Three-dimensional coordinate accurate positioning monitoring method and system based on BDS

Abstract

The invention discloses a BDS-based three-dimensional coordinate accurate positioning monitoring method and system, and belongs to the technical field of satellite navigation and positioning, and the method specifically comprises the steps: calculating a pseudo-range measurement value and a carrier phase observation value through receiving a navigation satellite signal; eliminating a multi-path effect by adopting a multi-path detection algorithm and a dynamic multi-path error model, and correcting a pseudo-range measurement value by utilizing an error source model; dynamic calibration parameters are calculated by combining real-time environment data and a preset calibration model, and the pseudo-range measurement value is recorrected; calculating the position by using a carrier phase difference algorithm, and outputting a three-dimensional coordinate and a displacement variation; according to the method, high-precision and real-time displacement monitoring is realized through comprehensive processing of a multi-path effect and dynamic calibration.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite navigation and positioning, and specifically relates to a three-dimensional coordinate precise positioning and monitoring method and system based on BDS. Background Technique

[0002] The technology of Beidou Navigation Satellite System (BDS) is widely used in fields such as surface displacement monitoring and building deformation monitoring. Traditional displacement monitoring stations usually rely on static calibration methods, that is, calibration is carried out once before the monitoring starts, and then fixed calibration parameters are used throughout the monitoring process. However, environmental factors such as temperature changes, atmospheric conditions, and multipath effects will cause the measurement errors of the monitoring station receiver to change over time, and the static calibration method cannot effectively cope with these dynamic changes, resulting in a decrease in monitoring accuracy.

[0003] As disclosed in the Chinese patent with the authorization announcement number CN115507733B, an electric tower displacement detection method, device, and computer equipment include: obtaining monitoring station data corresponding to the electric tower through a satellite monitoring device configured on the electric tower; constructing a double-difference equation corresponding to a target satellite at two consecutive epochs according to the monitoring station data, and combining each double-difference equation to obtain a least-squares observation equation corresponding to the target satellite; solving the least-squares observation equation according to a preset solution interval to obtain the three-direction displacement amounts and clock bias residuals corresponding to the solution interval; substituting the three-direction displacement amounts and clock bias residuals into the least-squares observation equation to calculate the residuals of each epoch in the least-squares observation equation; and detecting the displacement of the electric tower according to the three-direction displacement amounts and the residuals of each epoch. Using this technology can improve the accuracy of electric tower displacement detection.

[0004] The above existing technologies have the following problems: mainly based on the satellite monitoring device configured on the electric tower for data acquisition and processing, so its applicable range is relatively limited, mainly applicable to the displacement monitoring of specific structures such as electric towers; there is a lack of sufficient flexibility in data processing and displacement detection; the actual accuracy may be affected by various factors, resulting in low accuracy. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention proposes a three-dimensional coordinate precise positioning and monitoring method and system based on BDS. By receiving navigation satellite signals, pseudo-range measurement values and carrier phase observation values are calculated; a multipath detection algorithm and a dynamic multipath error model are used to eliminate the multipath effect, and the pseudo-range measurement values are corrected using an error source model; combining real-time environmental data with a preset calibration model to calculate dynamic calibration parameters, and re-correcting the pseudo-range measurement values; using the carrier phase differential algorithm to solve for the position and output the three-dimensional coordinates and displacement change amount; this method realizes high-precision and real-time displacement monitoring through comprehensive processing of the multipath effect and dynamic calibration.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A three-dimensional coordinate precise positioning and monitoring method based on BDS, comprising:

[0008] Step S1: The antenna of the displacement monitoring station receives signals from N navigation satellites, and records the pseudo-range measurement values and carrier phase observation values of each navigation satellite arriving at the displacement monitoring station;

[0009] Step S2: Combining a preset multipath detection algorithm and a dynamic multipath error model to correct the pseudo-range measurement values of each navigation satellite arriving at the displacement monitoring station, obtaining the preliminarily corrected pseudo-range measurement values; the multipath detection algorithm identifies multipath interference based on the signal reflection characteristics;

[0010] Step S3: According to the real-time obtained environmental data of the displacement monitoring station and a preset calibration model, calculate dynamic calibration parameters, and use the dynamic calibration parameters to re-correct the preliminarily corrected pseudo-range measurement values, obtaining the corrected pseudo-range measurement values;

[0011] Step S4: According to the corrected pseudo-range measurement values, combining the carrier phase observation values and the known navigation satellite position information, use the carrier phase differential algorithm for position solution, and output the three-dimensional coordinates and displacement change amount of the displacement monitoring station; the three-dimensional coordinates refer to longitude, latitude and elevation.

[0012] Specifically, the specific steps of the said Step S2 include:

[0013] S2.1: Obtain the pseudo-range measurement value ρ i and the carrier phase observation value of each navigation satellite arriving at the displacement monitoring station, and i ∈ [1, N];

[0014] S2.2: According to ρ i and use the multipath detection algorithm to analyze the received navigation satellite signals to detect whether there is a multipath effect;

[0015] If the detection result s of the multipath detection algorithm i is not within the standard value range [h left , h right when there is no multipath interference, it indicates that there is multipath interference, where h left represents the minimum fluctuation value of the standard value when there is no multipath interference, and h right represents the maximum fluctuation value of the standard value when there is no multipath interference;

[0016] If multipath interference is detected, the signal loss situation is evaluated through the signal-to-noise ratio η;

[0017] S2.3: According to the evaluated signal loss situation, adjust the parameters of the pre-constructed dynamic multipath error model.

[0018] Specifically, the specific steps of step S2 further include:

[0019] S2.4: Use the adjusted dynamic multipath error model to calculate the multipath error e1 in the received navigation satellite signal;

[0020] S2.5: According to the signal loss evaluation result, obtain the signal loss compensation value s bu ;

[0021] S2.6: Subtract the multipath error and the signal loss compensation value s bu from the received navigation satellite signal to obtain the pseudorange measurement value ρ i ' after double elimination of multipath effect and signal loss;

[0022] S2.7: Use the error source model to correct the pseudorange measurement value after double elimination of multipath effect and signal loss to obtain the optimized pseudorange measurement value ρ i ";

[0023] S2.8: Use the adaptive filtering algorithm to smooth the optimized pseudorange measurement value ρ i " to obtain the preliminarily corrected pseudorange measurement value

[0024] Specifically, the specific steps of S2.5 include:

[0025] S2.51: Obtain the signal loss evaluation result;

[0026] S2.52: Set the initial parameters of the adaptive compensation algorithm, and the initial parameters of the adaptive compensation algorithm include filter coefficients and learning rate;

[0027] S2.53: Input the signal loss evaluation result into the adaptive compensation algorithm;

[0028] The adaptive compensation algorithm automatically adjusts its own parameters according to the characteristics of the input signal and the changes in the environment. Through iterative calculation, the adaptive compensation algorithm converges to the optimal solution, and the signal loss compensation value s is obtained. bu 。

[0029] Specifically, the specific steps of S2.7 include:

[0030] S2.71: Obtain the pseudorange measurement value ρ i ′;

[0031] S2.72: Analyze the causes and characteristics of the receiver clock error, including the frequency stability of the crystal oscillator and the temperature effect;

[0032] S2.73: According to the analysis results, establish a mathematical model Δt of the receiver clock error R(t) ;

[0033] S2.74: Take ρ i ′ as the input, subtract the product of the mathematical model Δt of the receiver clock error and the speed of light c, and obtain the optimized pseudorange measurement value ρ R(t) ″. i ″.

[0034] Specifically, the specific steps of step S3 include:

[0035] S3.1: According to the position information of the displacement monitoring station, combined with map data and terrain features, construct a position compensation model; the position information is obtained based on map data and includes longitude, latitude, and altitude information;

[0036] S3.2: Input the position information of the displacement monitoring station and the terrain feature data into the position compensation model, and output the position compensation parameter Δρ pos ; the position compensation parameter includes terrain slope compensation Δρ slope 、terrain height difference compensation Δρ height ;

[0037] S3.3: Load the pre-constructed calibration model based on polynomial regression;

[0038] S3.4: Real-time collect environmental data through the displacement monitoring station, input the real-time collected environmental data into the pre-constructed calibration model based on polynomial regression, and combine with the position compensation parameter to output the dynamic calibration parameter Δρ dynamic ;

[0039] S3.5: Use the dynamic calibration parameter to correct the preliminarily corrected pseudorange measurement value to obtain the corrected pseudorange measurement value

[0040] Specifically, the specific steps of S3.5 include:

[0041] S3.51: Obtain the preliminarily corrected pseudorange measurement value

[0042] S3.52: Obtain the dynamic calibration parameter Δρ described in S3.4 dynamic ;

[0043] S3.53: Apply the dynamic calibration parameter to the preliminarily corrected pseudorange measurement value and adjust it by summation, and reassign the adjusted to obtain the corrected pseudorange measurement value

[0044] S3.54: Output the corrected pseudorange measurement value

[0045] Specifically, the specific steps of step S4 include:

[0046] S4.1: Obtain the corrected pseudorange measurement value carrier phase observation value and the known navigation satellite position information Y;

[0047] S4.2: Combine the corrected pseudorange measurement value and use the carrier phase difference algorithm to analyze the carrier phase observation value to obtain the carrier phase difference observation value for the same navigation satellite

[0048] S4.3: According to the carrier phase difference observation principle, establish an observation equation for each navigation satellite; the observation equation includes the three-dimensional coordinates of the displacement monitoring station, the three-dimensional coordinates of the navigation satellite, the carrier phase difference observation value the carrier wavelength λ i and the integer ambiguity M i , where the three-dimensional coordinates of the navigation satellite are known parameters and are obtained from the known navigation satellite position information;

[0049] S4.4: Initialize M i and the three-dimensional coordinates of the displacement monitoring station, and use them as the initial estimated values;

[0050] S4.5: Use the least squares method to iteratively solve the observation equation, update the estimated values of the three-dimensional coordinates of the displacement monitoring station and the integer ambiguity. At the same time, in each iteration, calculate the residual G(y) according to the observation equation and the current estimated value, and calculate miny||G(y)||2, where G(y) represents the residual function, y represents the parameter vector to be solved, including the three-dimensional coordinates of the displacement monitoring station and the integer ambiguity, and ||G(y)||2 represents the 2-norm of G(y).

[0051] S4.6: Check whether the residual is less than a preset threshold;

[0052] If the residual is less than the preset threshold, output the final three-dimensional coordinates of the displacement monitoring station and the integer ambiguity;

[0053] If the residual is greater than the preset threshold, re-initialize the parameters;

[0054] S4.7: Obtain the three-dimensional coordinates of the displacement monitoring station at different time points and use them as the reference three-dimensional coordinates;

[0055] S4.8: Calculate the displacement change by comparing the three-dimensional coordinates at the current time point with the reference three-dimensional coordinates;

[0056] S4.9: Output the three-dimensional coordinates of the displacement monitoring station and the displacement change.

[0057] A three-dimensional coordinate precise positioning and monitoring system based on BDS, comprising: a signal receiving module, a multipath effect module, a dynamic calibration module, and a position calculation module;

[0058] The signal receiving module is used to receive signals from navigation satellites and record information;

[0059] The multipath effect module is used to detect and eliminate the influence of multipath effects on the pseudorange measurement values;

[0060] The dynamic calibration module is used to calculate dynamic calibration parameters according to real-time environmental data and a preset calibration model, and re-correct the pseudorange measurement values;

[0061] The position calculation module is used to combine the corrected pseudorange measurement values, carrier phase observation values, and known navigation satellite position information, use the carrier phase differential algorithm for position calculation, and output the three-dimensional coordinates of the displacement monitoring station and the displacement change.

[0062] Specifically, the multipath effect module includes: a multipath detection unit, a multipath elimination unit, a signal loss compensation unit, an error correction unit, and an adaptive filtering unit;

[0063] The multi-path detection unit is used to analyze the received navigation satellite signals using a multi-path detection algorithm to determine whether there is multi-path interference;

[0064] The multi-path elimination unit is used to eliminate interference using a dynamic multi-path error model when multi-path interference is detected;

[0065] The signal loss compensation unit is used to calculate a signal loss compensation value according to the signal loss evaluation result;

[0066] The error correction unit is used to further correct the pseudo-range measurement value after multi-path effect elimination using an error source model;

[0067] The adaptive filtering unit is used to smooth the pseudo-range measurement value using an adaptive filtering algorithm to obtain a preliminarily corrected pseudo-range measurement value.

[0068] Compared with the prior art, the beneficial effects of the present invention are:

[0069] 1. The present invention proposes a three-dimensional coordinate precise positioning and monitoring system based on BDS, and has optimized improvements in architecture, operation steps and processes. The system has the advantages of simple process, low investment and operation costs, and low production work costs.

[0070] 2. The present invention proposes a three-dimensional coordinate precise positioning and monitoring method based on BDS. By multi-path detection and error correction, the accuracy of the pseudo-range measurement value is improved; the multi-path interference is identified and eliminated using a multi-path detection algorithm, and corrections are made in combination with a dynamic multi-path error model and an error source model, and then smoothed by an adaptive filtering algorithm to reduce the error sources.

[0071] 3. The present invention proposes a three-dimensional coordinate precise positioning and monitoring method based on BDS. Through dynamic calibration and position compensation, precise re-correction of the pseudo-range measurement value is achieved. Dynamic calibration parameters are calculated in combination with real-time environmental data and a preset calibration model, and position compensation parameters are considered at the same time to ensure that the corrected pseudo-range measurement value can truly reflect the actual position of the monitoring station; finally, carrier phase differential algorithm is used for position calculation to output high-precision three-dimensional coordinates and displacement changes. Description of the Drawings

[0072] Figure 1 It is a flowchart of a three-dimensional coordinate precise positioning and monitoring method based on BDS of the present invention;

[0073] Figure 2 It is a principle flowchart of a three-dimensional coordinate precise positioning and monitoring method based on BDS of the present invention;

[0074] Figure 3Schematic diagram of a three-dimensional coordinate precise positioning and monitoring method based on BDS according to the present invention;

[0075] Figure 4 Architecture diagram of a three-dimensional coordinate precise positioning and monitoring system based on BDS according to the present invention. Detailed implementation manners

[0076] Example 1

[0077] Please refer to Figures 1 - 3 , in Figure 3 , A represents a reference station, B represents a displacement monitoring station, C1, CN represent Beidou navigation satellites. When acquiring signals, the reference station and the displacement monitoring station respectively acquire signals transmitted by different navigation satellites, and signal transmission or communication can be carried out between the reference station and the displacement monitoring station. An example provided by the present invention: A three-dimensional coordinate precise positioning and monitoring method based on BDS, applicable to fields such as geological disaster monitoring, building deformation monitoring, and bridge health monitoring, includes the following steps:

[0078] Step S1: The antenna of the displacement monitoring station receives signals from N navigation satellites, and records the pseudorange measurement values and carrier phase observation values of each navigation satellite arriving at the displacement monitoring station;

[0079] Further, the specific steps of step S1 include:

[0080] (1) The displacement monitoring station is powered on and initialized, including antenna calibration and receiver parameter configuration;

[0081] (2) The antenna of the displacement monitoring station receives signals from N navigation satellites, and tracks these signals through the matching of pseudorandom noise codes;

[0082] (3) Record the time when each navigation satellite signal propagates to the antenna through the timestamp embedded in the satellite signal;

[0083] (4) Using the recorded time information, calculate the pseudorange of each navigation satellite arriving at the displacement monitoring station. Among them, the pseudorange is obtained by multiplying the signal propagation time by the speed of light, and the formula is: ρ = (t R - t T ) × c. However, due to the interference of factors such as the atmosphere and ionosphere, this distance is not the real distance, but the pseudorange. Among them, ρ represents the pseudorange measurement value, t R represents the time when the receiver receives the signal, t T represents the time when the navigation satellite transmits the signal, and c represents the speed of light;

[0084] (5) At the same time, the displacement monitoring station also records the carrier phase observation values of each navigation satellite. Among them, the carrier phase observation value is the phase change of the navigation satellite signal during propagation.

[0085] Step S2: Combine the preset multi-path detection algorithm and the dynamic multi-path error model to correct the pseudo-range measurement values of each navigation satellite to the displacement monitoring station, and obtain the preliminarily corrected pseudo-range measurement values; the multi-path detection algorithm identifies multi-path interference based on the signal reflection characteristics;

[0086] It should be understood that the pseudo-range measurement value of each navigation satellite refers to the distance measured by the BDS receiver between the receiver and the navigation satellite. This distance measurement includes the errors of the receiver and satellite clocks, the delay of factors such as atmospheric refraction, etc., so it is called pseudo-range. Pseudo-range measurement is the basis for the satellite navigation system to achieve navigation and positioning. The pseudo-range measurement value of each navigation satellite may have multi-path effects. The multi-path effect means that in addition to directly receiving the signal transmitted by the satellite, the receiver also simultaneously receives the signal reflected once or multiple times by the surface of the object near the displacement monitoring station. The superposition of the signals of different paths and the direct signal will produce a time-delay effect, that is, multi-path error. The multi-path error is one of the main error sources in pseudo-range measurement, which will affect the accuracy of pseudo-range measurement. Therefore, in displacement monitoring applications, it is necessary to use a multi-path detection algorithm to detect the multi-path effect of the received satellite signal. If there is multi-path interference, a corresponding error model is used to eliminate the interference to improve the accuracy and reliability of pseudo-range measurement.

[0087] In the present invention, the multi-path detection algorithm first determines whether there is multi-path interference by evaluating the phase or signal-to-noise ratio of the signal. If abnormal changes in these characteristics are detected, the signals with multi-path interference will be identified. Through multi-path detection, it can accurately identify which signals are affected by the multi-path effect; once the multi-path interference is identified, the dynamic multi-path error model will estimate the magnitude of the error caused by the multi-path effect based on the current environmental conditions and signal characteristics, and according to the estimated error, the dynamic multi-path error model will apply a compensation mechanism to reduce or eliminate these errors, reduce the influence of the multi-path effect on the pseudo-range measurement value, and thus improve the accuracy of navigation and positioning; while the error source model will comprehensively consider all known error sources and evaluate their influence on the pseudo-range measurement value. Based on these evaluation results, the error source model will formulate a set of correction strategies to reduce or eliminate these errors. Through the application of the error source model, the errors in the pseudo-range measurement value can be further reduced, thereby improving the accuracy and reliability of navigation and positioning. Especially after the multi-path effect is eliminated, the error source model can further correct other potential error sources to ensure that the finally obtained pseudo-range measurement value is more accurate.

[0088] Step S3: Calculate the dynamic calibration parameters according to the real-time acquired displacement monitoring station environmental data and the preset calibration model, and use the dynamic calibration parameters to re-correct the preliminarily corrected pseudo-range measurement values to obtain the corrected pseudo-range measurement values;

[0089] Step S4: Based on the corrected pseudorange measurement values, combined with the carrier phase observation values and the known navigation satellite position information, use the carrier phase differential algorithm for position calculation, and output the three-dimensional coordinates and displacement variation of the displacement monitoring station; the three-dimensional coordinates refer to longitude, latitude, and elevation.

[0090] Exemplarily, assume there is a displacement monitoring station in a complex environment in the city center, surrounded by high-rise buildings, trees, and other reflectors. The antenna of this displacement monitoring station can receive signals from 4 navigation satellites, that is, N = 4. The goal is to accurately calculate the three-dimensional coordinates (longitude, latitude, and elevation) and displacement variation of the displacement monitoring station;

[0091] First, the antenna of the displacement monitoring station receives signals from 4 navigation satellites, records the time for each navigation satellite signal to propagate to the displacement monitoring station, and calculates the pseudorange measurement value for each navigation satellite according to the signal propagation time and the speed of light. At the same time, record the carrier phase observation value for each navigation satellite, and these values will be used for subsequent position calculation; then, determine whether there is multipath interference in the received signals. If there is multipath interference, evaluate the signal loss situation, use the dynamic multipath error model combined with the signal loss compensation mechanism to eliminate the interference, and obtain the pseudorange measurement value after eliminating the multipath effect. At the same time, use the error source model, such as the ionosphere, to correct the pseudorange measurement value after eliminating the multipath effect, and combine the Kalman filter to smooth the corrected pseudorange measurement value to obtain the preliminarily corrected pseudorange measurement value. For example, the original pseudorange measurement value = 20000 m, the multipath error = 5 m, and the preliminarily corrected pseudorange measurement value = 19995 m; secondly, obtain the environmental data of the displacement monitoring station in real time, such as temperature, humidity, and air pressure, use the air pressure correction model to calculate the dynamic calibration parameters, and use the dynamic calibration parameters to further correct the preliminarily corrected pseudorange measurement value to obtain the corrected pseudorange measurement value. For example, if the preliminarily corrected pseudorange measurement value = 19995 m, then the corrected pseudorange measurement value = 19994.8 m; finally, based on the corrected pseudorange measurement value, the carrier phase observation value, and the known navigation satellite position information, use the carrier phase differential algorithm for position calculation, and output the three-dimensional coordinates and displacement variation of the displacement monitoring station. For example, the three-dimensional coordinates: longitude = 116.397128°, latitude = 39.916527°, elevation = 49.0 m; the displacement variation: east-west direction = 0.01 m, north-south direction = 0.02 m, vertical direction = 0.005 m.

[0092] In summary, the displacement detection method of the displacement monitoring station with dynamic calibration function mainly detects the displacement change of the monitoring target relative to the fixed reference point or the initial state, including but not limited to the absolute or relative displacement of surface points, buildings, and infrastructure, etc. Through technical means such as high-precision satellite signal reception and processing, data comparison and analysis, and dynamic calibration, displacement measurement accuracy at the millimeter level or even sub-millimeter level can be achieved, providing data support for the safety of various projects.

[0093] The specific steps of step S2 include:

[0094] S2.1: Obtain the pseudorange measurement value ρ of each navigation satellite to the displacement monitoring station i and the carrier phase observation value and i ∈ [1, N];

[0095] S2.2: According to ρ i and use the multipath detection algorithm to analyze the received navigation satellite signals to detect whether there is a multipath effect;

[0096] If the detection result s of the multipath detection algorithm i is not within the standard value range [h left , h right without multipath interference, it means that there is multipath interference. Among them, the formula of the multipath detection algorithm is: where λ i represents the carrier wavelength of the i-th navigation satellite, M i represents the integer ambiguity of the i-th navigation satellite, ε represents the ionospheric delay error term, f represents the observation frequency, f0 represents the L1 frequency, h left represents the minimum fluctuation value of the standard value without multipath interference, h right represents the maximum fluctuation value of the standard value without multipath interference;

[0097] If it means that there is multipath interference;

[0098] It should be noted that by accurately detecting multipath interference, the positioning error caused by the multipath effect can be identified and corrected in a timely manner, thereby improving the positioning accuracy of the satellite navigation system. The multipath detection algorithm used in the present invention comprehensively considers λ i , M iSum the error terms and combine with frequency-related factors to evaluate the presence or absence of multipath interference. When the detection result is not within the standard value range without multipath interference, the algorithm can accurately identify the presence of multipath interference, promptly detect and correct these errors, thereby improving the positioning accuracy and reducing the deviation caused by the multipath effect. Exemplarily, in complex environments such as urban canyons or mountainous areas, multipath interference is particularly significant. The multipath detection algorithm used in the present invention can enhance the system stability in complex environments and reduce operation errors or safety risks caused by inaccurate positioning.

[0099] If multipath interference is detected, then use the signal-to-noise ratio formula to evaluate the signal loss situation, where η represents the signal loss ratio, SNR B represents the signal-to-noise ratio standard value, and SNR R represents the actually measured signal-to-noise ratio value;

[0100] S2.3: Adjust the parameters of the pre-constructed dynamic multipath error model according to the evaluated signal loss situation;

[0101] Further, the specific steps of S2.3 include:

[0102] (1) Load the pre-constructed dynamic multipath error model;

[0103] (2) Extract the currently used parameter values r old from the pre-constructed dynamic multipath error model, including the reflection coefficient, path delay, and attenuation factor;

[0104] (3) Develop a parameter adjustment strategy F according to the signal loss evaluation result Q;

[0105] (4) Adjust the parameters of the dynamic multipath error model according to the developed parameter adjustment strategy F. The formula is: r new = r old + F(Q), where r new represents the adjusted parameter value of the dynamic multipath error model.

[0106] Among them, the construction process of the dynamic multipath error model includes:

[0107] (1) Collect multipath error data through actual observations or simulation experiments and perform preprocessing. The multipath error data includes satellite signal strength, phase observation values, and signal-to-noise ratio;

[0108] (2) Adopt a modeling method based on physical principles to construct the framework of the dynamic multipath error model. Among them, the modeling method based on physical principles is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here.

[0109] (3) Validate the dynamic multipath error model using an independent validation dataset, and deploy the validated dynamic multipath error model to an actual application scenario, where the independent validation dataset is divided from the preprocessed multipath error data.

[0110] S2.4: Calculate the multipath error e1 in the received navigation satellite signal using the adjusted dynamic multipath error model;

[0111] Further, the specific steps of S2.4 include:

[0112] (1) Obtain the signal data sent by the navigation satellite, including the direct signal and the possible reflected signal;

[0113] (2) Estimate the parameters of the dynamic multipath error model by the least squares method using the signal data sent by the navigation satellite;

[0114] (3) Obtain the adjusted dynamic multipath error model;

[0115] (4) Substitute the estimated model parameters into the adjusted dynamic multipath error model to calculate the multipath error e1 = f2(Δθ, α) in the received navigation satellite signal, where Δθ represents the phase difference between the direct wave and the reflected wave, α represents the reflection coefficient, and f2(·) represents the interference effect function of the direct wave and the reflected wave.

[0116] S2.5: Obtain the signal loss compensation value s using the adaptive compensation algorithm according to the signal loss evaluation result bu ;

[0117] S2.6: Subtract the multipath error and the signal loss compensation value s from the received navigation satellite signal bu , to obtain the pseudorange measurement value ρ i ′ after double elimination of the multipath effect and signal loss, and the formula is: ρ i ′ = ρ i - (e1 + s bu );

[0118] S2.7: Correct the pseudorange measurement value after double elimination of the multipath effect and signal loss using the error source model to obtain the optimized pseudorange measurement value ρ i ″;

[0119] S2.8: Smooth the optimized pseudorange measurement value ρ i ″ using the adaptive filtering algorithm to obtain the preliminarily corrected pseudorange measurement value The adaptive filtering algorithm is used to suppress the noise and random errors in the pseudorange measurement values. Among them, the adaptive filtering algorithm adopts the Kalman filtering algorithm, and the Kalman filtering algorithm is the prior art content in this field and is not the creative solution of this application, so it will not be elaborated here.

[0120] The specific steps of S2.5 include:

[0121] S2.51: Obtain the signal loss evaluation result;

[0122] S2.52: Set the initial parameters of the adaptive compensation algorithm. The initial parameters of the adaptive compensation algorithm include filter coefficients and learning rate;

[0123] S2.53: Input the signal loss evaluation result into the adaptive compensation algorithm;

[0124] The adaptive compensation algorithm automatically adjusts its own parameters according to the characteristics of the input signal and the changes in the environment. Through iterative calculation, the adaptive compensation algorithm converges to the optimal solution to obtain the signal loss compensation value s bu 。

[0125] The specific steps of S2.7 include:

[0126] S2.71: Obtain the pseudorange measurement value ρ i ′ after eliminating multipath effects and signal loss;

[0127] S2.72: Analyze the causes and characteristics of the receiver clock error, including the frequency stability and temperature effect of the crystal oscillator;

[0128] S2.73: According to the analysis results, establish a mathematical model of the receiver clock error. The mathematical model of the receiver clock error is Δt R(t) =b + βt, where Δt R(t) represents the receiver clock error at time t, b represents the constant term, and β represents the time coefficient;

[0129] S2.74: Take ρ i ′ as the input, subtract the product of the mathematical model Δt R(t) of the receiver clock error and the speed of light c to obtain the optimized pseudorange measurement value ρ i ″, and the formula is: ρ i ″=ρ i ′ - c×Δt R(t) 。

[0130] The specific steps of step S3 include:

[0131] S3.1: Construct a position compensation model based on the position information of the displacement monitoring station, combined with map data and terrain features; the position information is obtained based on map data and includes longitude, latitude, and altitude information. In the present invention, the position compensation model is a polynomial regression model, and the polynomial regression model is prior art content in the field and not a creative solution of this application, so it will not be elaborated here;

[0132] Among them, obtain the terrain data around the displacement monitoring station from the map data source, including altitude, terrain undulation, and slope, and the map data source refers to the geographic information system database; then, evaluate the possible impact on pseudorange measurement according to the terrain features, such as atmospheric refraction, multipath effect, and terrain occlusion.

[0133] It should be understood that the core of the position compensation model is to identify and correct positioning errors by accurately analyzing the position information of the displacement monitoring station, combined with map data and terrain features. These errors may come from various factors, such as atmospheric delay, multipath effect, receiver noise, and terrain occlusion. By constructing the position compensation model, accurate estimation and compensation of these errors can be achieved, thereby improving the positioning accuracy. Therefore, the position compensation model can significantly improve the positioning accuracy and stability by accurately identifying and compensating positioning errors.

[0134] S3.2: Input the position information of the displacement monitoring station and the terrain feature data into the position compensation model, and output the position compensation parameter Δρ pos ; the position compensation parameter includes terrain slope compensation Δρ slope 、terrain height difference compensation Δρ height , where the position compensation parameter Δρ pos The calculation formula is: Among them, L represents the length of the measurement line, δ represents the terrain slope angle, tan(δ) represents the tangent value of the terrain slope angle, n represents the number of contour lines between two points of the measurement line on the terrain, Δh j represents the height difference on the jth contour line, f3(·) represents the measurement line interpolation function, L j represents the jth contour line, and L = ∑L j ;

[0135] S3.3: Load the pre-constructed calibration model based on polynomial regression;

[0136] S3.4: Real-time collect environmental data through the displacement monitoring station, input the real-time collected environmental data into the pre-constructed calibration model based on polynomial regression, and combine the position compensation parameter to output the dynamic calibration parameter Δρ dynamic ;

[0137] Furthermore, the specific steps of S3.4 include:

[0138] (1) Load the pre-built calibration model based on polynomial regression from the storage medium;

[0139] (2) Collect environmental data in real time through the displacement monitoring station and perform preprocessing. Meanwhile, obtain the position compensation parameter Δρ pos ;

[0140] (3) Use the preprocessed environmental data as input variables and input them into the polynomial regression calibration model. Meanwhile, use the position compensation parameter as an additional input variable and input it into the polynomial regression calibration model;

[0141] (4) The polynomial regression calibration model calculates the output dynamic calibration parameter according to the input data;

[0142] (5) Output the dynamic calibration parameter and store the calibration parameter in the specified data storage medium.

[0143] S3.5: Use the dynamic calibration parameter to correct the preliminarily corrected pseudorange measurement value to obtain the corrected pseudorange measurement value

[0144] The specific steps of S3.5 include:

[0145] S3.51: Obtain the preliminarily corrected pseudorange measurement value

[0146] S3.52: Obtain the dynamic calibration parameter Δρ described in S3.4 dynamic ;

[0147] S3.53: Apply the dynamic calibration parameter to the preliminarily corrected pseudorange measurement value and adjust it through summation for and reassign the adjusted to obtain the corrected pseudorange measurement value

[0148] S3.54: Output the corrected pseudorange measurement value

[0149] The specific steps of step S4 include:

[0150] S4.1: Obtain the corrected pseudorange measurement value carrier phase observation value and the known navigation satellite position information Y;

[0151] S4.2: Combine the corrected pseudorange measurement value Analyze the carrier phase observations using the carrier phase differential algorithm to obtain the carrier phase differential observations for the same navigation satellite The formula for the carrier phase differential algorithm is as follows: where and respectively represent the carrier phase observations of the navigation satellite received by the reference station and the displacement monitoring station, and d u,i and d g,i respectively represent the geometric distances from the reference station and the displacement monitoring station to the navigation satellite, and M u,i and M g,i respectively represent the integer ambiguities of the reference station and the displacement monitoring station for the navigation satellite;

[0152] S4.3: According to the principle of carrier phase differential observation, establish an observation equation for each navigation satellite; the observation equation includes the three-dimensional coordinates of the displacement monitoring station, the three-dimensional coordinates of the navigation satellite, and the carrier phase differential observations The carrier wavelength λ i and the integer ambiguity M i , where the three-dimensional coordinates of the navigation satellite are known parameters and are obtained from the known navigation satellite position information;

[0153] where the form of the observation equation is: where noise i represents the observation noise.

[0154] S4.4: Initialize M i and the three-dimensional coordinates of the displacement monitoring station and use them as the initial estimated values;

[0155] S4.5: Use the least squares method to iteratively solve the observation equation, update the estimated values of the three-dimensional coordinates of the displacement monitoring station and the integer ambiguity. At the same time, in each iteration, calculate the residual G(y) according to the observation equation and the current estimated values, and calculate miny||G(y)‖2, where G(y) represents the residual function, which is obtained by calculating the difference between the result of the observation equation and the current estimated values, y represents the parameter vector to be solved, including the three-dimensional coordinates of the displacement monitoring station and the integer ambiguity, ||G(y)||2 represents the 2-norm of G(y), and the least squares method is the prior art content in the field and is not the creative solution of this application, so it will not be elaborated here;

[0156] S4.6: Check whether the residual is less than a preset threshold;

[0157] If the residual is less than the preset threshold, output the final three-dimensional coordinates of the displacement monitoring station and the integer ambiguity;

[0158] If the residual is greater than the preset threshold, re-initialize the parameters;

[0159] S4.7: Obtain the three-dimensional coordinates of the displacement monitoring station at different time points and use them as the reference three-dimensional coordinates;

[0160] S4.8: Calculate the displacement change amount by comparing the three-dimensional coordinates at the current time point with the reference three-dimensional coordinates;

[0161] S4.9: Output the three-dimensional coordinates and the displacement change amount of the displacement monitoring station.

[0162] Embodiment 2

[0163] Please refer to Figure 4 , another embodiment provided by the present invention: A three-dimensional coordinate precise positioning and monitoring system based on BDS, including:

[0164] A signal receiving module, a multipath effect module, a dynamic calibration module, and a position calculation module;

[0165] The signal receiving module is used to receive signals from navigation satellites and record information;

[0166] The multipath effect module is used to detect and eliminate the influence of the multipath effect on the pseudorange measurement value and improve the measurement accuracy;

[0167] The dynamic calibration module is used to calculate dynamic calibration parameters according to real-time environmental data and a preset calibration model, and re-correct the pseudorange measurement value;

[0168] The position calculation module is used to combine the corrected pseudorange measurement value, the carrier phase observation value, and the known position information of the navigation satellite, use the carrier phase differential algorithm to perform position calculation, and output the three-dimensional coordinates and the displacement change amount of the displacement monitoring station.

[0169] The multipath effect module includes: a multipath detection unit, a multipath elimination unit, a signal loss compensation unit, an error correction unit, and an adaptive filtering unit;

[0170] The multipath detection unit is used to analyze the received navigation satellite signals using a multipath detection algorithm to determine whether there is multipath interference;

[0171] The multipath elimination unit is used to eliminate interference using a dynamic multipath error model when multipath interference is detected;

[0172] The signal loss compensation unit is used to calculate a signal loss compensation value according to the signal loss evaluation result;

[0173] The error correction unit is used to further correct the pseudorange measurement value after eliminating the multipath effect using an error source model;

[0174] An adaptive filtering unit, which is used to smooth the pseudorange measurement value by using an adaptive filtering algorithm to obtain a preliminarily corrected pseudorange measurement value.

[0175] The dynamic calibration module includes: an environment monitoring unit, a position compensation unit, a calibration parameter calculation unit, and a re-correction unit;

[0176] The environment monitoring unit is used to obtain the environmental data of the displacement monitoring station in real time, such as temperature, humidity, and atmospheric pressure;

[0177] The position compensation unit is used to construct a position compensation model and generate position compensation parameters;

[0178] The calibration parameter calculation unit is used to calculate dynamic calibration parameters according to the environmental monitoring data and a preset calibration model;

[0179] The re-correction unit is used to re-correct the preliminarily corrected pseudorange measurement value by using the dynamic calibration parameters to obtain a corrected pseudorange measurement value.

[0180] The position calculation module includes: a position calculation unit, a displacement calculation unit, and an output unit;

[0181] The position calculation unit is used to calculate the three-dimensional coordinates of the displacement monitoring station by using the carrier phase differential algorithm in combination with the corrected pseudorange measurement value, the carrier phase observation value, and the navigation satellite position information;

[0182] The displacement calculation unit is used to calculate the displacement change amount of the displacement monitoring station according to the continuous position calculation results;

[0183] The output unit is used to display the calculated three-dimensional coordinates and displacement change amount.

[0184] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make changes, modifications, substitutions, and variations to the above embodiments without departing from the purpose and scope protected by the present invention. These all fall within the protection scope of the present invention.

Claims

1. A three-dimensional coordinate precise positioning and monitoring method based on BDS, characterized in that, Including: Step S1: The antenna of the displacement monitoring station receives signals from N navigation satellites, and records the pseudorange measurement values and carrier phase observation values of each navigation satellite arriving at the displacement monitoring station; Step S2: Combine the preset multipath detection algorithm and the dynamic multipath error model to correct the pseudorange measurement values of each navigation satellite arriving at the displacement monitoring station, and obtain the preliminarily corrected pseudorange measurement values; the multipath detection algorithm identifies multipath interference based on the signal reflection characteristics; Step S3: Calculate the dynamic calibration parameters according to the real-time obtained environmental data of the displacement monitoring station and the preset calibration model, and use the dynamic calibration parameters to further correct the preliminarily corrected pseudorange measurement values to obtain the corrected pseudorange measurement values; Step S4: According to the corrected pseudorange measurement values, combine the carrier phase observation values and the known position information of the navigation satellites, and use the carrier phase differential algorithm for position solution, and output the three-dimensional coordinates and displacement changes of the displacement monitoring station; the three-dimensional coordinates refer to longitude, latitude and elevation.

2. The three-dimensional coordinate precise positioning and monitoring method based on BDS according to claim 1, wherein, The specific steps of the said Step S2 include: S2.1: Obtain the pseudorange measurement value ρ of each navigation satellite reaching the displacement monitoring station i and the carrier phase observation value and i ∈ [1, N]; S2.2: According to ρ i and Use a multipath detection algorithm to analyze the received navigation satellite signals and detect whether there is a multipath effect; If the detection result s of the multipath detection algorithm i is not within the standard value range [h left , h right when there is no multipath interference, it means that there is multipath interference, where h left represents the minimum fluctuation value of the standard value when there is no multipath interference, and h right represents the maximum fluctuation value of the standard value when there is no multipath interference; If multipath interference is detected, evaluate the signal loss situation through the signal-to-noise ratio η; S2.3: Adjust the parameters of the pre-constructed dynamic multipath error model according to the evaluated signal loss situation.

3. The three-dimensional coordinate precise positioning and monitoring method based on BDS according to claim 2, characterized in that, The specific steps of the said Step S2 also include: S2.4: Use the adjusted dynamic multipath error model to calculate the multipath error e1 in the received navigation satellite signal; S2.5: According to the signal loss evaluation result, use the adaptive compensation algorithm to obtain the signal loss compensation value s bu ; S2.6: Subtract the multipath error and the signal loss compensation value s from the received navigation satellite signal bu to obtain the pseudorange measurement value ρ i ′ after double elimination of the multipath effect and signal loss. S2.7: Use the error source model to correct the pseudorange measurement values after double elimination of multipath effect and signal loss, and obtain the optimized pseudorange measurement value ρ i ″; S2.8: Smooth the optimized pseudorange measurement value ρ i ″ using an adaptive filtering algorithm to obtain the preliminarily corrected pseudorange measurement value 4. The three-dimensional coordinate precise positioning and monitoring method based on BDS according to claim 3, wherein, The specific steps of the said S2.5 include: S2.51: Obtain the signal loss evaluation result; S2.52: Set the initial parameters of the adaptive compensation algorithm, and the initial parameters of the adaptive compensation algorithm include filter coefficients and learning rates; S2.53: Input the signal loss evaluation result into the adaptive compensation algorithm; The adaptive compensation algorithm automatically adjusts its own parameters according to the characteristics of the input signal and changes in the environment. Through iterative calculation, the adaptive compensation algorithm converges to the optimal solution and obtains the signal loss compensation value s bu .

5. The three-dimensional coordinate precise positioning and monitoring method based on BDS according to claim 4, wherein, The specific steps of the said S2.7 include: S2.71: Obtain the pseudorange measurement value ρ i ′ S2.72: Analyze the causes and characteristics of the receiver clock error, including the frequency stability and temperature effect of the crystal oscillator; S2.73: Establish a mathematical model of the receiver clock offset Δt based on the analysis results R(t) ; S2.74: Take ρ i ′ as the input, subtract the product of the receiver clock error mathematical model Δt R(t) and the speed of light c to obtain the optimized pseudorange measurement value ρ i ″.

6. The three-dimensional coordinate precise positioning and monitoring method based on BDS according to claim 5, characterized in that The specific steps of the said Step S3 include: S3.1: According to the position information of the displacement monitoring station, combine the map data and terrain features to construct a position compensation model; the position information is obtained based on the map data and includes longitude, latitude and altitude information; S3.2: Input the position information of the displacement monitoring station and the terrain feature data into the position compensation model, and output the position compensation parameter Δρ pos ; The position compensation parameter includes the terrain slope compensation Δρ slope , the terrain height difference compensation Δρ height ; S3.3: Load the pre-constructed calibration model based on polynomial regression; S3.4: Collect environmental data in real time through displacement monitoring stations, input the collected environmental data in real time into a pre-constructed calibration model based on polynomial regression, and combine the position compensation parameters to output dynamic calibration parameter Δρ dynamic ; S3.5: Use the dynamic calibration parameters to correct the preliminarily corrected pseudorange measurement values to obtain the corrected pseudorange measurement values 7. The three-dimensional coordinate precise positioning and monitoring method based on BDS according to claim 6, wherein The specific steps of the said S3.5 include: S3.51: Obtain the pseudo-range measurement value after preliminary correction S3.52: Obtain the dynamic calibration parameter Δρ described in S3.4 dynamic ; S3.53: Apply the dynamic calibration parameters to the preliminarily corrected pseudorange measurements and adjust by summation, and reassign the adjusted to obtain the corrected pseudorange measurements S3.54: Output the corrected pseudorange measurement value 8. The three-dimensional coordinate precise positioning and monitoring method based on BDS according to claim 7, characterized in that The specific steps of the said Step S4 include: S4.1: Obtain the corrected pseudorange measurement value Carrier phase observation value and the known navigation satellite position information Y; S4.2: Combine the corrected pseudorange measurement value Use the carrier phase differential algorithm to analyze the carrier phase observation value to obtain the carrier phase differential observation value for the same navigation satellite S4.3: According to the principle of carrier phase differential observation, an observation equation is established for each navigation satellite; the observation equation includes the three-dimensional coordinates of the displacement monitoring station, the three-dimensional coordinates of the navigation satellite, the carrier phase differential observation value carrier wavelength λ i and the integer ambiguity M i , where the three-dimensional coordinates of the navigation satellite are known parameters and are obtained from the known navigation satellite position information S4.4: Initialize M i and the three-dimensional coordinates of the displacement monitoring station, and use them as the initial estimated values; S4.5: Use the least squares method to iteratively solve the observation equation, update the estimated values of the three-dimensional coordinates and integer ambiguity of the displacement monitoring station. At the same time, in each iteration, calculate the residual G(y) according to the observation equation and the current estimated value, and calculate miny||G(y)||2, where G(y) represents the residual function, y represents the parameter vector to be solved, including the three-dimensional coordinates and integer ambiguity of the displacement monitoring station, and ||G(y)||2 represents the 2-norm of G(y); S4.6: Check whether the residual is less than the preset threshold; If the residual is less than the preset threshold, output the final three-dimensional coordinates and integer ambiguity of the displacement monitoring station; If the residual is greater than the preset threshold, re-initialize the parameters; S4.7: Obtain the three-dimensional coordinates of the displacement monitoring station at different time points and use them as the reference three-dimensional coordinates; S4.8: Calculate the displacement change amount by comparing the three-dimensional coordinates at the current time point with the reference three-dimensional coordinates; S4.9: Output the three-dimensional coordinates and the displacement change amount of the displacement monitoring station.

9. A three-dimensional coordinate precise positioning and monitoring system based on BDS, which is used to implement a three-dimensional coordinate precise positioning and monitoring method according to any one of claims 1-8, and is characterized in that, It includes: a signal receiving module, a multipath effect module, a dynamic calibration module, and a position calculation module; The signal receiving module is used to receive signals from navigation satellites and record information; The multipath effect module is used to detect and eliminate the influence of the multipath effect on the pseudorange measurement value; The dynamic calibration module is used to calculate dynamic calibration parameters according to real-time environmental data and a preset calibration model, and further correct the pseudorange measurement value; The position calculation module is used to combine the corrected pseudorange measurement value, the carrier phase observation value, and the known position information of the navigation satellite, and use the carrier phase differential algorithm to perform position calculation, and output the three-dimensional coordinates and the displacement change amount of the displacement monitoring station.

10. A three-dimensional coordinate precise positioning and monitoring system based on BDS according to claim 9, characterized in that, The multipath effect module includes: a multipath detection unit, a multipath elimination unit, a signal loss compensation unit, an error correction unit, and an adaptive filtering unit; The multipath detection unit is used to analyze the received navigation satellite signals using a multipath detection algorithm to determine whether there is multipath interference; The multipath elimination unit is used to eliminate interference using a dynamic multipath error model when multipath interference is detected; The signal loss compensation unit is used to calculate a signal loss compensation value according to the signal loss evaluation result; The error correction unit is used to further correct the pseudorange measurement value after multipath effect elimination using an error source model; The adaptive filtering unit is used to smooth the pseudorange measurement value using an adaptive filtering algorithm to obtain a preliminarily corrected pseudorange measurement value.

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