PPP-B2b enhancement-based high-precision common-view method in urban environment

Abstract

The invention relates to a PPP-B2b enhancement-based high-precision common-view method in an urban environment, and relates to the technical field of information technology services, and the method comprises the steps: constructing a real-time precision ephemeris; correcting troposphere delay, a relativistic effect, a phase winding error and an antenna phase center error by using the model, and constructing a double-frequency ionosphere-free observation model in combination with a precise ephemeris; an improved IGG III algorithm is adopted to introduce a position precision attenuation factor PDOP and a satellite pitch angle double-factor adaptive fixed weight, a weighted pre-test residual error is calculated, and urban multipath interference is suppressed; based on the weighted pre-test residual error, parameter estimation is carried out by using Kalman filtering, and the precision clock error of the receiver relative to each satellite is solved one by one; and calculating a common-view comparison result by performing difference on each satellite, and outputting a high-precision time transfer result. The method has the beneficial effects that real-time high-precision precise common-view time transfer is realized, and the requirement of high-precision time transfer in an actual application scene is met.

Description

High-precision common-view method based on PPP-B2b enhancement in urban environments Technical Field

[0001] This invention relates to the field of information technology service technology, and more specifically, to a high-precision common-view method based on PPP-B2b enhancement in urban environments. Background Technology

[0002] Global navigation satellite systems (GNSS) have developed rapidly in recent decades. GNSS navigation, positioning and timing services have been integrated into all aspects of human life and social production and construction, from the establishment of global reference frames to vehicle navigation.

[0003] The BeiDou-3 navigation satellite system (BDS-3) officially commenced network coverage in 2020, providing users with positioning, navigation, and timing services. In 2020, my country released the "Performance Specification for Precise Point Positioning (PPP) Service of BeiDou Navigation Satellite System (Version 1.0)," broadcasting precise orbit and clock error correction parameters (PPP-B2b) for BDS-3 and other global navigation satellite systems to my country and surrounding regions via geostationary orbit satellites. Unlike the real-time services provided by international GNSS service organizations, users can still obtain real-time BDS-3 PPP services without an internet connection, making it one of the distinctive services of the BeiDou-3 global navigation satellite system.

[0004] Current research on PPP-B2b mainly focuses on PPP positioning and navigation, with limited practical engineering applications in time transfer. Time transfer is crucial for maintaining and transmitting standard time. Among time transfer methods, common view (CV) is one of the most commonly used, offering good continuity. CV can mitigate the effects of ionospheric, tropospheric, and satellite clock errors through inter-station differential measurement. Traditional CV methods use code pseudorange observations, resulting in lower time transfer accuracy. PPP utilizes high-precision carrier phase, providing high-precision positioning and timing performance, leading to the development of the precise common view (PCV) transfer method based on PPP. While some scholars are conducting research on PCV, it remains largely at the research level, with limited research on PCV in complex urban environments. In practical engineering applications, receivers experience greater interference, making better interference suppression a pressing issue. Summary of the Invention

[0005] The purpose of this invention is to provide a high-precision common-view method based on PPP-B2b enhancement in urban environments to improve the above-mentioned problems. To achieve the above objectives, the technical solution adopted by this invention is as follows: Firstly, this application provides a high-precision common-view method based on PPP-B2b enhancement in urban environments, comprising: receiving broadcast ephemeris, raw observation data, and PPP-B2b correction messages broadcast by BDS-3 satellites; parsing and matching the broadcast ephemeris with PPP-B2b orbital corrections and clock bias corrections; calculating precise satellite positions and precise satellite clock biases; constructing real-time precise ephemeris; judging elevation anomalies in the raw GNSS observation data, removing satellites with elevation angles below a 15° threshold; using ionospheric-free combinations, geometric distance-free combinations, and MW combinations for cycle slip detection and repair; using models to correct tropospheric delay, relativistic effects, phase winding errors, and antenna phase center errors; and constructing a dual-frequency ionospheric-free observation model based on the precise ephemeris; calculating the prior residuals based on the dual-frequency ionospheric-free model; using the 3-sigma principle to determine the residual statistical threshold for gross error detection; removing abnormal satellite observations; and using an improved IGG... Algorithm III introduces a position accuracy attenuation factor (PDOP) and a dual-factor adaptive weighting of satellite elevation angle to calculate weighted pre-hoc residuals and suppress urban multipath interference. Based on the weighted pre-hoc residuals, Kalman filtering is used for parameter estimation to obtain receiver position, receiver clock bias, and integer ambiguity. The estimated parameters are then substituted back into the carrier phase observation equation to solve the precise clock bias of the receiver relative to each satellite on a satellite-by-satellite basis. Based on the set of synchronously observed satellites from two common-view stations, the common-view comparison results are calculated by subtracting satellites one by one. Real-time Kalman filtering is applied to the common-view comparison results to output high-precision time transfer results.

[0006] Preferably, the PPP-B2b correction message is broadcast via the BDS-3 geostationary orbit satellite, achieving real-time precise ephemeris enhancement without relying on external network communication.

[0007] Preferably, the 3-sigma principle and the improved IGG III algorithm constitute a hierarchical progressive quality control architecture, wherein: the first layer uses the 3-sigma principle to perform hard threshold discrimination based on statistical distribution to generate an effective satellite observation subset; the second layer uses the improved IGG III algorithm to perform soft weighting based on a continuous function of PDOP and elevation angle within the effective satellite observation subset to generate weighted observations.

[0008] Preferably, in the satellite-by-satellite calculation of the receiver's precise clock bias relative to each satellite, the filtered estimated receiver position, tropospheric delay, and integer ambiguity are used as known parameters to substitute back into the carrier phase observation equations of each satellite, and the receiver clock bias corresponding to each satellite is calculated independently, wherein the clock bias calculation results of each satellite do not affect each other.

[0009] Preferably, the real-time Kalman filtering of the common-view alignment results includes: establishing the state transition equation and measurement equation of the common-view alignment results, setting the process noise covariance matrix and measurement noise covariance matrix, performing time update and measurement update recursive calculations, and outputting the filtered common-view alignment sequence.

[0010] Preferably, the state vector of the Kalman filter includes the receiver's three-dimensional position coordinates, receiver clock error, tropospheric zenith wet delay, and dual-frequency ionosphere-free combination integer ambiguity, wherein the measurement vector is the dual-frequency ionosphere-free combination pseudorange and carrier phase observation values.

[0011] Secondly, this application also provides a high-precision common-view system based on PPP-B2b enhancement in an urban environment, including: a precise ephemeris calculation module: used to receive broadcast ephemeris, raw observation data, and PPP-B2b correction messages broadcast by BDS-3 satellites, parse and match the broadcast ephemeris with PPP-B2b orbital corrections and clock bias corrections, calculate precise satellite positions and precise satellite clock biases, and construct real-time precise ephemeris; an urban environment adaptive preprocessing module: used to judge elevation anomalies in raw GNSS observation data, remove satellites with elevation angles below the 15° threshold, use ionospheric-free combinations, geometric distance-free combinations, and MW combinations for cycle slip detection and repair, use models to correct tropospheric delay, relativistic effects, phase winding errors, and antenna phase center errors, and construct a dual-frequency ionospheric-free observation model in conjunction with precise ephemeris; and an anti-multipath interference quality control module: used to calculate pre-approval residuals based on the dual-frequency ionospheric-free model, use the 3-sigma principle to determine the residual statistical threshold for gross error detection, remove abnormal satellite observations, and use an improved IGG... Algorithm III introduces a position accuracy attenuation factor (PDOP) and a dual-factor adaptive weighting of satellite elevation angle to calculate weighted pre-hoc residuals and suppress urban multipath interference. The precise single-satellite clock bias calculation module uses Kalman filtering to estimate parameters based on the weighted pre-hoc residuals, obtaining receiver position, receiver clock bias, and integer ambiguity. The estimated parameters are then substituted back into the carrier phase observation equation to calculate the precise clock bias of the receiver relative to each satellite. The common-view comparison optimization module calculates the common-view comparison results based on a set of synchronously observed satellites from two common-view stations, performs real-time Kalman filtering on the common-view comparison results, and outputs high-precision time transfer results.

[0012] Thirdly, this application also provides a high-precision precision co-viewing device based on PPP-B2b enhancement in an urban environment, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the high-precision precision co-viewing method based on PPP-B2b enhancement in an urban environment.

[0013] Fourthly, this application also provides a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described high-precision common-view method based on PPP-B2b enhancement in an urban environment.

[0014] The beneficial effects of this invention are as follows: This invention utilizes PPP-B2b correction information and, considering the greater multipath interference in urban environments, proposes an improved IGG III algorithm and a 3-sigma principle algorithm. First, the precise satellite positions and clock errors are calculated for the two co-viewing stations based on the broadcast ephemeris and BDS-3 PPP-B2b corrections, respectively. Then, for satellites after data anomaly detection, Kalman filtering is used to obtain the receiver clock errors of the co-viewing station relative to each satellite. Finally, CV data processing is performed using the receiver clock errors to obtain the co-viewing comparison results. This fully considers the abnormal increase in pseudorange noise caused by interference in complex urban environments and proposes an improved IGG III algorithm and a 3-sigma principle algorithm to address this phenomenon. Real-time Kalman filtering is then applied to the co-viewing comparison results to achieve real-time, high-precision co-viewing time transfer, meeting the high-precision time transfer requirements of practical application scenarios.

[0015] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 is a flowchart illustrating the high-precision precision co-viewing method based on PPP-B2b enhancement in an urban environment according to an embodiment of the present invention; Figure 2 is a structural diagram illustrating the high-precision precision co-viewing system based on PPP-B2b enhancement in an urban environment according to an embodiment of the present invention; Figure 3 is a structural diagram illustrating the high-precision precision co-viewing device based on PPP-B2b enhancement in an urban environment according to an embodiment of the present invention; Figure 4 is a schematic diagram illustrating the principle of the high-precision precision co-viewing method based on PPP-B2b enhancement in an urban environment according to an embodiment of the present invention; Figure 5 is a flowchart illustrating the PPP data processing in the high-precision precision co-viewing method based on PPP-B2b enhancement in an urban environment according to an embodiment of the present invention; Figure 6 is a schematic diagram illustrating the co-viewing comparison results in the high-precision precision co-viewing method based on PPP-B2b enhancement in an urban environment according to an embodiment of the present invention.

[0018] In the diagram: 701, Precise Ephemeris Calculation Module; 702, Urban Environment Adaptive Preprocessing Module; 703, Anti-Multipath Interference Quality Control Module; 704, Precise Single-Star Clock Error Calculation Module; 705, Common-View Comparison Optimization Module; 800, High-Precision Common-View Equipment Based on PPP-B2b Enhancement in Urban Environment; 801, Processor; 802, Memory; 803, Multimedia Component; 804, I / O Interface; 805, Communication Component. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] This embodiment provides a high-precision common-view method based on PPP-B2b enhancement for urban environments, overcoming the shortcomings of existing technologies. Unlike conventional PCV methods, this invention fully considers the abnormal increase in pseudorange noise caused by interference in complex urban environments and proposes an improved IGG III algorithm and a 3-sigma principle algorithm to address this phenomenon. Real-time Kalman filtering is then applied to the common-view comparison results to achieve real-time high-precision common-view time transfer, meeting the high-precision time transfer requirements of practical application scenarios.

[0022] Referring to Figure 1, the figure shows that the method includes steps S100, S200, S300, S400 and S500.

[0023] S100 receives broadcast ephemeris, raw observation data and PPP-B2b correction messages from the BDS-3 satellite, parses and matches the broadcast ephemeris with the PPP-B2b orbit corrections and clock corrections, calculates the precise satellite position and precise satellite clock error, and constructs a real-time precise ephemeris.

[0024] S200 performs elevation angle anomaly judgment on raw GNSS observation data, removes satellites with elevation angles below the 15° threshold, and uses ionospheric-free combination, geometric distance-free combination and MW combination for cycle slip detection and repair. It uses models to correct tropospheric delay, relativistic effects, phase winding error and antenna phase center error, and constructs a dual-frequency ionospheric-free observation model in combination with precise ephemeris.

[0025] S300 calculates pre-approval residuals based on a dual-frequency ionosphere-free model, uses the 3-sigma principle to determine the residual statistical threshold for gross error detection, eliminates abnormal satellite observations, and adopts an improved IGG III algorithm to introduce a position accuracy attenuation factor PDOP and satellite elevation angle dual-factor adaptive weighting to calculate weighted pre-approval residuals and suppress urban multipath interference.

[0026] S400: Based on weighted prior residuals, parameter estimation is performed using Kalman filtering to obtain receiver position, receiver clock error and integer ambiguity. The estimated parameters are then substituted back into the carrier phase observation equation to solve the precise clock error of the receiver relative to each satellite on a satellite-by-satellite basis.

[0027] The S500, based on a set of synchronously observed satellites from two stations, calculates the common-view comparison results by subtracting each satellite, performs real-time Kalman filtering on the common-view comparison results, and outputs high-precision time transfer results.

[0028] Specifically, in step S1, the broadcast ephemeris, raw observation data, and PPP-B2b correction data messages from the BDS-3 satellite are received and parsed according to the protocol to obtain the corresponding message structure for use in subsequent steps; in step S2, based on the broadcast ephemeris and PPP-B2b correction data messages parsed in step S1, the precise satellite position and satellite clock error are calculated by matching the broadcast ephemeris and PPP-B2b orbit and clock error corrections, as detailed below: 1) Precise satellite position: in, This represents the satellite positions in the Earth-center Earth-fixed (ECEF) system after orbital correction. The satellite positions in the ECEF coordinate system based on broadcast ephemeris. This represents the orbital correction in the ECEF coordinate system.

[0029] 2) Precision satellite clock bias: in, For satellite clock bias based on broadcast ephemeris; Satellite clock bias correction; This is the clock error correction. It is the speed of light.

[0030] Step S3 involves performing data anomaly detection and error correction on the raw GNSS observation data, and using the precise satellite positions and clock errors calculated in step S2 to form a dual-frequency ionospheric-free model, as detailed below: 1) Data anomaly detection mainly includes pitch angle anomaly judgment and cycle slip detection and repair. Pitch angle anomaly judgment refers to removing satellites below the elevation angle threshold (15°). Cycle slip detection and repair utilize ionospheric-free combination, geometric distance-free combination, and MW combination. Error correction mainly involves tropospheric delay, relativistic effects, phase winding error, and antenna phase center error. The above errors are corrected using relevant models.

[0031] First, anomaly detection is performed based on the elevation angle of each satellite, and satellites with elevation angles below the threshold (15°) are removed. Next, carrier cycle slips are detected and corrected using geometrically insensitive and MW combinations for carrier phase observations, resulting in observation data after anomaly detection. Then, errors in the propagation path (tropospheric delay, relativistic effects, phase winding errors, antenna phase center errors, etc.) are corrected sequentially using relevant models.

[0032] 2) Based on precise satellite position and clock bias, a dual-frequency ionospheric-free model is constructed from the observed values ​​after anomaly detection: in, , , For pseudoranges of different frequencies, For carrier phases of different frequencies, It is the frequency of the carrier phase. for The combined pseudorange, for The combined phase, This represents the geometric distance between the receiver and the satellite (using precise satellite positioning). For receiver clock bias, For satellite precision clock bias, For tropospheric delay, At the speed of light, To combine carrier phase integer ambiguity; and These represent the combined pseudorange and other phase error terms (tropospheric delay, relativistic effects, phase winding error, antenna phase center error, etc.).

[0033] Step S4: Based on the dual-frequency ionosphere-free model, pre-a priori residual detection based on the 3-sigma principle and pre-a priori residual weighting based on IGG III are performed. Finally, Kalman filtering is used for parameter estimation. The pre-a priori residuals are represented as follows: in, Indicates the current sampling time. Indicates the previous sampling time. and The first The combined pseudorange at time and the a priori residual of phase; For the first Combination pseudorange of time, For the first The combined phase of time, For the first The estimated receiver position and geometric distance to the satellite at that time. For the first Receiver clock bias for time estimation For the first Satellite clock bias at any time For the first The tropospheric delay at that moment, For the first Integer ambiguity in time estimation and The first The time-combined pseudorange and other phase error terms (tropospheric delay, relativistic effects, phase winding error, antenna phase center error, etc.).

[0034] Pre-test residual detection based on the 3-sigma principle: in, The mean of the pseudorange residuals. For the first a satellite in The pseudo-range residual at time, For the total number of satellites, The standard deviation of pseudorange residuals. This is the lower limit of the 3-sigma principle detection criterion. This represents the upper limit of the 3-sigma principle detection criterion.

[0035] Satellites whose pseudorange residuals meet the following conditions are retained: satellites whose pseudorange residuals are greater than the lower limit of the 3 sigma principle detection criterion and less than the upper limit of the 3 sigma principle detection criterion are retained: based on IGG III pre-detection residual weighting: in, , There are two weight values. For the first The position dilution of precision (PDOP) of each satellite. For the first The elevation angle of a satellite.

[0036] The weighted pre-test residuals are expressed as follows: in, and The first a satellite in The time-weighted pseudorange and carrier phase residual.

[0037] Parameter estimation is performed using Kalman filtering; in step S5, based on the estimation results of step S4, the receiver clock bias of the receiver relative to each satellite is calculated. That is, the estimated parameters other than the receiver clock bias are substituted back into the carrier phase observation equation in S3. Each equation yields a receiver clock bias, and the final result is the receiver clock bias of each receiver relative to each satellite. in, For the receiver relative to the first The receiver clock bias of each satellite For the first The combined phase of the satellites, For the receiver and the first The geometric distance between the satellites For the first The precise clock bias of each satellite For the first The tropospheric delay of a satellite At the speed of light, Indicates the first Combined phase integer ambiguity of satellites; Indicates the first Other error terms in the combined phase of the satellites.

[0038] Step S6: Based on step S5, calculate the common-view comparison result to obtain a common-view comparison result between the two sites: in, This is the common-view comparison result between receiver A and receiver B. For receiver A relative to the first The receiver clock bias of each satellite For receiver B relative to the first The receiver clock bias of each satellite This represents the total number of satellites.

[0039] Step S7: Perform real-time Kalman filtering on the common-view comparison results from step S6 to obtain more accurate common-view comparison results, thereby further improving the time transfer accuracy.

[0040] In this step, to evaluate the performance of the proposed method (a high-precision common-view method based on PPP-B2b enhancement in an urban environment), zero-baseline testing was performed using two Beidou Starbridge A2x receivers.

[0041] The specific experimental procedure is as follows: The experiment took place from 17:00:00 on January 30, 2026 to 16:00:00 the following day (UTC), on a rooftop in Chengdu. The antennas were connected to the Beidou satellite bridge receivers via power dividers for zero-baseline measurements. Both receivers acquired raw observations, broadcast ephemeris data, and PPP-B2b messages for real-time PPP data processing. Based on the estimation results, the receiver clock bias relative to each satellite was calculated. Finally, the clock bias of the two receivers was used for CV data processing to obtain the common-view comparison results.

[0042] Figure 6 shows the common-view alignment results curve and standard deviation (STD) statistics (approximately 23 hours) of the proposed method. It can be seen that the common-view alignment accuracy of the proposed method reaches 2 ns, exhibiting high time transfer accuracy, and can be widely applied in military fields without network communication environments.

[0043] As shown in Figure 4-6, the core of the real-time enhancement calculation of precise ephemeris in step one of this embodiment lies in constructing a real-time precise ephemeris that does not rely on external network communication. This is the fundamental prerequisite for achieving high-precision time transfer in urban environments. The BDS-3 system broadcasts PPP-B2b correction messages to users within its service area via geostationary orbit (GEO) satellites. This design is essentially a satellite-based augmentation architecture that broadcasts precise orbit and clock bias information through the navigation signals themselves. The calculation of precise ephemeris involves two key transformations. The application of orbit corrections requires converting the radial, tangential, and normal corrections provided by PPP-B2b to the geocentric-fixed (ECEF) coordinate system, which involves a rotational transformation of the satellite orbital coordinate system. Clock bias correction is relatively straightforward; the time deviation can be obtained by dividing the correction by the speed of light. It is worth noting that the update frequency of PPP-B2b corrections is typically on the order of tens of seconds to minutes, while the sampling rate of the original observation data is typically 1 Hz or higher. Therefore, it is necessary to design reasonable interpolation or extrapolation strategies to ensure time synchronization between the low-update-rate precise ephemeris and the high-sampling-rate observation data.

[0044] This step enables real-time precise ephemeris acquisition without the support of external networks such as the Internet or mobile communications, providing basic data support for high-precision time transfer in urban environments and breaking through the dependence of traditional precise point positioning on network RTK or international GNSS service (IGS) data streams.

[0045] The key bridge connecting the raw observation data and high-precision parameter estimation in step two is designed with full consideration of the special characteristics of the urban environment. The urban canyon effect complicates the satellite signal propagation environment, with multipath effects, signal blockage, and non-line-of-sight (NLOS) propagation becoming the main sources of error. Therefore, the quality control strategy in the preprocessing stage needs to be more stringent than in open environments. The elevation angle threshold (15°) is set based on a statistical trade-off between the average height of urban buildings and the geometric distribution of satellites. Satellites at low elevation angles have longer signal paths, and the increased thickness of the atmosphere they pass through amplifies the tropospheric delay modeling error. At the same time, low elevation angle signals are more susceptible to building reflections and blockages, significantly increasing multipath errors. In practical studies, this threshold can be adaptively adjusted according to the specific urban environment—for example, it may need to be increased to 20°-25° in densely populated CBD areas, while it can be reduced to 10° in open suburban roads.

[0046] Cycle slip detection and repair is the core step in carrier phase data processing. This step employs a triple combination strategy: ionospheric-free combination (LC) separates ionospheric delay from cycle slips; geometric distance-free combination (GF) eliminates the influence of geometric distance and receiver clock bias; and Melbourne-Wübbena (MW) combination utilizes the integer characteristics of wide-lane ambiguity for auxiliary detection. The advantage of this combination strategy is that each combination has different sensitivities to different error sources such as cycle slips, ionospheric variations, and receiver clock biases. Cross-validation can improve the reliability of cycle slip detection. In practical algorithm implementation, recursive filtering or polynomial fitting methods are typically used to establish a predictive model of the observations, and statistical tests (such as chi-square tests or t-tests) are used to determine the timing and magnitude of cycle slips. Tropospheric delay is separated and projected using a standard atmospheric model (such as the Saastamoinen model) and a mapping function (such as GMF or VMF1 / VMF3). Relativistic effects include two parts: special relativity (difference between satellite clock speed and receiver clock speed) and general relativity (difference in Earth's gravitational potential), which need to be calculated accurately based on satellite orbital parameters. Phase winding error originates from signal polarization rotation caused by satellite attitude control and can be corrected using satellite attitude quaternion data. Antenna phase center correction requires distinguishing between receiver antenna phase center offset (PCO) and phase center variation (PCV), and is usually performed based on antenna calibration documents (such as the atx file published by IGS).

[0047] Meanwhile, this model, through a linear combination of the B1 and B3 frequencies, can theoretically eliminate the first-order ionospheric delay (approximately 99.9% of the total ionospheric delay). The combined ionospheric pseudorange and carrier phase observations retain parameters such as geometric distance, receiver clock bias, satellite clock bias, tropospheric delay, and integer ambiguity, providing clean observational input for subsequent parameter estimation. In practical research, the construction of this model also needs to consider the handling of inter-frequency bias (IFB), especially the differences in delay between the B1 / B3 signal channels for different receiver types. This step, through multi-level data quality control, constructs an observation model suitable for complex urban environments, providing a standardized data foundation for subsequent multipath interference mitigation.

[0048] In step three, the multipath effect manifests as non-Gaussian gross errors in urban environments. Traditional least squares or standard Kalman filtering, which assumes observation noise follows a Gaussian distribution, suffers a sharp performance degradation in this scenario. The 3-sigma principle is applied based on the statistical distribution characteristics of the residuals, classifying observations exceeding three times the standard deviation of the mean as outliers. In practice, the calculation of the residual mean and standard deviation requires a recursive algorithm or a sliding window strategy to adapt to the time-varying characteristics of the observation environment. It is worth noting that the 3-sigma threshold is dynamically calculated—adaptively adjusting as observation conditions change (e.g., changes in the number of satellites or geometric configuration). This hard thresholding mechanism effectively eliminates significant gross errors, but may completely exclude "suspicious" observations in the boundary region, wasting available observation resources.

[0049] The traditional IGG III algorithm, based on the principle of equivalent weights, adjusts observation weights according to the residual magnitude to achieve robust estimation. The improvement of this invention lies in the introduction of a two-factor adaptive mechanism: the Position Accuracy Attenuation Factor (PDOP) reflects the quality of the satellite's spatial geometry; a larger PDOP value indicates a poorer geometry, making the accuracy of parameter estimation more susceptible to observation errors. The satellite elevation angle is directly related to atmospheric delay and multipath sensitivity of the signal propagation path. The introduction of these two factors ensures that weight adjustment considers not only the residual magnitude (the quality of the observations themselves) but also the contribution of the observations to parameter estimation (geometric quality) and the propagation environment (elevation angle). The PDOP weighting function maintains full weight (1.0) below 4, smoothly transitions between 4 and 8, and completely eliminates values ​​above 8. The elevation angle weighting function maintains full weight above 0.6 (approximately 36.9°), smoothly reduces weight between 0.3 and 0.6, and completely eliminates values ​​below 0.3 (approximately 17.5°). This piecewise function design ensures full utilization of high-quality observations while avoiding contamination of the estimation results by low-quality observations. In practical research, these thresholds can be optimized and adjusted according to specific application scenarios. For example, more stringent threshold settings may be required in high-precision time transfer applications.

[0050] Meanwhile, the 3-sigma principle serves as a coarse filter to handle significant anomalies, while the IGG III algorithm is used for fine-tuning to handle boundary quality observations. This architecture avoids the limitations of a single strategy—a pure hard threshold strategy may over-reject observations, while a pure soft weighting strategy may assign inappropriate weights to gross observations. In the actual algorithm implementation, there is also information transfer between the two layers: the effective satellite subset generated by the first layer serves as the input to the second layer, and the weight calculation of the second layer is based on the residual statistics of the first layer. This step, through a hierarchical and progressive quality control architecture, achieves adaptive screening and weighting of observations under urban multipath interference environments, balancing the contradiction between observation resource utilization and parameter estimation reliability.

[0051] The state vector used in step four includes: receiver three-dimensional position (3D), receiver clock bias (1D), tropospheric zenith wet delay (1D), and ionospheric combined integer ambiguity for each satellite (1D per satellite). This design assumes that the tropospheric dry delay can be accurately corrected through the model, the wet delay is estimated as a stochastic process, and the integer ambiguity is estimated as a time-invariant parameter (after cycle slip correction), whose converged fixed solution can significantly improve positioning accuracy. In practical studies, the dimensions of the state vector may be adjusted according to application requirements—for example, in time-transfer applications, the receiver position can be considered known (static station), and the state vector can be reduced accordingly. After obtaining the estimated values ​​of receiver position, tropospheric delay, and integer ambiguity through Kalman filtering, these parameters are fixed as known quantities and substituted back into the carrier phase observation equations for each satellite to independently solve for the receiver clock bias corresponding to each satellite. The advantage of this strategy is that the clock bias calculations for each satellite are independent, and anomalies in the observations of a single satellite will not affect the calculation results of other satellites. Furthermore, the single-satellite calculation results can be used for subsequent common-view comparisons, naturally satisfying the "independent observation" requirement of the common-view method. In practical implementation, single-satellite calculations can employ closed-form solutions (algebraic solutions) rather than iterative solutions, ensuring computational efficiency.

[0052] The core of the final step, step five, in the precise optimization of the common-view comparison results lies in the fact that when two stations observe the same satellite, common errors such as satellite clock bias and atmospheric delay are eliminated or significantly reduced through inter-station single-difference, thereby extracting the relative deviation of the receiver clock biases between the two stations. For a set of satellites observed synchronously by two stations with common-view bias, the receiver clock biases of the two stations for the same satellite are subtracted to obtain the inter-station clock bias difference corresponding to that satellite. Due to the different geometric paths of each satellite, the observation values ​​after single-difference still contain residual tropospheric delay differences (the difference in the zenith direction between the two stations projected onto the satellite direction) and ionospheric delay differences (second-order and higher terms), but these residual errors are much smaller than the original observation errors. In actual research, the selection strategy for common-view satellites affects the comparison accuracy—usually, satellites with moderate elevation angles and good observation quality are preferred, while satellites with low elevation angles or abnormal quality are eliminated. The common-view alignment result sequence can be viewed as clock bias state observations contaminated by noise. Kalman filtering achieves noise suppression and state prediction by establishing a state transition model (usually using a random walk or a first-order Gauss-Markov model to describe the time-frequency characteristics of the receiver clock) and a measurement model. Unlike the Kalman filtering in step four, the state dimension here is extremely low (only one dimension for inter-station clock bias or adding one dimension for clock speed), resulting in a light computational burden and suitability for real-time processing. In practical implementation, the process noise covariance matrix needs to be set based on the Allen variance characteristics of the receiver clock, while the measurement noise covariance can be determined based on the accuracy estimate of the single-satellite solution in step four.

[0053] This step combines common-view differential and real-time filtering to output high-precision and high-stability inter-station time transfer results, meeting the high-precision time synchronization requirements in urban environments without network conditions.

[0054] Therefore, this invention specifically relates to a high-precision common-view method based on PPP-B2b enhancement in urban environments. First, the two common-view stations calculate precise satellite positions and clock errors based on broadcast ephemeris and BDS-3 PPP-B2b correction information, respectively. Then, for satellites with anomaly detection and processing, Kalman filtering is used to obtain the receiver clock error of each station relative to each satellite. Finally, CV data processing is performed on the receiver clock errors of the two common-view stations to obtain the common-view comparison result. This invention achieves high-precision common-view based on PPP-B2b enhancement in complex urban environments. It fully considers the interference encountered in complex urban environments and proposes an improved IGG III algorithm and a 3-sigma principle algorithm to address interference. Furthermore, real-time Kalman filtering is applied to the common-view comparison result to further improve time transfer accuracy and achieve high-precision common-view time transfer.

[0055] Example 2: As shown in Figure 2, this example provides a high-precision common-view system based on PPP-B2b enhancement in an urban environment. The system, as described in Figure 2, includes: a precise ephemeris calculation module 701: used to receive broadcast ephemeris, raw observation data, and PPP-B2b correction messages from the BDS-3 satellite; parse and match the broadcast ephemeris with the PPP-B2b orbit corrections and clock bias corrections; calculate the precise satellite position and precise satellite clock bias; and construct a real-time precise ephemeris; and an urban environment adaptive preprocessing module 702: used to process raw GNSS observations. The data undergoes elevation angle anomaly detection, eliminating satellites with elevation angles below the 15° threshold. Cycle slip detection and repair are performed using ionospheric-free, geometrically-free, and MW combinations. Model corrections are applied to tropospheric delay, relativistic effects, phase winding errors, and antenna phase center errors. A dual-frequency ionospheric-free observation model is constructed using precise ephemeris data. The anti-multipath interference quality control module 703 calculates pre-hoc residuals based on the dual-frequency ionospheric-free model, uses the 3-sigma principle to determine residual statistical thresholds for gross error detection, eliminates anomalous satellite observations, and employs an improved IGG (In-Gross Gaussian Geometric Array) method. Algorithm III introduces a position accuracy attenuation factor (PDOP) and a dual-factor adaptive weighting of satellite elevation angle to calculate weighted pre-hoc residuals and suppress urban multipath interference. Precise single-satellite clock bias calculation module 704: Based on the weighted pre-hoc residuals, it uses Kalman filtering to estimate parameters, obtaining receiver position, receiver clock bias, and integer ambiguity. The estimated parameters are then substituted back into the carrier phase observation equation to calculate the precise clock bias of the receiver relative to each satellite. Common-view comparison optimization module 705: Based on a set of synchronously observed satellites from two common-view stations, it calculates the common-view comparison results by subtracting satellites one by one, performs real-time Kalman filtering on the common-view comparison results, and outputs high-precision time transfer results.

[0056] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0057] Example 3: Corresponding to the above method examples, this example also provides a high-precision precision co-viewing device based on PPP-B2b enhancement in an urban environment. The high-precision precision co-viewing device based on PPP-B2b enhancement in an urban environment described below can be referred to in conjunction with the high-precision precision co-viewing method based on PPP-B2b enhancement in an urban environment described above.

[0058] Figure 3 is a block diagram illustrating a high-precision shared-view device 800 based on PPP-B2b enhancement in an urban environment according to an exemplary embodiment. As shown in Figure 3, the high-precision shared-view device 800 based on PPP-B2b enhancement in an urban environment includes a processor 801 and a memory 802. The high-precision shared-view device 800 based on PPP-B2b enhancement in an urban environment also includes one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0059] The processor 801 controls the overall operation of the PPP-B2b-enhanced high-precision common-view device 800 in the urban environment to complete all or part of the steps in the PPP-B2b-enhanced high-precision common-view method in the urban environment. The memory 802 stores various types of data to support the operation of the PPP-B2b-enhanced high-precision common-view device 800 in the urban environment. This data may include, for example, instructions for any application or method operating on the PPP-B2b-enhanced high-precision common-view device 800 in the urban environment, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, or buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the high-precision, high-resolution co-viewing device 800 based on PPP-B2b enhancement and other devices in this urban environment. Wireless communication includes, for example, Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or one or more combinations thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0060] In an exemplary embodiment, the high-precision precision co-viewing device 800 based on PPP-B2b enhancement in an urban environment can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the aforementioned high-precision precision co-viewing method based on PPP-B2b enhancement in an urban environment.

[0061] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided. When executed by a processor, these program instructions implement the steps of the aforementioned high-precision precision co-viewing method based on PPP-B2b enhancement in an urban environment. For example, the computer-readable storage medium may be the aforementioned memory 802 including program instructions, which may be executed by the processor 801 of the high-precision precision co-viewing device 800 based on PPP-B2b enhancement in an urban environment to complete the aforementioned high-precision precision co-viewing method based on PPP-B2b enhancement in an urban environment.

[0062] Example 4: Corresponding to the above method embodiments, this example also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the high-precision precision co-view method based on PPP-B2b enhancement in an urban environment described above.

[0063] A computer program is stored on a readable storage medium. When the computer program is executed by a processor, it implements the steps of the high-precision common-view method based on PPP-B2b enhancement in an urban environment as described in the above method embodiments.

[0064] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0065] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A high-precision common-view method based on PPP-B2b enhancement in urban environments, characterized in that, include: The system receives broadcast ephemeris, raw observation data, and PPP-B2b correction messages from the BDS-3 satellite. It parses and matches the broadcast ephemeris with the PPP-B2b orbital and clock corrections, calculates precise satellite positions and clock errors, and constructs a real-time precise ephemeris. It performs elevation anomaly detection on the raw GNSS observation data, removing satellites with elevation angles below the 15° threshold. Cycle slip detection and repair are performed using ionospheric-free, geometric distance-free, and MW combinations. Model corrections are used to address tropospheric delay, relativistic effects, phase winding errors, and antenna phase center errors. A dual-frequency ionospheric-free observation model is constructed based on the precise ephemeris. Pre-hoc residuals are calculated based on the dual-frequency ionospheric-free model. Gross error detection is performed by determining the residual statistical threshold using the 3-sigma principle, removing anomalous satellite observations, and employing an improved IGG method. Algorithm III introduces a position accuracy attenuation factor (PDOP) and a dual-factor adaptive weighting of satellite elevation angle to calculate weighted pre-hoc residuals and suppress urban multipath interference. Based on the weighted pre-hoc residuals, Kalman filtering is used to estimate parameters to obtain receiver position, receiver clock error and integer ambiguity. The estimated parameters are then substituted back into the carrier phase observation equation to solve the precise clock error of the receiver relative to each satellite on a satellite-by-satellite basis. Based on the set of synchronous observation satellites from two stations, the common-view comparison results are calculated by subtracting satellites one by one, and real-time Kalman filtering is applied to the common-view comparison results to output high-precision time transfer results.

2. The high-precision common-view method based on PPP-B2b enhancement in urban environments according to claim 1, characterized in that, The PPP-B2b correction data message is broadcast via the BDS-3 geostationary orbit satellite, enabling real-time precise ephemeris without relying on external network communication.

3. The high-precision common-view method based on PPP-B2b enhancement in urban environments according to claim 1, characterized in that, The 3-sigma principle and the improved IGG III algorithm constitute a hierarchical progressive quality control architecture, wherein: the first layer uses the 3-sigma principle to perform hard threshold discrimination based on statistical distribution to generate an effective satellite observation subset; the second layer uses the improved IGG III algorithm to perform soft weighting based on a continuous function of PDOP and elevation angle as dual factors within the effective satellite observation subset to generate weighted observation values.

4. The high-precision common-view method based on PPP-B2b enhancement in urban environments according to claim 1, characterized in that, The two-factor adaptive weighting function of the improved IGG III algorithm is: in, 、 There are two weight values. For the first The position accuracy attenuation factor of each satellite For the first The elevation angle of a satellite.

5. The high-precision common-view method based on PPP-B2b enhancement in urban environments according to claim 1, characterized in that, In the satellite-by-satellite calculation of the receiver's precise clock bias relative to each satellite, the filtered estimated receiver position, tropospheric delay, and integer ambiguity are used as known parameters to substitute back into the carrier phase observation equations of each satellite, and the receiver clock bias corresponding to each satellite is calculated independently, wherein the clock bias calculation results of each satellite do not affect each other.

6. The high-precision common-view method based on PPP-B2b enhancement in urban environments according to claim 1, characterized in that, The real-time Kalman filtering of the common-view alignment results includes: establishing the state transition equation and measurement equation of the common-view alignment results, setting the process noise covariance matrix and measurement noise covariance matrix, performing time update and measurement update recursive calculations, and outputting the filtered common-view alignment sequence.

7. The high-precision common-view method based on PPP-B2b enhancement in urban environments according to claim 1, characterized in that, The calculation formula for the dual-frequency ionospheric observation model is as follows: in, , , For pseudoranges of different frequencies, For carrier phases of different frequencies, The frequency of the carrier phase. for The combined pseudorange, for The combined phase, The geometric distance between the receiver and the satellite. For receiver clock bias, For satellite precision clock bias, For tropospheric delay, At the speed of light, To combine carrier phase integer ambiguities, and These represent the combined pseudorange and other phase error terms, respectively.

8. The high-precision common-view method based on PPP-B2b enhancement in urban environments according to claim 1, characterized in that, The state vector of the Kalman filter includes the receiver's three-dimensional position coordinates, receiver clock error, tropospheric zenith wet delay, and dual-frequency ionosphere-free combination integer ambiguity. The measurement vector consists of the dual-frequency ionosphere-free combination pseudorange and carrier phase observations.

9. The high-precision common-view method based on PPP-B2b enhancement in urban environments according to claim 1, characterized in that, The method for determining the residual statistical threshold using the 3-sigma principle is as follows: Calculate the mean and standard deviation of the pseudorange residuals, using the following formula: Determine the lower limit and upper limit and retain the satisfaction Satellite observations.

10. A high-precision precision co-viewing system based on PPP-B2b enhancement in urban environments, based on the high-precision precision co-viewing method based on PPP-B2b enhancement in urban environments as described in claim 1, characterized in that... include: The Precise Ephemeris Calculation Module: Receives broadcast ephemeris, raw observation data, and PPP-B2b correction messages from the BDS-3 satellite; parses and matches the broadcast ephemeris with PPP-B2b orbital and clock corrections; calculates precise satellite positions and clock errors; and constructs a real-time precise ephemeris. The Urban Environment Adaptive Preprocessing Module: Identifies elevation anomalies in raw GNSS observation data, removes satellites with elevation angles below a 15° threshold, and uses ionospheric-free, geometric distance-free, and MW combinations for cycle slip detection and repair. It uses a model to correct tropospheric delay, relativistic effects, phase winding errors, and antenna phase center errors, and constructs a dual-frequency ionospheric-free observation model based on the precise ephemeris. The Anti-Multipath Interference Quality Control Module: Calculates pre-hoc residuals based on the dual-frequency ionospheric-free model, uses the 3-sigma principle to determine residual statistical thresholds for gross error detection, removes anomalous satellite observations, and employs an improved IGG (In-Gross Spectroscopy) method. Algorithm III introduces a position accuracy attenuation factor (PDOP) and a dual-factor adaptive weighting of satellite elevation angle to calculate weighted pre-hoc residuals and suppress urban multipath interference. The precise single-satellite clock bias calculation module uses Kalman filtering to estimate parameters based on the weighted pre-hoc residuals, obtaining receiver position, receiver clock bias, and integer ambiguity. The estimated parameters are then substituted back into the carrier phase observation equation to calculate the precise clock bias of the receiver relative to each satellite. The common-view comparison optimization module calculates the common-view comparison results based on a set of synchronously observed satellites from two common-view stations, performs real-time Kalman filtering on the common-view comparison results, and outputs high-precision time transfer results.

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