Multi-path suppression positioning method and system for band-shaped sparse single-beidou CORS network

Abstract

The application discloses a multi-path suppression positioning method and system for a belt-shaped sparse single-Beidou CORS network, and relates to the technical field of satellite navigation and positioning. The multi-path suppression positioning method for the belt-shaped sparse single-Beidou CORS network comprises the following steps: acquiring initial pseudo-range time sequence data, receiving time sequence data and regional state data, and performing correction analysis on the pseudo-range data; then, performing filtering processing by using a preset filtering rule, establishing a virtual reference station and performing data fusion; and finally, performing positioning calculation based on an adaptive weight rule to obtain accurate position information of a receiver. The pseudo-range time sequence data of each satellite is dynamically corrected, especially through comprehensive analysis of reflection surface information, environmental data and signal arrival time difference, so that errors caused by reflection surface and environmental changes can be identified and corrected in real time, and the stability and precision of the positioning system in a complex environment are significantly improved.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation and positioning technology, and in particular to a multipath suppression positioning method and system for a strip-shaped sparse single Beidou CORS network. Background Art

[0002] With the rapid development of satellite navigation technology, GNSS (such as GPS and BeiDou) has been widely used in various positioning, navigation and timing systems. However, in some complex environments (such as urban high-rise buildings, mountainous areas, near water bodies, etc.), traditional GNSS positioning methods often cannot provide high-precision positioning results due to signal reflection, obstruction and multipath effects. Multipath effect refers to the change in the path of the signal to the receiver after the satellite signal is reflected by obstacles or reflective surfaces (such as buildings, trees, water surfaces, etc.), thereby introducing pseudo-range errors, seriously affecting positioning accuracy.

[0003] In a strip-shaped sparse single Beidou CORS (Continuously Operating Reference Station) network, due to the sparse distribution of base stations, especially in some remote areas or special environments, traditional positioning methods cannot provide effective error correction when there are insufficient base stations, resulting in inaccurate positioning results. In addition, due to the limitations on the number of base stations and the complexity of the environment, error control of existing positioning systems in complex areas is more difficult, and it is impossible to achieve real-time and high-precision positioning correction in a multipath environment.

[0004] The limitations of the existing technology include at least the following problems: the positioning methods in the existing technology, especially in the strip-shaped and sparse single Beidou CORS network, have the problem of difficult to effectively suppress the multipath effect. In actual applications, after the satellite signal is reflected by the atmosphere and the ground, it may be interfered with by reflective surfaces such as buildings, mountains, and water surfaces, forming a multipath effect. This multipath effect will cause inaccurate pseudo-range data, thereby affecting positioning accuracy. However, traditional positioning methods usually rely on fixed and simple signal correction models, and do not fully consider the complexity and dynamic changes in the signal propagation process. Especially in complex urban environments or sparse network areas, the impact of multipath effects is more obvious. This limitation leads to poor stability of the positioning system in dynamic environments, low positioning accuracy, and it is difficult to meet the high-precision requirements in actual applications. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a multipath suppression positioning method and system for a strip-shaped sparse single Beidou CORS network, which solves the problem in the existing technology that the multipath effect is difficult to effectively suppress.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multipath suppression positioning method for a strip-shaped sparse single Beidou CORS network, comprising the following steps: obtaining initial pseudorange timing data, reception timing data, and regional status data within a set area of ​​the receiver for each satellite; correcting and analyzing the initial pseudorange timing data based on the reception timing data of each satellite for the receiver and the regional status data within the set area of ​​the receiver to obtain a pseudorange timing correction set for each satellite for the receiver; filtering the pseudorange timing correction set for each satellite for the receiver based on a preset filtering processing rule to obtain a pseudorange timing filter set for each satellite for the receiver; establishing a virtual reference station and performing fusion analysis on the pseudorange timing filter set for each satellite for the receiver to obtain a pseudorange timing fusion set for each satellite for the receiver; and performing positioning solution analysis on the pseudorange timing fusion set for each satellite for the receiver based on a preset adaptive weight rule to obtain position information of the receiver.

[0007] Furthermore, the initial pseudorange time series data includes initial pseudorange values ​​at several time points, the received time series data includes signal arrival time difference values, satellite signal strength values, satellite azimuth values, and satellite elevation values ​​at several time points, the regional status data includes reflection data and environmental status time series data, the reflection data includes type information of the reflecting surface and reflection angle values, the environmental status time series data includes environmental temperature values, environmental humidity values, light intensity values, thermal infrared values, ionospheric total electron content values, and electric field strength values ​​at several time points, the pseudorange time series correction set includes pseudorange correction values ​​at several time points, the pseudorange time series filter set includes pseudorange filter values ​​at several time points, and the pseudorange time series fusion set includes pseudorange fusion values ​​at several time points.

[0008] Furthermore, the specific steps for obtaining the pseudo-range timing correction set of each satellite for the receiver are as follows: based on the preset scoring rules, the type information of the reflecting surface in the set area of ​​the receiver is scored to obtain the reflection type score of the reflecting surface in the set area of ​​the receiver; the historical main reflection azimuth in the set area of ​​the receiver is obtained, and the reflection type score, reflection angle value, illumination intensity value, ambient humidity value, thermal infrared value, and satellite azimuth value of each satellite for the receiver at several time points in the set area of ​​the receiver are comprehensively analyzed to obtain the path error index of each satellite for the receiver at several time points; the path error index of each satellite for the receiver at several time points is obtained by reading the path error index of the reflecting surface in the set area of ​​the receiver. The ambient temperature value, ionospheric total electron content value, and light intensity value at several time points are combined with the satellite elevation angle value of each satellite for the receiver at several time points for a comprehensive analysis to obtain the environmental induced error index at several time points within the set area of ​​the receiver; the signal arrival time difference value, path error index, and environmental induced error index at several time points for each satellite for the receiver are comprehensively analyzed to obtain the error correction value of each satellite for the receiver at several time points; based on the error correction value and initial pseudorange value of each satellite for the receiver at several time points, the pseudorange correction value of each satellite for the receiver at several time points is analyzed.

[0009] Furthermore, the specific formula for calculating the error correction of a satellite for the receiver at each time point is as follows: ;in, The first The error correction amount at each time point, The first The arrival time difference of the signals at the time points, The first The path error index at each time point, is the path error influence coefficient stored in the database, Set the receiver to the first The environmental induced error index at each time point, is the environmental induced error influence coefficient stored in the database, =1, 2, 3, ..., , is the number of time points.

[0010] Furthermore, the specific steps for obtaining the pseudorange timing filter set of each satellite for the receiver are as follows: reading the pseudorange correction value of each satellite for the receiver at each time point, and performing change analysis respectively to obtain the pseudorange correction change rate of each satellite for the receiver at each time point; inputting the satellite signal strength value, pseudorange correction value, pseudorange correction change rate, and electric field strength value at each time point in the set area of ​​the receiver for each satellite for each time point into a preset filtering processing model for filtering analysis to obtain the pseudorange filter value of each satellite for the receiver at each time point.

[0011] Furthermore, the filtering processing model is specifically as follows: ;in, The first The pseudorange filtering value at each time point, The first The pseudorange correction value at a time point, The first The pseudorange correction change rate at each time point is: is the low change rate threshold stored in the database, The first The pseudorange correction value at a time point, The first The pseudorange correction value at a time point, The first Satellite signal strength value at a time point, is the signal strength influence coefficient stored in the database, is the high change rate threshold stored in the database, The first The pseudorange correction change rate at each time point is: Set the receiver to the first The electric field strength value at each time point, is the electric field strength influence coefficient stored in the database, =1, 2, 3, ..., , is the number of time points.

[0012] Furthermore, the specific steps for establishing a virtual reference station are as follows: obtaining the estimated position information of the receiver, the position information of each reference station within the receiver's set area, and analyzing the baseline distance value between each reference station and the receiver; performing a comprehensive analysis on the position information of each reference station within the receiver's set area and the baseline distance value between each reference station and the receiver to obtain the position information of the virtual reference station.

[0013] Furthermore, the specific steps for obtaining the pseudorange time series fusion set of each satellite for the receiver are as follows: obtaining the pseudorange differential correction value between each satellite and each reference station in the set area of ​​the receiver at each time point; analyzing the baseline distance value between the virtual reference station and each reference station in the set area of ​​the receiver based on the position information of each reference station and the position information of the virtual reference station; obtaining the station offset of each reference station in the set area of ​​the receiver at each time point, and performing a comprehensive analysis based on the pseudorange differential correction value between each satellite and each reference station in the set area of ​​the receiver at each time point, the baseline distance value between the virtual reference station and each reference station in the set area of ​​the receiver, and the baseline distance value between each reference station and the receiver to obtain the pseudorange virtual reference correction value of each satellite for the receiver at several time points; analyzing the pseudorange fusion value of each satellite for the receiver at several time points based on the pseudorange filter value and the pseudorange virtual reference correction value of each satellite for the receiver at several time points.

[0014] Furthermore, the specific steps for obtaining the position information of the receiver are as follows: based on a preset adaptive weight rule, a weight value is assigned to the pseudorange fusion value of each satellite for each time point of the receiver, and weighted processing is performed to obtain a weighted fusion value of the pseudorange time series of each satellite for the receiver; based on a preset positioning algorithm, the weighted fusion value of the pseudorange time series of each satellite for the receiver is solved to obtain the position information of the receiver.

[0015] A multipath suppression positioning system for a strip-shaped sparse single Beidou CORS network includes: a data acquisition unit for acquiring initial pseudorange timing data, reception timing data, and regional status data within a set area of ​​the receiver for each satellite; a pseudorange correction unit for correcting and analyzing the initial pseudorange timing data based on the reception timing data of each satellite for the receiver and the regional status data within the set area of ​​the receiver to obtain a pseudorange timing correction set for each satellite for the receiver; a pseudorange filtering unit for filtering the pseudorange timing correction set for each satellite for the receiver based on a preset filtering processing rule to obtain a pseudorange timing filter set for each satellite for the receiver; a pseudorange fusion unit for establishing a virtual reference station and fusing and analyzing the pseudorange timing filter set for each satellite for the receiver to obtain a pseudorange timing fusion set for each satellite for the receiver; and a positioning solution unit for performing positioning solution analysis on the pseudorange timing fusion set for each satellite for the receiver based on a preset adaptive weighting rule to obtain position information of the receiver.

[0016] The present invention has the following beneficial effects:

[0017] (1) This multipath suppression positioning method for a strip-shaped sparse single BeiDou CORS network can effectively suppress the multipath effect by dynamically correcting the pseudorange timing data of each satellite, especially through a comprehensive analysis of the reflecting surface information, environmental data and signal arrival time difference. The multipath effect is an important factor affecting the accuracy of GNSS positioning, especially in urban environments or complex terrains. After the signal is reflected by buildings, water surfaces or other obstacles, it often causes positioning errors. Traditional methods usually rely on simple signal models and it is difficult to fully consider the complexity of the reflecting surface and its impact on signal propagation. Therefore, the pseudorange correction method based on the reflecting surface type score and environmental factor analysis can identify and correct the errors caused by the changes in the reflecting surface and environment in real time, significantly improving the stability and accuracy of the positioning system in complex environments.

[0018] (2) This multipath suppression positioning method for a strip-shaped sparse single BeiDou CORS network obtains the preliminary position information of the receiver, the position information of the base station, and the baseline distance value between each base station and the receiver. The virtual base station can supplement and optimize the position based on the data and relative position of the existing base station. The virtual base station can not only make up for the shortcomings of the physical base station, but also make position corrections based on real-time network data to ensure that the system can still provide high-precision positioning results when the base stations are sparse. Especially in some remote areas or blind spots between high-rise buildings in cities, the virtual base station can effectively improve positioning stability and reduce errors.

[0019] (3) The multipath suppression positioning method for the strip-shaped sparse single Beidou CORS network introduces an adaptive weight allocation mechanism. Based on real-time signal strength, satellite azimuth, elevation and other information, the pseudo-range time series data of each satellite is dynamically assigned weights and weighted processing is performed. This method enables the system to fine-tune the positioning results according to the signal quality and environmental factors of each satellite, thereby improving the positioning accuracy. For example, when the signal strength is weak or the satellite azimuth is low, the system automatically reduces the weights of these satellites, thereby reducing their impact on the final positioning results; when the signal is strong and the azimuth and elevation are better, the system automatically increases the weights of these satellites to enhance their role in the positioning calculation. This adaptive weight allocation ensures that the positioning process can always be reasonably optimized according to the satellite signal quality, significantly improving the adaptability and accuracy of the system in a dynamically changing environment, and reducing the positioning error caused by factors such as signal fluctuations, interference or occlusion.

[0020] (4) The multipath suppression positioning system for the strip-shaped sparse single Beidou CORS network adopts a modular design. By dividing the system into independent modules such as data acquisition unit, pseudorange correction unit, pseudorange filtering unit, pseudorange fusion unit and positioning solution unit, the entire system has extremely high flexibility and scalability in different application scenarios. Each unit can be independently optimized or updated according to specific needs without affecting other parts of the system, thereby ensuring that the system can adapt to the ever-changing environment and technological development.

[0021] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of the multipath suppression positioning method for a strip-shaped sparse single Beidou CORS network according to the present invention.

[0023] Figure 2 The present invention is a flowchart of the specific steps of obtaining the pseudo-range timing filter set of each satellite for the receiver in the multipath suppression positioning method for the strip-shaped sparse single Beidou CORS network.

[0024] Figure 3 This is a block diagram of the multipath suppression positioning system for a strip-shaped sparse single Beidou CORS network according to the present invention. DETAILED DESCRIPTION

[0025] See also Figure 1 An embodiment of the present invention provides a technical solution: a multipath suppression positioning method for a strip-shaped sparse single Beidou CORS network, comprising the following steps: obtaining initial pseudorange timing data, reception timing data, and regional status data within a set area of ​​the receiver for each satellite; performing correction analysis on the initial pseudorange timing data based on the reception timing data of each satellite for the receiver and the regional status data within the set area of ​​the receiver to obtain a pseudorange timing correction set for each satellite for the receiver; performing filtering processing on the pseudorange timing correction set for each satellite for the receiver based on a preset filtering processing rule to obtain a pseudorange timing filter set for each satellite for the receiver; establishing a virtual reference station, and performing fusion analysis on the pseudorange timing filter set for each satellite for the receiver to obtain a pseudorange timing fusion set for each satellite for the receiver; performing positioning solution analysis on the pseudorange timing fusion set for each satellite for the receiver based on a preset adaptive weight rule to obtain position information of the receiver.

[0026] The initial pseudorange time series data includes the initial pseudorange values ​​at several time points, the received time series data includes the signal arrival time difference, satellite signal strength values, satellite azimuth values, and satellite elevation values ​​at several time points, the regional status data includes reflection data and environmental status time series data, the reflection data includes the type information of the reflecting surface and the reflection angle value, the environmental status time series data includes the ambient temperature values, ambient humidity values, light intensity values, thermal infrared values, ionospheric total electron content values, and electric field strength values ​​at several time points, the pseudorange time series correction set includes the pseudorange correction values ​​at several time points, the pseudorange time series filter set includes the pseudorange filter values ​​at several time points, and the pseudorange time series fusion set includes the pseudorange fusion values ​​at several time points.

[0027] Among them, the initial pseudorange value refers to the original pseudorange observation value measured by the receiver before the satellite signal undergoes any correction processing, that is, the estimated distance from each satellite to the receiver, which can be calculated in real time from the Beidou satellite signal through the GNSS receiver front-end module.

[0028] The signal arrival time difference refers to the difference between the arrival time of each satellite signal recorded by the receiver and the ideal model arrival time. It is calculated by the GNSS receiver by comparing the local time with the satellite timestamp.

[0029] The satellite signal strength value represents the signal-to-noise ratio or power strength during signal reception, reflecting the signal quality. It can be collected by the receiver's RF module and directly output by the GNSS chip.

[0030] The satellite azimuth value refers to the horizontal angle (0° to 360°) of the satellite relative to the true north direction in the receiver antenna reference system. It is calculated from the satellite ephemeris and the receiver's estimated position.

[0031] The satellite elevation angle value refers to the vertical angle when the satellite is 90° directly above the receiver antenna and the horizon is 0°. It is calculated from the satellite ephemeris and the receiver's estimated position information.

[0032] The type of reflecting surface identifies the main type of reflecting medium in the receiver area. This information is used to determine whether multipath is likely to occur. Environmental sensors can assist in identification (such as near-infrared image recognition of building materials).

[0033] The reflection angle value of the reflecting surface indicates the angle between the incident and reflected signals when a signal may be reflected. It is used to determine the strength of the reflection and can be calculated using the satellite's incident direction (azimuth + elevation) and the normal angle of the reflecting surface. If the reflecting surface direction can be extracted through infrared or map extraction, it can be automatically derived.

[0034] The ambient temperature value can be measured and obtained by a temperature sensor.

[0035] The ambient humidity value can be measured and obtained by a humidity sensor.

[0036] The light intensity value can be measured and obtained by a light sensor.

[0037] Thermal infrared values ​​can be measured and obtained using a thermal infrared camera or a thermal sensor.

[0038] The total ionospheric electron content (TEC) value represents the total electron density encountered by satellite signals as they pass through the ionosphere, affecting delay and error. It can be calculated by the receiver using dual-frequency signals or by accessing the real-time TEC estimate provided by the BeiDou Wide-area Augmentation System (SBAS).

[0039] The electric field strength value can be measured and obtained by an electric field sensor.

[0040] Specifically, the specific steps for obtaining the pseudorange timing correction set of each satellite for the receiver are as follows: based on the preset scoring rules, the type information of the reflecting surface in the set area of ​​the receiver is scored to obtain the reflection type score of the reflecting surface in the set area of ​​the receiver; the historical main reflection azimuth in the set area of ​​the receiver is obtained, and the reflection type score, reflection angle value, light intensity value at several time points, ambient humidity value, thermal infrared value, and satellite azimuth value of each satellite for the receiver at several time points are comprehensively analyzed to obtain the path error index of each satellite for the receiver at several time points; the ambient temperature at several time points in the set area of ​​the receiver is read; The degree value, ionospheric total electron content value, and light intensity value are comprehensively analyzed in combination with the satellite elevation angle value of each satellite for the receiver at several time points to obtain the environmental induced error index at several time points within the set area of ​​the receiver; the signal arrival time difference value, path error index, and environmental induced error index at several time points within the set area of ​​the receiver for each satellite for the receiver are comprehensively analyzed to obtain the error correction value of each satellite for the receiver at several time points; based on the error correction value and initial pseudorange value of each satellite for the receiver at several time points, the pseudorange correction value of each satellite for the receiver at several time points (i.e., the initial pseudorange value minus the error correction value) is analyzed.

[0041] The preset scoring rules include but are not limited to the following examples:

[0042] Water surface (such as lakes and rivers):

[0043] Reflection Type Score: Water surfaces are generally highly reflective, especially calm water, which can lead to significant multipath effects. Therefore, the Reflection Type score for water surfaces is high, perhaps 8 out of 10.

[0044] Concrete walls (such as the facade of a building):

[0045] Reflection Type Score: Although concrete walls can reflect signals, their reflective properties are generally poorer than those of water, resulting in a less pronounced multipath effect. A concrete wall can be assigned a Reflection Type Score of 5.

[0046] Glass curtain walls (such as the glass exterior walls of modern buildings):

[0047] Reflective Type Score: Glass curtain walls reflect a certain amount of signal, but the reflective effect of glass is generally weaker than that of water and concrete walls (especially if the building exterior is not specially coated). Glass is assigned a Reflective Type Score of 4.

[0048] Vegetation (such as trees, grass):

[0049] Reflection type score: The reflection effect of vegetation is relatively small because most of the signal is absorbed or scattered, and it is not easy to form a strong reflection. Therefore, the score is low, set at 2 points.

[0050] Bare soil:

[0051] Reflection type score: The soil has a small reflection effect. Basically, the signal will be absorbed by the ground and the reflection effect is not obvious. Therefore, the score is also low and can be set as 1 point.

[0052] The specific steps for obtaining the historical main reflection azimuth within the receiver's set area are as follows: first, record the pseudorange residual, signal strength drop rate, multipath identification characteristics, and other information of each satellite when it reaches the receiver at different azimuths; then cluster and statistically analyze this information by azimuth interval (e.g., one group every 10°), and calculate the multipath anomaly frequency and average error amplitude in each direction; then, combine the reflecting surface type annotation (e.g., glass, water surface, wall) and time conditions (e.g., sunny day, foggy day) to weight the directional reflection risk, and finally determine the main direction in which the reflection error occurred most frequently and significantly in the receiving area in history, and store it in the database as the main reflection azimuth.

[0053] The specific formulas for calculating the path error index of a satellite relative to a receiver at a certain time point and the environmental induced error index at a certain time point within the receiver's set area are as follows:

[0054] ;in, is the path error index of a satellite relative to the receiver at a certain time point, Score the reflection type of the reflective surfaces within the receiver setting area, Set the reflection angle value of the reflecting surface in the receiver setting area. Set the light intensity value at a certain time point in the area for the receiver. Set the ambient humidity value at a certain time point in the area for the receiver. is the satellite azimuth value of a certain satellite relative to the receiver at a certain time point, Set the historical main reflection azimuth in the area for the receiver, Set the thermal infrared value at a certain time point in the area for the receiver. is the thermal infrared influence coefficient stored in the database, Set the environmental induced error index for the receiver at a certain time point within the area. Set the ambient temperature value at a certain time point in the area for the receiver. Set the total electron content of the ionosphere at a certain time point in the receiver area. is the light intensity influencing factor stored in the database, and in this embodiment, the value is 10. is the satellite elevation angle value of a certain satellite relative to the receiver at a certain time point, is the elevation angle influence factor stored in the database, and its value is 30 in this embodiment.

[0055] It should be explained that the thermal infrared influence coefficient stored in the database The specific acquisition steps are as follows: based on the infrared sensor or thermal infrared camera, the thermal radiation distribution image in the area is captured at regular intervals, and matched with the pseudo-range residual data of the satellite signal at the same moment; by analyzing the pseudo-range error change trend under different infrared heat intensity levels, combining the reflection angle and azimuth information, a thermal infrared error response model is constructed, and the amplification factor of the signal error under different thermal reflection states is further extracted, and finally the thermal infrared influence coefficient is formed. It is then stored in the database as a regional thermal reflection error characteristic for a long time, and used for subsequent call and weight adjustment in environmental error modeling.

[0056] The specific formula for calculating the error correction of a satellite for each time point of the receiver is as follows: ;in, The first The error correction amount at each time point, The first The arrival time difference of the signals at the time points, The first The path error index at each time point, is the path error influence coefficient stored in the database, Set the receiver to the first The environmental induced error index at each time point, is the environmental induced error influence coefficient stored in the database, =1, 2, 3, ..., , is the number of time points.

[0057] Among them, it needs to be explained that the path error influence coefficient stored in the database , Environmental induced error influence coefficient The specific steps for obtaining are as follows: First, in actual operation, the system continuously records the pseudorange residuals of each satellite signal when it reaches the receiver at each time point, and groups them according to azimuth, elevation, reflection surface type, etc.; then, using the fitting model between the residuals and the signal propagation path characteristics (such as angle, reflection frequency), the average error under different paths is extracted, which is obtained At the same time, the system combines the relationship between environmental parameters such as temperature, humidity, ionospheric TEC, and light and pseudorange fluctuations to build a multivariate disturbance mapping model, extracting the error contribution rate of each environmental factor as and store it in the database for error correction at each moment.

[0058] In this implementation, the satellite pseudorange is dynamically corrected by comprehensively analyzing multiple factors, providing an accurate error correction mechanism and effectively improving the positioning accuracy. First, based on the comprehensive analysis of the reflecting surface type score and environmental factors (such as light, humidity, temperature, ionospheric TEC, etc.), it can identify and correct the multipath effects caused by reflecting surfaces (such as water surface, concrete wall, glass curtain wall, etc.) and environmental conditions (such as temperature and humidity, thermal infrared, etc.) in real time. By analyzing the historical main reflection azimuth, the system can identify the most frequent reflection path in the receiver setting area, thereby accurately compensating for the error source of the signal. Secondly, the calculation method of the path error index and the environmental induced error index is This method takes factors such as the type of reflecting surface, reflection angle, ambient temperature and humidity into consideration, ensuring that the pseudo-range data of each satellite can be reasonably corrected, thereby eliminating the influence of the external environment on positioning accuracy. Combined with characteristic quantities such as the thermal infrared influence coefficient and light intensity influence factor stored in the database, the system can cope with different environmental conditions and achieve precise correction. Compared with traditional methods, this multi-dimensional, multi-parameter dynamic correction method can better cope with error sources in complex environments and effectively improve the positioning accuracy and stability of the system in real scenarios. Therefore, it provides a strong technical guarantee for high-precision positioning in strip sparse networks and greatly enhances the adaptability and practicality of the system.

[0059] Specifically, if Figure 2As shown, the specific steps for obtaining the pseudorange timing filter set of each satellite for the receiver are as follows: reading the pseudorange correction value of each satellite for the receiver at each time point, and performing change analysis respectively to obtain the pseudorange correction change rate of each satellite for the receiver at each time point; inputting the satellite signal strength value, pseudorange correction value, pseudorange correction change rate, and electric field strength value at each time point in the set area of ​​the receiver for each satellite for each time point into a preset filtering processing model for filtering analysis to obtain the pseudorange filter value of each satellite for the receiver at each time point.

[0060] The filtering processing model is as follows:

[0061] ;in, The first The pseudorange filtering value at each time point, The first The pseudorange correction value at a time point, The first The pseudorange correction change rate at each time point is: is the low change rate threshold stored in the database, The first The pseudorange correction value at a time point, The first The pseudorange correction value at a time point, The first Satellite signal strength value at a time point, is the signal strength influence coefficient stored in the database, is the high change rate threshold stored in the database, The first The pseudorange correction change rate at each time point is: Set the receiver to the first The electric field strength value at each time point, is the electric field strength influence coefficient stored in the database, =1, 2, 3, ..., , is the number of time points.

[0062] It should be explained that the specific steps for obtaining the low change rate threshold and the high change rate threshold stored in the database are: through historical data collection and analysis, the typical range of the signal change rate is determined. For example, a low change rate indicates that the signal fluctuates less within a certain period of time, and a high change rate indicates that the signal changes more. According to the continuous pseudorange correction values, the pseudorange change rate of each satellite at different time points is calculated, and statistically analyzed to obtain the distribution of the change rate. According to the actual observation data, the low change rate and high change rate thresholds are automatically set by clustering or distributing the signal change rate. In this embodiment, the low change rate threshold can be set to the lowest percentile of the change rate, and the high change rate threshold can be set to a higher percentile.

[0063] Signal strength influence coefficient stored in the database , Electric field strength influence coefficient The specific acquisition steps are as follows: through signal quality measurement, the signal-to-noise ratio (SNR) of each satellite is recorded. Then, based on long-term data accumulation, the influence of signal strength on pseudorange correction is calculated. The signal strength influence coefficient can be obtained by performing regression analysis on the relationship between signal strength and error at each moment. The acquisition of the electric field strength influence coefficient depends on the measurement of the electric field strength in the environment. The electric field strength data is collected in real time through the electric field strength sensor, and the relationship between electric field strength and pseudorange error is analyzed. According to the correlation analysis of historical data, the influence coefficient of electric field strength on pseudorange correction, that is, the electric field strength influence coefficient, is obtained.

[0064] This implementation combines multiple environmental and signal quality parameters to dynamically filter and correct each satellite's pseudorange time series data, significantly improving the system's positioning accuracy and stability in complex environments. Specifically, rate-of-change analysis accurately identifies abnormal fluctuations in pseudorange correction data. By analyzing the distribution of signal changes, the low and high rate-of-change thresholds are automatically adjusted, effectively filtering out time periods with small or large signal changes, ensuring signal reliability and accuracy. Furthermore, the influence coefficients of signal strength and electric field strength on pseudorange correction are incorporated into the filtering model, further improving the system's adaptability to environmental changes. The signal strength influence coefficient, determined through long-term data accumulation and regression analysis, accurately measures the relationship between signal quality and pseudorange error, thereby dynamically adjusting the contribution of signal strength to pseudorange correction. Through this multi-parameter joint analysis and dynamic filtering adjustment, the system not only identifies and removes noise from the signal in real time, but also adapts to signal interference from varying environmental conditions, improving the accuracy of pseudorange data. Ultimately, these optimizations ensure that the system can provide high-precision positioning results even in complex urban or mountainous environments, significantly improving system reliability and practicality.

[0065] Specifically, the steps for establishing a virtual reference station are as follows: obtain the estimated position information of the receiver (which can be calculated through the preliminary positioning results of satellite navigation), the position information of each reference station in the receiver's set area, and analyze the baseline distance value between each reference station and the receiver; conduct a comprehensive analysis of the position information of each reference station in the receiver's set area and the baseline distance value between each reference station and the receiver to obtain the position information of the virtual reference station.

[0066] The specific steps for calculating the position information of the virtual reference station are as follows:

[0067] ;in, is the location information of the virtual reference station, Set the receiver to the first The location information of the base station, For the The baseline distance between the base station and the receiver, =1, 2, 3, ..., , is the number of base stations.

[0068] The specific steps for obtaining the pseudorange time series fusion set for each satellite for the receiver are as follows: obtaining the pseudorange differential correction value for each satellite and each reference station in the receiver's set area at each time point; analyzing the baseline distance value between the virtual reference station and each reference station in the receiver's set area based on the position information of each reference station and the position information of the virtual reference station; obtaining the station offset for each reference station in the receiver's set area at each time point (i.e., the X, Y, and Z axis difference between the receiver's estimated position information and the position of each reference station); and performing a comprehensive analysis based on the pseudorange differential correction value for each satellite and each reference station in the receiver's set area at each time point, the baseline distance value between the virtual reference station and each reference station in the receiver's set area, and the baseline distance value between each reference station and the receiver to obtain the pseudorange virtual reference correction value for each satellite for the receiver at several time points; and analyzing the pseudorange fusion value for each satellite for the receiver at several time points (i.e., the pseudorange filter value minus the virtual reference correction value) based on the pseudorange filtered value and pseudorange virtual reference correction value for each satellite for the receiver at several time points.

[0069] The specific formula for calculating the pseudorange virtual reference correction value of a satellite for a receiver at several time points is as follows: ;in, The first The pseudorange virtual reference correction value at a time point, Set the first The first base station The pseudorange difference correction value at each time point, For the The baseline distance between the base station and the receiver, Set the first The baseline distance value of each reference station, =1, 2, 3, ..., , is the number of time points, =1, 2, 3, ..., , is the number of base stations.

[0070] In this implementation plan, the sparse distribution problem of reference stations in the strip-shaped sparse single Beidou CORS network is effectively solved through the establishment of virtual reference stations and pseudo-range differential correction, which significantly improves the positioning accuracy and stability of the system. First, by obtaining the preliminary position information of the receiver and the position information of the reference stations in the area, the system can accurately calculate the baseline distance between the reference station and the receiver, and then construct a virtual reference station. The position information of the virtual reference station not only makes up for the shortcomings of the physical reference station, but also provides more accurate differential data, and realizes high-precision positioning correction in areas where the number of reference stations is insufficient. In addition, the pseudo-range differential correction is combined with the receiver and The position information of the reference station, the pseudorange correction value and the correction data of the virtual reference station can finely adjust the pseudorange data of each satellite and reduce the positioning error caused by the sparse reference station. By analyzing the difference between the pseudorange virtual reference correction value and the pseudorange filter value, the system can generate a pseudorange fusion value for each satellite, thereby improving the accuracy of the final positioning result. This method makes full use of the virtual reference station and multi-source data fusion, and can still ensure high-precision positioning when the reference stations are unevenly distributed, providing strong support for efficient positioning in complex environments. It is particularly suitable for the positioning needs of sparse networks and enhances the reliability and practicality of the positioning system.

[0071] Specifically, the specific steps for obtaining the position information of the receiver are as follows: based on a preset adaptive weight rule, a weight value is assigned to the pseudo-range fusion value of each satellite for each time point of the receiver (the satellite signal strength value, satellite azimuth value, satellite elevation angle value, distance between the receiver and the satellite, ambient temperature value, ambient humidity value, light intensity value, thermal infrared value, ionospheric total electron content value, and electric field strength value are first de-unitized and weighted analyzed to obtain the weight value), and weighted processing is performed to obtain the weighted fusion value of the pseudo-range time series of each satellite for the receiver; based on a preset positioning algorithm, the weighted fusion value of the pseudo-range time series of each satellite for the receiver is solved to obtain the position information of the receiver.

[0072] In this embodiment, the positioning algorithm refers to the least squares method, and the specific steps for calculating the weighted fusion value of the receiver pseudorange time series for each satellite are as follows:

[0073] Initialize the receiver's position based on the receiver's preliminary position information (which can be obtained through coarse satellite positioning or known reference points).

[0074] For each satellite, its pseudorange residual is calculated, which is the difference between the distance between the receiver position and the satellite position and the measured pseudorange.

[0075] Establish the equation: The goal of the least squares method is to minimize the pseudorange residual. Suppose we have N satellites, each providing a pseudorange data set (after weighting and correction) at a different time point. For each satellite and time point, analyze the pseudorange residual.

[0076] The least squares optimization objective is to minimize the sum of squares of the pseudorange residuals of all satellites.

[0077] The least squares method is used to minimize the objective function (the sum of squares of pseudorange residuals) by continuously adjusting the receiver's position information (longitude, latitude, and elevation).

[0078] Through each iteration, the position of the receiver is updated so that the pseudorange residual is minimized.

[0079] After each iteration, the value of the objective function is checked. If the change in the objective function is less than the preset threshold, it means that the solution has converged and the accurate position information of the receiver is obtained.

[0080] Once converged, the final position of the receiver is output, including longitude, latitude and elevation. This position is obtained by least squares optimization.

[0081] This implementation significantly improves receiver positioning accuracy and stability by introducing adaptive weighting rules and a least-squares positioning algorithm. First, by de-unitizing and weighting satellite signal strength, azimuth, elevation, range, and environmental factors (such as temperature, humidity, and light), the system dynamically assigns different weights to each satellite based on its quality and environmental conditions. This dynamic weighting mechanism ensures that more reliable satellite data plays a greater role in the final positioning. Especially in complex or dynamic environments where signal quality may fluctuate, this mechanism adaptively adjusts weights to reduce the impact of unreliable data on positioning results. Second, by using the least-squares method for positioning, the system accurately minimizes pseudorange residuals. By optimizing the receiver's three-dimensional position (longitude, latitude, and elevation) through multiple iterations, the least-squares method accurately estimates the receiver's exact position by optimizing the sum of squares of the pseudorange residuals, ensuring accuracy and stability in complex environments. Combined with the adaptive weighting mechanism, this method allows the system to converge to an accurate positioning result through the optimization process even when satellite signals are unstable or subject to multipath interference.

[0082] See also Figure 3 An embodiment of the present invention provides a technical solution: a multipath mitigation positioning system for a strip-shaped sparse single Beidou CORS network, comprising: a data acquisition unit, configured to acquire initial pseudorange timing data, reception timing data, and regional status data within a set area of ​​the receiver from each satellite; a pseudorange correction unit, configured to correct and analyze the initial pseudorange timing data based on the reception timing data from each satellite for the receiver and the regional status data within the set area of ​​the receiver, to obtain a pseudorange timing correction set for each satellite for the receiver; a pseudorange filtering unit, configured to filter the pseudorange timing correction set from each satellite for the receiver based on a preset filtering processing rule, to obtain a pseudorange timing filter set from each satellite for the receiver; a pseudorange fusion unit, configured to establish a virtual reference station and perform fusion analysis on the pseudorange timing filter set from each satellite for the receiver, to obtain a pseudorange timing fusion set from each satellite for the receiver; and a positioning solution unit, configured to perform positioning solution analysis on the pseudorange timing fusion set from each satellite for the receiver based on a preset adaptive weighting rule, to obtain position information of the receiver.

[0083] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0084] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A multipath suppression positioning method for a sparse single BeiDou CORS network, characterized in that: The following steps are involved: Obtain the initial pseudorange timing data of each satellite for the receiver, reception timing data, and regional status data within the receiver's set area. The reception timing data includes the signal arrival time difference, satellite signal strength value, satellite azimuth value, and satellite elevation angle value at several time points. The regional status data includes reflection data and environmental status timing data. The reflection data includes the type information of the reflecting surface and the reflection angle value. The environmental status timing data includes the ambient temperature value, ambient humidity value, light intensity value, thermal infrared value, ionospheric total electron content value, and electric field strength value at several time points. Based on the reception timing data of each satellite for the receiver and the regional status data within the receiver setting area, the initial pseudo-range timing data is corrected and analyzed to obtain the pseudo-range timing correction set of each satellite for the receiver; Based on a preset filtering processing rule, the pseudo-range timing correction set of each satellite for the receiver is filtered to obtain the pseudo-range timing filter set of each satellite for the receiver; Establish a virtual reference station and perform fusion analysis on the pseudo-range time series filter set of each satellite for the receiver to obtain the pseudo-range time series fusion set of each satellite for the receiver; Based on the preset adaptive weight rules, the positioning solution is analyzed for the pseudo-range time series fusion set of each satellite for the receiver to obtain the position information of the receiver.

2. The multipath suppression positioning method for a strip-shaped sparse single Beidou CORS network according to claim 1, characterized in that: The initial pseudorange time series data includes initial pseudorange values ​​at several time points, the pseudorange time series correction set includes pseudorange correction values ​​at several time points, the pseudorange time series filter set includes pseudorange filter values ​​at several time points, and the pseudorange time series fusion set includes pseudorange fusion values ​​at several time points.

3. The multipath suppression positioning method for a strip-shaped sparse single Beidou CORS network according to claim 2, characterized in that: The specific steps to obtain the pseudorange timing correction set of each satellite for the receiver are as follows: Based on a preset scoring rule, type information of the reflecting surface within the receiver setting area is scored to obtain a reflection type score of the reflecting surface within the receiver setting area; Obtain the historical main reflection azimuth within the receiver's set area, and perform a comprehensive analysis based on the reflection type score, reflection angle value, light intensity value, ambient humidity value, thermal infrared value, and satellite azimuth value of each satellite relative to the receiver at several time points. Calculate the path error index of each satellite relative to the receiver at several time points. The ambient temperature, ionospheric total electron content, and light intensity values ​​at several time points within the receiver's set area are read, and the satellite elevation angle values ​​of each satellite relative to the receiver at several time points are comprehensively analyzed to obtain the environmental induced error index at several time points within the receiver's set area; Comprehensively analyze the signal arrival time difference of each satellite with respect to the receiver at several time points, the path error index, and the environmental induced error index at several time points within the receiver's set area to obtain the error correction value of each satellite with respect to the receiver at several time points; Based on the error correction amount and initial pseudorange value of each satellite for the receiver at several time points, the pseudorange correction value of each satellite for the receiver at several time points is analyzed.

4. The multipath suppression positioning method for a strip-shaped sparse single Beidou CORS network according to claim 3, characterized in that: The specific formula for calculating the error correction of a satellite for each time point of the receiver is as follows: ； in, 、 、 The first Error correction amount at each time point, signal arrival time difference, path error index, Set the receiver to the first The environmental induced error index at each time point, 、 They are the path error influence coefficient and the environment induced error influence coefficient stored in the database, =1, 2, 3, ..., , is the number of time points.

5. The multipath suppression positioning method for a strip-shaped sparse single Beidou CORS network according to claim 2, characterized in that: The specific steps to obtain the pseudo-range timing filter set of each satellite for the receiver are as follows: Read the pseudorange correction value of each satellite for each time point of the receiver, and perform change analysis respectively to obtain the pseudorange correction change rate of each satellite for each time point of the receiver; The satellite signal strength value, pseudorange correction value, pseudorange correction change rate of each satellite at each time point of the receiver, and the electric field strength value at each time point in the set area of ​​the receiver are input into the preset filtering processing model for filtering analysis to obtain the pseudorange filtering value of each satellite at each time point of the receiver.

6. The multipath suppression positioning method for a strip-shaped sparse single Beidou CORS network according to claim 5, characterized in that: The filtering processing model is specifically as follows: ； in, 、 、 、 The first Pseudorange filter value, pseudorange correction value, pseudorange correction change rate, satellite signal strength value at each time point, 、 The first 、 The pseudorange correction value at a time point, 、 、 、 They are the low change rate threshold, signal strength influence coefficient, high change rate threshold, and electric field strength influence coefficient stored in the database. The first The pseudorange correction change rate at each time point is: Set the receiver to the first The electric field strength value at each time point, =1, 2, 3, ..., , is the number of time points.

7. The multipath suppression positioning method for a strip-shaped sparse single Beidou CORS network according to claim 2, characterized in that: The specific steps to establish a virtual base station are as follows: Obtain the estimated position information of the receiver and the position information of each reference station within the receiver's set area, and analyze the baseline distance value between each reference station and the receiver; The position information of each reference station in the receiver setting area and the baseline distance value between each reference station and the receiver are comprehensively analyzed to obtain the position information of the virtual reference station.

8. The multipath suppression positioning method for a strip-shaped sparse single Beidou CORS network according to claim 7, characterized in that: The specific steps to obtain the pseudorange time series fusion set of each satellite for the receiver are as follows: Obtain the pseudo-range differential correction value at each time point between each satellite and each reference station within the receiver's set area; Based on the position information of each reference station in the receiver setting area and the position information of the virtual reference station, the baseline distance value between the virtual reference station and each reference station in the receiver setting area is analyzed; Obtain the station offset of each reference station at each time point in the receiver setting area, and perform comprehensive analysis based on the pseudorange differential correction value of each satellite and each reference station at each time point in the receiver setting area, the baseline distance value between the virtual reference station and each reference station in the receiver setting area, and the baseline distance value between each reference station and the receiver to obtain the pseudorange virtual baseline correction value of each satellite for the receiver at several time points; Based on the pseudorange filtering value and pseudorange virtual reference correction value of each satellite for the receiver at several time points, the pseudorange fusion value of each satellite for the receiver at several time points is analyzed.

9. The multipath suppression positioning method for a strip-shaped sparse single Beidou CORS network according to claim 1, characterized in that: The specific steps to obtain the receiver's location information are as follows: Based on the preset adaptive weight rule, a weight value is assigned to the pseudorange fusion value of each satellite for each time point of the receiver, and weighted processing is performed to obtain the weighted fusion value of the pseudorange time series of each satellite for the receiver; Based on the preset positioning algorithm, the weighted fusion value of the receiver pseudorange time series of each satellite is solved and processed to obtain the position information of the receiver.

10. A multipath suppression positioning system for a strip-shaped sparse single Beidou CORS network, applying the multipath suppression positioning method for a strip-shaped sparse single Beidou CORS network according to any one of claims 1 to 9, characterized in that: include: A data acquisition unit is used to obtain initial pseudorange timing data of each satellite for the receiver, reception timing data, and regional status data within a set area of ​​the receiver; The pseudorange correction unit is used to correct and analyze the initial pseudorange timing data based on the reception timing data of each satellite for the receiver and the regional status data within the set area of ​​the receiver to obtain the pseudorange timing correction set of each satellite for the receiver; The pseudorange filtering unit is used to filter the pseudorange timing correction set of each satellite for the receiver based on a preset filtering processing rule to obtain the pseudorange timing filter set of each satellite for the receiver; The pseudorange fusion unit is used to establish a virtual reference station and perform fusion analysis on the pseudorange time series filter set of each satellite for the receiver to obtain the pseudorange time series fusion set of each satellite for the receiver; The positioning solution unit is used to perform positioning solution analysis on the pseudo-range time series fusion set of each satellite for the receiver based on a preset adaptive weight rule to obtain the position information of the receiver.

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