A fast Beidou differential positioning method and system under dynamic conditions

Abstract

The present invention belongs to the technical field of dynamic Beidou differential positioning, and discloses a method and system for fast Beidou differential positioning under dynamic conditions, constructing a single-difference error equation between epochs; selecting reference epochs for observation value selection; using a double-difference-single-difference model alternating strategy to achieve high sampling rate data processing; and data analysis. The fast Beidou differential positioning method under dynamic conditions proposed by the present invention solves the problem that when the double-difference model fails, by using a single-difference model between epochs and a constructed combined observation value with the minimum noise, a centimeter-level dynamic positioning result of the Beidou system is achieved. The method of selecting reference epochs used in the present invention avoids the influence of the accumulation of coordinate errors on the coordinate accuracy of the moving carrier, and is more suitable for real-time dynamic data processing. The present invention adopts the carrier phase combined observation value with the minimum noise, which further reduces the influence of the observation value noise on the positioning accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of dynamic Beidou differential positioning, and in particular relates to a method and system for rapid Beidou differential positioning under dynamic conditions. Background Art

[0002] In high-precision dynamic Beidou differential positioning, the carrier phase is generally used as the observation value, and a double difference model is constructed between the "star-station" to achieve the purpose of weakening or eliminating certain common errors, such as tropospheric delay, ionospheric delay, receiver clock error, satellite clock error, etc., so as to obtain high-precision positioning results. When using the double difference model, it is required that the base station and the mobile station on the moving carrier are equipped with receivers with the same sampling rate for synchronous observation. However, in many dynamic scenarios, the data sampling rate of the mobile station receiver on the moving carrier is much higher than that of the base station, resulting in no synchronous observation data between the mobile station and the base station on the moving carrier at some observation times, and the double difference model cannot be constructed. If the receiver with the same sampling rate is to be configured at the base station, the system cost will increase. At the same time, in actual applications, when the base station sends data to the mobile station receiver, there will inevitably be data transmission errors and other phenomena. At this time, the double difference model cannot be constructed, which affects the differential positioning.

[0003] To solve the above problems, the coordinates of the moving carrier are usually calculated in two steps. First, for the moment with synchronous observation values, the double difference model is used to solve the coordinates of the moving carrier; then the single difference observation value of the phase between epochs is used to solve the coordinates of the moving carrier at the moment of asynchronous observation. However, due to the different reference epoch selection methods, the existing methods will cause error accumulation, affect the accuracy of positioning results, or are only suitable for static data processing, not suitable for real-time data processing.

[0004] At present, the common method to solve the problem of asynchronous data sampling rate in high-precision dynamic Beidou differential positioning is to calculate the coordinates of the moving carrier in two steps, but this method has some disadvantages and problems: (1) Error accumulation: Due to the different reference epoch selection methods, the existing method will cause error accumulation, which in turn affects the accuracy of the positioning results. (2) Non-real-time: The existing method needs to collect the time of synchronous observation values ​​before solving, so it is not suitable for real-time data processing. (3) High cost: In order to ensure that the base station and the mobile station on the moving carrier have the same sampling rate, it is necessary to configure a receiver with the same sampling rate at the base station, which will increase the system cost. (4) Data transmission error: In the process of sending data from the base station to the mobile station receiver, data transmission errors and other phenomena are inevitable, which will also affect the accuracy of differential positioning. Summary of the invention

[0005] In order to solve the problem of asynchronous observation time caused by factors such as the sampling rate of the mobile station receiver on the moving carrier being higher than that of the base station receiver or the transmission error of the base station, which in turn causes the failure of the double difference model solution method, the present invention proposes a fast Beidou differential positioning method under dynamic conditions, which effectively solves the problem of real-time high-precision positioning of the moving carrier in the absence of synchronized observation values ​​by constructing a combined observation value with the minimum noise, and performing real-time positioning and solution without error accumulation.

[0006] The present invention is implemented as follows: a fast Beidou differential positioning method under dynamic conditions uses a single-difference error equation between epochs to construct a model, combines selected observations and reference epochs, and implements a double-difference-single-difference model alternating strategy for high sampling rate data processing.

[0007] Furthermore, the method specifically includes: S1: constructing a single-difference error equation between epochs; S2: selecting observation values; S3: selecting reference epochs; S4: using a double-difference-single-difference model alternating strategy to achieve high sampling rate data processing.

[0008] Furthermore, S1 specifically includes: a. Collecting and processing raw observation data: Obtaining raw observation data from Beidou satellite receiving equipment, including satellite pseudorange and carrier phase information; in the data processing process, removing and correcting abnormal data. b. Calculating single differences between epochs: For each epoch, calculating the difference between satellite observation values ​​between adjacent epochs, eliminating the influence of satellite clock error and atmospheric delay error.

[0009] Further, suppose If the coordinates of the moving carrier at the epoch time are known, it can be regarded as the reference epoch, and the error equation of the single difference observation value between epochs can be obtained: In the single-difference observation error equation, the carrier phase observation is to If there is no cycle slip between epochs, the ambiguities are equal, and the effects of tropospheric delay and ionospheric delay changes are ignored, the satellite clock error is , The clock error parameters can be calculated by broadcasting the ephemeris. There are four unknown parameters in the single difference error equation between epochs, namely the coordinates of the moving carrier and the difference of the receiver clock error between epochs. When the number of observations is greater than or equal to 4, the adjustment can be performed.

[0010] Furthermore, S2 specifically includes: a. Data smoothing and filtering: smoothing the observed data to reduce the impact of noise; b. Consistency detection and signal-to-noise ratio evaluation: checking the abnormal values ​​in the observed data through consistency detection; at the same time, evaluating the signal-to-noise ratio to determine the signal quality; based on the evaluation results, screening out high-quality observations.

[0011] Furthermore, from the single-difference observation error equation, it can be seen that the differential observations between epochs are mainly affected by observation noise, so observations with less noise should be selected for adjustment calculation. For the B1 and B3 frequency points of the BeiDou system, their linear combination observations and their variance can be expressed as: In the formula, is the linear combination observation value, , is the combination coefficient, , They are the wavelengths of B1 and B3 frequencies of the BeiDou system. is the mean error of the linear combination observations, , are the mean error of the carrier phase observation values ​​at the B1 and B3 frequency points respectively. , then the variance can be written as: To satisfy The minimum and observation equations have a satellite-to-ground distance coefficient of 1. , Need to meet: Then we can get: Then, by using this combination coefficient, we can obtain the combined observation value with the minimum noise.

[0012] Furthermore, the S3 specifically includes: a. Signal quality and satellite geometric distribution: when selecting a reference epoch, use an epoch with better signal quality and better satellite geometric distribution; b. Sliding window method: use the sliding window method to find the best reference epoch within a certain range; set a window of a fixed size, and in each iteration process, the window slides forward one epoch, and then selects the best reference epoch within the window range.

[0013] Further, suppose The epoch has synchronous observation data from the base station receiver and the mobile station receiver on the moving carrier. , , The epoch only has observation data from the mobile station receiver. The double difference model can be used to solve The coordinates of the moving carrier at the epoch time are solved using the single difference model between epochs , , The coordinates of the moving carrier at the epoch time.

[0014] Furthermore, S4 specifically includes: when the observation data of the mobile station receiver and the base station receiver on the moving carrier are synchronized, the double difference model is used to solve the coordinates of the moving carrier; when the observation data of the mobile station receiver and the base station receiver are not synchronized, the single difference model between epochs is used to solve the coordinates of the moving carrier. By using the above-mentioned double difference-single difference model alternating strategy, high sampling rate data processing can be achieved.

[0015] Another object of the present invention is to provide a fast Beidou differential positioning system under dynamic conditions to implement the above-mentioned differential positioning method. The system includes: an inter-epoch single-difference error equation construction module: this module is responsible for processing the original observation data and calculating the inter-epoch single-difference error equation; the input of this module is the original observation data, and the output is the inter-epoch single-difference error equation. Observation value selection module: this module is responsible for screening and eliminating abnormal observations and noise, selecting high-quality observations, the input is the inter-epoch single-difference error equation, and the output is the screened observations. Reference epoch selection module: this module is responsible for selecting a suitable reference epoch as the basis for positioning calculations, the input is the screened observations, and the output is the selected reference epoch. Double difference-single difference model alternating processing module: this module is responsible for implementing the double difference-single difference model alternating strategy and realizing high sampling rate data processing. The input is the screened observations and the selected reference epochs, and the output is the positioning results.

[0016] The advantages and positive effects of the present invention are specifically described as follows: (1) Improving positioning accuracy: By constructing a single-difference error equation between epochs, the influence of errors such as satellite clock error and atmospheric delay can be effectively eliminated, thereby improving the accuracy of differential positioning. At the same time, through the double-difference-single-difference model alternating strategy, it is possible to refine and optimize on the basis of coarse positioning to further improve positioning accuracy. (2) Improving positioning reliability and stability: Through observation value selection, outliers and noise can be effectively eliminated, and high-quality observation values ​​can be selected, thereby improving the reliability and stability of positioning results. (3) Reducing the amount of calculation: Through reference epoch selection, a suitable benchmark can be selected, thereby reducing the amount of calculation and improving positioning accuracy. In addition, the double-difference-single-difference model alternating strategy also helps to quickly obtain positioning results and save computing resources. (4) Adapting to dynamic scenarios: This method is optimized for dynamic conditions and can achieve fast Beidou differential positioning in dynamic scenarios. This is of great significance for scenarios that require real-time positioning (such as unmanned driving, ship navigation, etc.). (5) Flexibility: In practical applications, the method can be adjusted and optimized according to specific circumstances to meet the needs of different scenarios. This makes the method highly flexible and adaptable to a variety of application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a method for fast Beidou differential positioning under dynamic conditions provided by an embodiment of the present invention; Figure 2 The present invention provides a method for selecting a reference epoch schematic diagram (a) and a method for selecting a reference epoch schematic diagram (b) according to an embodiment of the present invention; Figure 3 is a flow chart of data processing of a single-difference model between epochs provided by an embodiment of the present invention; Figure 4 is a coordinate error curve diagram provided by an embodiment of the present invention; Figure 5 It is a coordinate error curve diagram of the prior art method. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] like Figure 1 As shown, the fast Beidou differential positioning method under dynamic conditions provided by the embodiment of the present invention adopts the single difference error equation between epochs to build a model, combines selected observations and reference epochs, and implements the double difference-single difference model alternating strategy to achieve high sampling rate data processing, and the steps are as follows:

[0020] 1. Single-difference error equation between epochs: Assume that the mobile station receiver on the moving carrier is , Epoch received Satellite carrier phase observations , ,in, , then the error equation of the Beidou carrier phase non-difference observation value is: Where: , , is the direction cosine from the moving carrier to the satellite;

[0021] , , is the coordinate correction number of the moving carrier; c is the speed of light; , are the clock differences between the receiver clock and the satellite clock respectively; , They are tropospheric delay and ionospheric delay respectively; is the distance from the moving carrier to the satellite; is the carrier wavelength; is the integer ambiguity.

[0022] set up If the coordinates of the moving carrier at the epoch time are known, it can be regarded as the reference epoch, and the error equation of the single difference observation value between epochs can be obtained: In the single-difference observation error equation, the carrier phase observations are in the range k to to If there is no cycle slip between epochs, the ambiguities are equal, and the effects of tropospheric delay and ionospheric delay changes are ignored, the satellite clock error is , The clock error parameters can be calculated by broadcasting the ephemeris. There are four unknown parameters in the single difference error equation between epochs, namely the coordinates of the moving carrier and the difference of the receiver clock error between epochs. When the number of observations is greater than or equal to 4, the adjustment can be performed.

[0023] 2. Observation value selection: From the single difference observation value error equation, it can be seen that the differential observation value between epochs is mainly affected by the observation noise, so the observation value with small noise should be selected for adjustment calculation. For the B1 and B3 frequency points of the Beidou system, the linear combination observation value and its variance can be expressed as: In the formula, is the linear combination observation value, , is the combination coefficient, , They are the wavelengths of B1 and B3 frequencies of the BeiDou system. is the mean error of the linear combination observations, , are the mean error of the carrier phase observation values ​​at the B1 and B3 frequency points respectively. , then the variance can be written as: To satisfy The minimum and observation equations have a satellite-to-ground distance coefficient of 1. , Need to meet: Then we can get: Then, by using this combination coefficient, we can obtain the combined observation value with the minimum noise.

[0024] 3. Reference epoch selection: The epoch has synchronous observation data from the base station receiver and the mobile station receiver on the moving carrier. , , The epoch only has observation data from the mobile station receiver. The double difference model can be used to solve The coordinates of the moving carrier at the epoch time are solved using the single difference model between epochs , , The coordinates of the moving carrier at the epoch time. When using the single difference model for calculation, you need to select the reference epoch. There are two methods for selecting the reference epoch: Method 1: Select , , epoch as a reference epoch, such as Figure 2 (a) as shown. Method 2: Select epoch as a reference epoch, such as Figure 2 (b) According to the error propagation law, when the reference epoch is selected using method 1, the coordinate error will gradually accumulate, thus causing the solution to , , The accuracy of the coordinates of the moving carrier at the epoch time is getting worse and worse, which is not conducive to high-precision positioning. The epoch is used as a reference epoch, which effectively avoids the disadvantages of method one. The present invention adopts method two to select the reference epoch.

[0025] 4. Processing strategy: When the observation data of the mobile station receiver and the base station receiver on the moving carrier are synchronized, the double difference model is used to calculate the coordinates of the moving carrier. When the observation data of the mobile station receiver and the base station receiver are not synchronized, the inter-epoch single difference model is used to solve the coordinates of the moving carrier. By using the above double difference-single difference model alternating strategy, high sampling rate data processing can be achieved. The inter-epoch single difference model data processing process is as follows Figure 3 shown.

[0026] As an optimization solution of the embodiment of the present invention, each step can adopt the following implementation solution:

[0027] (1) Construct the single-difference error equation between epochs (S1): a. Collect and process the original observation data: Obtain the original observation data from the Beidou satellite receiving device, including the satellite's pseudorange, carrier phase and other information. During the data processing process, abnormal data needs to be eliminated and corrected. b. Calculate the single difference between epochs: For each epoch, calculate the difference between the satellite observation values ​​between adjacent epochs. This can eliminate the influence of errors such as satellite clock error and atmospheric delay. The formula is expressed as: Δy = y_t y_(t-1), where y represents the observation value, t represents the current epoch, and t-1 represents the previous epoch.

[0028] (2) Observation selection (S2): a. Data smoothing and filtering: Smooth the observation data to reduce the impact of noise. Methods such as sliding average and Kalman filtering can be used. b. Consistency detection and signal-to-noise ratio evaluation: Check for outliers in the observation data through consistency detection. At the same time, evaluate the signal-to-noise ratio to determine the signal quality. Based on the evaluation results, select high-quality observations.

[0029] (3) Reference epoch selection (S3): a. Signal quality and satellite geometric distribution: When selecting reference epochs, epochs with better signal quality and better satellite geometric distribution should be considered. Satellite geometric distribution can be measured by indicators such as geometric sparsity (GDOP). b. Sliding window method: The sliding window method is used to find the best reference epoch within a certain range. A fixed-size window can be set. In each iteration, the window slides forward one epoch, and then the best reference epoch is selected within the window range.

[0030] (4) Use the double-difference-single-difference model alternating strategy to achieve high sampling rate data processing (S4): a. Double-difference model coarse positioning: First, the double-difference model is used to calculate the relative position between the base station and the mobile station. The double-difference model can eliminate the clock error between the satellite and the receiver, thereby improving the positioning accuracy. The calculated relative position is used as the preliminary position solution. b. Single-difference model refinement optimization: Based on the preliminary position solution obtained by the double-difference model, the single-difference model is used for further optimization. The single-difference model can eliminate the satellite clock error, thereby improving the positioning accuracy. The position solution is corrected and optimized by the single-difference model.

[0031] Through the above detailed steps, we can better understand and implement the fast Beidou differential positioning method under dynamic conditions. In practical applications, the method can also be adjusted and optimized according to specific needs and scenarios.

[0032] In order to realize the fast Beidou differential positioning method under dynamic conditions, the embodiment of the present invention also constructs a system including the following four main modules: (1) epoch-to-epoch single difference error equation construction module (S1): This module is responsible for processing the original observation data and calculating the epoch-to-epoch single difference error equation. The input of this module is the original observation data, and the output is the epoch-to-epoch single difference error equation. (2) Observation value selection module (S2): This module is responsible for screening and eliminating abnormal observations and noise, and selecting high-quality observations. The input is the epoch-to-epoch single difference error equation, and the output is the screened observation. (3) Reference epoch selection module (S3): This module is responsible for selecting a suitable reference epoch as the reference for positioning calculation. The input is the screened observation, and the output is the selected reference epoch. (4) Double difference-single difference model alternating processing module (S4): This module is responsible for implementing the double difference-single difference model alternating strategy to realize high sampling rate data processing. The input is the screened observation and the selected reference epoch, and the output is the positioning result. These four modules need to cooperate with each other to realize fast Beidou differential positioning under dynamic conditions. During specific implementation, the system modules can be adjusted and optimized according to actual needs and scenarios.

[0033] An embodiment of the present invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of a fast Beidou differential positioning method under dynamic conditions. An embodiment of the present invention also provides an information data processing terminal, which is used to implement the steps of a fast Beidou differential positioning method under dynamic conditions. The following is a simulation experiment for an embodiment of the present invention. To verify the correctness and feasibility of the algorithm, the experiment uses Beidou B1 and B3 dual-frequency receivers with a sampling rate of 20Hz. The sampling rate of the base station receiver is 1Hz. The data processing results are as follows. Figure 4 As shown by Figure 4It can be seen that the coordinate errors in the X, Y, and Z directions are all within the range of ±0.05m, and the root mean square errors in the three directions are 0.004m, 0.009m, and 0.009m, respectively. This shows that the method proposed in the present invention is feasible and can achieve centimeter-level positioning accuracy.

[0034] An embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of a fast Beidou differential positioning method under dynamic conditions.

[0035] An embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of a fast Beidou differential positioning method under dynamic conditions.

[0036] An embodiment of the present invention provides an information data processing terminal, which is used to implement the steps of a fast Beidou differential positioning method under dynamic conditions.

[0037] The positioning method provided by the application embodiment of the present invention is applied to the Beidou dynamic positioning, which does not require additional investment, improves economic benefits, and obtains high-precision real-time dynamic positioning results. The coordinate error curve obtained by using the existing technical method to process the non-combined original observations is as follows: Figure 5 Compared with the prior art, the accuracy of the embodiment of the present invention in the X, Y, and Z directions is improved by 42.8%, 18.2%, and 18.2%, respectively. This shows that the embodiment of the present invention can effectively improve the accuracy of Beidou real-time dynamic positioning, and has a great advantage over the prior art.

[0038] The following are six specific embodiments and specific implementation schemes of the present invention:

[0039] Embodiment 1: Rapid positioning method based on multi-epoch data fusion: Collect Beidou observation data of multiple consecutive epochs. Perform differential processing on the observation data of each epoch to generate a single difference error equation between epochs. Fuse the differential data of multiple epochs, for example, by weighted averaging or other fusion algorithms. Calculate the distance between the satellite and the receiver based on the fused differential data, and obtain the position solution using the pseudorange positioning method.

[0040] Embodiment 2: Observation value screening method based on Kalman filtering: Collect Beidou observation data of multiple consecutive epochs. Establish a state space model for the observation data of each epoch, and update the state vector and state covariance matrix according to the Kalman filtering algorithm. According to the filtering results, remove outliers and noise, and retain high-quality observation data. Use the screened observation data for differential positioning.

[0041] Embodiment 3: Reference epoch selection method based on machine learning: Collect Beidou observation data of a large number of epochs, and annotate each epoch with features such as signal quality and satellite geometric distribution. Use the annotated data to train a classification or regression model, such as a support vector machine, decision tree, or neural network. Apply the trained model to real-time observation data to automatically identify epochs with better signal quality and better satellite geometric distribution as reference epochs. Perform differential positioning calculations based on the selected reference epoch.

[0042] Embodiment 4: Double-difference-single-difference model alternating strategy based on weighted least squares method: Collect Beidou observation data of multiple consecutive epochs to construct double-difference models and single-difference models. Calculate the weight matrix (for example, based on the accuracy or signal-to-noise ratio of the observation value) for the observation data of the double-difference model. Apply the weighted least squares method and the weight matrix to perform coarse positioning on the double-difference model to obtain a preliminary position solution. Based on the preliminary position solution, the single-difference model is used to refine and optimize the position solution. The double-difference-single-difference model alternating strategy is used to achieve fast and high-precision positioning.

[0043] Embodiment 5: Dynamic differential positioning system based on multi-source data fusion: Collect observation data from the Beidou satellite navigation system and other satellite navigation systems (such as GPS, Galileo, etc.) or ground-based augmentation systems (such as RTK, etc.). Preprocess the observation data according to the characteristics of each data source. Fuse the preprocessed multi-source data, for example, using fusion algorithms such as Kalman filtering and particle filtering. Use the fused data for differential positioning calculation to obtain a high-precision position solution.

[0044] Example 6: Optimization of dynamic differential positioning method based on deep learning: Collect Beidou observation data of a large number of epochs, and annotate relevant features and position solutions for each epoch. Use the annotated data to train a deep learning model, such as a convolutional neural network (CNN), a recurrent neural network (RNN), or a long short-term memory network (LSTM). Apply the trained deep learning model to real-time observation data to automatically identify features and patterns related to positioning. According to the output results of the deep learning model, optimize the dynamic differential positioning method to achieve higher-precision positioning.

[0045] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated hardware. A person of ordinary skill in the art will understand that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware. The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by any technician familiar with the technical field within the technical scope disclosed by the present invention, within the spirit and principle of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A fast Beidou differential positioning method under dynamic conditions, characterized in that: The model is constructed by using the single-difference error equation between epochs, combining the selected observations and reference epochs, and implementing the double-difference-single-difference model alternating strategy to process the data at a high sampling rate; the method specifically includes: S1: constructing the single-difference error equation between epochs; S2: selecting observations; S3: selecting reference epochs; S4: using the double-difference-single-difference model alternating strategy to achieve high sampling rate data processing; S1 specifically includes: a. Collecting and processing raw observation data: Obtaining raw observation data from Beidou satellite receiving equipment, including satellite pseudorange and carrier phase information; During data processing, eliminating and correcting abnormal data; b. Calculating single differences between epochs: For each epoch, calculating the difference between satellite observation values ​​between adjacent epochs to eliminate the influence of satellite clock error and atmospheric delay error; S1 corresponds to the setting in b. If the coordinates of the moving carrier at the epoch time are known, it can be regarded as the reference epoch, and the error equation of the single difference observation value between epochs can be obtained: , in the single-difference observation error equation, the carrier phase observation is to If there is no cycle slip between epochs, the ambiguities are equal, and the influence of tropospheric delay and ionospheric delay changes is ignored, the satellite clock error , It can be calculated by broadcast ephemeris clock error parameters. There are 4 unknown parameters in the single difference error equation between epochs, namely the coordinates of the moving carrier and the difference of the receiver clock error between epochs. When the number of observation values ​​is greater than or equal to 4, the adjustment can be performed. S2 specifically includes: a. Data smoothing and filtering: smoothing the observation data to reduce the impact of noise; b. Consistency detection and signal-to-noise ratio evaluation: through consistency detection, check the abnormal values ​​in the observation data; at the same time, evaluate the signal-to-noise ratio to determine the signal quality; according to the evaluation results, screen out high-quality observations; in S2, it can be seen from the single-difference observation error equation that the differential observations between epochs are mainly affected by the observation noise, and the observations with small noise are selected for adjustment calculation; for the B1 and B3 frequency points of the Beidou system, the linear combination observations and their variances can be expressed as: , where is the linear combination observation value, , is the combination coefficient, , They are the wavelengths of B1 and B3 frequencies of the BeiDou system. is the mean error of the linear combination observations, , are the mean error of the carrier phase observation values ​​at the B1 and B3 frequency points respectively; , then the variance can be written as: , to satisfy The minimum and observation equations have a satellite-to-ground distance coefficient of 1. , Need to meet: , then we can get: , then using this combination coefficient, we can get the combined observation value with the minimum noise.

2. The method for fast Beidou differential positioning under dynamic conditions as claimed in claim 1, characterized in that: The S3 specifically includes: a. Signal quality and satellite geometric distribution: when selecting a reference epoch, use an epoch with better signal quality and better satellite geometric distribution; b. Sliding window method: use the sliding window method to find the best reference epoch within a certain range; set a window of fixed size, and in each iteration, the window slides forward one epoch, and then selects the best reference epoch within the window range.

3. The method for fast Beidou differential positioning under dynamic conditions as claimed in claim 2, characterized in that: S3 corresponds to the setting in b The epoch has synchronous observation data from the base station receiver and the mobile station receiver on the moving carrier. , , The epoch only has observation data from the mobile station receiver; the double difference model can be used to solve The coordinates of the moving carrier at the epoch time are solved using the single difference model between epochs , , The coordinates of the moving carrier at the epoch time.

4. The method for fast Beidou differential positioning under dynamic conditions as claimed in claim 1, characterized in that: The S4 specifically includes: when the observation data of the mobile station receiver and the base station receiver on the moving carrier are synchronized, the double difference model is used to solve the coordinates of the moving carrier; when the observation data of the mobile station receiver and the base station receiver are not synchronized, the single difference model between epochs is used to solve the coordinates of the moving carrier. By using the above-mentioned double difference-single difference model alternating strategy, high sampling rate data processing can be achieved.

5. A fast Beidou differential positioning system under dynamic conditions, implementing the differential positioning method as claimed in any one of claims 1 to 4, characterized in that: The system comprises: an epoch-to-epoch single-difference error equation construction module: the module is responsible for processing the original observation data and calculating the epoch-to-epoch single-difference error equation. The input of the module is the original observation data, and the output is the epoch-to-epoch single-difference error equation; an observation value selection module: the module is responsible for screening and eliminating abnormal observation values ​​and noise, selecting high-quality observation values, the input is the epoch-to-epoch single-difference error equation, and the output is the screened observation value; a reference epoch selection module: the module is responsible for selecting a suitable reference epoch as a benchmark for positioning calculation, the input is the screened observation value, and the output is the selected reference epoch; a double-difference-single-difference model alternating processing module: the module is responsible for implementing the double-difference-single-difference model alternating strategy and realizing high sampling rate data processing; the input is the screened observation value and the selected reference epoch, and the output is the positioning result.

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