Positioning method, device, computer device, storage medium and program product

Abstract

The present application relates to a positioning method, device, computer device, storage medium, and program product, which are used in a target terminal. The method includes: receiving uplink signal measurement data sent by a target base station, where the uplink signal measurement data includes AOA data and TOA data; inputting the uplink signal measurement data and the positioning data of the target terminal at the previous moment into a first positioning state space model, and solving the first positioning state space model based on a preset algorithm to obtain the first positioning data of the target terminal at the current moment; where the first positioning state space model includes a first positioning observation model and a first positioning state model, and the first positioning observation model includes a first incident angle observation model and a first three-dimensional TOA observation model. Using this method can improve the positioning accuracy.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technologies, and in particular, to a positioning method, apparatus, computer device, storage medium, and program product. Background Art

[0002] For the 2D (Two Dimension) positioning of a terminal, currently, it is achieved by a wireless base station measuring the 1D (One Dimension) AOA (Angle of Arrival) information and TOA (Time of Arrival) information of the terminal's uplink signal. Due to considering the deployment cost of the wireless base station, generally a single wireless base station is used for the 2D positioning of terminals in a certain area. However, in most actual positioning scenarios, the wireless base station and the terminal may be blocked by obstacles, resulting in non-line-of-sight propagation of the uplink signal, making the AOA information and TOA information measured by the wireless base station no longer reliable, thus leading to large positioning errors and even interruption of the positioning process. Using multiple wireless base stations can avoid the problems existing in the single wireless base station positioning mentioned above. However, deploying multiple wireless base stations will increase the cost. Therefore, the problem of accurately positioning the terminal while controlling the cost needs to be solved urgently.

[0003] In the traditional technology, based on the 1D AOA information and TOA information measured by a single wireless base station, terminal 2D positioning is performed based on the AAOM (Azimuth Angle Observation Model) and the 2D TOA observation model.

[0004] However, both the AAOM and the 2D TOA observation model are constructed based on the assumption that the terminal and the wireless base station are located on the same plane, and are only applicable to the case where the terminal is relatively far from the wireless base station. When the terminal is relatively close to the wireless base station, such as in the indoor positioning of a 5G small cell base station, the height difference between the terminal and the wireless base station will cause a non-negligible elevation angle. If the AAOM and the 2D TOA observation model are still used for terminal 2D positioning based on this assumption, the positioning error will increase significantly. Therefore, the traditional technology has the problem of low positioning accuracy in the 2D positioning of near-field terminals. Summary of the Invention

[0005] Based on this, it is necessary to provide a positioning method, apparatus, computer device, storage medium, and program product that can improve the positioning accuracy in the 2D positioning of near-field and far-field terminals for the above technical problems.

[0006] In a first aspect, the present application provides a positioning method. The positioning method includes: receiving uplink signal measurement data sent by a target base station, where the uplink signal measurement data includes angle of arrival (AOA) data and time of arrival (TOA) data; inputting the uplink signal measurement data and first positioning data of the target terminal at the previous moment into a first positioning state space model, and solving the first positioning state space model based on a preset algorithm to obtain first positioning data of the target terminal at the current moment; wherein, the first positioning state space model includes a first positioning observation model and a first positioning state model, the first positioning observation model includes a first incident angle observation model and a first three-dimensional TOA observation model, the first incident angle observation model is used to characterize the relationship between the AOA data, the first antenna data of the target terminal, and the second antenna data of the target base station, the first three-dimensional TOA observation model is used to characterize the relationship between the TOA data, the first antenna data, and the second antenna data, and the first positioning state model is used to characterize the relationship between the first positioning data of the target terminal at the current moment and the first positioning data of the target terminal at the previous moment.

[0007] In one embodiment, the first positioning state model is specifically used to characterize the relationship between the first positioning data of the target terminal at the current moment, the first positioning data of the target terminal at the previous moment, the state transition matrix at the previous moment, the noise input matrix at the previous moment, and the state noise vector at the previous moment.

[0008] In one embodiment, the state transition matrix at the previous moment and the noise input matrix at the previous moment are obtained according to the tracking sampling interval.

[0009] In one embodiment, the first antenna data includes the antenna position data of the target terminal, and the second antenna data includes the antenna position data and antenna attitude data of the target base station.

[0010] In one embodiment, the antenna position data of the target terminal includes the position coordinates of the phase center of the antenna of the target terminal in the local rectangular coordinate system; the antenna position data of the target base station includes the position coordinates of the phase center of the antenna of the target base station in the local rectangular coordinate system; the antenna attitude data of the target base station includes the attitude angles of the antenna of the target base station in the local rectangular coordinate system.

[0011] In one embodiment, the positioning method further includes: obtaining inertial measurement data of the target terminal; inputting the uplink signal measurement data, the inertial measurement data, and the second positioning data of the target terminal at the previous moment into a second positioning state space model, and solving the second positioning state space model based on a preset algorithm to obtain the second positioning data of the target terminal at the current moment; wherein the second positioning state space model includes a second positioning observation model and a second positioning state model, the second positioning observation model includes a second incident angle observation model and a second three-dimensional TOA observation model, the second incident angle observation model is used to characterize the relationship between the AOA data, the first antenna data, and the second antenna data, the second three-dimensional TOA observation model is used to characterize the relationship between the TOA data, the first antenna data, and the second antenna data, and the second positioning state model is used to characterize the relationship between the second positioning data of the target terminal at the current moment, the second positioning data of the target terminal at the previous moment, and the inertial measurement data.

[0012] In one embodiment, the positioning method further includes: constructing a target state equation between the first-order differential of the second positioning data of the target terminal at the current moment, the second positioning data of the target terminal at the current moment, and the inertial measurement data; discretizing the target state equation to obtain a second positioning state model.

[0013] In one embodiment, the second positioning data includes terminal attitude data, terminal velocity data, and terminal position data, and the target state equation includes a first state equation, a second state equation, and a third state equation; the first state equation is a state equation between the first-order differential of the terminal attitude data of the target terminal at the current moment, the terminal attitude data of the target terminal at the current moment, and the inertial measurement data; the second state equation is a state equation between the first-order differential of the terminal velocity data of the target terminal at the current moment, the terminal velocity data of the target terminal at the current moment, and the inertial measurement data; the third state equation is a state equation between the first-order differential of the terminal position data of the target terminal at the current moment, the terminal position data of the target terminal at the current moment, and the inertial measurement data.

[0014] In one embodiment, the inertial measurement data includes: white noise of the gyroscope carried on the target terminal in the body coordinate system, the rotation matrix between the body coordinate system and the navigation coordinate system, white noise of the accelerometer carried on the target terminal in the body coordinate system, the projection component of the angular velocity of the body coordinate system relative to the navigation coordinate system in the body coordinate system, random constant drift of the gyroscope in the body coordinate system, the specific force output by the accelerometer in the body coordinate system, and random constant drift of the accelerometer in the body coordinate system.

[0015] In one embodiment, the first positioning state space model is solved based on a preset algorithm to obtain the first positioning data of the target terminal at the current moment, including: obtaining the constraint condition between the first antenna data and the second antenna data; solving the first positioning state space model based on the preset algorithm and the constraint condition to obtain the first positioning data of the target terminal at the current moment.

[0016] In one embodiment, the second positioning state space model is solved based on a preset algorithm to obtain the second positioning data of the target terminal at the current moment, including: obtaining the constraint condition between the first antenna data and the second antenna data; solving the second positioning state space model based on the preset algorithm and the constraint condition to obtain the second positioning data of the target terminal at the current moment.

[0017] In one embodiment, the preset algorithm is a particle filter algorithm.

[0018] In a second aspect, the present application further provides a positioning device. The positioning device includes: a receiving module, configured to receive uplink signal measurement data sent by a target base station, where the uplink signal measurement data includes AOA data and TOA data; a calculation module, configured to input the uplink signal measurement data and the first positioning data of the target terminal at the previous moment into the first positioning state space model, and solve the first positioning state space model based on a preset algorithm to obtain the first positioning data of the target terminal at the current moment; where the first positioning state space model includes a first positioning observation model and a first positioning state model, the first positioning observation model includes a first incident angle observation model and a first three-dimensional TOA observation model, the first incident angle observation model is used to characterize the relationship between the AOA data, the first antenna data of the target terminal, and the second antenna data of the target base station, the first three-dimensional TOA observation model is used to characterize the relationship between the TOA data, the first antenna data, and the second antenna data, and the first positioning state model is used to characterize the relationship between the first positioning data of the target terminal at the current moment and the first positioning data of the target terminal at the previous moment.

[0019] In one embodiment, the first positioning state model is specifically used to characterize the relationship between the first positioning data of the target terminal at the current moment, the first positioning data of the target terminal at the previous moment, the state transition matrix at the previous moment, the noise input matrix at the previous moment, and the state noise vector at the previous moment.

[0020] In one embodiment, the state transition matrix at the previous moment and the noise input matrix at the previous moment are obtained according to the tracking sampling interval.

[0021] In one embodiment, the first antenna data includes the antenna position data of the target terminal, and the second antenna data includes the antenna position data and the antenna attitude data of the target base station.

[0022] In one embodiment, the antenna position data of the target terminal includes the position coordinates of the phase center of the antenna of the target terminal in the local rectangular coordinate system; the antenna position data of the target base station includes the position coordinates of the phase center of the antenna of the target base station in the local rectangular coordinate system; the antenna attitude data of the target base station includes the attitude angles of the antenna of the target base station in the local rectangular coordinate system.

[0023] In one embodiment, the positioning device further includes: an acquisition module, configured to acquire inertial measurement data of the target terminal; a calculation module, further configured to input the uplink signal measurement data, the inertial measurement data, and the second positioning data of the target terminal at the previous moment into a second positioning state space model, and solve the second positioning state space model based on a preset algorithm to obtain the second positioning data of the target terminal at the current moment; wherein, the second positioning state space model includes a second positioning observation model and a second positioning state model, the second positioning observation model includes a second incident angle observation model and a second three-dimensional TOA observation model, the second incident angle observation model is used to characterize the relationship between the AOA data and the first antenna data and the second antenna data, the second three-dimensional TOA observation model is used to characterize the relationship between the TOA data and the first antenna data and the second antenna data, and the second positioning state model is used to characterize the relationship between the second positioning data of the target terminal at the current moment and the second positioning data of the target terminal at the previous moment and the inertial measurement data.

[0024] In one embodiment, the device further includes: constructing a target state equation between the first-order differential of the second positioning data of the target terminal at the current moment and the second positioning data of the target terminal at the current moment and the inertial measurement data; discretizing the target state equation to obtain a second positioning state model.

[0025] In one embodiment, the second positioning data includes terminal attitude data, terminal velocity data, and terminal position data, and the target state equation includes a first state equation, a second state equation, and a third state equation; the first state equation is a state equation between the first-order differential of the terminal attitude data of the target terminal at the current moment and the terminal attitude data of the target terminal at the current moment and the inertial measurement data; the second state equation is a state equation between the first-order differential of the terminal velocity data of the target terminal at the current moment and the terminal velocity data of the target terminal at the current moment and the inertial measurement data; the third state equation is a state equation between the first-order differential of the terminal position data of the target terminal at the current moment and the terminal position data of the target terminal at the current moment and the inertial measurement data.

[0026] In one embodiment, the inertial measurement data includes: white noise of the gyroscope mounted on the target terminal in the body coordinate system, rotation matrix between the body coordinate system and the navigation coordinate system, white noise of the accelerometer mounted on the target terminal in the body coordinate system, projection component of the angular velocity of the body coordinate system relative to the navigation coordinate system in the body coordinate system, random constant drift of the gyroscope in the body coordinate system, specific force output by the accelerometer in the body coordinate system, and random constant drift of the accelerometer in the body coordinate system.

[0027] In one embodiment, the calculation module is specifically configured to obtain the constraint conditions between the first antenna data and the second antenna data; and solve the first positioning state space model based on a preset algorithm and the constraint conditions to obtain the first positioning data of the target terminal at the current moment.

[0028] In one embodiment, the calculation module is further specifically configured to solve the second positioning state space model based on a preset algorithm to obtain the second positioning data of the target terminal at the current moment, including: obtaining the constraint conditions between the first antenna data and the second antenna data; and solving the second positioning state space model based on the preset algorithm and the constraint conditions to obtain the second positioning data of the target terminal at the current moment.

[0029] In one embodiment, the preset algorithm is a particle filter algorithm.

[0030] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method described in any item of the first aspect above are implemented.

[0031] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in any item of the first aspect above are implemented.

[0032] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the method described in any item of the first aspect above are implemented.

[0033] The above positioning method, device, computer device, storage medium and program product receive the uplink signal measurement data sent by the target base station, where the uplink signal measurement data includes AOA data and TOA data, and then input the uplink signal measurement data and the first positioning data of the target terminal at the previous moment into the first positioning state space model, and solve the first positioning state space model based on a preset algorithm to obtain the first positioning data of the target terminal at the current moment; wherein, the first positioning state space model includes a first positioning observation model and a first positioning state model, the first positioning observation model includes a first angle of arrival observation model and a first three-dimensional TOA observation model, the first angle of arrival observation model is used to characterize the relationship between the AOA data, the first antenna data of the target terminal and the second antenna data of the target base station, the first three-dimensional TOA observation model is used to characterize the relationship between the TOA data, the first antenna data and the second antenna data, and the first positioning state model is used to characterize the relationship between the first positioning data of the target terminal at the current moment and the first positioning data of the target terminal at the previous moment. In this application, the first angle of arrival observation model and the first three-dimensional TOA observation model can take into account the height between the target terminal and the target base station, thereby improving the positioning accuracy in the 2D positioning of near and far field terminals. Description of the Drawings

[0034] Figure 1 It is a schematic flowchart of a positioning method in an embodiment;

[0035] Figure 2 It is an application environment diagram of a positioning method in an embodiment;

[0036] Figure 3 It is a schematic flowchart of another positioning method in an embodiment;

[0037] Figure 4 It is a schematic flowchart of solving the first positioning state space model based on the particle filter algorithm in an embodiment;

[0038] Figure 5 It is a schematic flowchart of solving the second positioning state space model based on the particle filter algorithm in an embodiment;

[0039] Figure 6 It is a simulation diagram of two positioning algorithms in a near-field case in an embodiment;

[0040] Figure 7 It is a simulation diagram of two positioning algorithms in a far-field case in an embodiment;

[0041] Figure 8 It is a structural block diagram of a positioning device in an embodiment;

[0042] Figure 9The internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0043] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] In one embodiment, as Figure 1 shown, a flowchart of a positioning method is provided. This embodiment is described by taking a terminal as an example. It can be understood that this method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be but is not limited to various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented by an independent server or a server cluster composed of multiple servers. In the embodiment of the present application, the method includes the following steps:

[0045] Step 101: Receive the uplink signal measurement data sent by the target base station. The uplink signal measurement data includes AOA data and TOA data.

[0046] Step 102: Input the uplink signal measurement data and the first positioning data of the target terminal at the previous moment into the first positioning state space model, and solve the first positioning state space model based on a preset algorithm to obtain the first positioning data of the target terminal at the current moment; wherein, the first positioning state space model includes a first positioning observation model and a first positioning state model. The first positioning observation model includes a first incident angle observation model and a first three-dimensional TOA observation model. The first incident angle observation model is used to characterize the relationship between the AOA data, the first antenna data of the target terminal and the second antenna data of the target base station. The first three-dimensional TOA observation model is used to characterize the relationship between the TOA data, the first antenna data and the second antenna data. The first positioning state model is used to characterize the relationship between the first positioning data of the target terminal at the current moment and the first positioning data of the target terminal at the previous moment.

[0047] Optionally, as Figure 2As described above, an application environment diagram of a positioning method is provided. The target base station is a single wireless base station. After the target terminal sends an uplink signal to the target base station, the target base station measures the received uplink signal to obtain AOA data and TOA data, and sends the AOA data, TOA data, and the second antenna data of the target base station to the target terminal through the antenna. The target terminal includes an antenna and a CPU (central processing unit). The antenna is used to receive the above-mentioned multiple data sent by the target base station, and the CPU is used to calculate the first positioning data of the target terminal at the current moment according to the AOA data, TOA data, and the positioning data of the target terminal at the previous moment.

[0048] In addition, the target base station can also send the AOA data, TOA data, and the second antenna data of the target base station to the server, and the server will calculate the first positioning data of the target terminal at the current moment according to the AOA data, TOA data, and the first positioning data of the target terminal at the previous moment.

[0049] Optionally, the first positioning state space model is

[0050] x k =F k-1 x k-1 +G k-1 w k-1 (1)

[0051] ξ k =ψ(x k )+υ k (2)

[0052] Where

[0053]

[0054]

[0055]

[0056] ψ(x k )=[h(x k )g(x k )] T (6)

[0057]

[0058]

[0059] α=cosψcosγ - sinψsinθsinγ (9)

[0060] β=sinψcosγ + cosψsinθsinγ (10)

[0061]

[0062]

[0063] υ k =[v k τ k T (13)

[0064]

[0065]

[0066]

[0067] The above formula (1) is the first positioning state model, formula (2) is the first positioning observation model, formula (14) is the first incidence angle observation model (IAOM), and formula (15) is the first three-dimensional TOA observation model. x k represents the first positioning data of the target terminal at the current moment (k moment), and x k-1 is the first positioning data of the target terminal at the previous moment (k - 1 moment). The interpretations of each quantity in formulas (1) to (16) are as follows:

[0068] and are the first antenna data of the target terminal, which are the antenna position data of the target terminal. Specifically, they are the horizontal 2D coordinates of the antenna phase center of the target terminal in the local rectangular coordinate system at the k moment; and respectively represent the velocity components of the antenna phase center of the target terminal on the x-axis and y-axis of the local rectangular coordinate system at the k moment; F k-1 is the state transition matrix at the k - 1 moment; G k-1 is the noise input matrix at the k - 1 moment; w k-1 is the state noise vector at the k - 1 moment; T is the tracking sampling interval; ξ k represents the AOA and TOA observation vectors measured by the target base station at the k moment; represents the incidence angle of the antenna phase center of the target terminal in the antenna coordinate system of the target base station at the k moment, that is, the AOA data measured by the target base station at the k moment; is the TOA data measured by the target base station at the k moment; ψ(x k ) represents the initial AOA and initial TOA observation vectors measured by the target terminal at the k moment; h(x k ) is the initial AOA data measured by the target terminal at the k moment; g(x​k ) is the initial TOA data measured by the target terminal at time k, where the speed of light c is omitted here; ψ, θ, γ, and are the data of the second antenna of the target base station, where ψ, θ, and γ are the antenna attitude data of the target base station, specifically the attitude angles of the antenna of the target base station in the local rectangular coordinate system, which are the azimuth angle, elevation angle, and roll angle respectively. and are the antenna position data of the target base station, specifically the position coordinates of the antenna phase center of the target base station in the local rectangular coordinate system; is the data of the first antenna of the target terminal, which is the antenna position data of the target terminal, specifically the height coordinate of the antenna phase center of the target terminal at time k in the local rectangular coordinate system; arccos(·) is the inverse cosine function; υ k is the total observation noise vector of the target base station at time k, v k is the AOA observation noise of the target base station at time k, τ k is the TOA observation noise of the target base station at time k;

[0069] Input the AOA data and TOA data measured by the target base station, the data of the second antenna of the target base station, and the first positioning data of the target terminal at the previous moment into Formulas (1) and (2), and then solve the first positioning state space model based on the Bayesian filtering algorithm to obtain the first positioning data of the target terminal at the current moment.

[0070] In summary, by receiving the uplink signal measurement data sent by the target base station, where the uplink signal measurement data includes AOA data and TOA data, and then inputting the uplink signal measurement data and the first positioning data of the target terminal at the previous moment into the first positioning state space model, and solving the first positioning state space model based on a preset algorithm, the first positioning data of the target terminal at the current moment is obtained; among them, the first positioning state space model includes a first positioning observation model and a first positioning state model. The first positioning observation model includes a first incident angle observation model and a first three-dimensional TOA observation model. The first incident angle observation model is used to characterize the relationship between the AOA data, the data of the first antenna of the target terminal, and the data of the second antenna of the target base station. The first three-dimensional TOA observation model is used to characterize the relationship between the TOA data, the data of the first antenna, and the data of the second antenna. The first positioning state model is used to characterize the relationship between the first positioning data of the target terminal at the current moment and the first positioning data of the target terminal at the previous moment. Through the first incident angle observation model and the first three-dimensional TOA observation model of the present application, the height between the target terminal and the target base station can be considered, thereby improving the positioning accuracy in the 2D positioning of near and far field terminals.

[0071] In one embodiment, the first positioning state model is specifically used to characterize the relationship between the first positioning data of the target terminal at the current moment, the first positioning data of the target terminal at the previous moment, the state transition matrix at the previous moment, the noise input matrix at the previous moment, and the state noise vector at the previous moment.

[0072] In one embodiment, the state transition matrix at the previous moment and the noise input matrix at the previous moment are obtained according to the tracking sampling interval.

[0073] Among them, x in formula (1) and formula (16) k is the first positioning data of the target terminal at the current moment, and x in formula (1) k-1 is the first positioning data of the target terminal at the previous moment. F in formula (1) and formula (3) k-1 is the state transition matrix at the previous moment. G in formula (1) and formula (4) k-1 is the noise input matrix at the previous moment. w in formula (1) k-1 is the state noise vector at the previous moment. T in formula (3) and formula (4) is the tracking sampling interval. By changing the value of T at the target terminal, the state transition matrix at the previous moment and the noise input matrix at the previous moment can be changed, and thus the first positioning data of the target terminal at the current moment is changed.

[0074] In one embodiment, the first antenna data includes the antenna position data of the target terminal, and the second antenna data includes the antenna position data and antenna attitude data of the target base station.

[0075] In one embodiment, the antenna position data of the target terminal includes the position coordinates of the phase center of the antenna of the target terminal in the local rectangular coordinate system; the antenna position data of the target base station includes the position coordinates of the phase center of the antenna of the target base station in the local rectangular coordinate system; the antenna attitude data of the target base station includes the attitude angles of the antenna of the target base station in the local rectangular coordinate system.

[0076] Among them, in the first positioning state space model and are the position coordinates of the phase center of the antenna of the target terminal in the local rectangular coordinate system at the kth moment, and are the position coordinates of the phase center of the antenna of the target base station in the local rectangular coordinate system, and ψ, θ, γ are the attitude angles of the antenna of the target base station in the local rectangular coordinate system.

[0077] In one embodiment, as Figure 3 shown, a flowchart of another positioning method is provided. The method includes the following steps:

[0078] Step 301: Receive the uplink signal measurement data sent by the target base station, and obtain the inertial measurement data of the target terminal. The uplink signal measurement data includes AOA data and TOA data.

[0079] Step 302: Input the uplink signal measurement data, the inertial measurement data, and the second positioning data of the target terminal at the previous moment into the second positioning state space model, and solve the second positioning state space model based on a preset algorithm to obtain the second positioning data of the target terminal at the current moment. Among them, the second positioning state space model includes a second positioning observation model and a second positioning state model. The second positioning observation model includes a second incident angle observation model and a second three-dimensional TOA observation model. The second incident angle observation model is used to characterize the relationship between the AOA data, the first antenna data, and the second antenna data. The second three-dimensional TOA observation model is used to characterize the relationship between the TOA data, the first antenna data, and the second antenna data. The second positioning state model is used to characterize the relationship between the second positioning data of the target terminal at the current moment, the second positioning data of the target terminal at the previous moment, and the inertial measurement data.

[0080] Optionally, the target base station is a single wireless base station. After the target terminal sends an uplink signal to the target base station, the target base station measures the received uplink signal to obtain AOA data and TOA data, and sends the AOA data, the TOA data, and the second antenna data of the target base station to the target terminal through the antenna. The target terminal includes an antenna, a CPU, and an IMU. The antenna is used to receive the above-mentioned multiple data sent by the target base station. The IMU includes a gyroscope and an accelerometer, which are used to measure the inertial measurement data of the target terminal. The CPU is used to calculate the two-positioning data of the target terminal at the current moment according to the AOA data, the TOA data, the inertial measurement data, and the second positioning data of the target terminal at the previous moment.

[0081] In addition, the target base station can also send the AOA data, the TOA data, and the second antenna data of the target base station to the server, and the target terminal also sends the inertial measurement data to the server. The server will calculate the second positioning data of the target terminal at the current moment according to the AOA data, the TOA data, the inertial measurement data, and the second positioning data of the target terminal at the previous moment.

[0082] Optionally, the target state equation is

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] Among them, represents the first-order differential of x(t); ψ, θ, and γ are the azimuth angle, pitch angle, and roll angle of the vehicle coordinate system relative to the "east-north-up" navigation coordinate system, respectively; v E , v N and v U are the eastward velocity, northward velocity, and upward velocity of the target terminal in the "east-north-up" navigation coordinate system, respectively; L, λ, and h are the latitude, longitude, and altitude of the target terminal in the earth-centered earth-fixed coordinate system, respectively; ε bx , ε by , ε bz , and are inertial measurement data, where ε bx , ε by and ε bz are the random constant drifts of the gyroscopes on the target terminal in the x-axis, y-axis, and z-axis directions in the vehicle coordinate system, respectively. and are the random constant drifts of the accelerometers on the target terminal in the x-axis, y-axis, and z-axis directions in the vehicle coordinate system, respectively. is the rotation matrix between the vehicle coordinate system and the navigation coordinate system, and 0 1×9 represents a pure zero row vector of dimension 9. and are the white noises of the gyroscopes on the x-axis, y-axis, and z-axis directions in the vehicle coordinate system, respectively. and are the white noises of the accelerometers on the x-axis, y-axis, and z-axis directions in the vehicle coordinate system, respectively; w(t) represents the total measurement white noise of the IMU of the target terminal at time t.

[0089] For the specific meaning of f[·], see Formulas (22) to (27).

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] Among them, and fbx , f by , f bz is inertial measurement data, and are the projection components of the angular velocity of the vehicle coordinate system relative to the "east - north - up" navigation coordinate system on the x - axis, y - axis, and z - axis directions in the vehicle coordinate system, respectively, f bx , f by and f bz are the specific forces output by the accelerometer on the x - axis, y - axis, and z - axis directions in the vehicle coordinate system, respectively, g is the acceleration due to gravity, ω ie is the angular velocity of the Earth's rotation, R M is the radius of curvature of the Earth's meridian, R N is the radius of curvature of the Earth's prime vertical.

[0097] Applying the Euler discretization method to discretize the target state equation, the second positioning state model can be obtained. The second positioning state model is as follows:

[0098] x k = F k-1 x k-1 + w k-1 (28)

[0099]

[0100] where x k represents the state information of the target terminal at the current moment (k - th moment), x k-1 is the state information of the target terminal at the previous moment (k - 1 moment), F k-1 is the discrete form of the function f[·], w k-1 is the discrete form of w(t), ψ k θ k γ k v E,k v N,k v U,k L k λ k h k ε bx,k ε by,k ε bz,k is the second positioning data of the target terminal at the current moment (k - th moment) at k - th moment.

[0101] Optionally, the second positioning observation model is

[0102] ξ k = ψ[Ξ(x k )]+ υ k (30)

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] α = cosψcosγ - sinψsinθsinγ (37)

[0110] β = sinψcosγ + cosψsinθsinγ (38)

[0111]

[0112]

[0113] υ k = [v k τ k σ k T (41)

[0114]

[0115]

[0116]

[0117] Equations (43) and (44) are the second incident angle observation model and the second three-dimensional TOA observation model respectively; where, ξ k represents the AOA and TOA observation vectors measured by the target base station at time k; represents the incident angle of the antenna phase center of the target terminal in the antenna coordinate system of the target base station at time k, that is, the AOA data measured by the target base station at time k; is the TOA data measured by the target base station at time k; ψ(χ k ) represents the initial AOA and initial TOA observation vectors measured by the target terminal at time k; h(χ k ) is the initial AOA data measured by the target terminal at time k; g(χ k ) is the initial TOA data measured by the target terminal at time k, where the speed of light c is omitted here; the meaning of h(x k ) is the same as that of h(χ k ); the meaning of g(x k ) is the same as that of g(χ​k ); ψ, θ, γ, and are the second antenna data of the target base station, where ψ, θ, and γ are the antenna attitude data of the target base station, specifically the antenna attitude data of the target base station in the local rectangular coordinate system, which are the azimuth angle, elevation angle, and roll angle respectively, and are the antenna position data of the target base station, specifically the antenna position data of the phase center of the target base station's antenna in the local rectangular coordinate system; and are the first antenna data of the target terminal, which is the antenna position data of the target terminal, specifically the antenna position data of the phase center of the target terminal's antenna at time k in the local rectangular coordinate system; arccos(g) is the inverse cosine function; υ k is the total observation noise vector of the target base station at time k, v k is the AOA observation noise of the target base station at time k, τ k is the TOA observation noise of the target base station at time k; is the height coordinate of the phase center of the target terminal's antenna in the local rectangular coordinate system at time k; is the known height coordinate of the phase center of the target terminal's antenna in the local rectangular coordinate system with error σ measured by the target base station k at time k, σ k represents the corresponding observation noise; f is usually taken as 1 / 298.257223563; L k , λ k and h k are the latitude, longitude, and height of the phase center of the target terminal's antenna in the Earth-centered Earth-fixed coordinate system at time k respectively, represents the coordinate transformation function from the Earth-centered Earth-fixed coordinate system to the local rectangular coordinate system, which can be directly obtained from the origin coordinates of the local rectangular coordinate system, that is

[0118]

[0119] where, and are the coordinates of the origin of the local rectangular coordinate system in the Earth-centered Earth-fixed coordinate system, L0 and λ0 are the latitude and longitude of the origin of the local rectangular coordinate system in the Earth-centered Earth-fixed coordinate system respectively, x e , y e and z e are independent variables, which are the coordinates of the target terminal in the x-axis, y-axis, and z-axis directions in the Earth-centered Earth-fixed coordinate system.

[0120] Therefore, the two-position state space model is

[0121] xk = F k-1 x k-1 + w k-1

[0122] ξ k = ψ[Ξ(x k )] + υ k (46)

[0123] Input the AOA data, TOA data measured by the target base station, the angular velocity and the specific force f bx 、f by 、f bz as well as the second positioning data of the target terminal at the previous moment into formula (46), and then solve the second positioning state space model based on the Bayesian filtering algorithm to obtain the second positioning data of the target terminal at the current moment.

[0124] In one embodiment, the positioning method further includes: constructing a target state equation between the first derivative of the second positioning data of the target terminal at the current moment, the second positioning data of the target terminal at the current moment, and the inertial measurement data; performing discretization processing on the target state equation to obtain a second positioning state model.

[0125] Among them, the first derivative of the second positioning data of the target terminal at the current moment is The second positioning data of the target terminal at the current moment is ψ θ γ v E v N v U L λ h ε bx ε by ε bz ▽ bx ▽ by ▽ bz , and the inertial measurement data is and f bx 、f by 、f bz . The constructed target state equation is formulas (17) to (27). Then, apply the Euler discretization method to perform discretization processing on the target state equation to obtain a positioning state model as shown in formulas (28) and (29).

[0126] In one embodiment, the second positioning data includes terminal attitude data, terminal velocity data, and terminal position data, and the target state equations include a first state equation, a second state equation, and a third state equation; the first state equation is a state equation between the first-order differential of the terminal attitude data at the current moment of the target terminal, the terminal attitude data at the current moment of the target terminal, and the inertial measurement data; the second state equation is a state equation between the first-order differential of the terminal velocity data at the current moment of the target terminal, the terminal velocity data at the current moment of the target terminal, and the inertial measurement data; the third state equation is a state equation between the first-order differential of the terminal position data at the current moment of the target terminal, the terminal position data at the current moment of the target terminal, and the inertial measurement data.

[0127] In one embodiment, the inertial measurement data includes: white noise of the gyroscope carried on the target terminal in the body coordinate system, rotation matrix between the body coordinate system and the navigation coordinate system, white noise of the accelerometer carried on the target terminal in the body coordinate system, projection component of the angular velocity of the body coordinate system relative to the navigation coordinate system in the body coordinate system, random constant drift of the gyroscope in the body coordinate system, specific force output by the accelerometer in the body coordinate system, and random constant drift of the accelerometer in the body coordinate system.

[0128] Among them, the terminal attitude data are ψ, θ, and γ in formulas (18) to (22), which respectively represent the azimuth angle, pitch angle, and roll angle of the body coordinate system relative to the "east-north-up" navigation coordinate system. The terminal velocity data are v E , v N , and v U in formulas (18), (19), and (23) to (25), which respectively represent the eastward velocity, northward velocity, and upward velocity of the target terminal in the "east-north-up" navigation coordinate system. The terminal position data are L, λ, and h in formulas (18), (19), (24), and (25), which respectively represent the latitude, longitude, and altitude of the target terminal in the earth-centered inertial coordinate system. The inertial measurement data includes f bx , f by , f bz , ε bx , ε by , ε bz , and where and represent the white noise of the gyroscope carried on the target terminal in the body coordinate system; and represent the white noise of the accelerometer carried on the target terminal in the body coordinate system; and Denote the projection components of the angular velocity of the vehicle coordinate system relative to the navigation coordinate system on the vehicle coordinate system; f bx 、f by and f bz Denote the specific force output by the accelerometer on the vehicle coordinate system; ε bx 、ε by and ε bz Denote the random constant drift of the gyroscope on the vehicle coordinate system; and Denote the random constant drift of the accelerometer on the vehicle coordinate system; Denote the rotation matrix between the vehicle coordinate system and the navigation coordinate system. It should be noted that the positioning data and inertial measurement data with subscript k in formula (29) have the same meaning as the corresponding quantities above, and are all the positioning data and inertial measurement data at time k, such as ψ k 、θ k and γ k , which respectively represent the azimuth angle, pitch angle and roll angle of the vehicle coordinate system relative to the "east-north-up" navigation coordinate system at time k.

[0129] In addition, the first state equation is formula (22); the second state equation is formula (23); the third state equation is (25).

[0130] For ease of reading, formulas (22), (23) and (25) are restated as follows.

[0131]

[0132]

[0133]

[0134] In one embodiment, based on a preset algorithm, solve the first positioning state space model to obtain the first positioning data of the target terminal at the current moment, including: obtaining the constraint conditions between the first antenna data and the second antenna data; based on the preset algorithm and the constraint conditions, solve the first positioning state space model to obtain the first positioning data of the target terminal at the current moment.

[0135] In one embodiment, the preset algorithm is a particle filter algorithm.

[0136] Optionally, the constraint condition is

[0137]

[0138] where

[0139]

[0140] and is the first antenna data, ψ, θ, and is the second antenna data, and other quantities in formula (48) are all explained below formula (27) and formula (44).

[0141] As Figure 4 shown, a flow diagram for solving the first positioning state space model based on the particle filter algorithm is provided. The process of solving the first positioning state space model based on the particle filter algorithm and the constraint conditions is as follows:

[0142] According to the AOA and TOA measurement values of the target base station, the prior probability density function p(x0) is obtained, and then the particle filter is initialized, that is, Λ particles are drawn from the prior probability density function p(x0) The initial particle weights are Subsequently, importance sampling is performed to obtain Λ particles using formula (12) That is

[0143]

[0144] Then the particle weights are updated, that is

[0145]

[0146]

[0147]

[0148]

[0149] Among them, is the likelihood function of the particle , ξ k and have the same meaning as in formulas (5) to (15), R k is the covariance matrix of the observation noise vector υ k of the target base station, is the normalized weight, and then resampling is performed to obtain a new particle set The corresponding particle weights are Resampling means that after the particles pass through weight update, that is, formula (53), according to the updated weight sizes of the particles, the particles are copied proportionally from large to small until the number of particles reaches Λ. For example, if there are 5 particles and their updated weights are 0.6, 0.4, 0, 0, 0 respectively, then after resampling, 3 particles among the 5 particles are the particles corresponding to 0.6, and 2 particles are the particles corresponding to 0.4. Finally, the first positioning data of the target terminal at the current moment can be expressed as

[0150]

[0151] In one embodiment, the second positioning state space model is solved based on a preset algorithm to obtain the second positioning data of the target terminal at the current moment, including: obtaining the constraint condition between the first antenna data and the second antenna data; solving the second positioning state space model based on the preset algorithm and the constraint condition to obtain the second positioning data of the target terminal at the current moment.

[0152] Optionally, as Figure 5 shown, a schematic flow chart of solving the second positioning state space model based on the particle filter algorithm is provided. The process of solving the second positioning state space model based on the particle filter algorithm and the constraint condition is as follows:

[0153] According to the AOA and TOA measurement values of the target base station, the prior probability density function p(x0) is obtained, and then the particle filter is initialized, that is, Λ particles are drawn from the prior probability density function p(x0) The initial particle weight is Subsequently, importance sampling is performed, and Λ particles are obtained using formula (28) That is

[0154]

[0155] Then the particle weight is updated, that is

[0156]

[0157]

[0158]

[0159]

[0160] Among them, is the likelihood function of the particle ξ k and have the same meaning as those in formulas (30) to (34), R k is the covariance matrix of the observation noise vector υ k of the target base station, is the normalized weight, and then resampling is performed to obtain a new particle set The corresponding particle weight is Resampling means that after the particles are updated in weight, that is, formula (59), according to the size of the updated particle weights, the particles are copied proportionally from large to small until the number of particles reaches Λ.

[0161] Finally, the second positioning data of the target terminal at the current moment can be expressed as

[0162]

[0163] In addition, in the above calculation process, since the AOA and TOA measurement values ξ of the target base station k need to be calculated on the target base station side first and then sent to the target terminal. Therefore, the AOA and TOA measurement values ξ of the target base station received by the target terminal k have a certain time delay. Since the length range of this time delay can be estimated in advance, for this problem, inertial measurement data from the current moment to a certain moment before (earlier than the occurrence moment of the AOA and TOA measurement values ξ of the target base station) can be stored in the target terminal. When the AOA and TOA measurement values η of the target base station k arrive at the terminal, the positioning data of the target terminal at the occurrence moment of the AOA and TOA measurement values ξ of the target base station can be estimated based on the stored inertial measurement data, and then the positioning data of the target terminal at the current moment can be inferred using the stored inertial measurement data. k reach the terminal, the positioning data of the target terminal at the occurrence moment of the AOA and TOA measurement values ξ of the target base station can be estimated based on the stored inertial measurement data, and then the positioning data of the target terminal at the current moment can be inferred using the stored inertial measurement data. k reach the terminal, the positioning data of the target terminal at the occurrence moment of the AOA and TOA measurement values ξ of the target base station can be estimated based on the stored inertial measurement data, and then the positioning data of the target terminal at the current moment can be inferred using the stored inertial measurement data.

[0164] This application also proves the effectiveness of this application through simulation experiments. In the simulation, the position coordinates of the target base station are set as and the attitude angles are ψ = 270°, θ = 0 and γ = 0. The standard deviation of the AOA observation error of the target base station is 1 degree, the standard deviation of the TOA observation error is 1 meter, and the number of particles of the particle filter is set to 500. A total of 100 Monte Carlo simulations are carried out.

[0165] In the near - field case, the positioning performance analysis of the traditional single - base - station positioning algorithm and the single - base - station positioning algorithm of this application is carried out. Among them, the initial positioning data of the target terminal are all set as

[0166]

[0167] As Figure 6 shown, it is the simulation diagram of the two positioning algorithms in the near - field case. Among them, the average root - mean - square positioning error of the traditional single - base - station positioning algorithm is 1.83m, and the average root - mean - square positioning error of the single - base - station positioning algorithm of this application is 0.308m. Therefore, in the near - field case, this application has higher positioning accuracy in the 2D positioning of the terminal, and the positioning performance is greatly improved, which can solve the problem of large positioning error of the traditional single - base - station positioning algorithm in the near - field case.

[0168] In the far - field case, the positioning performance analysis of the traditional single - base - station positioning algorithm and the single - base - station positioning algorithm of this application is carried out. Among them, the initial positioning data of the target terminal are all set as

[0169]

[0170] As Figure 7 shown, it is a simulation diagram of two positioning algorithms in the far - field case. Among them, the average root - mean - square positioning error of the traditional single - base - station positioning algorithm is 1.10 m, and the average root - mean - square positioning error of the single - base - station positioning algorithm of this application is 1.04 m. Therefore, in the far - field case, this application also has higher positioning accuracy in the 2D positioning of the terminal. So, this application can improve the positioning accuracy in the 2D positioning of the terminal.

[0171] In summary, the most detailed implementation of this application is as follows: First, receive the uplink signal measurement data sent by the target base station. The uplink signal measurement data includes AOA data and TOA data. Then, input the uplink signal measurement data and the first positioning data of the target terminal at the previous moment into the first positioning state - space model, and obtain the constraint conditions between the first antenna data and the second antenna data. Then, solve the first positioning state - space model based on the particle filter algorithm to obtain the first positioning data of the target terminal at the current moment; among them, the first positioning state - space model includes a first positioning observation model and a first positioning state model. The first positioning observation model includes a first incident - angle observation model and a first three - dimensional TOA observation model. The first incident - angle observation model is used to characterize the relationship between the AOA data, the first antenna data of the target terminal, and the second antenna data of the target base station. The first three - dimensional TOA observation model is used to characterize the relationship between the TOA data, the first antenna data, and the second antenna data. The first positioning state model is used to characterize the relationship between the first positioning data of the target terminal at the current moment and the first positioning data of the target terminal at the previous moment. Among them, the first positioning state model is specifically used to characterize the relationship between the first positioning data of the target terminal at the current moment, the first positioning data of the target terminal at the previous moment, the state - transition matrix at the previous moment, the noise - input matrix at the previous moment, and the state - noise vector at the previous moment. The state - transition matrix at the previous moment and the noise - input matrix at the previous moment are obtained according to the tracking sampling interval. The first antenna data includes the antenna position data of the target terminal. The second antenna data includes the antenna position data and antenna attitude data of the target base station. The antenna position data of the target terminal includes the position coordinates of the phase center of the antenna of the target terminal in the local rectangular coordinate system; the antenna position data of the target base station includes the position coordinates of the phase center of the antenna of the target base station in the local rectangular coordinate system; the antenna attitude data of the target base station includes the attitude angles of the antenna of the target base station in the local rectangular coordinate system.

[0172] In addition, another most detailed embodiment of this application is as follows: First, receive the uplink signal measurement data sent by the target base station and obtain the inertial measurement data of the target terminal. The uplink signal measurement data includes AOA data and TOA data, and the inertial measurement data includes: white noise of the gyroscope carried on the target terminal in the body coordinate system, rotation matrix between the body coordinate system and the navigation coordinate system, white noise of the accelerometer carried on the target terminal in the body coordinate system, projection component of the angular velocity of the body coordinate system relative to the navigation coordinate system in the body coordinate system, random constant drift of the gyroscope in the body coordinate system, specific force output by the accelerometer in the body coordinate system, and random constant drift of the accelerometer in the body coordinate system. Then, input the uplink signal measurement data, inertial measurement data, and the second positioning data of the target terminal at the previous moment into the second positioning state space model. Finally, obtain the constraint conditions between the first antenna data and the second antenna data, and solve the second positioning state space model based on the particle filter algorithm to obtain the second positioning data of the target terminal at the current moment; among them, the second positioning state space model includes a second positioning observation model and a second positioning state model. The second positioning observation model includes a second incident angle observation model and a second three-dimensional TOA observation model. The second incident angle observation model is used to characterize the relationship between the AOA data and the first antenna data and the second antenna data, and the second three-dimensional TOA observation model is used to characterize the relationship between the TOA data and the first antenna data and the second antenna data. The second positioning state model is used to characterize the relationship between the second positioning data of the target terminal at the current moment, the second positioning data of the target terminal at the previous moment, and the inertial measurement data. The construction process of the positioning state model includes: constructing a target state equation between the first-order differential of the second positioning data of the target terminal at the current moment, the second positioning data of the target terminal at the current moment, and the inertial measurement data; discretizing the target state equation to obtain the second positioning state model. The second positioning data includes terminal attitude data, terminal velocity data, and terminal position data, and the target state equation includes a first state equation, a second state equation, and a third state equation; the first state equation is the state equation between the first-order differential of the terminal attitude data of the target terminal at the current moment, the terminal attitude data of the target terminal at the current moment, and the inertial measurement data; the second state equation is the state equation between the first-order differential of the terminal velocity data of the target terminal at the current moment, the terminal velocity data of the target terminal at the current moment, and the inertial measurement data; the third state equation is the state equation between the first-order differential of the terminal position data of the target terminal at the current moment, the terminal position data of the target terminal at the current moment, and the inertial measurement data.

[0173] In summary, through the first incident angle observation model and the first 3D TOA observation model, or through the second incident angle observation model and the second 3D TOA observation model, the height between the target terminal and the target base station can be taken into account, thereby improving the positioning accuracy in the 2D positioning of near and far field terminals.

[0174] In addition, it should be noted that the first positioning state model, i.e., formula (1), and the first positioning observation model, i.e., formula (2), include the AOA data and TOA data measured by the target base station. Among them, formula (14) is the first IAOM, and formula (15) is the first 3D TOA observation model. The first positioning state model and the first positioning observation model constitute the first positioning state space model, enabling high-precision continuous 2D positioning of the target terminal only using the AOA data and TOA data of the target base station. There are two aspects to ensure that the first positioning state space model can achieve continuous high-precision positioning in the 2D positioning of near and far field terminals. On the one hand, it is the height coordinate of the antenna phase center of the target terminal at the known k moment in the local rectangular coordinate system. On the other hand, it is the constraint condition, i.e., formula (47).

[0175] Furthermore, the second positioning state model, i.e., formula (28), includes the measurement values of the IMU on the target terminal. The second positioning observation model, i.e., formula (30), includes the AOA data and TOA data measured by the target base station. Among them, formula (43) is the second IAOM, and formula (44) is the second 3D TOA observation model. The second positioning state model and the second positioning observation model constitute the second positioning state space model, i.e., formula (29), which realizes the tightly coupled positioning algorithm of the AOA and TOA of the target base station and the IMU of the target terminal. At the same time, using the AOA data and TOA data of the target base station and the IMU measurement values of the target terminal can achieve high-precision continuous 2D positioning of the target terminal. There are two aspects to ensure that the second positioning state space model can achieve continuous high-precision positioning in the 2D positioning of near and far field terminals. On the one hand, it is the height coordinate of the antenna phase center of the target terminal at the known k moment with error in the local rectangular coordinate system, i.e., On the other hand, it is the constraint condition, i.e., formula (47).

[0176] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0177] Based on the same inventive concept, an embodiment of the present application further provides a positioning device for implementing the positioning method described above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the positioning device provided below can refer to the limitations on the positioning method in the above text, and will not be repeated here.

[0178] In one embodiment, as Figure 8 shown, a structural block diagram of a positioning device is provided. The positioning device 800 includes: a receiving module 801 and a calculation module 802, where:

[0179] The receiving module 801 is configured to receive uplink signal measurement data sent by a target base station. The uplink signal measurement data includes AOA data and TOA data.

[0180] The calculation module 802 is configured to input the uplink signal measurement data and the first positioning data of the target terminal at the previous moment into a first positioning state space model, and solve the first positioning state space model based on a preset algorithm to obtain the first positioning data of the target terminal at the current moment; wherein, the first positioning state space model includes a first positioning observation model and a first positioning state model. The first positioning observation model includes a first incident angle observation model and a first three-dimensional TOA observation model. The first incident angle observation model is used to characterize the relationship between the AOA data, the first antenna data of the target terminal, and the second antenna data of the target base station. The first three-dimensional TOA observation model is used to characterize the relationship between the TOA data, the first antenna data, and the second antenna data. The first positioning state model is used to characterize the relationship between the first positioning data of the target terminal at the current moment and the first positioning data of the target terminal at the previous moment.

[0181] In one embodiment, the first positioning state model is specifically used to characterize the relationship between the first positioning data of the target terminal at the current moment, the first positioning data of the target terminal at the previous moment, the state transition matrix at the previous moment, the noise input matrix at the previous moment, and the state noise vector at the previous moment.

[0182] In one embodiment, the state transition matrix at the previous moment and the noise input matrix at the previous moment are obtained according to the tracking sampling interval.

[0183] In one embodiment, the first antenna data includes the antenna position data of the target terminal, and the second antenna data includes the antenna position data and the antenna attitude data of the target base station.

[0184] In one embodiment, the antenna position data of the target terminal includes the position coordinates of the phase center of the antenna of the target terminal in the local rectangular coordinate system; the antenna position data of the target base station includes the position coordinates of the phase center of the antenna of the target base station in the local rectangular coordinate system; the antenna attitude data of the target base station includes the attitude angle of the antenna of the target base station in the local rectangular coordinate system.

[0185] In one embodiment, the positioning device further includes: an acquisition module, configured to acquire the inertial measurement data of the target terminal; a calculation module 802, further configured to input the uplink signal measurement data, the inertial measurement data, and the second positioning data of the target terminal at the previous moment into the second positioning state space model, and solve the second positioning state space model based on a preset algorithm to obtain the second positioning data of the target terminal at the current moment; wherein, the second positioning state space model includes a second positioning observation model and a second positioning state model, the second positioning observation model includes a second incident angle observation model and a second three-dimensional TOA observation model, the second incident angle observation model is used to characterize the relationship between the AOA data, the first antenna data, and the second antenna data, the second three-dimensional TOA observation model is used to characterize the relationship between the TOA data, the first antenna data, and the second antenna data, and the second positioning state model is used to characterize the relationship between the second positioning data of the target terminal at the current moment, the second positioning data of the target terminal at the previous moment, and the inertial measurement data.

[0186] In one embodiment, the device further includes: constructing a target state equation between the first derivative of the second positioning data of the target terminal at the current moment, the second positioning data of the target terminal at the current moment, and the inertial measurement data; discretizing the target state equation to obtain the second positioning state model.

[0187] In one embodiment, the second positioning data includes terminal attitude data, terminal speed data, and terminal position data, and the target state equations include a first state equation, a second state equation, and a third state equation; the first state equation is a state equation between the first-order differential of the terminal attitude data at the current moment of the target terminal and the terminal attitude data and inertial measurement data at the current moment of the target terminal; the second state equation is a state equation between the first-order differential of the terminal speed data at the current moment of the target terminal and the terminal speed data and inertial measurement data at the current moment of the target terminal; the third state equation is a state equation between the first-order differential of the terminal position data at the current moment of the target terminal and the terminal position data and inertial measurement data at the current moment of the target terminal.

[0188] In one embodiment, the inertial measurement data includes: white noise of the gyroscope mounted on the target terminal in the body coordinate system, rotation matrix between the body coordinate system and the navigation coordinate system, white noise of the accelerometer mounted on the target terminal in the body coordinate system, projection component of the angular velocity of the body coordinate system relative to the navigation coordinate system in the body coordinate system, random constant drift of the gyroscope in the body coordinate system, specific force output by the accelerometer in the body coordinate system, and random constant drift of the accelerometer in the body coordinate system.

[0189] In one embodiment, the calculation module 802 is specifically configured to obtain the constraint conditions between the first antenna data and the second antenna data; solve the first positioning state space model based on a preset algorithm and the constraint conditions to obtain the first positioning data of the target terminal at the current moment.

[0190] In one embodiment, the calculation module 802 is further specifically configured to solve the second positioning state space model based on a preset algorithm to obtain the second positioning data of the target terminal at the current moment, including: obtaining the constraint conditions between the first antenna data and the second antenna data; solving the second positioning state space model based on a preset algorithm and the constraint conditions to obtain the second positioning data of the target terminal at the current moment.

[0191] In one embodiment, the preset algorithm is a particle filter algorithm.

[0192] Each module in the above positioning device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above respective modules.

[0193] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 9As shown in the figure. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. The computer program, when executed by the processor, implements a positioning method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0194] Those skilled in the art can understand that Figure 9 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0195] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0196] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0197] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0198] 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 for analysis, stored data, displayed data, etc.) involved in this application are all information and data that have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0199] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. 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), magnetoresistive 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 be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0200] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered to be within the scope described in this specification.

[0201] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A positioning method, characterized in that, Applied to a target terminal, the positioning method includes: Receiving uplink signal measurement data sent by a target base station, where the uplink signal measurement data includes angle of arrival (AOA) data and time of arrival (TOA) data; Inputting the uplink signal measurement data and first positioning data of the target terminal at the previous moment into a first positioning state space model, and solving the first positioning state space model based on a preset algorithm to obtain first positioning data of the target terminal at the current moment; Wherein, the first positioning state space model includes a first positioning observation model and a first positioning state model. The first positioning observation model includes a first incident angle observation model and a first three-dimensional TOA observation model. The first incident angle observation model is used to characterize the relationship between the AOA data, the first antenna data of the target terminal, and the second antenna data of the target base station. The first three-dimensional TOA observation model is used to characterize the relationship between the TOA data, the first antenna data, and the second antenna data. The first positioning state model is used to characterize the relationship between the first positioning data of the target terminal at the current moment and the first positioning data of the target terminal at the previous moment.

2. The method according to claim 1, wherein The first positioning state model is specifically used to characterize the relationship between the first positioning data of the target terminal at the current moment, the first positioning data of the target terminal at the previous moment, the state transition matrix at the previous moment, the noise input matrix at the previous moment, and the state noise vector at the previous moment.

3. The method according to claim 2, wherein The state transition matrix at the previous moment and the noise input matrix at the previous moment are obtained according to the tracking sampling interval.

4. The method according to claim 1, characterized in that, The first antenna data includes antenna position data of the target terminal, and the second antenna data includes antenna position data and antenna attitude data of the target base station.

5. The method according to claim 4, wherein The antenna position data of the target terminal includes the position coordinates of the phase center of the antenna of the target terminal in the local rectangular coordinate system; The antenna position data of the target base station includes the position coordinates of the phase center of the antenna of the target base station in the local rectangular coordinate system; The antenna attitude data of the target base station includes the attitude angles of the antenna of the target base station in the local rectangular coordinate system.

6. The method according to claim 1, wherein The positioning method further includes: Obtaining inertial measurement data of the target terminal; Inputting the uplink signal measurement data, the inertial measurement data, and second positioning data of the target terminal at the previous moment into a second positioning state space model, and solving the second positioning state space model based on a preset algorithm to obtain second positioning data of the target terminal at the current moment; Among them, the second positioning state space model includes a second positioning observation model and a second positioning state model. The second positioning observation model includes a second incident angle observation model and a second three-dimensional TOA observation model. The second incident angle observation model is used to characterize the relationship between the AOA data, the first antenna data, and the second antenna data. The second three-dimensional TOA observation model is used to characterize the relationship between the TOA data, the first antenna data, and the second antenna data. The second positioning state model is used to characterize the relationship between the second positioning data of the target terminal at the current moment, the second positioning data of the target terminal at the previous moment, and the inertial measurement data.

7. The method according to claim 6, characterized in that, The positioning method further includes: Constructing a target state equation between the first-order differential of the second positioning data of the target terminal at the current moment, the second positioning data of the target terminal at the current moment, and the inertial measurement data; Performing discretization processing on the target state equation to obtain the second positioning state model.

8. The method according to claim 7, wherein The second positioning data includes terminal attitude data, terminal velocity data, and terminal position data. The target state equation includes a first state equation, a second state equation, and a third state equation; The first state equation is a state equation between the first-order differential of the terminal attitude data of the target terminal at the current moment, the terminal attitude data of the target terminal at the current moment, and the inertial measurement data; The second state equation is a state equation between the first-order differential of the terminal velocity data of the target terminal at the current moment, the terminal velocity data of the target terminal at the current moment, and the inertial measurement data; The third state equation is a state equation between the first-order differential of the terminal position data of the target terminal at the current moment, the terminal position data of the target terminal at the current moment, and the inertial measurement data.

9. The method according to claim 8, characterized in that, The inertial measurement data includes: white noise of the gyroscope carried on the target terminal in the body coordinate system, rotation matrix between the body coordinate system and the navigation coordinate system, white noise of the accelerometer carried on the target terminal in the body coordinate system, projection component of the angular velocity of the body coordinate system relative to the navigation coordinate system in the body coordinate system, random constant drift of the gyroscope in the body coordinate system, specific force output by the accelerometer in the body coordinate system, and random constant drift of the accelerometer in the body coordinate system.

10. The method according to claim 1, characterized in that, Solving the first positioning state space model based on a preset algorithm to obtain the first positioning data of the target terminal at the current moment includes: Obtaining the constraint conditions between the first antenna data and the second antenna data; Solving the first positioning state space model based on the preset algorithm and the constraint conditions to obtain the first positioning data of the target terminal at the current moment.

11. The method according to claim 6, wherein Solving the second positioning state space model based on a preset algorithm to obtain the second positioning data of the target terminal at the current moment includes: Obtaining the constraint conditions between the first antenna data and the second antenna data; Solve the second positioning state space model based on the preset algorithm and the constraint conditions to obtain the second positioning data of the target terminal at the current moment.

12. According to the method according to any one of claims 1-11, characterized in that, The preset algorithm is a particle filter algorithm.

13. A positioning device, characterized in that, Applied to a target terminal, the positioning device includes: A receiving module, configured to receive uplink signal measurement data sent by a target base station, where the uplink signal measurement data includes AOA data and TOA data; A calculation module, configured to input the uplink signal measurement data and the first positioning data of the target terminal at the previous moment into a first positioning state space model, and solve the first positioning state space model based on a preset algorithm to obtain the first positioning data of the target terminal at the current moment; Wherein, the first positioning state space model includes a first positioning observation model and a first positioning state model, the first positioning observation model includes a first incident angle observation model and a first three-dimensional TOA observation model, the first incident angle observation model is used to characterize the relationship between the AOA data, the first antenna data of the target terminal, and the second antenna data of the target base station, the first three-dimensional TOA observation model is used to characterize the relationship between the TOA data, the first antenna data, and the second antenna data, and the first positioning state model is used to characterize the relationship between the first positioning data of the target terminal at the current moment and the first positioning data of the target terminal at the previous moment.

14. A computer device, comprising a memory and a processor, the memory storing 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 1 to 12.

15. 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 12.

16. A computer program product, comprising a computer program, 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 12.

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