Incoherent user positioning method and system based on integrated intelligent reflecting surface of unmanned aerial vehicle

Abstract

The invention provides an incoherent user positioning method and system based on an unmanned aerial vehicle integrated intelligent reflecting surface, and relates to the technical field of intelligent reflecting surface sensing and communication integration, and the method comprises the steps: constructing a system model; determining a known anchor point, enabling the intelligent reflecting surface to be in a closed state, and estimating angle information from a user to a base station; enabling the intelligent reflecting surface to be in an on state to obtain an interference-free user-intelligent reflecting surface-base station reflecting link signal, and estimating to obtain a direct component from the intelligent reflecting surface to a base station channel; calculating an orthogonal space of the direct component according to the direct component, and obtaining a receiving signal only containing a scattering component based on the orthogonal space; based on a receiving signal only containing a scattering component, solving through an alternately optimized exhaustive search incoherent maximum likelihood detection method to obtain angle estimation from a user to an intelligent reflecting surface; and obtaining the positioning information of the target user according to the relation between the position information and the angle based on the estimation of the angle from the user to the intelligent reflecting surface.

Description

Technical Field

[0001] The present disclosure relates to the technical field of intelligent reflective surface perception and communication integration, and in particular to an incoherent user positioning method and system based on an unmanned aerial vehicle (UAV) integrated intelligent reflective surface. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] With the development of fifth-generation wireless communication technology, the millimeter wave frequency band has become the key to improving data transmission speed, reducing latency and enhancing sensing capabilities due to its wide bandwidth. However, millimeter waves face serious path loss and susceptibility to obstacles in practical applications, especially in urban environments with tall buildings, which leads to limited signal coverage and inconsistent user experience. To solve these problems, smart reflective surface technology has been proposed as a new solution. By integrating a large number of programmable reflective units, the smart reflective surface can actively adjust the characteristics of the incident electromagnetic wave, thereby significantly improving the quality of the communication link without additional power consumption.

[0004] Although smart reflective surface technology has demonstrated its potential in enhancing signal coverage, it is still challenging to provide a stable line-of-sight link in mobile scenarios, mainly because smart reflective surfaces in fixed positions are susceptible to shadow effects or occlusion. To overcome this limitation, drone-assisted communication systems have been introduced as a flexible and efficient solution. Drones are highly maneuverable and easy to deploy. They can quickly reach designated locations as needed, carry smart reflective surface equipment, and dynamically optimize the establishment and maintenance of communication links, thereby ensuring high-quality millimeter-wave communication services in various environments.

[0005] However, most of the current UAV-assisted solutions focus on UAV-assisted communications, while there is relatively little research on the positioning of smart reflective surfaces carried by UAVs. In UAV swarm systems, existing research uses multiple smart reflective surfaces to obtain the arrival direction of the target through an arrival angle estimation method based on the atomic norm. Or through phase shifter optimization and deep reinforcement learning, UAV trajectory planning and smart reflective surface phase shift configuration are performed to achieve a balance between positioning and communication performance, or the joint optimization problem of UAV position and UAV-smart reflective surface passive beamforming is considered to improve positioning accuracy. However, the following problems still exist: 1) The base station-smart reflector channel estimation problem, which is crucial for user positioning and communication, is ignored; in fact, estimating the base station-smart reflector channel has always been a challenging problem, even when the channel between the base station and the smart reflector is a slowly time-varying channel.

[0006] 2) Due to power and cost limitations, smart reflectors usually do not have complex signal processing capabilities, which makes traditional channel estimation methods impossible to implement.

[0007] 3) The flying altitude and speed of the drone will cause fast fading, making the channel estimation overhead non-negligible. This may also corrupt the payload data transmission and is a long-standing problem. Summary of the invention

[0008] In order to solve the above problems, the present disclosure proposes an incoherent user positioning method and system based on an unmanned aerial vehicle integrated intelligent reflecting surface. Through the unmanned aerial vehicle integrated intelligent reflecting surface assisted millimeter wave communication framework, effective channel estimation is achieved between the base station and the intelligent reflecting surface. The second-order channel statistical information between the base station and the intelligent reflecting surface is used to simultaneously retrieve the positioning information of multiple users. A method for alternating exhaustive search of incoherent maximum likelihood detectors is designed to simplify complex multi-dimensional optimization problems into multiple one-dimensional optimization problems that are easier to handle, and the stability and reliability of the communication link can be maintained even under rapid fading conditions caused by relative motion.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions: The incoherent user positioning method based on the UAV integrated intelligent reflective surface includes: Based on the intelligent reflective surface and base station, the system model and channel model assisted by the intelligent reflective surface are constructed; Determine the block fading model, determine the time slot allocation, and divide the time interval within the coherence time into three stages; Determine a known anchor point, turn off the smart reflective surface, and estimate the angle from the user to the base station based on the pilot signal sent from the anchor point to the base station; Then, the smart reflective surface is turned on to obtain the interference-free user-smart reflective surface-base station reflection link signal, and the direct component of the smart reflective surface to base station channel is estimated according to the MUSIC algorithm; Calculating the orthogonal space of the direct component according to the direct component, and obtaining a received signal containing only the scattered component based on the orthogonal space; Based on the received signal containing only scattered components, the angle estimation from the user to the intelligent reflector is obtained by exhaustive search of non-coherent maximum likelihood detection method with alternating optimization. Based on the angle information from the user to the base station and the angle estimation from the user to the intelligent reflective surface, the positioning information of the target user is obtained according to the relationship between the position information and the angle.

[0010] According to some embodiments, the present disclosure adopts the following technical solutions: The incoherent user positioning system based on the UAV integrated intelligent reflective surface includes: A model building module is used to build a system model and a channel model assisted by an intelligent reflector based on an intelligent reflector and a base station; determine a block fading model, determine time slot allocation, and divide the time interval within the coherence time into three stages; The calculation and positioning module is used to determine a known anchor point, turn off the intelligent reflection surface, and estimate the angle information from the user to the base station according to the pilot signal sent by the anchor point to the base station; then turn on the intelligent reflection surface to obtain an interference-free user-intelligent reflection surface-base station reflection link signal, and estimate the direct component of the channel from the intelligent reflection surface to the base station according to the MUSIC algorithm; calculate the orthogonal space of the direct component according to the direct component, and obtain a received signal containing only scattered components based on the orthogonal space; based on the received signal containing only scattered components, solve it through an alternating optimized exhaustive search non-coherent maximum likelihood detection method to obtain an angle estimate from the user to the intelligent reflection surface; based on the angle information from the user to the base station and the angle estimate from the user to the intelligent reflection surface, and according to the relationship between the position information and the angle, obtain the positioning information of the target user.

[0011] According to some embodiments, the present disclosure adopts the following technical solutions: A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the incoherent user positioning method based on an unmanned aerial vehicle integrated intelligent reflective surface is implemented.

[0012] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the non-coherent user positioning method based on an unmanned aerial vehicle integrated intelligent reflective surface is implemented.

[0013] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the incoherent user positioning method based on the integrated intelligent reflective surface of a drone.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The disclosed incoherent user positioning method based on drone-integrated intelligent reflective surface proposes an innovative drone-integrated intelligent reflective surface-assisted millimeter wave communication framework, which can perform effective channel estimation between the base station and the intelligent reflective surface, and can simultaneously retrieve the positioning information (such as azimuth) of multiple users by only using the second-order channel statistics between the base station and the intelligent reflective surface, and can maintain the stability and reliability of the communication link even under the rapid fading conditions caused by relative motion. By combining the flexibility of drones and the signal adjustment capabilities of intelligent reflective surfaces, the disclosed invention provides new ideas and technical means for achieving more efficient and reliable millimeter wave communications, which is suitable for mobile communication scenarios in urban environments.

[0015] The non-coherent user positioning method based on the UAV integrated intelligent reflective surface disclosed in the present invention is particularly suitable for the situation where the channel between the intelligent reflective surface and the base station is in a fast fading environment. The scheme aims to achieve accurate positioning of the target without increasing the pilot overhead of channel estimation. The second-order channel statistical information between the base station and the intelligent reflective surface is utilized, and a method of alternating exhaustive search of non-coherent maximum likelihood detectors is designed to simplify the complex multi-dimensional optimization problem into multiple one-dimensional optimization problems that are easier to handle. This method not only effectively reduces the computational complexity, but also realizes accurate positioning estimation of the user. Therefore, compared with traditional methods, the present invention significantly reduces the pilot overhead required for channel estimation between the intelligent reflective surface and the base station.

[0016] The disclosed incoherent user positioning method based on the drone integrated intelligent reflective surface uses the idea of ​​alternating optimization combined with an exhaustive search algorithm to solve the incoherent maximum likelihood function, thereby achieving the purpose of locating the target user. The positioning of the target by a single base station is achieved, which saves cost and overhead, avoids the problem of collaboration between base stations, improves the user positioning accuracy and stability, and can achieve reliable location information estimation for multiple users even in an environment where the channel changes dramatically. It effectively solves the problem of the sharp decline in performance of traditional positioning methods under adverse conditions such as occlusion and reflection. In addition, it also reduces the possibility of interference between systems and improves the utilization rate of spectrum resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0018] Figure 1 This is an example diagram of a system model of intelligent reflective surface assistance according to an embodiment of the present disclosure; Figure 2 A schematic diagram of a fading time-varying channel model according to an embodiment of the present disclosure; Figure 3This is a flow chart of an incoherent user positioning method based on an intelligent reflective surface integrated with a drone according to an embodiment of the present disclosure; Figure 4 The figure is a schematic diagram for comparing the performance indicators of different methods according to the embodiments of the present disclosure. DETAILED DESCRIPTION

[0019] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0020] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0021] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0022] Example 1 In one embodiment of the present disclosure, a non-coherent user positioning method based on an intelligent reflective surface integrated with a drone is provided, and the steps include: Step 1: Building a smart reflective surface-assisted system model and channel model based on the smart reflective surface and base station; Step 2: Determine the block fading model, determine the time slot allocation, and divide the time interval within the coherence time into three stages; Step 3: Determine a known anchor point, turn off the smart reflective surface, and estimate the angle from the user to the base station based on the pilot signal sent by the anchor point to the base station; Step 4: The smart reflective surface is turned on to obtain a non-interference user-smart reflective surface-base station reflection link signal, and the direct component of the smart reflective surface to base station channel is estimated according to the MUSIC algorithm; Step 5: Calculate the orthogonal space of the direct component according to the direct component, and obtain a received signal containing only the scattered component based on the orthogonal space; Step 6: Based on the received signal containing only scattered components, the angle estimation from the user to the smart reflector is obtained by using the exhaustive search non-coherent maximum likelihood detection method with alternating optimization; Step 7: Based on the angle information from the user to the base station and the angle estimation from the user to the intelligent reflective surface, the positioning information of the target user is obtained according to the relationship between the position information and the angle.

[0023] As an embodiment, the incoherent user positioning method based on the drone integrated intelligent reflective surface disclosed in the present invention proposes a joint communication and positioning solution assisted by an intelligent reflective surface, which can realize single base station positioning, and can realize positioning and communication at the same time. The specific implementation process is as follows: Step 1: Building a smart reflective surface-assisted system model and channel model based on the smart reflective surface and base station; Specifically, the system model includes base stations and their antenna data. , Smart reflective surface and number of its components , single antenna user , as attached Figure 1 As shown in the figure, the intelligent reflective surface carried by the drone creates an additional channel path between the user and the base station blocked by the building, thus assisting in the communication and positioning services between the user and the base station. Uniform Linear Array (ULA) of 10 antennas, and smart reflectors are also equipped A uniform linear array of reflective elements, with base stations serving In addition, it is assumed that a single-antenna anchor point is deployed near the surface of the smart reflector to assist in estimating the channel from the smart reflector to the base station. At the same time, it is assumed that the base station, the user, and the smart reflector are far enough apart from each other, so they are in the far field region of each other. In addition, it is assumed that the drone can keep the two arrays parallel, and its direction is defined as the X-axis. It is assumed that the coordinates of the base station and the smart reflector have been determined, while the coordinates of the user are random and unknown.

[0024] All channels are modeled using the angle between the transmitter and the receiver. Since the smart reflector and the base station are basically fixed, the channel between the smart reflector and the base station remains almost unchanged over a long period of time. At this time, the received signal at the base station includes both the direct link from the user to the base station and the reflected link from the user to the smart reflector to the base station. The specific form of the signal is as follows:

[0025] in, represents the direct link from the user to the base station, represents the reflection coefficient of the smart reflective surface, Indicates the channel from the drone to the base station. represents the channel from the user to the smart reflective surface, represents the training symbol sent by the user, Indicates noise.

[0026] Step 2: Determine the time slot allocation, considering a block fading time-varying channel model, where the channel coefficients remain unchanged within each coherence time. The anchor node with known positioning sends a message of length The pilot sequence is divided into three phases: Figure 2 shown Assume that the duration of each pilot transmission phase is is fixed. In phase A, use The pilot symbol is used to estimate the direct channel between the user and the base station. When entering phase B, once the direct channel estimation between the user and the base station is completed, the smart reflector that was previously in the "off" state will be turned on to perform the channel estimation of the line-of-sight link in the smart reflector-base station channel. Finally, in phase C, the goal is to achieve azimuth estimation based on the user-smart reflector channel and ultimately achieve user positioning.

[0027] Step 2: Determine a known anchor point and turn off all smart reflective surface elements. At this time, the smart reflective surface will be in the "off" state. At this time, the angle information from the user to the base station is estimated by sending a pilot signal to the base station based on the anchor point; Specifically, a known anchor point is determined, and the reflection coefficient of the smart reflection surface is set to zero. Then the anchor point sends a pilot signal to the base station. At this time, the specific form of the pilot signal received by the base station in phase A is:

[0028] in, Y A is the pilot signal received by the base station in phase A, represents the direct link from the user to the base station, is the training symbol sent by the user in phase A, is the noise of phase A. The specific form is as follows:

[0029] in, Refers to K The steering vector of each user, Indicates K The azimuth from the user to the base station.

[0030] Then, based on the received direct link signal containing only the user to the base station, the channel matrix is ​​obtained using the classical least squares method: The estimate is:

[0031] in, , It refers to the pseudo-inverse matrix of the signal sent in stage A. yes The conjugate transposed matrix of . Assume that the power of each UE is subject to independent constraints such that:

[0032] in, It is k The power of each user is then transmitted by each user with a known pilot signal for a duration of symbols. Using orthogonal training sequences allows each user to transmit signals simultaneously with maximum power without interfering with other users. Therefore, a training pilot signal matrix with orthogonal rows is used. , the following conditions are met:

[0033] in represents a diagonal matrix whose diagonal elements are the transmission power of each user, is a "short" matrix with uniformly orthogonal row vectors such that Established, is the identity matrix, yes The conjugate transposed matrix of . Since the training matrix has infinite options, for example, if the training matrix Each element of has the same amplitude. For practical applications, it can be implemented using a normalized discrete Fourier transform (DFT) submatrix, which can be written as follows:

[0034] in, The matrix is ​​the discrete Fourier transform submatrix, represents the imaginary part, is the number of time slots, and K is the number of users. Then, we can get Get the channel estimate from the user to the base station and the corresponding azimuth estimate ,Right now:

[0035] in, is the channel between the kth user and the base station, is the channel estimation value between the kth user and the base station, Indicates that from The estimated azimuth from the user to the base station.

[0036] Step 4: The smart reflective surface is turned on to obtain a non-interference user-smart reflective surface-base station reflection link signal, and the direct component of the smart reflective surface to base station channel is estimated according to the MUSIC algorithm; Specifically, in step 3, after estimating the channel from the user to the base station After that, the smart reflective surface is "opened" and the reflective elements are configured according to the environmental information to provide services for the reflected users. At this time, the signal is sent to the base station and the smart reflective surface through the anchor point. At this time, the initial received signal at the base station contains both the link angle from the user to the base station and the link angle from the user to the smart reflective surface. With the help of the signal of the direct link known after the channel estimation in step 3, it is regarded as a constant and subtracted from the initial received signal obtained in step 3 to obtain a received signal containing only the reflected link. The specific form of the signal is as follows:

[0037] in, They are the received signal matrix, the channel matrix from the anchor node to the RIS, the training signal matrix, the RIS reflection coefficient matrix and the noise matrix at this stage. The received signal matrix, the channel matrix from the anchor node to the RIS, the training signal matrix, the RIS reflection coefficient matrix and the noise matrix at this stage are defined as follows:

[0038]

[0039]

[0040]

[0041]

[0042] in, , , , , It is the received signal vector under the first training symbol of this stage, the first element of the channel matrix from the anchor node to the RIS, the first training signal, the first element of the RIS reflection coefficient matrix and the noise vector under the first training symbol. The channel from the smart reflection surface to the base station is expressed as:

[0043] In the above formula and represents the normalized line-of-sight link component and the normalized non-line-of-sight link component. Among them, the Rice factor Determined by the relative strength of the line-of-sight link and the non-line-of-sight link. represents the large-scale fading coefficient of the channel from the smart reflector to the base station. Assuming the non-line-of-sight link component It follows a Rayleigh distribution whose elements are independently drawn from a circularly symmetric complex Gaussian distribution (CSCG) with zero mean and unit variance. The line-of-sight link component It can be written as:

[0044] in, and are the angle of arrival and angle of departure in the smart reflector-base station channel. More specifically, and are the array steering vectors related to the base station and the smart reflector, respectively. For a typical linear uniform array, the array steering vectors of the base station and the smart reflector can be written as:

[0045]

[0046] Where M is the number of base station antennas, is the transpose operation, and represent the array unit spacing of the base station and the smart reflector, respectively, and Indicates the carrier wavelength.

[0047] Next, the goal is to preliminarily estimate the line-of-sight link component of the smart reflector-base station channel. Therefore, the departure angle from the smart reflector is estimated by a multiple signal classification algorithm. , and the angle of arrival to the base station .for According to the received signal, the covariance matrix of the signal can be obtained:

[0048] in, is the expectation operator, is the received signal at this stage, represents the conjugate transpose operation, is the noise variance, is the identity matrix, Refers to the line-of-sight link component of the RIS-base station channel. is the transmission power of the 0th user (anchor node). Rice factor Determined by the relative strength of the line-of-sight link and the non-line-of-sight link. represents the large-scale fading coefficient of the channel from the smart reflector to the base station. In the above formula, the non-line-of-sight link component is independent of the line-of-sight link component, and all information about the 0th user is known, including the steering vector matrix In order to minimize the impact of irrelevant variables on the estimation, the matrix , so that , here is a dimension of The discrete Fourier transform matrix of can be written as:

[0049] therefore, represents the imaginary part, is the number of time slots sent, the above formula can be rewritten as:

[0050] in, is the transmission power of the 0th user (anchor node). Rice factor Determined by the relative strength of the line-of-sight link and the non-line-of-sight link. Represents the large-scale fading coefficient of the channel from the smart reflector to the base station. Refers to the line-of-sight link component of the RIS-base station channel. represents the conjugate transpose operation, is the noise variance, is the identity matrix, and are the angle of arrival and angle of departure in the smart reflector-base station channel, respectively. Next, a multiple signal classification algorithm is applied to estimate the line-of-sight link component. More specifically, Perform eigenvalue decomposition to obtain the signal subspace and noise subspace, so that:

[0051] in, is the eigenvector matrix, is a diagonal matrix with the diagonal elements being the eigenvalues, arranged in descending order, represents the conjugate transpose operation. In practice, there are usually multiple reflectors between the base station and the smart reflector, and the subspace of the reflected signal can be obtained by eigenvalue decomposition. To simplify the discussion, it is assumed that the channel between the base station and the smart reflector follows the Rice distribution and contains only one direct path. With the help of eigenvalue decomposition, the corresponding to the maximum eigenvalue can be determined The eigenvector of As part of the signal subspace. The remaining eigenvectors are used to construct the noise subspace matrix:

[0052] in, is the noise subspace matrix, is the eigenvector corresponding to the second eigenvalue is the eigenvector corresponding to the Mth eigenvalue, and then, the spectral function of multiple signal classification is constructed:

[0053] in, is the noise subspace matrix, Represents the conjugate transpose operation. is the test vector and can be written as:

[0054] in ,and Indicates the angle resolution. M indicates the number of antennas in the base station. and represents the array element spacing of the base station, while represents the carrier wavelength, is the transpose operation.

[0055] By searching the spectral peak of the spectral function of the above multiple signal classification, the angle corresponding to the peak is obtained. It is to estimate the azimuth from the intelligent reflecting surface to the base station.

[0056] In getting After estimating the steering vector Therefore, with the help of the received signal ,have:

[0057] in, is the transmission power of the 0th user (anchor node). Rice factor Determined by the relative strength of the line-of-sight link and the non-line-of-sight link, Represents the large-scale fading coefficient of the channel from the smart reflector to the base station, It refers to the line-of-sight link component of the RIS-base station channel, Refers to the non-line-of-sight link component of the RIS-base station channel, represents the conjugate transpose operation, is the noise variance, is the identity matrix, and They are the arrival angle and departure angle in the smart reflector-base station channel.

[0058] By observing the structure of the above formula, let changes in each time slot, in each time slot Can be set to Therefore, the above formula can be simplified to:

[0059] in, is the transmission power of the 0th user (anchor node). Rice factor Determined by the relative strength of the line-of-sight link and the non-line-of-sight link. represents the large-scale fading coefficient of the channel from the smart reflector to the base station. M is the number of base station antennas, is the discrete Fourier transform matrix. It refers to the line-of-sight link component of the RIS-base station channel, represents the conjugate transpose operation, is the noise variance, is the identity matrix, is the departure angle in the smart reflector-base station channel. Similarly, we can estimate .

[0060] Step 5: Calculate the orthogonal space of the direct component according to the direct component, and project the signal received by the base station into the orthogonal space to obtain a received signal containing only the scattered component; Specifically, in the final stage C, based on the received signal containing only the reflection link obtained in step 3, the corresponding The received signal matrix of a user ,Right now:

[0061] in, They are the received signal matrix, RIS-base station channel matrix, RIS phase matrix, user-to-intelligent reflection surface channel matrix, training signal matrix and noise matrix at this stage. The received signal matrix, anchor node-to-intelligent reflection surface channel matrix, training signal matrix and noise matrix at this stage are defined as follows:

[0062]

[0063]

[0064]

[0065] in, , , , is the received signal vector under the first training symbol of this stage, the channel vector from the first user to RIS, the training symbol vector sent by K users in the first time slot, and the noise vector under the training symbol vector of the first time slot. In the C stage, the smart reflection surface phase matrix is ​​set , so that its elements correspond to the discrete Fourier transform matrix For example, ,in is the discrete Fourier transform matrix To simplify the process, assume , that is, the number of pilot symbols is equal to the number of users, and all users have the same transmit power, which is set to 1, that is, .

[0066] Based on the line-of-sight link component in step 3 It is estimated that by Project to The orthogonal subspace is used to remove Due to the influence of The rank of is 1, which can be expressed using singular value decomposition , which can be written as:

[0067] in, and is a unitary matrix, is the singular value matrix, and are non-zero singular values. is a zero matrix, , and , The expression is as follows:

[0068] in, , and , are the singular vectors corresponding to the non-zero singular values, the matrix composed of the remaining singular vectors, M is the number of base station antennas, and L is the number of smart reflector elements. In fact, according to the Rice channel model, the above formula can be regarded as:

[0069]

[0070]

[0071] in, , represents the singular value vector corresponding to the non-zero singular value, are non-zero singular values, and are the arrival angle and departure angle in the smart reflector-base station channel, It refers to the norm operation.

[0072] Based on orthogonal matrix The nature of .therefore, can be written as:

[0073] in, , and , are the singular vectors corresponding to the non-zero singular values, and the matrix composed of the remaining singular vectors. M is the number of base station antennas, L is the number of smart reflector elements, , represents the singular value vector corresponding to the non-zero singular value, are non-zero singular values, The orthographic projection matrix can be written as , that is, The expression of the whitened projection matrix is ​​given as follows:

[0074] in, is the matrix of the remaining singular vectors Substituting the above formula into the received signal containing only the reflection link, we get the received signal containing only the non-line-of-sight link component, expressed as:

[0075] in, yes The orthogonal projection matrix, Rice factor Determined by the relative strength of the line-of-sight link and the non-line-of-sight link. Represents the large-scale fading coefficient of the channel from the smart reflector to the base station. represents the conjugate transpose operation, They are the received signal matrix, RIS phase matrix, RIS-base station channel matrix, training signal matrix and noise matrix at this stage. is the matrix of the remaining singular vectors The conjugate transposed matrix of . Refers to the non-line-of-sight link component of the RIS-base station channel.

[0076] By vectorizing the above formula, the covariance matrix of the received signal containing only the non-line-of-sight link component is calculated, and the specific form is:

[0077] in, is the expectation operator, the Rice factor Determined by the relative strength of the line-of-sight link and the non-line-of-sight link. Represents the large-scale fading coefficient of the channel from the smart reflector to the base station. It refers to the line-of-sight link component of the RIS-base station channel, represents the conjugate transpose operation, is the noise variance, and is the identity matrix, They are the RIS phase matrix of this stage, the channel matrix of RIS-base station, and the training signal matrix. yes After column vectorization, represents the Kronecker product, Represents the identity matrix.

[0078] Further simplification can be obtained:

[0079] in , Rice factor Determined by the relative strength of the line-of-sight link and the non-line-of-sight link. Represents the large-scale fading coefficient of the channel from the smart reflector to the base station. is the identity matrix, represents the Kronecker product, is the channel matrix of the RIS-base station, Represents the conjugate transpose operation.

[0080] Step 6: Based on the received signal containing only scattered components, the angle estimation from the user to the smart reflector is obtained by using an exhaustive search non-coherent maximum likelihood detection method with alternating optimization; Specifically, according to the covariance matrix obtained in step 5, the conditional distribution of the channel from any user to the smart reflector surface at the base station for the received signal containing only the scattered component can be written as:

[0081] in, is the received signal matrix containing only the non-line-of-sight link components After column vectorization, , M is the number of base station antennas, K is the number of users, represents the determinant operation, is the channel matrix of the RIS-base station, It refers to the exponent e.

[0082] Applying incoherent maximum likelihood detection to solve the channel from user to smart reflector , the specific form is:

[0083] in, is the channel matrix of the RIS-base station, is the estimated RIS-base station channel matrix, the Rice factor Determined by the relative strength of the line-of-sight link and the non-line-of-sight link, Represents the large-scale fading coefficient of the channel from the smart reflector to the base station, , is the received signal matrix containing only the non-line-of-sight link components, represents the conjugate transpose operation, , is the noise variance, is the identity matrix, It means to find the trace of the matrix, It means to find the determinant of a matrix.

[0084] By using the exhaustive search method, we can traverse all possible angles, calculate the value of the objective function, and further use the idea of ​​alternation to perform exhaustive search to avoid the algorithm complexity being too high when there are a large number of users. First, assuming there is only one user, we can get the angle estimate of the first user by performing an exhaustive search on the above function. ; Then fix the first user and solve the angle estimation of the second user. The specific form is:

[0085] in, From the perspective of the second user, is the angle estimate for the second user, is the channel matrix of the RIS-base station, the Rice factor Determined by the relative strength of the line-of-sight link and the non-line-of-sight link. Represents the large-scale fading coefficient of the channel from the smart reflector to the base station. , is the received signal matrix containing only the non-line-of-sight link components, represents the conjugate transpose operation, is the inverse operation, , is the noise variance, is the identity matrix, It means to find the trace of the matrix, It means to find the determinant of a matrix.

[0086] And so on, until you get Angle estimation for each user .

[0087] Step 7: Based on the angle information from the user to the base station and the angle estimation from the user to the intelligent reflective surface, the positioning information of the target user is obtained according to the relationship between the position information and the angle.

[0088] Specifically, according to the estimation of the angles in step 3 and step 6, and according to the relationship between the position information and the angle, a mathematical equation is used to express the relationship between the azimuth angles. For the convenience of expression, it is assumed that the spatial coordinates of the initial antenna of the base station and the initial elements of the smart reflective surface are expressed as and In order to realize the user's positioning function, it is necessary to associate the coordinates of the base station and the smart reflective surface with the obtained azimuth:

[0089]

[0090] in, They are the base station, the vertical coordinate of the user, the azimuth from the user to the base station, and the horizontal coordinates of the user and the base station. They are the ordinates of RIS and user, the azimuth of user to RIS, and the abscissa of user and RIS. Based on the azimuth estimates of user-base station and user-intelligent reflector, we have:

[0091]

[0092] in, is the user's horizontal coordinate estimate, They are the ordinate of the base station, the estimated azimuth from the user to the base station, and the abscissa of the base station. is the user's ordinate estimate, is the horizontal axis of RIS, It is the vertical coordinate of RIS. is the estimated azimuth from the user to the RIS. According to the above steps, the target user can be located and the user's location information can be obtained.

[0093] In order to verify the effectiveness of the present disclosure in user positioning accuracy, the performance differences of different methods are compared. The positioning performance is evaluated by using the minimum mean square error between the estimated value and the true value, and the result is Figure 4 The results shown are as follows, and the red curve represents the method disclosed herein. Figure 4 As shown, it can be obtained that compared with the existing solutions, the user positioning performance of the method disclosed in the present invention is better.

[0094] Example 2 In one embodiment of the present disclosure, a non-coherent user positioning system based on an unmanned aerial vehicle integrated intelligent reflective surface is provided, comprising: A model building module, used to build a system model and a channel model assisted by an intelligent reflecting surface based on an intelligent reflecting surface and a base station; The calculation and positioning module is used to determine a known anchor point, turn off the intelligent reflection surface, and estimate the angle information from the user to the base station according to the pilot signal sent by the anchor point to the base station; then turn on the intelligent reflection surface, obtain the interference-free user-intelligent reflection surface-base station reflection link signal based on the angle information from the user to the base station, and estimate the direct component of the channel from the intelligent reflection surface to the base station according to the MUSIC algorithm; calculate the orthogonal space of the direct component based on the direct component, and obtain the received signal containing only the scattered component based on the orthogonal space; based on the received signal containing only the scattered component, solve it through the exhaustive search non-coherent maximum likelihood detection method with alternating optimization to obtain the angle estimate from the user to the intelligent reflection surface; based on the angle estimate from the user to the intelligent reflection surface, and according to the relationship between the position information and the angle, obtain the positioning information of the target user.

[0095] Example 3 In one embodiment of the present disclosure, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the incoherent user positioning method based on the drone integrated intelligent reflective surface is implemented.

[0096] Example 4 In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the incoherent user positioning method based on the drone integrated intelligent reflective surface is implemented.

[0097] Example 5 In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the incoherent user positioning method based on the drone integrated intelligent reflective surface.

[0098] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0099] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0100] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A non-coherent user positioning method based on an intelligent reflective surface integrated with a UAV, characterized in that: include: Based on the intelligent reflector and base station, a system model and a channel model assisted by the intelligent reflector are constructed; a block fading model is determined, time slot allocation is determined, and the time interval within the coherence time is divided into three stages; Determine a known anchor point, turn off the smart reflective surface, and estimate the angle from the user to the base station based on the pilot signal sent from the anchor point to the base station; Then, the smart reflective surface is turned on to obtain the interference-free user-smart reflective surface-base station reflection link signal, and the direct component of the smart reflective surface to base station channel is estimated according to the MUSIC algorithm; Calculating the orthogonal space of the direct component according to the direct component, and obtaining a received signal containing only the scattered component based on the orthogonal space; Based on the received signal containing only scattered components, the angle estimation from the user to the intelligent reflector is obtained by exhaustive search of non-coherent maximum likelihood detection method with alternating optimization. Based on the angle information from the user to the base station and the angle estimation from the user to the intelligent reflective surface, the positioning information of the target user is obtained according to the relationship between the position information and the angle.

2. The incoherent user positioning method based on the drone integrated intelligent reflective surface as claimed in claim 1, characterized in that: Based on the smart reflecting surface and the base station, a system model and a channel model assisted by the smart reflecting surface are constructed, including: the system model includes the base station and its antenna, the smart reflecting surface and its components, and the single-antenna user, all channels are modeled with the angle between the transmitting end and the receiving end, the smart reflecting surface and the base station are fixed, and the channel between the smart reflecting surface and the base station is unchanged. At this time, the received signal at the base station includes a direct link from the user to the base station, and also includes a reflection link from the user to the smart reflecting surface to the base station.

3. The incoherent user positioning method based on the drone integrated intelligent reflective surface as claimed in claim 1, characterized in that: Determine the known anchor point, turn off the smart reflector, and then send a pilot signal to the base station. The specific form of the pilot signal received by the base station is: in, represents the direct link from the user to the base station, N represents noise, is the training symbol sent by the user in phase A, is the noise of phase A.

4. The incoherent user positioning method based on an unmanned aerial vehicle integrated intelligent reflective surface as claimed in claim 1, characterized in that: The smart reflector is turned on. At this time, the signal received by the base station contains both the link angle from the user to the base station and the link angle from the user to the smart reflector. Based on the received pilot signal, a received signal containing only the reflection link is obtained. The channel from the smart reflector to the base station is expressed as: in, and represents the normalized line-of-sight link component and the normalized non-line-of-sight link component; Rice factor Determined by the relative strength of the line-of-sight link and the non-line-of-sight link, Represents the large-scale fading coefficient of the channel from the smart reflector to the base station.

5. The incoherent user positioning method based on an unmanned aerial vehicle integrated intelligent reflective surface as claimed in claim 1, characterized in that: According to the direct component estimation, the orthogonal space of the direct component is calculated, and the received signal is projected into the orthogonal space. The projected signal is the received signal of only the scattered component. The received signal of only the scattered component is vectorized, and then the covariance matrix of the received signal of only the scattered component is calculated.

6. The incoherent user positioning method based on an unmanned aerial vehicle integrated intelligent reflective surface as claimed in claim 5, characterized in that: According to the covariance matrix, the conditional distribution of the channel from any user to the smart reflector at the base station with only the scattered component is obtained. The non-coherent maximum likelihood detection is used to solve the channel from the user to the smart reflector. First, assume that there is only one user and obtain the angle estimate of the first user by exhaustively searching the function. Then fix the first user and solve the angle estimate of the second user, and so on, until you get K Angle estimation for each user.

7. An incoherent user positioning system based on an unmanned aerial vehicle integrated with an intelligent reflective surface, characterized in that: include: A model building module is used to build a system model and a channel model assisted by an intelligent reflector based on an intelligent reflector and a base station; determine a block fading model, determine time slot allocation, and divide the time interval within the coherence time into three stages; The calculation and positioning module is used to determine a known anchor point, turn off the intelligent reflective surface, and estimate the angle information from the user to the base station based on the pilot signal sent by the anchor point to the base station; Then, the smart reflective surface is turned on to obtain the interference-free user-smart reflective surface-base station reflection link signal, and the direct component of the smart reflective surface to base station channel is estimated according to the MUSIC algorithm; Calculating the orthogonal space of the direct component according to the direct component, and obtaining a received signal containing only the scattered component based on the orthogonal space; Based on the received signal containing only scattered components, the angle estimation from the user to the intelligent reflecting surface is obtained through the exhaustive search of alternating optimization and non-coherent maximum likelihood detection method. Based on the angle information from the user to the base station and the angle estimation from the user to the intelligent reflecting surface, the positioning information of the target user is obtained according to the relationship between the position information and the angle.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the incoherent user positioning method based on the drone integrated intelligent reflective surface as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the incoherent user positioning method based on the drone integrated intelligent reflective surface as described in any one of claims 1-6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the incoherent user positioning method based on the drone integrated intelligent reflective surface as described in any one of claims 1-6.

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