Intelligent navigation positioning method and device based on multimode satellite communication

Abstract

The invention provides an intelligent navigation positioning method and device based on multimode satellite communication. The method provided by the invention comprises the following steps: determining a target navigation area; determining N candidate satellites according to the carrier phase measurement precision of the target navigation area; initial positions of N candidate satellites are determined, and the N candidate satellites move to the corresponding initial positions; calculating a first geometric accuracy factor based on the initial position; simultaneously receiving communication data of N candidate satellites; calculating the communication data coincidence degree of the N candidate satellites, and clustering the N candidate satellites based on the coincidence degree; selecting representative satellites from the clustered categories to obtain M target candidate satellites; selecting an optimal target satellite from the M target candidate satellites; and performing positioning calculation based on the communication data corresponding to the target satellite to realize navigation positioning. The intelligent navigation and positioning method and device based on multimode satellite communication are used for completing high-quality and reliable satellite navigation and positioning.

Description

Technical Field

[0001] The present application relates to the field of satellite communication technology, and in particular to an intelligent navigation and positioning method and device based on multi-mode satellite communication. Background Art

[0002] With the continuous improvement of global satellite navigation systems, navigation and positioning technologies have been widely used in fields such as autonomous driving, drones, smart logistics, precision agriculture, and emergency rescue. However, problems such as signal obstruction, multipath effects, and ionospheric interference in complex environments (such as urban canyons, forest cover, and inclement weather) continue to pose severe challenges to the accuracy and stability of navigation and positioning.

[0003] Existing navigation systems primarily rely on pseudoranges or single satellite signals for positioning. These methods can achieve high positioning accuracy in open environments with good signal strength. However, in complex environments, such as urban areas with tall buildings, dense tree cover, or areas with severe electromagnetic interference, single-signal positioning methods often encounter problems such as insufficient visible satellites and poor signal quality due to satellite signal attenuation, multipath effects, and interference. This leads to unstable positioning results, reduced accuracy, and even in extreme cases, inability to complete positioning. Therefore, a method that can ensure accurate positioning even in complex environments is urgently needed. Summary of the Invention

[0004] In view of this, the present application provides an intelligent navigation and positioning method and device based on multi-mode satellite communication to achieve high-quality and reliable satellite navigation and positioning.

[0005] Specifically, this application is implemented through the following technical solutions:

[0006] In a first aspect, the present application provides an intelligent navigation and positioning method based on multi-mode satellite communication, the method comprising:

[0007] Determine the target navigation area;

[0008] Determine N candidate satellites according to the carrier phase measurement accuracy of the target navigation area, where N is a positive integer greater than 2, and the candidate satellites include at least GPS satellites and BDS satellites;

[0009] Determining initial positions of the N candidate satellites according to the real-time object distribution density of the target navigation area, and moving the N candidate satellites to corresponding initial positions;

[0010] calculating a first dilution of precision based on the initial position;

[0011] Simultaneously receiving communication data of the N candidate satellites;

[0012] Calculating the communication data overlap of the N candidate satellites, and clustering the N candidate satellites based on the overlap;

[0013] Calculating a second geometric dilution of precision after clustering, comparing whether a difference between the second geometric dilution of precision and the first geometric dilution of precision is greater than a preset threshold, and if so, adjusting the number of clusters, and returning to the step of clustering the N candidate satellites based on the overlap;

[0014] Select representative satellites from each clustered category to obtain M target candidate satellites, where M is a positive integer less than N and greater than 2;

[0015] Selecting an optimal target satellite from the M target candidate satellites according to a satellite selection method, wherein the target satellite includes at least a GPS satellite and a BDS satellite;

[0016] Positioning is performed based on the communication data corresponding to the target satellite to achieve navigation positioning.

[0017] The second aspect of the present application provides an intelligent navigation and positioning device based on multi-mode satellite communication, the device comprising a determination module, a calculation module, a processing module and a screening module; wherein,

[0018] The determining module is used to determine the target navigation area;

[0019] The determining module is further configured to determine N candidate satellites according to the carrier phase measurement accuracy of the target navigation area, where N is a positive integer greater than 2, and the candidate satellites include at least GPS satellites and BDS satellites;

[0020] The determining module is further configured to determine the initial positions of the N candidate satellites according to the real-time object distribution density of the target navigation area, and the N candidate satellites move to the corresponding initial positions;

[0021] The calculation module is configured to calculate a first geometric dilution of precision based on the initial position;

[0022] The processing module is configured to simultaneously receive communication data of the N candidate satellites;

[0023] The calculation module is further configured to calculate the communication data overlap of the N candidate satellites, and cluster the N candidate satellites based on the overlap;

[0024] The processing module is further configured to calculate a second geometric dilution of precision after clustering, compare whether a difference between the second geometric dilution of precision and the first geometric dilution of precision is greater than a preset threshold, and if so, adjust the number of clusters and return to the step of clustering the N candidate satellites based on the overlap;

[0025] The screening module is used to select representative satellites from each clustered category to obtain M target candidate satellites, where M is a positive integer less than N and greater than 2;

[0026] The screening module is further configured to select an optimal target satellite from the M target candidate satellites according to a satellite selection method, wherein the target satellite includes at least a GPS satellite and a BDS satellite;

[0027] The processing module is further used to perform positioning calculation based on the communication data corresponding to the target satellite to achieve navigation positioning.

[0028] The intelligent navigation and positioning method and device based on multi-mode satellite communication provided by the present application provide an accurate positioning method based on GPS and BDS multi-mode satellites. On the basis of multi-mode satellite communication, a multi-level and multi-type satellite screening method is provided to realize an accurate satellite screening method adapted to the navigation and positioning environment. In terms of multi-mode satellite communication based on GPS and BDS satellites, the number of visible stars can be increased by the fusion of multi-mode signals, so that sufficient satellite observation data can still be obtained in a complex signal environment, reducing the positioning error caused by weak or failed signals of a single system. In the multiple types of satellite screening methods, the first level uses the positioning accuracy of the target area to select the range of the first satellite; the second level uses the data overlap to perform clustering to further reduce the number of satellites; the third level performs a satellite algorithm based on the star selection algorithm. After three levels of screening, the minimum number of satellites is achieved while the accuracy of satellite communication is optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flowchart of Example 1 of the intelligent navigation and positioning method based on multi-mode satellite communication provided by this application;

[0030] Figure 2 This is a hardware structure diagram of the intelligent navigation and positioning device based on multi-mode satellite communication in which the intelligent navigation and positioning device based on multi-mode satellite communication of this application is located;

[0031] Figure 3 This is a structural diagram of Example 1 of the intelligent navigation and positioning device based on multi-mode satellite communication provided in this application. DETAILED DESCRIPTION

[0032] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this application.

[0033] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a," "the," and "the" used in this application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0034] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0035] Specific embodiments are given below to introduce the technical solutions of the present application in detail.

[0036] Figure 1 This is a flow chart of the first embodiment of the intelligent navigation and positioning method based on multi-mode satellite communication provided by this application. Figure 1 The method provided in this embodiment may include:

[0037] S101: Determine a target navigation area.

[0038] Before introducing the method provided by this embodiment, the following briefly describes its application background: Before using satellites for navigation and positioning, satellites are in constant communication while in orbit, transmitting signals to the ground, and receivers receive this constantly transmitted data. However, traditional systems generally employ fixed satellite selection and positioning strategies. Even when satellite signals are constantly updated, the combination or position distribution of candidate satellites is rarely adjusted in real time. This often results in difficulties in real-time optimization of the satellite geometry in complex environments due to signal attenuation, multipath effects, and insufficient visible satellites, thus affecting positioning accuracy and stability. This embodiment builds on this foundation by utilizing continuously operating satellites to dynamically adjust the positions of required satellites, dynamically determine which satellites' data to select for communication, and determine target satellites through three rounds of screening. This data from the target satellites is then used to obtain more effective satellite signals for high-precision positioning.

[0039] The method provided in this embodiment is introduced below:

[0040] Specifically, the target navigation area refers to the specific geographical range that the navigation system needs to serve and focus on, representing the area that the system needs to locate, track and navigate.

[0041] In specific implementations, the target navigation area may include predefined geographic boundaries, as well as the signal environment, terrain characteristics, and application requirements within the area. For example, in one embodiment, the target navigation area may be a city, forest, mountainous area, etc. Objects requiring communication within the target navigation area are referred to as real-time objects.

[0042] S102: Determine N candidate satellites according to the carrier phase measurement accuracy of the target navigation area, where N is a positive integer greater than 2, and the candidate satellites include at least GPS satellites and BDS satellites.

[0043] Specifically, carrier phase measurement determines the position by measuring the phase difference between the satellite signal and a reference signal generated locally by the receiver; carrier phase measurement accuracy is used to characterize the accuracy of this phase difference measurement.

[0044] It is understood that the carrier phase measurement accuracy can be analyzed based on various factors such as the distance between the satellite and the receiver, the error in the signal propagation path, etc. For example, in one embodiment, the carrier phase measurement accuracy can be represented by the inverse of the distance between the satellite and the receiver.

[0045] Furthermore, candidate satellites are satellites that meet the usage requirements and are screened out through carrier phase measurement accuracy. Positioning can be achieved through candidate satellites. Candidate satellites already include two types of satellites, GPS and BDS. A large number of satellites can achieve accurate positioning, but too many satellites will lead to waste of resources. Therefore, screening is still required after completing the first level of screening.

[0046] A specific embodiment is given below to introduce the process of determining candidate satellites in detail:

[0047] (1) Acquire carrier phase observation data within the target navigation area.

[0048] Specifically, the carrier phase observation data is the carrier phase information in the satellite signal received by the satellite navigation receiver, that is, the phase difference between the satellite signal and the reference signal generated by the local oscillator of the receiver.

[0049] In specific implementation, the wave phase observation data can be obtained by analyzing the data received by the receiver.

[0050] (2) differentially processing the carrier phase observation data, and calculating the target residual between the differentially processed data and a pre-constructed satellite position theoretical model.

[0051] Specifically, the differential processing may be performed using a double-difference technique or a triple-difference technique.

[0052] In specific implementation, the observation equation corresponding to the carrier phase observation data is:

[0053] φ=ρ+c(δt-δT)+λN+∈;

[0054] After double difference technology difference processing, it becomes:

[0055] ΔΔφ=ΔΔρ+λΔΔN+ΔΔ∈;

[0056] where φ is the received carrier phase measurement; ρ is the geometric distance between the satellite and the receiver; c is the speed of light; δt and δT are the clock differences between the receiver and the satellite, respectively; λ is the signal wavelength; N is the ambiguity term; ∈ is the observation noise and other errors, and ΔΔ represents the differential observation value.

[0057] It can be understood that differential processing can reduce the impact of satellite clock error, receiver clock error and some atmospheric delay.

[0058] Furthermore, the observation equation is determined as a pre-built satellite position theoretical model through the least square method or Kalman filtering method, and the differentially processed data is matched with it to obtain the target residual by difference.

[0059] (3) Correcting the error distribution within the target navigation area based on dual-frequency observation.

[0060] In a specific implementation, two signals of different frequencies are received and processed simultaneously to correct for error distributions such as ionospheric delay. For example, in one embodiment, the difference between the signals corresponding to frequency A and frequency B is compared and the difference is determined as the error distribution.

[0061] (4) Weighted calculation of the root mean square of the target residual and the error distribution to obtain the carrier phase measurement accuracy.

[0062] In specific implementation, the root mean square of the target residual is calculated, and the root mean square and the error distribution are weighted to obtain the average value to obtain the final carrier phase measurement accuracy.

[0063] It should be noted that, in addition to weighted averaging, Kalman filtering can also be used to process the root mean square and error distribution to obtain the carrier phase measurement accuracy of the target navigation area.

[0064] (5) A satellite whose carrier phase measurement accuracy is greater than a preset measurement accuracy value is determined as the candidate satellite.

[0065] Specifically, the specific value of the preset measurement accuracy value is set according to actual needs and is not limited in this embodiment.

[0066] In specific implementation, the preset measurement accuracy value can be set at 80% to ensure that the number of candidate satellites meets the positioning requirements without being redundant.

[0067] Furthermore, satellites whose carrier phase measurement accuracy exceeds a preset measurement accuracy value are determined as candidate satellites.

[0068] It should be noted that it is necessary to ensure that the candidate satellites include both GPS satellites and BDS satellites. If it is found that the candidate satellites are only GPS satellites or only BDS satellites, it is necessary to readjust the preset measurement accuracy value and re-determine the candidate satellites.

[0069] The intelligent navigation and positioning method based on multi-mode satellite communication provided in this embodiment obtains carrier phase observation data within the target navigation area, uses differential processing technology to compare with a pre-built satellite position theoretical model, calculates the target residual, effectively filters out noise and errors in the measurement, improves data accuracy, and only selects satellites with high carrier phase measurement accuracy as candidate satellites, ensuring the reliability and accuracy of the subsequent navigation and positioning process. Especially in complex environments, it can significantly improve the stability and accuracy of positioning.

[0070] S103: Determine the initial positions of the N candidate satellites according to the real-time object distribution density of the target navigation area, and move the N candidate satellites to the corresponding initial positions.

[0071] Specifically, the real-time object can be any entity that needs to be located, such as personnel, vehicles, and equipment.

[0072] Furthermore, the real-time object distribution density is the ratio of the number of real-time objects to the area of ​​the target navigation area, which is used to describe the density of objects in the area.

[0073] Furthermore, the initial positions are the starting coordinates in space assigned to the candidate satellites. These positions are calculated based on the real-time object distribution density in the target navigation area. The purpose is to ensure that the candidate satellites are evenly distributed in the initial state to meet the needs of real-time object communication.

[0074] It is understandable that placing more candidate satellites in areas with high real-time object distribution density and placing fewer candidate satellites in areas with low real-time object distribution density can optimize the satellite layout and improve the navigation and positioning effect.

[0075] In specific implementation, sensor networks and surveillance cameras can be used to count the number and specific locations of people in the area in real time. Based on the statistical number and the area of ​​the target navigation area, the real-time object distribution density in the target navigation area can be calculated, and an initial position can be assigned to each candidate satellite to maximize the satellite's coverage of the target navigation area. At the same time, places with a high real-time object distribution density also have a large number of corresponding candidate satellites.

[0076] A specific embodiment is given below to introduce the process of determining the initial position in detail:

[0077] (1) Counting the number and positions of real-time objects in the target navigation area, and calculating the distribution density of real-time objects in the target navigation area.

[0078] Specifically, sensors, cameras, GPS trackers and other devices can be used to record the location information of all movable objects (such as vehicles and pedestrians) in the target navigation area in real time, and the recorded data can be counted to obtain the total number of real-time objects and their specific locations.

[0079] In specific implementation, for example, in one embodiment, the target navigation area is divided into several small areas, the number of real-time objects in each small area is counted, and then divided by the area of ​​the area to obtain the real-time object distribution density of the area, and the density values ​​of all small areas are averaged or weighted averaged to obtain the real-time object distribution density of the entire target navigation area.

[0080] (2) Determine the corresponding satellite communication range based on the satellite attributes of each candidate satellite.

[0081] Before determining the initial position, the satellite is already in the sky and capable of communication, meaning it already has an operational position. In practice, the communication coverage range of each satellite on the ground can be calculated based on the candidate satellite's orbital parameters, signal transmission power, and other properties, using mathematical models or data provided by the satellite manufacturer. The satellite's communication range, in this case, refers to the satellite's operational position during communication operations, prior to navigation and positioning.

[0082] (3) Calculating the distribution density of the real-time objects contained in the satellite communication range, and calculating the target density ratio corresponding to each satellite communication range.

[0083] Specifically, for each candidate satellite, the intersection area between its communication range and the target navigation area can be determined, and this intersection can be regarded as the satellite communication range.

[0084] Furthermore, the number of real-time objects within the satellite communication range is counted and divided by the area of ​​the intersection to obtain the real-time object distribution density within the satellite communication range. For example, in one embodiment, the satellite communication range area of ​​satellite a is S1, and there are B real-time objects within the satellite communication range. The real-time object distribution density is B / S1.

[0085] Furthermore, the target density ratio is the ratio of the satellite communication range to the total target navigation area. For example, in one embodiment, the area of ​​the target navigation area is the ratio of the target navigation area corresponding to satellite S2a to the target navigation area corresponding to satellite a, which is B / S1 / S2. The target density ratio is B / S1 / S2.

[0086] (4) Calculate the product of the target density ratio and the total number of the candidate satellites to obtain the number of the candidate satellites within the communication range of each satellite.

[0087] In specific implementation, for example, in one embodiment, the target density ratio of the satellite communication area corresponding to satellite a is B / S1 S2, and the total number of candidate satellites is N. The number of candidate satellites in the satellite communication area can be determined to be BN / S1 S2.

[0088] (5) Within the communication range of each satellite, determine the initial position of the candidate satellite according to the number of the candidate satellites.

[0089] In a specific implementation, for example, in one embodiment, a corresponding number of candidate satellites are allocated within the satellite communication range, ensuring that all adjacent satellites are spaced at equal distances to complete the allocation of the entire satellite communication range. In this case, the location of each candidate satellite is determined as the initial location of that candidate satellite. For another example, in another embodiment, signal interference within the satellite communication range can be determined. Candidate satellites are not placed in areas with signal interference, while candidate satellites are evenly distributed elsewhere. In this case, the location of each candidate satellite is determined as the initial location of that candidate satellite. After determining the initial location, the satellite is moved from its operating position to the corresponding initial location.

[0090] The intelligent navigation and positioning method based on multi-mode satellite communication provided in this embodiment can dynamically adjust the satellite layout to ensure the efficient use of satellite resources, especially increase satellite coverage in areas with dense objects, thereby improving the accuracy and reliability of navigation and positioning, and significantly improving the positioning effect in complex environments. Before satellite screening, the satellite position can be first adjusted to the optimal initial position so that the density of satellite distribution is adapted to the density of real-time objects, thereby achieving adaptive matching of the satellite distribution and the density of real-time objects that need to communicate.

[0091] S104: Calculate a first geometric dilution of precision based on the initial position.

[0092] Specifically, the first geometric precision factor is an indicator that measures the impact of the geometric distribution of candidate satellites at their initial positions on positioning accuracy. The first geometric precision factor combines factors such as the spatial distribution of satellites relative to user receivers and the directionality of satellite signals. It is a comprehensive accuracy indicator.

[0093] It should be noted that the smaller the first geometric dilution of precision value is, the better the geometric distribution of satellites is and the higher the positioning accuracy is.

[0094] A specific embodiment is given below to introduce the calculation process of the first geometric dilution of precision in detail:

[0095] (1) Obtain the first carrier phase observation data corresponding to each candidate satellite and generate a first target weight inverse matrix.

[0096] Specifically, since the candidate satellites continuously transmit data and are received by the receiver, when calculating the first geometric dilution of precision, the signals transmitted by each candidate satellite at the initial position can be obtained to obtain the corresponding first carrier phase observation data.

[0097] In specific implementation, the signal of the candidate satellite is obtained, the corresponding carrier phase, signal strength and other information are determined, and the first carrier phase observation data is constructed.

[0098] Furthermore, the first target weight inverse matrix reflects the weights of different satellite signals in the positioning solution. The least squares method can be used to construct the observation equation to calculate the first target weight inverse matrix corresponding to the first carrier phase.

[0099] In specific implementation, for example, in one embodiment, the constructed first target weight inverse matrix is ​​as follows:

[0100]

[0101] Among them, Q is the first target weight inverse matrix, H is the pseudorange, and n represents the first carrier phase observation data of each candidate satellite.

[0102] (2) Calculate a first measurement error when receiving the first carrier phase observation data, calculate the product of the first measurement error and the first target weight inverse matrix, and determine it as a first intermediate value.

[0103] In a specific implementation, the received first carrier phase observation data is subtracted from a theoretical value obtained by a pre-built theoretical model for characterizing the first carrier phase observation data and the corresponding satellite data. The difference is the first measurement error.

[0104] Furthermore, the first intermediate value can be expressed by the following formula:

[0105] ε1×Q;

[0106] Among them, ε1 is the first measurement error, and Q is the first target weight inverse matrix.

[0107] (3) Determine the first geometric precision dilution based on the first intermediate values ​​corresponding to each of the initial positions.

[0108] Specifically, all candidate satellites are combined to obtain a first intermediate value, and the first geometric dilution of precision is calculated based on the following formula:

[0109]

[0110] Among them, G1 is the first geometric precision dilution, ε1 is the first measurement error, H is the pseudorange, and n represents the first carrier phase observation data of each candidate satellite.

[0111] The intelligent navigation and positioning method based on multi-mode satellite communication provided in this embodiment generates a first inverse target weight matrix and then calculates a first geometric dilution of precision based on the first intermediate value corresponding to the initial position of each candidate satellite. This value accurately reflects the impact of the satellite geometric distribution on positioning accuracy. When the satellites are clustered later, the value of the geometric dilution of precision can be kept small, thus ensuring the rationality of the distribution while reducing the number of satellites. In other words, the satellite layout can be effectively evaluated and optimized during the satellite navigation and positioning process, thereby improving positioning accuracy and reliability.

[0112] S105. Receive communication data of the N candidate satellites simultaneously.

[0113] Specifically, a receiver may be used to receive communication data from all candidate satellites.

[0114] It should be noted that the communication data of each candidate satellite can be pre-processed to remove interference noise.

[0115] A specific embodiment is given below to introduce in detail the process of simultaneously receiving communication data from different candidate satellites:

[0116] (1) The satellite signals of the N candidate satellites are captured simultaneously by a multi-channel receiver, and the acquisition timestamp corresponding to each satellite signal is obtained.

[0117] Specifically, the multi-channel receiver is used to receive multiple satellite signals simultaneously, and can receive signals from different satellites at the same time.

[0118] In specific implementation, the multi-channel receiver records the acquisition timestamp of each satellite signal while capturing the signal to ensure that the signal's time information is accurate.

[0119] It should be noted that the multi-channel receiver not only captures the signal but also records the coordinate information represented by each satellite signal.

[0120] (2) Demodulate the satellite signal to generate a data frame in a standard format.

[0121] Specifically, the satellite signal received by each channel is demodulated to extract the satellite navigation information, and the extracted navigation information is encapsulated into a data frame according to a predefined standard format.

[0122] In a specific implementation, for example, in one embodiment, the satellite signal is demodulated and processed to generate data frames, each of which contains corresponding timestamp information and a position identifier of each detected object in a standard coordinate system.

[0123] (3) Using the acquisition timestamp as a reference, the data frames corresponding to the same moment are merged to obtain the communication data.

[0124] In specific implementation, the data frames are sorted and aligned according to the timestamps to ensure that the data of all satellites are consistent in time. At the same time, all data frames are aligned according to the position coordinates of the standard coordinate system to ensure that they remain consistent in spatial position.

[0125] Furthermore, the data frames with aligned timestamps and coordinate standards are fused to obtain the merged comprehensive data.

[0126] In a specific implementation, for example, in one embodiment, satellite A has a timestamp of T1 = 12:00:01.100, along with its location coordinates (X1, Y1, Z1); satellite B has a timestamp of T2 = 12:00:01.101, along with its coordinates (X2, Y2, Z2); and satellite C has a timestamp of T3 = 12:00:01.100, along with its coordinates (X3, Y3, Z3). After conversion into data frames, the data frame of satellite B, which has a slightly different timestamp, is aligned within a time window, and satellite B is assigned to the same time as satellites A and C. Then, based on the correspondence between each coordinate system and the standard coordinate system {A, B, C}, (X1, Y1, Z1), (X2, Y2, Z2), and (X3, Y3, Z3) are unified, and the final communication data is obtained after fusion.

[0127] The intelligent navigation and positioning method based on multi-mode satellite communication provided in this embodiment can significantly improve the efficiency of signal reception by simultaneously capturing signals of multiple candidate satellites through a multi-channel receiver, obtain the acquisition timestamp of each satellite signal, ensure the time synchronization of the signal, demodulate these satellite signals to generate data frames in a standard format, and facilitate subsequent processing. Based on the acquisition timestamp, the data frames received from multiple satellites at the same time are accurately fused while ensuring the consistency of coordinate standards, thereby obtaining complete and accurate communication data, effectively solving the problems of time asynchrony and inconsistent data formats of multi-source satellite signals, providing reliable and efficient data support for subsequent navigation and positioning solutions, and significantly improving the accuracy and real-time performance of satellite navigation and positioning.

[0128] S106: Calculate the communication data overlap of the N candidate satellites, and cluster the N candidate satellites based on the overlap.

[0129] Specifically, the communication data overlap is an indicator used to measure the similarity or consistency between the communication data transmitted by multiple candidate satellites.

[0130] It should be noted that when calculating the communication data overlap, it is necessary to target the communication data with the same communication content, sent at the same time, and received at the same time, to ensure that the communication data overlap can accurately characterize the relationship between the candidate satellites and avoid being affected by other interference.

[0131] In specific implementation, the similarity of semantic information and numerical information in different candidate satellite communication data can be compared, and the overlap of corresponding communication data can be determined from the semantics and numbers themselves.

[0132] A specific example is given below to introduce the clustering process in detail:

[0133] (1) Extracting the overlap between the semantic content and the numerical content of the communication data of each candidate satellite within the same time window, and calculating the comprehensive overlap.

[0134] In specific implementation, the communication data of the candidate satellites can be aligned according to the time window, and the semantic content and numerical content (such as the satellite's position coordinates, speed value, etc.) can be extracted from the communication data of each subsequent candidate satellite.

[0135] Furthermore, for semantic content, a text similarity algorithm can be used to calculate the semantic overlap between different candidate satellite communication data. For numerical content, a distance metric (such as Euclidean distance, Manhattan distance, etc.) can be used to calculate numerical overlap. The smaller the distance, the closer the numerical content and the higher the overlap.

[0136] Furthermore, the semantic overlap and numerical overlap are weighted to obtain the comprehensive overlap.

[0137] (2) The candidate satellites whose comprehensive overlap is greater than a preset overlap threshold are determined as an initial satellite combination to complete clustering; each initial satellite combination includes a different number of candidate satellites.

[0138] Specifically, the specific value of the preset overlap threshold is determined according to actual needs and is not limited in this embodiment. For example, in one embodiment, the preset overlap threshold can be set to 90%.

[0139] Furthermore, when the comprehensive overlap of two or more candidate satellites is greater than a preset overlap threshold, they are classified into the same initial satellite combination, and all candidate satellites are divided according to the comprehensive overlap to obtain multiple initial satellite combinations.

[0140] It can be understood that each initial satellite combination contains different numbers of candidate satellites, and the communication data of these satellites in the same time window have a high comprehensive overlap.

[0141] The intelligent navigation and positioning method based on multi-mode satellite communications provided in this embodiment extracts the overlap of semantic and numerical content of communication data of each candidate satellite within the same time window and calculates the comprehensive overlap. Satellites with comprehensive overlap higher than a preset threshold are then grouped together to form an initial satellite combination to complete clustering. This method can effectively screen out satellite combinations with high communication data similarity and consistent positioning performance, reduce redundant data, optimize satellite resource allocation, and improve the accuracy and efficiency of navigation and positioning.

[0142] S107: Calculate a second geometric dilution of precision after clustering, compare the difference between the second geometric dilution of precision and the first geometric dilution of precision to see whether it is greater than a preset threshold; if so, adjust the number of clusters, and return to the step of clustering the N candidate satellites based on the overlap.

[0143] In specific implementation, the representative satellite combination at the center position of each clustered category can be determined as a new satellite, and the second geometric precision dilution can be recalculated.

[0144] Furthermore, the specific value of the preset threshold is set according to actual needs and is not limited in this embodiment. For example, in one embodiment, the preset threshold can be set to 10%.

[0145] Furthermore, the difference between the second geometric DD of precision and the first geometric DD of precision is compared with a preset threshold. If the difference is greater than the preset threshold, it is considered that the clustering result does not meet the requirements, and the number of clusters needs to be adjusted and clustering processing needs to be performed again.

[0146] A specific embodiment is given below to introduce the process of adjusting the number of clusters in detail:

[0147] Obtaining second carrier phase observation data corresponding to each candidate satellite to generate a second target weight inverse matrix;

[0148] Calculating a second measurement error when receiving the second carrier phase observation data, calculating a product of the second measurement error and the second target weight inverse matrix, and determining the product as a second intermediate value;

[0149] determining the second geometric dilution of precision based on the second intermediate values ​​corresponding to the respective initial positions;

[0150] calculating a difference between the second geometric dilution of precision and the first geometric dilution of precision, and determining a ratio of the difference to a reference value as an adjustment coefficient;

[0151] The product of the adjustment coefficient and the number of clusters is calculated to obtain a cluster adjustment value, and the number of clusters is adjusted based on the cluster adjustment value.

[0152] It can be understood that after clustering processing, the positions of the candidate satellites with large comprehensive overlap are adjusted so that only one duplicate candidate satellite can be retained. Since the position of the candidate satellite has changed, it is necessary to calculate the second geometric precision factor at this time to characterize the positioning accuracy of the candidate satellite after clustering processing.

[0153] Furthermore, the specific process of calculating the second geometric dilution of precision may refer to the above-mentioned process of calculating the first geometric dilution of precision, which will not be repeated here.

[0154] Furthermore, the reference value is used to evaluate whether the current satellite can meet the positioning accuracy requirement.

[0155] During specific implementation, the first geometric dilution of precision may be determined as a reference value, or a preset value may be set as a reference value according to actual needs.

[0156] Furthermore, the difference between the second geometrical dilution of precision and the first geometrical dilution of precision is calculated, and then the ratio of the difference to the reference value is used as the adjustment coefficient.

[0157] It can be understood that the adjustment factor reflects the degree to which clustering improves the geometric dilution of precision and the degree to which further adjustment is needed.

[0158] Furthermore, the adjustment coefficient is multiplied by the current number of clusters to obtain a cluster adjustment value.

[0159] Furthermore, if the adjustment value is positive and large, the number of clusters can be increased to further optimize the satellite geometric distribution. If the adjustment value is negative or small, the number of clusters can be reduced to reduce computational complexity. Based on this adjustment value, the clustering step is returned to and clustering is performed again until the difference between the second geometric precision dilution and the first geometric precision dilution is no greater than a preset threshold.

[0160] The intelligent navigation and positioning method based on multi-mode satellite communication provided in this embodiment obtains the second geometric precision dilution of the candidate satellites after clustering and compares it with the first geometric precision dilution before clustering to obtain an adjustment coefficient. The clustering adjustment value is then determined based on the adjustment coefficient and the current number of clusters, and the number of clusters is dynamically adjusted accordingly. In this way, the degree to which the clustering effect optimizes the geometric distribution of satellites can be evaluated in real time. By iteratively adjusting the number of clusters, the optimal geometric distribution of the satellite combination is ensured, thereby significantly improving the accuracy and stability of navigation and positioning.

[0161] S108. Select representative satellites from each clustered category to obtain M target candidate satellites, where M is a positive integer less than N and greater than 2.

[0162] Specifically, for example, in one embodiment, satellites included in a clustered category after clustering may be compared, and the most central satellite may be determined as the target candidate satellite. For another example, in another embodiment, satellites included in a clustered category after clustering may be compared, and the satellite with the best performance may be determined as the target candidate satellite.

[0163] Furthermore, since the number of all satellites included in each category is N, only M target candidate satellites are selected. The number of M is less than N. At the same time, to ensure the accuracy of positioning, multiple satellites are required, so the number of M is greater than 2. At the same time, it is necessary to ensure that the target candidate satellites include both GPS satellites and BDS satellites.

[0164] S109 . Select an optimal target satellite from the M target candidate satellites according to a satellite selection method, where the target satellite includes at least a GPS satellite and a BDS satellite.

[0165] Specifically, the satellite selection method is used to select a satellite combination that can provide the best positioning accuracy and stability.

[0166] In specific implementation, the satellite selection method can be to select the satellite with the best signal quality based on indicators such as the signal-to-noise ratio of the satellite signal and the carrier phase measurement accuracy; the satellite selection method can be based on comprehensive consideration of multiple objectives such as positioning accuracy, signal quality, and cost, and select the optimal satellite combination through a multi-objective optimization algorithm.

[0167] A specific embodiment is given below to introduce in detail the process of selecting the optimal target satellite:

[0168] (1) Obtain the four candidate satellites in each satellite set after clustering, and form a five-vertex geometric body with the receiver.

[0169] In specific implementation, four candidate satellites may be selected from each satellite set.

[0170] It should be noted that the four candidate satellites need to include both GPS satellites and BDS satellites in order to achieve precise positioning by integrating multiple different types of satellites.

[0171] Furthermore, the four selected candidate satellites are combined with a receiver to form a five-vertex geometric body, which represents the spatial distribution relationship between the satellites and the receivers.

[0172] (2) Traverse all the candidate satellites in the satellite set and calculate the volumes of all the five-vertex geometric bodies.

[0173] Specifically, the volume of a five-vertex geometry can be calculated according to the following formula:

[0174]

[0175] in, It represents the direction vector of the line connecting the other three satellites with the position of one of the visible satellites as the starting point. It represents the direction vector of the line connecting the receiver and the other three satellites.

[0176] Furthermore, all candidate satellites are traversed, and for each five-vertex geometry consisting of four candidate satellites and one receiver, its volume is calculated.

[0177] (3) The candidate satellite corresponding to the largest five-vertex geometric body is determined as the target satellite.

[0178] In specific implementation, among all the calculated volumes of five-vertex geometric bodies, the geometric body with the largest volume is found, and the four candidate satellites corresponding to the geometric body are determined as target satellites.

[0179] It should be noted that the maximum volume of the five-vertex geometry usually means the optimal spatial distribution between the satellite and the receiver, which helps to improve positioning accuracy and stability.

[0180] The intelligent navigation and positioning method based on multi-mode satellite communication provided in this embodiment selects four candidate satellites from each satellite set to form a five-vertex geometric body with the receiver. By traversing and calculating the volumes of all possible combinations, the candidate satellite that can form the geometric body with the largest volume is finally selected as the target satellite. Compared with the traditional method of screening satellites one by one, the method significantly improves the satellite signal utilization and positioning accuracy in complex environments, while reducing the amount of calculation and improving system efficiency. At the same time, in traditional methods, the receiver is often regarded as a point that passively receives signals. However, this embodiment ensures the accuracy of positioning in complex scenarios by incorporating the receiver into the evaluation of satellite geometric distribution.

[0181] S110: Perform positioning calculation based on the communication data corresponding to the target satellite to achieve navigation positioning.

[0182] Specifically, positioning solution refers to the process of using the received satellite communication data to calculate the receiver's location information through a specific algorithm.

[0183] In specific implementation, positioning can be solved through carrier phase differential positioning. Two or more receivers simultaneously receive the carrier phase signal from the target satellite. The base station sends its own carrier phase observation value and known precise coordinates to the mobile station. The mobile station uses this differential information to correct its own carrier phase observation value, thereby eliminating or weakening the influence of the common error source and achieving positioning solution.

[0184] A specific embodiment is given below to introduce the positioning solution process in detail:

[0185] Constructing a pseudorange observation model for each of the target satellites;

[0186] Based on the position of each target satellite, determine the initial position and the initial clock error of the receiver based on a coarse positioning method;

[0187] Linearizing the pseudorange observation model, iteratively updating the receiver initial position and the receiver initial clock error, and obtaining a target correction value;

[0188] When the target correction value is less than the preset correction value, the receiver iteration position and receiver iteration clock difference at this time are output.

[0189] Specifically, the pseudorange observation model is a model that calculates the distance between the satellite and the receiver based on the satellite signal propagation time and the speed of light.

[0190] Furthermore, the receiver receives communication data from the target satellite and extracts pseudorange observation values ​​based on the communication data; then, the satellite position of the target satellite corresponding to the communication data is obtained, and a pseudorange observation model is constructed based on the pseudorange observation values ​​and the satellite position.

[0191] In specific implementation, the constructed pseudorange observation model can be represented by the following equation:

[0192] For each satellite, establish the pseudorange observation equation:

[0193]

[0194] Where (x, y, z) is the receiver position, (xi, yi, zi) is the satellite position of the target satellite, c is the speed of light, δt is the seed error of the receiver, δti is the clock error of the target satellite, ∈ ρi is the observation error.

[0195] Furthermore, based on the satellite pseudorange observations and satellite positions, the initial position and initial clock error of the receiver are determined by the three-sphere intersection method.

[0196] It should be noted that it is not necessary to calculate particularly precise satellite position and time information, because accurate positioning can be obtained through subsequent iterations using rough initial positions and initial clock differences.

[0197] Furthermore, the pseudorange observation model is expanded in Taylor series at the initial position and initial clock error of the receiver to obtain the linearized pseudorange observation equation.

[0198] Furthermore, the linearized pseudorange observation equation and the least squares iterative algorithm can be used to gradually adjust the receiver position and clock error to minimize the difference between the pseudorange observation value and the model prediction value.

[0199] It should be noted that in each iteration, it is necessary to recalculate the coefficients of the new pseudorange observation model based on the communication data sent by the target satellite, and update the receiver position and clock error.

[0200] Furthermore, the specific value of the preset correction value is set according to actual needs and is not limited in this embodiment.

[0201] Furthermore, after each iteration, the target correction value is calculated. When the target correction value is less than the preset correction value, the iteration is considered to have converged. At this time, the receiver iteration position and the receiver iteration clock difference are output as the final positioning result.

[0202] The intelligent navigation and positioning method based on multi-mode satellite communication provided in this embodiment uses a coarse positioning method to determine the initial position, and then gradually improves the positioning accuracy through linearization processing and iterative updates. In this way, the satellite signal utilization and positioning accuracy in complex environments can be significantly improved. In particular, in the case of signal obstruction or weak signal, the true position is approximated through multiple iterations, and finally a high-precision receiver position and clock deviation are output, which effectively improves the reliability and stability of the navigation and positioning system.

[0203] The intelligent navigation and positioning method based on multi-mode satellite communication provided in this embodiment combines multi-mode satellite signals such as GPS and BDS. The system can maintain a high number of visible satellites and signal quality in complex environments such as signal shielding and multipath effects, effectively reducing the impact of weak or failed signals of a single system on positioning accuracy. At the same time, by real-time analysis of the carrier phase measurement accuracy and real-time object distribution density in the target navigation area, candidate satellites and their initial positions are dynamically determined to ensure efficient use of satellite resources. In addition, the satellite combination and positioning strategy can be flexibly adjusted according to the dynamic changes of actual application scenarios, thereby enhancing the adaptability and reliability of the system and maintaining stable navigation and positioning performance even in extreme environments.

[0204] Corresponding to the aforementioned embodiment of an intelligent navigation and positioning method based on multi-mode satellite communication, the present application also provides an embodiment of an intelligent navigation and positioning device based on multi-mode satellite communication.

[0205] The embodiment of the intelligent navigation and positioning device based on multi-mode satellite communication of the present application can be applied to an intelligent navigation and positioning device based on multi-mode satellite communication. The embodiment of the device can be implemented by software, or by hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of the intelligent navigation and positioning device based on multi-mode satellite communication in which it is located reading the corresponding computer program instructions in the non-volatile memory into the memory and running them. From the hardware level, if Figure 2 As shown, this is a hardware structure diagram of the intelligent navigation and positioning device based on multi-mode satellite communication of the present application, in addition to Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the intelligent navigation and positioning device based on multi-mode satellite communication in which the device in the embodiment is located may also include other hardware according to the actual function of the intelligent navigation and positioning device based on multi-mode satellite communication, which will not be described in detail.

[0206] Figure 3 This is a structural diagram of the first embodiment of the intelligent navigation and positioning device based on multi-mode satellite communication provided by this application. Figure 3 The device provided in this embodiment includes a determination module 310, a calculation module 320, a processing module 330 and a screening module 340; wherein,

[0207] The determination module 310 is used to determine the target navigation area;

[0208] The determining module 310 is further configured to determine N candidate satellites according to the carrier phase measurement accuracy of the target navigation area, where N is a positive integer greater than 2, and the candidate satellites include at least GPS satellites and BDS satellites;

[0209] The determining module 310 is further configured to determine the initial positions of the N candidate satellites according to the real-time object distribution density of the target navigation area, and the N candidate satellites move to the corresponding initial positions;

[0210] The calculation module 320 is configured to calculate a first geometric dilution of precision based on the initial position;

[0211] The processing module 330 is configured to simultaneously receive communication data from the N candidate satellites;

[0212] The calculation module 320 is further configured to calculate the communication data overlap of the N candidate satellites, and cluster the N candidate satellites based on the overlap;

[0213] The processing module 330 is further configured to calculate a second geometric dilution of precision after clustering, compare the difference between the second geometric dilution of precision and the first geometric dilution of precision to see whether it is greater than a preset threshold, and if so, adjust the number of clusters and return to the step of clustering the N candidate satellites based on the overlap;

[0214] The screening module 340 is used to select representative satellites from each clustered category to obtain M target candidate satellites, where M is a positive integer less than N and greater than 2;

[0215] The screening module 340 is further configured to select an optimal target satellite from the M target candidate satellites according to a satellite selection method, wherein the target satellite includes at least a GPS satellite and a BDS satellite;

[0216] The processing module 330 is further configured to perform positioning calculation based on the communication data corresponding to the target satellite to achieve navigation positioning.

[0217] The device of this embodiment can be used to perform Figure 1 The steps, specific implementation principles and implementation processes of the method embodiment shown are similar and will not be repeated here.

[0218] Please continue to refer to Figure 2 The present application also provides an intelligent navigation and positioning device based on multi-mode satellite communication, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of any one of the methods provided in the first aspect of the present application are implemented.

[0219] The present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any one of the methods provided in the present application when the program is executed by a processor.

[0220] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0221] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0222] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. An intelligent navigation and positioning method based on multi-mode satellite communication, characterized in that: The method comprises: Determine the target navigation area; Determine N candidate satellites according to the carrier phase measurement accuracy of the target navigation area, where N is a positive integer greater than 2, and the candidate satellites include at least GPS satellites and BDS satellites; Determining initial positions of the N candidate satellites according to the real-time object distribution density of the target navigation area, and moving the N candidate satellites to corresponding initial positions; calculating a first dilution of precision based on the initial position; Simultaneously receiving communication data of the N candidate satellites; Calculating the communication data overlap of the N candidate satellites, and clustering the N candidate satellites based on the overlap; Calculating a second geometric dilution of precision after clustering, comparing whether a difference between the second geometric dilution of precision and the first geometric dilution of precision is greater than a preset threshold, and if so, adjusting the number of clusters, and returning to the step of clustering the N candidate satellites based on the overlap; Select representative satellites from each clustered category to obtain M target candidate satellites, where M is a positive integer less than N and greater than 2; Selecting an optimal target satellite from the M target candidate satellites according to a satellite selection method, wherein the target satellite includes at least a GPS satellite and a BDS satellite; Positioning is performed based on the communication data corresponding to the target satellite to achieve navigation positioning.

2. The method according to claim 1, characterized in that The determining N candidate satellites according to the carrier phase measurement accuracy of the target navigation area includes: Acquiring carrier phase observation data within the target navigation area; differentially processing the carrier phase observation data, and calculating a target residual between the differentially processed data and a pre-constructed satellite position theoretical model; Correcting error distribution within the target navigation area based on dual-frequency observation; Weighted calculation of the root mean square of the target residual and the error distribution to obtain the carrier phase measurement accuracy; The satellite whose carrier phase measurement accuracy is greater than a preset measurement accuracy value is determined as the candidate satellite.

3. The method according to claim 1, characterized in that The determining the initial positions of the N candidate satellites according to the real-time object distribution density of the target navigation area includes: Counting the number and positions of real-time objects in the target navigation area, and calculating the distribution density of real-time objects in the target navigation area; Determining a corresponding satellite communication range based on satellite attributes of each candidate satellite; Calculating the real-time object distribution density within the satellite communication range, and calculating the target density ratio corresponding to each satellite communication range; Calculating the product of the target density ratio and the total number of the candidate satellites to obtain the number of the candidate satellites within the communication range of each satellite; In each satellite communication range, the initial position of the candidate satellite is determined according to the number of the candidate satellites.

4. The method according to claim 1, wherein The calculating a first geometric dilution of precision based on the initial position includes: Obtaining first carrier phase observation data corresponding to each candidate satellite to generate a first target weight inverse matrix; Calculating a first measurement error when receiving the first carrier phase observation data, calculating a product of the first measurement error and the first target weight inverse matrix, and determining the product as a first intermediate value; The first geometric dilution of precision is determined based on the first intermediate values ​​corresponding to the respective initial positions.

5. The method according to claim 1, wherein The simultaneously receiving the communication data of the N candidate satellites includes: Simultaneously capturing satellite signals of the N candidate satellites based on a multi-channel receiver, and obtaining an acquisition timestamp corresponding to each satellite signal; Demodulating the satellite signal to generate a data frame in a standard format; With reference to the acquisition timestamp, the data frames corresponding to the same moment are fused to obtain the communication data.

6. The method according to claim 1, characterized in that Calculating the communication data overlap of the N candidate satellites and clustering the N candidate satellites based on the overlap includes: Extracting the overlap of semantic content and numerical content of the communication data of each candidate satellite within the same time window, and calculating the comprehensive overlap; The candidate satellites whose comprehensive overlap is greater than a preset overlap threshold are determined as an initial satellite combination to complete clustering; each initial satellite combination includes a different number of candidate satellites.

7. The method according to claim 1, characterized in that The adjusting the number of clusters includes: Obtaining second carrier phase observation data corresponding to each candidate satellite to generate a second target weight inverse matrix; Calculating a second measurement error when receiving the second carrier phase observation data, calculating a product of the second measurement error and the second target weight inverse matrix, and determining the product as a second intermediate value; determining the second geometric dilution of precision based on the second intermediate values ​​corresponding to the respective initial positions; calculating a difference between the second geometric dilution of precision and the first geometric dilution of precision, and determining a ratio of the difference to a reference value as an adjustment coefficient; The product of the adjustment coefficient and the number of clusters is calculated to obtain a cluster adjustment value, and the number of clusters is adjusted based on the cluster adjustment value.

8. The method according to claim 1, characterized in that The selecting an optimal target satellite from the M target candidate satellites according to the satellite selection method includes: Acquire the four candidate satellites in each satellite set after clustering, and form a five-vertex geometric body with the receiver; Traversing all the candidate satellites in the satellite set, and calculating the volumes of all the five-vertex geometric bodies; The candidate satellite corresponding to the largest five-vertex geometric body is determined as the target satellite.

9. The method according to claim 1, characterized in that The performing positioning calculation based on the communication data corresponding to the target satellite includes: Constructing a pseudorange observation model for each of the target satellites; Based on the position of each target satellite, determine the initial position and the initial clock error of the receiver based on a coarse positioning method; Linearizing the pseudorange observation model, iteratively updating the receiver initial position and the receiver initial clock error, and obtaining a target correction value; When the target correction value is less than the preset correction value, the receiver iteration position and receiver iteration clock difference at this time are output.

10. An intelligent navigation and positioning device based on multi-mode satellite communication, characterized in that: The device includes a determination module, a calculation module, a processing module and a screening module; wherein, The determining module is used to determine the target navigation area; The determining module is further configured to determine N candidate satellites according to the carrier phase measurement accuracy of the target navigation area, where N is a positive integer greater than 2, and the candidate satellites include at least GPS satellites and BDS satellites; The determining module is further configured to determine the initial positions of the N candidate satellites according to the real-time object distribution density of the target navigation area, and the N candidate satellites move to the corresponding initial positions; The calculation module is configured to calculate a first geometric dilution of precision based on the initial position; The processing module is configured to simultaneously receive communication data of the N candidate satellites; The calculation module is further configured to calculate the communication data overlap of the N candidate satellites, and cluster the N candidate satellites based on the overlap; The processing module is further configured to calculate a second geometric dilution of precision after clustering, compare whether a difference between the second geometric dilution of precision and the first geometric dilution of precision is greater than a preset threshold, and if so, adjust the number of clusters and return to the step of clustering the N candidate satellites based on the overlap; The screening module is used to select representative satellites from each clustered category to obtain M target candidate satellites, where M is a positive integer less than N and greater than 2; The screening module is further configured to select an optimal target satellite from the M target candidate satellites according to a satellite selection method, wherein the target satellite includes at least a GPS satellite and a BDS satellite; The processing module is further used to perform positioning calculation based on the communication data corresponding to the target satellite to achieve navigation positioning.

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