), J k δ is the Kalman gain, α is the satellite elevation angle; w θ represents the standard error of the predicted residuals within the sliding window. w V is the magnification factor. (k / k-1) To predict residuals.

4. The cycle slip detection method for BeiDou slope monitoring according to claim 3, characterized in that, Step B2, which uses an adaptive Kalman filter model to detect cycle slips, specifically includes the following steps: Step B2.1, Assume N mw The system state at time k is N mw (k) represents the width ambiguity at time k, and N′ mw (k) represents the corresponding rate of change, N″ mw (k) represents the corresponding changing acceleration; The system state equation is expressed as: S k =AS k-1 +GO k-1 5); In equation 5), A is the transition matrix. Ω is the coefficient matrix of the dynamic noise. k-1 This refers to the system's dynamic noise. The observation equation is: L k =BS k +dr k 6); In Equation 6), B = [1 0] is the observation coefficient matrix; dr k To observe noise; Step B2.2, the recursive formula for Kalman filtering is as follows: The system state predictions and variance matrix are as follows: S (k / k-1) =AS k-1 +GO (k-1) 7); In Equation 7), S (k / k-1) This is the one-step prediction value of the state vector; D(S (k / k-1) )=AD(S (k-1) )A T +ΓD(Ω (k-1) )C T 8); In equation 8), D(S) (k / k-1) D(Ω) is the one-step prediction of the variance matrix of the state vector. k-1 ) represents the dynamic noise variance matrix; The optimal valuation and variance matrix are: V (k / k-1) =L k -BS (k / k-1)) 9); In equation 9), V (k / k-1) To predict the residuals, L k The monitoring results at time k; In Equation 10), This is the optimal estimate of the state vector; In Equation 11), J is the variance matrix for the optimal estimate. k For Kalman gain; where J k =D(S (k / k-1) )B T [D(r k )+BD(S (k / k-1) )B T ] -1 12); In equation 12), D(r) k ) for t k The observation noise variance matrix at each time step.

5. The cycle slip detection method for BeiDou slope monitoring according to claim 3, characterized in that, Step B3, which uses the predicted residual to detect cycle slips in the Kalman filter model, specifically includes the following steps: Step B3.1: Calculate the initial value of the wide-lane ambiguity and the initial variance matrix of the wide-lane ambiguity; Step B3.2: Take the average value of the first 100 wide-lane ambiguities in the initial value of wide-lane ambiguity, and take the standard error of wide-lane ambiguity as the initial variance matrix of wide-lane ambiguity; Step B3.3, Cycle slip judgment: For GEO satellites, the original 4-fold error determination principle is adopted; For IGSO and MEO satellites, an amplification factor θ is added to the original 4 times the mean square error. w , i w =exp(-α / 20)+1 13); Where α is the satellite elevation angle; The judgment principle is as shown in Equation 14): |V (k / k-1) |≥4d w i w 14); When |V (k / k-1) |≥4δ w θ w Then, a cycle slip is determined to have occurred; where δ w This represents the standard error of the predicted residuals within the sliding window.

6. The cycle slip detection method for BeiDou slope monitoring according to claim 1, characterized in that, When a cycle slip is detected at this epoch, a polynomial fitting is first performed using the clean carrier observations within the smoothing window. Then, the predicted carrier observations for the cycle slip epoch are extrapolated based on the polynomial and compared with the actual observations. The residual terms are rounded using the integer characteristics of the cycle slip to obtain the cycle slip value. Finally, the cycle slip observations are repaired to obtain the BeiDou slope monitoring results. Δslip=|L i -L′ i | 15); Where Δslip is the cycle slip number, L′ i For the extrapolated carrier observation, L i These are actual observed values.

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