Fusion positioning method, terminal and storage medium

Abstract

The application provides a fusion positioning method, a terminal and a storage medium. The method comprises the following steps: at least one IRS is arranged at an obstacle or a missing base station; a terminal establishes a connection with a MEC server through a base station, and sends a Beidou positioning request to the MEC server; the terminal receives Beidou positioning information sent by the MEC server through the base station and the IRS; the terminal performs fusion positioning according to the Beidou positioning information and the IRS, and determines the position of the terminal. The application can realize accurate positioning in the case that base stations are unevenly distributed or are blocked by obstacles, and is conducive to the safe implementation of inspection and dispatch command in the case of obstacle blocking.

Description

Technical Field

[0001] This invention relates to the field of terminal device positioning, and more particularly to a fusion positioning method, terminal, and storage medium. Background Technology

[0002] Currently, there are various positioning methods for terminal devices, mainly including the Global Positioning System (GPS), Galileo Satellite Navigation System, GLONASS, BeiDou Satellite Navigation System (BDS), base station positioning, Wi-Fi hotspot positioning, and IP positioning. Among them, the BeiDou Satellite Navigation System can provide relatively reliable positioning information in open outdoor areas. In particular, BeiDou's enhanced regional differential technology, Real-Time Kinematic (RTK) technology, and wide-area enhancement technology can achieve high-precision positioning at the meter, sub-meter, and even centimeter levels based on BeiDou signals. However, due to the significant external interference affecting satellite signals, it is difficult to meet the requirements for indoor positioning and positioning in complex areas such as outdoor areas with obstructions.

[0003] With the advent of the 5G era, the multi-antenna, high-bandwidth, high-density base station deployment, high-speed, and low-latency characteristics of 5G systems provide strong support for communication and positioning. Combining the BeiDou positioning system with the 5G system allows for different positioning methods. In obstructed areas, 5G base stations measure the wireless signal characteristics of user terminals, and through collaboration between base stations and edge computing, calculate the terminal's location. In open areas, BeiDou positioning meets positioning requirements. This BeiDou + 5G fusion positioning system creates a seamless indoor and outdoor positioning system, improving positioning accuracy and efficiency while addressing the problem of severe satellite signal attenuation.

[0004] However, the inventors discovered that although the combination of BeiDou and 5G has significantly improved positioning accuracy and efficiency, it is impossible to achieve accurate positioning when 5G base stations are unevenly distributed or there are many obstacles blocking the way, and there may even be problems with positioning. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a fusion positioning method, terminal, and storage medium, aiming to enhance the signal power of direct-path signals and solve the problems of insufficient number of base stations and signal attenuation caused by non-direct-path signals in the prior art.

[0006] The first aspect provides a fusion localization method, which sets up at least one IRS at the location of an obstacle or where a base station is missing, including:

[0007] A connection is established between the base station and the MEC server, and a BeiDou positioning request is sent to the MEC server.

[0008] Receive BeiDou positioning information sent by the MEC server through the base station and the IRS;

[0009] The terminal's location is determined by fusing the BeiDou positioning information and the IRS.

[0010] In one possible implementation, the BeiDou positioning information is BeiDou differential data information;

[0011] The process by which the MEC server acquires the BeiDou differential data information includes:

[0012] A carrier phase double-difference model was established based on BeiDou satellites, ground observation stations, and terminals.

[0013] Obtain RTK positioning information and determine a state parameter vector based on the RTK positioning information;

[0014] The carrier vector is determined based on the carrier phase double-difference measurement and the double-difference pseudorange observation.

[0015] Based on the state parameter vector and the carrier vector, determine the state parameter vector at any given time and the covariance matrix corresponding to the state parameters;

[0016] Based on the state parameter vector and the covariance matrix corresponding to the state parameters, the carrier phase double-difference model is solved to determine the floating-point solution of the carrier phase.

[0017] Perform a space transformation on the floating-point solution and the covariance matrix to determine the objective function;

[0018] The objective function is calculated in the new space to obtain a fixed solution;

[0019] The carrier phase double-difference model is updated based on the fixed solution to determine the BeiDou differential data information.

[0020] In one possible implementation, the calculation of the objective function in the new space to obtain a fixed solution includes:

[0021] Based on the self-feedback factor, the objective function is calculated using the Cauchy mutation flock optimization algorithm to obtain the fixed solution. During the calculation process, each time the Cauchy mutation flock optimization algorithm reaches its optimal value, the self-feedback factor is updated, and the fixed solution is determined again using the current rooster position as the initial position, until the self-feedback factor is zero.

[0022] In one possible implementation, the step of calculating the objective function using a Cauchy mutation-based chicken flock optimization algorithm based on a self-feedback factor to obtain a fixed solution includes:

[0023] Set relevant parameters for the chicken flock optimization algorithm, including flock size, the ratio of roosters, hens, and chicks in the flock, number of iterations, iteration threshold, and self-feedback factor;

[0024] In the new space, the initial positions of all individuals among the rooster, hen, and chicks are randomly generated;

[0025] The rooster, hen, and chicks search for food in the new space, and the positions of the rooster, hen, and chicks are updated using the first method. The current first fitness value corresponding to the updated position is then calculated.

[0026] Detect whether the current first fitness value is less than the fitness value calculated in the previous iteration;

[0027] If the current first fitness value is less than the fitness value calculated in the previous iteration, the current iteration number is incremented by 1, and the process jumps to the step of "updating the positions of the rooster, hen, and chick by searching for food in the new space using the first method".

[0028] When the current fitness value is not less than the fitness value calculated in the previous iteration, the self-feedback factor is reduced, the current iteration number is incremented by 1, and the current positions of the rooster, hen, and chick are used as the initial positions. The rooster, hen, and chick search for food in the new space, and the positions of the rooster, hen, and chick are updated using the second method. The current second fitness value corresponding to the updated position is calculated. When the second fitness value is less than the fitness value calculated in the previous iteration, the process jumps to the step of "the rooster, hen, and chick search for food in the new space and update the positions of the rooster, hen, and chick using the second method". When the second fitness value is not less than the fitness value calculated in the previous iteration, the process jumps to the step of "the rooster, hen, and chick search for food in the new space and update the positions of the rooster, hen, and chick using the first method".

[0029] When the fitness value remains unchanged during a preset number of iterations, if the number of iterations is greater than or equal to the iteration threshold, the iteration is stopped, the optimal solution is determined, and the optimal solution is determined as the fixed solution of the objective function.

[0030] In one possible implementation, receiving the BeiDou positioning information sent by the MEC server through the base station and the IRS includes:

[0031] Obtain the phase control matrix;

[0032] Based on the phase control matrix, the first positioning information received through the IRS is determined;

[0033] Determine the second location information received through the base station;

[0034] The BeiDou positioning information is determined based on the first positioning information and the second positioning information.

[0035] In one possible implementation, obtaining the phase control matrix includes:

[0036] Acquire the received information for each snapshot corresponding to each scanning angle of the IRS reflective surface;

[0037] Based on the received information of each snapshot corresponding to each scanning angle, determine the energy of each snapshot corresponding to each scanning angle;

[0038] The energy of each scanning angle is determined based on the energy of each snapshot corresponding to each scanning angle;

[0039] Based on the energy at each scanning angle, an estimated value for the scanning angle is determined:

[0040] The phase control matrix is ​​determined based on the estimated value of the scanning angle.

[0041] In one possible implementation, determining the first positioning information received via the IRS based on the phase control matrix includes:

[0042] according to Determine the first location information received through the IRS;

[0043] Among them, H B,R,M ]n[ is the first location information received from the base station via the IRS, H B,R [n] represents the first positioning information transmitted from the base station to the IRS, H R,M [n] represents the first location information received from the IRS transmission. The phase control matrix is ​​L, where L represents IRS;

[0044] τ represents the spatial loss of the subcarrier during the transmission of the first positioning information from the base station to the IRS. B,R α is the time it takes for the first location information to be transmitted from the base station to the IRS. r Let α be the antenna array response vector. t This is the antenna array steering vector. Let be the transpose conjugate matrix of the antenna array steering vector. θ is the angle between the transmission channel from the base station to the IRS corresponding to the first positioning information and the base station. B,R The angle between the transmission channel from the base station to the IRS corresponding to the first positioning information and the IRS, j is an imaginary unit, representing that the first positioning information is a complex exponential signal, N is the number of equally spaced sampling points of the first positioning information, and n is the nth sampling point corresponding to the first positioning information.

[0045] τ represents the spatial loss of the subcarrier during the transmission of the first positioning information from the IRS. R,M α is the time it takes to receive the first location information transmitted from the IRS. r Let α be the antenna array response vector. t This is the antenna array steering vector. Let be the transpose conjugate matrix of the antenna array steering vector. θ is the angle between the transmission channel of the receiving IRS corresponding to the first positioning information and the IRS. R,M The angle between the transmission channel of the receiving IRS corresponding to the first positioning information and the terminal;

[0046] The determination of the second location information received through the base station includes:

[0047] according to Determine the second location information received through the base station;

[0048] Among them, H B,M [n] represents the second positioning information received from the base station. τ represents the spatial loss of the subcarrier during the transmission of the second positioning information. B,M The time for receiving the second positioning information, B is the total bandwidth of all subcarriers, and a r Let α be the antenna array response vector. t This is the antenna array steering vector. Let be the transpose conjugate matrix of the antenna array steering vector. θ is the angle between the transmission channel corresponding to the second positioning information and the base station. B,M The angle between the transmission channel corresponding to the second positioning information and the terminal is denoted by j, which is an imaginary unit, representing that the second positioning information is a complex exponential signal. N is the number of equally spaced sampling points of the second positioning information, and n is the nth sampling point corresponding to the second positioning information.

[0049] The BeiDou positioning information is determined based on the first positioning information and the second positioning information, including:

[0050] according to Determine BeiDou positioning information;

[0051] Where y[n] is the BeiDou positioning information, P is the transmission power of the positioning information, and F is the transmission power of the BeiDou positioning information. X[n] Let H[n] be the beamforming matrix for transmitting the positioning information, n[n] be the additive white Gaussian noise in the transmission space, and H[n] be the positioning information for transmission.

[0052] In one possible implementation, the step of determining the terminal location by fusing positioning based on the BeiDou positioning information and the IRS includes:

[0053] The BeiDou positioning information received from the base station via the IRS is determined. The BeiDou positioning information includes the time of transmission of the first positioning information from the base station to the IRS, the angle between the transmission channel of the base station to the IRS corresponding to the first positioning information and the IRS, the spatial loss of the subcarrier during the transmission of the first positioning information from the base station to the IRS, the time of receiving the first positioning information from the IRS, the angle between the transmission channel of the receiving IRS corresponding to the first positioning information and the terminal, and the spatial loss of the subcarrier during the transmission of the first positioning information from the IRS.

[0054] The channel parameters are determined based on the transmission time of the first positioning information from the base station to the IRS, the angle between the transmission channel of the first positioning information from the base station to the IRS and the IRS, the spatial loss of the subcarrier during the transmission of the first positioning information from the base station to the IRS, the transmission time of the first positioning information from the IRS, the angle between the transmission channel of the first positioning information from the receiving IRS and the terminal, and the spatial loss of the subcarrier during the transmission of the first positioning information from the IRS.

[0055] The channel parameters are estimated to obtain estimated channel parameter values;

[0056] Based on the estimated channel parameters, the location of the virtual base station corresponding to the IRS is obtained;

[0057] The information reception time for receiving the BeiDou positioning information is determined based on the time it takes for the first positioning information to be transmitted from the base station to the IRS and the time it takes to receive the first positioning information from the IRS.

[0058] Based on the information reception time, information transmission speed, the location of the virtual base station of the corresponding IRS, and the coordinates of the base station, the distance from the terminal to each IRS is determined;

[0059] Using the coordinates of each IRS as the origin and the distance from the terminal to each IRS as the radius, the range of each circle is determined respectively;

[0060] The target area is defined by identifying all areas where circles intersect.

[0061] The target area is solved to determine the terminal location.

[0062] In a second aspect, embodiments of the present invention provide a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the fusion positioning method as described in the first aspect or any possible implementation thereof.

[0063] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the fusion positioning method as described in the first aspect or any possible implementation thereof.

[0064] This invention provides a fusion positioning method, terminal, and storage medium. By setting up at least one IRS (Infrared Reception Stream) at obstacles or where base stations are missing, the IRS acts as a virtual base station to achieve accurate positioning. During the transmission of BeiDou positioning information, the IRS is set up where the transmission channel is blocked or where base stations are missing. By adjusting the phase or amplitude parameters of the IRS unit, a reflection path is established between the transceiver node and the IRS, allowing BeiDou positioning information to be transmitted to the receiving node via reflection from the IRS. This ensures reliable information transmission and enables target positioning even under line-of-sight obstruction. It also facilitates safe inspection, dispatch, and command operations under obstruction conditions. Through reasonable IRS deployment, IRS can replace base stations for information transmission, improving transmission power and addressing issues such as insufficient base station numbers and signal attenuation caused by non-direct paths. This achieves cost savings and reduced latency, ensuring secure transmission of positioning information. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a flowchart illustrating the implementation of the fusion positioning method provided in this embodiment of the invention.

[0067] Figure 2 This is a schematic diagram of the fusion positioning system provided in an embodiment of the present invention;

[0068] Figure 3This is a schematic diagram of BeiDou positioning information in the fusion positioning method provided in this embodiment of the invention;

[0069] Figure 4(a) is a schematic diagram of the number of iterations in the solution process using the traditional chicken flock optimization algorithm;

[0070] Figure 4(b) is a schematic diagram of the number of iterations in the solution process of the improved chicken flock optimization algorithm used in the fusion localization method provided in the embodiment of the present invention;

[0071] Figure 5 The above are simulation results of the fusion positioning method provided in this embodiment of the invention, and the error diagrams obtained in the three directions of east, north and vertical.

[0072] Figure 6 This is a schematic diagram of the terminal provided in an embodiment of the present invention. Detailed Implementation

[0073] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0074] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0075] In power scenarios such as substations and power plants, ensuring the safety of transmission and distribution lines and their equipment is of paramount importance. To facilitate dispatch and command and better respond to power emergencies, it is necessary to know the precise location information of the terminals in order to obtain real-time data on the status of the surrounding power facilities or equipment. Based on this, a fusion positioning method is proposed.

[0076] Figure 1 For a flowchart illustrating the implementation of the fusion positioning method provided in this embodiment of the invention, please refer to [link / reference]. Figure 2The fusion positioning system includes at least one Intelligent Reflection Surface (IRS) placed at obstacles or where base stations are missing, as well as: BeiDou satellites, ground observation stations, Mobile Edge Computing (MEC) servers, base stations, and terminals. Ground observation stations can continuously track BeiDou satellite signals over long periods, providing data support for satellite orbit determination, atmospheric inversion, and surface displacement monitoring. Especially in high-dynamic, high-precision positioning applications, they can provide terminals with fast and effective differential data, achieving centimeter-level positioning. MEC is a distributed computing approach based on mobile communication networks, extending cloud computing functionality at the network edge. By decoupling certain network services and functions from the core network, it achieves goals such as cost savings, reduced latency and round-trip time, optimized traffic, enhanced physical security, and improved caching efficiency. The fusion positioning method based on the fusion positioning system is detailed below:

[0077] Step 101: Establish a connection with the MEC server through the base station and send a BeiDou positioning request to the MEC server.

[0078] The MEC server receives BeiDou positioning requests and obtains BeiDou differential data information.

[0079] BeiDou differential data information is obtained through RTK technology. RTK uses the difference between the carrier phase observed by ground observation stations and terminals for positioning. However, due to the periodicity of the carrier phase, the receiver can only observe the fractional part of the phase, while the integer part of the phase is unknown. To obtain the true phase, it is necessary to solve the integer ambiguity.

[0080] The MEC server migrates intensive computing tasks for BeiDou differential data to nearby network edge servers, minimizing data backhaul to the cloud. This reduces waiting time and network costs associated with data travel to and from the cloud, and decreases the use of the core network and transmission network. The core network's main function is to route received call or data requests to different networks, while the transmission network serves as the transmission channel. This reduces congestion and burden on the core and transmission networks, alleviates network bandwidth pressure, achieves low latency, and enables real-time and accurate acquisition of base station IDs and user location information, thus optimizing the user experience of satellite navigation.

[0081] The process of obtaining BeiDou differential information is as follows:

[0082] A carrier phase double-difference model was established based on BeiDou satellites, ground observation stations, and terminals.

[0083]

[0084] in, These are carrier phase double-difference measurements, where k and s represent the two observed BeiDou satellites, A is the ground observation station, and B is the terminal. These are the double-difference pseudorange observations from the two BeiDou satellites and the ground observation station and terminal, respectively. Indicates error. Indicates the ambiguity of an integer.

[0085] Obtain RTK positioning information and determine the state parameter vector based on the RTK positioning information;

[0086]

[0087] Where x represents the state parameter vector, containing all the information required for RTK positioning, r represents the receiver's antenna position vector, V represents the velocity of the dynamic user, and B represents the corresponding single carrier L. i The integer ambiguity of the single-difference carrier phase in the frequency band, where T represents transpose.

[0088] The carrier vector is determined based on the carrier phase double-difference measurement and the double-difference pseudorange observation.

[0089]

[0090] Where y represents the carrier vector for carrier phase measurement, ρ represents the carrier phase double-difference measurement value, and ρ represents the double-difference pseudorange measurement values ​​of the two Beidou satellites and the ground observation station and terminal, respectively.

[0091] Based on the state parameter vector and the carrier vector, the extended Kalman filter algorithm is used to obtain the update of the state of the parameter to be estimated, and to determine the state parameter vector and the covariance matrix corresponding to the state parameter at any time.

[0092] x k (+)=x k x k (+)(y k -h((x k (-)));

[0093] Q k (+)=(1-K k H(x k (-)))Q k (-);

[0094]

[0095] Where, x k For t k The state parameter vector at time t, Q k For x k The corresponding covariance matrix, y kIt is the carrier observation phase vector, K k This belongs to the extended Kalman filter gain, where h((x0) represents the measurement function, H(x) is the partial derivative of h((x), and R... k Let represent the covariance matrix corresponding to the measurement residual vector. When the system is updated, (-) and (+) represent the measurement states of the system before and after the update, respectively, and k represents the solution obtained using the Kalman filter algorithm.

[0096] Based on the state parameter vector and the covariance matrix corresponding to the state parameters, the carrier phase double-difference model is solved to determine the floating-point solution of the carrier phase.

[0097] according to Determine the transformation relationship of the parameters, transform from the original space to the new space, and determine the objective function for the search.

[0098]

[0099] Let Z represent the objective function in the new space search, Z denote the Z-transform of the discrete sequence. The Z-transform is a mathematical transformation of the discrete sequence, which can transform the discrete time series into an expression in the complex frequency domain, and can transform the difference equation into an algebraic equation. N is the integer ambiguity corresponding to the original space. 0 It is the floating-point solution of the integer ambiguity calculated in the original space using the extended Kalman filter algorithm, Q N Let covariance be the original space. It is the integer ambiguity corresponding to the new space. It is the floating-point solution of integer ambiguity used for searching the new space. It is the covariance matrix corresponding to the new space. Min determines the size of the hyperellipsoid. This determines the shape of the new fuzzy space.

[0100] After the Z-transform, the parameters are transformed from the original space to a new space, and the correlation between the parameters is reduced, so as to quickly search for integer ambiguity combinations. At the same time, the variance of the ambiguity parameters after the transformation is also reduced, thereby improving the positioning accuracy.

[0101] The objective function is calculated within the defined new space to obtain a fixed solution.

[0102] The process of calculating the objective function using the improved chicken flock optimization algorithm within the defined new space is as follows:

[0103] Set up parameters for flock optimization, including flock size, the ratio of roosters, hens, and chicks in the flock, number of iterations, iteration threshold, and self-feedback factor.

[0104] In a flock of chickens, competition exists among different chickens when searching for food. Roosters are the most capable at searching for food and are therefore dominant, with a low fitness value. Hens are next, closely following the roosters in searching for food. Some hens also lead their chicks. Chicks are the least capable at searching for food, searching only around the hens, and have the highest fitness value, thus achieving a local search function. The flock is grouped according to the number of roosters, with each group consisting of one rooster, some hens, and chicks. The number of roosters is the number of groups. The fitness value is the value of the objective function substituted into the current position.

[0105] In the new space, the initial positions of all individuals among the roosters, hens, and chicks are randomly generated; the flock is sorted and ranked according to fitness values, and the roosters are determined to be among the top N based on the proportion of roosters, hens, and chicks in the flock. R There are individual entities, with the chick being the last N. C There are 1 individual chickens, the rest are hens, and the flock is divided into N groups according to the roosters. R There are N groups. Hens are randomly assigned to each group. The partnership between roosters and hens is determined. N groups are randomly selected. M There is one hen, who randomly leads the chicks, and the mother-child relationship between the hen and the chicks is determined.

[0106] The rooster, hen, and chicks search for food in the new space, and the positions of the rooster, hen, and chicks are updated respectively using the first method.

[0107] In the (t+1)th iteration, the position of the i-th rooster is:

[0108] X i (t+1)=X i (t)[1+rand(0,σ 2 )];

[0109] Among them, X i (t+1) represents the position of the i-th rooster, X i (t) represents the position of the i-th rooster at time t;

[0110] The position corresponding to the i-th hen is:

[0111] X i (t+1)=X i (t)+C1×rand[X1(t)-X i (t)]+C2×rand[X2(t)-X i (t)];

[0112] Among them, X i (t+1) represents the position of the i-th hen, X i X1(t) is the position of the i-th hen at time t, X2(t) is the position of the rooster in the same group as hen i at time t, and X3(t) is the position of the other hens and chicks of hen i at time t.

[0113] The position corresponding to the i-th chick is:

[0114] X i (t+1)=X i (t)+FL×rand[X m (t)-X i (t)];

[0115] Among them, X i (t+1) represents the position of the i-th chick, X i (t) represents the position of the i-th chick at time t, X m (t) represents the position of the hen that the chicks are following, and F and L are parameters between (0, 2), indicating that the chicks are following the hen to forage;

[0116] Calculate the current first fitness value corresponding to the updated position;

[0117] Check if the current fitness value is less than the fitness value calculated in the previous iteration;

[0118] If the current fitness value is less than the fitness value calculated in the previous iteration, it means that the current position may not be the optimal position, that is, the optimal solution has not been found and it is necessary to continue to search for the optimal position. In this case, the current iteration number is incremented by 1, and the process jumps to the step of "updating the positions of the rooster, hen and chick by searching for food in the new space using the first method" to continue updating the positions of the rooster, hen and chick in order to determine the optimal position of the rooster.

[0119] When the current fitness value is not less than the fitness value calculated in the previous iteration, it indicates that the current position may be the optimal position or it may be trapped in a local optimum. It is necessary to continue to find the optimal position to determine it. Therefore, the self-feedback factor is reduced, the current iteration number is incremented by 1, and the current positions of the rooster, hen and chick are used as the initial positions. The rooster, hen and chick search for food in the new space, and the positions of the rooster, hen and chick are updated using the second method.

[0120] Since rand(0, σ) 2 The rooster follows a normal distribution and tends to focus on searching its own local area, easily ignoring other areas and getting stuck in a local optimum. However, the Cauchy mutation process can generate random numbers with a wider and broader distribution. Therefore, a self-feedback factor is introduced. At the beginning of the iteration, the rooster first undergoes Gaussian mutation. After a certain number of iterations, the position is updated but the fitness value remains unchanged. It is determined that a local optimum may have occurred, so the number of self-feedback factors is reduced, causing the rooster to look for other paths and simultaneously initiating the Cauchy mutation process.

[0121] In the (t+n+1)th iteration, the position of the i-th rooster is:

[0122] X i (t+n+1)=X i (t+n)[1+candn(0,σ 2 )];

[0123] Among them, X i (t+n+1) represents the position of the i-th rooster, X i (t+n) represents the position of the i-th rooster in the (t+n)-th iteration;

[0124] The position corresponding to the i-th hen is:

[0125] X i (t+n+1)

[0126] =X i (t+n)+C1×rand[X1(t+n)-X i (t+n)]+C2×rand[X2(t+n)-X i (t+n)]

[0127] Among them, X i (t+n+1) represents the position of the i-th hen, X i (t+n) represents the position of the i-th hen in iteration t+n, X1(t+n) represents the position of the rooster in the same group as hen i in iteration t+n, and X2(t) represents the positions of the other hens and chicks of hen i in iteration t+n; the position of the i-th chick is:

[0128] X i (t+n+1)=X i (t+n)+FL×rand[X m (t+n)-X i (t+n)];

[0129] Among them, X i (t+n+1) represents the position of the i-th chick, X i (t+n) represents the position of the i-th chick in the (t+n)-th iteration, X m (t+n) is the position of the hen that the chicks are following, and F and L are parameters between (0, 2), indicating that the chicks are following the hen to forage;

[0130] Calculate the current second fitness value corresponding to the updated position. If the second fitness value is less than the fitness value calculated in the previous iteration, jump to the step of "searching for food in the fuzzy space by roosters, hens, and chicks and updating the positions of roosters, hens, and chicks using the second method". If the second fitness value is not less than the fitness value calculated in the previous iteration, jump to the step of "searching for food in the new space by roosters, hens, and chicks and updating the positions of roosters, hens, and chicks using the first method", until the optimal solution is determined.

[0131] If the fitness value remains unchanged during a preset number of iterations, and the number of iterations is greater than or equal to the iteration threshold, then the iteration is stopped, the optimal solution is determined, and the optimal solution is set as the fixed solution of the objective function.

[0132] By setting an iteration threshold, if the fitness value remains constant near the preset number of iterations, it indicates that the rooster is not trapped in a local optimum but has found the optimal position, thus allowing the iteration process to end early. If the fitness value continues to change, the iteration continues to search for the optimal solution until the fitness value remains constant or the preset number of iterations is reached, at which point the iteration stops, and the optimal solution is determined. Setting an iteration threshold facilitates the rapid determination of the optimal solution, reduces unnecessary iterations and iteration processes, and ensures a quick and accurate determination of the optimal solution.

[0133] The process of finding the fixed solution for integer ambiguity is as follows: using the improved chicken flocking optimization algorithm described above, find the integer solution that minimizes the objective function in the new space, and substitute it into the original space through the inverse transformation of the Z-transform relationship described above, and then obtain the fixed solution for integer ambiguity.

[0134] The carrier phase double-difference model is updated based on the fixed solution to determine the BeiDou differential data information.

[0135]

[0136] The above is the updated carrier phase double-difference model, where N F As a fixed solution for integer ambiguity, the BeiDou differential data information is determined based on the updated carrier phase double-difference model.

[0137] Step 102: Receive BeiDou positioning information sent by the MEC server through the base station and IRS.

[0138] A key advantage of IRS is its ability to customize wireless channels. When the signal transmission path is blocked, a reflection path can be established between the transmitting and receiving nodes by setting the phase or amplitude parameters of the IRS unit, thereby ensuring reliable information transmission and enabling target positioning under line-of-sight obstruction. In open environments where the line-of-sight path is unobstructed, the reflection path established by the IRS can also improve channel gain, thereby enhancing the system's information transmission capability and improving the accuracy of parameter estimation.

[0139] Obtain the phase control matrix;

[0140]

[0141]

[0142] in, For the designed phase control matrix, For the ideal phase control matrix, N R α is the number of units contained in the IRS. r Let θ be the antenna array response vector. B,R The angle between the base station to IRS transmission channel corresponding to the first location information and the IRS. L represents the angle between the transmission channel of the base station receiving the IRS corresponding to the first positioning information and the IRS.

[0143] Acquire the received information for each snapshot corresponding to each scanning angle of the IRS reflective surface;

[0144] Based on the received information of each snapshot corresponding to each scanning angle, determine the energy of each snapshot corresponding to each scanning angle;

[0145]

[0146] Assuming the scanning angle range is [-90, 90] degrees, the angle interval is t, and there are a total of N... i Each angle is used to adjust the corresponding phase control matrix, and the number of snapshots is set to N. k Obtain the positioning information y corresponding to each angle. L [k] i Where k = 1, 2, ..., N k i = 1, 2, ..., N i y L [k] i P represents the positioning information of the target node corresponding to the k-th snapshot at the i-th scanning angle. L [k] i The energy of the k-th snapshot corresponding to the i-th scanning angle;

[0147] The energy of each scanning angle is determined based on the energy of each snapshot corresponding to each scanning angle;

[0148]

[0149] Among them, P L The vector of energy for each scanning angle, P L [n]1 represents the energy of the nth snapshot corresponding to the first scanning angle. For the Nth i Each scanning angle corresponds to the energy of the nth snapshot;

[0150] Based on the energy at each scanning angle, an estimated value for the scanning angle is determined, including:

[0151] The scanning angle corresponding to the peak value of the energy at all scanning angles is selected as the estimated value of the scanning angle;

[0152]

[0153] in, This is an estimate of the scanning angle for the selected IRS;

[0154] Based on the estimated value of the scanning angle, the angle between the transmission channel of the terminal receiving the IRS corresponding to the first positioning information and the IRS is determined and recorded as the estimated value of the departure angle.

[0155] The phase control matrix is ​​determined based on the estimated values ​​of the scanning angle and the departure angle.

[0156]

[0157]

[0158] in, For the estimated phase control matrix, This is an estimate of the departure angle. Although the phase control matrix is ​​not ideal at this point, the transmitted positioning information contains the target node's position parameters, so using this positioning information as subsequent data for processing is reasonable and feasible.

[0159] In the actual location information transmission process, there is second location information transmitted via direct path, that is, the terminal directly receives the location information sent by the base station. There is also first location information transmitted via non-direct path due to obstruction by obstacles such as buildings, that is, the terminal receives the location information sent by the base station and forwarded by the IRS. In other words, the location information transmission path is base station—IRS—terminal. Therefore, we need to consider the coexistence of both possibilities.

[0160] Based on the estimated phase control matrix described above, the first positioning information received by the terminal via the IRS is determined, including:

[0161] according to Determine the first location information received via the IRS;

[0162] Among them, H B,R,M [n] represents the first location information received from the base station via the IRS, H B,R [n] represents the first location information transmitted from the base station to the IRS, HR,M [n] represents the first location information received by the terminal from the IRS transmission. This is the phase control matrix, where L represents the IRS;

[0163] τ represents the spatial loss of the subcarrier during the transmission of the first positioning information from the base station to the IRS. B,R α is the time it takes for the first location information to be transmitted from the base station to the IRS. r Let α be the antenna array response vector. t This is the antenna array steering vector. Let be the transpose conjugate matrix of the antenna array steering vector. θ is the angle between the base station and the base station, representing the transmission channel from the base station to the IRS corresponding to the first positioning information. B,R , where j is the angle between the transmission channel from the base station to the IRS corresponding to the first positioning information and the IRS, j is the imaginary unit, representing that the first positioning information is a complex exponential signal, N is the number of equally spaced sampling points of the first positioning information, and n is the nth sampling point corresponding to the first positioning information;

[0164] τ represents the spatial loss of the subcarrier during the transmission of the first positioning information from the IRS. R,M α is the time it takes for the terminal to receive the first location information transmitted from the IRS. r Let α be the antenna array response vector. t This is the antenna array steering vector. Let be the transpose conjugate matrix of the antenna array steering vector. θ is the angle between the transmission channel of the terminal receiving the IRS corresponding to the first positioning information and the IRS. R,M The angle between the transmission channel of the IRS received by the terminal corresponding to the first positioning information and the terminal itself;

[0165] The second location information received via the base station is determined, including:

[0166] according to Determine the second location information received by the terminal through the base station;

[0167] Among them, H B,M [n] represents the second positioning information received by the terminal from the base station. τ represents the spatial loss of the subcarrier during the transmission of the second positioning information. B,M The time for the terminal to receive the second positioning information, B is the total bandwidth of all subcarriers, and α is the time for the terminal to receive the second positioning information. r Let α be the antenna array response vector. t This is the antenna array steering vector. Let be the transpose conjugate matrix of the antenna array steering vector. θ is the angle between the transmission channel corresponding to the second positioning information and the base station. B,M The angle between the transmission channel corresponding to the second positioning information and the terminal is denoted by j, which is an imaginary unit, representing that the second positioning information is a complex exponential signal. N is the number of equally spaced sampling points of the second positioning information, and n is the nth sampling point corresponding to the second positioning information.

[0168] Based on the first and second positioning information, the BeiDou positioning information is determined, including:

[0169] according to Determine BeiDou positioning information;

[0170] Where y[n] represents BeiDou positioning information, P represents the transmission power for transmitting positioning information, and F represents the transmission power for transmitting positioning information. X[n] The beamforming matrix is ​​used to transmit positioning information, n[n] is additive white Gaussian noise in the transmission space, and H[n] is the positioning information to be transmitted.

[0171] Step 103: Perform fusion positioning based on BeiDou positioning information and IRS to determine the terminal location.

[0172] The BeiDou positioning information received by the base station through the IRS is determined. The BeiDou positioning information includes the time of transmission of the first positioning information from the base station to the IRS, the angle between the transmission channel of the base station to the IRS corresponding to the first positioning information and the IRS, the spatial loss of the subcarrier during the transmission of the first positioning information from the base station to the IRS, the time of terminal receiving the first positioning information from the IRS, the angle between the transmission channel of the terminal receiving the first positioning information from the IRS and the terminal, and the spatial loss of the subcarrier during the transmission of the first positioning information from the IRS.

[0173] Based on the transmission time of the first positioning information from the base station to the IRS, the angle between the transmission channel of the first positioning information from the base station to the IRS and the IRS, the spatial loss of the subcarrier during the transmission of the first positioning information from the base station to the IRS, the time of the terminal receiving the first positioning information from the IRS, the angle between the transmission channel of the terminal receiving the first positioning information from the IRS and the terminal, and the spatial loss of the subcarrier during the transmission of the first positioning information from the IRS, the channel parameters are determined.

[0174]

[0175] Where η is the channel parameter, τ B,R θ represents the time it takes for the first location information to be transmitted from the base station to the IRS. B,R The angle between the base station to IRS transmission channel corresponding to the first location information and the IRS. τ represents the spatial loss of the subcarrier during the transmission of the first positioning information from the base station to the IRS. R,Mθ is the time it takes for the terminal to receive the first location information transmitted from the IRS. R,M The angle between the transmission channel of the IRS received by the terminal corresponding to the first positioning information and the terminal itself. This refers to the spatial loss of the subcarrier during the transmission of the first positioning information from the IRS;

[0176] Since the estimated phase control matrix has a certain error, compressed sensing technology is used to reconstruct information through an improved orthogonal matching pursuit algorithm to effectively estimate the channel parameters and obtain the estimated channel parameter values.

[0177]

[0178] in, Let η be the estimated value of the channel parameters, W be the channel parameters, and W be the estimation error.

[0179] Based on the estimated channel parameters, the location of the virtual base station for the corresponding IRS is obtained;

[0180]

[0181] in, The coordinates of the virtual base station location for the IRS. This refers to the virtual base station direction of the IRS.

[0182] The information reception time for receiving BeiDou positioning information is determined based on the time it takes for the first positioning information to be transmitted from the base station to the IRS and the time it takes for the first positioning information to be received from the IRS.

[0183]

[0184] Where τ represents the information reception time of BeiDou positioning information, τ B,R τ is the time it takes for the first location information to be transmitted from the base station to the IRS. R,M The time it takes to receive the first location information transmitted from the IRS. This is a constant value assumed due to noise instability;

[0185] The total distance traversed by the first location information is from the base station to the IRS virtual base station to the terminal, which satisfies:

[0186] τ=(‖mr‖2+‖u′ l -r‖2) / v;

[0187] Based on the information reception time, information transmission speed, the location of the virtual base station of the corresponding IRS, and the coordinates of the base station, determine the distance from the terminal to each IRS:

[0188]

[0189] in, Let v be the distance from the terminal to each IRS, v be the information transmission speed of BeiDou positioning information, m be the location coordinates of the terminal, and u′ be the distance from the terminal to each IRS. l Let r be the location coordinates of the base station, r be the location coordinates of the IRS virtual base station, and l be the IRS;

[0190] Using the coordinates of each IRS as the origin and the distance from the terminal to each IRS as the radius, the range of each circle is determined.

[0191] The target area is defined by identifying all the areas where circles intersect.

[0192] Solve the target area to determine the terminal location;

[0193] Based on the target region, index blocks are divided to determine the second-order cone problem and convex optimization problem, including:

[0194] Among them, the second-order cone problem is determined by iteratively approximating the target region based on the index block division;

[0195]

[0196] M∈C Q×L ;

[0197] Where M is a matrix, M ql Complex gain for each index block;

[0198] Based on the second-order cone problem, determine the convex optimization problem;

[0199] min H ‖M‖ 2,1 ;

[0200]

[0201]

[0202] Where M is the optimization variable, To reconstruct information, y L This is the sum of the location information corresponding to all index blocks. The actual information received, where ε is a defined threshold, represents the received information. and reconstructing information The problem of maximum mismatch constraints caused by noise. To receive the first location information transmitted from the IRS, i.e., the first location information transmitted from the IRS to the terminal, For the phase control matrix, α rL This is the antenna array response vector of the terminal. The angle between the transmission channel of the terminal receiving the IRS corresponding to the first positioning information and the IRS.

[0203] Because the noise is Gaussian and unbounded, a high probability value needs to be determined to constrain it to the boundary. After the noise variance is normalized, the threshold is calculated using probability.

[0204]

[0205] This invention proposes a fusion positioning method that sets up at least one IRS at obstacles, missing base stations, or other required locations. It uses a chicken flock optimization algorithm with Cauchy variation and a self-feedback factor to solve the fixed solution of the integer ambiguity of the carrier phase double difference model. Then, based on the obtained BeiDou positioning information and the set IRS, it uses an improved convex optimization-based precise location estimation algorithm to determine the location of the terminal.

[0206] The beneficial effects of this embodiment are as follows: During the transmission of BeiDou positioning information, IRSs are set up at locations where the transmission channel is blocked or where base stations are missing. By adjusting the phase or amplitude parameters of the IRS unit, a reflection path is established between the transceiver node and the IRS, allowing BeiDou positioning information to be transmitted to the receiving node via reflection from the IRS. This ensures reliable information transmission and enables target positioning under line-of-sight obstruction conditions, facilitating safe inspection, dispatch, and command work under obstacle obstruction. Through reasonable IRS layout, IRSs can replace base stations for information transmission, strengthening information transmission power and solving the problems of insufficient base station numbers and power attenuation caused by non-direct paths, thus saving costs and reducing latency. The use of a self-feedback factor and a Cauchy mutation-based chicken flock optimization algorithm avoids getting trapped in local optima during the search process, improving the reliability and effectiveness of the search. This allows for more accurate calculation of fixed solutions for integer ambiguity, facilitating rapid and accurate positioning of equipment and personnel during inspection, dispatch, and command work. An improved precise location estimation algorithm based on convex optimization solves for terminal location information through successive iterative approximation, thereby achieving precise positioning of terminal devices and personnel.

[0207] 1) Simulation comparison of searching for fixed solutions with integer ambiguity

[0208] Figures 4(a) and 4(b) illustrate the number of iterations in the traditional flock optimization algorithm and the improved flock optimization algorithm used in the fusion positioning method of this invention, respectively. When obtaining a fixed solution for integer ambiguity, both the traditional and improved flock optimization algorithms are used to search for the optimal solution. The simulation is run in MATLAB, simulating a tall outdoor substation facility scenario. The maximum distance between the mobile terminal and the base station is 4 meters, the IRS is on the y=0 plane, and the IRS center is at the origin (0,0,0). The flock is grouped by the number of roosters, with 50 groups. Hens are randomly assigned to groups, and the maximum number of iterations is 100. Figures 4(a) and 4(b) show the objective function values ​​obtained by the traditional and improved flock optimization algorithms, respectively. It can be observed that the traditional flock optimization algorithm gets stuck in a local minimum at the 24th iteration and escapes around the 54th iteration, but has not yet reached the optimal solution. The improved flock optimization algorithm reaches its minimum objective function value after 10 iterations, demonstrating significantly better reliability and effectiveness than the traditional flock optimization algorithm. Therefore, the improved flock optimization algorithm enables the MEC server to more accurately calculate differential information, which is beneficial for precise positioning of equipment and personnel during inspection and dispatching operations.

[0209] 2) Simulation of errors in the fusion positioning system:

[0210] Positioning error such as Figure 5 As shown, the analysis is performed using a fusion localization method, by Figure 5 It can be seen that for the three directions of east, north, and vertical, the positional error in the east direction is approximately between 0 and 0.4 meters, the positional error in the north direction is approximately between 0 and 0.5 meters, and the error in the vertical direction is approximately between 0 and 1.2 meters. The error in the vertical direction is relatively large, but it is still possible to achieve accurate positioning.

[0211] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0212] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0213] Figure 2 A schematic diagram of the fusion positioning system provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below:

[0214] like Figure 2 As shown, the fusion positioning system includes: a terminal;

[0215] The terminal is used to establish a connection with the MEC server through the base station, send a BeiDou positioning request to the MEC server, receive BeiDou positioning information sent by the MEC server through the base station and IRS, and perform fusion positioning based on the BeiDou positioning information and IRS to determine the terminal's location.

[0216] Optionally, the BeiDou positioning information is BeiDou differential data information.

[0217] It also includes MEC servers;

[0218] The MEC server, used to acquire BeiDou differential data information, includes the following processes:

[0219] A carrier phase double-difference model was established based on BeiDou satellites, ground observation stations, and terminals.

[0220] Obtain RTK positioning information and determine the state parameter vector based on the RTK positioning information;

[0221] The carrier vector is determined based on the carrier phase double-difference measurement and the double-difference pseudorange observation.

[0222] Based on the state parameter vector and the carrier vector, determine the state parameter vector and the covariance matrix corresponding to the state parameters at any given time.

[0223] Based on the state parameter vector and the covariance matrix corresponding to the state parameters, the carrier phase double-difference model is solved to determine the floating-point solution of the carrier phase.

[0224] The objective function is determined by performing a spatial transformation on the floating-point solution and the covariance matrix.

[0225] The objective function is calculated in the new space to obtain a fixed solution;

[0226] The carrier phase double-difference model is updated based on the fixed solution to determine the BeiDou differential data information.

[0227] Optionally, when the MEC server calculates the objective function in the new space and obtains a fixed solution, it is used for:

[0228] Based on the self-feedback factor, the objective function is calculated using the Cauchy mutation flock optimization algorithm to obtain a fixed solution. During the calculation process, each time the Cauchy mutation flock optimization algorithm reaches its optimum, the self-feedback factor is updated, and the Cauchy mutation flock optimization algorithm is used again with the current rooster position as the initial position to determine the fixed solution, until the self-feedback factor is zero.

[0229] Optionally, the MEC server uses the Cauchy mutation chicken flock optimization algorithm to calculate the objective function, and when a fixed solution is obtained, it is used for:

[0230] Set the relevant parameters for the chicken flock optimization algorithm, including flock size, the ratio of roosters, hens and chicks in the flock, number of iterations, iteration threshold, and self-feedback factor;

[0231] In the new space, randomly generate the initial positions of all individuals among the roosters, hens, and chicks;

[0232] The rooster, hen, and chicks search for food in the new space. The positions of the rooster, hen, and chicks are updated using the first method, and the current first fitness value corresponding to the updated position is calculated.

[0233] Check if the current fitness value is less than the fitness value calculated in the previous iteration;

[0234] If the current fitness value is less than the fitness value calculated in the previous iteration, increment the current iteration number by 1 and jump to the step of "updating the positions of rooster, hen and chick by searching for food in the new space using the first method".

[0235] If the current fitness value is not less than the fitness value calculated in the previous iteration, reduce the self-feedback factor, increment the current iteration number by 1, and use the current positions of the rooster, hen, and chicks as the initial positions. Search for food in the new space using the rooster, hen, and chicks, and update their positions using the second method. Calculate the current second fitness value corresponding to the updated positions. If the second fitness value is less than the fitness value calculated in the previous iteration, jump to the step of "searching for food in the new space using the rooster, hen, and chicks, and updating their positions using the second method". If the second fitness value is not less than the fitness value calculated in the previous iteration, jump to the step of "searching for food in the new space using the rooster, hen, and chicks, and updating their positions using the first method", until the optimal solution is determined.

[0236] When the fitness value remains unchanged during a preset number of iterations, if the number of iterations is greater than or equal to the iteration threshold, the iteration is stopped, the optimal solution is determined, and the optimal solution is determined as the fixed solution of the objective function.

[0237] Optionally, when the terminal receives the BeiDou positioning information sent by the MEC server through the base station and the IRS, it is used for:

[0238] Obtain the phase control matrix;

[0239] Based on the phase control matrix, determine the first positioning information received via the IRS;

[0240] Determine the second location information received via the base station;

[0241] The BeiDou positioning information is determined based on the first and second positioning information.

[0242] Optionally, when the terminal acquires the phase control matrix, it is used for:

[0243] Acquire the received information for each snapshot corresponding to each scanning angle of the IRS reflective surface;

[0244] Based on the received information of each snapshot corresponding to each scanning angle, determine the energy of each snapshot corresponding to each scanning angle;

[0245] The energy of each scanning angle is determined based on the energy of each snapshot corresponding to each scanning angle;

[0246] Based on the energy at each scanning angle, determine the estimated value of the scanning angle:

[0247] The phase control matrix is ​​determined based on the estimated value of the scanning angle.

[0248] Optionally, when the terminal performs fusion positioning based on the BeiDou positioning information and the IRS to determine the terminal's location, it is used for:

[0249] The BeiDou positioning information received from the base station via the IRS is determined. The BeiDou positioning information includes the time of transmission of the first positioning information from the base station to the IRS, the angle between the transmission channel of the base station to the IRS corresponding to the first positioning information and the IRS, the spatial loss of the subcarrier during the transmission of the first positioning information from the base station to the IRS, the time of receiving the first positioning information from the IRS, the angle between the transmission channel of the receiving IRS corresponding to the first positioning information and the terminal, and the spatial loss of the subcarrier during the transmission of the first positioning information from the IRS.

[0250] Based on the transmission time of the first positioning information from the base station to the IRS, the angle between the transmission channel of the first positioning information from the base station to the IRS and the IRS, the spatial loss of the subcarrier during the transmission of the first positioning information from the base station to the IRS, the transmission time of the first positioning information from the IRS, the angle between the transmission channel of the first positioning information from the IRS and the terminal, and the spatial loss of the subcarrier during the transmission of the first positioning information from the IRS, the channel parameters are determined.

[0251] The channel parameters are estimated to obtain estimated channel parameter values;

[0252] Based on the estimated channel parameters, the location of the virtual base station for the corresponding IRS is obtained;

[0253] The information reception time for receiving BeiDou positioning information is determined based on the time it takes for the first positioning information to be transmitted from the base station to the IRS and the time it takes for the first positioning information to be received from the IRS.

[0254] The distance from the terminal to each IRS is determined based on the information reception time, information transmission speed, the location of the virtual base station of the corresponding IRS, and the coordinates of the base station.

[0255] Using the coordinates of each IRS as the origin and the distance from the terminal to each IRS as the radius, the range of each circle is determined.

[0256] The target area is defined by identifying all areas where circles intersect.

[0257] Solve the target area to determine the terminal location.

[0258] The embodiments of the present invention provide a fusion positioning system that sets up at least one IRS at obstacles, missing base stations, or other required locations. It uses a chicken flock optimization algorithm with Cauchy variation and an introduced self-feedback factor to solve the fixed solution of the integer ambiguity of the carrier phase double difference model. Then, based on the acquired BeiDou positioning information and the IRS, it uses an improved convex optimization-based precise position estimation algorithm to determine the terminal position.

[0259] The beneficial effects of this embodiment are as follows: During the transmission of BeiDou positioning information, IRSs are set up at locations where the transmission channel is blocked or where base stations are missing. By adjusting the phase or amplitude parameters of the IRS unit, a reflection path is established between the transceiver node and the IRS, allowing BeiDou positioning information to be transmitted to the receiving node via reflection from the IRS. This ensures reliable information transmission and enables target positioning under line-of-sight obstruction conditions, facilitating safe inspection, dispatch, and command work under obstacle obstruction. Through reasonable IRS layout, IRSs can replace base stations for information transmission, strengthening information transmission power and solving the problems of insufficient base station numbers and power attenuation caused by non-direct paths, thus saving costs and reducing latency. The use of a self-feedback factor and a Cauchy mutation-based chicken flock optimization algorithm avoids getting trapped in local optima during the search process, improving the reliability and effectiveness of the search. This allows for more accurate calculation of fixed solutions for integer ambiguity, facilitating rapid and accurate positioning of equipment and personnel during inspection, dispatch, and command work. An improved precise location estimation algorithm based on convex optimization solves for terminal location information through successive iterative approximation, thereby achieving precise positioning of terminal devices and personnel.

[0260] Figure 6 This is a schematic diagram of a terminal provided in an embodiment of the present invention. Figure 6 As shown, the terminal 6 in this embodiment includes a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60. When the processor 60 executes the computer program 62, it implements the steps described in the various fusion positioning method embodiments above, for example... Figure 1Steps 101 to 103 are shown. Alternatively, when processor 60 executes computer program 62, it implements the functions of each unit in the above system embodiments, for example... Figure 2 The functions of the MEC server and terminal are shown.

[0261] For example, computer program 62 can be divided into one or more modules, one or more modules are stored in memory 61 and executed by processor 60 to complete the present invention. One or more modules can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 62 in terminal 6. For example, computer program 62 can be divided into... Figure 2 The image shows the BeiDou satellite, ground observation station, MEC server, base station, IRS, and terminal.

[0262] Terminal 6 may include, but is not limited to, processor 60 and memory 61. Those skilled in the art will understand that... Figure 6 This is merely an example of terminal 6 and does not constitute a limitation on terminal 6. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.

[0263] The processor 60 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0264] The memory 61 can be an internal storage unit of the terminal 6, such as the hard disk or RAM of the terminal 6. The memory 61 can also be an external storage device of the terminal 6, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the terminal 6. Furthermore, the memory 61 can include both internal and external storage units of the terminal 6. The memory 61 is used to store computer programs and other programs and data required by the terminal. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0265] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0266] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0267] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0268] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0269] 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.

[0270] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0271] If the integrated module / 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, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various fusion positioning method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0272] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A fusion positioning method, characterized in that, At least one IRS shall be installed at the location of the obstacle or where the base station is missing, including: A connection is established between the base station and the MEC server, and a BeiDou positioning request is sent to the MEC server. Receive BeiDou positioning information sent by the MEC server through the base station and the IRS; The terminal location is determined by fusing the BeiDou positioning information and the IRS; The process by which the MEC server obtains the BeiDou positioning information includes: A carrier phase double-difference model was established based on BeiDou satellites, ground observation stations, and terminals. Obtain RTK positioning information and determine a state parameter vector based on the RTK positioning information; The carrier vector is determined based on the carrier phase double-difference measurement and the double-difference pseudorange observation. Based on the state parameter vector and the carrier vector, determine the state parameter vector at any given time and the covariance matrix corresponding to the state parameters; Based on the state parameter vector and the covariance matrix corresponding to the state parameters, the carrier phase double-difference model is solved to determine the floating-point solution of the carrier phase. Perform a space transformation on the floating-point solution and the covariance matrix to determine the objective function; The objective function is calculated in a new space using the Cauchy mutation-based chicken flock optimization algorithm to obtain a fixed solution, including: Step 1: Set the relevant parameters for the chicken flock optimization algorithm. The relevant parameters include the flock size, the ratio of roosters, hens, and chicks in the flock, the number of iterations, the iteration threshold, and the self-feedback factor. Step 2: Randomly generate the initial positions of all individuals among the rooster, hen, and chicks in the new space; Step 3: The rooster, hen, and chicks search for food in the new space, and the positions of the rooster, hen, and chicks are updated using the first method. Step 4: Calculate the current first fitness value corresponding to the updated position; Step 5: Check the current first fitness value. If the current first fitness value is less than the fitness value calculated in the previous iteration, increment the current iteration number by 1 and jump to step 3. Step 6: When the current fitness value is not less than the fitness value calculated in the previous iteration, reduce the self-feedback factor, increment the current iteration number by 1, and use the current position of the rooster, hen, and chick as the initial position. Step 7: The rooster, hen, and chicks search for food in the new space, and the positions of the rooster, hen, and chicks are updated using the second method. Step 8: Calculate the current second fitness value corresponding to the updated position; Step 9: If the second fitness value is less than the fitness value calculated in the previous iteration, jump to step 7; if the second fitness value is not less than the fitness value calculated in the previous iteration, jump to step 3. Step 10: When the fitness value remains unchanged during a preset number of iterations, if the number of iterations is greater than or equal to the iteration threshold, stop the iteration, determine the optimal solution, and set the optimal solution as the fixed solution of the objective function. In the first method, Gaussian mutation is used to update the rooster's position, while in the second method, Cauchy mutation is used to update the rooster's position. The carrier phase double-difference model is updated based on the fixed solution to determine the BeiDou positioning information.

2. The fusion positioning method according to claim 1, characterized in that, Receiving the BeiDou positioning information sent by the MEC server through the base station and the IRS includes: Obtain the phase control matrix; Based on the phase control matrix, the first positioning information received through the IRS is determined; Determine the second location information received through the base station; The BeiDou positioning information is determined based on the first positioning information and the second positioning information.

3. The fusion positioning method according to claim 2, characterized in that, Obtain the phase control matrix, including: Acquire the received information for each snapshot corresponding to each scanning angle of the IRS reflective surface; Based on the received information of each snapshot corresponding to each scanning angle, determine the energy of each snapshot corresponding to each scanning angle; The energy of each scanning angle is determined based on the energy of each snapshot corresponding to each scanning angle; Based on the energy at each scanning angle, an estimated value for the scanning angle is determined: The phase control matrix is ​​determined based on the estimated value of the scanning angle.

4. The fusion positioning method according to claim 2, characterized in that, Determining the first positioning information received via the IRS based on the phase control matrix includes: according to The first location information received through the IRS is determined; in, To receive the first location information transmitted by the base station through the IRS. The first location information transmitted from the base station to the IRS. In order to receive the first location information transmitted by the IRS, The phase control matrix, L Represents IRS; This refers to the spatial loss of subcarriers during the transmission of the first positioning information from the base station to the IRS. The time it takes for the first location information to be transmitted from the base station to the IRS. This is the antenna array response vector. This is the antenna array steering vector. Let be the transpose conjugate matrix of the antenna array steering vector. The angle between the base station to the IRS transmission channel corresponding to the first location information and the base station. The angle between the transmission channel from the base station to the IRS corresponding to the first positioning information and the IRS. j The imaginary unit indicates that the first positioning information is a complex exponential signal. N The number of equally spaced sampling points for the first positioning information. n The first location information corresponding to the first n One sampling point; This refers to the spatial loss of the subcarrier during the transmission of the first positioning information from the IRS. The time it takes to receive the first location information from the IRS This is the antenna array response vector. This is the antenna array steering vector. Let be the transpose conjugate matrix of the antenna array steering vector. The angle between the transmission channel of the receiving IRS corresponding to the first positioning information and the IRS itself. The angle between the transmission channel of the receiving IRS corresponding to the first positioning information and the terminal; The determination of the second location information received through the base station includes: according to The second positioning information received through the base station is determined; in, In order to receive the second positioning information transmitted by the base station, This refers to the spatial loss that occurs on the subcarrier during the transmission of the second positioning information. The time when the second location information was received. B The total bandwidth of all subcarriers, This is the antenna array response vector. This is the antenna array steering vector. Let be the transpose conjugate matrix of the antenna array steering vector. The angle between the transmission channel corresponding to the second positioning information and the base station. The angle between the transmission channel corresponding to the second positioning information and the terminal. j The imaginary unit indicates that the second positioning information is a complex exponential signal. N This represents the number of equally spaced sampling points for the second positioning information. n The second location information corresponds to the first n One sampling point; The BeiDou positioning information is determined based on the first positioning information and the second positioning information, including: according to To determine the BeiDou positioning information; in, The aforementioned BeiDou positioning information, P The transmission power for transmitting the positioning information, F X[n] For the beamforming matrix that transmits the positioning information, n [ n [This refers to additive white Gaussian noise in the transmission space.] H [ n [This refers to the transmitted positioning information.] 5. The fusion positioning method according to claim 2, characterized in that, The step of determining the terminal location by fusing positioning based on the BeiDou positioning information and the IRS includes: The BeiDou positioning information received from the base station via the IRS is determined. The BeiDou positioning information includes the time of transmission of the first positioning information from the base station to the IRS, the angle between the transmission channel of the base station to the IRS corresponding to the first positioning information and the IRS, the spatial loss of the subcarrier during the transmission of the first positioning information from the base station to the IRS, the time of receiving the first positioning information from the IRS, the angle between the transmission channel of the receiving IRS corresponding to the first positioning information and the terminal, and the spatial loss of the subcarrier during the transmission of the first positioning information from the IRS. The channel parameters are determined based on the transmission time of the first positioning information from the base station to the IRS, the angle between the transmission channel of the first positioning information from the base station to the IRS and the IRS, the spatial loss of the subcarrier during the transmission of the first positioning information from the base station to the IRS, the transmission time of the first positioning information from the IRS, the angle between the transmission channel of the first positioning information from the receiving IRS and the terminal, and the spatial loss of the subcarrier during the transmission of the first positioning information from the IRS. The channel parameters are estimated to obtain estimated channel parameter values; Based on the estimated channel parameters, the location of the virtual base station corresponding to the IRS is obtained; The information reception time for receiving the BeiDou positioning information is determined based on the time it takes for the first positioning information to be transmitted from the base station to the IRS and the time it takes to receive the first positioning information from the IRS. Based on the information reception time, information transmission speed, the location of the virtual base station of the corresponding IRS, and the coordinates of the base station, the distance from the terminal to each IRS is determined; Using the coordinates of each IRS as the origin and the distance from the terminal to each IRS as the radius, the range of each circle is determined respectively; The target area is defined by identifying all areas where circles intersect. The target area is solved to determine the terminal location.

6. A terminal, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5 above.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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