Multi-path suppression positioning method and system for banded sparse single Beidou CORS network

Abstract

The invention discloses a multi-path suppression positioning method and system for a banded sparse single Beidou CORS network, and relates to the technical field of satellite navigation positioning. According to the multi-path suppression positioning method for the banded sparse single Beidou CORS network, initial pseudo-range time sequence data are acquired, the time sequence data and regional state data are received, the pseudo-range data are corrected and analyzed, then, filtering processing is performed by using a preset filtering rule, a virtual base station is established, data fusion is performed, and the multi-path suppression positioning method for the banded sparse single Beidou CORS network is obtained. According to the method, the pseudo-range time sequence data of each satellite is dynamically corrected, and particularly, through comprehensive analysis of the reflecting surface information, the environment data and the signal arrival time difference, the accurate position information of the receiver is obtained. Errors caused by reflecting surface and environment changes can be recognized and corrected in real time, and the stability and precision of a positioning system in a complex environment are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite navigation and positioning, and specifically 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, Beidou, etc.) 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.), due to signal reflection, occlusion, and multipath effects, traditional GNSS positioning methods often cannot provide high-precision positioning results. The multipath effect refers to the situation where satellite signals are reflected by obstacles or reflecting surfaces (such as buildings, trees, water surfaces, etc.), resulting in changes in the signal paths reaching the receiver, thereby introducing pseudorange errors and seriously affecting the positioning accuracy.

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

[0004] The limitations of the existing technology at least include the following problems. In the existing positioning methods, especially in a strip-shaped sparse single Beidou CORS network, there is a problem that multipath effects are difficult to effectively suppress. In practical applications, after satellite signals are reflected by the atmosphere and the ground, they may be interfered by reflecting surfaces such as buildings, mountains, and water surfaces, forming multipath effects. Such multipath effects will lead to inaccurate pseudorange data, which in turn affects the 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 influence of multipath effects is more obvious. This limitation results in poor stability and low positioning accuracy of the positioning system in a dynamic environment, making it difficult to meet the high-precision requirements in practical applications. Summary of the Invention

[0005] Aiming at the deficiencies 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 that multipath effects are difficult to effectively suppress in the existing technology.

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

[0007] Further, the initial pseudorange time-series data includes the initial pseudorange values at several time points, the reception time-series data includes the signal arrival time differences, satellite signal strength values, satellite azimuth values, and satellite elevation values at several time points, the regional state data includes reflection data and environmental state time-series data, the reflection data includes the type information of the reflecting surface and the reflection angle value, the environmental state time-series data includes the environmental temperature value, environmental humidity value, light intensity value, thermal infrared value, total electron content value of the ionosphere, and electric field strength value at several time points, the pseudorange time-series correction set includes the pseudorange correction values at several time points, the pseudorange time-series filtering set includes the pseudorange filtering values at several time points, and the pseudorange time-series fusion set includes the pseudorange fusion values at several time points.

[0008] Further, the specific steps for obtaining the initial pseudorange timing correction set of the receiver for each satellite are as follows: Based on a preset scoring rule, perform type scoring on the type information of the reflecting surfaces within the set area of the receiver to obtain the reflection type scores of the reflecting surfaces within the set area of the receiver; obtain the historical main reflection azimuth angles within the set area of the receiver, and respectively combine the reflection type scores, reflection angle values, light intensity values at several time points, environmental humidity values, thermal infrared values, and satellite azimuth angle values of each satellite for the receiver at several time points within the set area of the receiver for comprehensive analysis to obtain the path error indices of each satellite for the receiver at several time points; read the environmental temperature values, total ionospheric electron content values, and light intensity values at several time points within the set area of the receiver, and respectively combine the satellite elevation angle values of each satellite for the receiver at several time points for comprehensive analysis to obtain the environmental-induced error indices at several time points within the set area of the receiver; perform comprehensive analysis on the signal arrival time differences, path error indices of each satellite for the receiver at several time points, and the environmental-induced error indices at several time points within the set area of the receiver to obtain the error correction amounts of each satellite for the receiver at several time points; based on the error correction amounts and initial pseudorange values of each satellite for the receiver at several time points, analyze the pseudorange correction values of each satellite for the receiver at several time points.

[0009] Further, the specific formula for calculating the error correction amount of a certain satellite for the receiver at each time point is as follows: Where, WxZ i is the error correction amount of a certain satellite for the receiver at the i-th time point, XsC i is the signal arrival time difference of a certain satellite for the receiver at the i-th time point, LwC i is the path error index of a certain satellite for the receiver at the i-th time point, δ1 is the path error influence coefficient stored in the database, HyW i is the environmental-induced error index at the i-th time point within the set area of the receiver, δ2 is the environmental-induced error influence coefficient stored in the database, i = 1, 2, 3, …, i0, and i0 is the number of time points.

[0010] Further, the specific steps to obtain the pseudo-range time series filtering set of each satellite with respect to the receiver are as follows: Read the pseudo-range correction values of each satellite with respect to the receiver at each time point, and perform variation analysis separately to obtain the pseudo-range correction change rate of each satellite with respect to the receiver at each time point; Input the satellite signal strength value, pseudo-range correction value, pseudo-range correction change rate of each satellite with respect to the receiver at each time point, and the electric field strength value at each time point within the set area of the receiver into a preset filtering processing model for filtering analysis to obtain the pseudo-range filtering value of each satellite with respect to the receiver at each time point.

[0011] Further, the filtering processing model is specifically as follows:

[0012]

[0013] Where, WjL i is the pseudo-range filtering value of a certain satellite with respect to the receiver at the i-th time point, WjX i is the pseudo-range correction value of a certain satellite with respect to the receiver at the i-th time point, XzB i is the pseudo-range correction change rate of a certain satellite with respect to the receiver at the i-th time point, DbH is the low change rate threshold stored in the database, WjX i-2 is the pseudo-range correction value of a certain satellite with respect to the receiver at the (i - 2)-th time point, WjX i-1 is the pseudo-range correction value of a certain satellite with respect to the receiver at the (i - 1)-th time point, XhQ i is the satellite signal strength value of a certain satellite with respect to the receiver at the i-th time point, ω is the signal strength influence coefficient stored in the database, GbH is the high change rate threshold stored in the database, XzB i-1 is the pseudo-range correction change rate of a certain satellite with respect to the receiver at the (i - 1)-th time point, DcQ i is the electric field strength value at the i-th time point within the set area of the receiver, ψ is the electric field strength influence coefficient stored in the database, i = 1, 2, ···, i0, and i0 is the number of time points.

[0014] Further, the specific steps to establish a virtual reference station are as follows: Obtain the estimated position information of the receiver and the position information of each reference station within the set area of the receiver, and analyze the baseline distance value between each reference station and the receiver; Comprehensively analyze the position information of each reference station within the set area of the receiver and the baseline distance value between each reference station and the receiver to obtain the position information of the virtual reference station.

[0015] Further, the specific steps to obtain the pseudo-range time-series fusion set of each satellite with respect to the receiver are as follows: Obtain the pseudo-range differential correction value of each satellite with respect to each reference station at each time point within the set area of the receiver; Based on the position information of each reference station within the set area of the receiver and the position information of the virtual reference station, analyze the baseline distance value between the virtual reference station and each reference station within the set area of the receiver; Obtain the site offset of each reference station at each time point within the set area of the receiver, and respectively combine the pseudo-range differential correction value of each satellite with respect to each reference station at each time point within the set area of the receiver, the baseline distance value between the virtual reference station and each reference station within the set area of the receiver, and the baseline distance value between each reference station and the receiver for comprehensive analysis to obtain the pseudo-range virtual reference correction value of each satellite with respect to the receiver at several time points; Based on the pseudo-range filtered value and the pseudo-range virtual reference correction value of each satellite with respect to the receiver at several time points, analyze the pseudo-range fusion value of each satellite with respect to the receiver at several time points.

[0016] Further, the specific steps to obtain the position information of the receiver are as follows: Based on the preset adaptive weight rule, assign weight values to the pseudo-range fusion value of each satellite with respect to the receiver at each time point respectively, and perform weighted processing to obtain the pseudo-range time-series weighted fusion value of each satellite with respect to the receiver; Based on the preset positioning algorithm, perform a solution process on the pseudo-range time-series weighted fusion value of each satellite with respect to the receiver to obtain the position information of the receiver.

[0017] The multipath suppression positioning system for a strip-shaped sparse single Beidou CORS network includes: a data acquisition unit for acquiring the initial pseudo-range time-series data, reception time-series data of each satellite with respect to the receiver, and the area status data within the set area of the receiver; a pseudo-range correction unit for performing correction analysis on the initial pseudo-range time-series data based on the reception time-series data of each satellite with respect to the receiver and the area status data within the set area of the receiver to obtain the pseudo-range time-series correction set of each satellite with respect to the receiver; a pseudo-range filtering unit for filtering the pseudo-range time-series correction set of each satellite with respect to the receiver based on the preset filtering processing rule to obtain the pseudo-range time-series filtering set of each satellite with respect to the receiver; a pseudo-range fusion unit for establishing a virtual reference station and performing fusion analysis on the pseudo-range time-series filtering set of each satellite with respect to the receiver to obtain the pseudo-range time-series fusion set of each satellite with respect to the receiver; a positioning solution unit for performing positioning solution analysis on the pseudo-range time-series fusion set of each satellite with respect to the receiver based on the preset adaptive weight rule to obtain the position information of the receiver.

[0018] The present invention has the following beneficial effects:

[0019] (1) The multipath suppression positioning method for the strip-shaped sparse single Beidou CORS network can effectively suppress the multipath effect by dynamically correcting the pseudorange time-series data of each satellite. Especially through the comprehensive analysis of the reflector information, environmental data, and time difference of signal arrival, the multipath effect is an important factor affecting GNSS positioning accuracy. 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 are difficult to fully consider the complexity of the reflector and its impact on signal propagation. Therefore, adopting a pseudorange correction method based on reflector type scoring and environmental factor analysis can identify and correct errors caused by reflector and environmental changes in real time, significantly improving the stability and accuracy of the positioning system in complex environments.

[0020] (2) The multipath suppression positioning method for the strip-shaped sparse single Beidou CORS network can obtain the preliminary position information of the receiver, the position information of the reference station, and the baseline distance value between each reference station and the receiver. The virtual reference station can perform position supplementation and optimization based on the data and relative positions of the existing reference stations. The virtual reference station can not only make up for the deficiencies of the physical reference stations but also perform position correction according to the real-time data of the network, ensuring that the system can still provide high-precision positioning results in the case of sparse reference stations. Especially in some remote areas or blind spots between high-rise buildings in the city, the virtual reference station can effectively improve the positioning stability and reduce errors.

[0021] (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 information such as signal strength, satellite azimuth, and elevation angle, weights are dynamically assigned to the pseudorange time-series data of each satellite and weighted processing is performed. This method enables the system to finely adjust the positioning results according to the signal quality of each satellite and environmental factors, thereby improving the positioning accuracy. For example, in the case of weak signal strength or low satellite azimuth, the system automatically reduces the weights of these satellites, thereby reducing their impact on the final positioning result; while in the case of strong signal, optimal azimuth, and elevation angle, the system automatically increases the weights of these satellites, enhancing 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 positioning errors caused by factors such as signal fluctuations, interference, or occlusion.

[0022] (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.

[0023] 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

[0024] 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.

[0025] 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.

[0026] 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

[0027] 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.

[0028] 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 differences, satellite signal strength values, satellite azimuth values, and satellite elevation values at several time points, the regional state data includes reflection data and environmental state time-series data, the reflection data includes the type information of the reflecting surface and the reflection angle value, the environmental state time-series data includes the environmental temperature value, environmental humidity value, light intensity value, thermal infrared value, total electron content value of the ionosphere, and electric field strength value at several time points, the pseudorange time-series correction set includes the pseudorange correction values at several time points, the pseudorange time-series filtering set includes the pseudorange filtering values at several time points, and the pseudorange time-series fusion set includes the pseudorange fusion values at several time points.

[0029] Among them, the initial pseudorange value refers to the original pseudorange observation value measured by the receiver before any correction processing of the satellite signal, that is, the distance estimation from each satellite to the receiver, which can be calculated in real time from the Beidou satellite signal by the front-end module of the GNSS receiver.

[0030] The signal arrival time difference refers to the difference between the arrival time of each satellite signal recorded by the receiver and the arrival time of the ideal model, which is calculated by the GNSS receiver by comparing the local time with the satellite timestamp.

[0031] The satellite signal strength value represents the signal-to-noise ratio or power intensity at the time of signal reception, reflecting the signal quality, which can be collected by the receiver RF module and directly output by the GNSS chip.

[0032] The satellite azimuth value refers to the horizontal angle (0° to 360°) of the satellite relative to the due north direction in the receiver antenna reference system, which is calculated from the satellite ephemeris and the estimated position of the receiver.

[0033] The satellite elevation value refers to the vertical angle with the satellite directly above the receiver antenna being 90° and the horizon being 0°, which is calculated from the satellite ephemeris and the receiver estimated position information.

[0034] The type information of the reflecting surface is used to identify the main reflecting medium types existing in the receiver area, which is used to judge whether multipath is likely to occur and can be assisted by environmental sensors for identification (such as identifying building materials by near-infrared images).

[0035] The reflection angle value of the reflecting surface represents the angle between the incident and reflection when the signal may be reflected, which is used to judge the reflection strength and can be calculated using the satellite incident direction (azimuth + elevation) and the normal angle of the reflecting surface; if the direction of the reflecting surface can be extracted by infrared or map, it can be automatically deduced.

[0036] The environmental temperature value can be measured and obtained through a temperature sensor.

[0037] The environmental humidity value can be measured and obtained through a humidity sensor.

[0038] The light intensity value can be measured and obtained through a light sensor.

[0039] The thermal infrared value can be measured and obtained through a thermal infrared camera or a thermal sensor.

[0040] The total electron content value of the ionosphere represents the total electron density encountered by the satellite signal passing through the ionosphere, which affects the delay and error. It can be calculated by the receiver through dual-frequency signals; or access the real-time estimated value of TEC provided by the Beidou Wide Area Augmentation System (SBAS).

[0041] The electric field intensity value can be measured and obtained through an electric field sensor.

[0042] Specifically, the specific steps to obtain the initial pseudorange timing correction set for each satellite by the receiver are as follows: Based on a preset scoring rule, perform type scoring on the type information of the reflecting surface within the set area of the receiver to obtain the reflection type score of the reflecting surface within the set area of the receiver; Obtain the historical main reflection azimuth angle within the set area of the receiver, and respectively combine the reflection type score, reflection angle value, light intensity values at several time points, environmental humidity value, thermal infrared value, and the satellite azimuth angle values of each satellite for the receiver at several time points within the set area of the receiver for comprehensive analysis to obtain the path error index of each satellite for the receiver at several time points; Read the environmental temperature value, total electron content value of the ionosphere, and light intensity value at several time points within the set area of the receiver, and respectively combine the satellite elevation angle values of each satellite for the receiver at several time points for comprehensive analysis to obtain the environmental induced error index at several time points within the set area of the receiver; Perform comprehensive analysis on the signal arrival time difference, path error index of each satellite for the receiver at several time points, and the environmental induced error index at several time points within the set area of the receiver to obtain the error correction amount of each satellite for 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, analyze the pseudorange correction value of each satellite for the receiver at several time points (that is, the initial pseudorange value minus the error correction amount).

[0043] Among them, the preset scoring rules include but are not limited to the following examples:

[0044] Water surface (such as lakes, rivers):

[0045] Reflection type score: The water surface usually has strong reflection characteristics. Especially for a calm water surface, it will cause obvious multipath effects. Therefore, the reflection type score of the water surface is relatively high and may be set at 8 points (out of 10 points).

[0046] Concrete wall (such as the exterior facade of a building):

[0047] Reflection type rating: Although a concrete wall can reflect signals, its reflection characteristics are usually worse than those of water surface. Therefore, the multipath effect generated is not as obvious as that of the water surface. The reflection type rating for the concrete wall can be set at 5 points.

[0048] Glass curtain wall (such as the glass exterior wall of a modern building):

[0049] Reflection type rating: A glass curtain wall can reflect a certain proportion of signals. However, compared with the water surface and the concrete wall, the reflection effect of glass is usually weaker (especially when there is no special coating on the building exterior wall). The reflection type rating for glass is set at 4 points.

[0050] Vegetation (such as trees, grasslands):

[0051] Reflection type rating: The reflection effect of vegetation is relatively small because most signals are absorbed or scattered and it is not easy to form a strong reflection. Therefore, the rating is relatively low and is set at 2 points.

[0052] Bare soil:

[0053] Reflection type rating: The reflection effect of the soil is small. Basically, the signal will be absorbed by the ground and the reflection effect is not obvious. Therefore, the rating is also relatively low and can be set at 1 point.

[0054] The specific steps to obtain the historical main reflection azimuth angle within the set area of the receiver are as follows: First, record information such as the pseudorange residual, signal strength degradation rate, and multipath identification characteristics when each satellite arrives at the receiver at different azimuth angles; then cluster and statistically analyze this information according to azimuth angle intervals (such as every 10° as a group), and calculate the multipath anomaly frequency and average error amplitude in each direction; then combine the reflection surface type annotation (such as glass, water surface, wall) and time conditions (such as sunny day, foggy day) to perform directional reflection risk weighting, and finally determine the main direction where reflection errors occur most frequently and significantly in the historical receiving area as the main reflection azimuth angle and store it in the database.

[0055] The specific formulas for calculating the path error index of a certain satellite for a certain time point of the receiver and the environmental induced error index at a certain time point within the set area of the receiver are as follows: Among them, LwC is the path error index of a certain satellite for the receiver at a certain time point, LxP is the reflection type score of the reflecting surface within the set area of the receiver, FsJ is the reflection angle value of the reflecting surface within the set area of the receiver, GzQ is the light intensity value at a certain time point within the set area of the receiver, HsD is the environmental humidity value at a certain time point within the set area of the receiver, FwJ is the satellite azimuth angle value of a certain satellite for the receiver at a certain time point, LzF is the historical main reflection azimuth angle within the set area of the receiver, RhW is the thermal infrared value at a certain time point within the set area of the receiver, α is the thermal infrared influence coefficient stored in the database, HyW is the environmental induced error index at a certain time point within the set area of the receiver, HwD is the environmental temperature value at a certain time point within the set area of the receiver, DhL is the total ionospheric electron content value at a certain time point within the set area of the receiver, β is the light intensity influence factor stored in the database, with a value of 10 in this embodiment, YjZ is the satellite elevation angle value of a certain satellite for the receiver at a certain time point, β is the elevation angle influence factor stored in the database, with a value of 30 in this embodiment.

[0056] It should be explained that the specific steps for obtaining the thermal infrared influence coefficient α stored in the database are as follows: Regularly capture the thermal radiation distribution image within the area by an infrared sensor or a thermal infrared camera, and match it with the pseudorange residual data of the satellite signal at the same time; By analyzing the change trend of the pseudorange error under different infrared thermal intensity levels, combining the reflection angle and azimuth angle information, construct a thermal infrared error response model, further extract the amplification factor of the signal error under different thermal reflection states, and finally form the thermal infrared influence coefficient α, and store it in the database as the regional thermal reflection error characteristic quantity for long-term storage, for subsequent calling and weighting in the environmental error modeling.

[0057] The specific formula for calculating the error correction amount of a certain satellite for the receiver at each time point is as follows: Among them, WxZ i is the error correction amount of a certain satellite for the receiver at the i-th time point, XsC i is the signal arrival time difference of a certain satellite for the receiver at the i-th time point, LwC i is the path error index of a certain satellite for the receiver at the i-th time point, δ1 is the path error influence coefficient stored in the database, HyW i is the environmental induced error index at the i-th time point within the set area of the receiver, δ2 is the environmental induced error influence coefficient stored in the database, i = 1, 2, 3,..., i0, and i0 is the number of time points.

[0058] Among them, it should be explained that the specific steps for obtaining the path error influence coefficient δ1 and the environmental induction error influence coefficient δ2 stored in the database are as follows: First, during actual operation, the system continuously records the pseudorange residuals of each satellite signal when it arrives at the receiver at each time point, and classifies them according to azimuth, elevation, reflector type, etc.; then, using the fitting model between the residuals and the signal propagation path characteristics (such as angle, reflection frequency), the average error amount under different paths is extracted, that is, δ1 is obtained; at the same time, the system combines environmental parameters such as temperature, humidity, ionospheric TEC, and illumination with the relationship between pseudorange fluctuations to construct a multivariate perturbation mapping model, extracts the error contribution rate of each environmental factor as δ2, and stores it in the database for error correction calls at each moment.

[0059] In this implementation plan, by comprehensively analyzing various factors to dynamically correct the satellite pseudorange, an accurate error correction mechanism is provided, effectively improving the positioning accuracy. First, based on the comprehensive analysis of the reflector type score and environmental factors (such as illumination, humidity, temperature, ionospheric TEC, etc.), the multipath effects caused by reflectors (such as water surfaces, concrete walls, glass curtain walls, etc.) and environmental conditions (such as temperature and humidity, thermal infrared, etc.) can be identified and corrected in real time. By analyzing the historical main reflection azimuth angle, the system can identify the most frequent reflection paths within the receiver's set area, thereby accurately compensating for the error sources of the signal. Second, the calculation methods of the path error index and the environmental induction error index take into account factors such as reflector type, reflection angle, environmental temperature and humidity, etc., ensuring that the pseudorange data of each satellite can be reasonably corrected, thereby eliminating the influence of the external environment on the positioning accuracy. Combining characteristic quantities such as the thermal infrared influence coefficient and the illumination intensity influence factor stored in the database, the system can cope with different environmental conditions and achieve accurate correction. This multi-dimensional and multi-parameter dynamic correction method can better cope with the error sources in complex environments compared with traditional methods, effectively improving the positioning accuracy and stability of the system in real scenarios. Therefore, it provides a strong technical guarantee for high-precision positioning in a strip-shaped sparse network, greatly enhancing the adaptability and practicality of the system.

[0060] Specifically, as Figure 2 shown, the specific steps for obtaining the pseudorange time series filtering set of each satellite for the receiver are as follows: Read the pseudorange correction values of each satellite for the receiver at each time point, and perform variation analysis respectively to obtain the pseudorange correction change rate of each satellite for the receiver at each time point; Input the satellite signal strength value, pseudorange correction value, pseudorange correction change rate of each satellite for the receiver at each time point, and the electric field strength value at each time point within the receiver's set area into a preset filtering processing model for filtering analysis to obtain the pseudorange filtering value of each satellite for the receiver at each time point.

[0061] The filtering processing model is as follows:

[0062]

[0063] Among them, WjL i is the pseudorange filtering value of a certain satellite for the receiver at the i-th time point, and WjX i is the pseudorange correction value of a certain satellite for the receiver at the i-th time point. XzB i is the pseudorange correction change rate of a certain satellite for the receiver at the i-th time point. DbH is the low change rate threshold stored in the database. WjX i-2 is the pseudorange correction value of a certain satellite for the receiver at the (i - 2)-th time point. WjX i-1 is the pseudorange correction value of a certain satellite for the receiver at the (i - 1)-th time point. XhQ i is the satellite signal strength value of a certain satellite for the receiver at the i-th time point. ω is the signal strength influence coefficient stored in the database. GbH is the high change rate threshold stored in the database. XzB i-1 is the pseudorange correction change rate of a certain satellite for the receiver at the (i - 1)-th time point. DcQ i is the electric field strength value at the i-th time point within the set area of the receiver. ψ is the electric field strength influence coefficient stored in the database. i = 1, 2, 3, …, i0, and i0 is the number of time points.

[0064] It should be explained that the specific acquisition steps of the low change rate threshold and the high change rate threshold stored in the database are as follows: Through historical data collection and analysis, determine the typical range of the signal change rate. For example, a low change rate means that the signal fluctuates less within a certain period of time, and a high change rate means that the signal changes greatly. According to the continuous pseudorange correction values, calculate the pseudorange change rate of each satellite at different time points, and conduct statistical analysis on it to obtain the distribution of the change rate. According to the actual observation data, automatically set the low change rate and high change rate thresholds by clustering or distribution analysis of the signal change rate. In this embodiment, the low change rate threshold can be set as the lowest percentile of the change rate, and the high change rate threshold can be set as a higher percentile.

[0065] The specific steps for obtaining the signal strength influence coefficient ω and the electric field strength influence coefficient ψ stored in the database are as follows: By measuring the signal quality, record the signal-to-noise ratio (SNR) of each satellite. Then, based on long-term data accumulation, calculate the influence degree of signal strength on pseudorange correction. The signal strength influence coefficient can be obtained through regression analysis of 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 an electric field strength sensor, and the relationship between the electric field strength and the pseudorange error is analyzed. According to the correlation analysis of historical data, the influence coefficient of the electric field strength on pseudorange correction, that is, the electric field strength influence coefficient, is obtained.

[0066] In this implementation scheme, by combining multiple environmental and signal quality parameters, dynamic filtering and correction are performed on the pseudorange time series data of each satellite, significantly improving the positioning accuracy and stability of the system in complex environments. Specifically, the rate of change analysis can accurately identify abnormal fluctuations in the pseudorange correction data. By analyzing the distribution of signal changes, the low change rate and high change rate thresholds are automatically adjusted, effectively screening out time periods with small or large signal changes, ensuring the reliability and accuracy of the signals. In addition, the influence coefficients of signal strength and electric field strength on pseudorange correction are introduced into the filtering model, further improving the system's adaptability to environmental changes. The influence coefficient of signal strength is determined through long-term data accumulation and regression analysis, which can accurately measure 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 can not only identify and eliminate noise in the signal in real time but also adapt to signal interference under different environmental conditions, improving the accuracy of pseudorange data. Finally, these optimizations ensure that the system can still provide high-precision positioning results in complex urban or mountainous environments, greatly improving the reliability and practicality of the system.

[0067] Specifically, the specific steps for establishing a virtual reference station are as follows: Obtain the estimated position information of the receiver (which can be deduced from the preliminary positioning result of satellite navigation), the position information of each reference station within the set area of the receiver, and analyze the baseline distance value between each reference station and the receiver; comprehensively analyze the position information of each reference station within the set area of the receiver and the baseline distance value between each reference station and the receiver to obtain the position information of the virtual reference station.

[0068] The specific steps for calculating the position information of the virtual reference station are as follows: Among them, (x′, y′, z′) is the position information of the virtual reference station, and (x u , y u , z u ) is the position information of the u-th reference station within the set area of the receiver, JxJu is the baseline distance value between the u-th reference station and the receiver, where u = 1, 2, 3, …, u0, and u0 is the number of reference stations.

[0069] The specific steps to obtain the pseudo-range time-series fusion set of each satellite with respect to the receiver are as follows: Obtain the pseudo-range differential correction value of each satellite and each reference station within the set area of the receiver at each time point; Based on the position information of each reference station within the set area of the receiver and the position information of the virtual reference station, analyze the baseline distance value between the virtual reference station and each reference station within the set area of the receiver; Obtain the site offset of each reference station within the set area of the receiver at each time point (i.e., the X, Y, and Z-axis differences between the estimated position information of the receiver and the position of each reference station), and comprehensively analyze it in combination with the pseudo-range differential correction value of each satellite and each reference station within the set area of the receiver at each time point, the baseline distance value between the virtual reference station and each reference station within the set area of the receiver, and the baseline distance value between each reference station and the receiver to obtain the pseudo-range virtual reference correction value of each satellite with respect to the receiver at several time points; Based on the pseudo-range filtered value and the pseudo-range virtual reference correction value of each satellite with respect to the receiver at several time points, analyze the pseudo-range fusion value of each satellite with respect to the receiver at several time points (i.e., the pseudo-range filtered value minus the virtual reference correction value).

[0070] Among them, the specific formula for calculating the pseudo-range virtual reference correction value of a certain satellite with respect to the receiver at several time points is as follows: Among them, XnX i is the pseudo-range virtual reference correction value of a certain satellite with respect to the receiver at the i-th time point, CfX ui is the pseudo-range differential correction value of a certain satellite and the u-th reference station within the set area of the receiver at the i-th time point, JxJ u is the baseline distance value between the u-th reference station and the receiver, XjJ u is the baseline distance value between the virtual reference station and the u-th reference station within the set area of the receiver, where i = 1, 2, 3, …, i0, i0 is the number of time points, and u = 1, 2, 3, …, u0, and u0 is the number of reference stations.

[0071] In this implementation scheme, through the establishment of a virtual reference station and pseudorange differential correction, the problem of sparse distribution of reference stations in a strip-shaped sparse single BeiDou CORS network is effectively solved, and the positioning accuracy and stability of the system are significantly improved. 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 stations and the receiver, and then construct a virtual reference station. The position information of the virtual reference station not only makes up for the deficiencies of the physical reference stations, but also can provide more accurate differential data to achieve high-precision positioning correction in areas with insufficient reference stations. In addition, through the combination of the position information of the receiver and the reference stations, the pseudorange correction amount, and the correction data of the virtual reference station, pseudorange differential correction can finely adjust the pseudorange data of each satellite, reducing the positioning error caused by sparse reference stations. By analyzing the difference between the pseudorange virtual reference correction value and the pseudorange filtered value, the system can generate the pseudorange fusion value of 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 in the case of uneven distribution of reference stations, providing strong support for efficient positioning in complex environments, especially suitable for the positioning requirements of sparse networks, and enhancing the reliability and practicability of the positioning system.

[0072] Specifically, the specific steps to obtain the position information of the receiver are as follows: Based on the preset adaptive weight rule, weight values are respectively assigned to the pseudorange fusion values of each satellite for each time point of the receiver (first, the satellite signal strength value, satellite azimuth value, satellite elevation value, distance between the receiver and the satellite, environmental temperature value, environmental humidity value, light intensity value, thermal infrared value, total electron content value of the ionosphere, and electric field strength value are de-unified and weighted analyzed to obtain the weight value), and weighted processing is performed to obtain the pseudorange time-series weighted fusion value of each satellite for the receiver; Based on the preset positioning algorithm, the pseudorange time-series weighted fusion value of each satellite for the receiver is solved to obtain the position information of the receiver.

[0073] Among them, in this embodiment, the positioning algorithm refers to the least squares method, and the specific steps for solving the pseudorange time-series weighted fusion value of each satellite for the receiver are as follows:

[0074] Initialize the position of the receiver according to the preliminary position information of the receiver (which can be obtained through rough satellite positioning or known reference points).

[0075] For each satellite, calculate its pseudorange residual, that is, the difference between the distance between the receiver position and the satellite position and the measured pseudorange.

[0076] Establish the equation: The goal of the least squares method is to minimize the pseudorange residuals. Assume we have N satellites, and each satellite provides a pseudorange data (after weighting and correction) at different time points. For each satellite and time point, analyze the pseudorange residuals.

[0077] Construct the least squares optimization objective: Minimize the sum of the squares of the pseudorange residuals of all satellites.

[0078] Use the least squares method to minimize the objective function (the sum of the squares of the pseudorange residuals) by continuously adjusting the receiver's position information (longitude, latitude, and altitude).

[0079] Update the receiver's position in each iteration to minimize the pseudorange residuals as much as possible.

[0080] After each iteration, check the value of the objective function. If the change in the objective function is less than a preset threshold, it means the solution has converged, and the accurate position information of the receiver is obtained.

[0081] Once convergence occurs, output the final position information of the receiver, including longitude, latitude, and altitude. This position is optimized by the least squares method.

[0082] In this implementation scheme, by introducing an adaptive weight rule and a least squares positioning algorithm, the positioning accuracy and stability of the receiver are significantly improved. First, through the unitless processing and weighted analysis of satellite signal strength, azimuth, elevation, distance, and environmental factors (such as temperature, humidity, light, etc.), the system can dynamically assign different weights according to the quality of each satellite and environmental conditions. This dynamic weighting mechanism can ensure that more reliable satellite data plays a greater role in the final positioning. Especially in complex or dynamic environments where the signal quality may change, this mechanism can adaptively adjust the weights to reduce the impact of unreliable data on the positioning result. Second, by using the least squares method for positioning calculation, the system can accurately minimize the pseudorange residuals and optimize the three-dimensional position (longitude, latitude, and altitude) of the receiver through multiple iterations. The least squares method can accurately estimate the accurate position of the receiver by optimizing the sum of the squares of the pseudorange residuals, ensuring accuracy and stability in complex environments. This method combined with the adaptive weight mechanism enables the system to still converge to an accurate positioning result through the optimization process even when satellite signals are unstable or affected by multipath effects.

[0083] Please refer to Figure 3, an embodiment of the present invention provides a technical solution: a multipath suppression positioning system for a strip-shaped sparse single Beidou CORS network, including: a data acquisition unit for acquiring the initial pseudorange time series data of each satellite with respect to the receiver, the reception time series data, and the area status data within the area set by the receiver; a pseudorange correction unit for performing correction analysis on the initial pseudorange time series data based on the reception time series data of each satellite with respect to the receiver and the area status data within the area set by the receiver to obtain a pseudorange time series correction set of each satellite with respect to the receiver; a pseudorange filtering unit for filtering the pseudorange time series correction set of each satellite with respect to the receiver based on a preset filtering processing rule to obtain a pseudorange time series filtering set of each satellite with respect to the receiver; a pseudorange fusion unit for establishing a virtual reference station and performing fusion analysis on the pseudorange time series filtering set of each satellite with respect to the receiver to obtain a pseudorange time series fusion set of each satellite with respect to the receiver; a positioning solution unit for performing positioning solution analysis on the pseudorange time series fusion set of each satellite with respect to the receiver based on a preset adaptive weight rule to obtain the position information of the receiver.

[0084] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0085] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. A multipath suppression positioning method for a sparse single BeiDou CORS network, characterized in that: It includes the following steps: Obtain the initial pseudorange time series data of each satellite with respect to the receiver, the reception time series data, and the regional status data within the area set by the receiver; Based on the reception time series data of each satellite with respect to the receiver and the regional status data within the area set by the receiver, perform correction analysis on the initial pseudorange time series data to obtain the pseudorange time series correction set of each satellite with respect to the receiver; Based on the preset filtering processing rules, perform filtering processing on the pseudorange time series correction set of each satellite with respect to the receiver to obtain the pseudorange time series filtering set of each satellite with respect to the receiver; Establish a virtual reference station, and perform fusion analysis on the pseudorange time series filtering set of each satellite with respect to the receiver to obtain the pseudorange time series fusion set of each satellite with respect to the receiver; Based on the preset adaptive weight rules, perform positioning solution analysis on the pseudorange time series fusion set of each satellite with respect to the receiver to obtain the position information of the receiver.

2. The multipath suppression positioning method for the strip-shaped sparse single Beidou CORS network according to claim 1, characterized in that The initial pseudorange time series data includes the initial pseudorange values at several time points, the reception time series data includes the signal arrival time difference, satellite signal strength value, satellite azimuth value, and satellite elevation value 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 environmental temperature value, environmental humidity value, light intensity value, thermal infrared value, total electron content value of the ionosphere, and electric field strength value at several time points, the pseudorange time series correction set includes the pseudorange correction values at several time points, the pseudorange time series filtering set includes the pseudorange filtering values at several time points, and the pseudorange time series fusion set includes the pseudorange fusion values at several time points.

3. The multipath suppression positioning method for the strip-shaped sparse single Beidou CORS network according to claim 2, wherein, The specific steps to obtain the initial pseudorange time series correction set of the receiver for each satellite are as follows: Based on the preset scoring rules, perform type scoring on the type information of the reflecting surface within the area set by the receiver to obtain the reflection type score of the reflecting surface within the area set by the receiver; Obtain the historical main reflection azimuth within the area set by the receiver, and respectively combine the reflection type score, reflection angle value, light intensity values at several time points, environmental humidity value, thermal infrared value of the reflecting surface within the area set by the receiver, and the satellite azimuth values of each satellite with respect to the receiver at several time points for comprehensive analysis to obtain the path error index of each satellite with respect to the receiver at several time points; Read the environmental temperature value, total electron content value of the ionosphere, and light intensity value at several time points within the area set by the receiver, and respectively combine the satellite elevation values of each satellite with respect to the receiver at several time points for comprehensive analysis to obtain the environmental induced error index at several time points within the area set by the receiver; Perform comprehensive analysis on the signal arrival time difference, path error index of each satellite with respect to the receiver at several time points, and the environmental induced error index at several time points within the area set by the receiver to obtain the error correction amount of each satellite with respect to the receiver at several time points; Analyze the pseudorange correction values of each satellite for the receiver based on the error correction amounts and initial pseudorange values of each satellite for several time points of the receiver.

4. The multipath suppression positioning method for the strip-shaped sparse single Beidou CORS network according to claim 3, wherein The specific formula for calculating the error correction amount of a certain satellite for each time point of the receiver is as follows: Among them, WxZ i , XsC i , LwC i are, in sequence, the error correction amount, the signal arrival time difference, and the path error index of a certain satellite for the receiver at the i-th time point. HyW i is the environmental induction error index at the i-th time point within the set area of the receiver. δ1 and δ2 are, in sequence, the path error influence coefficient and the environmental induction error influence coefficient stored in the database. i = 1, 2, 3,..., i0, where i0 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, wherein The specific steps for obtaining the pseudorange time series filtering set of each satellite for the receiver are as follows: Read the pseudorange correction values of each satellite for each time point of the receiver, and perform variation analysis respectively to obtain the pseudorange correction variation rate of each satellite for each time point of the receiver; Input the satellite signal strength value, pseudorange correction value, pseudorange correction variation rate of each satellite for each time point of the receiver, and the electric field strength value of each time point within the set area of the receiver into a preset filtering processing model for filtering analysis to obtain the pseudorange filtered value of each satellite for 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 specific filtering processing model is as follows: Among them, WjL i , WjX i , XzB i , XhQ i are respectively the pseudorange filtering value, pseudorange correction value, pseudorange correction change rate, and satellite signal strength value of a certain satellite for the receiver at the \(i\)-th time point. WjX i-2 , WjX i-1 are respectively the pseudorange correction values of a certain satellite for the receiver at the \((i - 2)\)-th and \((i - 1)\)-th time points. DbH, ω, GbH, ψ are respectively the low change rate threshold, signal strength influence coefficient, high change rate threshold, and electric field strength influence coefficient stored in the database. XzB i-1 is the pseudorange correction change rate of a certain satellite for the receiver at the \((i - 1)\)-th time point. DcQ i is the electric field strength value at the \(i\)-th time point within the set area of the receiver, where \(i = 1, 2, 3, \cdots, i_0\), and \(i_0\) 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 for establishing a virtual reference station are as follows: Obtain the estimated position information of the receiver and the position information of each reference station within the set area of the receiver, and analyze the baseline distance value between each reference station and the receiver; Comprehensively analyze the position information of each reference station within the set area of the receiver and the baseline distance value between each reference station and the receiver to obtain the position information of the virtual reference station.

8. The multipath suppression positioning method for the strip-shaped sparse single Beidou CORS network according to claim 7, wherein, The specific steps for obtaining the pseudorange time series fusion set of each satellite for the receiver are as follows: Obtain the pseudorange differential correction values of each satellite and each time point of each reference station within the set area of the receiver; Based on the position information of each reference station within the set area of the receiver and the position information of the virtual reference station, analyze the baseline distance value between the virtual reference station and each reference station within the set area of the receiver; Obtain the site offset of each time point of each reference station within the set area of the receiver, and comprehensively analyze it respectively in combination with the pseudorange differential correction values of each satellite and each time point of each reference station within the set area of the receiver, the baseline distance value between the virtual reference station and each reference station within 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 values of each satellite for several time points of the receiver; Based on the pseudorange filtered values and pseudorange virtual reference correction values of each satellite for several time points of the receiver, analyze the pseudorange fusion values of each satellite for several time points of the receiver.

9. The multipath suppression positioning method for a strip-shaped sparse single Beidou CORS network according to claim 1, wherein The specific steps for obtaining the position information of the receiver are as follows: Based on a preset adaptive weight rule, assign weight values to the pseudorange fusion values of each satellite for each time point of the receiver respectively, and perform weighted processing to obtain the pseudorange time series weighted fusion value of each satellite for the receiver; Based on a preset positioning algorithm, perform a solution process on the pseudorange time series weighted fusion value of each satellite for the receiver to obtain the position information of the receiver.

10. A multipath suppression positioning system for a strip-shaped sparse single Beidou CORS network, which applies the multipath suppression positioning method for a strip-shaped sparse single Beidou CORS network according to any one of claims 1-9, characterized in that Including: A data acquisition unit for acquiring the initial pseudorange time series data, reception time series data of each satellite for the receiver, and the regional status data within the set area of the receiver; A pseudorange correction unit, which is used to perform correction analysis on the initial pseudorange time series data based on the reception time series data of each satellite with respect to the receiver and the regional status data within the set area of the receiver, so as to obtain the pseudorange time series correction set of each satellite with respect to the receiver; A pseudorange filtering unit, which is used to perform filtering processing on the pseudorange time series correction set of each satellite with respect to the receiver based on the preset filtering processing rules, so as to obtain the pseudorange time series filtering set of each satellite with respect to the receiver; A pseudorange fusion unit, which is used to establish a virtual reference station and perform fusion analysis on the pseudorange time series filtering set of each satellite with respect to the receiver, so as to obtain the pseudorange time series fusion set of each satellite with respect to the receiver; A positioning solution unit, which is used to perform positioning solution analysis on the pseudorange time series fusion set of each satellite with respect to the receiver based on the preset adaptive weight rules, so as to obtain the position information of the receiver.

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