GNSS (Global Navigation Satellite System) positioning method of satellite navigation system

Abstract

The invention discloses a GNSS (Global Navigation Satellite System) positioning method of a satellite navigation system, which relates to the technical field of satellite navigation and positioning and comprises the following steps: acquiring a state quantity xi of a current epoch i; determining a solution space of the state quantity, and dividing the solution space into N subspaces; calculating a subspace j where the solution of the state quantity xi of the current epoch i is located; calculating the number nj of the state quantity solutions falling into the subspace j from the starting epoch to the current epoch i, iteratively updating and calculating the mean value mu j and variance # imgabs0 # of the state quantity solutions of the subspace j, enabling i to be equal to i + 1 until i is equal to n, obtaining the mean value and variance of the state quantity solutions of different subspaces, and further calculating the mean value and variance of the state quantity solutions in the whole probability distribution, dividing the subspace into a high probability range, a middle probability range and a low probability range according to the number nj of state quantity solutions from the starting epoch of the subspace to the current epoch i falling into the subspace j; the precision of a positioning output result can be improved.

Description

Technical Field

[0001] The present invention relates to the field of satellite navigation and positioning technology, and in particular to a satellite navigation system (GNSS) positioning method. Background Art

[0002] In many static positioning applications of satellite navigation systems, it is often necessary to estimate and output the positioning processing results such as position and attitude in real time. At this time, integration or filtering methods are usually used to process in order to obtain better output results. However, when the positioning conditions are poor and the signal is severely blocked, it is difficult to obtain ideal processing results by general integration or filtering methods due to measurement errors such as multipath, and it is difficult to meet the requirements of real-time positioning output by using post-processing methods. As a result, the use of satellite navigation positioning technology in the above application scenarios is limited.

[0003] The invention with patent announcement number CN110349002B discloses a static positioning method for a satellite navigation system, including: step 1, obtaining the state quantity x of the current epoch i i ; Step 2, determine the solution space of the state quantity, and divide the solution space into N subspaces; Step 3, calculate the state quantity x of the current epoch i i The solution of the subspace j, where 1 <= j <= N; Step 4, calculate the number n of state quantity solutions that fall into subspace j from the starting epoch to the current epoch i j , and iteratively update the mean μ of the state solution of subspace j j and variance Step 5: Let i=i+1, and repeat steps 3 to 4 until i equals n, to obtain the mean and variance of the state quantity solutions in different subspaces, and then calculate the mean and variance of the state quantity solutions in the entire probability distribution; this method has a small amount of computation and strong anti-error, and can improve the accuracy of the positioning output results. However, the following problems still exist: 1. The specific subspace obtained is matched by the state quantity, and the state quantity itself has deviations. Each subspace obtained is actually a subspace containing deviations; 2. In the absence of initial data accumulation, the specific location data is obtained through the state quantity, and the point probability obtained by the initial data will be continuously accumulated to the subsequent data, causing initial deviations and leading to probability deviation accumulation. Summary of the invention

[0004] The present invention aims at the technical problems existing in the prior art and provides a satellite navigation system (GNSS) positioning method.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: A satellite navigation system GNSS positioning method, comprising:

[0006] S101: Obtain the state quantity x of the current epoch ii , where i = 1, 2, ..., n;

[0007] S102: Based on the state quantity x of the current epoch i i Determine a solution space of the state quantity, and divide the solution space into N subspaces, where N is an integer greater than or equal to 1;

[0008] Preset the size range of the initial solution space of the state quantity and set the state quantity x i The minimum value x min and the maximum value x max , the set range can cover the expected deviation, and the range is set by the historical data of the state quantity of the historical epoch;

[0009] Presetting the range of the initial solution space of the state quantity can provide a basic range for the subsequent subspace division, ensuring that all results are included;

[0010] The initial solution space is divided into N subspaces, and the width of each subspace is Each subspace is represented as [x min +(j-1)Δx,x min +jΔx], where j = 1, 2, ..., N;

[0011] S103: Calculate the state quantity x of the current epoch i i The subspace j where the solution is located;

[0012] Get the state x of the current epoch i in real time i , calculate the state quantity x of the current epoch i i The subspace where the solution is located is calculated as j =<xi / Δx> , <·> is the rounding symbol;

[0013] S104: Calculate the number n of state quantity solutions that fall into subspace j from the starting epoch to the current epoch i j , and iteratively update the mean μ of the state solution of subspace j j and variance The calculation formula is:

[0014]

[0015] Among them, μ0 is the initial value of the mean of the state solution, and σ0 is the initial value of the variance of the state solution. Among them, for the current epoch, update the statistical number n of the state solution of the current epoch subspace j j , mean μ j and variance, where is the statistical number of state solutions of subspace j after update, is the mean of the state solutions of subspace j after update, is the variance of the state solutions of subspace j after update, n jis the statistical number of j state solutions in the subspace before updating, μ j is the mean of the state solution of subspace j before updating, σ j is the variance of the state solution of subspace j before updating. When j = 1, that is, when the formula is used for the first time, the initial value is n j =0,μ j =0,σ j =0;

[0016] S105: Let i=i+1, and repeat S103 to S104 until i equals n, so as to obtain the mean and variance of the state quantity solutions in different subspaces, and then calculate the mean and variance of the state quantity solutions in the entire probability distribution;

[0017] S106: The number n of state quantity solutions that fall into subspace j from the subspace starting epoch to the current epoch i j , divide the subspace into three probability ranges: high, medium and low;

[0018] Set the subspace probability threshold T high and T low , when n j >T high When , the subspace is a high probability range;

[0019] When T low <n j <T high When , the subspace is in the medium probability range;

[0020] When n j <T low When , the subspace is a low probability range;

[0021] Through probability division, data in high-probability areas are processed first, reducing the amount of calculation and improving positioning efficiency;

[0022] S107: Selecting a subspace with a multi-state quantity solution from the high probability range subspace set as the initial estimation center, and sequentially querying other high probability subspaces adjacent to the subspace to construct a continuous subspace set of maximum probability distribution;

[0023] The probability distribution of the state quantity is fitted through the probability distribution model, and the mean and variance in the probability distribution are calculated as the final point estimation;

[0024] The probability distribution model is Gaussian distribution.

[0025] In step S106, the center point of the state quantity solution space is calibrated each time, and the direction and distance of the subspace relative to the center point are obtained through the coordinate axis to obtain the probability direction, and the probability direction is calculated multiple times to form data intervals of multiple probability directions, which are sorted to form high, medium and low probability directions to further refine the probability range of the solution space.

[0026] S201: For each epoch state quantity solution space, calibrate the center point, and take the center point as the reference, calibrate the direction and distance of each subspace relative to the center point through the coordinate axis system to obtain the probability orientation;

[0027] Initialize the range of the solution space through the state quantity, calculate the mean of the solution space and calibrate the center point of the solution space;

[0028] S202: collecting probability azimuth data obtained by calibration at multiple epochs, and sorting the probability azimuth data to divide them into high, medium, and low probability azimuth intervals;

[0029] By processing the probability orientation data using a histogram, the main probability orientation and the secondary probability orientation are identified;

[0030] S203: updating the solution space probability distribution model according to the probability orientation sorting and interval division, and taking the high probability range interval as the main body of the solution space;

[0031] By using Bayesian statistical methods, the probability distribution information is integrated into the existing solution space probability distribution model;

[0032] By integrating the probabilistic position information, the solution space probability model can accurately reflect the distribution of actual positioning results and improve positioning accuracy and reliability.

[0033] Through the probability orientation, the probability orientation of the subspace is further obtained, and the high, medium and low probability orientations of the subspace are distinguished.

[0034] S301: Based on the probability orientation obtained in step S201, the solution space of each epoch state quantity is divided into N probability subspaces according to the continuity and density of the probability orientation;

[0035] By using the K-means clustering algorithm, the clusters are divided according to the similarity of probability orientation and spatial distribution;

[0036] S302: Count the number or frequency of state quantity solutions in each probability sub-interval, and divide the probability sub-interval into three probability levels: high, medium, and low according to the statistical results;

[0037] By setting a threshold value for the data in the statistical results, the data can be divided;

[0038] S303: According to the high, medium and low probability level division results of the probability subspace, the solution space probability distribution model is updated, and different weights are assigned to different probability subspaces;

[0039] A weighted probability distribution model is used to integrate the information of high, medium, and low probability subspaces.

[0040] For example, collect state data for a series of epochs, where each data point contains x and y coordinates;

[0041] For each epoch, the mean of all points in the state quantity solution space is calculated as the center point;

[0042] Calculate the direction and distance of each data point relative to the center point through the coordinate axis system;

[0043] Select K-means clustering algorithm and set the number of clusters N = 3;

[0044] Using the probability orientation data as input, the K-means clustering algorithm is executed, and the algorithm outputs three cluster centers and their corresponding probability subspaces;

[0045] Check the clustering results to ensure that each cluster center is located in the data-dense area of ​​its corresponding subspace;

[0046] If the clustering result is not ideal, you can adjust the value of N or reselect the initial cluster center and then execute the clustering algorithm again;

[0047] For each probability subspace, count the number of its internal state quantities;

[0048] According to the statistical results, set the thresholds of high, medium and low probability levels. For example, divide the subspace with the number of state quantities greater than 5 into the high probability area, the subspace with the number greater than 3 and less than 5 into the medium probability area, and the rest into the low probability area;

[0049] According to the set threshold, each probability subspace is divided into three probability levels: high, medium, and low;

[0050] Different weights are assigned to high, medium, and low probability areas. The weight of high probability area is 0.5, the weight of medium probability area is 0.3, and the weight of low probability area is 0.2.

[0051] Using the assigned weights, a weighted probability distribution model is constructed. When calculating the location probability of a certain location, it can be considered which probability area it is located in and adjusted using the corresponding weights.

[0052] Based on the azimuthal relationship between the obtained high-probability subinterval center point and the solution space center point, the algorithm equation of the solution space is corrected, so that the high-probability center point becomes the center point of the solution space. Through the relationship between the high-probability center point and the solution space center point, the high-probability point is directly output, and the subsequent state value points are recorded with the high-probability point as the center. Through the relationship between the high-probability center point and the state value points, a high-probability solution space is formed, and the calculation method for point acquisition is corrected to form an upgraded version of the high-probability solution space.

[0053] S401: Calculate the coordinates of the center point of each high probability subspace;

[0054] S402: Calculate the position and distance relationship between the center point of each high probability sub-interval and the center point of the original solution space;

[0055] The orientation and distance relationship between the center point of the high probability subinterval and the center point of the original solution space is calculated by using the coordinate geometry method;

[0056] S403: adjusting relevant parameters in the original solution space algorithm equation according to the orientation and distance between the center point of the high probability subinterval and the center point of the original solution space, so that the new algorithm generates a solution space centered on the high probability center point;

[0057] S404: applying the adjusted solution space algorithm equation to recalculate the solution space, and continuously recording the new probability subspace;

[0058] Integrate the optimization results to form an upgraded high-probability solution space model.

[0059] A number of loops is set, that is, after obtaining the state values ​​of the preset number of times, the high probability solution space is looped to obtain the center point, probability orientation, high probability point and high probability solution space, thereby eliminating the impact that may be caused by the deviation, dynamically correcting it, and improving the accuracy at the same time.

[0060] S501: Setting the number of loops w to control the number of times the state value is obtained and the frequency of loop optimization of the high probability solution space;

[0061] S502: In each cycle, the state quantity xi of the current epoch is obtained, and all state value points are recorded;

[0062] Repeat this step w times to collect a large amount of data for subsequent analysis;

[0063] S503: Calculate the high probability subspace and the center point based on the state value collected in the current cycle;

[0064] Use probabilistic statistical methods to identify high probability subspaces and use the mean method to calculate the center point;

[0065] S504: Analyze the relationship between the high probability center point and the center point of the current solution space, and modify the solution space algorithm equation according to the analysis result;

[0066] Optimize and adjust solution space parameters through coordinate geometry methods and algorithms;

[0067] S505: Use the newly acquired state value to verify the corrected solution space, and update the high probability solution space according to the verification result;

[0068] S506: After completing one cycle, check whether the set number of cycles w is reached;

[0069] If not, return to step S502 to continue the next cycle; if it is reached, perform the final optimization and output the result;

[0070] Output the final optimized upgraded high-probability solution space model.

[0071] The beneficial effects of the present invention are as follows: through the above-mentioned technical scheme, the problem of initial data deviation accumulation can be effectively solved, and the amount of calculation can be reduced and the positioning efficiency can be improved by means of probability division and priority processing of high-probability areas; at the same time, by dynamically adjusting the solution space range and probability division, it can adapt to different positioning environments and requirements, and improve the robustness and positioning accuracy of the system.

[0072] This method can handle the changes of state quantities at different epochs. By iteratively updating the mean and variance, it can adapt to the dynamic changes of state quantities, so that the method can still maintain positioning performance in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 The present invention is a flowchart of a satellite navigation system GNSS positioning method. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0075] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0076] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0077] Example 1

[0078] like Figure 1 As shown, the present application provides a satellite navigation system GNSS positioning method, comprising:

[0079] S101: Obtain the state quantity x of the current epoch i i , where i = 1, 2, ..., n;

[0080] S102: Based on the state quantity x of the current epoch i i Determine a solution space of the state quantity, and divide the solution space into N subspaces, where N is an integer greater than or equal to 1;

[0081] Preset the size range of the initial solution space of the state quantity and set the state quantity x i The minimum value x min and the maximum value x max , the set range can cover the expected deviation, and the range is set by the historical data of the state quantity of the historical epoch;

[0082] Presetting the range of the initial solution space of the state quantity can provide a basic range for the subsequent subspace division, ensuring that all results are included;

[0083] The initial solution space is divided into N subspaces, and the width of each subspace is Each subspace is represented as [x min +(j-1)Δx,x min +jΔx], where j = 1, 2, ..., N;

[0084] S103: Calculate the state quantity x of the current epoch i i The subspace j where the solution is located;

[0085] Get the state x of the current epoch i in real time i , calculate the state quantity x of the current epoch ii The subspace where the solution is located is calculated as j =<xi / Δx> , <·> is the rounding symbol;

[0086] S104: Calculate the number n of state quantity solutions that fall into subspace j from the starting epoch to the current epoch i j , and iteratively update the mean μ of the state solution of subspace j j and variance The calculation formula is:

[0087]

[0088] Among them, μ0 is the initial value of the mean of the state solution, and σ0 is the initial value of the variance of the state solution. Among them, for the current epoch, update the statistical number n of the state solution of the current epoch subspace j j , mean μ j and variance, where is the statistical number of state solutions of subspace j after update, is the mean of the state solutions of subspace j after update, is the variance of the state solutions of subspace j after update, n j is the statistical number of j state solutions in the subspace before updating, μ j is the mean of the state solution of subspace j before updating, σ j is the variance of the state solution of subspace j before updating. When j = 1, that is, when the formula is used for the first time, the initial value is n j =0,μ j =0,σ j =0;

[0089] S105: Let i=i+1, and repeat S103 to S104 until i equals n, so as to obtain the mean and variance of the state quantity solutions in different subspaces, and then calculate the mean and variance of the state quantity solutions in the entire probability distribution;

[0090] S106: The number n of state quantity solutions that fall into subspace j from the subspace starting epoch to the current epoch i j , divide the subspace into three probability ranges: high, medium and low;

[0091] Set the subspace probability threshold T high and T low , when n j >T high When , the subspace is a high probability range;

[0092] When T low <n j <T high When , the subspace is in the medium probability range;

[0093] When n j<T low When , the subspace is a low probability range;

[0094] Through probability division, data in high-probability areas are processed first, reducing the amount of calculation and improving positioning efficiency;

[0095] S107: Selecting a subspace with a multi-state quantity solution from the high probability range subspace set as the initial estimation center, and sequentially querying other high probability subspaces adjacent to the subspace to construct a continuous subspace set of maximum probability distribution;

[0096] The probability distribution of the state quantity is fitted through the probability distribution model, and the mean and variance in the probability distribution are calculated as the final point estimation;

[0097] The probability distribution model is Gaussian distribution.

[0098] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0099] The above technical solution can effectively solve the problem of initial data deviation accumulation, reduce the amount of calculation and improve positioning efficiency by means of probability division and priority processing of high-probability areas; at the same time, by dynamically adjusting the solution space range and probability division, it can adapt to different positioning environments and requirements and improve the system's robustness and positioning accuracy.

[0100] This method can handle the changes of state quantities at different epochs. By iteratively updating the mean and variance, it can adapt to the dynamic changes of state quantities, so that the method can still maintain positioning performance in complex environments.

[0101] Example 2

[0102] In Example 1, in step S106, the center point of the state quantity solution space is calibrated each time, and the direction and distance of the subspace relative to the center point are obtained through the coordinate axis to obtain the probability direction, and the probability direction is calculated multiple times to form data intervals of multiple probability directions, which are sorted to form high, medium and low probability directions to further accurately determine the probability range of the solution space.

[0103] Therefore, the embodiments of the present application are optimized based on the above embodiments.

[0104] S201: For each epoch state quantity solution space, calibrate the center point, and take the center point as the reference, calibrate the direction and distance of each subspace relative to the center point through the coordinate axis system to obtain the probability orientation;

[0105] Initialize the range of the solution space through the state quantity, calculate the mean of the solution space and calibrate the center point of the solution space;

[0106] S202: collecting probability azimuth data obtained by calibration at multiple epochs, and sorting the probability azimuth data to divide them into high, medium, and low probability azimuth intervals;

[0107] By processing the probability orientation data using a histogram, the main probability orientation and the secondary probability orientation are identified;

[0108] S203: updating the solution space probability distribution model according to the probability orientation sorting and interval division, and taking the high probability range interval as the main body of the solution space;

[0109] By using Bayesian statistical methods, the probability distribution information is integrated into the existing solution space probability distribution model;

[0110] By integrating the probabilistic position information, the solution space probability model can accurately reflect the distribution of actual positioning results and improve positioning accuracy and reliability.

[0111] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0112] This scheme adds the concept of probabilistic location and further accurately understands the probability range of space by calculating and sorting probabilistic location data, which not only helps to reduce the deviation and uncertainty of positioning results, but also improves positioning accuracy and reliability.

[0113] By accurately calibrating the probability range of the solution space, reducing the deviation and uncertainty of the positioning results, introducing the concept of probabilistic orientation and sorting the intervals, the adaptability and robustness of the model in different positioning environments are improved, giving priority to processing data in high-probability areas, reducing the overall calculation amount, and improving real-time performance.

[0114] Example 3

[0115] In Example 1 and Example 2, the probability orientation of the subspace is further obtained through the probability orientation, and the high, medium and low probability orientations of the subspace are distinguished.

[0116] Therefore, the embodiments of the present application are optimized based on the above embodiments.

[0117] S301: Based on the probability orientation obtained in step S201, the solution space of each epoch state quantity is divided into N probability subspaces according to the continuity and density of the probability orientation;

[0118] By using the K-means clustering algorithm, the clusters are divided according to the similarity of probability orientation and spatial distribution;

[0119] S302: Count the number or frequency of state quantity solutions in each probability sub-interval, and divide the probability sub-interval into three probability levels: high, medium, and low according to the statistical results;

[0120] By setting a threshold value for the data in the statistical results, the data can be divided;

[0121] S303: According to the high, medium and low probability level division results of the probability subspace, the solution space probability distribution model is updated, and different weights are assigned to different probability subspaces;

[0122] A weighted probability distribution model is used to integrate the information of high, medium, and low probability subspaces.

[0123] For example, collect state data for a series of epochs, each data point contains x and y coordinates;

[0124] For each epoch, the mean of all points in the state quantity solution space is calculated as the center point;

[0125] Calculate the direction and distance of each data point relative to the center point through the coordinate axis system;

[0126] Select K-means clustering algorithm and set the number of clusters N = 3;

[0127] Using the probability orientation data as input, the K-means clustering algorithm is executed, and the algorithm outputs three cluster centers and their corresponding probability subspaces;

[0128] Check the clustering results to ensure that each cluster center is located in the data-dense area of ​​its corresponding subspace;

[0129] If the clustering result is not ideal, you can adjust the value of N or reselect the initial cluster center and then execute the clustering algorithm again;

[0130] For each probability subspace, count the number of its internal state quantities;

[0131] According to the statistical results, set the thresholds of high, medium and low probability levels. For example, divide the subspace with the number of state quantities greater than 5 into the high probability area, the subspace with the number greater than 3 and less than 5 into the medium probability area, and the rest into the low probability area;

[0132] According to the set threshold, each probability subspace is divided into three probability levels: high, medium, and low;

[0133] Different weights are assigned to high, medium, and low probability areas. The weight of high probability area is 0.5, the weight of medium probability area is 0.3, and the weight of low probability area is 0.2.

[0134] Using the assigned weights, a weighted probability distribution model is constructed. When calculating the location probability of a certain location, it can be considered which probability area it is located in and adjusted using the corresponding weights.

[0135] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0136] By refining the probability interval and distinguishing between high, medium and low probabilities, the deviation of the positioning results is reduced, the positioning accuracy is improved, and the solution space probability model is significantly improved, which can more accurately reflect the uncertainty distribution of the positioning results.

[0137] The adaptability and stability of the model in different positioning environments have been improved, and it can better cope with complex and changeable signal environments. The optimized model effectively controls the computational complexity while maintaining high precision, thereby improving real-time performance. The updated model has shown higher positioning accuracy and stability in practical applications, especially in environments with severe signal obstruction.

[0138] Example 4

[0139] In the above embodiment, the algorithm equation of the solution space is corrected based on the azimuthal relationship between the obtained high probability sub-interval center point and the solution space center point, so that the high probability center point is formed as the center point of the solution space. Through the relationship between the high probability center point and the solution space center point, the high probability point is directly output, and the subsequent state value points are recorded with the high probability point as the center. Through the relationship between the high probability center point and the state value points, a high probability solution space is formed, and the calculation method for point acquisition is corrected to form an upgraded version of the high probability solution space.

[0140] Therefore, the embodiments of the present application are optimized based on the above embodiments.

[0141] S401: Calculate the coordinates of the center point of each high probability subspace;

[0142] S402: Calculate the position and distance relationship between the center point of each high probability subinterval and the center point of the original solution space;

[0143] The orientation and distance relationship between the center point of the high probability subinterval and the center point of the original solution space is calculated by using the coordinate geometry method;

[0144] S403: adjusting relevant parameters in the original solution space algorithm equation according to the orientation and distance between the center point of the high probability subinterval and the center point of the original solution space, so that the new algorithm generates a solution space centered on the high probability center point;

[0145] S404: applying the adjusted solution space algorithm equation to recalculate the solution space, and continuously recording the new probability subspace;

[0146] Integrate the optimization results to form an upgraded high-probability solution space model.

[0147] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0148] This scheme forms an optimized solution space model based on high-probability areas, significantly improving positioning accuracy and system robustness. Through continuous data collection and model optimization, it ensures the long-term stability of the positioning system in complex environments.

[0149] By optimizing the solution space, the system deviation is reduced and the accuracy of the positioning results is improved. The model can adaptively respond to environmental changes and improve the positioning stability in complex signal environments. The algorithm optimization reduces the computational complexity and ensures rapid response of real-time positioning.

[0150] Example 5

[0151] In the above embodiment, a number of loops is set, that is, after obtaining the state values ​​of a preset number of times, the high probability solution space is looped to obtain the center point, probability orientation, high probability point and high probability solution space, thereby eliminating the impact that may be caused by the deviation, dynamically correcting it, and improving the accuracy at the same time.

[0152] Therefore, the embodiments of the present application are optimized based on the above embodiments.

[0153] S501: Setting the number of loops w to control the number of times the state value is obtained and the frequency of loop optimization of the high probability solution space;

[0154] S502: In each cycle, obtain the state quantity x of the current epoch i , and record all status value points;

[0155] Repeat this step w times to collect a large amount of data for subsequent analysis;

[0156] S503: Calculate the high probability subspace and the center point based on the state value collected in the current cycle;

[0157] Use probabilistic statistical methods to identify high probability subspaces and use the mean method to calculate the center point;

[0158] S504: Analyze the relationship between the high probability center point and the center point of the current solution space, and modify the solution space algorithm equation according to the analysis result;

[0159] Optimize and adjust solution space parameters through coordinate geometry methods and algorithms;

[0160] S505: Use the newly acquired state value to verify the corrected solution space, and update the high probability solution space according to the verification result;

[0161] S506: After completing one cycle, check whether the set number of cycles w is reached;

[0162] If not, return to step S502 to continue the next cycle; if it is reached, perform the final optimization and output the result;

[0163] Output the final optimized upgraded high-probability solution space model.

[0164] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0165] By setting the number of cycles, the system dynamically optimizes the high-probability solution space based on the newly acquired data, thereby eliminating possible deviations and further improving positioning accuracy.

[0166] The cyclic optimization process enhances the system's adaptability to environmental changes and improves positioning stability in complex environments.

[0167] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0168] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0169] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0170] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0171] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0172] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0173] 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 equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A satellite navigation system GNSS positioning method, characterized in that: include: S101: Obtain the state quantity x of the current epoch i i ; S102: Based on the state quantity x of the current epoch i i Determine a solution space of the state quantity, and divide the solution space into N subspaces; S103: Calculate the state quantity x of the current epoch i i The subspace j where the solution is located; S104: Calculate the number n of state quantity solutions that fall into subspace j from the starting epoch to the current epoch i j , and iteratively update the mean μ of the state solution of subspace j j and variance S105: Let i=i+1, and repeat S103 to S104 until i equals n, so as to obtain the mean and variance of the state quantity solutions in different subspaces, and then calculate the mean and variance of the state quantity solutions in the entire probability distribution; S106: The number n of state quantity solutions that fall into subspace j from the subspace starting epoch to the current epoch i j , divide the subspace into three probability ranges: high, medium and low; S107: Selecting a subspace with a multi-state quantity solution from the high probability range subspace set as the initial estimation center, and sequentially querying other high probability subspaces adjacent to the subspace to construct a continuous subspace set of maximum probability distribution; The probability distribution of the state quantity is fitted through the probability distribution model, and the mean and variance in the probability distribution are calculated as the final point estimation.

2. A satellite navigation system GNSS positioning method according to claim 1, characterized in that: The state quantity x according to the current epoch i i Determine a solution space of the state quantity, and divide the solution space into N subspaces, where N is an integer greater than or equal to 1, specifically including: Preset the size range of the initial solution space of the state quantity and set the state quantity x i The minimum value x min and the maximum value x max , the setting range is set by the historical data of the state quantity of the historical epoch; The initial solution space is divided into N subspaces, and the width of each subspace is Each subspace is represented as [x min +(j-1)Δx,x min +jΔx], where j=1, 2,…,N.

3. A satellite navigation system GNSS positioning method according to claim 1, characterized in that: The state quantity x of the current epoch i is calculated i The subspace j where the solution is located includes: Get the state x of the current epoch i in real time i , calculate the state quantity x of the current epoch i i The subspace where the solution is located is calculated as j =<xi / Δx> , <·> is the rounding symbol.

4. The GNSS positioning method according to claim 1, characterized in that: The number n of state solutions that fall into subspace j from the starting epoch to the current epoch i is calculated. j , and iteratively update the mean μ of the state solution of subspace j j and variance The calculation formula is: Among them, μ0 is the initial value of the mean of the state solution, σ0 is the initial value of the variance of the state solution, and for the current epoch, the statistical number n of the state solution of the current epoch subspace j is updated j , mean μ j and variance, where is the statistical number of state solutions of subspace j after update, is the mean of the state solutions of subspace j after update, is the variance of the state solutions of subspace j after update, n j is the statistical number of j state solutions in the subspace before updating, μ j is the mean of the state solution of subspace j before updating, σ j is the variance of the state solution of subspace j before updating. When j = 1, that is, when the formula is used for the first time, the initial value is n j =0,μ j =0,σ j =0.

5. The GNSS positioning method according to claim 1, characterized in that: The subspace is divided into three probability ranges of high, medium and low according to the number nj of state quantity solutions falling into the subspace j from the starting epoch of the subspace to the current epoch i, specifically including: Set the subspace probability threshold T high and T low , when n j >T high When , the subspace is a high probability range; When T low <n j <T high When , the subspace is in the medium probability range; When n j <T low When , the subspace is a low probability range.

6. The GNSS positioning method according to claim 1, characterized in that: In step S106, the center point of the state quantity solution space is calibrated each time, and the direction and distance of the subspace relative to the center point are obtained through the coordinate axis to obtain the probability orientation, and the probability orientation is calculated multiple times to form a data interval of multiple probability orientations, which are sorted to form high, medium and low probability orientations, including: S201: For each epoch state quantity solution space, calibrate the center point, and take the center point as the reference, calibrate the direction and distance of each subspace relative to the center point through the coordinate axis system to obtain the probability orientation; S202: collecting probability azimuth data obtained by calibration at multiple epochs, and sorting the probability azimuth data to divide them into high, medium, and low probability azimuth intervals; By processing the probability orientation data using a histogram, the main probability orientation and the secondary probability orientation are identified; S203: updating the solution space probability distribution model according to the probability orientation sorting and interval division, and taking the high probability range interval as the main body of the solution space; By using Bayesian statistical methods, the probability distribution information is integrated into the existing solution space probability distribution model; By integrating the probabilistic position information, the solution space probability model can accurately reflect the distribution of actual positioning results and improve positioning accuracy and reliability.

7. A satellite navigation system GNSS positioning method according to claim 6, characterized in that: The center point of the state quantity solution space of each epoch is calibrated, specifically, the range of the solution space is initialized by the state quantity, the mean of the solution space is calculated, and the center point of the solution space is calibrated.

8. A satellite navigation system GNSS positioning method according to claim 6, characterized in that: In step S201, the probability orientation of the subspace is further obtained through the probability orientation, and the high, medium and low probability orientations of the subspace are distinguished, which specifically includes the following steps: S301: Based on the probability orientation obtained in step S201, the solution space of each epoch state quantity is divided into N probability subspaces according to the continuity and density of the probability orientation; By using the K-means clustering algorithm, the clusters are divided according to the similarity of probability orientation and spatial distribution; S302: Count the number or frequency of state quantity solutions in each probability sub-interval, and divide the probability sub-interval into three probability levels: high, medium, and low according to the statistical results; By setting a threshold value for the data in the statistical results, the data can be divided; S303: According to the high, medium and low probability level division results of the probability subspace, the solution space probability distribution model is updated, and different weights are assigned to different probability subspaces; A weighted probability distribution model is used to integrate the information of high, medium, and low probability subspaces.

9. A satellite navigation system GNSS positioning method according to claim 8, characterized in that: In step S302, the algorithm equation of the solution space is corrected based on the azimuth relationship between the obtained high probability subinterval center point and the solution space center point, so that the high probability center point forms the center point of the solution space, and the high probability point is directly output through the relationship between the high probability center point and the solution space center point, and the subsequent state value points are recorded with the high probability point as the center, and the high probability solution space is formed through the relationship between the high probability center point and the state value point, and the calculation method of point acquisition is corrected to form an upgraded high probability solution space, which specifically includes the following steps: S401: Calculate the coordinates of the center point of each high probability subspace; S402: Calculate the position and distance relationship between the center point of each high probability sub-interval and the center point of the original solution space; The orientation and distance relationship between the center point of the high probability subinterval and the center point of the original solution space is calculated by using the coordinate geometry method; S403: adjusting relevant parameters in the original solution space algorithm equation according to the orientation and distance between the center point of the high probability subinterval and the center point of the original solution space, so that the new algorithm generates a solution space centered on the high probability center point; S404: applying the adjusted solution space algorithm equation to recalculate the solution space, and continuously recording the new probability subspace; Integrate the optimization results to form an upgraded high-probability solution space model.

10. A satellite navigation system GNSS positioning method according to claim 9, characterized in that: A number of cycles is set, that is, after obtaining the state value of the preset number of times, the high probability solution space is cycled to obtain the center point, probability orientation, high probability point and high probability solution space, specifically including the following steps: S501: Setting the number of loops w to control the number of times the state value is obtained and the frequency of loop optimization of the high probability solution space; S502: In each cycle, the state quantity xi of the current epoch is obtained, and all state value points are recorded; Repeat this step w times to collect a large amount of data for subsequent analysis; S503: Calculate the high probability subspace and the center point based on the state value collected in the current cycle; Use probabilistic statistical methods to identify high probability subspaces and use the mean method to calculate the center point; S504: Analyze the relationship between the high probability center point and the center point of the current solution space, and modify the solution space algorithm equation according to the analysis result; Optimize and adjust solution space parameters through coordinate geometry methods and algorithms; S505: Use the newly acquired state value to verify the corrected solution space, and update the high probability solution space according to the verification result; S506: After completing one cycle, check whether the set number of cycles w is reached; If not, return to step S502 to continue the next cycle; if it is reached, perform the final optimization and output the result; Output the final optimized upgraded high-probability solution space model.

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