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

Abstract

The application relates to a positioning method and device, computer equipment, a storage medium and a program product, which are used for a target terminal, and the method comprises the following steps: receiving uplink signal measurement data respectively transmitted by a plurality of base stations, and acquiring inertial measurement data of the target terminal, wherein the uplink signal measurement data comprises AOA data; inputting each uplink signal measurement data, the inertial measurement data and positioning data of the target terminal at a previous moment into a positioning state space model, and solving the positioning state space model based on a preset algorithm to obtain positioning data of the target terminal at a current moment; wherein the positioning state space model comprises a positioning observation model and a positioning state model, the positioning observation model comprises an incident angle observation model, and the incident angle observation model is used for representing the relationship between the AOA data in each uplink signal measurement data and first antenna data of the target terminal and second antenna data of each base station. The method can improve positioning accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, and particularly relates to a positioning method and device, computer equipment, a storage medium and a program product. BACKGROUND

[0002] The wireless base station can measure the AOA (Angle of Arrival) of the terminal. According to the position information and attitude information of the plurality of wireless base stations, the measured AOA of the terminal and the measurement information of the IMU (Inertial Measurement Unit) of the terminal, the position of the terminal can be estimated.

[0003] In the conventional technology, the AOA of the terminal measured by the wireless base station is taken as the azimuth angle, an AAOM (Azimuth Angle Observation Model) is established, and then an IMU loose coupling positioning algorithm based on the AAOM is used to position the terminal.

[0004] However, the AAOM is an effective approximation of the real-world observation model in the far-field positioning tracking case. In the actual wireless base station deployment environment, the terminal is usually very close to the wireless base station antenna, which belongs to the near-field positioning case. At this time, there is a certain height difference between the installation position of the wireless base station antenna and the position of the terminal, which causes the AAOM to be unable to effectively approximate the real-world observation model. In addition, the loose coupling positioning algorithm is easy to implement, but the precision is low. Therefore, the conventional technology has the problem of low positioning precision. SUMMARY

[0005] Therefore, it is necessary to provide a positioning method, device, computer equipment, computer readable storage medium and computer program product capable of improving the positioning precision in view of the above technical problems.

[0006] In a first aspect, the present application provides a positioning method. For a target terminal, the method comprises: receiving uplink signal measurement data respectively transmitted by a plurality of base stations, and obtaining inertial measurement data of the target terminal, the uplink signal measurement data comprising angle of arrival (AOA) data; inputting each uplink signal measurement data, the inertial measurement data and positioning data of the target terminal at a previous time into a positioning state space model, and solving the positioning state space model based on a preset algorithm to obtain positioning data of the target terminal at a current time; wherein the positioning state space model comprises a positioning observation model and a positioning state model, the positioning observation model comprises an angle of incidence observation model, the angle of incidence observation model is used to represent a relationship between the AOA data in each uplink signal measurement data and first antenna data of the target terminal and second antenna data of each base station, and the positioning state model is used to represent a relationship between the positioning data of the target terminal at the current time and the positioning data of the target terminal at the previous time and the inertial measurement data.

[0007] In one of the embodiments, the uplink signal measurement data further comprises time difference of arrival (TDOA) data; the positioning observation model further comprises a TDOA observation model, and the TDOA observation model is used to represent a relationship between the TDOA data in each uplink signal measurement data and the first antenna data and each second antenna data.

[0008] In one of the embodiments, the first antenna data comprises antenna position data of the target terminal, and each second antenna data comprises antenna position data and antenna attitude data of each base station.

[0009] In one of the embodiments, the antenna position data of the target terminal comprises position coordinates of a phase center of an antenna of the target terminal in a local rectangular coordinate system; the antenna position data of each base station comprises position coordinates of a phase center of an antenna of each base station in the local rectangular coordinate system; and the antenna attitude data of each base station comprises an attitude angle of the antenna of each base station in the local rectangular coordinate system.

[0010] In one of the embodiments, the method further comprises: constructing a target state equation between a first-order differential of the positioning data of the target terminal at the current time and the positioning data of the target terminal at the current time and the inertial measurement data; and performing discretization processing on the target state equation to obtain the positioning state model.

[0011] In one of the embodiments, the positioning data comprises terminal attitude data, terminal velocity data and terminal position data, the target state equation comprises a first state equation, a second state equation and a third state equation; the first state equation is a first-order differential of the terminal attitude data of the target terminal at a current time and a state equation between the terminal attitude data of the target terminal at the current time and the inertial measurement data; the second state equation is a first-order differential of the terminal velocity data of the target terminal at the current time and a state equation between the terminal velocity data of the target terminal at the current time and the inertial measurement data; and the third state equation is a first-order differential of the terminal position data of the target terminal at the current time and a state equation between the terminal position data of the target terminal at the current time and the inertial measurement data.

[0012] In one of the embodiments, the inertial measurement data comprises white noise of a gyroscope carried on the target terminal in the carrier coordinate system, a rotation matrix between the carrier coordinate system and the navigation coordinate system, white noise of an accelerometer carried on the target terminal in the carrier coordinate system, a projection component of an angular velocity of the carrier coordinate system relative to the navigation coordinate system in the carrier coordinate system, random constant drift of the gyroscope in the carrier coordinate system, specific force output by the accelerometer in the carrier coordinate system, and random constant drift of the accelerometer in the carrier coordinate system.

[0013] In one of the embodiments, the positioning state space model is solved based on a preset algorithm to obtain the positioning data of the target terminal at the current time, comprising: obtaining a constraint condition between the first antenna data and the second antenna data; and solving the positioning state space model based on the preset algorithm and the constraint condition to obtain the positioning data of the target terminal at the current time.

[0014] In one of the embodiments, the preset algorithm is a particle filter algorithm.

[0015] In a second aspect, the application further provides a positioning device. The device is used in a target terminal, and comprises: a receiving module, configured to receive uplink signal measurement data respectively sent by a plurality of base stations, and to obtain inertial measurement data of the target terminal, wherein the uplink signal measurement data comprises AOA data; and a calculating module, configured to input each uplink signal measurement data, the inertial measurement data and positioning data of the target terminal at a previous time into a positioning state space model, and to solve the positioning state space model based on a preset algorithm to obtain positioning data of the target terminal at a current time; wherein the positioning state space model comprises a positioning observation model and a positioning state model, the positioning observation model comprises an angle of incidence observation model, the angle of incidence observation model is used to represent a relationship between the AOA data in each uplink signal measurement data and first antenna data of the target terminal and second antenna data of each base station, and the positioning state model is used to represent a relationship between the positioning data of the target terminal at the current time and the positioning data of the target terminal at the previous time and the inertial measurement data.

[0016] In one of the embodiments, the uplink signal measurement data further comprises TDOA data; and the positioning observation model further comprises a TDOA observation model, the TDOA observation model being used to represent the relationship between the TDOA data in each of the uplink signal measurement data and the first antenna data and each of the second antenna data.

[0017] In one of the embodiments, the first antenna data comprises antenna position data of the target terminal, and each of the second antenna data comprises antenna position data and antenna attitude data of each of the base stations.

[0018] In one of the embodiments, the antenna position data of the target terminal comprises position coordinates of a phase center of an antenna of the target terminal in a local rectangular coordinate system; the antenna position data of each of the base stations comprises position coordinates of a phase center of an antenna of each of the base stations in the local rectangular coordinate system; and the antenna attitude data of each of the base stations comprises an attitude angle of the antenna of each of the base stations in the local rectangular coordinate system.

[0019] In one of the embodiments, the apparatus further comprises: constructing a target state equation between a first-order differential of the positioning data of the target terminal at a current time and the positioning data of the target terminal at the current time and the inertial measurement data; and discretizing the target state equation to obtain the positioning state model.

[0020] In one of the embodiments, the positioning data comprises terminal attitude data, terminal velocity data and terminal position data, and the target state equation comprises a first state equation, a second state equation and a third state equation; the first state equation is a state equation between a first-order differential of the terminal attitude data of the target terminal at a current time and the terminal attitude data of the target terminal at the current time and the inertial measurement data; the second state equation is a state equation between a first-order differential of the terminal velocity data of the target terminal at the current time and the terminal velocity data of the target terminal at the current time and the inertial measurement data; and the third state equation is a state equation between a first-order differential of the terminal position data of the target terminal at the current time and the terminal position data of the target terminal at the current time and the inertial measurement data.

[0021] In one of the embodiments, the inertial measurement data comprises: white noise of a gyroscope carried on the target terminal in a body coordinate system, a rotation matrix between the body coordinate system and a navigation coordinate system, white noise of an accelerometer carried on the target terminal in the body coordinate system, a projection component of an 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.

[0022] In one of the embodiments, the computing module is specifically configured to acquire a constraint condition between the first antenna data and the second antenna data; and solve the positioning state space model based on a preset algorithm and the constraint condition to obtain the positioning data of the target terminal at the current time.

[0023] In one of the embodiments, the preset algorithm is a particle filter algorithm.

[0024] In a third aspect, the present application further provides a computer device. The computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the method of any one of the first aspect when executing the computer program.

[0025] In a fourth aspect, the present application further provides a computer readable storage medium. The computer readable storage medium stores a computer program, and the computer program implements the steps of the method of any one of the first aspect when executed by a processor.

[0026] In a fifth aspect, the present application further provides a computer program product. The computer program product comprises a computer program, and the computer program implements the steps of the method of any one of the first aspect when executed by a processor.

[0027] The positioning method, device, computer device, storage medium and program product are used in a target terminal, and the positioning data of the target terminal at the current time is obtained by receiving uplink signal measurement data respectively sent by multiple base stations, acquiring inertial measurement data of the target terminal, the uplink signal measurement data comprising AOA data, then inputting each uplink signal measurement data, the inertial measurement data and the positioning data of the target terminal at the previous time into a positioning state space model, and solving the positioning state space model based on a preset algorithm to obtain the positioning data of the target terminal at the current time; wherein the positioning state space model comprises a positioning observation model and a positioning state model, the positioning observation model comprises an angle of incidence observation model, the angle of incidence observation model is used to represent the relationship between the AOA data in each uplink signal measurement data and the first antenna data of the target terminal and the second antenna data of each base station, and the positioning state model is used to represent the relationship between the positioning data of the target terminal at the current time and the positioning data of the target terminal at the previous time and the inertial measurement data. The angle of incidence observation model can consider the height between the target terminal and the base station, and the positioning state space model realizes the tight coupling of each uplink signal measurement data and the inertial measurement data, and thus the positioning precision can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 For a wireless base station antenna coordinate system in one embodiment;

[0029] Figure 2Fig. 1 is a schematic diagram of modeling AOA data as azimuth angle in one embodiment;

[0030] Figure 3 Fig. 2 is a schematic diagram of modeling AOA data as incident angle in one embodiment;

[0031] Figure 4 Fig. 3 is a schematic diagram of a positioning method in one embodiment;

[0032] Figure 5 Fig. 4 is an application environment diagram of a positioning method in one embodiment;

[0033] Figure 6 Fig. 5 is a schematic diagram of solving a positioning state space model based on a particle filter algorithm in one embodiment;

[0034] Figure 7 Fig. 6 is a structural block diagram of a positioning device in one embodiment;

[0035] Figure 8 Fig. 7 is an internal structure diagram of a computer device in one embodiment. DETAILED DESCRIPTION

[0036] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application.

[0037] The wireless base station can measure the AOA data of the terminal uplink signal, thereby obtaining the AOA data of the terminal. The position of the terminal can be estimated according to the positions and attitudes of the plurality of wireless base stations and the measured AOA data of the terminal. In the deployment of a wireless network, the number of wireless base stations that can simultaneously reach the terminal signal is limited, resulting in that some areas do not meet the convergence condition of the conventional positioning algorithm. In addition, in an indoor environment, there are many obstacles, and the non-line-of-sight propagation phenomenon of the signal between the wireless base station and the terminal is common, which will cause the positioning error to increase. Moreover, the update period of the positioning signal of the wireless base station is usually greater than the order of magnitude of hundreds of milliseconds, which is difficult to meet the positioning requirements of the medium and high speed mobile terminal. The fusion of the IMU of the terminal and the positioning result of the wireless base station can improve the accuracy, reliability and continuity of the positioning, and meet the requirements of the medium and high speed mobile terminal on the update rate of the positioning. The wireless base station and IMU combined positioning based on AOA data can be divided into a loose coupling mode and a tight coupling mode, and there are few reports on this aspect at present, and the loose coupling mode is usually used. Compared with the tight coupling mode, the implementation of the loose coupling mode is relatively simple, but the accuracy and stability are relatively low. In the conventional technology of the wireless base station and IMU combined positioning based on AOA data, the wireless base station observation quantity used is the azimuth angle, the elevation angle and the distance of the terminal, and the loose coupling mode is adopted. However, due to cost reasons, the number of antennas of most wireless base stations is relatively small, and only one-dimensional AOA data can be accurately measured, so the combined positioning algorithm in the conventional technology cannot be widely applied. In addition, in the conventional positioning algorithm, the one-dimensional AOA data is usually modeled as an azimuth angle and the AAOM is used for terminal positioning, as shown in Figure 1 , a wireless base station antenna coordinate system is provided, in which the AOA data is modeled as an azimuth angle, as shown in Figure 2 . The AAOM is an effective approximation of the real-world observation model in the far-field positioning and tracking case, which can reduce the number of parameters of the observation model and simplify the calculation on the one hand, and the performance loss of the positioning and tracking caused by the approximation of the observation model in the far-field positioning case can be ignored on the other hand. However, in the actual deployment environment of the wireless base station, the terminal is usually very close to the wireless base station antenna, which belongs to the near-field positioning case. At this time, due to the height difference between the installation position of the wireless base station antenna and the position of the terminal, the AAOM cannot effectively approximate the real-world observation model, and the performance of the existing positioning algorithm based on the AAOM will deteriorate sharply, therefore, it is necessary to propose a positioning method that can accurately position in both far-field and near-field. In the present application, the AOA data measured by the base station is modeled as an incidence angle η and the IAOM ((Incidence Angle Observation Model, incidence angle observation model) is used for terminal positioning, and the AOA data is modeled as an incidence angle, as shown in Figure 3 , and the specific implementation is as follows.

[0038] In one embodiment, asFigure 4 The diagram illustrates a positioning method. This embodiment applies to a terminal, which can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. In this embodiment, the method is used in the target terminal and includes the following steps:

[0039] Step 401: Receive uplink signal measurement data sent by multiple base stations respectively, and acquire inertial measurement data of the target terminal. The uplink signal measurement data includes AOA data.

[0040] Step 402: Input the uplink signal measurement data, inertial measurement data, and the target terminal's previous positioning data into the positioning state space model, and solve the positioning state space model based on a preset algorithm to obtain the target terminal's current positioning data. The positioning state space model includes a positioning observation model and a positioning state model. The positioning observation model includes an incident angle observation model, which is used to characterize the relationship between the AOA data in each uplink signal measurement data and the target terminal's first antenna data and the second antenna data of each base station. The positioning state model is used to characterize the relationship between the target terminal's current positioning data and the target terminal's previous positioning data and inertial measurement data.

[0041] Optional, such as Figure 5 The diagram illustrates an application environment for a positioning method. The base station is a wireless base station. When the target terminal sends uplink signals to each base station, each base station measures the received uplink signals to obtain AOA data. The AOA data and the base station's second antenna data are then transmitted to the target terminal via the antenna. The target terminal includes an antenna, a CPU (central processing unit), and an IMU. The antenna receives the aforementioned data transmitted by each base station. The IMU includes a gyroscope and an accelerometer, used to measure the target terminal's inertial measurement data. The CPU calculates the target terminal's current positioning data based on the AOA data, the inertial measurement data, and the target terminal's positioning data from the previous moment.

[0042] Optionally, the target state equation is:

[0043]

[0044]

[0045] x(t)=[ψ θ γ v E v N vU L λ h ε bx ε by ε bz ▽ bx ▽ by ▽ bz ] T (3)

[0046]

[0047]

[0048] wherein, denotes the first derivative of x(t); ψ, θ and γ are the azimuth, pitch and roll angles of the body coordinate system with respect to the East-North-Up navigation coordinate system, respectively; v E , v N and v U are the East, North and Up velocities 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 , ▽ bx , ▽ by , ▽ bz , and are the inertial measurement data, wherein ε bx , ε by and ε bz are the random constant biases of the gyroscope on the target terminal in the x, y and z axes of the body coordinate system, respectively; ▽ bx , ▽ by and ▽ bz are the random constant biases of the accelerometer on the target terminal in the x, y and z axes of the body coordinate system, respectively, is the rotation matrix between the body coordinate system and the navigation coordinate system, 0 1×9 denotes a pure zero row vector with dimension 9, and are the white noises of the gyroscope in the x, y and z axes of the body coordinate system, respectively, and are the white noises of the accelerometer in the x, y and z axes of the body coordinate system, respectively; w(t) denotes the total measurement white noise of the IMU of the target terminal at time t.

[0049] The specific meaning of f[·] is shown in formula (6) to formula (11)

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] wherein, and f bx , f by , f bz are inertial measurement data, and are the projection components of the angular velocity of the body coordinate system relative to the “East-North-Sky” navigation coordinate system in the x-axis, y-axis and z-axis directions of the body coordinate system, respectively, f bx , f by and f bz are the specific forces output by the accelerometer in the x-axis, y-axis and z-axis directions of the body coordinate system, respectively, g is the gravitational acceleration, ω ie is the Earth rotation angular velocity, R M is the Earth meridian principal curvature radius, R N is the Earth prime vertical principal curvature radius.

[0057] The target state equation is discretized by using the Euler discretization method, and a positioning state model can be obtained, and the positioning state model is as follows:

[0058] x k = F k-1 x k-1 + w k-1 (12)

[0059] x k = [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 ▽ bx,k ▽ by,k ▽ bz,k (13) T ​

[0060] where x k denotes the state information of the target terminal at the current time (k time), x k-1 is the state information of the target terminal at the last time (k-1 time), 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 ▽ bx,k ▽ by,k ▽ bz,k is the positioning data of the target terminal at the current time (k time).

[0061] Optionally, the positioning observation model is the Incidence Angle Observation Model (IAOM), which is

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] α i = cos ψ i cos γ i -sin ψ i sin θ i sin γ i (20)

[0069] β i =sin ψ i cos γ i +cos ψ i sin θ i sin γ i (21)

[0070]

[0071]

[0072] υ k =[v 1,k v 2,k … v N,k σ k ] T (24)

[0073]

[0074]

[0075] wherein formula (14) is also an Incidence Angle Observation Model (IAOM), ξ k represents the AOA observation vector measured by multiple base stations at time k; represents the incidence angle of the antenna phase center of the target terminal in the i-th base station antenna coordinate system at time k, i.e., the AOA data measured by the i-th base station at time k; ψ(χ k ) represents the initial AOA observation vector measured by the target terminal at time k; h i (χ k ) is the i-th initial AOA data measured by the target terminal at time k; ψ i , θ i , γ i and is the second antenna data of the i-th base station, wherein ψ i , θ i and γ i are the antenna attitude data of the i-th base station, specifically the antenna attitude data of the antenna of the i-th base station in the local rectangular coordinate system, which are azimuth angle, elevation angle and roll angle, respectively, and are the antenna position data of the i-th base station, specifically the antenna position data of the antenna phase center of the i-th base station in the local rectangular coordinate system. and is 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 antenna phase center of the target terminal in the local rectangular coordinate system at time k; arccos(·) is the inverse cosine function; υ k is the total observation noise vector of multiple base stations at time k; v i,k is the AOA observation noise of the i-th base station at time k; the subscript N represents the number of base stations; is the height coordinate of the antenna phase center of the target terminal in the local rectangular coordinate system at time k; Known error-inclusive amount σ measured for multiple base stations k Height coordinate of the antenna phase center of the target terminal at time k in the local rectangular coordinate system, σ k Indicates 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 antenna phase center of the target terminal at time k in the geocentric coordinate system, respectively, Indicates the coordinate conversion function of the geocentric 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,

[0076]

[0077] Wherein, and are the coordinates of the origin of the local rectangular coordinate system in the geocentric coordinate system, L0 and λ0 are the latitude and longitude of the origin of the local rectangular coordinate system in the geocentric 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 geocentric coordinate system.

[0078] Therefore, the positioning state space model is

[0079]

[0080] The AOA data measured by each base station, angular velocity Specific force f bx , f by , f bz and the positioning data of the target terminal at the last time are input into formula (28), and then the positioning state space model is solved based on the Bayesian filtering algorithm, so that the positioning data of the target terminal at the current time can be obtained.

[0081] In summary, the target terminal obtains the inertial measurement data of the target terminal by receiving the uplink signal measurement data respectively transmitted by the plurality of base stations, and the uplink signal measurement data comprises AOA data, and then inputs each uplink signal measurement data, the inertial measurement data and the positioning data of the target terminal at the last time into the positioning state space model, and solves the positioning state space model based on a preset algorithm to obtain the positioning data of the target terminal at the current time; wherein the positioning state space model comprises a positioning observation model and a positioning state model, the positioning observation model comprises an angle of incidence observation model, the angle of incidence observation model is used to represent the relationship between the AOA data in each uplink signal measurement data and the first antenna data of the target terminal and the second antenna data of each base station, and the positioning state model is used to represent the relationship between the positioning data of the target terminal at the current time and the positioning data of the target terminal at the last time and the inertial measurement data. The height between the target terminal and the base station can be considered by the angle of incidence observation model, and the positioning state space model realizes the close coupling of each uplink signal measurement data and the inertial measurement data, and thus the positioning precision can be improved.

[0082] In one embodiment, the uplink signal measurement data further comprises TDOA data; the positioning observation model further comprises a TDOA observation model, and the TDOA observation model is used to represent the relationship between the TDOA data in each uplink signal measurement data and the first antenna data and each second antenna data.

[0083] Optionally, after the positioning observation model comprises the TDOA observation model, the positioning observation model is also represented by formula (14), but the changed amount is as follows:

[0084]

[0085]

[0086]

[0087] υ k = [v 1,k v 2,k … v N,k τ 1,k τ 2,k … τ N-1,k σ k ] T (32)

[0088]

[0089]

[0090] Equations (33) and (34) are the incident angle observation model and the TDOA observation model respectively; wherein, ξ k represents the AOA and TDOA observation vectors measured by the plurality of base stations at the kth moment; ψ(χ k ) represents the initial AOA and initial TDOA observation vectors measured by the target terminal at the kth moment; represents the TDOA data between the antenna phase center of the target terminal at the kth moment and the antenna of the i+1th base station and the antenna of the 1st base station, i.e., the TDOA data measured by the i th base station at the kth moment; g i (χ k ) is the initial TDOA data measured by the target terminal at the kth moment, wherein the light speed c is omitted; τ i,k is the TDOA observation noise of the i th base station at the kth moment; and the meanings of other quantities are the same as those of the corresponding quantities in equations (15) to (26).

[0091] In one of the embodiments, the first antenna data includes antenna position data of the target terminal, and each second antenna data includes antenna position data and antenna attitude data of each base station.

[0092] In one of the embodiments, the antenna position data of the target terminal includes position coordinates of the phase center of the antenna of the target terminal in a local rectangular coordinate system; the antenna position data of each base station includes position coordinates of the phase center of the antenna of each base station in the local rectangular coordinate system; and the antenna attitude data of each base station includes attitude angles of the antenna of each base station in the local rectangular coordinate system.

[0093] In the positioning observation model, ξ and are position coordinates of the phase center of the antenna of the target terminal in a local rectangular coordinate system at the kth moment. In the positioning observation model, ξ and are position coordinates of the phase center of the antenna of the i th base station in the local rectangular coordinate system. In the positioning observation model, ψ i , θ i and γ i are attitude angles of the antenna of the i th base station in the local rectangular coordinate system.

[0094] In one of the embodiments, the method further includes: constructing a target state equation between the first-order differential of the positioning data of the target terminal at the current moment and the positioning data of the target terminal at the current moment and the inertial measurement data; and discretizing the target state equation to obtain a positioning state model.

[0095] In the target state equation, the first-order differential of the positioning data of the target terminal at the current moment is the positioning data of the target terminal at the current moment is ψθ γ v E v Nv U L λ h ε bx ε by ε bz ▽ bx ▽ by ▽ bz , the inertial measurement data is and f bx , f by , f bz The constructed target state equation is formula (1) to (11). Then the Euler discretization method is applied to discretize the target state equation to obtain the positioning state model as formula (12) and (13).

[0096] In one embodiment, the 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 first-order differential of the terminal attitude data of the target terminal at the current time and a state equation between the terminal attitude data of the target terminal at the current time and the inertial measurement data; the second state equation is a first-order differential of the terminal velocity data of the target terminal at the current time and a state equation between the terminal velocity data of the target terminal at the current time and the inertial measurement data; and the third state equation is a first-order differential of the terminal position data of the target terminal at the current time and a state equation between the terminal position data of the target terminal at the current time and the inertial measurement data.

[0097] In one embodiment, the inertial measurement data includes: white noise of a gyroscope carried on the target terminal in the carrier coordinate system, a rotation matrix between the carrier coordinate system and the navigation coordinate system, white noise of an accelerometer carried on the target terminal in the carrier coordinate system, a projection component of the angular velocity of the carrier coordinate system relative to the navigation coordinate system in the carrier coordinate system, random constant drift of the gyroscope in the carrier coordinate system, specific force output by the accelerometer in the carrier coordinate system, and random constant drift of the accelerometer in the carrier coordinate system.

[0098] Wherein, the terminal attitude data is ψ, θ and γ in formula (2) to (6), which respectively represent the azimuth angle, the pitch angle and the roll angle of the carrier coordinate system relative to the "East-North-Sky" navigation coordinate system. The terminal velocity data is v E , v N and v U, respectively, represent the eastward, northward and skyward velocities of the target terminal in the "East-North-Sky" navigation coordinate system. The terminal position data are L, λ and h in equations (2), (3), (8) and (9), respectively, represent the latitude, longitude and altitude of the target terminal in the Earth-Centered Earth-Fixed coordinate system. The inertial measurement data include f bx , f by , f bz , ε bx , ε by , ε bz , ▽ bx , ▽ by , ▽ bz and where and represent the white noise of the gyroscope mounted on the target terminal in the body coordinate system; and represent the white noise of the accelerometer mounted on the target terminal in the body coordinate system; and represent the projection components of the angular velocity of the body coordinate system relative to the navigation coordinate system in the body coordinate system;f bx , f by and f bz represent the specific forces output by the accelerometer in the body coordinate system; ε bx , ε by and ε bz represent the random constant drift of the gyroscope in the body coordinate system; ▽ bx , ▽ by and ▽ bz represent the random constant drift of the accelerometer in the body coordinate system; represents the rotation matrix between the body coordinate system and the navigation coordinate system. It should be noted that the positioning data and inertial measurement data with subscript k in equation (13) have the same meaning as the corresponding quantities described above, and are the positioning data and inertial measurement data at time k, such as ψ k , θ k and γ k , respectively, represent the azimuth angle, pitch angle and roll angle of the body coordinate system relative to the "East-North-Sky" navigation coordinate system at time k.

[0099] In addition, the first state equation is equation (6); the second state equation is equation (7); and the third state equation is (9). For ease of reading, equations (6), (7) and (9) are restated as follows.

[0100]

[0101]

[0102]

[0103] In one of the embodiments, the positioning state space model is solved based on a preset algorithm to obtain the positioning data of the target terminal at the current time, comprising: obtaining a constraint condition between the first antenna data and the second antenna data; solving the positioning state space model based on the preset algorithm and the constraint condition to obtain the positioning data of the target terminal at the current time.

[0104] In one of the embodiments, the preset algorithm is a particle filter algorithm.

[0105] Optionally, the constraint condition is

[0106]

[0107] wherein, and is the first antenna data, ψi, θ i 、 and is the second antenna data.

[0108] As shown in Figure 6 , a flowchart for solving a positioning state space model based on a particle filter algorithm is provided, and the process of solving the positioning state space model based on the particle filter algorithm and the constraint condition is as follows:

[0109] It can be that only the total base station AOA measurement value is used to obtain the prior probability density function p(x0), or the total base station AOA and TDOA measurement value is used to obtain the prior probability density function p(x0), and then the particle filter is initialized, i.e. Λ particles are extracted from the prior probability density function p(x0) The initial particle weight is Then importance sampling is performed, and Λ particles are obtained by using formula (12) i.e.

[0110]

[0111] Then the particle weight is updated, i.e.

[0112]

[0113]

[0114]

[0115]

[0116] wherein, is the particle likelihood function of ξ k and The meaning of R can be consistent with the meaning in formula (14) to formula (26), and can also be consistent with the meaning in formula (29) to formula (34); R k υ is an observation noise vector of a single wireless base station k is a covariance matrix of ξ is a normalized weight, and then resampling is performed to obtain a new particle set The corresponding particle weight is Resampling refers to copying particles in proportion from large to small according to the size of the updated weight of the particles after the weight update, that is, formula (40), until the number of particles reaches Λ. For example, there are 5 particles, and their updated weights are 0.6, 0.4, 0, 0, and 0. After resampling, 3 particles of 5 particles are 0.6 corresponding particles, and 2 particles are 0.4 corresponding particles.

[0117] Finally, the positioning data of the target terminal at the current time can be expressed as

[0118]

[0119] In addition, in the above calculation process, since the total base station AOA and TDOA measurement value ξ k needs to be calculated at the base station side first and then issued to the target terminal, therefore, there is a certain time delay for the total base station AOA and TDOA measurement value ξ k received by the target terminal, since the length range of this time delay can be estimated in advance, therefore, for this problem, the inertial measurement data from the current time to a certain time (earlier than the time when the total base station AOA and TDOA measurement value ξ k occurs) can be stored in the target terminal, when the total base station AOA and TDOA measurement value η k arrives at the terminal, the positioning data estimation of the target terminal at the time when the total base station AOA and TDOA measurement value ξ k occurs can be performed according to the stored inertial measurement data, and then the positioning data of the target terminal at the current time is inferred using the stored inertial measurement data.

[0120] In summary, the application is used in a target terminal, first receives uplink signal measurement data respectively sent by multiple base stations, and obtains inertial measurement data of the target terminal, the uplink signal measurement data includes AOA data and TDOA data, and the inertial measurement data includes white noise of a gyroscope carried on the target terminal in a carrier coordinate system, a rotation matrix between the carrier coordinate system and a navigation coordinate system, white noise of an accelerometer carried on the target terminal in the carrier coordinate system, a projection component of an angular velocity of the carrier coordinate system relative to the navigation coordinate system in the carrier coordinate system, random constant drift of the gyroscope in the carrier coordinate system, specific force output by the accelerometer in the carrier coordinate system, and random constant drift of the accelerometer in the carrier coordinate system. Then, the uplink signal measurement data, the inertial measurement data, and positioning data of the target terminal at a previous moment are input into a positioning state space model. Finally, a constraint condition between first antenna data and second antenna data is obtained; the positioning state space model is solved based on a particle filtering algorithm and the constraint condition, to obtain positioning data of the target terminal at a current moment. The positioning state space model includes a positioning observation model and a positioning state model, the positioning observation model includes an angle of arrival (AOA) observation model and a time difference of arrival (TDOA) observation model, the AOA observation model is used to represent a relationship between AOA data in the uplink signal measurement data and the first antenna data of the target terminal and second antenna data of the base stations, the TDOA observation model is used to represent a relationship between TDOA data in the uplink signal measurement data and the first antenna data and the second antenna data, and the positioning state model is used to represent a relationship between the positioning data of the target terminal at the current moment and the positioning data of the target terminal at the previous moment and the inertial measurement data; the first antenna data includes position coordinates of a phase center of an antenna of the target terminal in a local rectangular coordinate system, the second antenna data includes antenna position data of the base stations and antenna attitude data of the base stations, the antenna position data includes position coordinates of phase centers of antennas of the base stations in the local rectangular coordinate system, and the antenna attitude data includes attitude angles of the antennas of the base stations in the local rectangular coordinate system. The construction process of the positioning state model includes: constructing a target state equation between a first-order differential of the positioning data of the target terminal at the current moment and the positioning data of the target terminal at the current moment and the inertial measurement data; and performing discretization processing on the target state equation to obtain the positioning state model.The positioning data comprises terminal attitude data, terminal speed data and terminal position data, and the target state equation comprises a first state equation, a second state equation and a third state equation; the first state equation is a first-order differential of terminal attitude data of the target terminal at a current time and a state equation between the terminal attitude data of the target terminal at the current time and inertial measurement data; the second state equation is a first-order differential of terminal speed data of the target terminal at the current time and a state equation between the terminal speed data of the target terminal at the current time and the inertial measurement data; and the third state equation is a first-order differential of terminal position data of the target terminal at the current time and a state equation between the terminal position data of the target terminal at the current time and the inertial measurement data.

[0121] In addition, it should be noted that the positioning state model, i.e., equation (12), contains the measurement value of the IMU on the target terminal, and the positioning observation model, i.e., equation (14), contains the AOA data and TDOA data measured by multiple base stations, wherein equation (33) is the IAOM, and equation (34) is the TDOA observation model. The positioning state model and the positioning observation model constitute the positioning state space model, i.e., equation (28), to realize the tightly coupled positioning algorithm of the AOA and TDOA of multiple base stations and the IMU of the target terminal. Meanwhile, using the AOA data and TDOA data of multiple base stations and the IMU measurement value of the target terminal can realize high-precision continuous positioning of the target terminal. To ensure that the positioning state space model can realize continuous high-precision positioning in the near field and the far field, two aspects are required. One aspect is the height coordinate of the antenna phase center of the target terminal at time k with an error amount in the local rectangular coordinate system, i.e., The other aspect is the constraint condition, i.e., equation (35).

[0122] It should be understood that, although each step in the flowchart involved in each of the above-described embodiments is shown in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each of the above-described embodiments can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0123] Based on the same inventive concept, the embodiments of the present application also provide a positioning device for implementing the positioning method described above. The implementation scheme of the device for solving the problem is similar to the implementation scheme described in the above method, so the specific limitations in one or more positioning device embodiments provided below can refer to the limitations of the positioning method described above, which will not be described here.

[0124] In one embodiment, as shown in Figure 7 a structural block diagram of a positioning device 700 is provided, which is used in a target terminal and includes a receiving module 701 and a computing module 702, wherein:

[0125] The receiving module 701 is configured to receive uplink signal measurement data respectively sent by a plurality of base stations and obtain inertial measurement data of the target terminal, wherein the uplink signal measurement data includes AOA data.

[0126] The computing module 702 is configured to input each uplink signal measurement data, the inertial measurement data, and positioning data of the target terminal at a previous time into a positioning state space model, and solve the positioning state space model based on a preset algorithm to obtain the positioning data of the target terminal at a current time; wherein the positioning state space model includes a positioning observation model and a positioning state model, the positioning observation model includes an angle of arrival (AOA) observation model, the AOA observation model is configured to represent a relationship between the AOA data in each uplink signal measurement data and first antenna data of the target terminal and second antenna data of each base station, and the positioning state model is configured to represent a relationship between the positioning data of the target terminal at the current time and the positioning data of the target terminal at the previous time and the inertial measurement data.

[0127] In one embodiment, the uplink signal measurement data further includes TDOA data; the positioning observation model further includes a time difference of arrival (TDOA) observation model, and the TDOA observation model is configured to represent a relationship between the TDOA data in each uplink signal measurement data and the first antenna data and each second antenna data.

[0128] In one embodiment, the first antenna data includes antenna position data of the target terminal, and each second antenna data includes antenna position data and antenna attitude data of each base station.

[0129] In one embodiment, the antenna position data of the target terminal includes position coordinates of a phase center of an antenna of the target terminal in a local rectangular coordinate system; the antenna position data of each base station includes position coordinates of a phase center of an antenna of each base station in the local rectangular coordinate system; and the antenna attitude data of each base station includes an attitude angle of the antenna of each base station in the local rectangular coordinate system.

[0130] In one of the embodiments, the apparatus further comprises: constructing a first-order differential of the positioning data of the target terminal at the current time and a target state equation between the positioning data of the target terminal at the current time and the inertial measurement data; and discretizing the target state equation to obtain the positioning state model.

[0131] In one of the embodiments, the positioning data comprises terminal attitude data, terminal velocity data and terminal position data, and the target state equation comprises a first state equation, a second state equation and a third state equation; the first state equation is a state equation between a first-order differential of the terminal attitude data of the target terminal at the current time and the terminal attitude data of the target terminal at the current time and the inertial measurement data; the second state equation is a state equation between a first-order differential of the terminal velocity data of the target terminal at the current time and the terminal velocity data of the target terminal at the current time and the inertial measurement data; and the third state equation is a state equation between a first-order differential of the terminal position data of the target terminal at the current time and the terminal position data of the target terminal at the current time and the inertial measurement data.

[0132] In one of the embodiments, the inertial measurement data comprises: white noise of a gyroscope carried on the target terminal in a carrier coordinate system, a rotation matrix between the carrier coordinate system and a navigation coordinate system, white noise of an accelerometer carried on the target terminal in the carrier coordinate system, a projection component of an angular velocity of the carrier coordinate system relative to the navigation coordinate system in the carrier coordinate system, random constant drift of the gyroscope in the carrier coordinate system, specific force output by the accelerometer in the carrier coordinate system, and random constant drift of the accelerometer in the carrier coordinate system.

[0133] In one of the embodiments, the computing module 702 is specifically configured to acquire a constraint condition between the first antenna data and the second antenna data; and solve the positioning state space model based on a preset algorithm and the constraint condition to obtain the positioning data of the target terminal at the current time.

[0134] In one of the embodiments, the preset algorithm is a particle filter algorithm.

[0135] The above positioning apparatus can be implemented by software, hardware and combinations thereof. The above modules can be embedded in or independent of a processor in a computer device in a hardware form, or stored in a memory in a computer device in a software form, so as to be called and executed by a processor to perform operations corresponding to the above modules.

[0136] In one of the embodiments, a computer device is provided, which can be a terminal, and an internal structure diagram of the computer device can be as shown in Figure 8The computer device shown in the figure 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 capability. 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 operating system and the computer program in the non-volatile storage medium to run. 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. The wireless manner can be realized through WIFI, mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by the processor to realize 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 overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0137] Those skilled in the art can understand that, Figure 8 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0138] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the above method embodiments.

[0139] In one embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0140] In one embodiment, a computer program product is provided, including a computer program, and the computer program is executed by a processor to implement the steps in the above method embodiments.

[0141] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of the country and region.

[0142] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of each method can be included. Any reference to a memory, database or other medium used in the embodiments provided by the present application can include at least one of a non-volatile and volatile memory. The non-volatile memory can include a read-only memory (Read-Only Memory, ROM), a magnetic tape, a floppy disk, a flash memory, an optical storage, a high-density embedded non-volatile memory, a resistive memory (ReRAM), a magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), a ferroelectric memory (Ferroelectric Random Access Memory, FRAM), a phase change memory (Phase Change Memory, PCM), a graphene memory, etc. The volatile memory can include a random access memory (Random Access Memory, RAM) or an external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0143] The technical features of the above embodiments can be combined in any way. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present disclosure.

[0144] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A positioning method, characterized in that, For use in a target terminal, the positioning method includes: The system receives uplink signal measurement data transmitted by multiple base stations and acquires inertial measurement data of the target terminal, wherein the uplink signal measurement data includes angle of arrival (AOA) data. The uplink signal measurement data, the inertial measurement data, and the positioning data of the target terminal at the previous moment are input into the positioning state space model, and the positioning state space model is solved based on the preset algorithm to obtain the positioning data of the target terminal at the current moment. The positioning state space model includes a positioning observation model and a positioning state model. The positioning observation model includes an incident angle observation model, which is used to characterize the relationship between the AOA data in each of the uplink signal measurement data and the first antenna data of the target terminal and the second antenna data of each of the base stations. The positioning state model is used to characterize the relationship between the positioning data of the target terminal at the current moment, the positioning data of the target terminal at the previous moment, and the inertial measurement data.

2. The method according to claim 1, characterized in that, The uplink signal measurement data also includes Time Difference of Arrival (TDOA) data; The positioning observation model also includes a TDOA observation model, which is used to characterize the relationship between the TDOA data in each of the uplink signal measurement data and the first antenna data and each of the second antenna data.

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

4. The method according to claim 3, characterized in that, 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 each base station includes the position coordinates of the phase center of the antenna of each base station in the local rectangular coordinate system; The antenna attitude data of each base station includes the attitude angle of the antenna of each base station in the local Cartesian coordinate system.

5. The method according to any one of claims 1 to 4, characterized in that, The method further includes: constructing a target state equation between the first derivative of the current positioning data of the target terminal and the current positioning data of the target terminal and the inertial measurement data; The target state equation is discretized to obtain the positioning state model.

6. The method according to claim 5, characterized in that, The positioning data includes terminal attitude data, terminal speed 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 derivative of the target terminal's current attitude data and the target terminal's current attitude data and the inertial measurement data; The second state equation is the state equation between the first derivative of the target terminal's current velocity data and the target terminal's current velocity data and the inertial measurement data; The third state equation is the state equation between the first derivative of the target terminal's current position data and the target terminal's current position data and the inertial measurement data.

7. The method according to claim 5, characterized in that, The inertial measurement data includes: white noise of the gyroscope mounted on the target terminal in the carrier coordinate system, rotation matrix between the carrier coordinate system and the navigation coordinate system, white noise of the accelerometer mounted on the target terminal in the carrier coordinate system, projection component of the angular velocity of the carrier coordinate system relative to the navigation coordinate system in the carrier coordinate system, random constant drift of the gyroscope in the carrier coordinate system, specific force output by the accelerometer in the carrier coordinate system, and random constant drift of the accelerometer in the carrier coordinate system.

8. The method according to any one of claims 1 to 4, characterized in that, The step of solving the positioning state space model based on a preset algorithm to obtain the positioning data of the target terminal at the current moment includes: Obtain the constraints between the first antenna data and the second antenna data; The positioning state space model is solved based on the preset algorithm and the constraints to obtain the positioning data of the target terminal at the current moment.

9. The method according to claim 8, characterized in that, The preset algorithm is a particle filter algorithm.

10. A positioning device, characterized in that, For use in a target terminal, the positioning device includes: The receiving module is used to receive uplink signal measurement data sent by multiple base stations respectively, and to acquire inertial measurement data of the target terminal, wherein the uplink signal measurement data includes AOA data; The calculation module is used to input the uplink signal measurement data, the inertial measurement data, and the positioning data of the target terminal at the previous moment into the positioning state space model, and solve the positioning state space model based on a preset algorithm to obtain the positioning data of the target terminal at the current moment. The positioning state space model includes a positioning observation model and a positioning state model. The positioning observation model includes an incident angle observation model, which is used to characterize the relationship between the AOA data in each of the uplink signal measurement data and the first antenna data of the target terminal and the second antenna data of each of the base stations. The positioning state model is used to characterize the relationship between the positioning data of the target terminal at the current moment, the positioning data of the target terminal at the previous moment, and the inertial measurement data.

11. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 9.

12. 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 9.

13. 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 9.

Images