Terminal position determination method and apparatus, computer device, and storage medium

Abstract

The application relates to a terminal position determination method and device, a computer device and a storage medium. Position information and speed information of a target terminal at a previous moment are acquired; the position information and the speed information of the target terminal at the previous moment are processed through a state space model to obtain position information of the target terminal at a current moment; wherein the state space model is constructed based on an incidence angle formed according to a spatial position relationship between the terminal and a base station. In the method, the incidence angle is determined by considering the spatial position relationship between the terminal and the base station, and then the state space model is constructed based on the incidence angle, so that the terminal position determination method in the application can be applied to high-precision positioning and tracking of far-field and near-field terminals, and accurate terminal positions can be obtained whether far-field positioning and tracking or near-field positioning and tracking is performed.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method, apparatus, computer equipment, and storage medium for determining the location of a terminal. Background Technology

[0002] With the development of internet technology, people have increasingly higher requirements for location services.

[0003] In existing technologies, wireless base stations can measure the angle of arrival of the uplink signal of the terminal, and then establish an azimuth observation model based on the wireless base station's own pose information and the measured angle of arrival information. The position of the terminal is determined through the azimuth observation model. The azimuth observation model is an effective approximation of the real-world observation model in far-field positioning and tracking.

[0004] However, in actual wireless base station deployment environments, terminals are usually very close to the wireless base station, which is a near-field positioning and tracking scenario, and the azimuth observation model cannot accurately determine the terminal's position. Summary of the Invention

[0005] Therefore, it is necessary to provide a terminal location determination method, apparatus, computer equipment, and storage medium to address the aforementioned technical problems, which can accurately determine the terminal's location in near-field positioning and tracking.

[0006] In a first aspect, this application provides a method for determining the location of a terminal, the method comprising:

[0007] Obtain the target terminal's position and velocity information from the previous moment;

[0008] The position and velocity information of the target terminal at the previous moment are processed by the state space model to obtain the position information of the target terminal at the current moment; the state space model is constructed based on the incident angle formed by the spatial position relationship between the terminal and the base station.

[0009] In one embodiment, the process of constructing the state-space model includes:

[0010] Obtain the sample location information and sample velocity information of the sample terminal, as well as the antenna status information of the sample base station;

[0011] The incident angle information is determined based on the sample location information, antenna status information, and measurement error of the sample base station.

[0012] A state-space model is constructed based on the incident angle information, antenna state information, sample position information, and sample velocity information.

[0013] In one embodiment, the antenna state information further includes the tracking sampling interval, noise input matrix, state noise, and observation noise; based on the incident angle information, antenna state information, sample position information, and sample velocity information, a state space model is constructed, including:

[0014] Based on the tracking sampling interval, sample position information, and sample velocity information, the candidate position information of the sample terminal at the next moment is determined;

[0015] Based on the candidate location information, the noise input matrix, and the state noise, the terminal state information is generated.

[0016] Determine the incident angle observation information based on the incident angle information and observation noise;

[0017] The state-space model is determined based on the terminal status information and the incident angle observation information.

[0018] In one embodiment, the state-space model includes the time difference of arrival information between the terminal and the base station; based on the terminal state information and the incident angle observation information, the state-space model is determined, including:

[0019] Based on the candidate location information and the location information of the sample base station, the arrival time difference information between the sample terminal and the sample base station is obtained;

[0020] The arrival time difference information, incident angle observation information, and terminal status information are used to define the state space model.

[0021] In one embodiment, the position and velocity information of the target terminal at the previous moment are processed using a state-space model to obtain the position information of the target terminal at the current moment, including:

[0022] Based on the preset position constraints of the base station and the terminal, and the position and velocity information of the target terminal at the previous moment, the state space model is solved to determine the position information of the target terminal at the current moment.

[0023] In one embodiment, based on preset position constraints of the base station and the terminal, and the position and velocity information of the target terminal at the previous moment, the state space model is solved to determine the position information of the target terminal at the current moment, including:

[0024] The position and velocity information of the target terminal at the previous moment are input into the state space model for solution to obtain the candidate position information of the target terminal at the current moment;

[0025] Based on the candidate location information, the covariance matrix of the base station's observation noise, and the location constraints, the initial weights of the location information are updated to obtain the updated weights corresponding to the candidate location information.

[0026] Based on the updated weights, the candidate location information is filtered to obtain the target terminal's location information at the current moment.

[0027] In one embodiment, the initial weights of the location information are updated based on the candidate location information, the covariance matrix of the base station's observation noise, and location constraints to obtain updated weights corresponding to the candidate location information, including:

[0028] The candidate position information is input into the state space model for solution to obtain the incident angle information of the target terminal;

[0029] Based on the incident angle information, the incident angle observation information of the base station, and the covariance matrix of the observation noise, the likelihood function value corresponding to the candidate location information is determined; the likelihood function value is used to represent the probability value of the target terminal in the candidate location information.

[0030] The initial weights are updated based on the initial weights of the location information, the likelihood function value, and the location constraints to obtain the updated weights.

[0031] In one embodiment, the likelihood function value corresponding to the candidate location information is determined based on the incident angle information, the incident angle observation information of the base station, and the covariance matrix of the observation noise, including:

[0032] The candidate location information is input into the state space model for solution to obtain the arrival time difference information between the target terminal and the base station;

[0033] Based on the incident angle information, time difference of arrival information, the incident angle observation information of the base station, the time difference of arrival observation information, and the covariance matrix of the observation noise, the likelihood function value corresponding to the candidate location information is determined.

[0034] In one embodiment, the candidate location information is filtered according to the updated weights to obtain the target terminal's location information at the current time, including:

[0035] Based on the updated weights, the candidate location information is resampled to obtain the updated candidate location information;

[0036] The updated candidate location information is transformed to obtain the target terminal's location information at the current moment.

[0037] Secondly, this application also provides a terminal location determination device, the device comprising:

[0038] The information acquisition module is used to acquire the target terminal's position and velocity information at the previous moment;

[0039] The position determination module is used to process the position and velocity information of the target terminal at the previous moment through the state space model to obtain the position information of the target terminal at the current moment; the state space model is constructed based on the incident angle formed by the spatial position relationship between the terminal and the base station.

[0040] Thirdly, embodiments of this application provide a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods provided in the first aspect of the embodiments described above.

[0041] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods provided in the first aspect of the embodiments described above.

[0042] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the methods provided in the first aspect of the embodiments described above.

[0043] The aforementioned terminal location determination method, apparatus, computer equipment, and storage medium acquire the target terminal's location and velocity information at the previous moment; process the target terminal's location and velocity information at the previous moment using a state-space model to obtain the target terminal's location information at the current moment; wherein, the state-space model is constructed based on the incident angle formed by the spatial positional relationship between the terminal and the base station. In this method, the incident angle is determined by considering the spatial positional relationship between the terminal and the base station, and then a state-space model is constructed using the incident angle. This makes the terminal location determination method in this application applicable to high-precision positioning and tracking of near-field and far-field terminals, ensuring accurate terminal location for both far-field and near-field positioning and tracking. Attached Figure Description

[0044] Figure 1a This is an application environment diagram of the terminal location determination method in one embodiment;

[0045] Figure 1b This is a schematic diagram of the coordinate system for a wireless base station antenna.

[0046] Figure 1c A schematic diagram of the azimuth angle measured for a wireless base station;

[0047] Figure 2 This is a flowchart illustrating a terminal location determination method in one embodiment;

[0048] Figure 3 A schematic diagram of the incident angle measured for a wireless base station;

[0049] Figure 4This is a flowchart illustrating the terminal location determination method in another embodiment;

[0050] Figure 5 This is a flowchart illustrating the terminal location determination method in another embodiment;

[0051] Figure 6 This is a flowchart illustrating the terminal location determination method in another embodiment;

[0052] Figure 7 This is a flowchart illustrating the terminal location determination method in another embodiment;

[0053] Figure 8 This is a flowchart illustrating the terminal location determination method in another embodiment;

[0054] Figure 9 This is a flowchart illustrating the terminal location determination method in another embodiment;

[0055] Figure 10 This is a flowchart illustrating the terminal location determination method in another embodiment;

[0056] Figure 11 This is a flowchart illustrating the terminal location determination method in another embodiment;

[0057] Figure 12 This is a simulation diagram of the near-field case in one embodiment;

[0058] Figure 13 This is a simulation diagram of the far-field case in one embodiment;

[0059] Figure 14 This is a flowchart illustrating the terminal location determination method in another embodiment;

[0060] Figure 15 This is a simulation diagram of the near-field case in another embodiment;

[0061] Figure 16 This is a simulation diagram of the far-field case in another embodiment;

[0062] Figure 17 This is a flowchart illustrating the terminal location determination method in another embodiment;

[0063] Figure 18 This is a structural block diagram of a terminal position determination device in one embodiment;

[0064] Figure 19 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0066] The terminal location determination method provided in this application embodiment can be applied to, for example, Figure 1a The application environment shown. Figure 1a The target terminal 102 communicates with the server 104 via a network. A data storage system can store the data that the server 104 needs to process. The data storage system can be integrated onto the server 104, or it can be located in the cloud or on other network servers. The target terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, etc., and the server 104 can be a standalone server or a server cluster consisting of multiple servers.

[0067] Currently, wireless base stations can measure the Angle of Arrival (AOA) of the uplink signal of a terminal to obtain the terminal's AOA information; based on the position and attitude information of multiple wireless base stations and the measured terminal AOA information, the position of the terminal can be estimated.

[0068] However, due to cost reasons, most wireless base stations have a relatively small number of antennas, which can only perform accurate measurement of one-dimensional AOA information. In addition, there is usually a certain height difference between the installation position of the wireless base station antenna and the terminal position.

[0069] In traditional positioning and tracking algorithms, the one-dimensional AOA information is typically modeled as an azimuth angle, and an Azimuth Angle Observation Model (AAOM) is used for terminal positioning and tracking. AAOM is an effective approximation of the real-world observation model in far-field positioning and tracking. On the one hand, it can reduce the number of parameters in the observation model and simplify the calculation; on the other hand, the performance loss of positioning and tracking caused by the observation model approximation in far-field positioning and tracking is negligible. For example, as shown in 1b... Figure 1b This is a schematic diagram defining the coordinate system of a wireless base station antenna, where 101 represents the wireless base station. Figure 1c This is a schematic diagram of the azimuth angle of the azimuth angle observation model, where A is the azimuth angle.

[0070] However, in actual wireless base station deployment environments, terminals are usually very close to the wireless base station antenna, which is a near-field positioning and tracking situation. In this case, due to the height difference between the installation position of the wireless base station antenna and the terminal position, AAOM cannot effectively approximate the real-world observation model, and the performance of existing positioning and tracking algorithms based on AAOM will deteriorate sharply.

[0071] Existing positioning and tracking algorithms use AAOM for terminal positioning and tracking, such as Figure 1c As shown, the one-dimensional AOA information measured by the wireless base station is approximated as follows: Figure 1c When the terminal position is calculated using the azimuth angle θ, if there is a height difference between the installation position of the wireless base station antenna and the terminal position, the closer the terminal is to the wireless base station antenna, the greater the approximation error of the AAOM model, which leads to a significant increase in near-field positioning and tracking error.

[0072] Based on this, embodiments of this application provide a terminal location determination method, apparatus, computer device, and storage medium, which can accurately determine the terminal's location in near-field positioning and tracking.

[0073] The technical solutions of this application and how they solve the aforementioned technical problems will be described in detail below through embodiments and in conjunction with the accompanying drawings. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments.

[0074] In one embodiment, such as Figure 2 As shown, a method for determining the location of a terminal is provided, which can be applied to... Figure 1a Taking the application environment as an example, this embodiment involves first obtaining the target terminal's position and velocity information at the previous moment, and then processing this information using a state-space model to obtain the target terminal's position information at the current moment. This embodiment includes the following steps:

[0075] S201, Obtain the target terminal's position and speed information at the previous moment.

[0076] The target terminal is any terminal device in the base station. The location of the target terminal is not fixed. The location information of the target terminal in the next moment can be determined based on the location information and speed information of the target terminal in the previous moment.

[0077] In one embodiment, the location of the target terminal is not fixed. Therefore, the location and speed information of the target terminal at the previous moment can be randomly initialized based on the location information of the base station. Optionally, multiple location information can be randomly initialized.

[0078] Alternatively, the location and speed of the target terminal at the previous moment can be determined by combining the location of the base station with the location information of historical terminals in the base station, i.e., historical experience.

[0079] The location information can be the target terminal's position coordinates in a global coordinate system, which can include x-axis, y-axis, and z-axis coordinates. Correspondingly, the velocity information can be the velocity along the x-axis, y-axis, and z-axis.

[0080] S202, the position and velocity information of the target terminal at the previous moment are processed by the state space model to obtain the position information of the target terminal at the current moment; the state space model is constructed based on the incident angle formed by the spatial position relationship between the terminal and the base station.

[0081] like Figure 3 As shown, Figure 3 This diagram illustrates the incident angle from the terminal to the base station as measured by the base station. The incident angle takes into account the spatial positional relationship (height difference) between the base station and the terminal. Therefore, constructing a state space model using the incident angle allows the state space model to take into account the height difference between the base station and the terminal, thus enabling the state space model to be used for near-field tracking and positioning as well as far-field tracking and positioning.

[0082] Therefore, by using a state-space model to process the position and velocity information of the target terminal at the previous moment, the position information of the target terminal at the current moment can be obtained. Specifically, a filtering algorithm can be used to filter the position and velocity information of the target terminal at the previous moment based on the state-space model to obtain the position information of the target terminal at the current moment.

[0083] The position and velocity information of the target terminal at the previous moment are processed by the state space model to obtain multiple position information of the target terminal at the current moment. Then, the multiple position information is filtered to obtain the position information of the target terminal at the current moment.

[0084] The location information of the target terminal at the current moment can be location information in a global coordinate system.

[0085] It should be noted that after obtaining the target terminal's location information at the current moment, the target terminal's location information at the next moment can also be determined based on the target terminal's location information at the current moment, until it is no longer necessary to locate the terminal's location.

[0086] The aforementioned terminal location determination method acquires the target terminal's location and velocity information from the previous moment; it then processes this information using a state-space model to obtain the target terminal's current location information. The state-space model is constructed based on the incident angle formed by the spatial relationship between the terminal and the base station. This method determines the incident angle by considering the spatial relationship between the terminal and the base station, and then constructs the state-space model using the incident angle. This makes the terminal location determination method applicable to high-precision positioning and tracking of both near-field and far-field terminals, ensuring accurate terminal location for both near-field and far-field positioning and tracking.

[0087] In one embodiment, such as Figure 4 As shown, the process of constructing a state-space model includes the following steps:

[0088] S401, obtain the sample location information and sample speed information of the sample terminal, as well as the antenna status information of the sample base station.

[0089] The sample terminal is a terminal device that uses the established state space model as a sample, and the sample base station is a base station that uses the established state space model as a sample. The sample terminal and the sample base station correspond to each other. There can be multiple sample base stations. Therefore, the antenna state information of the sample base station is the antenna state information of multiple sample base stations. The sample base station can be a wireless base station.

[0090] The antenna status information of a base station is the status information of the antenna phase center of the base station. A base station may include multiple antennas. Therefore, the antenna status information of a base station is obtained based on the phase center of the antennas in the base station.

[0091] Among them, the sample position information of the sample terminal is the position coordinates of the sample terminal antenna phase center in the local coordinate system along the x-axis, y-axis and z-axis. Correspondingly, the sample velocity information can be the velocity vector of the sample terminal antenna phase center in the local coordinate system along the x-axis, y-axis and z-axis.

[0092] The antenna status information of the sample base station includes the attitude angle information of the sample base station in the local coordinate system and the position information of the antenna phase center of the sample base station in the local coordinate system. The attitude angle information of the sample base station in the local coordinate system includes azimuth, elevation and roll angles. The position information of the antenna phase center of the sample base station in the local coordinate system includes the position coordinates of the antenna phase center of the sample base station on the x-axis, y-axis and z-axis in the local coordinate system.

[0093] It should be noted that the local coordinate system is the coordinate system of the location of the terminal and base station, and the local coordinate system can also be the global coordinate system.

[0094] S402, determine the incident angle information based on the sample location information and antenna status information.

[0095] Based on the sample location information and antenna status information, the incident angle information is determined as shown in formulas (1)-(5).

[0096]

[0097] α i =cosψ i cosγ i -sinψ i sinθ i sinγ i (2)

[0098] β i =sinψ i cosγ i +cosψ i sinθ i sinγ i (3)

[0099]

[0100]

[0101] Among them, h i (x k () represents the incident angle information of the terminal's antenna phase center in the i-th wireless base station antenna coordinate system. This represents the state information of terminal k at time k. and Let k be the coordinates of the terminal antenna phase center in the local Cartesian coordinate system at time k. and Let ψ represent the velocity components of the terminal antenna phase center at time k along the x-axis and y-axis of the local rectangular coordinate system, respectively. i θ i and γ i These are the attitude angles of the i-th wireless base station antenna in the local Cartesian coordinate system, namely the azimuth, elevation, and roll angles. and Let be the position coordinates of the phase center of the i-th wireless base station antenna in the local rectangular coordinate system, and let arccos(g) be the inverse cosine function, α i β i and ε i μ is a parameter factor. i This represents the square of the difference between the height coordinates of the sample terminal and the height coordinates of the i-th base station at time k.

[0102] S403 constructs a state-space model based on incident angle information, antenna state information, sample position information, and sample velocity information.

[0103] Based on the incident angle information obtained above, as well as the antenna state information of the sample base station, the sample position information and sample velocity information of the sample terminal, a state space model can be constructed according to the following embodiments.

[0104] In one embodiment, such as Figure 5 As shown, the antenna state information also includes the tracking sampling interval, noise input matrix, state noise, and observation noise; therefore, based on the incident angle information, antenna state information, sample position information, and sample velocity information, a state space model is constructed, including the following steps:

[0105] S501 determines the candidate position information of the sample terminal at the next moment based on the tracking sampling interval, sample position information and sample velocity information.

[0106] The tracking sampling interval is the time difference between adjacent moments. Based on the tracking sampling interval and sample velocity information, the position change of the sample terminal within the tracking sampling interval can be determined. Then, based on the position change and the sample position information of the sample terminal at the previous moment, the candidate position information of the sample terminal at the next moment can be determined, as shown in formula (6). The tracking sampling interval is the time difference between the previous moment and the next moment.

[0107] x′ k =F k-1 x k-1 (6)

[0108]

[0109] in, x′ k In This provides candidate location information for the sample terminal at the next time step (time k). and F represents the velocity components of the sample terminal antenna phase center on the x-axis and y-axis of the local rectangular coordinate system at the next time moment (time k), respectively. k-1 Let T be the state transition matrix at time k-1, and T be the tracking sampling interval. x k-1 This provides the sample position and velocity information at time k-1 of the sample terminal.

[0110] In formula (6), F k-1 It is a state transition matrix constructed based on the tracking sampling interval.

[0111] S502 generates terminal status information based on candidate location information, noise input matrix, and status noise.

[0112] In order to make the obtained position information of the sample terminal more accurate in the next moment, the noise of the sample base station also needs to be considered. Therefore, the candidate position information of the sample terminal in the next moment can be updated according to the noise input matrix and state noise of the sample base station, as shown in formula (8).

[0113] x k =F k-1 x k-1 +G k-1 w k-1 (8)

[0114]

[0115] Where, x k G represents the terminal state information of the sample terminal at time k (the next time step). k-1 Let w be the noise input matrix at time k-1 (the previous time step). k-1 It is the state noise vector at time k-1.

[0116] S503 determines the incident angle observation information based on the incident angle information and observation noise.

[0117] The incident angle information can be determined based on the terminal's location information, the base station's antenna status information, and the base station's measurement error. Therefore, the incident angle information can be obtained by substituting the terminal status information obtained above into formula (1). Then, the incident angle observation information can be determined based on the incident angle information and the observation noise, as shown in formula (10).

[0118]

[0119] η k =h(x k )+v k (11)

[0120]

[0121] h(x k )=[h1(x k h2(x) k ) … h N (x k )] T (13)

[0122] v k =[v 1,k v 2,k … v N,k ] T (14)

[0123] in, This represents the incident angle observation information of the terminal's antenna phase center in the antenna coordinate system of the i-th wireless base station at time k, i.e., the terminal's AOA observation information measured by the i-th wireless base station at time k. i Let η be the AOA observation noise of the i-th wireless base station at time k, which is the AOA measurement error of the base station. k v represents the multi-base station AOA incident angle observation information at time k. k Let be the total observation noise of the base station at time k, which is in vector form.

[0124] S504 determines the state-space model based on the terminal status information and the incident angle observation information.

[0125] Therefore, the terminal state information and incident angle observation information can be determined as a state space model, that is, formula (8) and formula (11) can be determined as a state space model, and formula (11) is a simplified form of formula (10).

[0126] It should be noted that if the base station in this application includes multiple base stations, the constructed state space model is determined by using the incident angle observation information of multiple base stations for terminal positioning and tracking.

[0127] For example, if multiple wireless base stations in a certain area can measure the incident angle of a terminal's uplink signal, then the total incident angle observation information can be expressed as:

[0128]

[0129] Where N is the number of wireless base stations.

[0130] The aforementioned terminal location determination method acquires sample terminal location and velocity information, as well as sample base station antenna status information. Based on the sample location and antenna status information, it determines the incident angle information. Then, based on the incident angle information, antenna status information, sample location information, and sample velocity information, it constructs a state space model. This method, by constructing a spatial state model of the base station using incident angle information, ensures high-precision positioning and tracking of the terminal for both far-field and near-field positioning.

[0131] If the state-space model also includes the time difference of arrival information between the terminal and the base station, then in one embodiment, such as Figure 6 As shown, the state-space model is determined based on the terminal state information and the incident angle observation information, including the following steps:

[0132] S601, based on the candidate location information and the location information of the sample base station, obtain the arrival time difference information between the sample terminal and the sample base station.

[0133] The Time Difference of Arrival (TDOA) information is the absolute time difference between the arrival of the detection signal at the two base stations. In this application, the TDOA information can be the TDOA information between the sample terminal and the antenna of the first wireless base station and other wireless base stations, which can be expressed by formula (16):

[0134]

[0135]

[0136] Among them, g i (x k ) represents the sample terminal at candidate position x k The TDOA information of the (i+1)th base station and the first base station in the sample base station, μ i,k This represents the square of the difference between the height coordinate of the sample terminal and the height coordinate of the i-th base station in the sample base station at time k.

[0137] S602 defines the arrival time difference information, incident angle observation information, and terminal status information as a state-space model.

[0138] Therefore, the arrival time difference information, incident angle observation information and terminal state information can be determined as the state space model; the state space model formula (11) can be rewritten as formula (18).

[0139] ξ k =ψ(x k )+υ k (18)

[0140]

[0141] ψ(x k )=[h1(x k )h2(x k )...h N (x k )g1(x k )g2(x k )...g N-1 (x k )] T (20)

[0142] υ k =[v 1,k v 2,k …v N,k τ 1,k τ 2,k …τ N-1,k ] T (twenty one)

[0143]

[0144]

[0145] Where, ξ k ψ(x) represents the AOA and TDOA observation information vector of multiple wireless base stations in the sample base stations at time k. k () represents the AOA and TDOA information of the sample terminal at time k. Let represent the angle of incidence of the sample terminal antenna phase center in the antenna coordinate system of the i-th wireless base station among the sample base stations at time k, that is, the terminal AOA information measured by the i-th wireless base station among the sample base stations at time k. This represents the TDOA information between the phase center of the sample terminal antenna at time k and the (i+1)th and 1st wireless base station antennas in the sample base station. k Let v be the total observed noise vector of multiple wireless base stations in the sample base stations at time k. i,k Let τ be the AOA observation noise of the i-th wireless base station at time k. i,k Let denot be the TDOA observation noise at time k, and N be the number of wireless base stations in the sample base stations.

[0146] The aforementioned terminal location determination method obtains the time difference of arrival (TDOA) information between the sample terminal and the sample base station based on candidate location information and sample base station location information. The TDOA information, incident angle observation information, and terminal state information are then used to define a state-space model. This method achieves high-precision positioning and tracking of the terminal through a state-space model fused from multi-base station uplink AOA and TDOA information.

[0147] In one embodiment, the position and velocity information of the target terminal at the previous moment are processed by a state space model to obtain the position information of the target terminal at the current moment. This includes: solving the state space model according to preset position constraints of the base station and the terminal, and the position and velocity information of the target terminal at the previous moment, to determine the position information of the target terminal at the current moment.

[0148] Typically, multiple wireless base stations within a region can measure the AOA information of a terminal's uplink signal. Therefore, for a single terminal, given the AOA information measured by multiple wireless base stations, calculating the terminal's position coordinates using only the incident angle observation information shown in equation (15) would yield multiple or even infinitely many position coordinate solutions (the incident angle forms a cone, and multiple cones form a curved surface), making it impossible to accurately estimate the terminal's position coordinates. To ensure the feasibility of positioning and tracking, i.e., the uniqueness of the positioning and tracking solution, this embodiment proposes the following approach: to eliminate redundant position coordinate solutions and obtain a unique and accurate estimate of the terminal's position coordinates, on the one hand, the terminal's height value is known, i.e., the terminal's height coordinates in the local rectangular coordinate system (measured); on the other hand, position constraints are used to constrain the feasible region of the terminal's position coordinates, where the position constraints can be expressed as:

[0149]

[0150] In equation (24), the meaning of each symbol is the same as that of the symbols in the above embodiment, and k represents time k.

[0151] Therefore, based on the aforementioned positional constraints of the base station and the terminal, as well as the position and velocity information of the target terminal at the previous moment, the state-space model is solved to determine the position information of the target terminal at the current moment.

[0152] Optionally, based on the position constraints of the base station and the terminal, and the position and velocity information of the target terminal at the previous moment, the state space model can be solved using algorithms such as particle filtering and Bayesian filtering to determine the position information of the target terminal at the current moment.

[0153] In one embodiment, such as Figure 7 As shown, based on the preset position constraints of the base station and the terminal, and the position and velocity information of the target terminal at the previous moment, the state space model is solved to determine the position information of the target terminal at the current moment, including the following steps:

[0154] S701, input the position and velocity information of the target terminal at the previous moment into the state space model for solution, and obtain the candidate position information of the target terminal at the current moment.

[0155] First, the position and velocity information of the target terminal can be initialized using a particle filter, and Λ particles can be randomly initialized. Each particle corresponds to a position and velocity information of the target terminal.

[0156] Alternatively, multiple location and speed information of the target terminal can be determined based on historical experience.

[0157] The multiple location and velocity information of the target terminal are determined as the multiple location and velocity information of the target terminal at the previous moment.

[0158] Then, the multiple position and velocity information of the target terminal at the previous time step are input into the state space model, that is, into formula (8), to obtain the candidate position information of the target terminal at the current time step.

[0159] S702, based on the candidate location information, the covariance matrix of the base station's observation noise, and the location constraints, the initial weights of the location information are updated to obtain the updated weights corresponding to the candidate location information.

[0160] When determining the position and velocity information of the target terminal at the previous moment, the initial weights of each position information corresponding to the target terminal are initialized. For example, if there are Λ position information items, the initial weights corresponding to each position information item are:

[0161] In one embodiment, such as Figure 8 As shown, the initial weights of the location information are updated based on the candidate location information, the covariance matrix of the base station's observation noise, and the location constraints to obtain the updated weights corresponding to the candidate location information. This includes the following steps:

[0162] S801, the candidate position information is input into the state space model for solution, and the incident angle observation information of the target terminal is obtained.

[0163] Candidate location information Input the information into formula (1) to obtain the incident angle.

[0164] S802 determines the likelihood function value corresponding to the candidate location information based on the incident angle information, the incident angle observation information of the base station, and the covariance matrix of the observation noise.

[0165] The likelihood function value represents the probability of the target terminal being in the candidate location information.

[0166] Calculate the likelihood function value corresponding to the candidate position according to formula (25).

[0167]

[0168] in, For candidate location information The likelihood function value, R k Observation noise v for the incident angle of multiple base stations k The covariance matrix, η k For the incident angle observation information of the base station, This refers to the incident angle information of the target terminal.

[0169] Incident angle observation information η k The base station measures the incident angle of the target terminal and sends this information to the terminal.

[0170] S803 updates the initial weights based on the initial weights of the location information, the likelihood function value, and the location constraints to obtain the updated weights.

[0171] The initial weights can be updated according to formulas (26) and (27).

[0172]

[0173]

[0174] in, Location information Update weights, Location information The initial weights.

[0175] S703, based on the updated weights, filters the candidate location information to obtain the target terminal's location information at the current moment.

[0176] Before filtering the candidate position information, the update weights need to be normalized, as shown in formula (28).

[0177]

[0178] in, Location information The weight at time k, Location information The likelihood function, These are the normalized weights.

[0179] One way to filter candidate location information is to weight the candidate location information according to the normalized update weights. The result of the weighted location information is the location information of the target terminal at the current time.

[0180] The aforementioned terminal location determination method inputs the target terminal's position and velocity information from the previous moment into a state-space model for solution, obtaining candidate position information for the target terminal at the current moment. Based on the candidate position information, the covariance matrix of the base station's observation noise, and position constraints, the initial weights of the position information are updated to obtain updated weights corresponding to the candidate position information. Based on the updated weights, the candidate position information is filtered to obtain the target terminal's position information at the current moment. In this method, the use of a filtering algorithm to solve the state-space model to obtain the target terminal's position information at the current moment improves the accuracy of the obtained target terminal position information.

[0181] If the state-space model also includes the time difference of arrival (TDOA) between the terminal and the base station, then the likelihood function value of the candidate location also needs to be determined using the TDOA information. This will be explained in detail below with an example. In one example, such as... Figure 9 As shown, based on the incident angle information, the base station's incident angle observation information, and the covariance matrix of the observation noise, the likelihood function value corresponding to the candidate location information is determined, including the following steps:

[0182] S901, the candidate location information is input into the state space model for solution to obtain the arrival time difference information between the target terminal and the base station.

[0183] Candidate location information Inputting the data into formula (16) yields the time difference of arrival between the target terminal and the base station.

[0184] S902, based on the incident angle information, time difference of arrival information, the incident angle observation information of the base station, the time difference of arrival observation information, and the covariance matrix of the observation noise, determine the likelihood function value corresponding to the candidate location information.

[0185] Observation noise v k This includes incident angle observation noise and time difference of arrival observation noise.

[0186] Based on the incident angle information calculated above and arrival time difference information Determine formula (20), and then determine the likelihood function value corresponding to the candidate position according to formula (29).

[0187]

[0188] in, For candidate location information The likelihood function value, R k Observation noise v for multiple base stations k The covariance matrix, ξ kFor the base station's incident angle observation information and time difference of arrival observation information, ψ(x) k ) represents the incident angle information and arrival time difference information of the target terminal in formula (20).

[0189] The incident angle observation information and the time difference of arrival observation information were obtained from the base station measurements.

[0190] The aforementioned terminal location determination method inputs candidate location information into a state-space model for solution, obtaining the time difference of arrival (TDOA) between the target terminal and the base station. Based on the incident angle information, TDOA information, base station incident angle observation information, TDOA observation information, and the covariance matrix of observation noise, the likelihood function value corresponding to the candidate location information is determined. This method uses incident angle information and TDOA information for fused positioning of the terminal, improving the accuracy of the calculated likelihood function value of the candidate location information, thereby improving the accuracy of the subsequent calculation of the target terminal's location at the next time step.

[0191] In one embodiment, such as Figure 10 As shown, the candidate location information is filtered according to the updated weights to obtain the target terminal's location information at the current time, including the following steps:

[0192] S1001, Based on the updated weights, the candidate position information is resampled to obtain the updated candidate position information.

[0193] Based on the above embodiments, the update weights are normalized to obtain normalized update weights, and then the candidate position information is resampled according to the normalized weights.

[0194] Specifically, candidate position information corresponding to weights whose normalized updated weights are less than a preset threshold is deleted, and then corresponding candidate position information is added. The number of added candidate position information is the same as the number of deleted candidate position information, and the added candidate position information can all be the candidate position information corresponding to the largest weight among the normalized updated weights, thus obtaining new candidate position information. The new candidate location information is determined as the updated candidate location information, and the corresponding weights are transformed back to...

[0195] S1002, perform information transformation processing on the updated candidate location information to obtain the location information of the target terminal at the current time.

[0196] Based on the updated candidate position information obtained above, a transformation process is performed. The transformation process can be a mean calculation process, as shown in formula (30).

[0197]

[0198] in, This represents the location information of the target terminal at time k, where time k is the current time.

[0199] The aforementioned terminal location determination method resamples candidate location information based on updated weights to obtain updated candidate location information. This updated candidate location information is then transformed to obtain the target terminal's location information at the current moment. By resampling the candidate location information and then transforming the updated candidate location information, this method considers the updated weights corresponding to the candidate locations, thus improving the accuracy of the target terminal's location information at the current moment.

[0200] It should be noted that the terminal position determination method in this application can obtain the position information of the target terminal at each time after time k through iteration. At this time, if the position information of the target terminal at time k+1 is calculated, the candidate position information updated at time k can be used as the position information of the target terminal at the previous time. Then, the position information and velocity information of the target terminal at the previous time are processed through the state space model to obtain the position information of the target terminal at the current time. Thus, the position information of the target terminal at time k+1 can be obtained. In this way, the position information of the target terminal at each time can be obtained.

[0201] In one embodiment, to solve the problem of large near-field positioning and tracking error in existing positioning and tracking algorithms based on AAOM, the one-dimensional AOA information measured by the wireless base station is modeled as the incident angle and a positioning and tracking algorithm based on the Incidence Angle Observation Model (IAOM) is proposed: (1) An IAOM is proposed to accurately model the near-field wireless base station observation model, that is, the one-dimensional AOA information measured by the wireless base station is modeled as the incident angle; (2) A position constraint condition is proposed to constrain the feasible domain of the terminal position coordinates to ensure the uniqueness of the positioning solution; (3) A state space model for terminal positioning and tracking is proposed; (4) A constrained particle filter algorithm is proposed based on the particle filter framework and the above (1)-(3) to perform terminal positioning and tracking, which can simultaneously perform high-precision positioning and tracking of near and far-field terminals.

[0202] In one embodiment, such as Figure 11 As shown, this embodiment includes the following steps:

[0203] S1101, Establish a state space model for terminal positioning and tracking;

[0204] The terminal positioning and tracking state space model is shown in formulas (8) and (11).

[0205] S1102, Particle filter initialization.

[0206] The particle filtering initialization process is performed to obtain the initial motion state of the particles. The motion state of the particles is the motion state of the terminal, which includes the position coordinates and velocity information of the terminal.

[0207] S1103, Particle importance sampling.

[0208] Particle importance sampling is performed based on the particle's motion state, that is, the motion state of the particle at each moment is determined by the terminal positioning and tracking state model.

[0209] S1104, Particle weight update.

[0210] Based on the terminal positioning and tracking state model, the AOA value of the particle is calculated by the motion state at the initial moment, and the particle weight is updated according to the AOA value, the AOA measurement value of the base station at the corresponding moment and the terminal position constraint in formula (24), so as to obtain the updated particle weight.

[0211] S1105, Particle resampling.

[0212] The particles are resampled based on the updated particle weights to obtain the resampling results.

[0213] S1106, terminal state estimate, and based on the terminal state estimate, continue to execute S1103 to perform iterative updates, obtain the terminal state estimate at each time point, until the iteration ends.

[0214] The estimated terminal state of the particle at the next moment is obtained based on the resampling results, which determines the position information of the terminal. The position information of the particle can be iteratively processed, that is, the estimated terminal state of the particle at the current moment is used to determine the estimated terminal state of the next moment, and the estimated terminal state of the next moment is used as the current moment. Then, the estimated terminal state of the current moment is used to determine the estimated terminal state of the next moment, until the iteration ends, and the position information of the terminal at each moment is obtained.

[0215] This embodiment can significantly improve near-field positioning and tracking accuracy on the one hand, and also improve far-field positioning and tracking accuracy to a certain extent on the other hand. The proposed IAOM and the corresponding positioning and tracking algorithm can perform high-precision positioning and tracking of near and far-field terminals simultaneously.

[0216] To verify the effectiveness of the terminal location determination method in this application, in one embodiment, a simulation experiment is conducted to illustrate the terminal location determination method including AOA information in this application. The simulation sets the location coordinates of wireless base station 1 as follows: and The attitude angles are ψ1 = 270°, θ1 = 0 and γ1 = 0. The location coordinates of wireless base station 2 are... and The attitude angles were ψ1 = 270°, θ1 = 0 and γ1 = 0. The standard deviation of the AOA observation error of the two wireless base stations was 2 degrees. The particle number of the particle filter was set to 500. A total of 100 Monte Carlo simulations were performed.

[0217] When performing performance analysis of two localization and tracking algorithms in the near field, the initial state of the terminal is set as follows:

[0218]

[0219] like Figure 12 As shown, Figure 12 This paper presents a performance comparison between a positioning and tracking algorithm based on AAOM and a positioning and tracking algorithm based on IAOM (the terminal location determination method including AOA information in this application). The horizontal axis represents time k, and the vertical axis represents the root mean square error (RMSE). The AAOM-based positioning and tracking algorithm has a RMSE of 1.294 m, while the IAOM-based algorithm has a RMSE of 0.335 m. Figure 12 It can be seen that the IAOM-based positioning and tracking algorithm in this embodiment has higher accuracy and significantly improved positioning and tracking performance in the near field, which can solve the problem of large positioning and tracking errors in the existing AAOM-based positioning and tracking algorithms in the near field.

[0220] When performing positioning and tracking performance analysis of two positioning and tracking algorithms in the far-field situation, the initial state of the terminal is set as follows:

[0221]

[0222] like Figure 13 As shown, Figure 13 This paper presents a performance comparison between localization and tracking algorithms based on AAOM and IAOM. The AAOM-based algorithm has a mean root mean square (RMS) tracking error of 0.395m, while the IAOM-based algorithm has a mean SMS tracking error of 0.372m. Figure 13 It can be seen that even in far-field conditions, the IAOM-based positioning and tracking algorithm in this embodiment has higher accuracy. In summary, the IAOM-based positioning and tracking algorithm in this embodiment can be applied to high-precision positioning and tracking of both near-field and far-field terminals.

[0223] Since terminal 2D positioning typically requires at least two wireless base stations covering the same area, the Time Difference of Arrival (TDOA) information measured by multiple wireless base stations can be used to improve the terminal's 2D positioning accuracy. Because at least two wireless base stations cover the same area, high-precision TDOA measurement can usually be achieved by ensuring high-precision time synchronization between the wireless base stations. Generally speaking, the more observations available, the higher the accuracy of the terminal's position estimation.

[0224] Therefore, in one embodiment, a multi-base station uplink AOA and TDOA fusion 2D tracking algorithm is proposed based on the above-mentioned AOA information and further combined with TDOA information, which can effectively improve the 2D positioning accuracy of the terminal. This algorithm includes: (1) fusing one-dimensional AOA information and TDOA information measured by multiple base stations for terminal 2D positioning; (2) proposing a state space model for multi-base station uplink AOA and TDOA fusion 2D tracking; (3) proposing state constraints for multi-base station uplink AOA and TDOA fusion 2D tracking; and (4) proposing a multi-base station uplink AOA and TDOA fusion 2D tracking algorithm based on constrained particle filtering. It should be noted that the contents of the embodiments in this application have been described in detail in the above embodiments and will not be repeated here.

[0225] In one embodiment, such as Figure 14 As shown, this embodiment includes the following steps:

[0226] S1401, Establish the terminal positioning and tracking state space model;

[0227] The terminal positioning and tracking state space model is shown in formulas (8) and (18).

[0228] S1402, Particle filter initialization.

[0229] The particle filtering initialization process is performed to obtain the initial motion state of the particles. The motion state of the particles is the motion state of the terminal, which includes the position coordinates and velocity information of the terminal.

[0230] S1403, Particle Importance Sampling.

[0231] Particle importance sampling is performed based on the particle's motion state, that is, the motion state of the particle at each moment is determined by the terminal positioning and tracking state model.

[0232] S1404, Update the particle weights based on the terminal positioning and tracking status model.

[0233] Based on the terminal positioning and tracking state model, the AOA and TDOA values ​​of the particles are calculated by the motion state at the initial moment. The particle weights are updated according to the AOA and TDOA values, the AOA and TDOA measurement values ​​of the base station at the corresponding moment, and the terminal position constraints in formula (24), so as to obtain the updated particle weights.

[0234] S1405, Particle resampling.

[0235] The particles are resampled based on the updated particle weights to obtain the resampling results.

[0236] S1406, terminal state estimate, and based on the terminal state estimate, continue to execute S1403 to perform iterative updates, obtain the terminal state estimate at each time point, until the iteration ends.

[0237] The estimated terminal state of the particle at the next moment is obtained based on the resampling results, which determines the position information of the terminal. The position information of the particle can be iteratively processed, that is, the estimated terminal state of the particle at the current moment is used to determine the estimated terminal state of the next moment, and the estimated terminal state of the next moment is used as the current moment. Then, the estimated terminal state of the current moment is used to determine the estimated terminal state of the next moment, until the iteration ends, and the position information of the terminal at each moment is obtained.

[0238] To verify the effectiveness of the terminal location determination method in this application, in one embodiment, a simulation experiment is conducted to illustrate the terminal location determination method including AOA information and TDOA information in this application. The simulation settings are as follows: The location coordinates of wireless base station 1 are... and The attitude angles are ψ1 = 270°, θ1 = 0 and γ1 = 0. The location coordinates of wireless base station 2 are... and The attitude angles were ψ2 = 270°, θ2 = 0 and γ2 = 0. The standard deviation of the AOA observation error for both wireless base stations was 1 degree, and the standard deviation of the TDOA observation error was 0.1 meters. The particle number of the particle filter was set to 500. A total of 100 Monte Carlo simulations were performed.

[0239] When performing positioning performance analysis of two tracking algorithms in near-field conditions, the initial state of the terminal is set as follows:

[0240]

[0241] Figure 15This paper presents a comparison of the positioning performance of a pure AOA 2D tracking algorithm based on AAOM and a multi-base station uplink AOA and TDOA fusion 2D tracking algorithm based on constrained particle filtering (a terminal location determination method including AOA and TDOA information in this application). The average root mean square (RMS) positioning error of the pure AOA 2D tracking algorithm based on AAOM is 1.34 m, while the average RMS positioning error of the multi-base station uplink AOA and TDOA fusion 2D tracking algorithm based on constrained particle filtering is 9.75 × 10⁻⁶ m. -2 m, based on Figure 15 It can be seen that in the near field, the multi-base station uplink AOA and TDOA fusion 2D tracking algorithm proposed in this embodiment based on constrained particle filtering can effectively fuse AOA and TDOA information measured by multiple wireless base stations. At the same time, with the help of the proposed IAOM, high-precision positioning of the terminal can be achieved. Compared with the traditional pure AOA 2D tracking algorithm based on AAOM, it has higher accuracy and significantly improved positioning performance.

[0242] When performing positioning performance analysis of the two tracking algorithms in the far-field scenario, the initial state of the terminal is set as follows:

[0243]

[0244] Figure 16 This paper presents a comparison of the positioning performance of a pure AOA 2D tracking algorithm based on AAOM and a multi-base station uplink AOA and TDOA fusion 2D tracking algorithm based on constrained particle filtering in this embodiment. The average root mean square (RMS) positioning error of the pure AOA 2D tracking algorithm based on AAOM is 0.238m, while the average RMS positioning error of the multi-base station uplink AOA and TDOA fusion 2D tracking algorithm based on constrained particle filtering is 0.145m. Figure 16 It can be seen that even in far-field conditions, the multi-base station uplink AOA and TDOA fusion 2D tracking algorithm based on constrained particle filtering in this embodiment still has higher accuracy. In summary, the multi-base station uplink AOA and TDOA fusion 2D tracking algorithm based on constrained particle filtering in this embodiment can effectively improve the positioning accuracy of the terminal.

[0245] In one embodiment, such as Figure 17 As shown, this embodiment includes the following steps:

[0246] S1701, establish a state space model of multiple base stations based on the location information of the terminal, the location information and measurement error of the base station, and the attitude angle information of the phase center of the base station antenna;

[0247] The state-space model includes incident angle information or incident angle information and TDOA information.

[0248] S1702, Initialize the particles according to the particle filtering algorithm to obtain the initial state of the particles;

[0249] The state includes position and velocity.

[0250] S1703 updates the particle position using a state-space model to obtain candidate positions for the particle.

[0251] S1704, based on the preset location constraints of the terminal and base station and the state space model of the multi-base station, the initial weights of the particles are updated to obtain the updated weights.

[0252] S1705, the initial state of the particle is resampled according to the updated weights to obtain the updated candidate positions.

[0253] S1706, the updated candidate positions are averaged to obtain the next position of the terminal.

[0254] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0255] Based on the same inventive concept, this application also provides a terminal location determination device for implementing the terminal location determination method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more terminal location determination device embodiments provided below can be found in the limitations of the terminal location determination method described above, and will not be repeated here.

[0256] In one embodiment, such as Figure 18 As shown, a terminal location determination device is provided, including: an information acquisition module 1801 and a location determination module 1802, wherein:

[0257] The information acquisition module 1801 is used to acquire the position and speed information of the target terminal at the previous moment.

[0258] The position determination module 1802 is used to process the position and velocity information of the target terminal at the previous moment through the state space model to obtain the position information of the target terminal at the current moment; the state space model is constructed based on the incident angle formed by the spatial position relationship between the terminal and the base station.

[0259] In one embodiment, the device 1800 further includes:

[0260] The sample information acquisition module is used to acquire the sample location information and sample velocity information of the sample terminal, as well as the antenna status information of the sample base station.

[0261] The observation information determination module is used to determine the incident angle information based on the sample location information, antenna status information, and measurement error of the sample base station;

[0262] The model building module is used to construct a state-space model based on incident angle information, antenna state information, sample position information, and sample velocity information.

[0263] In one embodiment, the model building module includes:

[0264] The information determination unit is used to determine the candidate position information of the sample terminal at the next moment based on the tracking sampling interval, sample position information and sample velocity information;

[0265] The information generation unit is used to generate terminal state information based on candidate position information, noise input matrix and state noise;

[0266] An incident angle determination unit is used to determine the incident angle observation information based on the incident angle information and the observation noise;

[0267] The model determination unit is used to determine the state-space model based on the terminal state information and the incident angle observation information.

[0268] In one embodiment, the model determination unit includes:

[0269] The first determining subunit is used to obtain the arrival time difference information between the sample terminal and the sample base station based on the candidate location information and the location information of the sample base station.

[0270] The second determining sub-unit is used to determine the arrival time difference information, incident angle observation information, and terminal status information into a state-space model.

[0271] In one embodiment, the location determination module 1802 includes:

[0272] The location determination unit is used to solve the state space model based on the preset location constraints of the base station and the terminal, as well as the location and velocity information of the target terminal at the previous moment, and to determine the location information of the target terminal at the current moment.

[0273] In one embodiment, the location determination unit includes:

[0274] The solution sub-unit is used to input the position and velocity information of the target terminal at the previous moment into the state space model for solution, so as to obtain the candidate position information of the target terminal at the current moment;

[0275] The update subunit is used to update the initial weights of the location information based on the candidate location information, the covariance matrix of the base station's observation noise, and the location constraints, so as to obtain the updated weights corresponding to the candidate location information.

[0276] The filtering subunit is used to filter the candidate location information according to the updated weights to obtain the location information of the target terminal at the current time.

[0277] In one embodiment, updating the subunit includes:

[0278] The first input sub-unit is used to input the candidate position information into the state space model for solving to obtain the incident angle information of the target terminal;

[0279] The third determining subunit is used to determine the likelihood function value corresponding to the candidate location information based on the incident angle information, the incident angle observation information of the base station, and the covariance matrix of the observation noise; the likelihood function value is used to represent the probability value of the target terminal in the candidate location information.

[0280] The resulting sub-unit is used to update the initial weights based on the initial weights of the location information, the likelihood function value, and the location constraints, thus obtaining the updated weights.

[0281] In one embodiment, the third determining subunit includes:

[0282] The second input sub-unit is used to input the candidate location information into the state space model for solving, so as to obtain the arrival time difference information between the target terminal and the base station;

[0283] The fourth determining subunit is used to determine the likelihood function value corresponding to the candidate location information based on the incident angle information, the time difference of arrival information, the incident angle observation information of the base station, the time difference of arrival observation information, and the covariance matrix of the observation noise.

[0284] In one embodiment, the filtering subunit includes:

[0285] The resampling subunit is used to resample the candidate position information according to the updated weights to obtain the updated candidate position information.

[0286] The processing subunit is used to perform information transformation processing on the updated candidate location information to obtain the location information of the target terminal at the current time.

[0287] Each module in the aforementioned terminal location determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0288] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 19 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a terminal location determination method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0289] Those skilled in the art will understand that Figure 19 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0290] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0291] The implementation principles and technical effects of each step in this embodiment are similar to those of the terminal location determination method described above, and will not be repeated here.

[0292] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0293] The implementation principles and technical effects of each step in this embodiment when the computer program is executed by the processor are similar to those of the terminal location determination method described above, and will not be repeated here.

[0294] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0295] The implementation principles and technical effects of each step in this embodiment when the computer program is executed by the processor are similar to those of the terminal location determination method described above, and will not be repeated here.

[0296] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0297] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0298] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0299] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining the location of a terminal, characterized in that, The method includes: Obtain the target terminal's position and velocity information from the previous moment; The position and velocity information of the target terminal at the previous moment are processed by a state-space model to obtain the position information of the target terminal at the current moment. The state-space model is constructed based on the incident angle formed by the spatial positional relationship between the terminal and the base station. The step of processing the position and velocity information of the target terminal at the previous moment using a state-space model to obtain the position information of the target terminal at the current moment includes: Based on the preset position constraints of the base station and the terminal, and the position and velocity information of the target terminal at the previous moment, the state space model is solved to determine the position information of the target terminal at the current moment.

2. The method according to claim 1, characterized in that, The process of constructing the state-space model includes: Obtain the sample location information and sample velocity information of the sample terminal, as well as the antenna status information of the sample base station; The incident angle information is determined based on the sample location information and the antenna status information; The state space model is constructed based on the incident angle information, the antenna state information, the sample position information, and the sample velocity information.

3. The method according to claim 2, characterized in that, The antenna state information further includes the tracking sampling interval, noise input matrix, state noise, and observation noise; the construction of the state space model based on the incident angle information, the antenna state information, the sample position information, and the sample velocity information includes: Based on the tracking sampling interval, the sample position information, and the sample velocity information, determine the candidate position information of the sample terminal at the next moment; Based on the candidate location information, the noise input matrix, and the state noise, terminal state information is generated; Based on the incident angle information and the observation noise, the incident angle observation information is determined; The state space model is determined based on the terminal state information and the incident angle observation information.

4. The method according to claim 3, characterized in that, The state-space model includes the time difference of arrival information between the terminal and the base station; Determining the state space model based on the terminal state information and the incident angle observation information includes: Based on the candidate location information and the location information of the sample base station, the arrival time difference information between the sample terminal and the sample base station is obtained; The arrival time difference information, the incident angle observation information, and the terminal state information are used to determine the state space model.

5. The method according to claim 1, characterized in that, The step of solving the state space model based on preset position constraints of the base station and the terminal, and the position and velocity information of the target terminal at the previous moment, to determine the position information of the target terminal at the current moment, includes: The position and velocity information of the target terminal at the previous moment are input into the state space model for solution to obtain the candidate position information of the target terminal at the current moment; Based on the candidate location information, the covariance matrix of the observation noise of the base station, and the location constraints, the initial weights of the location information are updated to obtain the updated weights corresponding to the candidate location information. Based on the updated weights, the candidate location information is filtered to obtain the location information of the target terminal at the current time.

6. The method according to claim 5, characterized in that, The step of updating the initial weights of the location information based on the candidate location information, the covariance matrix of the observation noise of the base station, and the location constraints to obtain the updated weights corresponding to the candidate location information includes: The candidate location information is input into the state space model for solution to obtain the incident angle information of the target terminal; Based on the incident angle information, the incident angle observation information of the base station, and the covariance matrix of the observation noise, the likelihood function value corresponding to the candidate location information is determined; the likelihood function value is used to represent the probability value of the target terminal in the candidate location information. The initial weights are updated based on the initial weights of the location information, the likelihood function value, and the location constraints to obtain the updated weights.

7. The method according to claim 6, characterized in that, Based on the incident angle information, the incident angle observation information of the base station, and the covariance matrix of the observation noise, the likelihood function value corresponding to the candidate location information is determined, including: The candidate location information is input into the state space model for solution to obtain the arrival time difference information between the target terminal and the base station; Based on the incident angle information, the time difference of arrival information, the incident angle observation information of the base station, the time difference of arrival observation information, and the covariance matrix of the observation noise, the likelihood function value corresponding to the candidate location information is determined.

8. The method according to claim 5, characterized in that, The step of filtering the candidate location information according to the updated weight to obtain the location information of the target terminal at the current time includes: Based on the updated weights, the candidate location information is resampled to obtain the updated candidate location information; The updated candidate location information is processed by information transformation to obtain the location information of the target terminal at the current time.

9. A terminal location determination device, characterized in that, The device includes: The information acquisition module is used to acquire the target terminal's position and velocity information at the previous moment; The location determination module is used to process the position and velocity information of the target terminal at the previous moment using a state-space model to obtain the position information of the target terminal at the current moment; the state-space model is constructed based on the incident angle formed by the spatial positional relationship between the terminal and the base station. The location determination module includes: The location determination unit is used to solve the state space model based on preset location constraints of the base station and the terminal, and the location and velocity information of the target terminal at the previous moment, to determine the location information of the target terminal at the current moment.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 8 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

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