A processing method and related device for GNSS pseudorange diffraction error determination and extraction

Abstract

The embodiment of the application provides a kind of GNSS pseudorange diffraction error determination and extraction processing method and related equipment, belong to satellite navigation positioning field.The method includes collecting the first pseudorange observation data of reference station and the second pseudorange observation data of monitoring station;Reference station is set in open area, and monitoring station is set in signal shielding area;According to the first pseudorange observation data and the second pseudorange observation data, the double difference observation equation of station and satellite is constructed;According to the signal-to-noise ratio sequence of monitoring station relative to satellite and satellite sky map position, diffraction period is identified, and the multiple target diffraction periods of satellite are obtained;The double difference observation equation of multiple satellites in the same epoch is combined to carry out parameter solution, and the pseudorange residual sequence of each satellite is obtained;According to the multiple target diffraction periods of satellite, pseudorange residual sequence is extracted, and the pseudorange diffraction error sequence is obtained.The application can realize the accurate prediction of pseudorange diffraction error, and then improve the positioning solution accuracy.

Description

Technical Field

[0001] This application relates to the field of satellite navigation and positioning, and in particular to a method and related equipment for determining and extracting GNSS pseudorange diffraction errors. Background Technology

[0002] With the completion of the BeiDou Global Navigation Satellite System and its entry into a new stage of global service and large-scale application, the demand for centimeter-level high-precision positioning in fields such as surveying, autonomous driving, and automated construction is becoming increasingly urgent. In complex urban canyons, dense forests, and other obstructed environments, satellite signals often experience path curvature and delay due to diffraction, leading to systematic deviations in pseudorange observations—pseudorange diffraction errors. These errors are difficult to eliminate effectively using conventional differential or modeling methods and have become one of the key bottlenecks restricting the extreme positioning accuracy and reliability of BeiDou in complex scenarios. In-depth research into the generation mechanism, modeling methods, and correction strategies of pseudorange diffraction errors is crucial for improving the service continuity and accuracy of the BeiDou system in harsh environments. This not only helps to promote the penetration of high-precision positioning technology into key scenarios such as seamless indoor and outdoor integration and disaster emergency response, but also provides important support for the development of next-generation navigation signal design and error processing theories, possessing significant scientific research value and engineering application significance.

[0003] In related technologies, pseudorange diffraction errors contaminate observations, making it impossible to completely eliminate pseudoranges containing severe systematic biases using conventional differencing or modeling methods, thus compromising the purity of the observation equations. This, in turn, undermines the fundamental assumptions of positioning solutions. Due to the high spatial correlation and asymmetry of errors, the residual statistical characteristics of algorithms such as least squares or Kalman filtering deviate from Gaussian white noise, significantly reducing the reliability of parameter estimation (especially for fixed ambiguity). This ultimately manifests as systematic positioning bias and decreased availability: in complex urban canyons and other scenarios, it not only causes positioning results to decimeter-level or even meter-level deviations but also significantly increases the divergence of the solution results, even leading to errors in fixed solutions or interruptions in positioning services. This severely damages the accuracy, continuity, and integrity of navigation systems, making them unsuitable for the stringent requirements of critical applications such as precision measurement and accurate construction. Summary of the Invention

[0004] The main objective of this application is to propose a processing method and related equipment for determining and extracting GNSS pseudorange diffraction errors, aiming to achieve accurate prediction of pseudorange diffraction errors and thereby improve the positioning accuracy.

[0005] To achieve the above objectives, one aspect of this application proposes a method for determining and extracting GNSS pseudorange diffraction errors, comprising the following steps: The system collects first pseudorange observation data from a base station and second pseudorange observation data from a monitoring station; the base station is located in an open area, and the monitoring station is located in a signal-obstructed area. Based on the first pseudorange observation data and the second pseudorange observation data, construct the inter-station and inter-satellite double-difference observation equations; Based on the signal-to-noise ratio sequence of the monitoring station relative to the satellite and the satellite sky map position, diffraction period identification is performed to obtain multiple target diffraction periods of the satellite; By combining the double-difference observation equations of multiple satellites at the same epoch, the parameters are solved to obtain the pseudorange residual sequence of each satellite; The pseudorange residual sequence is extracted based on multiple target diffraction periods of the satellite to obtain the pseudorange diffraction error sequence.

[0006] In some embodiments, constructing the inter-station and inter-satellite double-difference observation equations based on the first pseudorange observation data and the second pseudorange observation data includes the following steps: The second pseudorange observation equation of the monitoring station relative to the satellite is constructed based on the second pseudorange observation data, and the first pseudorange observation equation of the reference station relative to the satellite is constructed based on the first pseudorange observation data. The inter-station difference observation equation of the observation satellite is obtained by performing a difference operation between the second pseudorange observation equation and the first pseudorange observation equation relative to the same observation satellite. The inter-station difference observation equation of the observed satellite and the inter-station difference observation equation of the reference satellite are subtracted to obtain the double difference observation equation.

[0007] In some embodiments, the step of identifying multiple target diffraction periods of the satellite based on the signal-to-noise ratio sequence of the monitoring station relative to the satellite and the satellite sky map position includes the following steps: A sliding window-based statistical analysis was performed on the signal-to-noise ratio sequence of the monitoring station relative to the satellite to obtain the signal-to-noise ratio statistical value for each window. The signal-to-noise ratio (SNR) of each epoch in the SNR sequence is compared with the SNR statistics in the corresponding window to determine the candidate time period for signal attenuation. Based on the satellite sky map position, the diffraction effect of the satellite signal during the candidate signal attenuation period is identified to determine the target diffraction period.

[0008] In some embodiments, the step of identifying the diffraction effect of satellite signals during the candidate signal attenuation period based on the satellite sky map position, and determining the target diffraction period, includes the following steps: For each satellite signal in the candidate period of signal attenuation, the first Fresnel zone radius of the satellite signal at the edge of the obstruction is calculated based on the satellite sky map position, and then the diffraction threshold corresponding to the first Fresnel zone obstruction range is calculated based on the first Fresnel zone radius; Calculate the diffraction parameters of the satellite signal and the edge of the obstruction based on the satellite sky map position; The diffraction parameters and the diffraction threshold are compared to determine whether the candidate signal attenuation period is the target diffraction period.

[0009] In some embodiments, the method of solving the parameters of the double-difference observation equations of multiple satellites at the same epoch to obtain the pseudorange residual sequence of each satellite includes the following steps: The weight matrix is ​​obtained by calculating the weights of multiple satellites at the same epoch using a signal-to-noise ratio weighting model. Subtract the true double-difference geometric distance from the double-difference observations of each satellite to construct the residual vector; The least squares parameter estimation method is used to perform double-difference residual estimation based on the double-difference observation equations, weight matrices and residual vectors of multiple satellites at the same epoch, so as to obtain the pseudorange residual sequence of each satellite.

[0010] In some embodiments, the GNSS pseudorange diffraction error determination and extraction method further includes the following steps: A pseudorange diffraction error prediction model is constructed based on the pseudorange diffraction error sequence and the corresponding path geometric parameters. The constructed pseudorange diffraction error prediction model is added to the pseudorange observation equation of the monitoring station for positioning calculation, and the positioning calculation result is obtained.

[0011] To achieve the above objectives, another aspect of this application proposes a processing system for determining and extracting GNSS pseudorange diffraction errors, comprising: The first module is used to collect the first pseudorange observation data of the base station and the second pseudorange observation data of the monitoring station; the base station is set in an open area and the monitoring station is set in a signal-obstructed area. The second module is used to construct inter-station and inter-satellite double-difference observation equations based on the first pseudorange observation data and the second pseudorange observation data. The third module is used to identify diffraction periods based on the signal-to-noise ratio sequence of the monitoring station relative to the satellite and the satellite sky map position, thereby obtaining multiple target diffraction periods of the satellite. The fourth module is used to solve the parameters of the double-difference observation equations of multiple satellites at the same epoch, and obtain the pseudorange residual sequence of each satellite. The fifth module is used to extract the pseudorange residual sequence based on multiple target diffraction periods of the satellite to obtain the pseudorange diffraction error sequence.

[0012] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0013] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0014] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a processing method and related equipment for determining and extracting GNSS pseudorange diffraction errors. The scheme first constructs inter-station and inter-satellite double-difference observation equations for the raw pseudorange observation data of the reference station and the monitoring station, effectively eliminating satellite clock errors and receiver clock errors, and significantly reducing common errors such as ionospheric and tropospheric delays. In this pure double-difference observation domain, the residual pseudorange double-difference residuals are mainly dominated by diffraction errors on the path from the observation station to the satellite. Further, based on the signal-to-noise ratio sequence of the monitoring station relative to the satellite and the satellite sky map position, diffraction period identification based on systematic bias and geometric relationship is performed to obtain multiple target diffraction periods of the satellite. The parameters are solved by combining the double-difference observation equations of multiple satellites at the same epoch, and the pseudorange residual sequence of each satellite is obtained. Then, the pseudorange residual sequence is extracted based on the multiple target diffraction periods of the satellite to obtain the pseudorange diffraction error sequence. This method can realize the quantitative extraction and identification of diffraction errors. This method effectively utilizes the powerful elimination capability of double-difference technology for common errors, and provides a key data foundation for subsequent modeling and correction of diffraction errors. Attached Figure Description

[0016] Figure 1 This is a flowchart of the GNSS pseudorange diffraction error determination and extraction processing method provided in the embodiments of this application; Figure 2 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0019] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0020] This application provides a method and related equipment for determining and extracting GNSS pseudorange diffraction errors. This method constructs precise geometric constraints to separate spatially non-common error components, thereby achieving quantitative extraction and identification of diffraction errors. This method effectively utilizes the ability of double-difference technology to eliminate common errors, providing a crucial data foundation for subsequent modeling and correction of diffraction errors.

[0021] The GNSS pseudorange diffraction error determination and extraction method provided in this application relates to the field of satellite navigation and positioning. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the GNSS pseudorange diffraction error determination and extraction method, but is not limited to the above forms.

[0022] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0023] Figure 1 This is an optional flowchart of the GNSS pseudorange diffraction error determination and extraction processing method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0024] S101, collect the first pseudorange observation data of the base station and the second pseudorange observation data of the monitoring station; the base station is set in an open area and the monitoring station is set in a signal-obstructed area. S102, construct the inter-station and inter-satellite double-difference observation equations based on the first pseudorange observation data and the second pseudorange observation data; S103, based on the signal-to-noise ratio sequence of the monitoring station relative to the satellite and the position of the satellite sky map, the diffraction period is identified to obtain multiple target diffraction periods of the satellite; S104, combined with the double-difference observation equations of multiple satellites at the same epoch, parameter solutions are performed to obtain the pseudorange residual sequence of each satellite; S105. Based on the multiple target diffraction periods of the satellite, the pseudorange residual sequence is extracted to obtain the pseudorange diffraction error sequence.

[0025] Steps S101 to S105 of this embodiment first construct inter-station and inter-satellite double-difference observation equations for the raw pseudorange observation data of the base station and monitoring station. This effectively eliminates satellite clock errors and receiver clock errors, and significantly reduces common errors such as ionospheric and tropospheric delays. In this pure double-difference observation domain, the residual pseudorange double-difference residuals are mainly dominated by diffraction errors along the path from the observation station to the satellite. Further, based on the signal-to-noise ratio sequence of the monitoring station relative to the satellite and the satellite sky map position, diffraction time periods are identified based on systematic bias and geometric relationships to obtain multiple target diffraction time periods for the satellite. The parameters of the double-difference observation equations for multiple satellites at the same epoch are then combined to obtain the pseudorange residual sequence for each satellite. Finally, the pseudorange residual sequence is extracted based on the multiple target diffraction time periods of the satellite to obtain the pseudorange diffraction error sequence. This enables quantitative extraction and identification of diffraction errors. This embodiment effectively utilizes the powerful elimination capability of double-difference technology for common errors, providing a crucial data foundation for subsequent modeling and correction of diffraction errors.

[0026] In step S101 of some embodiments, a base station is set up in an open area, and a mobile monitoring station is set up in an area obstructed by buildings; this embodiment is also referred to as a rover station or target station. The coordinates of both the base station and the monitoring station are known or can be obtained through high-precision static calculation. The first pseudorange observation data from the base station and the second pseudorange observation data from the monitoring station are read, as well as the signal-to-noise ratio sequences of each satellite observed by the base station and the monitoring station are read.

[0027] In step S102 of some embodiments, inter-station and inter-satellite double-difference observation equations are constructed based on the first pseudorange observation data and the second pseudorange observation data. Specifically, firstly, the original pseudorange observation values ​​of the base station and the rover for the same satellite are subjected to inter-station single difference to effectively eliminate satellite clock errors. Then, a common reference satellite with similar elevation angle and azimuth angle and not affected by diffraction is selected, and inter-satellite double difference is performed with the observation values ​​of the suspected satellite affected by diffraction, thereby further eliminating receiver clock errors and significantly reducing common errors such as ionospheric and tropospheric delays.

[0028] In some embodiments, step S102 may include, but is not limited to, the following steps: S201, construct the second pseudorange observation equation of the monitoring station relative to the satellite based on the second pseudorange observation data, and construct the first pseudorange observation equation of the reference station relative to the satellite based on the first pseudorange observation data; S202, perform a difference operation on the second pseudorange observation equation and the first pseudorange observation equation relative to the same observation satellite to obtain the inter-station difference observation equation of the observation satellite; S203, perform a difference operation between the inter-station difference observation equations of the observation satellite and the inter-station difference observation equations of the reference satellite to obtain the double difference observation equations.

[0029] In this embodiment, the pseudorange observation equation of the monitoring station relative to the satellite It is expressed as follows: (1) in, and These are designated as satellite and station (i.e., monitoring station) numbers, respectively. The geometric distance from the satellite to the monitoring station receiver. and These represent the clock biases of the receiver and the satellite, respectively. At the speed of light, For tropospheric wet delay parameters, Ionospheric delay in the direction of satellite incidence. It is a collection of pseudorange hardware delay, relativistic effects, Earth's rotation, tidal and multipath effects errors, and other unmodeled errors. This refers to the pseudorange diffraction error caused by the diffraction effect at the edge of obstructions affecting the monitoring station. The pseudorange observation noise is represented by the symbol (1), where all symbols are in meters. Substituting the second pseudorange observation data into formula (1)... In this process, the second pseudorange observation equation of the monitoring station relative to satellite i is obtained.

[0030] Similarly, the pseudorange observation equation of the base station relative to satellite i It is expressed as follows: (2) In the formula, The reference station number is indicated by the symbol; other symbols are the same as in equation (1). Substitute the first pseudorange observation data into equation (2). In this process, the second pseudorange observation equation of the monitoring station relative to satellite i is obtained.

[0031] Furthermore, a double-difference observation equation is constructed using observations from the base station and monitoring station to eliminate satellite clock errors, receiver clock errors, and most atmospheric delay errors. Specifically: For satellite The second pseudorange observation equation and the first pseudorange observation equation of the base station and the observation station are differentiated to construct the satellite. Single-difference station-to-station difference observation equation The expression is as follows: (3) In the formula, As a single-difference operator, the orbital and clock errors associated with the satellite are eliminated through single-difference operations. At the same time, since the distance between the two stations is short, single-difference can also greatly reduce the ionospheric and tropospheric delay errors. Therefore, it can be seen from formula (3) that the parameter to be estimated in the observation equation is the satellite-to-ground distance. Receiver clock bias and diffraction error .

[0032] A satellite with a high elevation angle, no obstructions, and a stable signal-to-noise ratio was selected as the reference satellite. Other satellites The single-difference observation equation (i.e., the single-difference inter-station difference observation equation) Subtract the reference satellite Inter-station difference observation equation Eliminating the receiver clock bias term yields the double-difference observation equation. , means as follows: (4) In step S103 of some embodiments, the diffraction period is identified by using the signal-to-noise ratio sequence between the monitoring station and the satellite and the satellite sky map position, so as to obtain multiple target diffraction periods in which the diffraction effect occurs for each satellite, thereby distinguishing between conventional multipath and diffraction effect.

[0033] In some embodiments, step S103 may include, but is not limited to, the following steps: S301, Perform a sliding window-based statistical analysis on the signal-to-noise ratio sequence of the monitoring station relative to the satellite to obtain the signal-to-noise ratio statistical value for each window; S302, compare the signal-to-noise ratio of each epoch in the signal-to-noise ratio sequence with the signal-to-noise ratio statistics in the corresponding window to determine the candidate time period for signal attenuation; S303, based on the satellite sky map position, identifies the diffraction effect of satellite signals during candidate signal attenuation periods to determine the target diffraction period.

[0034] In this embodiment, the signal-to-noise ratio (SNR) sequence of each satellite is segmented into sliding windows, and the SNR statistics within each segment window are calculated. The SNR statistics include the mean. and standard deviation They are represented as follows: (5) (6) Calculate the lower envelope of the signal-to-noise ratio sequence for each satellite. The envelope is defined as the mean of each window minus N times (e.g., 3 times) the standard deviation, as shown below: (7) The signal-to-noise ratio statistics for each window are calculated according to formula (7) to form a lower envelope sequence. Each S in the lower envelope sequence is used as the threshold for determining whether the signal is affected by occlusion within that window.

[0035] Specifically, if a certain epoch satisfy Then mark the time period. If the signal attenuation is a candidate period, proceed to the next step of geometric verification; otherwise, it is determined to be normal line-of-sight propagation. Indicates the signal-to-noise ratio. The threshold can be set to 5 dB-Hz.

[0036] This embodiment analyzes the signal-to-noise ratio (SNR) time series of a single satellite received by the monitoring station. It performs sliding segmentation according to a preset window length and step size, calculating the mean, standard deviation, and other statistical values ​​for the SNR sequence within each window. This yields a dynamically updated local benchmark, avoiding the judgment bias caused by using a globally fixed threshold. The measured SNR at each epoch is compared with the statistical benchmark value of its corresponding sliding window, marking that epoch as an attenuation point and forming a candidate period for signal attenuation. This initial screening filters out interference from normal signal fluctuations, laying the foundation for subsequent accurate diffraction effect identification.

[0037] In some embodiments, step S303 may include, but is not limited to, the following steps: S401, for each candidate period of signal attenuation, calculate the radius of the first Fresnel zone of the satellite signal at the edge of the obstruction based on the satellite sky map position, and then calculate the diffraction threshold corresponding to the obstruction range of the first Fresnel zone based on the radius of the first Fresnel zone. S402, calculate the diffraction parameters of the satellite signal and the edge of the obstruction based on the satellite sky map position; S403 compares the diffraction parameters and the diffraction threshold to determine whether the candidate period for signal attenuation is the target diffraction period.

[0038] In this embodiment, the satellite sky image location is a two-dimensional polar coordinate diagram used in GNSS (receiver or post-processing software) to visualize the distribution of visible satellites in three-dimensional space, with the horizontal axis representing azimuth and the vertical axis representing elevation. For satellite signals marked as candidate signal attenuation periods, the radius of the first Fresnel zone at the edge of the obstruction is calculated based on the Knife-Edge Diffraction Model. Due to satellite distance Much greater than the distance from the receiver to the obstruction ( The formula for calculating the radius of the first Fresnel zone is simplified to the following formula: (8) In the formula, The wavelength of the GNSS signal. This is the horizontal distance from the receiver to the edge of the obstruction. The effective obstruction distance can be estimated by combining the location of satellite sky images with the digital surface model of open source maps and the elevation angle of the obstruction.

[0039] Calculate the vertical angle between the satellite signal line of sight and the edge of the obstruction. And further calculation of dimensionless diffraction parameters. The calculation formulas are as follows: (9) (10) In the formula, This refers to the satellite's real-time elevation angle. This is the cutoff elevation angle of the obstruction at that azimuth (i.e., the elevation angle of the edge of the obstruction). When When the satellite's line of sight is higher than the obstruction (this is a direct signal); when When the satellite's line of sight just passes through the edge of the obstruction (this is the critical point where diffraction occurs); when When the signal is 0, it indicates that the satellite's direct line of sight is blocked by an obstruction (in this case, it is a diffraction signal).

[0040] According to diffraction parameters The numerical range is used to determine whether the signal is within the dominance range of diffraction. A significant diffraction effect is defined as the signal being within the first Fresnel zone blocking range, as determined by the following formula: (11) In the formula, When propagated in free space, there is no diffraction. At this stage, grazing diffraction occurs, resulting in signal enhancement or slight distortion. At that time, the first Fresnel zone will block diffraction, resulting in a significant positive pseudorange deviation. These areas are considered deep shadow regions, where signals will lose lock or have extremely low signal-to-noise ratios, and will not participate in localization. It is understood that -0.78 and 2.4 are diffraction thresholds (including upper and lower diffraction limits) calculated based on the radius of the first Fresnel zone, corresponding to the occlusion range of the first Fresnel zone. -0.78 and 2.4 are merely illustrative examples; the specific values ​​can be determined by calculating the diffraction thresholds corresponding to the occlusion range of the first Fresnel zone.

[0041] After combining the above signal-to-noise ratio with geometric determination, a binary diffraction marker sequence is generated for each epoch. As shown in equation (12). Only when At that time, it was determined that the satellite of that epoch had a pseudorange diffraction error, that is, the corresponding time period was the target diffraction period, and the subsequent steps would extract the residual for this time period.

[0042] (12) In step S104 of some embodiments, the parameters of the double-difference observation equations of multiple satellites at the same epoch are solved together to obtain the pseudorange residual sequence of each satellite. The pseudorange residual sequence refers to the difference between the pseudorange observed by the satellite and the true double-difference geometric distance. The true double-difference geometric distance can be calculated from the actual position of the monitoring station and the actual position of the satellite.

[0043] In some embodiments, step S104 may include, but is not limited to, the following steps: S501 uses a signal-to-noise ratio weighting model to calculate the weights of multiple satellites at the same epoch, thus obtaining a weight matrix. S502, subtract the true double-difference geometric distance from the double-difference observations of each satellite to construct the residual vector; S503 uses the least squares parameter estimation method to perform double-difference residual estimation based on the double-difference observation equations, weight matrices and residual vectors of multiple satellites at the same epoch, and obtains the pseudorange residual sequence of each satellite.

[0044] In this embodiment, after obtaining the double-difference observation equations for multiple satellites at the same epoch using the above-described double-difference observation equation construction method, a signal-to-noise ratio weighting strategy is adopted, and the diffraction components in the double-difference residuals are separated by least squares estimation, as follows: The weight matrix is ​​determined using a signal-to-noise ratio weighting model. The variance model of pseudorange observations is expressed as: (13) In the formula, This represents an inherent error term that is independent of signal strength. These are coefficients related to the signal-to-noise ratio. Then the weight matrix... The expression is: (14) In the formula, diag represents the diagonal element of the square matrix, and the subscript number is the satellite number. This represents the total number of satellites.

[0045] The parameters to be solved in the double-difference observation equations of multiple satellites at the same epoch are extracted as state vectors. These state vectors include the monitoring station positions and error parameters (such as receiver clock errors) to be solved. Based on the state vectors, weight matrices, and residual vectors, a parameter estimation solution formula using the weighted least squares method is constructed, as follows: (15) In the formula, To design the matrix, It is the residual vector obtained by subtracting the true double-difference geometric distance (i.e., the true double-difference geometric distance between the satellite and the station) from the double-difference observations.

[0046] Based on the above parameter estimation formula, the least squares parameter estimation method is used to estimate the parameters of the double-difference residuals for each epoch, and the state vector is solved. This allows us to obtain the pseudorange residual sequence for each satellite i. .

[0047] In step S105 of some embodiments, the pseudorange residual sequence is extracted based on multiple target diffraction periods of the satellite to obtain the pseudorange diffraction error sequence. Specifically, this is combined with the judgment flags of each satellite in each time period. The pseudorange residuals located in the target diffraction period are extracted from the pseudorange residual sequence to form the pseudorange diffraction error sequence. The extraction formula is as follows: (16) In some embodiments, the GNSS pseudorange diffraction error determination and extraction method of this application may also include, but is not limited to, the following steps: S601, construct a pseudorange diffraction error prediction model based on the pseudorange diffraction error sequence and the corresponding path geometric parameters; S602, the constructed pseudorange diffraction error prediction model is added to the pseudorange observation equation of the monitoring station for positioning calculation, and the positioning calculation result is obtained.

[0048] In this embodiment, the extracted pseudorange diffraction error sequence is fitted with the corresponding path geometric parameters to construct a pseudorange diffraction error prediction model for empirical correction; wherein, the path geometric parameters represent the satellite elevation angle, azimuth angle, and the geometric relationship of the obstruction edge. In high-precision positioning calculation, the constructed pseudorange diffraction error prediction model is added as a priori correction to the pseudorange observation equation to achieve real-time correction of diffraction errors.

[0049] This application proposes a GNSS pseudorange diffraction error extraction method based on double-difference technology. First, a base station is deployed in an open, unobstructed area near the target region affected by diffraction to ensure its observations are unaffected by diffraction effects. In data processing, the raw pseudorange observations from the base station and rover are first subjected to inter-station single-difference to effectively eliminate satellite clock errors. Then, a common-view reference satellite with similar elevation and azimuth angles and unaffected by diffraction is selected, and its observations are subjected to inter-satellite double-difference with those of the suspected satellite affected by diffraction, further eliminating receiver clock errors and significantly reducing common errors such as ionospheric and tropospheric delays. In this pure double-difference observation domain, the residual pseudorange double-difference residual is mainly dominated by diffraction errors along the path from the rover to the suspected satellite. Finally, by analyzing the systematic deviation characteristics of this residual and its dynamic correlation with the relative geometry of the satellite and obstacle, quantitative extraction and identification of diffraction errors can be achieved. This method effectively utilizes the powerful elimination capability of double-difference technology for common errors, providing a crucial data foundation for subsequent modeling and correction of diffraction errors. The method proposed in this application can be widely applied to various satellite navigation and positioning systems and all their signal frequency scenarios.

[0050] This application also provides a processing system for determining and extracting GNSS pseudorange diffraction errors, including: The first module is used to collect the first pseudorange observation data of the base station and the second pseudorange observation data of the monitoring station; the base station is set in an open area and the monitoring station is set in a signal-obstructed area. The second module is used to construct inter-station and inter-satellite double-difference observation equations based on the first pseudorange observation data and the second pseudorange observation data. The third module is used to identify diffraction periods based on the signal-to-noise ratio sequence of the monitoring station relative to the satellite and the satellite sky map position, thereby obtaining multiple target diffraction periods of the satellite. The fourth module is used to solve the parameters of the double-difference observation equations of multiple satellites at the same epoch, and obtain the pseudorange residual sequence of each satellite. The fifth module is used to extract the pseudorange residual sequence based on multiple target diffraction periods of the satellite to obtain the pseudorange diffraction error sequence.

[0051] It is understood that the methods described in the above method embodiments are applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0052] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0053] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0054] Please see Figure 2 , Figure 2 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 902 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901. The input / output interface 903 is used to implement information input and output; The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904); The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0055] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0056] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0057] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0058] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0059] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0060] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0061] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0062] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0063] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0064] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0065] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0066] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0067] The modules described above as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0068] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0069] If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0070] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for determining and extracting GNSS pseudorange diffraction errors, characterized in that, Includes the following steps: The system collects first pseudorange observation data from a base station and second pseudorange observation data from a monitoring station; the base station is located in an open area, and the monitoring station is located in a signal-obstructed area. Based on the first pseudorange observation data and the second pseudorange observation data, construct the inter-station and inter-satellite double-difference observation equations; Based on the signal-to-noise ratio sequence of the monitoring station relative to the satellite and the satellite sky map position, diffraction period identification is performed to obtain multiple target diffraction periods of the satellite; By combining the double-difference observation equations of multiple satellites at the same epoch, the parameters are solved to obtain the pseudorange residual sequence of each satellite; The pseudorange residual sequence is extracted based on multiple target diffraction periods of the satellite to obtain the pseudorange diffraction error sequence.

2. The processing method for determining and extracting GNSS pseudorange diffraction errors according to claim 1, characterized in that, The construction of inter-station and inter-satellite double-difference observation equations based on the first pseudorange observation data and the second pseudorange observation data includes the following steps: The second pseudorange observation equation of the monitoring station relative to the satellite is constructed based on the second pseudorange observation data, and the first pseudorange observation equation of the reference station relative to the satellite is constructed based on the first pseudorange observation data. The inter-station difference observation equation of the observation satellite is obtained by performing a difference operation between the second pseudorange observation equation and the first pseudorange observation equation relative to the same observation satellite. The inter-station difference observation equation of the observed satellite and the inter-station difference observation equation of the reference satellite are subjected to a difference operation to obtain the double difference observation equation.

3. The processing method for determining and extracting GNSS pseudorange diffraction errors according to claim 1, characterized in that, The step of identifying multiple target diffraction periods of the satellite based on the signal-to-noise ratio sequence of the monitoring station relative to the satellite and the satellite sky map position includes the following steps: A sliding window-based statistical analysis was performed on the signal-to-noise ratio sequence of the monitoring station relative to the satellite to obtain the signal-to-noise ratio statistical value for each window. The signal-to-noise ratio (SNR) of each epoch in the SNR sequence is compared with the SNR statistics in the corresponding window to determine the candidate time period for signal attenuation. Based on the satellite sky map position, the diffraction effect of the satellite signal during the candidate signal attenuation period is identified to determine the target diffraction period.

4. The processing method for determining and extracting GNSS pseudorange diffraction errors according to claim 3, characterized in that, The step of identifying the diffraction effect of satellite signals during the candidate signal attenuation period based on the satellite sky map position, and determining the target diffraction period, includes the following steps: For each satellite signal in the candidate period of signal attenuation, the first Fresnel zone radius of the satellite signal at the edge of the obstruction is calculated based on the satellite sky map position, and then the diffraction threshold corresponding to the first Fresnel zone obstruction range is calculated based on the first Fresnel zone radius; Calculate the diffraction parameters of the satellite signal and the edge of the obstruction based on the satellite sky map position; The diffraction parameters and the diffraction threshold are compared to determine whether the candidate signal attenuation period is the target diffraction period.

5. The processing method for determining and extracting GNSS pseudorange diffraction errors according to claim 1, characterized in that, The method of solving the parameters of the double-difference observation equations of multiple satellites at the same epoch to obtain the pseudorange residual sequence of each satellite includes the following steps: The weight matrix is ​​obtained by calculating the weights of multiple satellites at the same epoch using a signal-to-noise ratio weighting model. Subtract the true double-difference geometric distance from the double-difference observations of each satellite to construct the residual vector; The least squares parameter estimation method is used to perform double-difference residual estimation based on the double-difference observation equations, weight matrices and residual vectors of multiple satellites at the same epoch, so as to obtain the pseudorange residual sequence of each satellite.

6. The processing method for determining and extracting GNSS pseudorange diffraction errors according to any one of claims 1 to 5, characterized in that, The method for determining and extracting GNSS pseudorange diffraction errors also includes the following steps: A pseudorange diffraction error prediction model is constructed based on the pseudorange diffraction error sequence and the corresponding path geometric parameters. The constructed pseudorange diffraction error prediction model is added to the pseudorange observation equation of the monitoring station for positioning calculation, and the positioning calculation result is obtained.

7. A processing system for determining and extracting GNSS pseudorange diffraction errors, characterized in that, include: The first module is used to collect the first pseudorange observation data of the base station and the second pseudorange observation data of the monitoring station. The base station is located in an open area, and the monitoring station is located in an area with signal obstruction. The second module is used to construct inter-station and inter-satellite double-difference observation equations based on the first pseudorange observation data and the second pseudorange observation data. The third module is used to identify diffraction periods based on the signal-to-noise ratio sequence of the monitoring station relative to the satellite and the satellite sky map position, thereby obtaining multiple target diffraction periods of the satellite. The fourth module is used to solve the parameters of the double-difference observation equations of multiple satellites at the same epoch, and obtain the pseudorange residual sequence of each satellite. The fifth module is used to extract the pseudorange residual sequence based on multiple target diffraction periods of the satellite to obtain the pseudorange diffraction error sequence.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

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