Terminal positioning method and device, nonvolatile storage medium and computer device

Abstract

The application discloses a terminal positioning method and device, a nonvolatile storage medium and a computer device. The method comprises the following steps: acquiring distance measurement values of multiple base stations, measurement variances corresponding to the distance measurement values of the multiple base stations and base station positions of the multiple base stations, wherein the distance measurement values are obtained by measuring distances between the multiple base stations and a terminal by using a line-of-sight distance measurement method; and determining a terminal position of the terminal by using an approximate maximum likelihood time difference of arrival algorithm according to the base station positions, the distance measurement values and the measurement variances. The application solves the technical problem of reducing the positioning accuracy of the terminal due to the non-line-of-sight effect in a scene.

Description

Technical Field

[0001] This invention relates to the field of communications, and more specifically, to a terminal positioning method, apparatus, non-volatile storage medium, and computer equipment. Background Technology

[0002] Spatial location awareness is a key foundational technology supporting various mobile applications. In recent years, high-precision 3D positioning has attracted great interest from both academia and industry. In millimeter-wave multiple-input multiple-output networks, collaborative positioning based on channel state information has broad application value.

[0003] Terminal location can be achieved based on the time difference of arrival (TDOA) of signals sent from the terminal to the base station. In the process of terminal location, the Approximate Maximum Likelihood Time Difference of Arrival (AML TDoA) algorithm used in related technologies is mostly based on the Line of Sight (LoS) assumption. However, in real-world scenarios, many measurements taken by the base station from the terminal are non-line of sight (NLoS), which significantly reduces location accuracy.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a terminal positioning method, apparatus, non-volatile storage medium, and computer device to at least solve the technical problem of reduced positioning accuracy of the terminal due to non-line-of-sight effects in the scene.

[0006] According to one aspect of the present invention, a terminal positioning method is provided, comprising: acquiring distance measurement values ​​of a plurality of base stations, measurement variances corresponding to the distance measurement values ​​of the plurality of base stations, and base station locations of the plurality of base stations, wherein the distance measurement values ​​are distances between the plurality of base stations and the terminal respectively measured using a line-of-sight measurement method; and determining the terminal location of the terminal using an approximate maximum likelihood time difference of arrival algorithm based on the base station locations, the distance measurement values, and the measurement variances.

[0007] Optionally, the terminal location is determined using an approximate maximum likelihood time difference of arrival algorithm based on the base station location, the distance measurement value, and the measurement variance. This includes: dividing the plurality of base stations into reference base stations and non-reference base stations; determining a first unbiased measurement value of the distance between the reference base station and the terminal, and determining a second unbiased measurement value of the distance between the non-reference base station and the terminal; constructing a measured distance difference vector based on the first unbiased measurement value and the second unbiased measurement value, wherein the elements in the measured distance difference vector represent the difference between the second unbiased measurement value and the first unbiased measurement value; determining the covariance matrix of the measured distance difference vector based on the measurement variance, wherein the elements in the covariance matrix are represented by the measurement variance; and determining the terminal location using the approximate maximum likelihood time difference of arrival algorithm based on the base station location, the measured distance difference vector, and the covariance matrix.

[0008] Optionally, determining the terminal location using the approximate maximum likelihood time difference of arrival algorithm based on the base station location, the measured distance difference vector, and the covariance matrix includes: determining the objective function of the approximate maximum likelihood time difference of arrival algorithm based on the measured distance difference vector and the covariance matrix; and determining the terminal location using the approximate maximum likelihood time difference of arrival algorithm based on the base station location and the objective function.

[0009] Optionally, dividing the plurality of base stations into reference base stations and non-reference base stations includes: ignoring the geometric precision factor, determining a reference base station judgment value for each of the plurality of base stations based on the measurement variance and a preset reference base station judgment function, wherein the reference base station judgment function is derived based on the minimum mean square error; and dividing the plurality of base stations into the reference base station and the non-reference base station based on the reference base station judgment values ​​for each of the plurality of base stations, wherein the reference base station judgment value corresponding to the reference base station is less than the reference base station judgment value corresponding to the non-reference base station.

[0010] Optionally, dividing the plurality of base stations into reference base stations and non-reference base stations includes: the reference base station determination function includes: in, i represents the i-th base station, N represents the total number of base stations, and σ i 2 This represents the measurement variance corresponding to the distance measurement value of the i-th base station.

[0011] Optionally, the terminal location of the terminal is determined by using an approximate maximum likelihood time difference of arrival algorithm based on the base station location, the distance measurement value, and the measurement variance. This includes: determining that the approximate maximum likelihood time difference of arrival algorithm is a two-dimensional approximate maximum likelihood time difference of arrival algorithm, wherein the solution process of the two-dimensional approximate maximum likelihood time difference of arrival algorithm assumes that the height of the terminal is predetermined; and determining the terminal location of the terminal by using the two-dimensional approximate maximum likelihood time difference of arrival algorithm based on the base station location, the distance measurement value, and the measurement variance.

[0012] Optionally, obtaining the distance measurement values ​​of each of the multiple base stations includes: obtaining the signal transmission time between each of the multiple base stations and the terminal; and multiplying the signal transmission time by the speed of light to obtain the distance measurement value.

[0013] According to another aspect of the present invention, a terminal positioning device is also provided, comprising: an acquisition module, configured to acquire distance measurement values ​​of a plurality of base stations, measurement variances corresponding to the distance measurement values ​​of the plurality of base stations, and base station locations of the plurality of base stations, wherein the distance measurement values ​​are distances between the plurality of base stations and a terminal respectively measured using a line-of-sight measurement method; and a determination module, configured to determine the terminal location of the terminal based on the base station locations, the distance measurement values, and the measurement variances, using an approximate maximum likelihood time difference of arrival algorithm.

[0014] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is executed, the device where the non-volatile storage medium is located is controlled to execute any of the terminal positioning methods described above.

[0015] According to another aspect of the present invention, a computer device is also provided, the computer device including a memory and a processor, the memory being used to store a program, and the processor being used to run the program stored in the memory, wherein the program, when running, executes any of the terminal positioning methods described above.

[0016] In this embodiment of the invention, by introducing the measurement error of the base station, the distance measurement value, measurement variance, and location of each of the multiple base stations are first obtained. The distance measurement value is the distance between the multiple base stations and the terminal measured by the line-of-sight method. Then, considering the measurement error of the base station, the above distance measurement value and the base station location are input into the approximate maximum likelihood time difference of arrival algorithm, which achieves the purpose of making the algorithm estimate the terminal location more accurately, thereby realizing the technical effect of improving the positioning accuracy of the terminal and solving the technical problem of reduced positioning accuracy of the terminal due to non-line-of-sight effects in the scene. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 A hardware structure block diagram of a computer terminal for implementing a terminal positioning method is shown.

[0019] Figure 2 This is a flowchart illustrating the terminal positioning method provided according to an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram comparing the AML algorithm with and without Loss of Spectrum (LOS) under optional implementation methods.

[0021] Figure 4 This is a schematic diagram comparing AML algorithms with and without prior knowledge of the z-axis based on optional implementations;

[0022] Figure 5 This is a schematic diagram comparing the positioning performance of the alternative implementation method with that of the selected reference BS;

[0023] Figure 6 This is a structural block diagram of a terminal positioning device provided according to an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] First, some nouns or terms that appear in the description of the embodiments of this application shall be interpreted as follows:

[0027] Geometric Dilution of Precision (GDOP) is an indicator used to evaluate the quality of satellite navigation system receivers, reflecting the degree to which geometric factors of the receiver's location affect positioning errors.

[0028] Line of sight (LoS) refers to the situation in wireless communication where there are no obstacles in the transmission path and the signal can directly reach the target device. Non-line of sight (NLoS) refers to the situation in wireless communication where, due to obstructions, reflections, or other reasons, there are obstacles in the transmission path that prevent the signal from directly reaching the target device.

[0029] A base station (BS) is a collection of communication equipment that can transmit and receive radio frequency signals and provides the basic physical structure and technical support for establishing and maintaining telephone and data services in mobile communication networks.

[0030] A mobile user (MU) refers to a user who uses a mobile phone or other mobile device to communicate in a mobile communication network. The device to be located by the user is the user equipment (UE).

[0031] Time of Arrival (ToA) refers to the time it takes for a signal sent by a terminal to reach the base station.

[0032] The Time Difference of Arrival (TDoA) refers to the time difference between when a terminal signal arrives at multiple base stations.

[0033] The Central Unit (CU) is the core device in a communication system responsible for controlling and managing data transmission.

[0034] Mean Square Error (MSE) is a measure of the average deviation between a predicted result and the true value.

[0035] Measurement variance, in this invention, is the variance of the distance measurement values ​​from the base station to the terminal.

[0036] According to an embodiment of the present invention, an embodiment of a terminal positioning method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0037] The methods and embodiments provided in this application can be executed on mobile terminals, computer terminals, or similar computing devices. Figure 1 A hardware structure block diagram of a computer terminal for implementing a terminal positioning method is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as processors 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0038] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be implemented wholly or partially as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be wholly or partially integrated into any other element in the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0039] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the terminal positioning method in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the terminal positioning method of the aforementioned application. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0040] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0041] Existing Approximate Maximum Likelihood Time Difference of Arrival (AML TDoA) algorithms are mostly based on the line-of-sight assumption. However, many measurements in real-world systems are non-line-of-sight, which severely reduces positioning accuracy. This application proposes to overcome the negative impact of NLoS by obtaining second-order statistics, such as variance, of the base station measurement arrival time (ToA).

[0042] Figure 2 This is a flowchart illustrating a terminal positioning method according to an embodiment of the present invention. This method can be applied to a central unit, which is the core device in a communication system responsible for controlling and managing data transmission. Figure 2 As shown, the method includes the following steps:

[0043] Step S202: Obtain the distance measurement values ​​of multiple base stations, the measurement variance corresponding to the distance measurement values ​​of each base station, and the location of each base station. The distance measurement values ​​are the distances between the multiple base stations and the terminal, measured using a line-of-sight (LOS) measurement method. The LOS measurement method is a method for measuring the line-of-sight distance between two points on the Earth's surface. Line-of-sight distance refers to the straight-line distance between two points, without considering the effects of Earth's curvature and terrain undulations. Optionally, the LOS measurement method can include both line-of-sight and non-line-of-sight measurements; the specific method used depends on the actual situation and measurement requirements. Line-of-sight measurement refers to the method of calculating the line-of-sight distance by measuring the straight-line distance between two points when the target object can be directly seen; non-line-of-sight measurement refers to the method of calculating the line-of-sight distance indirectly when the target object cannot be directly seen. This method includes using technologies such as radio waves, radar, and lasers to measure the distance between two points and then calculating the line-of-sight distance. The measurement variance is the variance of the distance measurement values ​​from the base station to the terminal.

[0044] Since the measurement variance of the distance measurements from each of the multiple base stations introduced in this step depends on the characteristics of that base station, the measurement variance values ​​of each base station are not consistent. Therefore, the measurement variance in this application can also be called non-uniform variance. By introducing the measurement variance into the approximate maximum likelihood time difference of arrival algorithm, the algorithm model for solving the terminal location can be made more accurate, thereby allowing for a more accurate estimation of the terminal's location. The approximate maximum likelihood time difference of arrival algorithm is a model-driven method, inherently scalable, and usually based on the line-of-sight measurement assumption. However, its positioning accuracy is severely reduced due to the significant non-line-of-sight effects in outdoor scenarios. The line-of-sight measurement assumption assumes that the signals received by the base station from the terminal are transmitted to the base station in a straight line, without refraction or obstruction along the way.

[0045] The distances between multiple base stations are determined based on the line-of-sight measurement assumption, representing distance values ​​determined based on the signal received by the terminal and assumed to travel in a straight line through space to each base station. Since the base stations are fixed hardware devices, the individual locations of each base station do not need to be measured in real time; the central unit can directly read the precise locations of each base station.

[0046] Optionally, for a millimeter-wave multiple-input multiple-output (MIMO) communication network consisting of N base stations (N≥2), the N base stations can be connected to a central unit (CU) via a fronthaul link. The clocks of the N base stations are synchronized, and each base station is equipped with a large-scale planar antenna array. The spatial three-dimensional coordinates of BSi (the i-th base station) can be denoted as s. i =[x i y i , z i ]T The three-dimensional coordinates of a mobile user's terminal (UE) can be denoted as s = [x, y, z]. T The actual distance between the UE and BSi is:

[0047] r i =||ss i ||2 (1)

[0048] Where, r i s represents the distance measurement value from the i-th base station to the mobile terminal. i The central unit can obtain the precise location coordinates of each base station in advance, while the terminal's three-dimensional coordinates s need to be estimated through measurement.

[0049] As an optional embodiment, the distance measurements of multiple base stations can be obtained as follows: the signal transmission time between each base station and the terminal is obtained; the signal transmission time is multiplied by the speed of light to obtain the distance measurement. The signal transmission time between each base station and the terminal is determined based on the arrival time measured by each base station. Since the distance measurement value in the AML algorithm with introduced measurement variance proposed in this invention can be obtained under the line-of-sight measurement assumption, multiplying the aforementioned signal transmission time by the speed of light is equivalent to measuring the distance using the line-of-sight measurement assumption.

[0050] Optionally, the measurement results of the signal transmission time for N base stations can be denoted as: τ i Let represent the signal transmission time between the i-th base station and the terminal as measured by itself. Based on the line-of-sight measurement assumption, the signal transmission time of all N base stations is measured using LoS, which can be expressed as follows, where c represents the speed of light:

[0051]

[0052] Step S204: Based on the base station location, distance measurement value, and measurement variance, the terminal location is determined using the approximate maximum likelihood time difference of arrival algorithm.

[0053] Since the measurement variances corresponding to the distance measurements of multiple base stations are not uniform, the method provided by this invention can introduce the different measurement variances of different base stations into the approximate maximum likelihood time difference of arrival algorithm, that is, introduce a location estimation algorithm. This method replaces the basic assumption in related technologies that the measurement variances of each base station are equal, thus obtaining a model that is closer to reality, reducing modeling errors, and improving the accuracy of terminal location estimation during the calculation process.

[0054] As an optional embodiment, the terminal location can be determined as follows: Multiple base stations are divided into reference base stations and non-reference base stations; a first unbiased measurement value is determined for the distance between the reference base station and the terminal, and a second unbiased measurement value is determined for the distance between the non-reference base station and the terminal; a measured distance difference vector is constructed based on the first and second unbiased measurement values, wherein the elements in the measured distance difference vector represent the difference between the second and first unbiased measurement values; a covariance matrix of the measured distance difference vector is determined based on the measurement variance, wherein the elements in the covariance matrix are represented by the measurement variance; and the terminal location is determined using an approximate maximum likelihood time difference of arrival algorithm based on the base station location, the measured distance difference vector, and the covariance matrix.

[0055] Optionally, one base station can be selected from multiple base stations as a reference base station, and the others are non-reference base stations. The first unbiased measurement value for the reference base station is obtained by adding its measurement variance to the distance measurement value. Similarly, the second unbiased measurement value for each non-reference base station is obtained by adding its measurement variance to the distance measurement value. Because the time delay and distance between different base stations and the terminal are different, without a reference base station or by selecting an incorrect one, the time difference cannot be accurately measured and the terminal's location information cannot be calculated. Therefore, selecting a suitable reference base station is crucial for accurate ToA positioning.

[0056] Optionally, the index of the reference base station can be denoted as q, and the distance measurement value corresponding to the reference base station can be: r q , q∈{1,…,N}, where r q This indicates that the reference base station is the q-th base station out of N base stations, where q ranges from 1 to N. Then, the i-th base station BS... i Distance measurement to the mobile terminal and the reference base station BS q The difference between the distance measurements to the mobile terminal is denoted as d. i，q ,Right now:

[0057] d i，q =r i -r q (3)

[0058] According to equation (3), the ideal distance difference vector d = [d 1，q , ...,d q-1，q d q+1，q , ...,d N，q ] T .

[0059] As an optional embodiment, the first unbiased measurement and the second unbiased measurement can be characterized by adding the zero-mean additive white Gaussian noise of each of the multiple base stations and the distance measurement of each of the multiple base stations to obtain the first unbiased measurement and the second unbiased measurement, respectively.

[0060] Optionally, considering distance measurements with non-uniform measurement variance, the first and second unbiased measurement values ​​corresponding to the reference base station and non-reference base station are uniformly represented as:

[0061] r n，i =r i +n i (4)

[0062] Where, r n，i This represents the unbiased measurement value of the base station. When the base station is the reference base station, r n，i This represents the first unbiased measurement value; when the base station is a non-reference base station, r n，i This represents the second unbiased measurement value, where the subscript i indicates the i-th base station and its value ranges from 1 to N, and the subscript n indicates the unbiased measurement value r of the base station. n，i This is a measurement that includes noise. i It can be based on the measurement variance corresponding to each base station. A defined zero-mean additive white Gaussian noise (AWGN), n i The mean is zero and the variance is Therefore, the measured distance difference vector can be defined as δ=[δ 1，q , …, δ q-1，q δ q+1，q , …, δ N，q ] T Its i-th element is

[0063] δ i，q =r n，i -r n，q (5)

[0064] The covariance matrix ∑ of the measured distance difference vector δ can be represented as follows:

[0065]

[0066] The elements in the covariance matrix ∑ are represented using measurement variance. to σ represents the measurement variance corresponding to each base station with indices 1 to N. q 2 This represents the measurement variance corresponding to the reference base station q.

[0067] The AML algorithm in related technologies does not consider the non-uniformity of measurement variance among different base stations, meaning that different base stations have different measurement variances. Therefore, the AML algorithm in related technologies does not differentiate between measurement noise from different base stations, resulting in inaccurate estimated terminal locations. The method provided in this invention improves the accuracy of the predicted terminal location by introducing measurement variance to correct the process of estimating the terminal location using the AML algorithm.

[0068] As an optional embodiment, based on the base station location, the measured distance difference vector, and the covariance matrix, the terminal location can be determined by using the approximate maximum likelihood time difference of arrival algorithm through the following steps: determining the objective function of the approximate maximum likelihood time difference of arrival algorithm based on the measured distance difference vector and the covariance matrix; and determining the terminal location by using the approximate maximum likelihood time difference of arrival algorithm based on the base station location and the objective function.

[0069] Optionally, based on the approximate maximum likelihood time difference of arrival algorithm provided in this invention, the likelihood function of δ with respect to s is:

[0070]

[0071] Where J is the objective function of the approximate maximum likelihood time difference of arrival algorithm, and the objective function J can be expressed as follows:

[0072] J = [δ - d] T ∑ -1 [δ-d] (8)

[0073] Where d is the ideal distance difference vector represented in formula (3), and δ is the measured distance difference vector. Terminal localization based on the approximate maximum likelihood time difference of arrival algorithm is to estimate the terminal position s that minimizes the objective function J, formally expressed as:

[0074]

[0075] The estimated terminal location s obtained at this time is the more accurate terminal location determined by the approximate maximum likelihood time difference of arrival algorithm, taking into account the measurement variance of each base station.

[0076] To address the issue of how to calculate the terminal location using an approximate maximum likelihood time difference of arrival algorithm when measurement variance is introduced, this invention provides the following optional implementation methods:

[0077] Let the partial derivative of J with respect to s in (8) be zero, then we have

[0078]

[0079] definition Based on the above formulas (1), (3), (4), (5) and (10), the following matrix equation can be obtained:

[0080] 2ΦDs=Φ(u+2r q δ) (11)

[0081] in,

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] Equation (11) is a linear equation with respect to s. The weighted matrix Φ contains elements of s, so we can let Φ be an identity matrix and simplify equation (11) using weighted least squares:

[0088]

[0089] This solution gives the use of r q The value of s is represented by s. Substituting s into formula (1) at this time yields the value of r. q The quadratic equation can be optimized by selecting the optimal root according to the root selection convention (RSR). In the approximate maximum likelihood time difference of arrival algorithm, Φ is calculated using s in equation (17) as the initial solution. Then, from equation (11), we can obtain:

[0090]

[0091] Similarly, s can be used with r q This means that the updated solution for s can be given by following the steps after formula (17). By iterating and repeating formula (18) multiple times, the solution that minimizes the value of J can be determined, and the terminal position with higher positioning accuracy can be predicted.

[0092] As an optional embodiment, to divide multiple base stations into reference base stations and non-reference base stations, the following method can be adopted: Ignoring the geometric precision factor, determine the reference base station judgment value corresponding to each of the multiple base stations based on the measurement variance and a preset reference base station judgment function, wherein the reference base station judgment function is derived based on the minimum mean square error; divide the multiple base stations into reference base stations and non-reference base stations based on the reference base station judgment values ​​corresponding to each of the multiple base stations, wherein the reference base station judgment value corresponding to the reference base station is less than the reference base station judgment value corresponding to the non-reference base station.

[0093] Optionally, the reference base station determination function can be: in, i represents the i-th base station, N represents the total number of base stations, and σ i 2 This represents the measurement variance corresponding to the distance measurement value of the i-th base station.

[0094] The Cramer-Rao lower bound for each of the multiple base stations can be determined based on the measurement variance, and the definition is as follows: When given measurement variance When Ξ is a constant.

[0095] In the above optional embodiments, the measurement variance of each base station can be... Sort in ascending order, ignoring the geometrical precision factor (GDOP), in terms of measurement variance, the index q of the reference base station corresponding to the optimal TDoA positioning can be obtained from the formula... Determine, where argmin represents minimizing the value of the function's independent variable, i.e., minimizing the function's minimum value. Alternatively, it can be described as finding the base station index i that minimizes the function, where the function... The judgment formula is derived based on the minimum mean square error. Using base station q as the reference base station, and taking the ToA measurement value and measurement variance of the reference base station as references, TDoA positioning is performed.

[0096] The Geometric Precision Factor (GDOP) is a metric used to measure the accuracy or potential error of the position solution calculated by the Global Positioning System (GPS) and other satellite navigation systems at a given time, location, and satellite combination. A smaller GDOP indicates that the selected satellite combination provides a better position determination and is likely to produce more accurate results. In this optional embodiment, the GDOP is ignored because it is related to the terminal's position coordinates s, which are unknown.

[0097] The Cramerow lower bound is a fundamental theory for estimating parameters, which gives a lower bound (i.e., the minimum possible error) on the variance of any unbiased estimator. More specifically, for an unknown parameter θ and a sample X1, X2, ..., Xn, the Cramerow lower bound is defined as the reciprocal of the expectation of Fisher information with respect to θ. This bound tells us under what conditions we can find the best feasible unbiased estimator and determines that a higher level of accuracy cannot be achieved without special measures.

[0098] By introducing measurement errors from the base station through the above steps, the algorithm achieves a more accurate estimation of the terminal's location, thereby improving the positioning accuracy of the terminal and solving the problem of reduced positioning accuracy due to non-line-of-sight (NLOS) effects in the scenario. The AML algorithm based on non-uniform variance proposed in this invention can estimate the mobile terminal coordinates using the estimated ToA statistics, effectively improving positioning performance in NLoS scenarios.

[0099] As an optional embodiment, when using the approximate maximum likelihood time difference of arrival algorithm to determine the terminal location, the following method can be adopted: the approximate maximum likelihood time difference of arrival algorithm is determined to be a two-dimensional approximate maximum likelihood time difference of arrival algorithm, wherein the terminal height is assumed to be predetermined during the solution process of the two-dimensional approximate maximum likelihood time difference of arrival algorithm; based on the base station location, distance measurement value and measurement variance, the two-dimensional approximate maximum likelihood time difference of arrival algorithm is used to determine the terminal location.

[0100] The AML TDoA algorithm in related technologies considers three-dimensional space and does not fully utilize prior information that mobile users typically move at a fixed height on the ground in real-world scenarios. This invention proposes an AMLTDoA algorithm that utilizes the prior vertical z-axis coordinates of the mobile terminal, for example, assuming the terminal's height is known, to improve the positioning accuracy of the terminal in the xy-plane.

[0101] In real-world scenarios, mobile users typically move at a fixed height on the ground, meaning that the terminal carried by the mobile user has a deterministic prior on the z-axis, i.e., z in s is known. Therefore, a two-dimensional approximate maximum likelihood time difference of arrival algorithm using 3D measurements can be employed to utilize the assumption of the terminal's z-axis prior in a model-driven manner.

[0102] Optionally, the steps of the AML algorithm using z-axis prior provided in this embodiment are slightly modified from the steps of the optional implementation corresponding to formulas (10) to (18), and formulas (13), (15) and (16) are modified as follows:

[0103]

[0104]

[0105]

[0106] The above method simplifies the calculation process and improves the positioning accuracy of the terminal in the xy plane by ignoring the influence of z-axis coordinate error on the calculation process.

[0107] Figure 3This is a schematic diagram comparing the AML algorithm with and without Loss of Spectrum (LOS) under optional implementations, where "σ" 2 =1" indicates the standard AML algorithm based on the assumption of equal measurement variances. correspond Figure 3 Curves ② and ③ in the diagram show that in scene 1, because the measured statistics of different BSs are very similar, AMLs with and without variance estimation have similar positioning performance. In scene 2, the AML algorithm with measurement variance estimation provided by this invention significantly outperforms the ordinary AML algorithm, verifying the effectiveness of the improvement. Optionally, the ToA statistic can be measured using broadband uplink MIMO signals. The geometric layouts of the two millimeter-wave networks represent suburban and urban scenes, respectively. BSs are located on building rooftops or along streets, with heights ranging from 15 to 40 meters. The heights of buildings or trees are also marked. Outdoor positioning data is collected uniformly.

[0108] Although the proposed positioning method is performed in three-dimensional space, the planar squared error can be considered as a performance indicator.

[0109] Figure 4 This is a schematic diagram comparing AML algorithms with and without providing z-axis prior knowledge according to optional implementations. It compares the localization performance of AML algorithms with and without this z-axis prior knowledge. Figure 4 As shown, "prior" indicates that AML uses a z-axis prior. The AML algorithm with prior outperforms the AML algorithm with non-uniform variance in both scenarios, which verifies the effectiveness of the proposed improvement.

[0110] Figure 5 This is a comparative diagram of the positioning performance of the alternative implementation method and the selected reference BS, such as... Figure 5 As shown, experiments can be conducted in scenario 2 where GDOP is not significant. Calculations show that the optimal reference is the reference value with the smallest variance. Therefore, theoretically, the lower bound of positioning increases monotonically with increasing reference variance. (The text then abruptly shifts to a different topic:) Sort the data in ascending order and select the corresponding measurement values ​​as references to obtain the corresponding positioning errors, such as... Figure 5 As shown in the figure. The x-axis represents the variance index after sorting, the red curve is the MSE, and the blue curve is the 90% squared error of all sorted squared errors.

[0111] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0112] Through the above description of the embodiments, those skilled in the art can clearly understand that the terminal positioning method according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0113] According to embodiments of the present invention, a terminal positioning device for implementing the above-described terminal positioning method is also provided. Figure 6 This is a structural block diagram of a terminal positioning device provided according to an embodiment of the present invention, such as... Figure 6 As shown, the terminal positioning device includes an acquisition module 62 and a determination module 64. The terminal positioning device will be described below.

[0114] The acquisition module 62 is used to acquire the distance measurement values ​​of each of the multiple base stations, the measurement variance corresponding to the distance measurement values ​​of each of the multiple base stations, and the base station location of each of the multiple base stations. The distance measurement values ​​are the distances between the multiple base stations and the terminal measured by the line-of-sight measurement method.

[0115] The determination module 64, connected to the acquisition module 62, is used to determine the terminal location of the terminal based on the base station location, distance measurement value, and measurement variance, using an approximate maximum likelihood time difference of arrival algorithm.

[0116] It should be noted that the acquisition module 62 and the determination module 64 mentioned above correspond to steps S202 to S204 in the embodiments. The two modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in the computer terminal 10 provided in the embodiments.

[0117] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0118] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the terminal positioning method and device in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned terminal positioning method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0119] The processor can call the information and application program stored in the memory through the transmission device to perform the following steps: obtain the distance measurement values ​​of each of the multiple base stations, the measurement variance corresponding to the distance measurement values ​​of each of the multiple base stations, and the base station location of each of the multiple base stations, wherein the distance measurement values ​​are the distances between the multiple base stations and the terminal respectively measured by the line-of-sight measurement method; and determine the terminal location of the terminal by using the approximate maximum likelihood time difference of arrival algorithm based on the base station location, the distance measurement values, and the measurement variance.

[0120] Optionally, the processor may also execute program code for the following steps: determining the terminal location using an approximate maximum likelihood time difference of arrival algorithm based on the base station location, distance measurement values, and measurement variance, including: dividing multiple base stations into reference base stations and non-reference base stations; determining a first unbiased measurement value for the distance between the reference base station and the terminal, and a second unbiased measurement value for the distance between the non-reference base station and the terminal; constructing a measured distance difference vector based on the first and second unbiased measurement values, wherein the elements in the measured distance difference vector represent the difference between the second and first unbiased measurement values; determining the covariance matrix of the measured distance difference vector based on the measurement variance, wherein the elements in the covariance matrix are represented by the measurement variance; and determining the terminal location using an approximate maximum likelihood time difference of arrival algorithm based on the base station location, the measured distance difference vector, and the covariance matrix.

[0121] Optionally, the processor may also execute program code for the following steps: determining the terminal location using an approximate maximum likelihood time difference of arrival algorithm based on the base station location, the measured distance difference vector, and the covariance matrix, including: determining the objective function of the approximate maximum likelihood time difference of arrival algorithm based on the measured distance difference vector and the covariance matrix; and determining the terminal location using the approximate maximum likelihood time difference of arrival algorithm based on the base station location and the objective function.

[0122] Optionally, the processor may also execute program code for the following steps: dividing multiple base stations into reference base stations and non-reference base stations, including: ignoring the geometric precision factor, determining the reference base station judgment value corresponding to each of the multiple base stations based on the measurement variance and a preset reference base station judgment function, wherein the reference base station judgment function is derived based on the minimum mean square error; dividing the multiple base stations into reference base stations and non-reference base stations based on the reference base station judgment values ​​corresponding to each of the multiple base stations, wherein the reference base station judgment value corresponding to the reference base station is less than the reference base station judgment value corresponding to the non-reference base station.

[0123] Optionally, the processor may also execute program code that performs the following steps: dividing multiple base stations into reference base stations and non-reference base stations, including: the reference base station determination function includes: in, i represents the i-th base station, N represents the total number of base stations, and σ i 2 This represents the measurement variance corresponding to the distance measurement value of the i-th base station.

[0124] Optionally, the processor may also execute program code for the following steps: determining the terminal location of the terminal using an approximate maximum likelihood time difference of arrival algorithm based on the base station location, distance measurement value, and measurement variance, including: determining that the approximate maximum likelihood time difference of arrival algorithm is a two-dimensional approximate maximum likelihood time difference of arrival algorithm, wherein the solution process of the two-dimensional approximate maximum likelihood time difference of arrival algorithm assumes that the height of the terminal is predetermined; and determining the terminal location of the terminal using a two-dimensional approximate maximum likelihood time difference of arrival algorithm based on the base station location, distance measurement value, and measurement variance.

[0125] Optionally, the processor may also execute program code that performs the following steps: obtaining distance measurement values ​​from multiple base stations, including: obtaining the signal transmission time between the terminal and each of the multiple base stations; and multiplying the signal transmission time by the speed of light to obtain the distance measurement value.

[0126] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0127] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the terminal positioning method provided in the above embodiments.

[0128] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0129] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining the distance measurement values ​​of each of the multiple base stations, the measurement variance corresponding to the distance measurement values ​​of each of the multiple base stations, and the base station location of each of the multiple base stations, wherein the distance measurement values ​​are the distances between the multiple base stations and the terminal respectively measured using the line-of-sight measurement method; and determining the terminal location of the terminal using an approximate maximum likelihood time difference of arrival algorithm based on the base station location, the distance measurement values, and the measurement variance.

[0130] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the terminal location of the terminal using an approximate maximum likelihood time difference of arrival algorithm based on the base station location, distance measurement value, and measurement variance, including: dividing multiple base stations into reference base stations and non-reference base stations; determining a first unbiased measurement value of the distance between the reference base station and the terminal, and determining a second unbiased measurement value of the distance between the non-reference base station and the terminal; constructing a measured distance difference vector based on the first and second unbiased measurement values, wherein the elements in the measured distance difference vector represent the difference between the second and first unbiased measurement values; determining the covariance matrix of the measured distance difference vector based on the measurement variance, wherein the elements in the covariance matrix are represented by the measurement variance; and determining the terminal location using an approximate maximum likelihood time difference of arrival algorithm based on the base station location, the measured distance difference vector, and the covariance matrix.

[0131] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the terminal location using an approximate maximum likelihood time difference of arrival algorithm based on the base station location, the measured distance difference vector, and the covariance matrix, including: determining the objective function of the approximate maximum likelihood time difference of arrival algorithm based on the measured distance difference vector and the covariance matrix; and determining the terminal location using the approximate maximum likelihood time difference of arrival algorithm based on the base station location and the objective function.

[0132] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: dividing multiple base stations into reference base stations and non-reference base stations, including: ignoring the geometric precision factor, determining the reference base station judgment value corresponding to each of the multiple base stations based on the measurement variance and a preset reference base station judgment function, wherein the reference base station judgment function is derived based on the minimum mean square error; dividing the multiple base stations into reference base stations and non-reference base stations based on the reference base station judgment values ​​corresponding to each of the multiple base stations, wherein the reference base station judgment value corresponding to the reference base station is less than the reference base station judgment value corresponding to the non-reference base station.

[0133] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: dividing multiple base stations into reference base stations and non-reference base stations, including: the reference base station determination function includes: in, i represents the i-th base station, N represents the total number of base stations, and σ i 2 This represents the measurement variance corresponding to the distance measurement value of the i-th base station.

[0134] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining the terminal location of the terminal using an approximate maximum likelihood time difference of arrival algorithm based on the base station location, distance measurement value, and measurement variance, including: determining that the approximate maximum likelihood time difference of arrival algorithm is a two-dimensional approximate maximum likelihood time difference of arrival algorithm, wherein the solution process of the two-dimensional approximate maximum likelihood time difference of arrival algorithm assumes that the height of the terminal is predetermined; and determining the terminal location of the terminal using the two-dimensional approximate maximum likelihood time difference of arrival algorithm based on the base station location, distance measurement value, and measurement variance.

[0135] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining distance measurement values ​​of multiple base stations, including: obtaining the signal transmission time between the terminal and each of the multiple base stations; multiplying the signal transmission time by the speed of light to obtain the distance measurement value.

[0136] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0137] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0139] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0140] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0141] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0142] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A terminal positioning method, characterized in that, include: The distance measurement values ​​of each of the multiple base stations, the measurement variance corresponding to the distance measurement values ​​of each of the multiple base stations, and the base station location of each of the multiple base stations are obtained, wherein the distance measurement values ​​are the distances between the multiple base stations and the terminal respectively, measured by the line-of-sight measurement method; Based on the base station location, the distance measurement value, and the measurement variance, the terminal location of the terminal is determined using an approximate maximum likelihood time difference of arrival algorithm. The process of determining the terminal location using an approximate maximum likelihood time difference of arrival algorithm, based on the base station location, the distance measurement value, and the measurement variance, includes: dividing the plurality of base stations into reference base stations and non-reference base stations; determining a first unbiased measurement value of the distance between the reference base station and the terminal, and determining a second unbiased measurement value of the distance between the non-reference base station and the terminal; constructing a measured distance difference vector based on the first and second unbiased measurement values, wherein the elements in the measured distance difference vector represent the difference between the second and first unbiased measurement values; determining the covariance matrix of the measured distance difference vector based on the measurement variance, wherein the elements in the covariance matrix are represented by the measurement variance; and determining the terminal location using the approximate maximum likelihood time difference of arrival algorithm based on the base station location, the measured distance difference vector, and the covariance matrix.

2. The method according to claim 1, characterized in that, Based on the base station location, the measured distance difference vector, and the covariance matrix, the terminal location is determined using the approximate maximum likelihood time difference of arrival algorithm, including: The objective function of the approximate maximum likelihood time difference of arrival algorithm is determined based on the measured distance difference vector and the covariance matrix. The terminal location is determined using the approximate maximum likelihood time difference of arrival algorithm based on the base station location and the objective function.

3. The method according to claim 1, characterized in that, The plurality of base stations are divided into reference base stations and non-reference base stations, including: Ignoring the geometric precision factor, the reference base station judgment value corresponding to each of the plurality of base stations is determined according to the measurement variance and the preset reference base station judgment function, wherein the reference base station judgment function is derived based on the minimum mean square error. Based on the reference base station judgment value corresponding to each of the multiple base stations, the multiple base stations are divided into reference base stations and non-reference base stations, wherein the reference base station judgment value corresponding to the reference base station is less than the reference base station judgment value corresponding to the non-reference base station.

4. The method according to claim 3, characterized in that, The reference base station determination function includes: ,in, where i represents the i-th base station and N represents the total number of base stations. This represents the measurement variance corresponding to the distance measurement value of the i-th base station.

5. The method according to claim 1, characterized in that, Based on the base station location, the distance measurement value, and the measurement variance, the terminal location is determined using an approximate maximum likelihood time difference of arrival algorithm, including: The approximate maximum likelihood time difference of arrival algorithm is determined to be a two-dimensional approximate maximum likelihood time difference of arrival algorithm, wherein the height of the terminal is assumed to be predetermined during the solution process of the two-dimensional approximate maximum likelihood time difference of arrival algorithm; Based on the base station location, the distance measurement value, and the measurement variance, the terminal location is determined using the two-dimensional approximate maximum likelihood time difference of arrival algorithm.

6. The method according to any one of claims 1 to 5, characterized in that, The acquisition of distance measurement values ​​for each of the multiple base stations includes: The signal transmission time between the terminal and each of the multiple base stations is obtained from their respective measurements. The distance measurement value is obtained by multiplying the signal transmission time by the speed of light.

7. A terminal positioning device, characterized in that, include: The acquisition module is used to acquire the distance measurement values ​​of each of the multiple base stations, the measurement variance corresponding to the distance measurement values ​​of each of the multiple base stations, and the base station location of each of the multiple base stations, wherein the distance measurement values ​​are the distances between the multiple base stations and the terminal respectively measured using the line-of-sight measurement method; The determination module is used to determine the terminal location of the terminal based on the base station location, the distance measurement value, and the measurement variance, using an approximate maximum likelihood time difference of arrival algorithm. The determining module is further configured to: divide the plurality of base stations into reference base stations and non-reference base stations; determine a first unbiased measurement value of the distance between the reference base station and the terminal, and determine a second unbiased measurement value of the distance between the non-reference base station and the terminal; construct a measured distance difference vector based on the first unbiased measurement value and the second unbiased measurement value, wherein the elements in the measured distance difference vector represent the difference between the second unbiased measurement value and the first unbiased measurement value; determine the covariance matrix of the measured distance difference vector based on the measurement variance, wherein the elements in the covariance matrix are represented by the measurement variance; and determine the terminal location using the approximate maximum likelihood time difference of arrival algorithm based on the base station location, the measured distance difference vector, and the covariance matrix.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the terminal positioning method according to any one of claims 1 to 6.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store a program, and the processor is used to run the program stored in the memory. When the program is run, it executes the terminal positioning method according to any one of claims 1 to 6.

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