A three-dimensional coordinate precision positioning monitoring method and system based on BDS

Abstract

The application discloses a kind of three-dimensional coordinate precision positioning monitoring method and system based on BDS, belong to satellite navigation and positioning technical field, it specifically includes: by receiving navigation satellite signal, calculating pseudorange measurement and carrier phase observation value;Adopt multi-path detection algorithm and dynamic multi-path error model eliminates multi-path effect, and utilize error source model correction pseudorange measurement;Combining real-time environmental data with preset calibration model calculates dynamic calibration parameter, and re-modifies pseudorange measurement;Using carrier phase difference algorithm resolves position, outputs three-dimensional coordinate and displacement variation;The method is by comprehensive processing multi-path effect and dynamic calibration, realizes high-precision, real-time displacement monitoring.

Description

TECHNICAL FIELD

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

[0002] The Beidou satellite navigation system (BDS) technology is widely used in fields such as ground surface displacement monitoring and building deformation monitoring. Traditional displacement monitoring stations usually rely on a static calibration method, i.e., a calibration is performed once before the monitoring begins, and then fixed calibration parameters are used throughout the monitoring process. However, environmental factors such as temperature changes, atmospheric conditions, and multipath effects can cause the measurement error of the monitoring station receiver to change over time. The static calibration method cannot effectively cope with these dynamic changes, resulting in a decrease in monitoring accuracy.

[0003] A Chinese patent with the authorized publication number CN115507733B discloses a method and device for detecting displacement of an electric tower, and a computer device. The method includes: obtaining monitoring station data corresponding to the electric tower through a satellite monitoring device configured on the electric tower; constructing double-difference equations corresponding to a target satellite at two consecutive epochs based on the monitoring station data, and combining each double-difference equation to obtain a least squares observation equation corresponding to the target satellite; performing calculation on the least squares observation equation according to a preset calculation interval to obtain three-direction displacement and clock error residuals corresponding to the calculation interval; substituting the three-direction displacement and the clock error residuals into the least squares observation equation to calculate residuals of each epoch in the least squares observation equation; and detecting displacement of the electric tower according to the three-direction displacement and the residuals of each epoch. The technology can improve the precision of displacement detection of the electric tower.

[0004] The above prior art has the following problems: mainly based on satellite monitoring devices configured on the electric tower for data acquisition and processing, so its application scope is relatively limited, mainly suitable for displacement monitoring of specific structures such as electric towers; lacks sufficient flexibility in data processing and displacement detection; actual precision may be affected by various factors, resulting in low precision. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a three-dimensional coordinate accurate positioning monitoring method and system based on BDS, which receives navigation satellite signals, calculates pseudo-range measurement values and carrier phase observation values, adopts a multi-path detection algorithm and a dynamic multi-path error model to eliminate multi-path effects, and uses an error source model to correct the pseudo-range measurement values; real-time environmental data and a preset calibration model are combined to calculate dynamic calibration parameters, and the pseudo-range measurement values are further corrected; a carrier phase difference algorithm is used to solve the position, and three-dimensional coordinates and displacement changes are output; the method realizes high-precision and real-time displacement monitoring by comprehensively processing multi-path effects and dynamic calibration.

[0006] To achieve the above object, the present application provides the following technical scheme:

[0007] A three-dimensional coordinate accurate positioning 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 value and the carrier phase observation value of each navigation satellite to the displacement monitoring station;

[0009] Step S2: the pseudo-range measurement value of each navigation satellite to the displacement monitoring station is corrected by combining a preset multi-path detection algorithm and a dynamic multi-path error model, and the preliminary corrected pseudo-range measurement value is obtained; the multi-path detection algorithm identifies multi-path interference based on signal reflection characteristics;

[0010] Step S3: according to the real-time acquired environmental data of the displacement monitoring station and the preset calibration model, the dynamic calibration parameters are calculated, and the preliminary corrected pseudo-range measurement value is further corrected using the dynamic calibration parameters, and the corrected pseudo-range measurement value is obtained;

[0011] Step S4: according to the corrected pseudo-range measurement value, combining the carrier phase observation value and the known navigation satellite position information, using the carrier phase difference algorithm for position solving, and outputting the three-dimensional coordinates and displacement changes of the displacement monitoring station; the three-dimensional coordinates refer to longitude, latitude and elevation.

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

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

[0014] S2.2: according to ρ i and the received navigation satellite signals are analyzed using a multi-path detection algorithm to detect whether there is a multi-path effect;

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

[0016] If multipath interference is detected, the signal loss is assessed using the signal-to-noise ratio η.

[0017] S2.3: Adjust the parameters of the pre-built dynamic multipath error model based on the assessed signal loss.

[0018] Specifically, step S2 further includes the following steps:

[0019] S2.4: Using the adjusted dynamic multipath error model, calculate the multipath error e1 in the received navigation satellite signals;

[0020] S2.5: Based on the signal loss assessment results, the signal loss compensation value s is obtained using an adaptive compensation algorithm. bu ;

[0021] S2.6: Subtract the multipath error and signal loss compensation value s from the received navigation satellite signal. bu The pseudorange measurement value ρ is obtained after the dual elimination of multipath effect and signal loss. i ′;

[0022] S2.7: The pseudorange measurement value after dual elimination of multipath effects and signal loss is corrected using the error source model to obtain the optimized pseudorange measurement value ρ. i ";

[0023] S2.8: Use an adaptive filtering algorithm to optimize the pseudorange measurement value ρ i "After smoothing, the pseudorange measurement value is obtained after preliminary correction."

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

[0025] S2.51: Obtain the signal loss assessment results;

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

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

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

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

[0030] S2.71: Obtain the pseudo-range measurement value p after the double elimination of the multipath effect and signal loss 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 result, establish the mathematical model of the receiver clock error At R(t) ;

[0033] S2.74: Take p i ′ as input, subtract the product of the mathematical model of the receiver clock error At R(t) and the speed of light c, to obtain the optimized pseudo-range measurement value p i ″.

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

[0035] S3.1: According to the position information of the displacement monitoring station, combined with map data and terrain characteristics, a position compensation model is constructed; the position information is obtained based on map data, including longitude, latitude and height 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 Ap pos ; The position compensation parameter includes terrain slope compensation Ap slope , terrain elevation compensation Ap height ;

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

[0038] S3.4: Real-time acquisition of environmental data by the displacement monitoring station, input the real-time acquisition of environmental data into the pre-constructed calibration model based on polynomial regression, combined with the position compensation parameter, output the dynamic calibration parameter Ap dynamic ;

[0039] S3.5: Use the dynamic calibration parameter to correct the preliminary corrected pseudo-range measurement value p , to obtain the corrected pseudo-range measurement value p

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

[0041] S3.51: obtaining the preliminary corrected pseudo-range measurement value

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

[0043] S3.53: applying the dynamic calibration parameter to the preliminary corrected pseudo-range measurement value , and reassigning the adjusted to obtain the corrected pseudo-range measurement value

[0044] S3.54: outputting the corrected pseudo-range measurement value

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

[0046] S4.1: obtaining the corrected pseudo-range measurement value carrier phase observation value and known navigation satellite position information Y;

[0047] S4.2: combining the corrected pseudo-range measurement value using 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: establishing an observation equation for each navigation satellite according to the carrier phase difference observation principle; 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 , carrier wavelength λ i and integer ambiguity M i , wherein the three-dimensional coordinates of the navigation satellite are known parameters, which are obtained from the known navigation satellite position information;

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

[0050] ​S4.5: iteratively solving the observation equation using the least square method to update the estimated values of the three-dimensional coordinates of the displacement monitoring station and the integer ambiguity, and in each iteration, calculating the residual G(y) according to the observation equation and the current estimated values, and calculating miny||G(y)||2, wherein 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: checking whether the residual is less than a preset threshold value;

[0052] If the residual is less than the preset threshold value, 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 value, reinitialize the parameters;

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

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

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

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

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

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

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

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

[0062] Specifically, the multipath effect module comprises: 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 configured to analyze the received navigation satellite signals using a multi-path detection algorithm to determine whether multi-path interference exists.

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

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

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

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

[0068] Compared with the prior art, the present application has the following advantages:

[0069] 1. The present application proposes a three-dimensional coordinate accurate positioning monitoring system based on BDS, and optimizes and improves the architecture, operation steps and process, the system has the advantages of simple process, low investment and operation cost, and low production cost.

[0070] 2. The present application proposes a three-dimensional coordinate accurate positioning monitoring method based on BDS, which improves the accuracy of the pseudo-range measurement value through multi-path detection and error correction; the multi-path detection algorithm is used to identify and eliminate multi-path interference, and the dynamic multi-path error model and error source model are used for correction, and then the adaptive filtering algorithm is used for smoothing processing, which reduces the error sources.

[0071] 3. The present application proposes a three-dimensional coordinate accurate positioning monitoring method based on BDS, which realizes accurate re-correction of the pseudo-range measurement value through dynamic calibration and position compensation, combines real-time environmental data and a preset calibration model to calculate dynamic calibration parameters, and considers position compensation parameters to ensure that the corrected pseudo-range measurement value can truly reflect the actual position of the monitoring station; finally, the carrier phase difference algorithm is used for position solving to output high-precision three-dimensional coordinates and displacement change. BRIEF DESCRIPTION OF DRAWINGS

[0072] Figure 1 A three-dimensional coordinate accurate positioning monitoring method based on BDS is provided.

[0073] Figure 2 A three-dimensional coordinate accurate positioning monitoring method based on BDS is provided.

[0074] Figure 3A schematic diagram of a three-dimensional coordinate accurate positioning monitoring method based on BDS according to the present application;

[0075] Figure 4 A schematic diagram of a three-dimensional coordinate accurate positioning monitoring system architecture based on BDS according to the present application. DETAILED DESCRIPTION

[0076] Embodiment 1

[0077] Please refer to Figures 1-3 , in Figure 3 , A represents a reference station, B represents a displacement monitoring station, C1, CN represents a Beidou navigation satellite, when signal acquisition is performed, the reference station and the displacement monitoring station acquire signals transmitted by different navigation satellites respectively, and signal transmission or communication can be performed between the reference station and the displacement monitoring station, and the present application provides an embodiment: a three-dimensional coordinate accurate positioning monitoring method based on BDS, which is suitable for geological disaster monitoring, building deformation monitoring, bridge health monitoring and other fields, comprising the following steps:

[0078] Step S1: the antenna of the displacement monitoring station receives signals from N navigation satellites, and records the pseudo-range measurement value and the carrier phase observation value of each navigation satellite to the displacement monitoring station;

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

[0080] (1) the displacement monitoring station is turned 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 pseudo-random noise codes;

[0082] (3) the time of each navigation satellite signal propagating to the antenna is recorded through the time stamp embedded in the satellite signal;

[0083] (4) the pseudo-range of each navigation satellite to the displacement monitoring station is calculated by using the recorded time information, wherein the pseudo-range is obtained by multiplying the signal propagation time by the speed of light, and the formula is: p = (t R -t T ) x c, but due to the interference of factors such as the atmosphere and the ionosphere, this distance is not the true distance, but the pseudo-range, wherein p represents the pseudo-range 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 value of each navigation satellite, wherein the carrier phase observation value is the phase change of the navigation satellite signal in the propagation process.

[0085] Step S2: The pseudorange measurement value of each navigation satellite to the displacement monitoring station is corrected by combining the preset multipath detection algorithm and the dynamic multipath error model, to obtain a preliminary corrected pseudorange measurement value; the multipath detection algorithm identifies multipath interference based on signal reflection characteristics;

[0086] It should be understood that the pseudorange measurement value of each navigation satellite refers to the distance between the receiver and the navigation satellite measured by the BDS receiver. This distance measurement includes the errors of the receiver and satellite clock, the delay of atmospheric refraction and other factors, so it is called pseudorange. Pseudorange measurement is the basis for satellite navigation system to realize navigation and positioning. However, the pseudorange measurement value of each navigation satellite may be affected by multipath effect. Multipath effect refers to the fact that the receiver receives signals reflected once or more times on the surface of objects near the displacement monitoring station in addition to the direct signals transmitted by the satellite. The superposition of signals from different paths and direct signals will cause time delay effect, i.e. multipath error, which is one of the main error sources in pseudorange measurement and will affect the accuracy of pseudorange measurement. Therefore, in displacement monitoring applications, a multipath detection algorithm is needed to detect the multipath effect of the received satellite signals. If there is multipath interference, a corresponding error model is used to eliminate the interference to improve the accuracy and reliability of the pseudorange measurement.

[0087] In the present application, the multipath detection algorithm first determines whether there is multipath interference by evaluating the phase or signal-to-noise ratio of the signal. If abnormal changes in these characteristics are detected, it will be identified that the signal is affected by multipath interference. Through multipath detection, it can be accurately identified which signals are affected by multipath effect. Once the multipath interference is identified, the dynamic multipath error model will estimate the error size caused by the multipath effect based on the current environmental conditions and signal characteristics. According to the estimated error, the dynamic multipath error model will apply a compensation mechanism to reduce or eliminate these errors, reduce the impact of multipath effect on the pseudorange measurement value, and improve the accuracy of navigation and positioning. The error source model will consider all known error sources and evaluate their impact on the pseudorange measurement value. Based on these evaluation results, the error source model will develop a correction strategy to reduce or eliminate these errors. Through the application of the error source model, the errors in the pseudorange measurement value can be further reduced, thereby improving the accuracy and reliability of navigation and positioning. In particular, after the multipath effect is eliminated, the error source model can further correct other potential error sources to ensure that the final pseudorange measurement value is more accurate.

[0088] Step S3: Calculate the dynamic calibration parameter according to the real-time acquired environmental data of the displacement monitoring station and the preset calibration model, and use the dynamic calibration parameter to re-correct the preliminary corrected pseudorange measurement value to obtain a corrected pseudorange measurement value;

[0089] Step S4: According to the corrected pseudo-range measurement value, combined with the carrier phase observation value and the known navigation satellite position information, using the carrier phase difference algorithm for position solution, and output the three-dimensional coordinates and displacement change of the displacement monitoring station; the three-dimensional coordinates refer to longitude, latitude and elevation.

[0090] For example, assuming that there is a displacement monitoring station located in a complex environment in the center of a city, surrounded by high-rise buildings, trees and other reflectors, the antenna of the displacement monitoring station can receive signals from 4 navigation satellites, i.e. N = 4, the target is to accurately calculate the three-dimensional coordinates (longitude, latitude and elevation) and displacement change of the displacement monitoring station;

[0091] Firstly, the antenna of the displacement monitoring station receives signals from 4 navigation satellites, records the time of each navigation satellite signal propagation to the displacement monitoring station, and calculates the pseudo-range measurement value of each navigation satellite according to the signal propagation time and the speed of light, while recording the carrier phase observation value of each navigation satellite, which will be used for subsequent position solution; Then, judge whether there is multipath interference in the received signal, if there is multipath interference, evaluate the signal loss situation, use dynamic multipath error model combined with signal loss compensation mechanism to eliminate interference, get the pseudo-range measurement value after multipath effect elimination, at the same time, use error source model such as ionosphere to correct the pseudo-range measurement value after multipath effect elimination, combined with Kalman filter to smooth the corrected pseudo-range measurement value, get the preliminary corrected pseudo-range measurement value, for example, the original pseudo-range measurement value = 20000 meters, the multipath error = 5 meters, the preliminary corrected pseudo-range measurement value = 19995 meters; Secondly, real-time acquisition of the environmental data of the displacement monitoring station, such as temperature, humidity, air pressure, using air pressure correction model to calculate dynamic calibration parameters, and using dynamic calibration parameters to correct the preliminary corrected pseudo-range measurement value again, get the corrected pseudo-range measurement value, for example, the preliminary corrected pseudo-range measurement value = 19995 meters, then the corrected pseudo-range measurement value = 19994.8 meters; Finally, according to the corrected pseudo-range measurement value, carrier phase observation value and known navigation satellite position information, using carrier phase difference algorithm for position solution, output the three-dimensional coordinates and displacement change of the displacement monitoring station. For example, three-dimensional coordinates: longitude = 116.397128°, latitude = 39.916527°, elevation = 49.0 meters; displacement change: east-west direction = 0.01 meters, north-south direction = 0.02 meters, vertical direction = 0.005 meters.

[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 initial state, including but not limited to the absolute or relative displacement of the ground points, buildings and infrastructures, etc. Through high-precision satellite signal receiving and processing, data comparison and analysis, dynamic calibration and other technical means, the displacement measurement accuracy of millimeter level or even sub-millimeter level can be realized, which provides data support for various engineering safety.

[0093] The specific steps of step S2 include:

[0094] S2.1: Obtain the pseudo-range 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 analyze the received navigation satellite signal using a multi-path detection algorithm to detect whether there is a multi-path effect;

[0096] If the detection result s of the multi-path detection algorithm is not within the standard value range [h i , left h right ] when there is no multi-path interference, it indicates that there is multi-path interference, wherein the formula of the multi-path detection algorithm is: wherein 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 when there is no multi-path interference, and h right represents the maximum fluctuation value of the standard value when there is no multi-path interference.

[0097] If s , it indicates that there is multi-path interference.

[0098] It should be noted that by accurately detecting the multi-path interference, the positioning error caused by the multi-path effect can be identified and corrected in time, thereby improving the positioning accuracy of the satellite navigation system. The multi-path detection algorithm used in the present application considers λ i , M iand an error term, and combines frequency-related factors to evaluate the presence or absence of multipath interference. When the detection result is not within the standard value range in the absence of multipath interference, the algorithm can accurately identify the presence of multipath interference, and can timely detect and correct these errors, thereby improving positioning accuracy and reducing deviations caused by multipath effects. For example, in complex environments such as urban canyons or mountainous areas, multipath interference is particularly significant. The multipath detection algorithm used in the present application can improve system stability in complex environments and reduce operational errors or safety risks caused by inaccurate positioning.

[0099] If multipath interference is detected, the signal-to-noise ratio formula is used to evaluate the signal loss, where η represents the signal loss ratio, SNR B represents the standard value of the signal-to-noise ratio, SNR R represents the actual measured value of the signal-to-noise ratio.

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

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

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

[0103] (2) Extract the current parameter value 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, with the formula: r new = r old + F(Q), where r new represents the adjusted parameter value of the dynamic multipath error model.

[0106] where the construction process of the dynamic multipath error model includes:

[0107] (1) Collect multipath error data through actual observation or simulation experiments, and preprocess the data. The multipath error data includes satellite signal strength, phase observation value, and signal-to-noise ratio.

[0108] (2) Use a modeling method based on physical principles to construct the framework of the dynamic multipath error model. The modeling method based on physical principles is a prior art in the field and is not part of the inventive concept of the present application, so it will not be described here.

[0109] (3) using an independent verification data set to verify the dynamic multipath error model, and deploying the verified dynamic multipath error model to an actual application scenario, wherein the independent verification data set is divided from the preprocessed multipath error data.

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

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

[0112] (1) obtaining signal data transmitted by the navigation satellite, including direct signals and possibly existing reflected signals;

[0113] (2) using the signal data transmitted by the navigation satellite, estimating the dynamic multipath error model parameters by the least square method;

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

[0115] (4) substituting the estimated model parameters into the adjusted dynamic multipath error model to calculate the multipath error e1 in the received navigation satellite signal, e1 = f2(Δθ, α), wherein 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: according to the signal loss evaluation result, using an adaptive compensation algorithm to obtain a signal loss compensation value s bu ;

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

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

[0119] S2.8: using an adaptive filtering algorithm to smooth the optimized pseudo-range measurement value ρ i ″ to obtain a preliminarily corrected pseudo-range measurement value The adaptive filtering algorithm is used to suppress noise and random errors in the pseudo-range measurement value, wherein the adaptive filtering algorithm adopts a Kalman filtering algorithm, which is prior art in the field and is not the creative scheme of the present application, and thus is not described herein.

[0120] S2.5 includes the following specific steps:

[0121] S2.51: obtaining a signal loss evaluation result;

[0122] S2.52: setting initial parameters of the adaptive compensation algorithm, wherein the initial parameters of the adaptive compensation algorithm include filter coefficients and a learning rate;

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

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

[0125] S2.7 includes the following specific steps:

[0126] S2.71: obtaining a pseudo-range measurement value ρ i ′ after double elimination of the multipath effect and the signal loss;

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

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

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

[0130] S3 includes the following specific steps:

[0131] S3.1: According to the position information of the displacement monitoring station, combined with map data and terrain features, a position compensation model is constructed; the position information is obtained based on map data and includes longitude, latitude and height information. In the present application, the position compensation model is a polynomial regression model, which is prior art in the field and is not the creative scheme of the present application, and will not be described here.

[0132] Among them, the terrain data around the displacement monitoring station is obtained from the map data source, including elevation, terrain undulation and slope, and the map data source refers to the geographic information system database; then, according to the terrain features, the influence on the pseudo-range measurement is evaluated, such as atmospheric refraction, multipath effect and terrain shielding.

[0133] It should be understood that the core of the position compensation model is to accurately analyze the position information of the displacement monitoring station, combined with map data and terrain features, to identify and correct positioning errors, which may be caused by various factors such as atmospheric delay, multipath effect, receiver noise and terrain shielding. Through the construction of the position compensation model, the errors can be accurately estimated and compensated, thereby improving the positioning accuracy. Therefore, the position compensation model can significantly improve the positioning accuracy and stability by accurately identifying and compensating the positioning errors.

[0134] S3.2: The position information and terrain feature data of the displacement monitoring station are input into the position compensation model, and the position compensation parameter Δρ pos is output; the position compensation parameter includes terrain slope compensation Δρ slope , terrain elevation difference compensation Δρ height , wherein the calculation formula of the position compensation parameter Δρ pos is: wherein 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 the two points on the terrain, Δh j represents the height difference on the jth contour line, f3(·) represents the measurement line segment 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 acquisition of environmental data by the displacement monitoring station, input of the real-time acquired environmental data into the pre-constructed calibration model based on polynomial regression, combination of the position compensation parameter, and output of the dynamic calibration parameter Δρ dynamic .

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

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

[0139] (2) Collect environmental data in real time through the displacement monitoring station, and pre-process, at the same time, obtain the position compensation parameter Δρ pos ;

[0140] (3) The pre-processed environmental data is input into the polynomial regression calibration model as an input variable, and the position compensation parameter is input into the polynomial regression calibration model as an additional input variable;

[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 designated data storage medium.

[0143] S3.5: Use the dynamic calibration parameter to correct the preliminary corrected pseudo-range measurement value to obtain the corrected pseudo-range measurement value

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

[0145] S3.51: Obtain the preliminary corrected pseudo-range measurement value

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

[0147] S3.53: Apply the dynamic calibration parameter to the preliminary corrected pseudo-range measurement value , adjust it by summation, and reassign the adjusted to obtain the corrected pseudo-range measurement value

[0148] S3.54: Output the corrected pseudo-range measurement value

[0149] The specific steps of step S4 include:

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

[0151] S4.2: Combine the corrected pseudo-range measurement value ​The carrier phase differential algorithm is used to analyze the carrier phase observations to obtain the carrier phase differential observations for the same navigation satellite. The formula for the carrier phase differential algorithm is: in, and d represents the navigation satellite carrier phase observation values ​​received by the base station and the displacement monitoring station, respectively. u,i and d g,i M represents the geometric distance from the base station and the displacement monitoring station to the navigation satellite, respectively. u,i and M g,i These represent the integer ambiguity of the navigation satellite for the reference station and the displacement monitoring station, respectively.

[0152] S4.3: Based on the carrier phase differential observation principle, 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, and the carrier phase differential observation values. carrier wavelength λ i And integer ambiguity M i The three-dimensional coordinates of the navigation satellites are known parameters, obtained from the known position information of the navigation satellites.

[0153] The observation equation is in the form of: Among them, noise i This indicates observation noise.

[0154] S4.4: Initialize M i The three-dimensional coordinates of the displacement monitoring station were used as initial estimates;

[0155] S4.5: The least squares method is used to iteratively solve the observation equation, updating the estimated values ​​of the three-dimensional coordinates and integer ambiguity of the displacement monitoring station. At the same time, in each iteration, the residual G(y) is calculated based on the observation equation and the current estimated value, and miny||G(y)‖2 is calculated, 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 value, 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). The least squares method is the prior art in this field and is not an inventive solution of this application, so it will not be described in detail here.

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

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

[0158] If the residual is greater than the preset threshold, the parameters are reinitialized.

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

[0160] S4.8: Calculate the displacement change 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 of the displacement monitoring station and the displacement change.

[0162] Embodiment 2

[0163] Please refer to Figure 4 , the application provides another embodiment: a three-dimensional coordinate accurate positioning monitoring system based on BDS, comprising:

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

[0165] The signal receiving module is used for receiving signals from navigation satellites and recording information;

[0166] The multipath effect module is used for detecting and eliminating the influence of multipath effect on pseudorange measurement values, thereby improving measurement accuracy;

[0167] The dynamic calibration module is used for calculating dynamic calibration parameters according to real-time environmental data and a preset calibration model, and re-modifying the pseudorange measurement values;

[0168] The position solving module is used for combining the modified pseudorange measurement values, carrier phase observation values, and known navigation satellite position information, using a carrier phase difference algorithm to solve the position, and outputting the three-dimensional coordinates of the displacement monitoring station and the displacement change.

[0169] The multipath effect module comprises 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 for analyzing the received navigation satellite signals using a multipath detection algorithm to determine whether there is multipath interference;

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

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

[0173] The error correction unit is used for further correcting the pseudorange measurement values after multipath effect elimination using an error source model;

[0174] An adaptive filtering unit is configured to smooth the pseudo-range measurement value using an adaptive filtering algorithm to obtain a preliminary corrected pseudo-range measurement value.

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

[0176] The environment monitoring unit is configured to acquire environment data of the displacement monitoring station in real time, such as temperature, humidity and atmospheric pressure.

[0177] The position compensation unit is configured to construct a position compensation model and generate position compensation parameters.

[0178] The calibration parameter calculation unit is configured to calculate dynamic calibration parameters according to the environment monitoring data and a preset calibration model.

[0179] The re-correction unit is configured to re-correct the preliminary corrected pseudo-range measurement value using the dynamic calibration parameters to obtain a corrected pseudo-range measurement value.

[0180] The position solution module comprises a position solution unit, a displacement calculation unit and an output unit.

[0181] The position solution unit is configured to calculate the three-dimensional coordinates of the displacement monitoring station using a carrier phase differential algorithm in combination with the corrected pseudo-range measurement value, carrier phase observation value and navigation satellite position information.

[0182] The displacement calculation unit is configured to calculate the displacement change of the displacement monitoring station according to continuous position solution results.

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

[0184] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the above specific embodiments, and the above specific embodiments are only illustrative but not restrictive, and the person skilled in the art can make changes, modifications, replacements and variations to the above embodiments without departing from the purpose and the protected scope of the present application, and these are all within the protection of the present application.

Claims

1.A method for monitoring precise positioning of three-dimensional coordinates based on BDS, characterized in that, The method comprises the following steps: Step S1: the antenna of the displacement monitoring station receives signals from N navigation satellites, and records the pseudorange measurement value and the carrier phase observation value of each navigation satellite to the displacement monitoring station; Step S2: the pseudorange measurement value of each navigation satellite to the displacement monitoring station is corrected by combining a preset multipath detection algorithm and a dynamic multipath error model, to obtain a preliminary corrected pseudorange measurement value; the multipath detection algorithm is based on signal reflection characteristics to identify multipath interference; Step S3: a dynamic calibration parameter is calculated according to the real-time acquired displacement monitoring station environment data and a preset calibration model, and the preliminary corrected pseudorange measurement value is re-corrected by using the dynamic calibration parameter, to obtain a corrected pseudorange measurement value; Step S4: according to the corrected pseudorange measurement value, the carrier phase observation value and the known navigation satellite position information, the position is solved by using a carrier phase difference algorithm, and the three-dimensional coordinates and the displacement change of the displacement monitoring station are output; the three-dimensional coordinates refer to longitude, latitude and elevation. 2.The method of claim 1, wherein, The specific steps of step S2 comprise: S2.1: Obtain the pseudorange measurement value p of each navigation satellite to the displacement monitoring station i and carrier phase observations and i ∈ [1, N] S2.2: According to p i and The received navigation satellite signals are analyzed using a multipath detection algorithm to detect whether a multipath effect exists. If the detection result s of the multipath detection algorithm i Not within the standard value range when there is no multipath interference [h left ,h right If the value is within ], it indicates the presence of multipath interference, where h left h represents the minimum fluctuation value of the standard value when there is no multipath interference. right This represents the maximum fluctuation of the standard value when there is no multipath interference. If multipath interference is detected, the signal loss condition is evaluated by a signal-to-noise ratio η; S2.3: according to the evaluated signal loss condition, the parameters of the pre-constructed dynamic multipath error model are adjusted. 3.The method of claim 2, wherein, The specific steps of step S2 further comprise: S2.4: using the adjusted dynamic multipath error model, the multipath error e1 in the received navigation satellite signal is calculated; 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 signal loss compensation value s from the received navigation satellite signal bu , to obtain a pseudorange measurement value p that is double-eliminated by multipath effects and signal loss i ′; S2.7: Correct the pseudo-range measurement value after the double elimination of multipath effect and signal loss by using the error source model to obtain the optimized pseudo-range measurement value p i "; S2.8: Using an adaptive filtering algorithm on the optimized pseudorange measurement value p i "Smoothed, resulting in a preliminary corrected pseudorange measurement value 4.The method of claim 3, wherein, The specific steps of S2.5 comprise: S2.51: the signal loss evaluation result is obtained; S2.52: the initial parameters of the adaptive compensation algorithm are set, including filter coefficients and learning rate; S2.53: the signal loss evaluation result is input into the adaptive compensation algorithm; The adaptive compensation algorithm automatically adjusts its parameters according to the characteristics of the input signal and the changes in the environment, and through iterative calculation, the adaptive compensation algorithm converges to an optimal solution to obtain a signal loss compensation value s bu . 5.The three-dimensional coordinate precision positioning monitoring method based on BDS of claim 4, wherein, The specific steps of S2.7 comprise: S2.71: Obtain the pseudo-range measurement value p after the double elimination of multipath effect and signal loss i ′; S2.72: the causes and characteristics of the receiver clock bias are analyzed, including the frequency stability of the crystal oscillator and the temperature effect; S2.73: Based on the analysis results, a mathematical model of the receiver clock error Δt is established R(t) ; S2.74: Subtract the mathematical model of the receiver clock difference Δt i and the speed of light c from the input ρ R(t) to obtain the optimized pseudo-range measurement ρ i ". 6.The method of claim 5, wherein, The specific steps of step S3 comprise: S3.1: according to the position information of the displacement monitoring station, a position compensation model is constructed by combining map data and terrain characteristics; the position information is obtained based on the map data, including longitude, latitude and height 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 comprises a terrain slope compensation Δρ slope , a terrain elevation difference compensation Δρ height ; S3.3: a pre-constructed calibration model based on polynomial regression is loaded; S3.4: Real-time collection of environmental data by the displacement monitoring station, input of the real-time collected environmental data into a pre-constructed calibration model based on polynomial regression, combination with the position compensation parameter, and output of the dynamic calibration parameter Δρ dynamic ; S3.5: Using the dynamic calibration parameters to correct the preliminary corrected pseudorange measurements correcting the preliminary pseudorange measurements to obtain corrected pseudorange measurements 7.The three-dimensional coordinate precision positioning monitoring method based on BDS of claim 6, wherein, The specific steps of S3.5 comprise: S3.51 : Obtain the preliminary corrected pseudorange measurement S3.52: Obtain the dynamic calibration parameter Δρ described in S3.4 dynamic ; S3.53: Apply the dynamic calibration parameters to the preliminary corrected pseudorange measurement The above, by the way of summation to Adjustment, and the adjusted Reassignment, get the corrected pseudorange measurement S3.54: output the corrected pseudo-range measurement 8.The method of claim 7, wherein, The specific steps of step S4 comprise: S4.1: Obtain the corrected pseudo-range measurement carrier phase observations and known navigation satellite position information Y; S4.2: Combining the corrected pseudorange measurements The carrier phase observations are analyzed using a carrier phase differential algorithm to obtain carrier phase differential observations for the same navigation satellite S4.3: According to the carrier phase difference observation principle, 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, and the carrier phase difference observation value carrier wavelength λ i and the integer ambiguity M i wherein the three-dimensional coordinates of the navigation satellite are known parameters, obtained from known navigation satellite position information; S4.4: initialize M i and the three-dimensional coordinates of the displacement monitoring station as initial estimates; S4.5: the observation equation is iteratively solved by using the least squares method, to update the three-dimensional coordinates of the displacement monitoring station and the estimated value of the integer ambiguity, and in each iteration, the residual G(y) is calculated according to the observation equation and the current estimated value, and miny||G(y)||2 is calculated, wherein 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); S4.6: whether the residual is less than a preset threshold is checked; If the residual is less than the preset threshold, the final three-dimensional coordinates of the displacement monitoring station and the integer ambiguity are output; If the residual is greater than the preset threshold, the parameters are reinitialized. S4.7: Obtain the three-dimensional coordinates of the displacement monitoring station at different time points as the reference three-dimensional coordinates; S4.8: Calculate the displacement change 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 displacement change of the displacement monitoring station. 9.A BDS-based three-dimensional coordinate precision positioning monitoring system, used to implement the BDS-based three-dimensional coordinate precision positioning monitoring method of any one of claims 1-8, characterized in that, Comprise: Signal receiving module, multipath effect module, dynamic calibration module, position solving module; The signal receiving module is used for receiving signals from navigation satellites and recording information; The multipath effect module is used for detecting and eliminating the influence of multipath effect on pseudorange measurement values; The dynamic calibration module is used for calculating dynamic calibration parameters according to real-time environmental data and a preset calibration model, and re-modifying the pseudorange measurement values; The position solving module is used for combining the modified pseudorange measurement values, carrier phase observation values and known navigation satellite position information, using a carrier phase difference algorithm to solve the position, and outputting the three-dimensional coordinates and displacement change of the displacement monitoring station. 10.The three-dimensional coordinate precision positioning monitoring system based on BDS of claim 9, wherein, The multipath effect module comprises 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 for analyzing the received navigation satellite signals using a multipath detection algorithm to determine whether there is multipath interference; The multipath elimination unit is used for eliminating interference using a dynamic multipath error model when multipath interference is detected; The signal loss compensation unit is used for calculating signal loss compensation values according to signal loss evaluation results; The error correction unit is used for further correcting the pseudorange measurement values after multipath effect elimination using an error source model; The adaptive filtering unit is used for smoothing the pseudorange measurement values using an adaptive filtering algorithm to obtain the preliminary modified pseudorange measurement values.

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