A 5G positioning method, system, electronic device, and computer-readable storage medium

Abstract

This application relates to a positioning method that addresses the technical problems of current 5G-based positioning methods, such as large errors, frequent database maintenance, and inability to adapt to dynamic environmental changes. It provides a 5G positioning method, system, electronic device, and computer-readable storage medium. Based on the geometric accuracy attenuation factor degradation coefficient of candidate base station combinations, an initial base station combination is selected. After RSS signal noise reduction processing and TDOA path calibration of the original signal, the initial position of the 5G user terminal device is calculated. Simultaneously, a dynamic allocation strategy for RSS and TDOA weights based on environmental factors is formulated. A weighted least squares error function of RSS and TDOA weights is constructed as the objective function, and minimizing the objective function yields the estimated position coordinates of the 5G user terminal device. Finally, the base station combination is dynamically adjusted to obtain the optimal base station combination. This method offers stronger environmental adaptability and more accurate positioning.

Description

Technical Field

[0001] This application pertains to a positioning method, specifically relating to a 5G positioning method, system, electronic device, and computer-readable storage medium. Background Technology

[0002] 5G communication technology offers new opportunities for indoor positioning due to its high-frequency characteristics, but its high-frequency signals are susceptible to multipath fading and non-line-of-sight (NLOS) effects, making it difficult for a single positioning method to meet the high-precision requirements.

[0003] In recent years, academia and industry have conducted in-depth research on base station layout optimization. For example, Chinese invention patent application CN119582931A proposes a pseudo-satellite layout optimization method based on deep belief networks and particle swarm optimization, using machine learning algorithms to optimize base station locations and improve navigation system performance. Chinese invention patent application CN119155794A, targeting mobile base stations, avoids ill-conditioned matrices by dynamically selecting base station locations, thus improving positioning accuracy. Chinese invention patent application CN119227515A proposes a multi-objective base station layout optimization method, which, by introducing system signal coverage, improves the coverage of the land-based navigation system while optimizing the distribution of accuracy factors. However, most of these methods are designed for specific scenarios, do not consider changes in signal quality under dynamic interference, and rely on offline training or preset constraints, making them difficult to adapt to the real-time positioning needs of open scenarios.

[0004] Existing 5G-based positioning methods are mainly divided into geometric positioning, fingerprint matching, and fusion methods. Among them, geometric positioning methods, particularly TDOA (Time Difference of Arrival), have high requirements for clock synchronization and exhibit significant errors in multipath environments. Fingerprint matching methods are susceptible to dynamic interference, requiring frequent database maintenance. Fusion methods often employ fixed weight strategies, which cannot adapt to dynamic environmental changes. Furthermore, existing technologies generally neglect the impact of three-dimensional spatial geometric layout on positioning accuracy. When base stations are collinear or have unreasonable angular distributions, the Geometric Dilution of Precision (GDOP) factor increases significantly, leading to an exponential increase in positioning errors. Summary of the Invention

[0005] This application addresses the technical problems of current 5G-based positioning methods, such as large errors, the need for frequent database maintenance, and the inability to adapt to dynamic environmental changes, by providing a 5G positioning method, system, electronic device, and computer-readable storage medium.

[0006] To achieve the above objectives, this application adopts the following technical solution: Firstly, this application proposes a 5G positioning method, including: Candidate base station combinations are determined by polyhedral volume constraints and elevation angle constraints; Based on the geometric precision attenuation factor deterioration coefficient of the candidate base station combinations, an initial base station combination is obtained through screening. Signals are collected from 5G user terminal devices to obtain the raw signals; The original signal is subjected to received signal strength denoising and arrival time difference path calibration respectively, resulting in a received signal strength sequence after time-series smoothing and a purified arrival time difference dataset. Based on the three-dimensional coordinates of the initial base station combination and the purified time difference of arrival dataset, the initial position of the 5G user terminal equipment is calculated. Based on the received signal strength sequence after time-series smoothing and the purified time difference of arrival dataset, a dynamic allocation strategy for received signal strength weight and time difference of arrival weight based on environmental factors is formulated. Based on the dynamic allocation strategy of received signal strength weight and time difference of arrival weight, a weighted least squares error function of received signal strength weight and time difference of arrival weight is constructed as the objective function. By combining the initial position of the 5G user terminal equipment, the objective function is minimized to obtain the estimated position coordinates of the 5G user terminal equipment. Based on the estimated location coordinates of the 5G user terminal equipment, the base station combination is dynamically adjusted to obtain the optimal base station combination for 5G positioning.

[0007] Furthermore, the polyhedral volume constraint includes: Calculate the volume of the three-dimensional polyhedron formed by any combination of base stations; If the volume of the three-dimensional polyhedron is less than the dynamic threshold, the corresponding base station combination is removed; otherwise, the corresponding base station combination is retained. The pitch angle constraint includes: For any combination of base stations, if the elevation angle of each base station and the standard deviation of the elevation angle of each base station in the same combination meet the preset requirements, the corresponding base station combination is retained; otherwise, the corresponding base station combination is removed.

[0008] Furthermore, the method for calculating the initial position of the 5G user terminal equipment includes: Construct the hyperbola positioning equation:

[0009] in, This is the initial location of the 5G user terminal equipment to be located. for N A collection of base stations For the first One base station, The coordinates of the calibrated main base station. , To determine the values ​​of the hyperbola's positioning equation, At the speed of light, For the first in the purified arrival time difference dataset One data point; the main base station is the base station with the smallest standard deviation within the preset sliding window; By linearizing the hyperbolic positioning equation using Taylor expansion, an observation matrix is ​​constructed. The initial position of the 5G user terminal equipment is then solved using the weighted least squares method, and the solution is optimized through residual analysis.

[0010] Furthermore, the dynamic allocation strategy for received signal strength weight and time difference of arrival weight based on environmental factors includes: Time: Employs the time difference of arrival (TDOA) dominant model; Time: The received signal strength weight and the time difference of arrival weight are smoothly adjusted using the Sigmoid function; At this time: the received signal strength-dominated mode is used; in, As environmental factors, The calculation method is as follows:

[0011] in, The standard deviation of the received signal strength sequence samples after time-series smoothing. The mean of the received signal strength sequence after time-series smoothing. The standard deviation of the cleaned arrival time difference dataset. This represents the mean of the purified arrival time difference dataset.

[0012] Furthermore, the objective function includes:

[0013] in, Let be the objective function. For received signal strength weighting, This represents the total number of base stations in the base station combination. This represents the number of base station pairs in the base station combination. For the first The received signal strength ranging value of each base station, Estimating the location of 5G user terminal equipment. For the first One base station, For arrival time difference weighting, This refers to the base station number in the base station combination. Let be the base station log number in the base station combination, and , The base station logarithmic sequence number is The average arrival time difference of the base station combination after purification. At the speed of light, For the first A combination of base stations, For the first A combination of base stations, Estimating the location of 5G user terminal equipment To the base stations Euclidean distance, Estimating the location of 5G user terminal equipment To the Base station combination Euclidean distance, Estimating the location of 5G user terminal equipment To the Base station combination The Euclidean distance.

[0014] Furthermore, dynamically adjusting the base station combination based on the estimated location coordinates of the 5G user terminal equipment also includes: Based on the estimated location coordinates of the 5G user terminal equipment, calculate the received signal strength ranging residual and the time difference of arrival residual respectively. The joint residual standard deviation of 5G user terminal equipment is determined based on the received signal strength ranging residual and the arrival time difference residual. Determine whether the accuracy of the current base station combination meets the requirements based on the joint residual standard deviation, and dynamically adjust the base station combination.

[0015] Further, based on the geometric precision attenuation factor degradation coefficient of the candidate base station combinations, an initial base station combination is obtained, including: For each base station in the candidate base station combination, a base station observation matrix is ​​constructed. Based on the base station observation matrix, calculate the geometric precision attenuation factor of the candidate base station combination; Based on the geometric precision attenuation factor of the candidate base station combination, calculate the geometric precision attenuation factor degradation coefficient. If the geometric precision attenuation factor degradation coefficient is greater than a preset value, reselect from the candidate base station combinations; otherwise, retain the corresponding base station combination and proceed. If the standard deviation of the joint residual is greater than the preset threshold for the standard deviation of the joint residual, then a new selection is made from the candidate base station combinations; otherwise, the corresponding base station combination is retained and the process is executed. Based on the geometric precision attenuation factor of the candidate base station combination, a multi-objective scoring function is calculated. If the value of the multi-objective scoring function is less than the preset value, the corresponding base station combination is eliminated; otherwise, the corresponding base station combination is retained. The initial base station combination is obtained.

[0016] Secondly, this application proposes a 5G positioning system, comprising: The candidate module is used to determine the combination of candidate base stations through polyhedral volume constraints and elevation angle constraints; The optimization module is used to select an initial base station combination based on the geometric precision attenuation factor degradation coefficient of the candidate base station combination; The acquisition module is used to acquire signals from 5G user terminal devices to obtain raw signals; The calibration module is used to perform received signal strength noise reduction processing and arrival time difference path calibration on the original signal respectively, so as to obtain the received signal strength sequence after time-series smoothing and the purified arrival time difference dataset. The preliminary calculation module is used to calculate the initial position of the 5G user terminal equipment based on the three-dimensional coordinates of the initial base station combination and the purified time difference of arrival dataset. The strategy module is used to formulate a dynamic allocation strategy for received signal strength weights and time difference of arrival weights based on environmental factors, according to the received signal strength sequence after time-series smoothing and the purified time difference of arrival dataset. The objective function module is used to construct a weighted least squares error function of the received signal strength weight and the time difference of arrival weight as the objective function based on the dynamic allocation strategy of the received signal strength weight and the time difference of arrival weight. The solution module is used to solve the minimum objective function based on the initial position of the 5G user terminal equipment to obtain the estimated position coordinates of the 5G user terminal equipment; The dynamic adjustment module is used to dynamically adjust the base station combination based on the estimated location coordinates of the 5G user terminal equipment to obtain the optimal base station combination for 5G positioning.

[0017] Thirdly, this application proposes an electronic device, including: a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the steps of the aforementioned 5G positioning method.

[0018] Fourthly, this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned 5G positioning method.

[0019] Compared with the prior art, this application has the following beneficial effects: This application proposes a 5G positioning method. Based on the confidence level of candidate base station combinations, an optimal base station combination is obtained. The raw signals obtained from signal acquisition by the 5G user terminal device are subjected to received signal strength noise reduction processing and time difference of arrival (TDOA) path calibration to calculate the initial position of the 5G user terminal device. Simultaneously, a dynamic allocation strategy for received signal strength weights and TDOA weights based on environmental factors is formulated. A weighted least squares error function of the received signal strength weights and TDOA weights is constructed as the objective function, and minimizing the objective function yields the estimated position coordinates of the 5G user terminal device. This application selects the initial base station combination based on the geometric accuracy attenuation factor degradation coefficient of the candidate base station combinations and dynamically adjusts the base station combination after obtaining the estimated position coordinates of the 5G user terminal device, reducing positioning errors from a layout perspective and achieving more accurate positioning. Furthermore, the dynamic allocation strategy for received signal strength weights and TDOA weights based on environmental factors can be adjusted in real time according to environmental factors. Compared with fixed weights, it is more adaptable to different interference scenarios, has stronger environmental adaptability, and does not require frequent database maintenance.

[0020] This application also proposes a 5G positioning system, an electronic device, and a computer-readable storage medium, which possess all the advantages of the aforementioned 5G positioning methods. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of one embodiment of the 5G positioning method of this application; Figure 2 This is a schematic diagram of another embodiment of the 5G positioning method of this application; Figure 3 This is the calibration curve of the exponential decay coefficient in the embodiments of this application; Figure 4 This is a schematic diagram of the positioning results obtained in an embodiment of this application; Figure 5 for A diagram comparing the error distribution of dynamic weights and fixed weights; Figure 6 for A diagram comparing the error distribution of dynamic weights and fixed weights; Figure 7for K A diagram comparing the error distribution of dynamic weights and fixed weights when the value is ≥1.0; Figure 8 This is a schematic diagram of a 5G positioning system according to this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0024] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0025] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0026] In the description of the embodiments of this application, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this application. In addition, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0028] In the description of the embodiments of this application, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0029] Indoor positioning has significant demand in fields such as smart logistics and industrial automation. 5G positioning is affected by multipath fading and NLOS effects. Single positioning methods, such as TDOA, rely on clock synchronization, and RSS (Received Signal Strength) requires frequent database maintenance, making it difficult to meet the demands. Some existing solutions fuse RSS and TDOA data, but use fixed-weight strategies. Furthermore, base station selection often follows fixed layouts or simple rules, such as based on experience or signal strength, without systematically considering the impact of three-dimensional geometric constraints on GDOP. Therefore, base station selection lacks optimized geometric layout and fails to quantify the impact of relative position and angle on GDOP, resulting in suboptimal positioning geometry. Moreover, fixed-weight fusion strategies cannot adapt to dynamic environmental changes, leading to large fluctuations in positioning accuracy under different interference intensities.

[0030] Based on the above, this application proposes a 5G positioning method, system, electronic device, and computer-readable storage medium. The following describes this application in detail with reference to embodiments and accompanying drawings.

[0031] like Figure 1 The diagram shown is a schematic representation of an embodiment of the 5G positioning method of this application, which may include: S101, candidate base station combinations are determined by polyhedral volume constraints and elevation angle constraints.

[0032] In indoor 5G positioning scenarios, the area covered by multiple base stations can be approximated as a polyhedron. Using methods for calculating the volume of a polyhedron, the volume of the polyhedron formed by different combinations of base stations is analyzed. Only base station combinations that meet certain volume conditions can provide more accurate positioning information. For example, a volume that is too small may result in an overly limited positioning area, while a volume that is too large may contain too many interference factors. Furthermore, base stations transmit signals within a certain range of elevation angles, and different elevation angles affect signal coverage and reception. By restricting the elevation angles of base stations, base station combinations that meet the signal reception conditions of 5G user terminal devices are selected. For example, when a 5G user terminal device is in a specific location, only base station signals within certain elevation angle ranges can effectively reach it, thus determining candidate base station combinations. This eliminates base stations that cannot provide effective positioning information due to unsuitable elevation angles, improving positioning accuracy.

[0033] S102, Based on the geometric precision attenuation factor deterioration coefficient of the candidate base station combination, an initial base station combination is obtained.

[0034] After determining the candidate base station combinations, a base station observation matrix is ​​constructed for each combination, and the corresponding geometric precision attenuation factor is calculated. Then, based on the change of the current geometric precision attenuation factor relative to the initially selected optimal value, and combined with the current continuous running time, the geometric precision attenuation factor degradation coefficient is obtained. When the geometric precision attenuation factor degradation coefficient is greater than a preset value, it indicates that the spatial geometric distribution of the candidate base station combination has deteriorated, easily amplifying ranging errors; therefore, a new selection is made from the candidate base station combinations. When the geometric precision attenuation factor degradation coefficient is not greater than the preset value, the corresponding base station combination is retained.

[0035] S103 collects signals from 5G user terminal equipment to obtain the raw signal.

[0036] 5G user terminal equipment is typically equipped with a dedicated signal receiving module to collect signals from base stations. These modules can receive 5G signals of different frequency bands and convert them into electrical signals. The raw signals collected usually contain various information, such as RSS and TDOA. However, the raw signals may be affected by factors such as noise and multipath effects, resulting in certain errors and interference. Therefore, subsequent processing is required to improve the quality of the raw signals.

[0037] S104, the original signal is subjected to received signal strength noise reduction processing and arrival time difference path calibration respectively, to obtain the received signal strength sequence after time-series smoothing and the purified arrival time difference dataset.

[0038] Because RSS signals are susceptible to noise interference, such as indoor electromagnetic interference and reflections, they require noise reduction algorithms to smooth the signal and improve its reliability. TDOA signals may be affected by multipath effects during transmission, leading to errors in signal arrival time measurement. By calibrating the path of the TDOA signal, errors caused by multipath effects are eliminated, resulting in more accurate time difference data and providing a more accurate basis for subsequent positioning calculations.

[0039] S105, based on the three-dimensional coordinates of the initial base station combination and the purified time difference of arrival dataset, the initial position of the 5G user terminal equipment is calculated.

[0040] Each base station in the initial base station array has its corresponding three-dimensional coordinates, which can be obtained from the base station's installation location and relevant geographic information systems. Based on the base station's three-dimensional coordinates, a certain positioning algorithm can be used to calculate the initial position of the 5G user terminal equipment. This initial position is generated solely based on TDOA geometric positioning and serves as the starting point for subsequent nonlinear optimization iterations.

[0041] The initial position solution serves as the input basis for joint optimization and forms a progressive optimization relationship with the final estimated position coordinates: the initial position is obtained by solving a single TDOA data set, while the final estimated position coordinates are obtained by optimizing the objective function with the RSS / TDOA data, which are fused with dynamic weights, starting from the initial position.

[0042] S106. Based on the received signal strength sequence after time-series smoothing and the purified time difference of arrival dataset, formulate a dynamic allocation strategy for received signal strength weight and time difference of arrival weight based on environmental factors.

[0043] The RSS and TDOA data in the original signal have different characteristic parameters, such as the signal strength variation of RSS data and the time difference accuracy of TDOA data. Meanwhile, indoor environmental factors can have different effects on RSS and TDOA data. For example, in areas with many obstacles, RSS data may attenuate significantly, while TDOA data may be relatively more accurate. Based on the analysis of these characteristic parameters and environmental factors, this application formulates a strategy for dynamically allocating RSS and TDOA weights. Under different environmental conditions, the RSS and TDOA weights are adjusted in real time to fully utilize their advantages and improve positioning accuracy. For example, in open areas, where RSS data is strong and accurate, the RSS weight can be appropriately increased; in complex environments, where TDOA data may be more reliable, the TDOA weight can be increased.

[0044] S107. Based on the dynamic allocation strategy of received signal strength weight and time difference of arrival weight, a weighted least squares error function of received signal strength weight and time difference of arrival weight is constructed as the objective function.

[0045] It should be noted that weighted least squares is a commonly used optimization method that weights the error according to the different weights of the data. In 5G positioning, the RSS weight and TDOA weight are weighted according to a dynamic allocation strategy. By weighting the RSS weight and TDOA weight, a weighted least squares error function is constructed as the objective function. The goal of this objective function is to minimize the positioning error; that is, by adjusting the location parameters of the 5G user terminal equipment, the value of the objective function is minimized, resulting in more accurate positioning results.

[0046] S108, combined with the initial position of the 5G user terminal equipment, solve the minimum objective function to obtain the estimated position coordinates of the 5G user terminal equipment.

[0047] The initial position of the 5G user terminal device provides an initial reference point for solving the objective function. During the solution process, based on this initial position, the position parameters are continuously adjusted to minimize the objective function. In practical applications, optimization algorithms such as Newton's iteration method and gradient descent can be used to solve the objective function. Through iterative calculations, the position parameters that minimize the objective function are gradually approximated, ultimately yielding the estimated position coordinates of the 5G user terminal device. The obtained estimated position coordinates are more accurate and better adaptable to the complex environment of indoor 5G positioning.

[0048] S109, Based on the estimated location coordinates of the 5G user terminal equipment, dynamically adjust the base station combination to obtain the optimal base station combination for 5G positioning.

[0049] After obtaining the estimated location coordinates of the 5G user terminal equipment, the positioning accuracy of the current base station combination is reassessed based on these estimated coordinates. This dynamic adjustment process allows the base station combination to be updated in a closed loop as the location of the 5G user terminal equipment and the environment change, improving the stability and environmental adaptability of the continuous positioning process while ensuring positioning accuracy.

[0050] This application addresses the problems of existing 5G indoor positioning technologies, such as the deterioration of geometric accuracy due to the randomness of base station layout and the inability of fixed-weight fusion strategies to adapt to dynamic environments.

[0051] like Figure 2 The diagram shown illustrates another embodiment of the 5G positioning method of this application. The implementation method of this application is divided into four modules: a geometric optimization module, a data processing module, a dynamic weight allocation module, and a joint optimization module. The methods executed in each module are described in detail below.

[0052] 1. Geometry optimization module.

[0053] The core objective of the geometry optimization module is to select an initial base station combination from multiple candidate base station combinations to improve positioning accuracy and reliability. This application introduces a three-dimensional geometric constraint criterion, employing an optimization decision combining polyhedral volume constraints and pitch angle constraints (pitch angle hierarchical selection): First, the volume of the three-dimensional polyhedron formed by the candidate base station combinations is calculated. When the volume of a three-dimensional polyhedron The value exceeds the preset dynamic threshold. When the distance to the base station and signal fluctuations are considered, the candidate base station combination is determined to have a reasonable spatial distribution. Simultaneously, candidate base station combinations with excessively concentrated vertical distribution are eliminated through hierarchical screening using elevation angles. Through the joint constraint mechanism of the above-mentioned three-dimensional spatial layout, the geometry optimization module selects the initial base station combination from a geometric perspective. Specific screening methods may include: (1) Initialization phase.

[0054] Assume the initial location of the 5G user terminal device to be determined is... Loading the first Coordinates of available base stations .in, This indicates the base station number in the base station combination.

[0055] Introducing dual constraints: a. Polyhedral volume constraint: For any combination of base stations, this embodiment takes four base stations as an example to calculate the volume of the three-dimensional polyhedron formed. :

[0056] in, to These represent the x-axis coordinates of the 1st to the 4th base stations, respectively. to These represent the y-axis coordinates of the 1st to the 4th base stations, respectively. to These represent the z-axis coordinates of the 1st to the 4th base stations, respectively. To obtain the determinant of the corresponding square matrix.

[0057] When base stations are laid out too closely together, either coplanar or collinear, If the value approaches zero, such base station combinations are excluded. Only base station combinations with a volume greater than the dynamic threshold are retained.

[0058] Among them, dynamic threshold The calculation equation is as follows:

[0059] in, For the first The Euclidean distance from each base station to the location point (the estimated initial location of the 5G user terminal equipment to be determined), in meters. The standard deviation of the received signal strength sequence after time-series smoothing is expressed in dBm, representing the amplitude of signal fluctuation. This is the exponential decay coefficient, which can be determined through offline calibration in practical applications. For example... Figure 3 The figure shows the calibration curve of the exponential decay coefficient in this embodiment. Figure 3 In the middle, the horizontal axis Reflecting RSS intensity fluctuations, the vertical axis... The threshold volume represents a threshold related to the RSS data in the simulation experiment, which varies with the x-axis variable. It represents the goodness of fit of the regression model, that is, the degree of fit between the exponential decay coefficient calibration model and the actual measurement data. The closer the curve is to 1, the better it fits the simulated experimental data. Figure 3 The higher the goodness of fit (represented by black dots), the better the fit. The exponential decay coefficient is determined by minimizing the positioning error under different signal-to-noise ratios. The optimal value is , This represents the number of base stations in the candidate base station combination. This represents the true correlation coefficient of the spatial distribution of base station combinations, which is the theoretically designed optimal geometric parameter and serves as a theoretical reference benchmark for measuring the fitting results. The correlation coefficient represents the spatial distribution obtained by fitting simulated experimental data, and serves as a quantitative indicator of the actual fitting result.

[0060] b. Pitch angle constraint: To ensure the rationality of the vertical distribution of base stations, this application constrains the elevation angle of the base stations based on empirical statistics. To avoid base stations being overly concentrated on the horizontal plane, which could easily lead to increased vertical positioning errors, the following provisions are made: The elevation angle of each base station Must meet ( ), and the standard deviation of the elevation angle of each base station within the same base station combination. satisfy The formula for calculating the standard deviation of the elevation angle of each base station within the same base station combination is as follows:

[0061] in, This represents the average elevation angle of all base stations within the same base station group. For the firsti The z-axis coordinates of each base station The z-axis coordinate is the estimated initial position of the 5G user terminal device to be determined.

[0062] Candidate base station combinations are obtained by screening base station combinations using polyhedral volume constraints and elevation angle constraints.

[0063] (2) Calculation of confidence of candidate base station combination.

[0064] In the candidate base station combinations selected through polyhedral volume constraints and elevation angle constraints, the confidence level of the candidate base station combinations is calculated. This can be achieved through the following steps: a. Construct an observation matrix based on the three-dimensional coordinates of the base station.

[0065] A base station observation matrix is ​​constructed by calculating the GDOP value of the selected candidate base station combinations. :

[0066] in, to The first base station to the second base station are respectively The Euclidean distance from each base station to the location point to The first base station to the second base station are respectively The x-axis coordinates of each base station to The first base station to the second base station are respectively The y-axis coordinates of each base station to The first base station to the second base station are respectively The z-axis coordinates of each base station.

[0067] b. GDOP calculation.

[0068] Calculate the GDOP of the candidate base station combination. GDOP characterizes the amplification effect of positioning geometry on ranging error, and the calculation formula is: .

[0069] in, The geometric accuracy attenuation factor, This represents the trace operation of the base station observation matrix. (Selection) The three smallest candidate base station combinations are used as a backup combination pool. This method geometrically reduces the theoretical lower limit of subsequent positioning errors.

[0070] (3) Base station combination selection decision.

[0071] Real-time monitoring of geometric accuracy attenuation factor and degradation coefficient during the positioning phase Geometric accuracy attenuation factor deterioration coefficient The calculation formula is: .

[0072] in, express Time geometric precision attenuation factor observations This represents the optimal geometric precision attenuation factor value in the initial screening. This indicates the current continuous running time, in seconds. The thermal stability time constant for 5G user terminal equipment is calibrated to 180 seconds in this embodiment. It is believed that the current base station combination geometry has deteriorated, triggering a reselection mechanism.

[0073] If no reselection is required, the joint residual standard deviation is then calculated to assess positioning accuracy. This involves calculating the RSS ranging residual and TDOA residual based on the estimated location coordinates from the 5G user terminal equipment. The method for calculating RSS ranging residuals is as follows:

[0074] in, For the first The received signal strength ranging residual of each base station, in square meters, is used to reflect the degree of matching between the signal ranging model and the actual geometric distance. Estimate the location coordinates of 5G user terminal equipment. For the first One base station, For the first The received signal strength ranging value of each base station, in meters. It can reflect the deviation between the signal ranging model and the actual geometric distance.

[0075] The method for calculating TDOA residuals is as follows:

[0076] in, For the first Each base station's TDOA residual, To measure the true value of TDOA, For the first One base station pair, For the first base station pair, At the speed of light, This represents the number of base station pairs in the base station combination. Estimating the location of 5G user terminal equipment To the base stations The Euclidean distance, in meters, is calculated from the estimated location coordinates and the base station coordinates. This represents the theoretical distance difference between the 5G user terminal equipment and the base station pair, expressed in meters. This reflects the difference between the TDOA measured value and the theoretical distance.

[0077] In summary, the formula for calculating the standard deviation of the joint residuals is:

[0078] in, The combined residual standard deviation, in meters, is used to characterize the overall error level of the estimated location.

[0079] As an example, based on historical data statistics, the preset threshold for the joint residual standard deviation is: ,like If the accuracy of the current base station combination is insufficient, the geometric optimization module can be triggered to re-select the base station combination, and abnormal base station combinations can be eliminated in conjunction with the joint residual standard deviation. Specifically, when using the joint residual standard deviation for judgment, when... At that time, the multi-objective scoring function is activated. :

[0080] in, This represents the number of base station handovers. In the formula above, 0.5, 0.3, and 0.2 represent the weights corresponding to GDOP optimization, RSS stability, and base station handover penalty in the multi-objective scoring function, respectively. The weight corresponding to GDOP optimization is set to 0.5, meaning that the base station combination with the smallest geometric precision attenuation factor is selected first. The weight corresponding to RSS stability is 0.3, used to suppress the misselection of base station combinations caused by signal fluctuations. For example, The frequency of 5 times per minute was derived from stability testing. It should be noted that all data calibrations in this application are derived from empirical statistical analysis.

[0081] Ultimately, the degradation coefficient is determined by the geometric accuracy attenuation factor. and the combined residual standard deviation Joint decision to select new active base station combinations: first, by using the geometric precision attenuation factor deterioration coefficient. Identify base station combinations with deteriorated geometry and trigger reselection, then utilize the joint residual standard deviation. Candidate base station combinations that meet the positioning accuracy standard are selected. Finally, based on a multi-objective scoring function (combining GDOP, residual standard deviation, and number of handovers), the base station combination with the highest score is selected as the newly activated base station combination, ensuring dual optimization of geometric layout and positioning accuracy.

[0082] 2. Data processing module.

[0083] After the initial base station combination is selected through the geometry optimization module, the data processing module begins processing the raw signal. The data processing module preprocesses the acquired raw signal to eliminate noise and outlier interference. Key steps include: (1) Multi-source data acquisition and preprocessing.

[0084] This embodiment uses the USRP B210 (Universal Software Radio Peripheral B210) software radio platform to collect the raw signals from the 5G user equipment (UE) and simultaneously performs dual-link processing: In the RSS link, SS-RSRP (Synchronization Signal - Reference Signal Received Power) measurements are collected at 100ms intervals and a noise reduction algorithm is implemented; in the TDOA (Time Difference of Arrival) link, the time delay difference between the base station's ID (identity number) and timestamp is obtained based on the cross-correlation calculation of the PRS (Positioning Reference Signal), and path calibration is performed to eliminate multipath effects. This process improves the reliability of the data input to the positioning algorithm and the overall positioning accuracy and robustness of the system by filtering out noise interference and correcting system deviations (including clock deviations and time-varying obstructions). Specifically: a. RSS noise reduction processing.

[0085] To address the signal abrupt changes in RSS due to multipath effects and human occlusion, Kalman filtering is employed for timing smoothing. The specific processing flow includes: 1> State prediction.

[0086] Based on the previous optimal RSS estimate and combined with a pre-defined process noise model, the theoretical range of the current RSS is predicted.

[0087] 2> Observation correction.

[0088] The real-time RSS measurement value and the predicted value are weighted and fused, and the Kalman gain coefficient is dynamically adjusted to reduce the impact of sudden electromagnetic interference.

[0089] 3> Iterative convergence.

[0090] Through multiple rounds of prediction-correction loops, the received signal strength sequence after time-smoothing is output. It can effectively suppress short-time impulse noise and low-frequency drift.

[0091] b. TDOA path calibration.

[0092] To address systematic biases caused by clock skew and multiple access interference in TDOA measurements, the following calibration strategies can be employed: 1> Primary base station selection.

[0093] Periodically evaluate the signal stability of each base station and select the base station with the smallest standard deviation within the sliding window as the main base station.

[0094] 2> Sliding window averaging.

[0095] Calculate the average TDOA of the same base station pair within a continuous time window (e.g., 1 second) to filter out abnormal false peaks caused by sudden reflection paths.

[0096] 3> Residual compensation.

[0097] The deviation of statistical measurements from the mean of TDOA is used to remove outliers exceeding the threshold, and missing values ​​are repaired using linear interpolation to generate a cleaned-up Time Difference of Arrival (TDOA) dataset. .

[0098] (2) Feature parameter extraction.

[0099] After completing RSS signal noise reduction and TDOA path calibration, the data processing module further extracts key feature parameters for environmental factor calculation.

[0100] a. Calculation of RSS characteristic parameters.

[0101] Calculate the mean of the received signal strength sequence after time-smooth processing:

[0102] in, The mean of the received signal strength sequence after time-series smoothing. This is the RSS index in the received signal strength sequence after time-smoothing processing. This indicates that the RSS value is being processed for the first time. k The measured values ​​obtained from this measurement.

[0103] Calculate the mean of the received signal strength sequence after time-smooth processing It reflects the average level of signal strength and is used for subsequent normalization processing.

[0104] Calculate the sample standard deviation of the received signal strength sequence after time-smooth processing. :

[0105] in, It reflects the degree of signal strength fluctuation.

[0106] b. Calculation of TDOA characteristic parameters.

[0107] Calculate the mean of the purified arrival time difference dataset:

[0108] in, The mean of the purified arrival time difference dataset. The mean TDOA within the sliding window. For window length, For the first in the purified arrival time difference dataset Data.

[0109] Calculate the standard deviation of the purified arrival time difference dataset:

[0110] in, The standard deviation of the cleaned arrival time difference dataset. Measure the true value of TDOA.

[0111] (3) Initial position estimation.

[0112] This step, based on the initial base station combination selected by the geometry optimization module, uses the purified time difference of arrival (TDOA) dataset to construct a hyperbolic positioning equation, enabling rapid calculation of the initial position of 5G user terminal equipment:

[0113] in, This is the initial location of the 5G user terminal equipment to be located. for N A collection of base stations The coordinates of the calibrated main base station. , At the speed of light, For the first in the purified arrival time difference dataset The data includes the coordinates of the main base station after calibration by the data processing module (the base station with the smallest standard deviation within the sliding window is the main base station).

[0114] The solution process involves linearizing the hyperbolic positioning equation using Taylor expansion, constructing an observation matrix, and then using the weighted least squares method to solve for the initial position of the 5G user terminal equipment. Finally, residual analysis is used to optimize the solution results and improve positioning accuracy.

[0115] The above processing significantly improves the effective utilization of RSS and TDOA data, laying a high-quality data foundation for subsequent weight allocation and joint optimization. Simultaneously, the coordinated output of these parameters provides a complete signal quality feature vector for environmental factor calculation, ensuring that the dynamic weight allocation module can comprehensively evaluate signal quality.

[0116] 3. Dynamic weight allocation module.

[0117] The dynamic weight allocation module constructs environmental factors based on the core parameters obtained from the data processing module, quantifies the signal quality level in real time, and dynamically adjusts the fusion weights of RSS and TDOA accordingly. This achieves an optimized balance between positioning accuracy and system robustness in complex electromagnetic environments. Specific methods may include: (1) Calculate environmental factors .

[0118] The environmental disturbance level is determined by statistically analyzing the characteristic parameters of RSS and TDOA and normalizing the fluctuation ratio.

[0119] The smaller the standard deviation, the more stable the signal, and the higher its reliability in weight allocation; conversely, the larger the standard deviation, the more drastic the signal fluctuation, in which case the signal weight should be reduced or an error correction mechanism should be triggered.

[0120] (2) Decision-making based on environmental factors.

[0121] The calculation of environmental factors is not only a quantitative representation of signal quality, but also the foundation for constructing a hierarchical decision-making system. By converting RSS volatility and TDOA consistency into normalized ratios, an actionable decision-making basis is provided for subsequent interference level classification. Based on this calculation framework, the environmental factor classification system establishes a complete link from signal characteristic analysis to system strategy adjustment through theoretical threshold derivation, experimental data verification, and dynamic response optimization, thereby achieving precise adaptation to complex electromagnetic environments.

[0122] In this embodiment, the specific grading criteria are as follows: a. Low-quality range ( ): RSS fluctuates wildly ( (The movement of people or obstruction of equipment leads to a significant multipath effect.) b. Medium quality range ( RSS and TDOA performance fluctuate alternately, requiring fine-grained dynamic balancing; c. High-quality range ( ): Signal propagation is stable ( RSS has superior accuracy.

[0123] (3) Weighting strategy.

[0124] The environmental factor classification system not only enables quantitative assessment of signal quality but also provides a clear decision boundary for weight allocation strategies. and These are the RSS weights and TDOA weights, respectively. Then we have: Time: Adopting the TDOA dominant mode, the time delay differential is used to improve the robustness of multipath and enhance positioning stability.

[0125] Time: The weights are smoothly adjusted using the Sigmoid function to ensure a continuous and seamless fusion process.

[0126] At this time: switch to RSS-dominant mode to enhance the constraint effect of the signal ranging model on the positioning solution.

[0127] This grading system combines theoretical thresholds with measured data to construct a three-interval weighted decision-making model based on environmental factors. Among them, the low-quality interval ( ) and high-quality range ( A fixed-weight strategy is adopted, corresponding to TDOA-dominated ( ) mode and RSS-dominated ( ) mode. And the medium quality range ( Then, the Sigmoid function is introduced to achieve a non-linear smooth transition of weights, which not only preserves the gradual change characteristics of signal quality, but also avoids the decision jump problem at the threshold boundary of traditional piecewise functions.

[0128] The specific adaptive weight formula is as follows:

[0129] The dynamic weight allocation module dynamically adjusts the RSS and TDOA weights based on environmental factors, achieving adaptive fusion of multi-source data. This strategy enhances robustness to complex environments while maintaining positioning accuracy, making it particularly suitable for mixed line-of-sight and non-line-of-sight scenarios.

[0130] 4. Joint optimization module.

[0131] The joint optimization module fuses the RSS ranging model and the TDOA positioning model with dynamic weights to construct a joint positioning objective function. The specific optimization method is as follows: (1) Model input initialization.

[0132] a. Dynamic weight input.

[0133] Receive RSS weights output by the dynamic weight allocation module With TDOA weight In practical applications, the input weights need to be normalized and validated to ensure... If an error occurs, the system will be forcibly reset to the default value. Then, the weights are adjusted according to the weight allocation strategy.

[0134] b. Base station combination and initial coordinate loading.

[0135] The set of three-dimensional coordinates of the initial base station combination selected by the loading geometry optimization module. It also receives the initial location estimate of the 5G user terminal equipment from the data processing module. .

[0136] c. Input of feature parameters.

[0137] RSS ranging value: The RSS value after time-series smoothing is converted into a ranging value, using the following formula:

[0138] in, For the first The received signal strength ranging value of each base station, The received signal power at the reference distance, in dBm. For the first The mean RSS of each base station after time-series smoothing, in dBm. The path loss factor can be obtained through offline calibration.

[0139] (2) Joint positioning objective function.

[0140] The joint positioning objective function is constructed by fusing the RSS ranging model and the TDOA positioning model, and a weighted least squares error function is generated. :

[0141] in, Let be the objective function. For received signal strength weighting, This represents the total number of base stations in the base station combination. This represents the number of base station pairs in the base station combination. For the first The received signal strength ranging value of each base station, Estimating the location of 5G user terminal equipment. For the first One base station, For arrival time difference weighting, This refers to the base station number in the base station combination. Let be the base station log number in the base station combination, and , The base station logarithmic sequence number is The data in the purified time difference of arrival dataset of the base station combination, For the first One base station pair, For the first One base station pair, Estimating the location of 5G user terminal equipment To the base stations Euclidean distance, Estimating the location of 5G user terminal equipment To the Each base station pair Euclidean distance, Estimating the location of 5G user terminal equipment To the Each base station pair The Euclidean distance.

[0142] The RSS term constrains the distance between the 5G user terminal device and the base station to conform to the signal strength attenuation law through the logarithmic distance path loss model. The TDOA term converts the time difference into a distance difference based on the speed of light conversion to ensure geometric position consistency. The dynamic weights adjust the reliability allocation of RSS and TDOA in real time through environmental factors, thereby enhancing the robustness of TDOA and the accuracy advantage of RSS in multipath interference and signal stability scenarios, respectively, thus achieving high-precision positioning in complex environments.

[0143] (3) Model solution.

[0144] a. Initial value iterative optimization.

[0145] The Levenberg-Marquardt (LM) algorithm is used to perform nonlinear least squares optimization. The initial position of the 5G user terminal device is considered. Starting from the point of iteration, the objective function is evaluated using the LM algorithm. Iterative optimization is performed to achieve minimization. Among these, To optimize the variables, i.e., the objective function The position vector to be solved is continuously updated along the gradient direction by the LM algorithm during the iteration process.

[0146] As the initial value for iteration, it needs to be obtained by analytically solving the hyperbolic positioning equation, which provides the initial search point for the entire optimization process; This is the convergent solution of the algorithm, when the objective function... The slope at the current position is almost zero (i.e., it satisfies...). The algorithm terminates when the gradient approaches zero or when the number of iterations exceeds 50 (the termination condition set in this embodiment), and the output position at this time is the final estimated coordinate. .

[0147] b. Calculate the standard deviation of the measurement residuals.

[0148] The formula for calculating the RSS ranging residual is as follows:

[0149] No. Each base station for TDOA residuals :

[0150] Joint residual standard deviation (corresponding to the logic of the multi-objective scoring function):

[0151] The preset residual standard deviation threshold is This can be obtained based on historical data statistics, for example, if If the accuracy of the current base station combination is insufficient, the geometric optimization module can be triggered to re-select the base station combination and exclude abnormal base stations with residual data.

[0152] (4) Output results and feedback.

[0153] The final output of this method is the positioning coordinates of the 5G user terminal equipment. : and the combined residual standard deviation .in, Mapping to a multi-objective scoring function Handover serves as the basis for base station combination handover.

[0154] In summary, this application combines the screening mechanism of the geometry optimization module with a dynamic weight allocation strategy, such as... Figure 4 The figure shown is a schematic diagram of the positioning results obtained in this embodiment. It illustrates an example of base station layout in three-dimensional space and provides a direct comparison of the positioning results before and after optimization. Figure 4 In this context, BS1, BS2, BS3, BS4, and BS5 represent five base stations. It should be noted that... Figure 4 The simulation positioning points before optimization are positioning points that did not use the method of this application, while the simulation positioning points after optimization are positioning points that used the method of this application. The true positioning value is the actual position result. Figures 5 to 7 The diagram shown is a comparison of the error distributions between dynamic weights and fixed weights. Figure 5 for A diagram comparing the error distribution of dynamic weights and fixed weights. Figure 6for A diagram comparing the error distribution of dynamic weights and fixed weights. Figure 7 for A diagram comparing the error distributions of dynamic weights and fixed weights. The effectiveness of the dynamic weight allocation strategy can be verified through... Figures 5 to 7 The error distribution comparison and the error statistics in Table 1 provide evidence that the experimental data fully verify the superiority of the method in this application.

[0155] Table 1 Comparison of positioning errors between dynamic weights and fixed weights

[0156] The core technology of this application lies in a multi-source data fusion algorithm, which achieves high-precision positioning through residual analysis, environmental factor classification, and adaptive weight allocation. The experimental results above show that in low-quality environments (… K Under conditions of <0.6), the dynamic weighting strategy proposed in this application reduces the average positioning error by 33.7% compared to the fixed weighting (6:4) by increasing the TDOA weight to 60%. In high-quality environments (…),… K In the ≥1.0 range, dynamic weights account for over 70% of the RSS, resulting in a 40.7% reduction in average error compared to the fixed weight (5:5) strategy. Furthermore, the standard deviation of the positioning error obtained by the dynamic weight strategy is lower than that of the fixed weight strategy. This fully demonstrates that the dynamic weight strategy has stronger robustness under different interference scenarios, effectively reducing fluctuations in positioning error and providing more stable and reliable results for indoor positioning.

[0157] This application employs a closed-loop control positioning method through the collaborative operation of a geometric optimization module, a data processing module, a dynamic weight allocation module, and a joint optimization module. The initial base station combination output by the geometric optimization module is first fed into the data processing module for signal acquisition and preprocessing. The noise-reduced data is then input into the dynamic weight allocation module for quality evaluation. Finally, the joint optimization module completes the multi-source data fusion positioning calculation, and the positioning residual is fed back to the geometric optimization module to trigger the base station combination reselection mechanism.

[0158] like Figure 8 The diagram shown is a schematic of a 5G positioning system according to this application, which may include: The candidate module is used to determine the combination of candidate base stations through polyhedral volume constraints and elevation angle constraints; The optimization module is used to select an initial base station combination based on the geometric precision attenuation factor degradation coefficient of the candidate base station combination; The acquisition module is used to acquire signals from 5G user terminal devices to obtain raw signals; The calibration module is used to perform received signal strength noise reduction processing and arrival time difference path calibration on the original signal respectively, so as to obtain the received signal strength sequence after time-series smoothing and the purified arrival time difference dataset. The preliminary calculation module is used to calculate the initial position of the 5G user terminal equipment as the iteration starting point based on the three-dimensional coordinates of the initial base station combination and the purified time difference of arrival dataset. The strategy module is used to formulate a dynamic allocation strategy for received signal strength weights and time difference of arrival weights based on environmental factors, according to the received signal strength sequence after time-series smoothing and the purified time difference of arrival dataset. The objective function module is used to construct a weighted least squares error function of the received signal strength weight and the time difference of arrival weight as the objective function based on the dynamic allocation strategy of the received signal strength weight and the time difference of arrival weight. The solution module is used to solve the objective function starting from the aforementioned initial position to obtain the optimized estimated position coordinates of the 5G user terminal equipment. The dynamic adjustment module is used to dynamically adjust the base station combination based on the estimated location coordinates of the 5G user terminal equipment to obtain the optimal base station combination for 5G positioning.

[0159] It should be noted that, in the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For instance, the division of each module is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another device, or some features may be ignored or not executed. The modules described as separate components may or may not be physically separated. The components shown as modules may be one or more physical units, that is, they may be located in one place or distributed in multiple different places. Some or all of the modules can be selected to achieve the purpose of the solution in this embodiment according to actual needs.

[0160] Furthermore, in the various embodiments of the present invention, the modules can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The integrated unit described above can be implemented in hardware or as a software functional unit.

[0161] This application also provides an electronic device, which may include one or more processors, memory and communication interfaces.

[0162] The memory, communication interface, and processor are coupled together. For example, the memory, communication interface, and processor can be coupled together via a bus.

[0163] The communication interface is used for data transmission with other devices. The memory stores computer program code. This computer program code includes computer instructions, which, when executed by the processor, cause the electronic device to perform the steps of the aforementioned 5G positioning method.

[0164] The processor can be a processor or controller, such as a Central Processing Unit (CPU), a general-purpose processor, a Digital Signal Processor (DSP), an Application-Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with this disclosure. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The processor can be used to support an electronic device in performing the method steps provided in the above embodiments.

[0165] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. These buses can be categorized as address buses, data buses, control buses, etc.

[0166] This application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the 5G positioning method described above.

[0167] The computer-readable storage media involved in this application include random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage media known in the art.

[0168] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A 5G positioning method, characterized in that, include: Candidate base station combinations are determined by polyhedral volume constraints and elevation angle constraints; Based on the geometric precision attenuation factor deterioration coefficient of the candidate base station combinations, an initial base station combination is obtained through screening. Signal acquisition is performed on 5G user terminal equipment to obtain the raw signal; The original signal is subjected to received signal strength denoising and arrival time difference path calibration respectively, resulting in a received signal strength sequence after time-series smoothing and a purified arrival time difference dataset. Based on the three-dimensional coordinates of the initial base station combination and the purified time difference of arrival dataset, the initial position of the 5G user terminal equipment is calculated. Based on the received signal strength sequence after time-series smoothing and the purified time difference of arrival dataset, a dynamic allocation strategy for received signal strength weight and time difference of arrival weight based on environmental factors is formulated. Based on the dynamic allocation strategy of received signal strength weight and time difference of arrival weight, a weighted least squares error function of received signal strength weight and time difference of arrival weight is constructed as the objective function. By combining the initial position of the 5G user terminal equipment, the objective function is minimized to obtain the estimated position coordinates of the 5G user terminal equipment. Based on the estimated location coordinates of the 5G user terminal equipment, the base station combination is dynamically adjusted to obtain the optimal base station combination for 5G positioning.

2. The 5G positioning method according to claim 1, characterized in that, The polyhedral volume constraint includes: Calculate the volume of the three-dimensional polyhedron formed by any combination of base stations; If the volume of the three-dimensional polyhedron is less than the dynamic threshold, the corresponding base station combination is removed; otherwise, the corresponding base station combination is retained. The pitch angle constraint includes: For any combination of base stations, if the elevation angle of each base station and the standard deviation of the elevation angle of each base station in the same combination meet the preset requirements, the corresponding base station combination is retained; otherwise, the corresponding base station combination is removed.

3. The 5G positioning method according to claim 1, characterized in that, The method for calculating the initial position of the 5G user terminal equipment includes: Construct the hyperbola positioning equation: in, This is the initial location of the 5G user terminal equipment to be located. for N A collection of base stations For the first One base station, The coordinates of the calibrated main base station. , To determine the values ​​of the hyperbola's positioning equation, At the speed of light, For the first in the purified arrival time difference dataset One data point; the main base station is the base station with the smallest standard deviation within the preset sliding window; By linearizing the hyperbolic positioning equation using Taylor expansion, an observation matrix is ​​constructed. The initial position of the 5G user terminal equipment is then solved using the weighted least squares method, and the solution is optimized through residual analysis.

4. The 5G positioning method according to claim 1, characterized in that, The dynamic allocation strategy for received signal strength weight and time difference of arrival weight based on environmental factors includes: Time: Employs the time difference of arrival (TDOA) dominant model; Time: The received signal strength weight and the time difference of arrival weight are smoothly adjusted using the Sigmoid function; At this time: the received signal strength-dominated mode is adopted; in, As environmental factors, The calculation method is as follows: in, The standard deviation of the received signal strength sequence samples after time-series smoothing. The mean of the received signal strength sequence after time-series smoothing. The standard deviation of the cleaned arrival time difference dataset. This represents the mean of the arrival time difference dataset after purification.

5. The 5G positioning method according to claim 1, characterized in that, The objective function includes: in, Let be the objective function. For received signal strength weighting, This represents the total number of base stations in the base station combination. This represents the number of base station pairs in the base station combination. For the first The received signal strength ranging value of each base station, Estimating the location of 5G user terminal equipment. For the first One base station, For arrival time difference weighting, This refers to the base station number in the base station combination. Let be the base station log number in the base station combination, and , The base station logarithmic sequence number is The average arrival time difference of the base station combination after purification. At the speed of light, For the first One base station pair, For the first One base station pair, Estimating the location of 5G user terminal equipment To the base stations Euclidean distance, Estimating the location of 5G user terminal equipment To the Each base station pair Euclidean distance, Estimating the location of 5G user terminal equipment To the Each base station pair The Euclidean distance.

6. The 5G positioning method according to claim 1, characterized in that, Based on the estimated location coordinates of the 5G user terminal equipment, the base station combination is dynamically adjusted, including: Based on the estimated location coordinates of the 5G user terminal equipment, calculate the received signal strength ranging residual and the time difference of arrival residual respectively. The joint residual standard deviation of 5G user terminal equipment is determined based on the received signal strength ranging residual and the arrival time difference residual. Determine whether the accuracy of the current base station combination meets the requirements based on the joint residual standard deviation, and dynamically adjust the base station combination.

7. The 5G positioning method according to claim 6, characterized in that, Based on the geometric accuracy attenuation factor degradation coefficient of the candidate base station combinations, an initial base station combination is obtained, including: For each base station in the candidate base station combination, a base station observation matrix is ​​constructed. Based on the base station observation matrix, calculate the geometric precision attenuation factor of the candidate base station combination; Based on the geometric precision attenuation factor of the candidate base station combination, calculate the geometric precision attenuation factor degradation coefficient. If the geometric precision attenuation factor degradation coefficient is greater than a preset value, reselect from the candidate base station combinations; otherwise, retain the corresponding base station combination and proceed. If the standard deviation of the joint residual is greater than the preset threshold for the standard deviation of the joint residual, then a new selection is made from the candidate base station combinations; otherwise, the corresponding base station combination is retained and the process is executed. Based on the geometric precision attenuation factor of the candidate base station combination, a multi-objective scoring function is calculated. If the value of the multi-objective scoring function is less than the preset value, the corresponding base station combination is eliminated; otherwise, the corresponding base station combination is retained. The initial base station combination is obtained.

8. A 5G positioning system, characterized in that, include: The candidate module is used to determine the combination of candidate base stations through polyhedral volume constraints and elevation angle constraints; The optimization module is used to select an initial base station combination based on the geometric precision attenuation factor degradation coefficient of the candidate base station combination; The acquisition module is used to acquire signals from 5G user terminal devices to obtain raw signals; The calibration module is used to perform received signal strength noise reduction processing and arrival time difference path calibration on the original signal respectively, so as to obtain the received signal strength sequence after time-series smoothing and the purified arrival time difference dataset. The preliminary calculation module is used to calculate the initial position of the 5G user terminal equipment based on the three-dimensional coordinates of the initial base station combination and the purified time difference of arrival dataset. The strategy module is used to formulate a dynamic allocation strategy for received signal strength weights and time difference of arrival weights based on environmental factors, according to the received signal strength sequence after time-series smoothing and the purified time difference of arrival dataset. The objective function module is used to construct a weighted least squares error function of the received signal strength weight and the time difference of arrival weight as the objective function based on the dynamic allocation strategy of the received signal strength weight and the time difference of arrival weight. The solution module is used to solve the minimum objective function based on the initial position of the 5G user terminal equipment to obtain the estimated position coordinates of the 5G user terminal equipment. The dynamic adjustment module is used to dynamically adjust the base station combination based on the estimated location coordinates of the 5G user terminal equipment to obtain the optimal base station combination for 5G positioning.

9. An electronic device, characterized in that, include: A memory, one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the steps of a 5G positioning method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a 5G positioning method as described in any one of claims 1-7.

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