Tracking and positioning methods and devices, electronic devices, storage media

Abstract

This application provides a tracking and positioning method and apparatus, electronic device, and storage medium, relating to the field of positioning technology. The method includes: acquiring first channel state information, second channel state information, and third channel state information; calculating the quotient of the first and second channel state information to obtain a channel state quotient; calculating the Doppler frequency shift based on the two channel state quotients to obtain a reference Doppler frequency shift component; dividing the channel state information into a dynamic part and a static part, and then performing conjugate multiplication based on the channel state information to obtain a first path component, a second path component, and a third path component; inputting the first path component, the second path component, the third path component, and the reference Doppler frequency shift component into a preset propagation path estimation model to perform path estimation, obtaining the target propagation path, thereby obtaining the positioning result. This embodiment improves positioning accuracy, reduces the number of receivers, and is more universal.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of positioning, in particular to a tracking positioning method and device, electronic equipment and storage medium. BACKGROUND

[0002] In the related art, positioning and tracking technologies develop rapidly. Among them, passive positioning of mobile targets without carrying any equipment has attracted great attention due to its wide potential applications.

[0003] Wi-Fi-based positioning schemes can be roughly divided into two categories from the algorithm perspective: data-driven methods and model-driven methods. Data-driven methods require building a dataset and training a model in advance, which is time-consuming and highly related to the environment, and has poor universality. Model-driven methods model and estimate channel parameters based on Channel State Information (CSI), and then locate the target. This method does not require training and is not sensitive to the environment. Therefore, model-based positioning is the mainstream indoor positioning method for Wi-Fi.

[0004] However, the current model-driven method requires multiple receivers, which does not meet the requirements in many practical application scenarios. Therefore, how to provide a positioning method to reduce the number of receivers while ensuring positioning accuracy has become a technical problem to be solved. SUMMARY

[0005] The main purpose of the embodiments of the present application is to provide a tracking positioning method and device, electronic equipment and storage medium, which improves the positioning accuracy and reduces the number of receivers.

[0006] To achieve the above purpose, a first aspect of the embodiments of the present application provides a tracking positioning method, which comprises:

[0007] obtaining channel state information data; wherein the channel state information data comprises first channel state information of a first antenna, second channel state information of a second antenna and third channel state information of a third antenna;

[0008] calculating the quotient of the first channel state information and the second channel state information to obtain channel state quotients, calculating the Doppler shift based on the two channel state quotients to obtain a reference Doppler shift component;

[0009] The first channel state information, the second channel state information, and the third channel state information are divided into dynamic and static components. The first channel state information and the second channel state information are multiplied by their conjugates to obtain the first path component. The first channel state information and the second channel state information are multiplied by their conjugates to obtain the second path component. The first channel state information and the third channel state information are multiplied by their conjugates to obtain the third path component. The first path component, the second path component, and the third path component are filtered to remove the static component and amplitude-adjusted to remove interference terms. The processed results are still denoted as the first path component, the second path component, and the third path component.

[0010] The first path component, the second path component, the third path component, and the reference Doppler frequency shift component are input into a preset propagation path estimation model to perform path estimation and obtain the target propagation path; the parameters of the target propagation path include the signal Doppler frequency shift, the signal flight time, and the signal angle of arrival, and the signal Doppler frequency shift is the same as the reference Doppler frequency shift component;

[0011] The positioning result is obtained by using the Doppler frequency shift of the signal, the time of flight of the signal, and the angle of arrival of the signal.

[0012] In some embodiments, the step of locating based on the signal Doppler frequency shift, the signal time of flight, and the signal angle of arrival to obtain the positioning result includes:

[0013] The target's velocity is obtained based on the Doppler frequency shift of the signal;

[0014] The target propagation distance is obtained based on the target's speed and the signal's flight time.

[0015] The target location is determined based on the target propagation distance and the signal angle of arrival, and the positioning result is obtained based on the target location.

[0016] In some embodiments, determining the target position based on the target propagation distance and the signal angle of arrival includes:

[0017] The first reference position component is obtained by constructing the observation vector of the unscented Kalman filter based on the target propagation distance, target speed, and signal angle of arrival.

[0018] Based on the preset initial object position, velocity, and state equations, the second reference position component is obtained;

[0019] Position calibration is performed based on the first reference position component and the second reference position component to obtain the target position.

[0020] In some embodiments, the target propagation distance is obtained according to the target motion velocity and the signal time of flight.

[0021] The offset time of flight is obtained according to the signal Doppler shift and a preset sampling time interval.

[0022] The reference time of flight is obtained according to a sum of the offset time of flight and a preset initial time of flight.

[0023] The fused time of flight is obtained according to a weighted sum of the signal time of flight and the reference time of flight.

[0024] The target propagation distance is obtained according to the target motion velocity and the fused time of flight.

[0025] In some embodiments, before the reference time of flight is obtained according to the sum of the offset time of flight and the preset initial time of flight, the method further comprises:

[0026] The preset initial time of flight is set, specifically comprising:

[0027] The first path component, the second path component, the third path component and the reference Doppler shift component at the initial time are input into the propagation path estimation model for time of flight estimation to obtain a time of flight at the initial time.

[0028] The preset initial time of flight is obtained according to the time of flight at the initial time.

[0029] In some embodiments, the target propagation path is obtained by inputting the first path component, the second path component, the third path component and the reference Doppler shift component into a preset propagation path estimation model for path estimation, comprising:

[0030] The first path component, the second path component, the third path component and the reference Doppler shift component are input into a preset propagation path estimation model for path estimation to obtain a candidate path parameter set; the candidate path parameter set comprises a plurality of path parameters, and the plurality of path parameters are used to represent path parameters at different time instants of a plurality of candidate paths.

[0031] The plurality of path parameters are path matched according to a preset path matching algorithm to obtain a candidate path set; the candidate path set comprises a plurality of candidate paths, and each candidate path comprises path parameters at different time instants.

[0032] The target propagation path is obtained by screening the plurality of candidate paths according to path attenuation.

[0033] In some embodiments, the step of performing path matching on multiple path parameters according to a preset graph path matching algorithm to obtain a candidate path set includes:

[0034] A weighted multipart graph is constructed based on multiple path parameters; each vertex of the weighted multipart graph is used to indicate the path parameter at different times, and the weight of the edge is used to indicate the distance between two path parameters.

[0035] The matching result of each vertex and edge is determined by comparing the weights of the multiple edges.

[0036] A complete path graph is constructed based on the matching results, and each candidate path is obtained based on each complete path graph.

[0037] The candidate paths are merged to obtain the candidate path set.

[0038] To achieve the above objectives, a second aspect of this application provides a tracking and positioning device, the device comprising:

[0039] The data acquisition module is used to acquire channel state information data; wherein, the channel state information data includes the first channel state information of the first antenna, the second channel state information of the second antenna, and the third channel state information of the third antenna;

[0040] The frequency shift component determination module is used to calculate the quotient of the first channel state information and the second channel state information to obtain the channel state quotient, and calculate the Doppler frequency shift based on the two channel state quotients to obtain the reference Doppler frequency shift component.

[0041] The path component determination module is used to perform conjugate multiplication of the first channel state information and the second channel state information to obtain a first path component; perform conjugate multiplication of the first channel state information and the second channel state information to obtain a second path component; and perform conjugate multiplication of the first channel state information and the third channel state information to obtain a third path component; the first path component, the second path component, and the third path component are filtered to remove static components and amplitude-adjusted to remove interference terms, and the processing results are still recorded as the first path component, the second path component, and the third path component;

[0042] The path estimation module is used to input the first path component, the second path component, the third path component, and the reference Doppler frequency shift component into a preset propagation path estimation model to perform path estimation and obtain the target propagation path; the parameters of the target propagation path include the signal Doppler frequency shift, the signal flight time, and the signal angle of arrival, and the signal Doppler frequency shift is the same as the reference Doppler frequency shift component;

[0043] The positioning module is used to perform positioning based on the Doppler frequency shift of the signal, the time of flight of the signal, and the angle of arrival of the signal, and obtain the positioning result.

[0044] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0045] To achieve the above objectives, a fourth aspect of the present application provides a storage medium, which is a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0046] This application proposes a tracking and positioning method, apparatus, device, and medium. This method overcomes the noise amplification problem caused by conjugate multiplication of channel state information by using channel state information quotient, avoiding ambiguity in estimating the reference Doppler frequency shift component. Furthermore, the reference Doppler frequency shift component estimated by the channel state quotient, along with the first path component, second path component, and third path component, are jointly input into the propagation path estimation model. The estimated Doppler frequency shift of the target path is identical to the reference Doppler frequency shift component, thereby improving the estimation accuracy of signal attenuation, signal flight time, and signal angle of arrival, thus enhancing positioning accuracy. In addition, this embodiment requires only one transmitter and one receiver, reducing the number of transmitters and receivers and making it more universal compared to existing technologies. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of an implementation scenario of the tracking and positioning method provided in this application.

[0048] Figure 2 This is a schematic diagram of a positioning result of the tracking and positioning method provided in the embodiments of this application;

[0049] Figure 3 This is a schematic diagram of the channel response provided in an embodiment of this application;

[0050] Figure 4 This is a flowchart illustrating the tracking and positioning method provided in an embodiment of this application;

[0051] Figure 5 yes Figure 4 The detailed flowchart of step 104 in the document;

[0052] Figure 6 yes Figure 4 The detailed flowchart of step 105 in the document;

[0053] Figure 7 This is a schematic diagram illustrating the determination of object location provided in an embodiment of this application;

[0054] Figure 8 yes Figure 6 The detailed flowchart of step 302 in the text;

[0055] Figure 9 This is a flowchart illustrating the overall framework of a tracking and localization method provided in an application example of this application.

[0056] Figure 10 This is a schematic diagram of the structure of the tracking and positioning device provided in the embodiments of this application;

[0057] Figure 11 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0061] First, let me explain some of the terms used in this application.

[0062] Channel State Information (CSI): In the field of wireless communication, CSI refers to the channel attributes of a communication link. CSI describes the fading factors of a signal along each transmission path, i.e., the value of each element in the channel gain matrix H, such as signal scattering, environmental fading (multipath fading or shadowing fading), and power decay of distance. CSI allows the communication system to adapt to current channel conditions, providing a guarantee for high-reliability and high-speed communication in multi-antenna systems. CSI presents the amplitude and phase of multipath propagation at different frequencies (corresponding to different subcarriers), thus more accurately characterizing channels with frequency-selective fading characteristics.

[0063] The Space Alternating Generalized Expectation Maximization (SAGE) algorithm is an iterative algorithm for parameter estimation when data is incomplete or missing variables exist. Each iteration of the algorithm consists of two steps: expectation and maximization. It is also a generalized asymptotic approximation optimization algorithm. After defining an optimization function, it mainly consists of the following two steps: 1. Adjusting the model according to the parameters (E-step); 2. Adjusting the parameters according to the model (M-step); The E-step and M-step are performed alternately until the optimal solution is obtained.

[0064] Signal attenuation: When a signal propagates through a transmission medium, some of its energy is converted into heat or absorbed by the medium, causing the signal strength to weaken continuously. This phenomenon is called attenuation. Signal attenuation is an important characteristic of communication transmission. The degree of signal attenuation is not only an important indicator for evaluating the quality of communication, but also directly affects the characteristics of communication system expansion and upgrades, as well as the relay distance in communication transmission cable layout.

[0065] Angle of Arrival (AoA): Used to indicate the direction of reception when the transmitted signal from the transmitter reaches the receiver.

[0066] Time of Flight (ToF): Used to represent the time delay it takes for a transmitted signal from a transmitter to travel through the propagation path and reach the receiver.

[0067] Doppler Frequency Shift (DFS): Used to represent the frequency difference of a received signal. This is because the movement of a target object causes a change in the path length of the signal reflected from the target object, resulting in a shift in the observed signal frequency.

[0068] Kalman filtering is an algorithm that uses the state equations of a linear system to make an optimal estimate of the system state using the system's input and output observation data. Since the observation data includes the effects of noise and interference in the system, the optimal estimation can also be viewed as a filtering process.

[0069] Unscented Kalman Filter (UKF): This algorithm combines Unscented Transform (UT) with the standard Kalman filter system. It uses lossless transformation to adapt nonlinear system equations to the standard Kalman system under the linear assumption.

[0070] Currently, tracking and positioning technologies that target objects without carrying any devices are attracting significant attention, aiming to track and locate moving objects indoors. For example, a positioning method called LiFS estimates the target position of a moving object by establishing a relationship between signal power attenuation and target position, based on changes in CSI amplitude. Another example is a positioning scheme based on angle of arrival (AoA), which uses a two-dimensional multi-signal classification algorithm to estimate the absolute AoA and relative time of flight (ToF) of static and dynamic paths. However, both of these schemes require multiple receivers, which is unacceptable in many home environments.

[0071] The tracking and positioning method of this application can improve the accuracy of positioning and reduce the number of receivers.

[0072] The tracking and location method in this application can be executed by the server alone, by the terminal alone, or by both the terminal and the server. Furthermore, the tracking and location method provided in this application can also be software running on a server. The server can be configured as a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The software can be an application implementing the tracking and location method, but is not limited to the above forms.

[0073] An example of an implementation scenario for this application is as follows: Figure 1The diagram shows a transmitter with one antenna and a receiver with three antennas (the first, second, and third antennas, as described below). One object is in motion, while all other objects are stationary; line-of-sight paths exist between the transmitter and receiver, between the transmitter and the target, and between the receiver and the target.

[0074] The tracking and positioning method in this application is essentially a Wi-Fi-based single-link positioning method. It primarily models CSI as a function of signal attenuation, DFS, AoA, and ToF, uses an improved SAGE algorithm to jointly estimate multiple parameters of the multipath channel, and employs graph matching technology to obtain the optimal solution for the length of the path reflected from the target and the target's azimuth information, ultimately achieving the tracking and positioning of the target object. The tracking and positioning results are as follows: Figure 2 As shown.

[0075] Before explaining the tracking and positioning method of the embodiments of this application in detail, the wireless channel modeling will be described first.

[0076] The receiver's channel response at data packet i, subcarrier j, and antenna k is as follows: Figure 3 As shown, the channel response formula is expressed as follows:

[0077]

[0078] Where L is the multipath number, P l It is the signal of the l-th path, α l It is the decay of the l-th path, τ l Let be the transmission delay of the l-th path, and N be zero-mean unit complex white Gaussian noise, with both its real and imaginary parts following a zero-mean unit Gaussian distribution. The signal for each path can be represented as:

[0079]

[0080] in,

[0081] With H(0, 0, 0) as a reference, the signal phase of path l in H(i, j, k) can be expressed as:

[0082]

[0083] Among them, f c It is the carrier frequency; Δt i , Δf i Δs k τ represents the difference between H(i,j,k) and H(0,0,0) in time, frequency, and spatial location, respectively; l , φ l , These are the time of flight (ToF), angle of arrival (AoA), and Doppler shift (DFS) of the l-th path of H(0,0,0), respectively.

[0084] remember i = 0, 1, ..., T-1, j = 0, 1, ..., F-1, k = 0, 1, ..., S-1, T, F, s represent the number of data packets, the number of subcarriers, and the number of antennas, respectively. The term f in formula (3) c τ l The same applies to all measurements; this is incorporated into the signal attenuation α. l In the middle, the channel response becomes:

[0085]

[0086] in,

[0087] Let the estimated parameters of path l be... The first step in localization is to estimate the multidimensional parameters θ of the target's reflection path.

[0088] The following embodiments of this application provide tracking and positioning methods and apparatus, electronic devices, and computer-readable storage media, which will be specifically described through the following embodiments. First, the tracking and positioning method in the embodiments of this application will be described.

[0089] Reference Figure 4 The tracking and positioning method according to one embodiment of this application includes, but is not limited to, steps 101 to 105:

[0090] Step 101: Obtain channel state information data; wherein, the channel state information data includes the first channel state information of the first antenna, the second channel state information of the second antenna, and the third channel state information of the third antenna;

[0091] Step 102: Calculate the quotient of the first channel state information and the second channel state information to obtain the channel state quotient, and calculate the Doppler frequency shift based on the two channel state quotients to obtain the reference Doppler frequency shift component;

[0092] Step 103: Divide the first channel state information into dynamic and static parts, the second channel state information into dynamic and static parts, and the third channel state information into dynamic and static parts; perform conjugate multiplication of the first channel state information and the second channel state information to obtain the first path component; perform conjugate multiplication of the first channel state information and the second channel state information to obtain the second path component; perform conjugate multiplication of the first channel state information and the third channel state information to obtain the third path component; filter out the static part of the first path component, the second path component, and the third path component, and remove interference terms by amplitude adjustment. The processing results are still recorded as the first path component, the second path component, and the third path component.

[0093] Step 104: Input the first path component, the second path component, the third path component, and the reference Doppler frequency shift component into a preset propagation path estimation model to perform path estimation and obtain the target propagation path; the parameters of the target propagation path include the signal Doppler frequency shift, the signal flight time, and the signal angle of arrival, and the signal Doppler frequency shift is the same as the reference Doppler frequency shift component;

[0094] Step 105: Positioning is performed based on the signal Doppler frequency shift, signal flight time, and signal angle of arrival to obtain the positioning result.

[0095] Through steps 101-105 above, this embodiment aims to reduce the noise amplification problem caused by CSI conjugate multiplication by using CSI quotient, thus avoiding ambiguity in DFS estimation. Furthermore, the DFS estimated by CSI quotient is input into the propagation path estimation model, ensuring that the estimated Doppler frequency shift of the target path is the same as the reference Doppler frequency shift component, improving the estimation accuracy of signal attenuation, ToF, and AoA, thereby enhancing positioning accuracy. In addition, this embodiment requires only one transmitter and one receiver, reducing the number of transmitters and receivers, making it more universal than existing technologies.

[0096] Steps 101-105 are described in detail below.

[0097] In step 101, channel state information data is acquired. This channel state information data includes the first channel state information of the first antenna, the second channel state information of the second antenna, and the third channel state information of the third antenna. The first antenna, the second antenna, and the third antenna are located at the same receiver.

[0098] In one example, in packet i, the channel state information (CSI) H(i,j,K) on the j-th (1,...,N) subcarrier and the k-th (1,...,M) receive antenna can be represented as:

[0099] H(i,j,K)=[H(i,1,1),H(i,2,2),...,H(i,N,M)];

[0100] Where j(1,…,N) is the subcarrier number, N is the number of Wi-Fi receiver subcarriers, and M is the number of Wi-Fi receiver antennas.

[0101] Specifically, the first channel state information of the first antenna can be represented as:

[0102] H(i,j,1)=[H(i,1,1),H(i,2,1),...,H(i,N,1)];

[0103] The second channel state information of the second antenna can be represented as:

[0104] H(i,j,2)=[H(i,1,1),H(i,2,2),...,H(i,N,2)];

[0105] The third channel state information of the third antenna can be represented as:

[0106] H(i,j,3)=[H(i,1,3),H(i,2,3),...,H(i,N,3)].

[0107] The following section describes the methods for obtaining channel state information data.

[0108] One way to obtain channel state information (CSO) data is by using OFDM (Orthogonal Frequency Division Multiplexing) modulation technology as an example. In OFDM, the entire frequency band is divided into several small, overlapping sub-bands. At the transmitter, the digital signal carried by each subcarrier is superimposed using IFFT (Inverse Fast Fourier Transform). Then, the real and imaginary parts of the signals are converted into analog quantities using digital-to-analog converter (DAC) and propagated from the transmitter into space. At the receiver, these signals are received, sampled, and demodulated (i.e., analog-to-digital converter (ADC) operations are performed), and then FFT (Fast Fourier Transform) is performed to convert the signal back to the frequency domain. Finally, the channel state information is obtained in the frequency domain or in the form of multiple subcarriers.

[0109] Another way to obtain channel status information data is to receive channel status information data sent by the transmitter through the server's network interface. For example, the channel status information data can be channel status information data sent by the terminal to the server through the network interface after requesting the tracking and positioning service from the server via a preset link; that is, the terminal sends a tracking and positioning request.

[0110] Another way to obtain channel status information data is for the server to access a preset storage location and read the channel status information data from that location. For example, the channel status information data can be pre-stored in a preset storage location on the server. During tracking and positioning, the server reads the channel status information data from the preset storage location to obtain the channel status information data.

[0111] After obtaining the channel state information data in step 101, considering that the initial purpose of CSI is to equalize the channel for data demodulation, CSI includes not only the channel response but also various phase noises caused by asynchrony between transceivers and hardware defects. Therefore, noise filtering is required after obtaining the channel state information data.

[0112] Generally, when filtering noise from Channel State Information (CSI) data, the CSI values ​​of two different antennas are multiplied by conjugate to remove noise. However, in practical applications, it has been found that while multiplying the CSI values ​​of two different antennas by conjugate eliminates the phase shift of the CSI, it also doubles the amplitude noise of the CSI.

[0113] Furthermore, since conjugate multiplication introduces amplitude noise, the CSI amplitude needs to be adjusted to remove interference terms when separating dynamic paths using the propagation path estimation model. At this point, filtering methods can only roughly filter out static paths, not completely eliminate them, leading to inaccurate parameter estimation. For the interference terms introduced by conjugate multiplication, different constants are added or subtracted to the CSI amplitudes of the reference antenna and other antennas to make the target term much larger than the interference term and thus ignore it. This introduces some error, resulting in ambiguity and low accuracy in DFS estimation.

[0114] Therefore, in this embodiment of the application, after obtaining the channel state information data in step 101, the channel state information data is used in parallel through steps 102 and 103 to jointly complete noise filtering.

[0115] In step 102, the first channel state information and the second channel state information are divided to obtain the channel state quotient. The Doppler frequency shift is calculated based on the two channel state quotients to obtain the reference Doppler frequency shift component.

[0116] Specifically, considering the severe phase noise of commercial network cards, and the fact that the phase noise of different antennas on the same receiver is basically the same, phase noise is removed by using different antenna CSI quotients. These different antennas can be any two of the first, second, and third antennas. For example, in step 102, the first and second antennas were selected.

[0117] For example, arbitrarily select two receiving antennas k1 and k2, where k1 represents the first antenna and k2 represents the second antenna. The channel state quotient (CSI) obtained by quotienting the CSI between the antennas is expressed as:

[0118]

[0119] Among them, H s For static components, H d For dynamic components, As can be seen, only z is related to time i. Therefore, for a given frequency j, parameters a, b, c, and d are all constants. The above equation is a Möbius transform, which has the property of preserving circle. Thus, the key conclusion is obtained: H... q The number of rotations per second (i, j) equals the Doppler frequency shift. The target DFS expression is calculated using the CSI quotient of two adjacent sampling points as follows:

[0120]

[0121] Where ΔT represents the sampling interval, Δρ i This represents the change in the central angle of the rotating circle of the CSI quotient between adjacent sampling points, which can be calculated from the angle difference of the circle's tangents.

[0122] In parallel with step 102, step 103 performs CSI conjugate multiplication between antennas.

[0123] In step 103, the first channel state information is divided into dynamic and static parts, the second channel state information is divided into dynamic and static parts, and the third channel state information is divided into dynamic and static parts; the first channel state information and the second channel state information are multiplied by their conjugates to obtain the first path component; the first channel state information and the second channel state information are multiplied by their conjugates to obtain the second path component; the first channel state information and the third channel state information are multiplied by their conjugates to obtain the third path component; the first path component, the second path component, and the third path component are filtered to remove the static part and the interference term is removed by amplitude adjustment. The processing result is still recorded as the first path component, the second path component, and the third path component.

[0124] Specifically, the first antenna is selected as the reference antenna, and the CSI of the first antenna itself is multiplied by the CSI of the reference antenna by conjugate. The CSI of the second antenna is multiplied by the CSI of the reference antenna by conjugate, and the CSI of the third antenna is multiplied by the CSI of the reference antenna by conjugate.

[0125] The remaining CSI signal after amplitude adjustment, conjugate multiplication, and filtering is represented as follows:

[0126]

[0127] Among them, P d P represents the dynamic path. s Representing the static path, Z(m) can be approximated as complex Gaussian white noise. C(m) has the same phase structure as H(m) in equation (4). Definition The summation index is denoted as l, l = 1, ..., L. Path decay α l The magnitude of reflects the probability that path l is the target path. Slightly different is that the target ToF in C(m) becomes the target ToF in hit(m) minus the ToF of the direct transmission / receive path, but this does not affect parameter estimation; the ToF of the direct transmission / receive path can be obtained based on the known transceiver positions.

[0128] Next, we need to calculate the parameters of the L paths based on C(m). This problem can be described as a maximum likelihood estimation problem, with the log-likelihood function being:

[0129]

[0130] Where c(m) represents the value of C(m) at m. Maximize A to solve for Θ:

[0131]

[0132] After steps 102 and 103, the parameters of the path need to be estimated. However, estimating the parameters of all paths simultaneously is too complex. Therefore, in step 104, the SAGE algorithm is used as the propagation path estimation model to estimate the parameters of the propagation path.

[0133] In step 104, the first path component, the second path component, the third path component, and the reference Doppler frequency shift component are input into a preset propagation path estimation model to perform path estimation and obtain the target propagation path. The parameters of the target propagation path include the signal Doppler frequency shift, the signal flight time, and the signal angle of arrival, and the signal Doppler frequency shift is the same as the reference Doppler frequency shift component.

[0134] The following section will provide a detailed explanation of step 104.

[0135] The propagation path estimation model in this embodiment is the SAGE algorithm model. After inputting the first path component, the second path component, and the reference Doppler frequency shift component into the SAGE algorithm model, the SAGE algorithm model will perform path estimation to obtain path parameters, including signal attenuation, signal Doppler frequency shift, signal time of flight, and signal angle of arrival; based on the path parameters, the parameters of the target propagation path are obtained.

[0136] The SAGE algorithm, after defining an optimization function, mainly consists of the following two steps: 1. Adjusting the model based on the parameters (E-step); 2. Adjusting the parameters based on the model (M-step); The E-step and M-step are performed alternately until the optimal solution is obtained. E-step: For path l, we have...

[0137]

[0138] in, These are the parameters estimated in the previous iteration, initialized to 0, β l It is a non-negative parameter that controls the convergence speed of the algorithm, and its default value is 1.

[0139] M-step: First, the estimated parameters of the SAGE algorithm in Widar2.0 are denoted as Θ0 as the initial values ​​for the improved SAGE. Then, the parameter indices of the target paths at different times are obtained through a graph-based path matching algorithm. The DFS of the target paths in Θ0 is replaced with the DFS of the CSI quotient, and this replacement is kept unchanged during the iteration process.

[0140] For example, if the target path index is l = s, the DFS obtained by using CSI as the quotient is: The M-steps of the target path are then:

[0141]

[0142]

[0143] It should be noted that while parameter estimation yields parameters for L paths at different times, the parameters of the same path at different times are not correlated. Path matching aims to link the path parameters at different times. This embodiment employs a graph-based path matching method, and the core idea of ​​this method is to minimize the parameter changes of the same path at different times within a short period.

[0144] Reference Figure 5 Step 104 specifically includes steps 201 to 203:

[0145] Step 201: Input the first path component, the second path component, the third path component and the reference Doppler frequency shift component into the preset propagation path estimation model to perform path estimation and obtain a candidate path parameter set; the candidate path parameter set includes multiple path parameters, which are used to characterize the path parameters of multiple candidate paths at different times;

[0146] Step 202: Perform path matching on multiple path parameters according to the preset graph path matching algorithm to obtain a candidate path set; the candidate path set includes multiple candidate paths, and each candidate path includes path parameters at different times.

[0147] Step 203: Filter multiple candidate paths based on path decay to obtain the target propagation path.

[0148] The graph path matching method in steps 201-203 can quickly find the target propagation path, further improving the positioning efficiency.

[0149] Step 202 specifically includes:

[0150] A weighted multipart graph is constructed based on multiple path parameters; each vertex of the weighted multipart graph is used to indicate the path parameter at different times, and the weight of the edge is used to indicate the distance between two path parameters.

[0151] The matching result of each vertex and edge is determined by comparing the weights of multiple edges.

[0152] Construct a complete path graph based on the matching results, and obtain each candidate path based on each complete path graph.

[0153] The candidate paths are merged to obtain a candidate path set.

[0154] Specifically, considering the path parameters at N time points, each time point contains L paths, a weighted N-part graph G = (V, E, W) is constructed, where v i，j ∈V represents the parameter θ of path j at time i. ij The weight of an edge is the distance between the parameters.

[0155]

[0156] Where c is the parameter normalization vector.

[0157] binary variables Indicates whether to select an edge. Perform matching. Therefore, the objective function is...

[0158] x opt =argmin x w T x (14)

[0159] Where w and x represent the vectorized weights and variables.

[0160] The edges and vertices selected for each path must form a complete graph of order N, resulting in L complete graphs. Vertices cannot be selected repeatedly. The parameters associated with each path at different times are obtained using the above method, and the target path has the maximum average gain.

[0161] Generally, the path reflected from the object will have the maximum average gain. Therefore, the target propagation path can be obtained by filtering multiple path parameter sets based on the magnitude of signal attenuation.

[0162] After obtaining the parameters of the target propagation path (including signal Doppler shift, signal flight time, and signal angle of arrival) in step 104, step 105 requires positioning.

[0163] In step 105, positioning is performed based on the signal Doppler frequency shift, signal flight time, and signal angle of arrival to obtain the positioning result.

[0164] Reference Figure 6 In one implementation, step 105 specifically includes steps 301 to 303:

[0165] Step 301: Obtain the target's velocity based on the Doppler frequency shift of the signal;

[0166] Step 302: Obtain the target propagation distance based on the target's speed and signal flight time;

[0167] Step 303: Determine the location based on the target propagation distance and the signal angle of arrival to obtain the target location, and obtain the positioning result based on the target location.

[0168] For reference Figure 7 The target propagation distance *d* refers to the path length from the transmitter to the object, and from the object to the receiver. The angle of arrival *φ* refers to the direction in which the object is located within the receiver's antenna array. Therefore, the target object can be determined based on the target propagation distance and the angle of arrival, thus achieving localization.

[0169] In one embodiment, the accuracy of the time of flight can be improved by adjusting the signal's time of flight with reference to the Doppler frequency component. Therefore, referencing... Figure 8 Step 302 specifically includes steps 401 to 403:

[0170] Step 401: Obtain the deviation flight time based on the reference Doppler frequency shift component and the preset sampling time interval; obtain the reference flight time based on the sum of the deviation flight time and the preset initial flight time.

[0171] Step 402: Obtain the fused flight time based on the weighted sum of the signal flight time and the reference flight time;

[0172] Step 403: Calculate the distance based on the target's speed and fused flight time to obtain the target's propagation distance.

[0173] It should be noted that the calculation process in step 401 is as follows:

[0174] If the initial ToF is known to be τ d (0), then the target ToF corresponding to time i can be expressed as:

[0175]

[0176] Where λ is the wavelength and c is the speed of light.

[0177] If the initial ToF is unknown, the ToF value at time 0 estimated by the improved SAGE algorithm can be used as τ. d (0). Therefore, prior to step 401, this embodiment further includes setting a preset initial flight time, specifically including:

[0178] The first path component, the second path component, the third path component, and the reference Doppler frequency shift component at the initial moment are input into the propagation path estimation model to estimate the flight time at the initial moment.

[0179] The preset initial flight time is obtained based on the flight time at the initial moment.

[0180] The calculation process in step 402 specifically includes:

[0181] After path matching, the Time-of-Flight (ToF) estimated by the CSI quotient is fused according to the actual environment. d τ obtained by matching (reference flight time) with the path l The new Time-of-Flight (ToF) is obtained from the (signal time of flight), i.e.

[0182] τ=aτ d +(1-a)τ l (16)

[0183] Where a∈[0,1] are weighting coefficients used to control the proportion of the two Time-of-Flight (ToF) operations, since τ d There is an error accumulation problem; the larger the tracking area, the smaller 'a' becomes.

[0184] Based on the position determination in step 303, and considering that positioning based on distance and angle is a nonlinear problem, which UKF can handle, UKF obtains a more accurate result by fusing the predicted state with the measured state. Therefore, this embodiment will use the UKF state equation to predict the position at the next moment, and calibrate the predicted position according to the distances corresponding to AoA, DFS, and ToF to obtain the final target position (x, y).

[0185] In one embodiment, step 303 specifically includes:

[0186] The first reference position component is obtained by constructing the observation vector of the unscented Kalman filter based on the target propagation distance, target speed, and signal angle of arrival.

[0187] The second reference position component is obtained based on the preset initial object position, preset velocity, and preset state equation;

[0188] Position calibration is performed based on the first reference position component and the second reference position component to obtain the target position.

[0189] Specifically, the state vector is represented as:

[0190] x = [x, v] x ,y,v y ] T (17)

[0191] Where x represents the x-coordinate of the target position, y represents the y-coordinate, and v x v y These represent the velocity components of the target along the x and y axes, respectively.

[0192] The observation vector is represented as:

[0193] z = [d, v, φ] T (18)

[0194] Where d represents the reflection path length, v represents the target velocity, and φ represents AoA.

[0195] The state equation is expressed as:

[0196] x k =Fx k-1 +w

[0197] in, ΔT is the time interval between adjacent moments.

[0198] acceleration variance

[0199] The observation equation is expressed as:

[0200] in,

[0201] UKF positioning state vector initial settings: If the initial position of the target is known, the initial x and y of the state vector can be obtained directly, and the velocity can be set according to the actual situation.

[0202] It should be noted that in many indoor scenarios, the initial position is easily obtained, such as the position of a door. Even if the initial position is unknown, a relatively accurate initial position can be obtained using appropriate processing methods. If the target's initial position is unknown, the following method can be used to obtain a rough initial position of the target.

[0203] The relationship between the approximate position of the target (x, y) and the distance d and angle φ is expressed by the formula:

[0204]

[0205] Where, x r Let φ represent the receiver's x-coordinate, and φ0 represent the angle of the receiving antenna array. Therefore:

[0206]

[0207] in, The x-coordinate of the target activity area must be less than x. r .

[0208] In one application example, the overall framework of the tracking and localization method is as follows: Figure 9 As shown. First, the CSI signal is acquired and processed in two paths: the first branch selects the CSI signals on the two antennas to calculate the quotient, and then calculates the DFS based on the tangent angle difference of the CSI quotient. This gives us the target velocity, and then we can obtain ToFτ based on the sampling interval and the initial position. d The second branch uses conjugate multiplication to remove phase noise, combined with the first branch. Estimating ToFτ using an improved SAGE algorithm s With AoAφ s The following is a fusion of τ. d and τ s And refine it to obtain the distance d, and finally according to d、φ s The target's trajectory is obtained by using UKF to locate the initial position.

[0209] Please see Figure 10 This application also provides a tracking and positioning device that can implement the above-described tracking and positioning method. Figure 10The block diagram of the tracking and positioning device provided in this application embodiment is shown. The device includes: a data acquisition module 501, a frequency shift component determination module 502, a path component determination module 503, a path estimation module 504, and a positioning module 505. The acquisition module 501 is used to acquire channel state information data, including first channel state information of a first antenna, second channel state information of a second antenna, and third channel state information of a third antenna. The frequency shift component determination module 502 is used to calculate the quotient of the first channel state information and the second channel state information to obtain a channel state quotient, and to calculate the Doppler frequency shift based on the two channel state quotients of adjacent sampling points to obtain a reference Doppler frequency shift component. The path component determination module 503 is used to divide the first channel state information into dynamic and static parts, the second channel state information into dynamic and static parts, and the third channel state information into dynamic and static parts; to perform conjugate multiplication of the first channel state information and the second channel state information to obtain a first path component; and to calculate the reference Doppler frequency shift component based on the two channel state quotients of adjacent sampling points. The first and third channel state information are multiplied by their conjugates to obtain the second path component; the first and third path state information are multiplied by their conjugates to obtain the third path component; the first, second, and third path components are filtered to remove static components and amplitude-adjusted to remove interference terms, and the processed results are still recorded as the first, second, and third path components; the path estimation module 504 is used to input the first, second, and third path components and the reference Doppler frequency shift component into a preset propagation path estimation model to perform path estimation and obtain the target propagation path; the parameters of the target propagation path include the signal Doppler frequency shift, signal flight time, and signal angle of arrival, and the signal Doppler frequency shift is the same as the reference Doppler frequency shift component; the positioning module 505 is used to perform positioning based on the signal Doppler frequency shift, signal flight time, and signal angle of arrival to obtain the positioning result.

[0210] It should be noted that the specific implementation of this tracking and positioning device is basically the same as the specific implementation of the tracking and positioning method described above, and will not be repeated here.

[0211] This application also provides an electronic device, which includes: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for communication between the processor and the memory. When the program is executed by the processor, it implements the aforementioned tracking and positioning method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0212] Please see Figure 11 , Figure 11 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0213] The processor 601 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0214] The memory 602 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 602 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 602 and is called and executed by the processor 601 using the tracking and positioning method of the embodiments of this application.

[0215] The input / output interface 603 is used to implement information input and output;

[0216] The communication interface 604 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, Wi-Fi, Bluetooth, etc.).

[0217] Bus 605 transmits information between various components of the device (e.g., processor 601, memory 602, input / output interface 603, and communication interface 604);

[0218] The processor 601, memory 602, input / output interface 603, and communication interface 604 are connected to each other within the device via bus 605.

[0219] This application embodiment also provides a storage medium, which is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, which can be executed by one or more processors to implement the above-described tracking and positioning method.

[0220] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0221] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0222] It will be understood by those skilled in the art that Figures 4-6 and Figure 8 The technical solutions shown do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0223] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0224] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0225] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0226] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0227] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0228] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0229] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method of tracking positioning, characterized by, The method comprises: obtaining channel state information data; wherein the channel state information data comprises first channel state information of a first antenna, second channel state information of a second antenna and third channel state information of a third antenna; wherein the first antenna, the second antenna and the third antenna are antennas located on the same receiver; calculating the quotient of the first channel state information and the second channel state information to obtain a channel state quotient, and calculating the Doppler shift according to the two channel state quotients to obtain a reference Doppler shift component; dividing the first channel state information into a dynamic part and a static part, dividing the second channel state information into a dynamic part and a static part, and dividing the third channel state information into a dynamic part and a static part; multiplying the first channel state information and the first channel state information to obtain a first path component; multiplying the first channel state information and the second channel state information to obtain a second path component; and multiplying the first channel state information and the third channel state information to obtain a third path component; the first path component, the second path component and the third path component are processed by filtering to remove the static part and by amplitude adjustment to remove the interference term, and the processing result is still recorded as the first path component, the second path component and the third path component; inputting the first path component, the second path component, the third path component and the reference Doppler shift component into a preset propagation path estimation model for path estimation to obtain a target propagation path; the parameters of the target propagation path include a signal Doppler shift, a signal time of flight and a signal angle of arrival, and the signal Doppler shift is the same as the reference Doppler shift component; positioning according to the signal Doppler shift, the signal time of flight and the signal angle of arrival to obtain a positioning result.

2. The method of claim 1, wherein, Positioning according to the signal Doppler shift, the signal time of flight and the signal angle of arrival to obtain a positioning result, comprising: obtaining a target motion speed according to the signal Doppler shift; obtaining a target propagation distance according to the target motion speed and the signal time of flight; determining a target position according to the target propagation distance and the signal angle of arrival, and obtaining the positioning result according to the target position.

3. The method of claim 2, wherein, Determining a target position according to the target propagation distance and the signal angle of arrival, comprising: constructing an observation vector of an unscented Kalman filter according to the target propagation distance, the target motion speed and the signal angle of arrival to obtain a first reference position component; obtaining a second reference position component according to a preset initial object position, a preset speed and a preset state equation; calibrating the position according to the first reference position component and the second reference position component to obtain the target position.

4. The method of claim 2, wherein, Obtaining a target propagation distance according to the target motion speed and the signal time of flight, comprising: obtaining a deviated time of flight according to the signal Doppler shift and a preset sampling time interval; obtaining a reference time of flight according to a sum of the deviated time of flight and a preset initial time of flight; obtaining a fused time of flight according to a weighted sum of the signal time of flight and the reference time of flight; obtaining the target propagation distance according to the target motion speed and the fused time of flight.

5. The method of claim 4, wherein, Before the step of obtaining the reference time of flight according to the sum of the deviated time of flight and the preset initial time of flight, the method further comprises: setting the preset initial time of flight, specifically comprising: inputting the first path component, the second path component, the third path component and the reference Doppler shift component at the initial moment into the propagation path estimation model for time of flight estimation to obtain a time of flight at the initial moment; obtaining the preset initial time of flight according to the time of flight at the initial moment.

6. The method according to any one of claims 1 to 5, characterized in that, inputting the first path component, the second path component, the third path component and the reference Doppler shift component into a preset propagation path estimation model for path estimation to obtain a target propagation path, comprising: inputting the first path component, the second path component, the third path component and the reference Doppler shift component into a preset propagation path estimation model for path estimation to obtain a candidate path parameter set; the candidate path parameter set comprises a plurality of path parameters, and the plurality of path parameters are used to represent path parameters at different moments of a plurality of candidate paths; performing path matching on a plurality of the path parameters according to a preset graph path matching algorithm to obtain a candidate path set; the candidate path set comprises a plurality of the candidate paths, and each candidate path comprises path parameters at different moments; performing screening on a plurality of the candidate paths according to path attenuation to obtain the target propagation path.

7. The method of claim 6, wherein, The step of performing path matching on a plurality of the path parameters according to a preset graph path matching algorithm to obtain a candidate path set, comprising: constructing a weighted multi-partite graph according to a plurality of the path parameters; each vertex of the weighted multi-partite graph is used to indicate the path parameter at the different moment, and the weight of an edge is used to indicate the distance between two path parameters; comparing the weights of a plurality of the edges to determine the matching result of each vertex and the edge; constructing a path complete graph according to the matching result, and obtaining each candidate path according to each path complete graph; merging the candidate paths to obtain the candidate path set.

8. A tracking and positioning device, characterized by The device comprises: a data acquisition module configured to acquire channel state information data; wherein the channel state information data comprises first channel state information of a first antenna, second channel state information of a second antenna and third channel state information of a third antenna; wherein the first antenna, the second antenna and the third antenna are antennas located on the same receiver; a shift component determination module configured to calculate a quotient of the first channel state information and the second channel state information to obtain channel state quotients, calculate a Doppler shift according to two channel state quotients, and obtain a reference Doppler shift component; The path component determination module is configured to divide the first channel state information into a dynamic part and a static part, divide the second channel state information into a dynamic part and a static part, and divide the third channel state information into a dynamic part and a static part; multiply the first channel state information and the first channel state information to obtain a first path component; multiply the first channel state information and the second channel state information to obtain a second path component; and multiply the first channel state information and the third channel state information to obtain a third path component; the first path component, the second path component, and the third path component are filtered to remove the static part, and are amplitude-adjusted to remove interference terms, and the processing results are still recorded as the first path component, the second path component, and the third path component; The path estimation module is configured to input the first path component, the second path component, the third path component, and the reference Doppler shift component into a preset propagation path estimation model to perform path estimation, and obtain a target propagation path; parameters of the target propagation path include a signal Doppler shift, a signal time of flight, and a signal angle of arrival, and the signal Doppler shift is the same as the reference Doppler shift component; The positioning module is configured to perform positioning according to the signal Doppler shift, the signal time of flight, and the signal angle of arrival, and obtain a positioning result.

9. An electronic device, comprising: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method in any one of claims 1 to 7 when executing the computer program.

10. A computer readable storage medium, the storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method in any one of claims 1 to 7.

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