Method, device, server and readable storage medium for multi-lateration of base stations

Abstract

The application discloses a base station multilateral positioning method, device, server and readable storage medium, and belongs to the technical field of data service and wireless. The method comprises the following steps: acquiring pseudo-range observation values of distances between multiple base station antennas and a terminal antenna, and constructing a multilateral positioning random model according to the pseudo-range observation values; the multilateral positioning random model comprises an observation value cofactor matrix and an observation value weight matrix; obtaining a residual error vector according to the pseudo-range observation values; the residual error vector is determined by a coefficient matrix and a model parameter vector estimation value; a weighted residual error vector is constructed, and a residual error vector meeting a target function of a minimum first norm of the weighted residual error vector is determined as a target residual error vector; the observation value cofactor matrix and the observation value weight matrix are corrected according to the target residual error vector; and multilateral positioning calculation is carried out according to the corrected multilateral positioning random model, so that the coordinates of the terminal antenna are obtained. In the application, the ranging observation value data is subjected to quality control, and the positioning reliability is improved.

Description

Technical Field

[0001] This application belongs to the field of data services and wireless technology, and specifically relates to a method, apparatus, server and readable storage medium for base station multilateral positioning. Background Technology

[0002] Existing methods for quality control of ranging observation data can be divided into two types: outlier removal algorithms based on test statistic thresholds and weighting algorithms. The performance of outlier removal algorithms based on test statistic thresholds is greatly affected by the threshold value, and there are often problems of missed or false outliers, which affects the final positioning performance. The performance of weighting algorithms often depends on the robustness of the initial value and the form of the weight function. When the robustness of the initial value is poor and the weight function is not suitable, the performance of the weighting algorithm will be poor. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, server, and readable storage medium for base station multilateral positioning to solve the problem of poor quality of ranging observation data.

[0004] To solve the above-mentioned technical problems, this application is implemented as follows:

[0005] Firstly, a method for base station multilateral positioning is provided, including:

[0006] Obtain pseudorange observations of the distances between multiple base station antennas and terminal antennas, and construct a multilateral positioning stochastic model based on the pseudorange observations; the multilateral positioning stochastic model includes: an observation cofactor matrix and an observation weight matrix;

[0007] The residual vector is obtained based on the pseudorange observations; the residual vector is determined by the coefficient matrix and the estimated values ​​of the model parameter vector.

[0008] Construct a weighted residual vector and determine the residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector as the target residual vector;

[0009] The cofactor matrix and weight matrix of the observed values ​​are corrected based on the target residual vector;

[0010] The terminal antenna coordinates are obtained by performing a multilateral positioning solution based on the modified multilateral positioning stochastic model.

[0011] Optionally, the multilateral positioning stochastic model is Among them, Q k For in t k The cofactor matrix of the observed values ​​at time P; k For in t k The weighted matrix of observations at time ρ i Let be the pseudorange observation value of the i-th base station; i = 1, 2, ..., n.

[0012] Optionally, the residual vector V k for in, The coefficient matrix, For constant terms; These are the estimated values ​​of the model parameter vector; The pseudorange observation value of the i-th base station is an approximation; (x i ,y i (x0, y0) represents the base station antenna coordinates, and (x0, y0) represents the approximate values ​​of the terminal antenna coordinates.

[0013] Optionally, constructing the weighted residual vector and determining the residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector as the target residual vector includes:

[0014] Obtain n multilateral positioning observation equations, wherein the n multilateral positioning observation equations include s unknown parameters, where n>s+1;

[0015] Select s+1 equations from the n observation equations. This yields m sets of equations, according to the formula... Solve for the model parameter vector estimates for each of the m sets of equations; where A w P w and L w Is the above A k P k and L k A subarray, where w takes integer values ​​from 1 to m;

[0016] Will Substitute the residual vector In this process, m sets of weighted residual vectors are obtained;

[0017] Take the absolute value of the m sets of weighted residual vectors, and sort the absolute values ​​of the m sets of weighted residual vectors in ascending order to obtain |V w | (1) ≤|V w | (2) ≤...≤|V w | (n) ;

[0018] Extract | V w | (1) ≤|V w | (2) ≤...≤|V w | (n) The first t terms of the m groups of t-dimensional weighted residual absolute value vectors |V w | (1) ≤|Vw | (2) ≤...≤|V w | (t) ;

[0019] The objective function is solved based on the m-group t-dimensional weighted residual absolute value vector. The residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector is obtained, thus yielding the objective residual vector and the corresponding solution.

[0020] Will contain the corresponding solution Substitute the target residual vector Obtain the unit weighted mean error

[0021] The multilateral positioning stochastic model is corrected based on the unit weight mean square error.

[0022] Optionally, the step of correcting the multilateral positioning stochastic model based on the unit weight mean square error includes:

[0023] The stochastic model is corrected based on the unit weight error and the IGG III weight function;

[0024] The corrected formula is: in, k0 = 1.5, k1 = 2.5; the corrected observation cofactor matrix is The corrected observation weight matrix is

[0025] Optionally, the step of performing multilateral positioning calculations based on the modified multilateral positioning stochastic model to obtain the terminal antenna coordinates includes:

[0026] The modified multilateral positioning stochastic model is regularized and a sequential algorithm is used to perform multilateral positioning calculations to obtain the terminal antenna coordinates.

[0027] Secondly, a base station multilateral positioning device is provided, comprising:

[0028] The first processing module is used to acquire pseudorange observations of the distance between multiple base station antennas and terminal antennas, and to construct a multilateral positioning stochastic model based on the pseudorange observations; the multilateral positioning stochastic model includes: an observation cofactor matrix and an observation weight matrix;

[0029] The second processing module is used to obtain the residual vector based on the pseudorange observations; the residual vector is determined by the coefficient matrix and the estimated values ​​of the model parameter vector.

[0030] A construction module is used to construct a weighted residual vector and determine the residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector as the target residual vector;

[0031] The correction module is used to correct the observation cofactor matrix and the observation weight matrix according to the target residual vector;

[0032] The calculation module is used to perform multilateral positioning calculations based on the modified multilateral positioning stochastic model to obtain the terminal antenna coordinates.

[0033] Optionally, the multilateral positioning stochastic model is Among them, Q k For in t k The cofactor matrix of the observed values ​​at time P; k For in t k The weighted matrix of observations at time ρ i Let be the pseudorange observation value of the i-th base station; i = 1, 2, ..., n.

[0034] Optionally, the residual vector V k for in, The coefficient matrix, For constant terms; These are the estimated values ​​of the model parameter vector; The pseudorange observation value of the i-th base station is an approximation; (x i ,y i (x0, y0) represents the base station antenna coordinates, and (x0, y0) represents the approximate values ​​of the terminal antenna coordinates.

[0035] Optionally, the construction module includes:

[0036] The acquisition submodule is used to acquire n multilateral positioning observation equations, wherein the n multilateral positioning observation equations include s unknown parameters, where n>s+1;

[0037] The first processing submodule is used to select s+1 equations from the n observation equations. This yields m sets of equations, according to the formula... Solve for the model parameter vector estimates for each of the m sets of equations; where A w P w and L w Is the above A k P k and L k A subarray, where w takes integer values ​​from 1 to m;

[0038] The second processing submodule is used to... Substitute the residual vector In this process, m sets of weighted residual vectors are obtained;

[0039] The third processing submodule is used to take the absolute value of the m sets of weighted residual vectors and sort the absolute values ​​of the m sets of weighted residual vectors in ascending order to obtain |V w | (1) ≤|V w | (2) ≤...≤|V w | (n) ;

[0040] The fourth processing submodule is used to extract |V w | (1) ≤|V w | (2) ≤...≤|V w | (n) The first t terms of the m groups of t-dimensional weighted residual absolute value vectors |V w | (1) ≤|V w | (2) ≤...≤|V w | (t) ;

[0041] The first calculation submodule is used to solve the objective function based on the m sets of t-dimensional weighted residual absolute value vectors. The residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector is obtained, thus yielding the objective residual vector and the corresponding solution.

[0042] The second calculation submodule is used to process the corresponding solutions. Substitute the target residual vector Obtain the unit weighted mean error

[0043] The correction submodule is used to correct the multilateral positioning stochastic model based on the unit weight error.

[0044] Optionally, the correction submodule includes: correcting the stochastic model based on the unit weight mean square error and the IGG III weight function;

[0045] The corrected formula is: in, k0 = 1.5, k1 = 2.5; the corrected observation cofactor matrix is The corrected observation weight matrix is

[0046] Optionally, the computing module includes:

[0047] The third calculation submodule is used to apply a regularization method to the modified multilateral positioning stochastic model and to perform multilateral positioning calculation using a sequential algorithm to obtain the terminal antenna coordinates.

[0048] Thirdly, a server is provided, including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect, or the steps of the method described in the second aspect.

[0049] Fourthly, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect, or the steps of the method described in the second aspect.

[0050] In the embodiments of this application, the ranging observation data is corrected and data quality control is performed. This takes into account a lot of positioning prior information and does not sacrifice the number of effective equations, thereby reducing positioning latency and improving positioning reliability. Attached Figure Description

[0051] Figure 1 This is a flowchart of a base station multilateral positioning method provided in an embodiment of this application;

[0052] Figure 2 This is a general flowchart of a base station multilateral positioning method provided in an embodiment of this application;

[0053] Figure 3 This is a schematic diagram of the structure of a base station multilateral positioning device provided in an embodiment of this application;

[0054] Figure 4 This is a schematic diagram of the structure of a server provided in an embodiment of this application. Detailed Implementation

[0055] 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, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0056] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0057] The following description, in conjunction with the accompanying drawings, details a method, apparatus, server, and readable storage medium for base station multilateral positioning provided in this application, through specific embodiments and application scenarios.

[0058] Please see Figure 1 , Figure 1 This is a flowchart of a base station multilateral positioning method provided in an embodiment of this application. The method is applied to a server, such as... Figure 1 As shown, the method includes the following steps:

[0059] Step 11: Obtain pseudorange observations of the distances between multiple base station antennas and terminal antennas, and construct a multilateral positioning stochastic model based on the pseudorange observations; the multilateral positioning stochastic model includes: an observation cofactor matrix and an observation weight matrix;

[0060] In this embodiment of the invention, optionally, the multilateral positioning stochastic model is: Among them, Q k For in t k The cofactor matrix of the observed values ​​at time P; k For in t k The weighted matrix of observations at time ρ i Let be the pseudorange observation value of the i-th base station; i = 1, 2, ..., n.

[0061] In this embodiment of the invention, the multilateral positioning stochastic model is based on the multilateral positioning function model: ρ i =L i +σ i The linearized formula for the Taylor expansion of i = 1, 2, ..., n is as follows: Constructed; where ρ i L is calculated from TOA or RSRP data. i This refers to the distance between the base station antenna and the terminal antenna. This is an approximate value for the distance between the base station antenna and the terminal antenna, (x) i ,y i(x0, y0) represents the base station antenna coordinates, (x0, y0) represents the approximate terminal antenna coordinates, and (x, y) represents the terminal antenna coordinates. i To observe noise.

[0062] Step 12: Obtain the residual vector based on the pseudorange observations; the residual vector is determined by the coefficient matrix and the estimated values ​​of the model parameter vector.

[0063] In this embodiment of the invention, optionally, the residual vector V k for in, The coefficient matrix, For constant terms; These are the estimated values ​​of the model parameter vector; The pseudorange observation value of the i-th base station is an approximation; (x i ,y i (x0, y0) represents the base station antenna coordinates, and (x0, y0) represents the approximate terminal antenna coordinates. The residual vector is used to correct the multilateral positioning stochastic model, thereby improving positioning reliability.

[0064] Step 13: Construct a weighted residual vector and determine the residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector as the target residual vector;

[0065] Step 14: Correct the observation cofactor matrix and observation weight matrix according to the target residual vector;

[0066] In this embodiment of the invention, optionally, constructing a weighted residual vector and determining the residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector as the target residual vector includes:

[0067] Obtain n multilateral positioning observation equations, wherein the n multilateral positioning observation equations include s unknown parameters, where n>s+1;

[0068] Select s+1 equations from the n observation equations. This yields m sets of equations, according to the formula... Solve for the model parameter vector estimates for each of the m sets of equations; where A w P w and L w Is the above A k P k and L k A subarray, where w takes integer values ​​from 1 to m;

[0069] Will Substitute the residual vector In this process, m sets of weighted residual vectors are obtained;

[0070] Take the absolute value of the m sets of weighted residual vectors, and sort the absolute values ​​of the m sets of weighted residual vectors in ascending order to obtain |V w | (1) ≤|V w | (2) ≤...≤|V w | (n) ;

[0071] Extract | V w | (1) ≤|V w | (2) ≤...≤|V w | (n) The first t terms of the m groups of t-dimensional weighted residual absolute value vectors |V w | (1) ≤|V w | (2) ≤...≤|V w | (t) ;

[0072] The objective function is solved based on the m-group t-dimensional weighted residual absolute value vector. The residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector is obtained, thus yielding the objective residual vector and the corresponding solution.

[0073] Will contain the corresponding solution Target residual vector Obtain the unit weighted mean error

[0074] The multilateral positioning stochastic model is corrected based on the unit weight mean square error.

[0075] In this embodiment of the invention, optionally, the step of correcting the multilateral positioning stochastic model based on the unit weight mean square error includes:

[0076] The stochastic model is corrected based on the unit weight error and the IGG III weight function;

[0077] The corrected formula is: in, k0 = 1.5, k1 = 2.5; the corrected observation cofactor matrix is The corrected observation weight matrix is

[0078] In this embodiment of the invention, the residual vector of the objective function that minimizes the first norm of the weighted residual vector is obtained to correct the multilateral positioning model. This solves the problems that the factorization least squares algorithm sacrifices an effective equation during linearization, increases the number of necessary positioning observations, and makes it difficult to accurately determine weights when performing least squares estimation, thus affecting accuracy analysis.

[0079] Step 15: Perform multilateral positioning calculations based on the modified multilateral positioning stochastic model to obtain the terminal antenna coordinates.

[0080] In this embodiment of the invention, optionally, the step of performing multilateral positioning calculations based on the modified multilateral positioning stochastic model to obtain the terminal antenna coordinates includes:

[0081] The modified multilateral positioning stochastic model is regularized and a sequential algorithm is used to perform multilateral positioning calculations to obtain the terminal antenna coordinates.

[0082] In this embodiment of the invention, the multilateral positioning is solved using a sequential algorithm. The observation weight array has been updated and corrected again. Obtain the calculated observation weight matrix The addition of a regularization matrix R to the coefficient matrix is ​​to improve the state of the normal matrix and solve the ill-conditioned problem; the sequential solution process is as follows:

[0083]

[0084]

[0085]

[0086] The initial value is:

[0087] The terminal antenna coordinates are finally obtained through sequential calculation.

[0088] In this embodiment of the invention, the ranging observation data is corrected and data quality control is performed. This takes into account a lot of prior positioning information and does not sacrifice the number of effective equations, thereby reducing positioning latency and improving positioning reliability.

[0089] Please refer to Figure 2 In this embodiment of the invention, the coefficient matrix A is first constructed. k constant term L k and observation weight matrix P k This involves acquiring pseudorange observations of the distances between multiple base station antennas and terminal antennas, and constructing a multilateral positioning stochastic model based on these pseudorange observations. The multilateral positioning stochastic model includes an observation cofactor matrix Q. kand observation weight matrix P k The residual vector is obtained based on the pseudorange observations; the residual vector is determined by the coefficient matrix and the estimated values ​​of the model parameter vector; the residual vector V k for in, The coefficient matrix, For constant terms; These are the estimated values ​​of the model parameter vector; The pseudorange observation value of the i-th base station is an approximation; (x i ,y i (x0, y0) represents the base station antenna coordinates, and (x0, y0) represents the approximate values ​​of the terminal antenna coordinates.

[0090] The original system of equations is split into A set of sub-equations is obtained, namely, n multilateral positioning observation equations, each containing s unknown parameters, where n > s+1; s+1 equations are selected from these n observation equations. This yields m sets of equations, according to the formula... Solve for the model parameter vector estimates for each of the m sets of equations; where A w P w and L w Is the above A k P k and L k A subarray, where w takes integer values ​​from 1 to m;

[0091] calculate and V w Soon Substitute the residual vector In the process, m sets of weighted residual vectors are obtained; for the residual vector V w Take the absolute value and arrange them in ascending order. That is, take the absolute value of the m sets of weighted residual vectors and sort the absolute values ​​of the m sets of weighted residual vectors in ascending order to obtain |V w | (1) ≤|V w | (2) ≤...≤|V w | (n) ;

[0092] Extract the first t terms of the absolute value vector of the residuals, i.e., extract |V w | (1) ≤|V w | (2) ≤...≤|V w | (n) The first t terms of the m groups of t-dimensional weighted residual absolute value vectors |V w |(1) ≤|V w | (2) ≤...≤|V w | (t) ;

[0093] Solving the objective function yields the w′-th group of t-dimensional residual absolute value vectors, which is obtained by solving the objective function based on the m groups of t-dimensional weighted residual absolute value vectors. The residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector is obtained, thus yielding the objective residual vector and the corresponding solution.

[0094] To calculate the estimated unit weight error, substitute i = 1, 2, ..., n into... Obtain the unit weighted mean error

[0095] The modified stochastic model is obtained based on the IGG III weight function. and That is, the stochastic model is corrected based on the unit weight error and the IGG III weight function;

[0096] The corrected formula is: in, k0 = 1.5, k1 = 2.5; the corrected observation cofactor matrix is The corrected observation weight matrix is

[0097] Singular value decomposition selects a regularization matrix R, which is the corrected observation weight matrix. Unitize, and obtain right Singular value decomposition yields Where U and V are orthogonal matrices; D is The singular value matrix arranged in descending order;

[0098] The corrected weight matrix is ​​obtained based on the regularization matrix R. That is, the regularization matrix R is chosen as R = VV T The location estimate is: Where 'a' is the regularization coefficient, which can be set based on experience;

[0099] Determine the first epoch and calculate the initial value. and At the same time, based on the previous epoch and Perform sequential multivariate positioning for this epoch, i.e., use a sequential algorithm for solution. The observation weight array has been updated and corrected again. Obtain the calculated observation weight matrix The addition of a regularization matrix R to the coefficient matrix is ​​to improve the state of the normal matrix and solve the ill-conditioned problem; the sequential solution process is as follows:

[0100]

[0101]

[0102]

[0103] The initial value is: The terminal antenna coordinates are finally obtained through sequential calculation.

[0104] Please refer to Figure 3 A base station multilateral positioning device is provided, comprising:

[0105] The first processing module 31 is used to acquire pseudorange observations of the distance between multiple base station antennas and terminal antennas, and to construct a multilateral positioning stochastic model based on the pseudorange observations; the multilateral positioning stochastic model includes: an observation cofactor matrix and an observation weight matrix;

[0106] The second processing module 32 is used to obtain a residual vector based on the pseudorange observations; the residual vector is determined by the coefficient matrix and the estimated values ​​of the model parameter vector.

[0107] Construction module 33 is used to construct a weighted residual vector and determine the residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector as the target residual vector;

[0108] The correction module 34 is used to correct the observation cofactor matrix and the observation weight matrix according to the target residual vector;

[0109] The calculation module 35 is used to perform multilateral positioning calculations based on the modified multilateral positioning stochastic model to obtain the terminal antenna coordinates.

[0110] In this embodiment of the invention, optionally, the multilateral positioning stochastic model is: Among them, Q k For in t k The cofactor matrix of the observed values ​​at time P; k For in t k The weighted matrix of observations at time ρ i Let be the pseudorange observation value of the i-th base station; i = 1, 2, ..., n.

[0111] In this embodiment of the invention, optionally, the residual vector V k for in, The coefficient matrix, For constant terms; These are the estimated values ​​of the model parameter vector; The pseudorange observation value of the i-th base station is an approximation; (x i ,y i (x0, y0) represents the base station antenna coordinates, and (x0, y0) represents the approximate values ​​of the terminal antenna coordinates.

[0112] In this embodiment of the invention, optionally, the construction module includes:

[0113] The acquisition submodule is used to acquire n multilateral positioning observation equations, wherein the n multilateral positioning observation equations include s unknown parameters, where n>s+1;

[0114] The first processing submodule is used to select s+1 equations from the n observation equations. This yields m sets of equations, according to the formula... Solve for the model parameter vector estimates for each of the m sets of equations; where A w P w and L w Is the above A k P k and L k A subarray, where w takes integer values ​​from 1 to m;

[0115] The second processing submodule is used to... Substitute the residual vector In this process, m sets of weighted residual vectors are obtained;

[0116] The third processing submodule is used to take the absolute value of the m sets of weighted residual vectors and sort the absolute values ​​of the m sets of weighted residual vectors in ascending order to obtain |V w | (1) ≤|V w | (2) ≤...≤|V w | (n) ;

[0117] The fourth processing submodule is used to extract |V w | (1) ≤|V w | (2) ≤...≤|V w | (n) The first t terms of the m groups of t-dimensional weighted residual absolute value vectors |V w | (1) ≤|V w | (2) ≤...≤|V w | (t) ;

[0118] The first calculation submodule is used to solve the objective function based on the m sets of t-dimensional weighted residual absolute value vectors. The residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector is obtained, thus yielding the objective residual vector and the corresponding solution.

[0119] The second calculation submodule is used to process the corresponding solutions. Substitute the target residual vector Obtain the unit weighted mean error

[0120] The correction submodule is used to correct the multilateral positioning stochastic model based on the unit weight error.

[0121] In this embodiment of the invention, optionally, the correction submodule includes: correcting the stochastic model based on the unit weight error and the IGGIII weight function;

[0122] The corrected formula is: in, k0 = 1.5, k1 = 2.5; the corrected observation cofactor matrix is The corrected observation weight matrix is

[0123] In this embodiment of the invention, optionally, the computing module includes:

[0124] The third calculation submodule is used to apply a regularization method to the modified multilateral positioning stochastic model and to perform multilateral positioning calculation using a sequential algorithm to obtain the terminal antenna coordinates.

[0125] The base station multiple positioning device 30 of this application embodiment can achieve the above-mentioned... Figure 1 The various processes of the method embodiments shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0126] In this embodiment of the invention, optionally, such as Figure 4 As shown, this application embodiment also provides a server 40, including a processor 41, a memory 42, and a program or instructions stored in the memory 42 and executable on the processor 41. For example, when the server 40 is a terminal, the program or instructions executed by the processor 41 implement the various processes of the above-described base station multi-directional positioning method embodiment and achieve the same technical effect. When the server 40 is a network-side device, the program or instructions executed by the processor 41 implement the various processes of the above-described base station multi-directional positioning method embodiment and achieve the same technical effect; to avoid repetition, further details are omitted here.

[0127] This application also provides a readable storage medium storing a program or instructions that, when executed by a processor, can achieve the above-described functions. Figure 1 The various processes of the method embodiments shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0128] Computer-readable media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0129] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0130] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0131] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a service classification device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0132] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for base station multilateral positioning, characterized in that, include: Obtain pseudorange observations of the distances between multiple base station antennas and terminal antennas, and construct a multilateral positioning stochastic model based on the pseudorange observations; The multilateral positioning stochastic model includes: an observation cofactor matrix and an observation weight matrix; The residual vector is obtained based on the pseudorange observations; the residual vector is determined by the coefficient matrix and the estimated values ​​of the model parameter vector. Construct a weighted residual vector and determine the residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector as the target residual vector; The cofactor matrix and weight matrix of the observed values ​​are corrected based on the target residual vector; The terminal antenna coordinates are obtained by performing a multilateral positioning solution based on the modified multilateral positioning stochastic model.

2. The base station multilateral positioning method according to claim 1, characterized in that, The multilateral positioning stochastic model is , ;in, In order to be in The cofactor matrix of observations at time t; In order to be in The weighted matrix of observations at each moment; Let be the pseudorange observation value of the i-th base station; .

3. The base station multilateral positioning method according to claim 2, characterized in that, residual vector for ;in, The coefficient matrix, For constant terms; These are the estimated values ​​of the model parameter vector; This is an approximation of the pseudorange observation value of the i-th base station; , For base station antenna coordinates, , These are approximate values ​​for the terminal antenna coordinates.

4. The base station multilateral positioning method according to claim 3, characterized in that, The process of constructing a weighted residual vector and determining the residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector as the target residual vector includes: Obtain n multilateral positioning observation equations, wherein the n multilateral positioning observation equations include s unknown parameters, where, ; From the n multilateral positioning observation equations, select s+1 equations, and select... This yields m sets of equations, according to the formula... Solve for the model parameter vector estimates for each of the m sets of equations; where, , and yes , and The subarray, where w takes integer values ​​from 1 to m; Will Substitute the residual vector In this process, m sets of weighted residual vectors are obtained; Take the absolute value of each of the m weighted residual vectors, and sort the absolute values ​​of the m weighted residual vectors in ascending order to obtain... ; Cut The first t terms of the m-group t-dimensional weighted residual absolute value vector are obtained. ; The objective function is solved by using the m-group t-dimensional weighted residual absolute value vector. The residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector is obtained, thus yielding the objective residual vector and the corresponding solution. ; Will contain the corresponding solution Substitute the target residual vector The unit weighted mean error is obtained. ; The multilateral positioning stochastic model is corrected based on the unit weight mean square error.

5. The base station multilateral positioning method according to claim 4, characterized in that, The step of correcting the multilateral positioning stochastic model based on the unit weight mean square error includes: The stochastic model is corrected based on the unit weight error and the IGG III weight function; The corrected formula is: ;in, ; , The corrected cofactor matrix of the observations is The corrected observation weight matrix is: .

6. The base station multilateral positioning method according to claim 5, characterized in that, The step of performing multilateral positioning calculations based on the modified multilateral positioning stochastic model to obtain the terminal antenna coordinates includes: The modified multilateral positioning stochastic model is regularized and a sequential algorithm is used to perform multilateral positioning calculations to obtain the terminal antenna coordinates.

7. A device for base station multilateral positioning, characterized in that, include: The first processing module is used to obtain pseudorange observations of the distance between multiple base station antennas and terminal antennas, and to construct a multilateral positioning stochastic model based on the pseudorange observations. The multilateral positioning stochastic model includes: an observation cofactor matrix and an observation weight matrix; The second processing module is used to obtain the residual vector based on the pseudorange observations; the residual vector is determined by the coefficient matrix and the estimated values ​​of the model parameter vector. A construction module is used to construct a weighted residual vector and determine the residual vector that satisfies the objective function of minimizing the first norm of the weighted residual vector as the target residual vector; The correction module is used to correct the observation cofactor matrix and the observation weight matrix according to the target residual vector; The calculation module is used to perform multilateral positioning calculations based on the modified multilateral positioning stochastic model to obtain the terminal antenna coordinates.

8. The apparatus for base station multilateral positioning according to claim 7, characterized in that, The multilateral positioning stochastic model is , ;in, In order to be in The cofactor matrix of observations at time t; In order to be in The weighted matrix of observations at each moment; Let be the pseudorange observation value of the i-th base station.

9. A server, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the base station multilateral positioning method as described in any one of claims 1 to 6.

10. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the base station multilateral positioning method as described in any one of claims 1 to 6.

Images