Positioning method, device and processor-readable storage medium

Abstract

The present invention provides a positioning method, device and processor-readable storage medium, which relate to the field of communication technology. The method comprises: obtaining first data including multiple measured time differences and second data including multiple distance differences; obtaining the location information of the target terminal determined by M iterations based on an iterative algorithm according to the first data and the second data; wherein the location information determined by the mth iteration is determined based on the first distance corresponding to the location information determined by the m-1th iteration, the first data and the second data; the first distance is the distance from the target terminal to the first base station; M and m are positive integers, 1≤m≤M; the target terminal is positioned according to the location information determined by M iterations. The present invention can solve the problem of inaccurate terminal positioning in the current positioning method.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and in particular to a positioning method, a positioning device, and a processor-readable storage medium. Background Art

[0002] The terminal can be positioned based on multiple base stations. The current positioning method usually uses the approximate maximum likelihood (AML) method and the measurement value of the time difference of arrival (TDOA) (i.e., the measurement value of the time difference of signal transmission from the terminal to different base stations) to solve the terminal's position. This method of solving the terminal position based on AML and TDOA may have multiple solutions. If multiple solutions of the terminal position are obtained, and then one of the multiple solutions of the terminal position is selected to locate the terminal, this method may have the problem of selecting the wrong solution, which may lead to inaccurate terminal positioning. Summary of the invention

[0003] The present invention provides a positioning method, a device and a processor-readable storage medium, which solve the problem of inaccurate terminal positioning in current positioning methods.

[0004] An embodiment of the present invention provides a positioning method, including:

[0005] Acquire first data including multiple measured time differences and second data including multiple distance differences; wherein one of the measured time differences is a measured time difference of a signal transmitted from a target terminal to a first base station and a second base station, and one of the distance differences is a distance difference from the target terminal to the first base station and a second base station; the first base station is a base station in a positioning system, and the second base station is a base station other than the first base station in the positioning system;

[0006] According to the first data and the second data, the location information of the target terminal determined by M iterations is obtained based on an iterative algorithm; wherein the location information determined by the m-th iteration is determined based on the first distance corresponding to the location information determined by the m-1-th iteration, the first data, and the second data; the first distance is the distance from the target terminal to the first base station; M and m are positive integers, 1≤m≤M;

[0007] The target terminal is positioned according to the position information determined by M iterations.

[0008] Optionally, when m=1, obtaining the location information of the target terminal determined by M iterations based on an iterative algorithm according to the first data and the second data includes:

[0009] According to the first data and the second data, the position information determined for the m-1th time is estimated based on the least square (LS) method, and the mth first position information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the position information determined for the m-1th time and the mth first position information are both related to the first distance;

[0010] If the value of the first distance calculated from the mth first location information does not meet the positioning condition of the target terminal, the location information determined by the mth iteration is determined based on the first distance corresponding to the location information determined for the m-1th time, the first data and the second data.

[0011] Optionally, when m>1, the determining the location information of the target terminal for M iterations based on an iterative algorithm according to the first data and the second data includes:

[0012] According to the first data and the second data, the mth first location information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the mth first location information is related to the first distance;

[0013] If the value of the first distance calculated from the m-th first location information does not meet the positioning condition of the target terminal, determining the location information determined in the m-th iteration based on the first distance corresponding to the location information determined in the m-1th iteration, the first data, and the second data;

[0014] The position information determined at the m-1th iteration is the position information determined at the m-1th iteration based on an iterative algorithm.

[0015] Optionally, obtaining the mth first location information of the target terminal by estimating based on the first data and the second data based on an approximate maximum likelihood method includes:

[0016] Determining a first maximum likelihood error function according to the first data and the second data;

[0017] The mth first position information is determined according to the first maximum likelihood error function.

[0018] Optionally, determining the m-th first position information according to the first maximum likelihood error function includes:

[0019] Determine, according to the derivative of the first maximum likelihood error function with respect to θ, a first equivalent formula corresponding to the derivative being zero; wherein θ is the preset location information of the target terminal;

[0020] According to the first equivalent formula, the mth first position information is determined.

[0021] Optionally, the first maximum likelihood error function J d for:

[0022]

[0023] Among them, T d is the first data; d(θ) is the second data; c is the transmission speed of the signal; Q d Based on T d Determine the covariance matrix.

[0024] Optionally, the first equivalent formula is: 2ΦDθ=Φv;

[0025] in, Φ=WΛ,V=v1+r1Ψ;

[0026]

[0027] v1=[(Δ 21 2 +k1-k2)…(Δ N1 2 +k1-k N )] T ;Ψ=2[Δ 21 …Δ N1 ] T ;

[0028] Among them, the location information of the i-th second base station among the N-1 second base stations is (x i ,y i ), i = 2, ..., N; N is the number of base stations in the positioning system; r i is the distance from the target terminal to the i-th second base station, k i =x i 2 +y i 2 ; The location information of the first base station is (x1, y1); r1 is the distance from the target terminal to the first base station, k1 = x1 2 +y1 2 ; Δ i1 is the measured distance difference from the target terminal to the first base station and the i-th second base station.

[0029] Optionally, if a value of the first distance calculated from the m-th first position information does not meet the positioning condition of the target terminal, determining the position information determined by the m-th iteration based on the first distance corresponding to the position information determined for the m-1th time, the first data, and the second data, includes:

[0030] If the two values ​​of the first distance calculated from the mth first position information meet the preset condition, it is determined that the value of the first distance r1 calculated from the mth first position information does not meet the positioning condition of the target terminal; wherein the two values ​​meet the preset condition if: both values ​​are positive real numbers, or both values ​​are negative real numbers, or both values ​​are complex numbers;

[0031] The position information determined in the m-th iteration is determined based on the first distance corresponding to the position information determined in the (m-1)th iteration, the first data, and the second data.

[0032] Optionally, determining the position information determined in the m-th iteration based on the first distance corresponding to the position information determined in the (m-1)th iteration, the first data, and the second data includes:

[0033] Determine a second maximum likelihood error function according to the first distance corresponding to the position information determined for the m-1th time, the first data, the second data, and third data including a plurality of measurement times; wherein one of the measurement times is a measurement time for a signal to be transmitted from the target terminal to a base station in the positioning system;

[0034] The position information determined in the mth iteration is determined according to the second maximum likelihood error function.

[0035] Optionally, determining a second maximum likelihood error function according to the first distance corresponding to the position information determined for the (m-1)th time, the first data, the second data, and third data including multiple measurement times includes:

[0036] Determine fourth data according to the first distance corresponding to the position information determined for the (m-1)th time, the transmission speed of the signal, and the first data;

[0037] determining fifth data according to the first distance corresponding to the position information determined for the (m-1)th time and the second data;

[0038] A second maximum likelihood error function is determined based on the third data, the fourth data and the fifth data.

[0039] Optionally, the second maximum likelihood error function is J d ′:

[0040]

[0041] Among them, T d ' is the fourth data, T d =[t 21 …t N1 ] T , t i1 is the measured time difference of the signal transmitted from the target terminal to the first base station and the i-th second base station, i=2,…,N; N is the number of base stations in the positioning system; r 1,m-1 is the first distance corresponding to the position information determined for the m-1th time;

[0042] r d (θ) is the fifth data, r d (θ)=[r 1,m-1 ,d(θ)] T , d(θ)=[d 21 …d N1 ] T , d i1 is the distance difference between the target terminal and the first base station and the i-th second base station;

[0043] c is the transmission speed of the signal; Q is the covariance matrix determined based on the third data.

[0044] Optionally, positioning the target terminal according to the position information determined by M iterations includes:

[0045] Determining a maximum likelihood error value corresponding to the position information determined in each iteration;

[0046] Determine the target location information according to the minimum of the maximum likelihood error values ​​corresponding to the location information determined in M ​​iterations;

[0047] The target location information is determined as the positioning position of the target terminal.

[0048] An embodiment of the present invention provides a positioning device, including a memory, a transceiver, and a processor;

[0049] The memory is used to store computer programs; the transceiver is used to send and receive data under the control of the processor; the processor is used to read the computer program in the memory and perform the following operations:

[0050] Acquire first data including multiple measured time differences and second data including multiple distance differences; wherein one of the measured time differences is a measured time difference of a signal transmitted from a target terminal to a first base station and a second base station, and one of the distance differences is a distance difference from the target terminal to the first base station and a second base station; the first base station is a base station in a positioning system, and the second base station is a base station other than the first base station in the positioning system;

[0051] According to the first data and the second data, the location information of the target terminal determined by M iterations is obtained based on an iterative algorithm; wherein the location information determined by the m-th iteration is determined based on the first distance corresponding to the location information determined by the m-1-th iteration, the first data, and the second data; the first distance is the distance from the target terminal to the first base station; M and m are positive integers, 1≤m≤M;

[0052] The target terminal is positioned according to the position information determined by M iterations.

[0053] Optionally, when m=1, the processor is configured to read the computer program in the memory and perform the following operations:

[0054] According to the first data and the second data, the position information determined for the m-1th time is estimated based on the least squares method, and the mth first position information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the position information determined for the m-1th time and the mth first position information are both related to the first distance;

[0055] If the value of the first distance calculated from the mth first location information does not meet the positioning condition of the target terminal, the location information determined by the mth iteration is determined based on the first distance corresponding to the location information determined for the m-1th time, the first data and the second data.

[0056] Optionally, when m>1, the processor is configured to read the computer program in the memory and perform the following operations:

[0057] According to the first data and the second data, the mth first location information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the mth first location information is related to the first distance;

[0058] If the value of the first distance calculated from the m-th first location information does not meet the positioning condition of the target terminal, determining the location information determined in the m-th iteration based on the first distance corresponding to the location information determined in the m-1th iteration, the first data, and the second data;

[0059] The position information determined at the m-1th iteration is the position information determined at the m-1th iteration based on an iterative algorithm.

[0060] Optionally, the processor is configured to read the computer program in the memory and perform the following operations:

[0061] Determining a first maximum likelihood error function according to the first data and the second data;

[0062] The mth first position information is determined according to the first maximum likelihood error function.

[0063] Optionally, the processor is configured to read the computer program in the memory and perform the following operations:

[0064] Determine, according to the derivative of the first maximum likelihood error function with respect to θ, a first equivalent formula corresponding to the derivative being zero; wherein θ is the preset location information of the target terminal;

[0065] According to the first equivalent formula, the mth first position information is determined.

[0066] Optionally, the first maximum likelihood error function J d for:

[0067]

[0068] Among them, T d is the first data; d(θ) is the second data; c is the transmission speed of the signal; Q d Based on T d Determine the covariance matrix.

[0069] Optionally, the first equivalent formula is: 2ΦDθ=Φv;

[0070] in, Φ=WΛ,V=v1+r1Ψ;

[0071]

[0072] v1=[(Δ 21 2 +k1-k2)…(Δ N1 2 +k1-k N )] T ;Ψ=2[Δ 21 …Δ N1 ] T ;

[0073] Among them, the location information of the i-th second base station among the N-1 second base stations is (xi ,y i ), i = 2, ..., N; N is the number of base stations in the positioning system; r i is the distance from the target terminal to the i-th second base station, k i =x i 2 +y i 2 ; The location information of the first base station is (x1, y1); r1 is the distance from the target terminal to the first base station, k1 = x1 2 +y1 2 ; Δ i1 is the measured distance difference from the target terminal to the first base station and the i-th second base station.

[0074] Optionally, the processor is configured to read the computer program in the memory and perform the following operations:

[0075] If the two values ​​of the first distance calculated from the mth first position information meet the preset condition, it is determined that the value of the first distance r1 calculated from the mth first position information does not meet the positioning condition of the target terminal; wherein the two values ​​meet the preset condition if: both values ​​are positive real numbers, or both values ​​are negative real numbers, or both values ​​are complex numbers;

[0076] The position information determined in the m-th iteration is determined based on the first distance corresponding to the position information determined in the (m-1)th iteration, the first data, and the second data.

[0077] Optionally, the processor is configured to read the computer program in the memory and perform the following operations:

[0078] Determine a second maximum likelihood error function according to the first distance corresponding to the position information determined for the m-1th time, the first data, the second data, and third data including a plurality of measurement times; wherein one of the measurement times is a measurement time for a signal to be transmitted from the target terminal to a base station in the positioning system;

[0079] The position information determined in the mth iteration is determined according to the second maximum likelihood error function.

[0080] Optionally, the processor is configured to read the computer program in the memory and perform the following operations:

[0081] Determine fourth data according to the first distance corresponding to the position information determined for the (m-1)th time, the transmission speed of the signal, and the first data;

[0082] determining fifth data according to the first distance corresponding to the position information determined for the (m-1)th time and the second data;

[0083] A second maximum likelihood error function is determined based on the third data, the fourth data and the fifth data.

[0084] Optionally, the second maximum likelihood error function is J d ′:

[0085]

[0086] Among them, T d ' is the fourth data, T d =[t 21 …t N1 ] T , t i1 is the measured time difference of the signal transmitted from the target terminal to the first base station and the i-th second base station, i=2,…,N; N is the number of base stations in the positioning system; r 1,m-1 is the first distance corresponding to the position information determined for the m-1th time;

[0087] r d (θ) is the fifth data, r d (θ)=[r 1,m-1 ,d(θ)] T , d(θ)=[d 21 …d N1 ] T , d i1 is the distance difference between the target terminal and the first base station and the i-th second base station;

[0088] c is the transmission speed of the signal; Q is the covariance matrix determined based on the third data.

[0089] Optionally, the processor is configured to read the computer program in the memory and perform the following operations:

[0090] Determining a maximum likelihood error value corresponding to the position information determined in each iteration;

[0091] Determine the target location information according to the minimum of the maximum likelihood error values ​​corresponding to the location information determined in M ​​iterations;

[0092] The target location information is determined as the positioning position of the target terminal.

[0093] The embodiment of the present invention further provides a positioning device, including:

[0094] An acquisition unit is used to acquire first data including multiple measured time differences and second data including multiple distance differences; wherein one of the measured time differences is a measured time difference of a signal transmitted from a target terminal to a first base station and a second base station, and one of the distance differences is a distance difference from the target terminal to the first base station and a second base station; the first base station is a base station in a positioning system, and the second base station is a base station other than the first base station in the positioning system;

[0095] a processing unit, configured to obtain, based on the first data and the second data, the location information of the target terminal determined by M iterations based on an iterative algorithm; wherein the location information determined by the m-th iteration is determined based on the first distance corresponding to the location information determined by the m-1th iteration, the first data, and the second data; the first distance is the distance from the target terminal to the first base station; M and m are positive integers, 1≤m≤M;

[0096] A positioning unit is used to locate the target terminal according to the position information determined by M iterations.

[0097] An embodiment of the present invention further provides a processor-readable storage medium, wherein the processor-readable storage medium stores a computer program, and the computer program is used to enable the processor to execute the steps in the above-mentioned positioning method.

[0098] The beneficial effects of the above technical solution of the present invention are:

[0099] In the above scheme, based on the first data including multiple measured time differences of the signal transmitted from the target terminal to different base stations and the second data including multiple distance differences from the target terminal to different base stations, the location information of the target terminal determined by M iterations is obtained based on an iterative algorithm; the first distance is the distance from the target terminal to the first base station; M and m are positive integers, 1≤m≤M; based on the location information determined by M iterations, the target terminal is located. In this way, since the location information determined by the mth iteration is determined based on the first distance, the first data and the second data corresponding to the location information determined by the m-1th iteration, the problem of inaccurate positioning when selecting one of the multiple target terminal locations obtained to locate the target terminal in the current positioning method can be solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 A flowchart showing a positioning method according to an embodiment of the present invention;

[0101] Figure 2 A flow chart showing a root selection method according to an embodiment of the present invention;

[0102] Figure 3A schematic diagram showing positioning scenario 1 according to an embodiment of the present invention;

[0103] Figure 4 A schematic diagram showing the error CDF of an embodiment of the present invention in a positioning scenario;

[0104] Figure 5 A schematic diagram showing positioning scenario 2 according to an embodiment of the present invention;

[0105] Figure 6 A schematic diagram showing the error CDF of an embodiment of the present invention in positioning scenario 2;

[0106] Figure 7 The error distribution diagram of the traditional positioning method is shown;

[0107] Figure 8 A diagram showing an error distribution of a positioning method using an embodiment of the present invention;

[0108] Fig. 9 A schematic diagram showing the error between the positioning method according to the embodiment of the present invention and the traditional positioning method;

[0109] Fig.10a One of the error CDF diagrams of the conventional positioning method and the positioning method according to the embodiment of the present invention is shown;

[0110] Fig.10b A second schematic diagram showing CDF errors using a conventional positioning method and a positioning method according to an embodiment of the present invention;

[0111] Fig.11 One of the block diagrams showing the positioning device according to the embodiment of the present invention;

[0112] Fig.12 The second block diagram of the positioning device according to the embodiment of the present invention. DETAILED DESCRIPTION

[0113] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail in conjunction with the accompanying drawings and specific embodiments. In the following description, specific details such as specific configurations and components are provided only to help fully understand the embodiments of the present invention. Therefore, it should be clear to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. In addition, for clarity and brevity, the description of known functions and structures is omitted.

[0114] It should be understood that the references to "one embodiment" or "an embodiment" throughout the specification mean that the specific features, structures, or characteristics associated with the embodiment are included in at least one embodiment of the present invention. Therefore, the references to "in one embodiment" or "in an embodiment" appearing throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0115] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the following processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0116] In the embodiments of the present invention, the term "and / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.

[0117] In the embodiments of the present application, the term "plurality" refers to two or more than two, and other quantifiers are similar.

[0118] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0119] The embodiments of the present application provide a positioning method and a positioning device to solve the problem of inaccurate terminal positioning in current positioning methods.

[0120] Among them, the method and the device are based on the same application concept. Since the method and the device solve the problem in a similar principle, the implementation of the device and the method can refer to each other, and the repeated parts will not be repeated.

[0121] like Figure 1 As shown, an embodiment of the present invention provides a positioning method, including:

[0122] Step 11: Acquire first data including a plurality of measured time differences and second data including a plurality of distance differences.

[0123] Among them, one of the measured time differences is the measured time difference of the signal transmitted from the target terminal to the first base station and a second base station, and one of the distance differences is the distance difference from the target terminal to the first base station and a second base station; the first base station is a base station in the positioning system, and the second base station is a base station in the positioning system other than the first base station.

[0124] Optionally, the positioning system includes at least three base stations, such as the positioning system including three base stations (base station A, base station B and base station C) for example. Base station A is the first base station (i.e., the reference base station), and base station B and base station C are both second base stations; the first data includes: the difference between the first measurement time when the signal is transmitted from the target terminal to base station B and the second measurement time when the signal is transmitted from the target terminal to base station A (i.e., the measurement time difference), and the difference between the third measurement time when the signal is transmitted from the target terminal to base station C and the second measurement time when the signal is transmitted from the target terminal to base station A (i.e., the measurement time difference). The second data includes: the difference between the first distance from the target terminal to base station B and the second distance from the target terminal to base station A (i.e., the distance difference), and the difference between the third distance from the target terminal to base station C and the second distance from the target terminal to base station A (i.e., the distance difference).

[0125] Here, the distance from the target terminal to the base station can be understood as the actual distance. For example, the position coordinates of the target terminal are preset to (x, y), and the position coordinates of each base station are known, such as the base station position is (x i ,y i ), so according to the location coordinates of the target terminal and the location coordinates of each base station, the actual distance (or true distance) from the target terminal to each base station can be determined based on the distance formula. The distance difference can be understood as the difference between the actual distances. For example, taking base station A and base station B as an example, the distance difference between the target terminal and base station A and base station B is: the difference between the actual distance from the target terminal to base station A and the actual distance from the target terminal to base station B.

[0126] Step 12: According to the first data and the second data, the location information of the target terminal is obtained based on an iterative algorithm after M iterations.

[0127] Among them, the location information determined in the mth iteration is determined based on the first distance, the first data and the second data corresponding to the location information determined in the m-1th iteration; the first distance is the distance from the target terminal to the first base station; M and m are positive integers, 1≤m≤M.

[0128] Optionally, when m>1, the location information determined for the m-1th time can be understood as the location information determined at the m-1th iteration based on the iterative algorithm; when m=1, the location information determined for the m-1th time can be an initial value determined based on a preset algorithm (such as determining the location information of the target terminal based on LS estimation and using it as the initial value).

[0129] Optionally, the iterative process based on the iterative algorithm can be: obtaining the location information of the target terminal based on LS estimation, taking the location information determined based on LS as the initial value, performing the first iterative processing, and subsequent iterations can be based on the location information determined in the previous iteration of the current iteration as input for iteration, and so on for multiple iterations (such as 5 iterations), the embodiments of the present invention are not limited to this.

[0130] Step 13: Position the target terminal according to the location information determined by M iterations.

[0131] In the above scheme, based on the first data including multiple measured time differences of the signal transmitted from the target terminal to different base stations and the second data including multiple distance differences from the target terminal to different base stations, the location information of the target terminal determined by M iterations is obtained based on an iterative algorithm; the first distance is the distance from the target terminal to the first base station; M and m are positive integers, 1≤m≤M; based on the location information determined by M iterations, the target terminal is located. In this way, since the location information determined by the mth iteration is determined based on the first distance, the first data and the second data corresponding to the location information determined by the m-1th iteration, the problem of inaccurate positioning when selecting one of the multiple target terminal locations obtained to locate the target terminal in the current positioning method can be solved.

[0132] Optionally, when m=1, obtaining the location information of the target terminal determined by M iterations based on an iterative algorithm according to the first data and the second data includes:

[0133] According to the first data and the second data, the position information determined for the m-1th time is estimated based on the least squares method, and the mth first position information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the position information determined for the m-1th time and the mth first position information are both related to the first distance;

[0134] If the value of the first distance calculated from the mth first location information does not meet the positioning condition of the target terminal, the location information determined by the mth iteration is determined based on the first distance corresponding to the location information determined for the m-1th time, the first data and the second data.

[0135] For example: the location information θ(x, y) estimated based on LS can be represented by the first distance r1. On this basis, the distance formula based on the first distance r1 can be solved to obtain the value of r1. Based on the solved value of r1 and the location information θ estimated based on LS, the final location information (such as location coordinates) of the target terminal can be determined.

[0136] Similarly, the location information θ(x, y) estimated based on the AML method can also be represented by the first distance r1, on this basis, the distance formula based on the first distance r1 can be solved to obtain the value of r1. If the value of r1 obtained based on the AML method does not meet the positioning condition, it is necessary to determine a value of r1 that meets the positioning condition among the values ​​of r1 obtained based on the AML method based on the first distance corresponding to the location information determined for the m-1th time (here, the location information of the final target terminal determined based on the LS estimation), the first data, and the second data, so as to determine the location information of the target terminal determined in the mth iteration, that is, the location information (such as the location coordinates) of the target terminal determined in the first iteration, based on the determined value of r1 that meets the positioning condition and the location information θ estimated based on the AML method.

[0137] Optionally, when m>1, the determining the location information of the target terminal for M iterations based on an iterative algorithm according to the first data and the second data includes:

[0138] According to the first data and the second data, the mth first location information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the mth first location information is related to the first distance;

[0139] If the value of the first distance calculated from the m-th first location information does not meet the positioning condition of the target terminal, determining the location information determined in the m-th iteration based on the first distance corresponding to the location information determined in the m-1th iteration, the first data, and the second data;

[0140] The position information determined at the m-1th iteration is the position information determined at the m-1th iteration based on an iterative algorithm.

[0141] For example, the location information θ(x, y) estimated based on the AML method can be represented by the first distance r1, and on this basis, the distance formula based on the first distance r1 can be solved to obtain the value of r1. If the value of r1 obtained based on the AML method does not meet the positioning condition, it is necessary to determine a value of r1 that meets the positioning condition among the values ​​of r1 obtained based on the AML method based on the first distance corresponding to the location information determined in the m-1th iteration, the first data, and the second data, so as to determine the location information of the target terminal determined in the mth iteration according to the determined value of r1 that meets the positioning condition and the location information θ estimated based on the AML method, that is, the location information of the target terminal determined in the mth iteration.

[0142] Optionally, obtaining the mth first location information of the target terminal by estimating based on the first data and the second data based on an approximate maximum likelihood method includes:

[0143] Determining a first maximum likelihood error function according to the first data and the second data;

[0144] The mth first position information is determined according to the first maximum likelihood error function.

[0145] For example, based on the first maximum likelihood error function, the mth first location information estimated based on the AML method can be determined. The location information of the target terminal estimated based on the LS can also be determined based on the first maximum likelihood error function, that is, when m=1, the location information of the target terminal determined for the m-1th time.

[0146] Optionally, determining the m-th first position information according to the first maximum likelihood error function includes:

[0147] According to the derivative of the first maximum likelihood error function with respect to θ, determine the first equivalent formula corresponding to the derivative being zero; wherein θ is the preset location information of the target terminal; such as presetting the location information of the target terminal as coordinates θ(x, y).

[0148] According to the first equivalent formula, the mth first position information is determined.

[0149] Optionally, the first maximum likelihood error function J d for:

[0150]

[0151] Among them, T d is the first data; d(θ) is the second data; c is the transmission speed of the signal; Q d Based on T d Determine the covariance matrix, such as Qd =E{T d T d T}, Q d -1 It's Q d The inverse matrix of .

[0152] For example: Based on the first maximum likelihood error function J d Taking the derivative with respect to θ and then simplifying the transformation, we can get the first equivalent formula.

[0153] Optionally, the first equivalent formula is: 2ΦDθ=Φv;

[0154] in, Φ=WΛ,V=v1+r1Ψ;

[0155]

[0156] v1=[(Δ 21 2 +k1-k2)…(Δ N1 2 +k1-k N )] T ;Ψ=2[Δ 21 …Δ N1 ] T ;

[0157] Wherein, N is the number of base stations in the positioning system, and the location information of the i-th second base station among the N-1 second base stations is (x i ,y i ), i = 2, ..., N; r i is the distance from the target terminal to the i-th second base station, k i =x i 2 +y i 2 .

[0158] The location information of the first base station is (x1, y1); r1 is the distance from the target terminal to the first base station (which can be understood as the actual distance or true distance from the target terminal to the first base station), k1=x1 2 +y1 2 .

[0159] Δ i1 is the measured distance difference from the target terminal to the first base station and the i-th second base station, such as Δ i1 =c*t i1 ,t i1 is the measured time difference of the signal transmitted from the target terminal to the first base station and the i-th second base station.

[0160] For example: based on the first equivalent formula, let Φ = I, I is the unit matrix, and solve to obtain the location information θ(x, y) of the target terminal, that is, determine the location information of the target terminal estimated based on LS. The location information of the target terminal estimated based on LS is used as the initial value, and the initial value is used as the input of the first iteration. Based on the first equivalent formula, the location information θ(x, y) of the target terminal is solved to obtain the location information of the target terminal, that is, determine the location information of the target terminal estimated based on the AML method. The location information θ(x, y) of the target terminal determined in the previous iteration is used as the input in the next iteration, so as to determine the location information of the target terminal estimated based on the AML method next time, and so on, which will not be repeated here.

[0161] Optionally, if a value of the first distance calculated from the m-th first position information does not meet the positioning condition of the target terminal, determining the position information determined by the m-th iteration based on the first distance corresponding to the position information determined for the m-1th time, the first data, and the second data, includes:

[0162] If the two values ​​of the first distance calculated from the mth first position information meet the preset condition, it is determined that the value of the first distance r1 calculated from the mth first position information does not meet the positioning condition of the target terminal; wherein the two values ​​meet the preset condition if: both values ​​are positive real numbers, or both values ​​are negative real numbers, or both values ​​are complex numbers;

[0163] The position information determined in the m-th iteration is determined based on the first distance corresponding to the position information determined in the (m-1)th iteration, the first data, and the second data.

[0164] For example: the position information θ(x, y) estimated based on the AML method can be represented by the first distance r1, and on this basis, the distance formula based on the first distance r1 can be solved to obtain the value of r1. If the two values ​​of r1 obtained by solving (i.e., by using θ represented by r1 and the distance formula of r1, a quadratic equation based on r1 is determined, and the two roots of r1 obtained by solving the quadratic equation) are both positive real numbers, or the two values ​​of r1 are both negative real numbers, or the two values ​​of r1 are complex numbers, then if the first maximum likelihood error function J corresponding to each of the two values ​​of r1 is d The value of r1 is selected based on the value of (i.e., the value of the approximate maximum likelihood error), and it is determined that the position information determined by the final m-th iteration may cause inaccurate terminal positioning, that is, the positioning condition of the target terminal is not met.

[0165] The embodiment of the present invention can reconstruct the maximum likelihood error function based on the first distance corresponding to the location information determined for the m-1th time, the first data and the second data to increase the maximum likelihood error of the mirror solution and the abnormal solution of r1, so as to reduce the probability of the wrong solution being selected, thereby determining an accurate solution for r1, and then determining the location information of the target terminal determined in the final mth iteration based on the determined accurate solution of r1, so as to ensure the accuracy of terminal positioning.

[0166] Optionally, determining the position information determined in the m-th iteration based on the first distance corresponding to the position information determined in the (m-1)th iteration, the first data, and the second data includes:

[0167] Determine a second maximum likelihood error function according to the first distance corresponding to the position information determined for the m-1th time, the first data, the second data, and third data including a plurality of measurement times; wherein one of the measurement times is a measurement time for a signal to be transmitted from the target terminal to a base station in the positioning system;

[0168] The position information determined in the mth iteration is determined according to the second maximum likelihood error function.

[0169] Optionally, the positioning system includes three base stations (base station A, base station B and base station C) as an example for description. Among them, base station A is the first base station (i.e., the reference base station), base station B and base station C are both second base stations; the third data includes: a first measurement time when the signal is transmitted from the target terminal to base station A, a second measurement time when the signal is transmitted from the target terminal to base station B, and a third measurement time when the signal is transmitted from the target terminal to base station C.

[0170] In this embodiment, a second maximum likelihood error function is reconstructed based on the first distance corresponding to the position information determined for the m-1th time and third data containing multiple measurement times, and based on the reconstructed second maximum likelihood error function, when the value of the first distance r1 calculated from the mth first position information does not meet the positioning condition, the maximum likelihood error of the mirror solution and the abnormal solution of r1 can be increased based on the reconstructed second maximum likelihood error function to reduce the probability of an erroneous solution being selected, thereby selecting an accurate value from the two values ​​of the first distance that do not meet the positioning condition, thereby ensuring the accuracy of the position information of the target terminal determined by the final iteration.

[0171] Optionally, determining a second maximum likelihood error function according to the first distance corresponding to the position information determined for the (m-1)th time, the first data, the second data, and third data including multiple measurement times includes:

[0172] Determine fourth data according to the first distance corresponding to the position information determined for the (m-1)th time, the transmission speed of the signal, and the first data;

[0173] determining fifth data according to the first distance corresponding to the position information determined for the (m-1)th time and the second data;

[0174] A second maximum likelihood error function is determined based on the third data, the fourth data and the fifth data.

[0175] In this embodiment, the first data is reconstructed based on the first distance corresponding to the position information determined for the m-1th time, that is, the first data is updated to obtain the fourth data; d 'for: T d =[t 21 …t N1 ] T , where T d is the first data, r 1,m-1 It is the first distance corresponding to the position information determined for the m-1th time.

[0176] The second data is reconstructed based on the first distance corresponding to the position information determined for the m-1th time, that is, the second data is updated to obtain the fifth data, such as the fifth data r d (θ) is: r d (θ)=[r 1,m-1 ,d(θ)] T , d(θ)=[d 21 …d N1 ] T , where d(θ) is the second data.

[0177] Determine a covariance matrix based on the third data according to the third data, such as the covariance matrix Q based on the third data is: Q = E{TT T}, where T is the third data.

[0178] Optionally, the second maximum likelihood error function is J d ′:

[0179]

[0180] Among them, T d ' is the fourth data, T d =[t 21 …t N1 ] T , t i1is the measured time difference of the signal transmitted from the target terminal to the first base station and the i-th second base station, i=2,…,N; N is the number of base stations in the positioning system; r 1,m-1 is the first distance corresponding to the position information determined for the m-1th time;

[0181] r d (θ) is the fifth data, r d (θ)=[r 1,m-1 ,d(θ)] T , d(θ)=[d 21 …d N1 ] T , d i1 is the distance difference between the target terminal and the first base station and the i-th second base station;

[0182] c is the transmission speed of the signal; Q is the covariance matrix determined based on the third data, Q- 1 is the inverse matrix of Q.

[0183] Optionally, positioning the target terminal according to the position information determined by M iterations includes:

[0184] Determining a maximum likelihood error value corresponding to the position information determined in each iteration;

[0185] Determine the target location information according to the minimum of the maximum likelihood error values ​​corresponding to the location information determined in M ​​iterations;

[0186] The target location information is determined as the positioning position of the target terminal.

[0187] For example, the location information of the target terminal determined by M iterations is determined based on an iterative algorithm, such as obtaining the location information of 5 target terminals by 5 iterations. For the location information of the target terminal determined by M iterations, the maximum likelihood error values ​​corresponding to each of them can be determined based on the first maximum likelihood error function, and a location information with the smallest maximum likelihood error value is selected as the final positioning information of the target terminal, thereby realizing the positioning of the target terminal and ensuring the accuracy of the positioning result.

[0188] The following combination Figure 2 The root selection method of the embodiment of the present invention is described as follows:

[0189] Based on the maximum likelihood error function J dDetermine a first equivalent formula, and determine the location information of the target terminal estimated based on LS according to the first equivalent formula, and this location information corresponds to a first distance r1; and determine the location information of the target terminal estimated based on AML method according to the first equivalent formula, and this location information is represented by the first distance r1. Please refer to the above embodiment and will not repeat it here.

[0190] Step 21: Based on the location information of the target terminal estimated by the AML method and the distance formula of the first distance r1, two solutions of r1 are obtained.

[0191] Step 22: Determine whether both solutions are real numbers and one is positive and the other is negative.

[0192] Step 23: If both solutions are real numbers and both are positive or negative, or both solutions are complex numbers, proceed to step 24.

[0193] Step 24: Reconstruct the maximum likelihood error function and obtain the second maximum likelihood error function as J d ′:

[0194]

[0195] Among them, T d ' is the fourth data, T d =[t 21 …t N1 ] T , t i1 is the measured time difference of the signal transmitted from the target terminal to the first base station and the i-th second base station, i=2,…,N; N is the number of base stations in the positioning system; r 1,m-1 is the first distance corresponding to the position information determined for the m-1th time; r d (θ) is the fifth data, r d (θ)=[r 1,m-1 ,d(θ)] T , d(θ)=[d 21 …d N1 ] T , d i1 is the distance difference between the target terminal and the first base station and the i-th second base station; c is the signal transmission speed; and Q is the covariance matrix determined based on the third data.

[0196] Step 25: Based on the reconstructed second maximum likelihood error function J d ′, determine the J corresponding to the two solutions d The smaller of the values ​​of ′ is taken as the exact solution for r1.

[0197] Step 26: If after step 22, it is determined that both solutions are real numbers and one is positive and the other is negative, then the solution with r1 being a positive number is determined to be the correct solution, and the solution is J based on the first maximum likelihood error function. d Determine the J corresponding to the exact solution d The value of .

[0198] Step 27: Determine an exact solution for r1 and the corresponding J d Value output result.

[0199] It should be noted that if the location information of the target terminal estimated based on the AML method is the first iteration, then in the second iteration, it is determined that the two solutions of r1 obtained are also both positive real numbers / negative real numbers / complex numbers. J can be reconstructed based on the r1 value corresponding to the output result obtained in the first iteration. d ′.

[0200] The second maximum likelihood error function based on the reconstruction is J d ′, to limit the correct interval of the solution of the first iteration in the two solutions of the hyperbola, so as to continuously reduce the error during the iteration process to reduce the probability of selecting the wrong solution under non-ideal conditions, thereby reducing the positioning error. At the same time, it will be continuously updated with iterations and become more accurate to ensure rapid convergence.

[0201] The effect of the positioning method of the embodiment of the present invention is described below in conjunction with specific scenarios:

[0202] Scenario 1: Figure 3 Take the classic indoor positioning scenario shown as an example: the target UE sends an uplink sounding reference signal (Sounding Reference Signal, SRS) signal, and 6 base stations TRP in different locations complete the signal reception and distance measurement. The local management function (Location Management Function, LMF) server collects the distance measurement information of all TRPs, uses TDOA and TRP coordinates to complete position positioning and tracking, and solves the position based on the positioning method of the embodiment of the present invention. In addition to the measurement error, it reduces the solution error introduced by the two solutions of r1 being positive real numbers or negative real numbers or no solution (complex number). If the target UE moves within the coverage area of ​​​​the TRP, the measurement error is a Gaussian truncation error with an amplitude of 1m. Each TRP is independent of each other, the total TDOA error is within 5m, and the final position error is within 5m. The statistical results of the error distribution function (CDF) are as follows Figure 4 shown.

[0203] Scenario 2: For scenarios where downlink signal positioning is used on the terminal side, such as Figure 5As shown in the figure, four base stations TRP at different locations send downlink phase tracking reference signal (PTRS) signals, and the target UE completes the signal reception and distance measurement. Positioning and tracking are completed using TDOA and TRP coordinates. The position coordinates of the target UE within the irregular quadrilateral coverage formed by the TRP lines are traversed. The measurement error is the Gaussian truncation error with an amplitude of 0.5m. Each TRP is independent of each other, and the total TDOA error is within 2m. The CDF statistical results of the error are shown as follows: Figure 6 shown.

[0204] Generally speaking, since base stations are generally irregularly distributed in actual station deployment scenarios, 4 BSs are the minimum number of base stations that meet stable positioning conditions. In this scenario, the boundary problem is more obvious, and the positioning error at the boundary can be reduced to a normal range by using the embodiment of the present invention. The embodiment of the present invention can be applied to different base station distribution scenarios and numbers of base stations, and can well solve the problem of no solution and wrong solution of the position solution algorithm of the existing positioning method at the base station coverage boundary.

[0205] The following describes the effect of the positioning method according to the embodiment of the present invention in combination with a scenario where positioning measurement is performed based on 6 base stations distributed in a rectangular shape:

[0206] like Figure 7 is the error distribution diagram using the traditional positioning method, such as Figure 8 This is an error distribution diagram of the positioning method using an embodiment of the present invention, where the measurement error is a Gaussian truncation error with an amplitude of 0.5m, the number of iterations of the AML algorithm is 5, the measurement errors of all positions within the observation rectangle, the coordinate resolution is 0.2m, the vertical axis represents the position settlement error (unit: meter), and -1m means the error is greater than 5m. Figure 7 and Figure 8 It can be seen that the traditional positioning method has a large solution error near the rectangular boundary and the four corner base stations; the positioning method of the embodiment of the present invention has a large error only in the four corners, but it is all within 5m, and the average settlement error is smaller than the traditional positioning method. It can be seen that the positioning method of the embodiment of the present invention can limit the solution range through the first LS estimation when the error is large or the position is close to the base station (when there is a high probability of two solutions), ensure iterative convergence, avoid selecting the wrong solution, and thus reduce the solution error.

[0207] The terminal moves from the leftmost end to the rightmost end within the rectangular range, and the measurement error is a Gaussian truncation error with an amplitude of 1m. The rest of the configuration is the same as above. The error comparison between the positioning method of the embodiment of the present invention and the traditional positioning method is as follows: Fig. 9 shown.

[0208] like Fig.10aA schematic diagram of the error CDF statistics of the positioning method of the present invention and the traditional positioning method based on the AML algorithm is given. Fig.10a It can be seen that the positioning method using the embodiment of the present invention has no maximum error points at both ends of the rectangle. However, the traditional positioning method is prone to selecting the wrong mirror solution when the position is not good or the measurement error is large, resulting in a large solution error, which can reach up to 80m, while the maximum of the positioning method using the embodiment of the present invention is only about 10m.

[0209] like Fig.10b A schematic diagram of the error CDF statistics of the positioning method of the present invention and the traditional positioning method based on the CHAN algorithm is given. Since the traditional positioning method based on the CHAN algorithm uses two LS solutions and the final result is based on the first measured base station, there will be no mirror solution, but due to the poor position at both ends of the rectangle, there will be an error greater than 20m. The positioning method of the embodiment of the present invention has a maximum of 5 iterations, so the accuracy is higher than the traditional positioning method.

[0210] The positioning method of the present invention is introduced above, and the corresponding positioning device will be further described in conjunction with the accompanying drawings in the following embodiments.

[0211] Specifically, Fig.11 As shown, the positioning device 1100 of the embodiment of the present invention includes:

[0212] The acquisition unit 1110 is configured to acquire first data including a plurality of measured time differences and second data including a plurality of distance differences; wherein one of the measured time differences is a measured time difference of a signal transmitted from a target terminal to a first base station and a second base station, and one of the distance differences is a distance difference from the target terminal to the first base station and a second base station; the first base station is a base station in a positioning system, and the second base station is a base station other than the first base station in the positioning system;

[0213] The processing unit 1120 is configured to obtain the location information of the target terminal determined by M iterations based on an iterative algorithm according to the first data and the second data; wherein the location information determined by the m-th iteration is determined based on the first distance corresponding to the location information determined by the m-1-th iteration, the first data, and the second data; the first distance is the distance from the target terminal to the first base station; M and m are positive integers, and 1≤m≤M;

[0214] The positioning unit 1130 is used to locate the target terminal according to the position information determined by M iterations.

[0215] Optionally, when m=1, the processing unit 1120 is further configured to:

[0216] According to the first data and the second data, the position information determined for the m-1th time is estimated based on the least squares method, and the mth first position information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the position information determined for the m-1th time and the mth first position information are both related to the first distance;

[0217] If the value of the first distance calculated from the mth first location information does not meet the positioning condition of the target terminal, the location information determined by the mth iteration is determined based on the first distance corresponding to the location information determined for the m-1th time, the first data and the second data.

[0218] Optionally, when m>1, the processing unit 1120 is further configured to:

[0219] According to the first data and the second data, the mth first location information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the mth first location information is related to the first distance;

[0220] If the value of the first distance calculated from the m-th first location information does not meet the positioning condition of the target terminal, determining the location information determined in the m-th iteration based on the first distance corresponding to the location information determined in the m-1th iteration, the first data, and the second data;

[0221] The position information determined at the m-1th iteration is the position information determined at the m-1th iteration based on an iterative algorithm.

[0222] Optionally, the processing unit 1120 is further configured to:

[0223] Determining a first maximum likelihood error function according to the first data and the second data;

[0224] The mth first position information is determined according to the first maximum likelihood error function.

[0225] Optionally, the processing unit 1120 is further configured to:

[0226] Determine, according to the derivative of the first maximum likelihood error function with respect to θ, a first equivalent formula corresponding to the derivative being zero; wherein θ is the preset location information of the target terminal;

[0227] According to the first equivalent formula, the mth first position information is determined.

[0228] Optionally, the first maximum likelihood error function J d for:

[0229]

[0230] Among them, T d is the first data; d(θ) is the second data; c is the transmission speed of the signal; Q d Based on T d Determine the covariance matrix.

[0231] Optionally, the first equivalent formula is: 2ΦDθ=Φv;

[0232] in, Φ=WΛ,V=v1+r1Ψ;

[0233]

[0234] v1=[(Δ 21 2 +k1-k2)…(Δ N1 2 +k1-k N )] T ;Ψ=2[Δ 21 …Δ N1 ] T ;

[0235] Among them, the location information of the i-th second base station among the N-1 second base stations is (x i ,y i ), i = 2, ..., N; N is the number of base stations in the positioning system; r i is the distance from the target terminal to the i-th second base station, k i =x i 2 +y i 2 ; The location information of the first base station is (x1, y1); r1 is the distance from the target terminal to the first base station, k1 = x1 2 +y1 2 ; Δ i1 is the measured distance difference from the target terminal to the first base station and the i-th second base station.

[0236] Optionally, the processing unit 1120 is further configured to:

[0237] If the two values ​​of the first distance calculated from the mth first position information meet the preset condition, it is determined that the value of the first distance r1 calculated from the mth first position information does not meet the positioning condition of the target terminal; wherein the two values ​​meet the preset condition if: both values ​​are positive real numbers, or both values ​​are negative real numbers, or both values ​​are complex numbers;

[0238] The position information determined in the m-th iteration is determined based on the first distance corresponding to the position information determined in the (m-1)th iteration, the first data, and the second data.

[0239] Optionally, the processing unit 1120 is further configured to:

[0240] Determine a second maximum likelihood error function according to the first distance corresponding to the position information determined for the m-1th time, the first data, the second data, and third data including a plurality of measurement times; wherein one of the measurement times is a measurement time for a signal to be transmitted from the target terminal to a base station in the positioning system;

[0241] The position information determined in the mth iteration is determined according to the second maximum likelihood error function.

[0242] Optionally, the processing unit 1120 is further configured to:

[0243] Determine fourth data according to the first distance corresponding to the position information determined for the (m-1)th time, the transmission speed of the signal, and the first data;

[0244] determining fifth data according to the first distance corresponding to the position information determined for the (m-1)th time and the second data;

[0245] A second maximum likelihood error function is determined based on the third data, the fourth data and the fifth data.

[0246] Optionally, the second maximum likelihood error function is J d ′:

[0247]

[0248] Among them, T d ' is the fourth data, T d =[t 21 …t N1 ] T , t i1 is the measured time difference of the signal transmitted from the target terminal to the first base station and the i-th second base station, i=2,…,N; N is the number of base stations in the positioning system; r 1,m-1 is the first distance corresponding to the position information determined for the m-1th time;

[0249] r d (θ) is the fifth data, r d (θ)=[r 1,m-1 ,d(θ)] T , d(θ)=[d 21 …d N1] T , d i1 is the distance difference between the target terminal and the first base station and the i-th second base station;

[0250] c is the transmission speed of the signal; Q is the covariance matrix determined based on the third data.

[0251] Optionally, the positioning unit 1130 is further configured to:

[0252] Determining a maximum likelihood error value corresponding to the position information determined in each iteration;

[0253] Determine the target location information according to the minimum of the maximum likelihood error values ​​corresponding to the location information determined in M ​​iterations;

[0254] The target location information is determined as the positioning position of the target terminal.

[0255] It should be noted that the division of units in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0256] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0257] It should be noted here that the above-mentioned device provided in the embodiment of the present invention can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0258] In order to better achieve the above goals, Fig.12 As shown, a positioning device according to an embodiment of the present invention includes a memory 1220, a transceiver 1210, and a processor 1200; wherein the memory 1220 is used to store a computer program; the transceiver 1210 is used to send and receive data under the control of the processor 1200; the transceiver 1210 is used to receive and send data under the control of the processor 1200; the processor 1200 is used to read the computer program in the memory and perform the following operations:

[0259] Acquire first data including multiple measured time differences and second data including multiple distance differences; wherein one of the measured time differences is a measured time difference of a signal transmitted from a target terminal to a first base station and a second base station, and one of the distance differences is a distance difference from the target terminal to the first base station and a second base station; the first base station is a base station in a positioning system, and the second base station is a base station other than the first base station in the positioning system;

[0260] According to the first data and the second data, the location information of the target terminal determined by M iterations is obtained based on an iterative algorithm; wherein the location information determined by the m-th iteration is determined based on the first distance corresponding to the location information determined by the m-1-th iteration, the first data, and the second data; the first distance is the distance from the target terminal to the first base station; M and m are positive integers, 1≤m≤M;

[0261] The target terminal is positioned according to the position information determined by M iterations.

[0262] Optionally, when m=1, the processor 1200 is configured to read the computer program in the memory and perform the following operations:

[0263] According to the first data and the second data, the position information determined for the m-1th time is estimated based on the least squares method, and the mth first position information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the position information determined for the m-1th time and the mth first position information are both related to the first distance;

[0264] If the value of the first distance calculated from the mth first location information does not meet the positioning condition of the target terminal, the location information determined by the mth iteration is determined based on the first distance corresponding to the location information determined for the m-1th time, the first data and the second data.

[0265] Optionally, when m>1, the processor 1200 is configured to read the computer program in the memory and perform the following operations:

[0266] According to the first data and the second data, the mth first location information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the mth first location information is related to the first distance;

[0267] If the value of the first distance calculated from the m-th first location information does not meet the positioning condition of the target terminal, determining the location information determined in the m-th iteration based on the first distance corresponding to the location information determined in the m-1th iteration, the first data, and the second data;

[0268] The position information determined at the m-1th iteration is the position information determined at the m-1th iteration based on an iterative algorithm.

[0269] Optionally, the processor 1200 is configured to read the computer program in the memory and perform the following operations:

[0270] Determining a first maximum likelihood error function according to the first data and the second data;

[0271] The mth first position information is determined according to the first maximum likelihood error function.

[0272] Optionally, the processor 1200 is configured to read the computer program in the memory and perform the following operations:

[0273] Determine, according to the derivative of the first maximum likelihood error function with respect to θ, a first equivalent formula corresponding to the derivative being zero; wherein θ is the preset location information of the target terminal;

[0274] According to the first equivalent formula, the mth first position information is determined.

[0275] Optionally, the first maximum likelihood error function J d for:

[0276]

[0277] Among them, T d is the first data; d(θ) is the second data; c is the transmission speed of the signal; Q d Based on T d Determine the covariance matrix.

[0278] Optionally, the first equivalent formula is: 2ΦDθ=Φv;

[0279] in, Φ=WΛ,V=v1+r1Ψ;

[0280]

[0281] v1=[(Δ 21 2 +k1-k2)…(Δ N1 2 +k1-k N )] T ;Ψ=2[Δ 21 …Δ N1 ] T ;

[0282] Among them, the location information of the i-th second base station among the N-1 second base stations is (x i ,y i ), i = 2, ..., N; N is the number of base stations in the positioning system; r i is the distance from the target terminal to the i-th second base station, k i =x i 2 +y i 2 ; The location information of the first base station is (x1, y1); r1 is the distance from the target terminal to the first base station, k1 = x1 2 +y1 2 ; Δ i1 is the measured distance difference from the target terminal to the first base station and the i-th second base station.

[0283] Optionally, the processor 1200 is configured to read the computer program in the memory and perform the following operations:

[0284] If the two values ​​of the first distance calculated from the mth first position information meet the preset condition, it is determined that the value of the first distance r1 calculated from the mth first position information does not meet the positioning condition of the target terminal; wherein the two values ​​meet the preset condition if: both values ​​are positive real numbers, or both values ​​are negative real numbers, or both values ​​are complex numbers;

[0285] The position information determined in the m-th iteration is determined based on the first distance corresponding to the position information determined in the (m-1)th iteration, the first data, and the second data.

[0286] Optionally, the processor 1200 is configured to read the computer program in the memory and perform the following operations:

[0287] Determine a second maximum likelihood error function according to the first distance corresponding to the position information determined for the m-1th time, the first data, the second data, and third data including a plurality of measurement times; wherein one of the measurement times is a measurement time for a signal to be transmitted from the target terminal to a base station in the positioning system;

[0288] The position information determined in the mth iteration is determined according to the second maximum likelihood error function.

[0289] Optionally, the processor 1200 is configured to read the computer program in the memory and perform the following operations:

[0290] Determine fourth data according to the first distance corresponding to the position information determined for the (m-1)th time, the transmission speed of the signal, and the first data;

[0291] determining fifth data according to the first distance corresponding to the position information determined for the (m-1)th time and the second data;

[0292] A second maximum likelihood error function is determined based on the third data, the fourth data and the fifth data.

[0293] Optionally, the second maximum likelihood error function is J d ′:

[0294]

[0295] Among them, T d ' is the fourth data, T d =[t 21 …t N1 ] T , t i1 is the measured time difference of the signal transmitted from the target terminal to the first base station and the i-th second base station, i=2,…,N; N is the number of base stations in the positioning system; r 1,m-1 is the first distance corresponding to the position information determined for the m-1th time;

[0296] r d (θ) is the fifth data, r d (θ)=[r 1,m-1 ,d(θ)] T , d(θ)=[d 21 …d N1 ] T , d i1 is the distance difference between the target terminal and the first base station and the i-th second base station;

[0297] c is the transmission speed of the signal; Q is the covariance matrix determined based on the third data.

[0298] Optionally, the processor 1200 is configured to read the computer program in the memory and perform the following operations:

[0299] Determining a maximum likelihood error value corresponding to the position information determined in each iteration;

[0300] Determine the target location information according to the minimum of the maximum likelihood error values ​​corresponding to the location information determined in M ​​iterations;

[0301] The target location information is determined as the positioning position of the target terminal.

[0302] Among them, Fig.12 In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically one or more processors represented by processor 1200 and various circuits of memory represented by memory 1220 are linked together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 1210 may be a plurality of components, namely, a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium, which transmission medium may include a wireless channel, a wired channel, an optical cable, and other transmission media. The processor 1200 is responsible for managing the bus architecture and general processing, and the memory 1220 may store data used by the processor 1200 when performing operations.

[0303] The processor 1200 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or a complex programmable logic device (CPLD). The processor may also adopt a multi-core architecture.

[0304] It should be noted here that the above-mentioned device provided in the embodiment of the present invention can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0305] An embodiment of the present invention further provides a processor-readable storage medium, wherein the processor-readable storage medium stores a computer program, and the computer program is used to enable the processor to execute the steps in the above positioning method.

[0306] The processor-readable storage medium can be any available medium or data storage device that can be accessed by the processor, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drive (SSD)), etc.

[0307] The terminal device involved in the embodiment of the present application may be a device that provides voice and / or data connectivity to a user, a handheld device with a wireless connection function, or other processing devices connected to a wireless modem. In different systems, the name of the terminal device may also be different. For example, in a 5G system, the terminal device may be called a user equipment (UE). A wireless terminal device can communicate with one or more core networks (CN) via a radio access network (RAN). The wireless terminal device may be a mobile terminal device, such as a mobile phone (or a "cellular" phone) and a computer with a mobile terminal device. For example, it may be a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device that exchanges language and / or data with a wireless access network. For example, personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs) and other devices. The wireless terminal device may also be referred to as a system, a subscriber unit, a subscriber station, a mobile station, a mobile station, a remote station, an access point, a remote terminal device, an access terminal device, a user terminal device, a user agent, and a user device, but is not limited in the embodiments of the present application.

[0308] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0309] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer executable instructions. These computer executable instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0310] These processor executable instructions may also be stored in a processor readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the processor readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0311] These processor-executable instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0312] In addition, it should be noted that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it is understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0313] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A positioning method, characterized in that: include: Acquire first data including multiple measured time differences and second data including multiple distance differences; wherein one of the measured time differences is a measured time difference of a signal transmitted from a target terminal to a first base station and a second base station, and one of the distance differences is a distance difference from the target terminal to the first base station and a second base station; the first base station is a base station in a positioning system, and the second base station is a base station other than the first base station in the positioning system; According to the first data and the second data, the location information of the target terminal determined by M iterations is obtained based on an iterative algorithm; wherein the location information determined by the m-th iteration is determined based on the first distance corresponding to the location information determined by the m-1-th iteration, the first data, and the second data; the first distance is the distance from the target terminal to the first base station; M and m are positive integers, 1≤m≤M; Positioning the target terminal according to the position information determined by M iterations; Wherein, in the case where m=1, obtaining the location information of the target terminal determined by M iterations based on an iterative algorithm according to the first data and the second data includes: According to the first data and the second data, the position information determined for the m-1th time is estimated based on the least squares method, and the mth first position information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the position information determined for the m-1th time and the mth first position information are both related to the first distance; If the value of the first distance calculated from the m-th first position information does not meet the positioning condition of the target terminal, determining the iteratively determined position information determined in the m-th iteration based on the first distance corresponding to the position information determined for the m-1th time, the first data, and the second data; or In the case where m>1, the determining the location information of the target terminal for M iterations based on an iterative algorithm according to the first data and the second data includes: According to the first data and the second data, the mth first location information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the mth first location information is related to the first distance; If the value of the first distance calculated from the m-th first location information does not meet the positioning condition of the target terminal, determining the location information determined in the m-th iteration based on the first distance corresponding to the location information determined in the m-1th iteration, the first data, and the second data; The position information determined at the m-1th iteration is the position information determined at the m-1th iteration based on the iterative algorithm; Wherein, if the value of the first distance calculated by the m-th first position information does not meet the positioning condition of the target terminal, determining the position information determined by the m-th iteration based on the first distance corresponding to the position information determined for the m-1th time, the first data, and the second data, includes: If the two values ​​of the first distance calculated from the mth first position information meet the preset condition, it is determined that the value of the first distance r1 calculated from the mth first position information does not meet the positioning condition of the target terminal; wherein the two values ​​meet the preset condition if: both values ​​are positive real numbers, or both values ​​are negative real numbers, or both values ​​are complex numbers; The position information determined in the m-th iteration is determined based on the first distance corresponding to the position information determined in the (m-1)th iteration, the first data, and the second data.

2. The positioning method according to claim 1, characterized in that: Obtaining the mth first location information of the target terminal based on the approximate maximum likelihood method according to the first data and the second data, including: Determining a first maximum likelihood error function according to the first data and the second data; The mth first position information is determined according to the first maximum likelihood error function.

3. The positioning method according to claim 2, characterized in that: Determining the mth first position information according to the first maximum likelihood error function includes: Determine, according to the derivative of the first maximum likelihood error function with respect to θ, a first equivalent formula corresponding to the derivative being zero; wherein θ is the preset location information of the target terminal; According to the first equivalent formula, the mth first position information is determined.

4. The positioning method according to claim 3, characterized in that: The first maximum likelihood error function J d for: Among them, T d is the first data; d(θ) is the second data; c is the transmission speed of the signal; Q d Based on T d Determine the covariance matrix.

5. The positioning method according to claim 3, characterized in that: The first equivalent formula is: 2ΦDθ=Φv; Among them, Φ=WΛ,V=v1+r1Ψ; v1=[(D 21 2 +k1-k2)…(D N1 2 +k1-k N )] T ;Ψ=2[D 21 …D N1 ] T ; Among them, the location information of the i-th second base station among the N-1 second base stations is (x i ,y i ), i = 2, ..., N; N is the number of base stations in the positioning system; r i is the distance from the target terminal to the i-th second base station, k i =x i 2 +y i 2 ; The location information of the first base station is (x1, y1); r1 is the distance from the target terminal to the first base station, k1 = x1 2 +y1 2 ; Δ i1 is the measured distance difference from the target terminal to the first base station and the i-th second base station.

6. The positioning method according to claim 1, characterized in that: Determining the position information determined in the m-th iteration based on the first distance corresponding to the position information determined in the m-1th iteration, the first data, and the second data, includes: Determine a second maximum likelihood error function according to the first distance corresponding to the position information determined for the m-1th time, the first data, the second data, and third data including a plurality of measurement times; wherein one of the measurement times is a measurement time for a signal to be transmitted from the target terminal to a base station in the positioning system; The position information determined in the mth iteration is determined according to the second maximum likelihood error function.

7. The positioning method according to claim 6, characterized in that: Determining a second maximum likelihood error function according to the first distance corresponding to the position information determined for the (m-1)th time, the first data, the second data, and third data including a plurality of measurement times, includes: Determine fourth data according to the first distance corresponding to the position information determined for the (m-1)th time, the transmission speed of the signal, and the first data; determining fifth data according to the first distance corresponding to the position information determined for the (m-1)th time and the second data; A second maximum likelihood error function is determined based on the third data, the fourth data and the fifth data.

8. The positioning method according to claim 7, characterized in that: The second maximum likelihood error function is J d ′: Among them, T d ' is the fourth data, T d =[t 21 …t N1 ] T , t i1 is the measured time difference of the signal transmitted from the target terminal to the first base station and the i-th second base station, i=2,…,N; N is the number of base stations in the positioning system; r 1,m-1 is the first distance corresponding to the position information determined for the m-1th time; r d (θ) is the fifth data, r d (θ)=[r 1,m-1 ,d(θ)] T , d(θ)=[d 21 …d N1 ] T , d i1 is the distance difference between the target terminal and the first base station and the i-th second base station; c is the transmission speed of the signal; Q is the covariance matrix determined based on the third data.

9. The positioning method according to claim 1, characterized in that: Positioning the target terminal according to the position information determined by M iterations includes: Determining a maximum likelihood error value corresponding to the position information determined in each iteration; Determine the target location information according to the minimum of the maximum likelihood error values ​​corresponding to the location information determined in M ​​iterations; The target location information is determined as the positioning position of the target terminal.

10. A positioning device, characterized in that: Including memory, transceiver, processor; The memory is used to store computer programs; the transceiver is used to send and receive data under the control of the processor; the processor is used to read the computer program in the memory and perform the following operations: Acquire first data including multiple measured time differences and second data including multiple distance differences; wherein one of the measured time differences is a measured time difference of a signal transmitted from a target terminal to a first base station and a second base station, and one of the distance differences is a distance difference from the target terminal to the first base station and a second base station; the first base station is a base station in a positioning system, and the second base station is a base station other than the first base station in the positioning system; According to the first data and the second data, the location information of the target terminal determined by M iterations is obtained based on an iterative algorithm; wherein the location information determined by the m-th iteration is determined based on the first distance corresponding to the location information determined by the m-1-th iteration, the first data, and the second data; the first distance is the distance from the target terminal to the first base station; M and m are positive integers, 1≤m≤M; Positioning the target terminal according to the position information determined by M iterations; Wherein, when m=1, the processor is used to read the computer program in the memory and perform the following operations: According to the first data and the second data, the position information determined for the m-1th time is estimated based on the least squares method, and the mth first position information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the position information determined for the m-1th time and the mth first position information are both related to the first distance; If the value of the first distance calculated from the m-th first location information does not meet the positioning condition of the target terminal, determining the location information determined by the m-th iteration based on the first distance corresponding to the location information determined for the m-1th time, the first data and the second data; or When m>1, the processor is configured to read the computer program in the memory and perform the following operations: According to the first data and the second data, the mth first location information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the mth first location information is related to the first distance; If the value of the first distance calculated from the m-th first location information does not meet the positioning condition of the target terminal, determining the location information determined in the m-th iteration based on the first distance corresponding to the location information determined in the m-1th iteration, the first data, and the second data; The position information determined at the m-1th iteration is the position information determined at the m-1th iteration based on the iterative algorithm; The processor is used to read the computer program in the memory and perform the following operations: If the two values ​​of the first distance calculated from the mth first position information meet the preset condition, it is determined that the value of the first distance r1 calculated from the mth first position information does not meet the positioning condition of the target terminal; wherein the two values ​​meet the preset condition if: both values ​​are positive real numbers, or both values ​​are negative real numbers, or both values ​​are complex numbers; The position information determined in the m-th iteration is determined based on the first distance corresponding to the position information determined in the (m-1)th iteration, the first data, and the second data.

11. The positioning device according to claim 10, characterized in that: The processor is configured to read the computer program in the memory and perform the following operations: Determining a first maximum likelihood error function according to the first data and the second data; The mth first position information is determined according to the first maximum likelihood error function.

12. The positioning device according to claim 11, characterized in that: The processor is configured to read the computer program in the memory and perform the following operations: Determine, according to the derivative of the first maximum likelihood error function with respect to θ, a first equivalent formula corresponding to the derivative being zero; wherein θ is the preset location information of the target terminal; According to the first equivalent formula, the mth first position information is determined.

13. The positioning device according to claim 12, characterized in that: The first maximum likelihood error function J d for: Among them, T d is the first data; d(θ) is the second data; c is the transmission speed of the signal; Q d Based on T d Determine the covariance matrix.

14. The positioning device according to claim 12, characterized in that: The first equivalent formula is: 2ΦDθ=Φv; Among them, Φ=WΛ,V=v1+r1Ψ; v1=[(D 21 2 +k1-k2)…(D N1 2 +k1-k N )] T ;Ψ=2[D 21 …D N1 ] T ; Among them, the location information of the i-th second base station among the N-1 second base stations is (x i ,y i ), i = 2, ..., N; N is the number of base stations in the positioning system; r i is the distance from the target terminal to the i-th second base station, k i =x i 2 +y i 2 ; The location information of the first base station is (x1, y1); r1 is the distance from the target terminal to the first base station, k1 = x1 2 +y1 2 ; Δ i1 is the measured distance difference from the target terminal to the first base station and the i-th second base station.

15. The positioning device according to claim 10, characterized in that: The processor is configured to read the computer program in the memory and perform the following operations: Determine a second maximum likelihood error function according to the first distance corresponding to the position information determined for the m-1th time, the first data, the second data, and third data including a plurality of measurement times; wherein one of the measurement times is a measurement time for a signal to be transmitted from the target terminal to a base station in the positioning system; The position information determined in the mth iteration is determined according to the second maximum likelihood error function.

16. The positioning device according to claim 15, characterized in that: The processor is configured to read the computer program in the memory and perform the following operations: Determine fourth data according to the first distance corresponding to the position information determined for the (m-1)th time, the transmission speed of the signal, and the first data; determining fifth data according to the first distance corresponding to the position information determined for the (m-1)th time and the second data; A second maximum likelihood error function is determined based on the third data, the fourth data and the fifth data.

17. The positioning device according to claim 16, characterized in that: The second maximum likelihood error function is J d ′: Among them, T d ' is the fourth data, T d =[t 21 …t N1 ] T , t i1 is the measured time difference of the signal transmitted from the target terminal to the first base station and the i-th second base station, i=2,…,N; N is the number of base stations in the positioning system; r 1,m-1 is the first distance corresponding to the position information determined for the m-1th time; r d (θ) is the fifth data, r d (θ)=[r 1,m-1 ,d(θ)] T , d(θ)=[d 21 …d N1 ] T , d i1 is the distance difference between the target terminal and the first base station and the i-th second base station; c is the transmission speed of the signal; Q is the covariance matrix determined based on the third data.

18. The positioning device according to claim 10, characterized in that: The processor is configured to read the computer program in the memory and perform the following operations: Determining a maximum likelihood error value corresponding to the position information determined in each iteration; Determine the target location information according to the minimum of the maximum likelihood error values ​​corresponding to the location information determined in M ​​iterations; The target location information is determined as the positioning position of the target terminal.

19. A positioning device, characterized in that: include: An acquisition unit is used to acquire first data including multiple measured time differences and second data including multiple distance differences; wherein one of the measured time differences is a measured time difference of a signal transmitted from a target terminal to a first base station and a second base station, and one of the distance differences is a distance difference from the target terminal to the first base station and a second base station; the first base station is a base station in a positioning system, and the second base station is a base station other than the first base station in the positioning system; a processing unit, configured to obtain, based on the first data and the second data, the location information of the target terminal determined by M iterations based on an iterative algorithm; wherein the location information determined by the m-th iteration is determined based on the first distance corresponding to the location information determined by the m-1th iteration, the first data, and the second data; the first distance is the distance from the target terminal to the first base station; M and m are positive integers, 1≤m≤M; A positioning unit, configured to locate the target terminal according to the position information determined by M iterations; Wherein, when m=1, the processing unit is further configured to: According to the first data and the second data, the position information determined for the m-1th time is estimated based on the least squares method, and the mth first position information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the position information determined for the m-1th time and the mth first position information are both related to the first distance; If the value of the first distance calculated from the m-th first location information does not meet the positioning condition of the target terminal, determining the location information determined by the m-th iteration based on the first distance corresponding to the location information determined for the m-1th time, the first data and the second data; or When m>1, the processing unit is further configured to: According to the first data and the second data, the mth first location information of the target terminal is estimated based on the approximate maximum likelihood method; wherein the mth first location information is related to the first distance; If the value of the first distance calculated from the m-th first location information does not meet the positioning condition of the target terminal, determining the location information determined in the m-th iteration based on the first distance corresponding to the location information determined in the m-1th iteration, the first data, and the second data; The position information determined at the m-1th iteration is the position information determined at the m-1th iteration based on the iterative algorithm; Wherein, the processing unit is further used for: If the two values ​​of the first distance calculated from the mth first position information meet the preset condition, it is determined that the value of the first distance r1 calculated from the mth first position information does not meet the positioning condition of the target terminal; wherein the two values ​​meet the preset condition if: both values ​​are positive real numbers, or both values ​​are negative real numbers, or both values ​​are complex numbers; The position information determined in the m-th iteration is determined based on the first distance corresponding to the position information determined in the (m-1)th iteration, the first data, and the second data.

20. A processor-readable storage medium, characterized in that: The processor-readable storage medium stores a computer program, and the computer program is used to enable the processor to execute the steps in the positioning method according to any one of claims 1 to 9.

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