Target carrying reconfigurable intelligent metasurface enabled MIMO-OFDM system positioning method

Abstract

The embodiment of the invention relates to a target-carried reconfigurable intelligent metasurface enabled MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) system positioning method, which comprises the following steps of: constructing a wireless communication channel model of an RIS-assisted MIMO-OFDM system, and determining a channel matrix of the wireless communication channel model and a receiving signal of a receiver; the received signal is converted into a tensor based on the channel model, channel parameters are independently extracted from the tensor by using a CP decomposition algorithm, and the channel parameters comprise a time delay parameter and an angle parameter; constructing a first linear equation of a path from the transmitter to the RIS and a second linear equation of a path from the RIS to the receiver based on a geometric principle according to the extracted channel parameters; and based on the first linear equation and the second linear equation, solving a positioning result by applying an ellipsoid positioning algorithm. The method provided by the embodiment of the invention can effectively cope with a multipath effect environment, and can improve the algorithm performance.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a MIMO-OFDM system positioning method enabled by a target equipped with a reconfigurable intelligent metasurface. Background Art

[0002] In recent years, high-precision positioning has become a key issue in the field of sensing, and the demand is particularly urgent in 6G application scenarios. 6G communication signals have high-resolution characteristics in the angular and delay domains, as well as large-scale antenna arrays and wide spectrum bandwidth, making high-precision positioning based on radio signals possible. However, in mobile communication scenarios, due to the highly complex propagation environment, there is usually a lack of line-of-sight (LoS) path and long-distance signal attenuation between the base station (BS) and the target, which affects the positioning accuracy and reliability.

[0003] The prior art proposes to use reconfigurable intelligent surfaces (RIS) to establish a virtual line-of-sight path between the base station and the target, eliminating blind area coverage, and using a large number of low-power, low-cost passive reflective elements to cleverly reconfigure the wireless propagation environment and significantly improve the performance of wireless communication networks and sensors. Specifically, RIS can be installed in a device-free sensing system on the sensing target, and the position of the target can be equivalently obtained by estimating the position of RIS.

[0004] However, the actual propagation path will be affected by various scattering and diffraction, causing the signal sent from the transmitter to reach the receiver through multiple paths. As a result, although the reconfigurable intelligent metasurface is used to assist in estimating the target's position, the positioning accuracy of the RIS position is not high enough.

[0005] Therefore, it is necessary to provide a new technical solution to improve one or more problems existing in the above solutions.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0007] The purpose of the embodiments of the present disclosure is to provide a MIMO-OFDM system positioning method enabled by a reconfigurable intelligent metasurface, so as to improve the accuracy of target positioning according to the transmission conditions of signals under multiple propagation paths.

[0008] According to an embodiment of the present disclosure, a MIMO-OFDM system positioning method enabled by a target equipped with a reconfigurable intelligent metasurface is provided, comprising:

[0009] Establishing a wireless communication channel model of a MIMO-OFDM system assisted by RIS, and determining a channel matrix of the wireless communication channel model and a received signal of a receiver;

[0010] Based on the wireless communication channel model, the received signal is converted into a tensor, and channel parameters are independently extracted from the tensor using a CP decomposition algorithm, wherein the channel parameters include a delay parameter and an angle parameter;

[0011] According to the extracted channel parameters, constructing a first straight line equation of a transmitter-to-RIS path and a second straight line equation of a RIS-to-receiver path based on geometric principles;

[0012] Based on the first straight line equation and the second straight line equation, an ellipsoid positioning algorithm is applied to solve a positioning result.

[0013] The technical solution provided by the present disclosure may have the following beneficial effects:

[0014] In the embodiments of the present disclosure, on the one hand, in view of the transmission of signals in multiple propagation paths, the CP decomposition algorithm is used to independently extract delay parameters and angle parameters from the received signal to achieve joint estimation of channel parameters related to positioning, which ensures the accuracy of positioning to a certain extent; on the other hand, the second straight line equation of the RIS to receiver path is constructed based on the geometric principle according to the extracted channel parameters, and the first straight line equation of the transmitter to RIS path is also added, which effectively enhances the adaptability and accuracy of the positioning method in complex scenarios, so that the positioning result solved by the ellipsoid positioning algorithm based on the first straight line equation and the second straight line equation is more accurate. The method proposed in the embodiments of the present disclosure can also effectively cope with the multipath effect environment and improve the algorithm performance.

[0015] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0017] Figure 1 A flowchart showing the steps of a method for positioning a target-carrying reconfigurable smart metasurface-enabled MIMO-OFDM system in an exemplary embodiment of the present disclosure;

[0018] Figure 2 A schematic diagram showing a wireless communication channel model based on a RIS-assisted MIMO-OFDM system in an exemplary embodiment of the present disclosure;

[0019] Figure 3 A block diagram showing a MIMO-OFDM transceiver with digital precoding in an exemplary embodiment of the present disclosure is shown;

[0020] Figure 4 The local coordinate system in the exemplary embodiment of the present disclosure is shown Schematic diagram of

[0021] Figure 5 The channel parameter θ in the simulation experiment of the exemplary embodiment of the present disclosure is shown. t Schematic diagram of the simulation results of MSE performance and CRB relative to the signal-to-noise ratio SNR;

[0022] Figure 6 The channel parameter φ in the simulation experiment of the exemplary embodiment of the present disclosure is shown. t Schematic diagram of the simulation results of MSE performance and CRB relative to the signal-to-noise ratio SNR;

[0023] Figure 7 The channel parameters in the simulation experiment of the exemplary embodiment of the present disclosure are shown as follows: Schematic diagram of the simulation results of MSE performance and CRB relative to the signal-to-noise ratio SNR;

[0024] Figure 8 Channel parameters in a simulation experiment showing an exemplary embodiment of the present disclosure Schematic diagram of the simulation results of MSE performance and CRB relative to the signal-to-noise ratio SNR;

[0025] Fig. 9 Channel parameters in a simulation experiment showing an exemplary embodiment of the present disclosure Schematic diagram of the simulation results of MSE performance and CRB relative to the signal-to-noise ratio SNR;

[0026] Fig.10 The relationship between the MSE of the ellipsoid positioning method proposed for the prior art and the CRB and signal-to-noise ratio of the target position according to the exemplary embodiment of the present disclosure is shown;

[0027] Fig.11 A schematic diagram showing a three-dimensional mapping of an estimated target position when the signal-to-noise ratio is 20 dB in a simulation experiment of an exemplary embodiment of the present disclosure;

[0028] Fig.12 A schematic diagram showing the normalized mean square error results of various methods in the simulation experiment of the exemplary embodiment of the present disclosure as a function of the signal-to-noise ratio;

[0029] Fig.13 A schematic diagram showing the relationship between the NMSE of position estimation and the number of RXs in a simulation experiment of an exemplary embodiment of the present disclosure;

[0030] Fig.14 A schematic diagram showing the relationship between NMSE and the number of RIS elements of various methods in a simulation experiment of an exemplary embodiment of the present disclosure;

[0031] Fig.15 A schematic diagram showing the comparison results of NMSE and running time in a simulation experiment of an exemplary embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0032] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0033] In this example implementation, a MIMO-OFDM system positioning method enabled by a reconfigurable intelligent metasurface is first provided. Figure 1 As shown in , the method may include the following steps:

[0034] Step S101: establishing a wireless communication channel model based on a RIS-assisted MIMO-OFDM system, and determining a channel matrix of the wireless communication channel model and a received signal of a receiver;

[0035] Step S102: converting the received signal into a tensor based on the wireless communication channel model, and independently extracting channel parameters from the tensor using a CP decomposition algorithm, wherein the channel parameters include a delay parameter and an angle parameter;

[0036] Step S103: constructing a first straight line equation of a path from the transmitter to the RIS and a second straight line equation of a path from the RIS to the receiver based on the geometric principle according to the extracted channel parameters;

[0037] Step S104: Based on the first straight line equation and the second straight line equation, an ellipsoid positioning algorithm is applied to solve the positioning result.

[0038] In the embodiments of the present disclosure, on the one hand, in view of the transmission of signals in multiple propagation paths, a tensor-based method is considered to model the received signal, and the received signal in the multipath environment is mapped to different dimensions of the fourth-order tensor through the corresponding angle parameters and delay parameters, so as to clearly distinguish the signal characteristics of each path. And the canonical polyadic (CP) decomposition method is used to decompose the received signal into multiple tensors of rank one, so as to independently extract the delay and angle information, which ensures the accuracy of the channel parameter estimation to a certain extent; on the other hand, based on the extracted channel parameters, the second straight line equation of the RIS to receiver path is constructed according to the geometric principle, and in addition, the first straight line equation of the transmitter to RIS path is constructed, which not only completes the geometric description of the signal propagation path, but also strengthens the constraints of the simultaneous equations, thereby reducing the positioning error, so that the positioning result solved by the ellipsoid positioning algorithm based on the first straight line equation and the second straight line equation is more accurate. The method proposed in the embodiments of the present disclosure can also effectively deal with the multipath effect environment and improve the algorithm performance.

[0039] Next, we will refer to Figures 1 to 4 Each step of the above method in this example implementation is described in more detail.

[0040] Example, reference Figure 2 As shown in , the RIS-assisted MIMO-OFDM system mentioned in step S101 is a RIS-assisted multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. The system includes a target, a transmitter and a group of distributed receivers, and the group of distributed receivers can be composed of Q receivers, wherein a RIS is installed on the outer surface of the target, and the controllable signal reflection of the RIS is used to estimate the target position.

[0041] It should be noted that in Figure 2 The RIS-assisted MIMO-OFDM system shown in FIG. 1 also includes multiple scatterers. Due to the presence of these scatterers, the signal emitted from the transmitter reaches the receiving end through multiple paths. Figure 2 In , TX represents a transmitter, RX represents a receiver, and RXq represents the qth receiver in a group of distributed receivers.

[0042] In one embodiment, the step of establishing a wireless communication channel model based on a RIS-assisted MIMO-OFDM system in step S101 may include the following steps:

[0043] Step S1011: Establishing a global Cartesian coordinate system C based on the transmitter B ;

[0044] Step S1012: Establish a local Cartesian coordinate system C based on the qth receiver Rq ;

[0045] Step S1013: configuring uniform planar arrays and radio frequency chains for both the transmitter and the receiver.

[0046] Specifically, regarding the transmitter-based global Cartesian Coordinate System (CCS) C established in step S1011 B ,refer to Figure 2 The global CCS shown in the figure has a global Cartesian coordinate system with its origin set to an angle C of the transmitter. B , the y-axis and z-axis are along the antenna direction of the transmitter, and the x-axis can be determined by the right-hand rule.

[0047] Specifically, regarding the local Cartesian coordinate system based on the qth receiver established in step S1012 refer to Figure 2 The local CCS is shown in , where the local Cartesian coordinate system The establishment method and global Cartesian coordinate system C B is established in a similar way.

[0048] Specifically, in step S1013, for the transmitter, the transmitter is configured with a Uniform Planar Array (UPA) and M T Radio Frequency (RF) chain, where: and They represent the coordinates along the global Cartesian coordinate system C B The number of antennas on the y-axis and z-axis.

[0049] Specifically, in step S1013, for the receiver, the receiver is configured with Uniform planar array and M R RF chain, where and They represent the local Cartesian coordinate system The number of antennas on the y-axis and z-axis.

[0050] Specifically, in step S101, for RIS, RIS is composed of M=M x ×M y elements, where M xand M y They represent the coordinates along the global Cartesian coordinate system C B The number of elements on the x-axis and y-axis.

[0051] The determination of the channel matrix of the wireless communication channel model and the received signal of the receiver in step S101 may be implemented in the following specific manner.

[0052] The RIS-assisted MIMO-OFDM system in this embodiment occupies K subcarriers, and the center carrier frequency is f c , bandwidth is f s , assign K tr subcarriers are used for training. In this embodiment, a geometric broadband millimeter wave channel model is used to characterize the channel between the transmitter and the RIS and the channel between the RIS and the qth receiver. Hereinafter, the transmitter to the RIS may be abbreviated as TX-RIS, and the RIS to the qth receiver may be abbreviated as RIS-RXq. Then the channel between TX-RIS can be expressed in the delay domain as:

[0053]

[0054] Among them, L 1 Indicates the total number of paths between TX-RIS, Indicates that 1 The random complex gain associated with each path, Indicates that 1 The delay associated with each path, represents the Dirac function, I t The subscript indicating the angle of the RIS end of the TX-RIS path, and denote the elevation and azimuth of the departure angle at TX, respectively. and denote the elevation and azimuth of the arrival angle at RIS on the TX-RIS path, represents the array response at TX on the TX-RIS path, represents the array response at RIS on the TX-RIS path,

[0055] Array response at TX on TX-RIS path and the array response at RIS on the TX-RIS path It can be expressed as:

[0056]

[0057] Among them, d t and d Idenotes the antenna spacing of UPA at TX and RIS, λ denotes the wavelength, j denotes a complex number, M x Represents the number of elements along x, M y Represents the number of elements along the y-axis, represents the number of antennas along the z-axis, represents the Kronecker product.

[0058] Then the channel between RIS-RXq can be expressed in the delay domain as:

[0059]

[0060] Among them, L 2 Indicates the total number of paths between RIS-RXq, Indicates that 2 The random complex gain associated with each path, Indicates that 2 The delay associated with each path, represents the Dirac function, The subscript representing the angle of the RIS end on the RIS-RXq path, r q The subscript representing the angle of the qth RX end, and denote the elevation and azimuth of the departure angle at RIS on the RIS-RXq path, and denote the elevation and azimuth of the arrival angle at RXq, respectively, represents the array response at RXq on the RIS-RXq path, represents the array response at RIS on the RIS-RXq path,

[0061] The array response at RXq on the RIS-RXq path and the array response at RIS on the RIS-RXq path can be expressed as:

[0062]

[0063]

[0064] Among them, d r Indicates the antenna spacing of UPA at RX.

[0065] Based on the above array response, the channel matrix of the wireless communication channel model can be determined, specifically: the channel matrix on the TX-RIS path at the kth subcarrier and channel matrix on the RIS-RXq path Channel matrix on TX-RIS path In the frequency domain it can be expressed as:

[0066]

[0067] Among them, L 1 Indicates the total number of paths between TX-RIS, Indicates that 1 The random complex gain associated with each path, j represents a complex number, k represents the sequence number of the subcarrier, and f s represents bandwidth, K represents the number of subcarriers, Indicates that 1 The delay associated with each path, represents the array response at TX on the TX-RIS path, represents the array response at RIS on the TX-RIS path, express The transpose of and denote the elevation and azimuth of the departure angle at TX, respectively. and denote the elevation and azimuth of the arrival angle at RIS on the TX-RIS path, respectively;

[0068] Channel matrix on the RIS-RXq path In the frequency domain it can be expressed as:

[0069]

[0070] Among them, L 2 Indicates the total number of paths between RIS-RXq, Indicates that 2 The random complex gain associated with each path, Indicates that 2 The delay associated with each path, The subscript representing the angle of the RIS end on the RIS-RXq path, r q The subscript represents the qth RX end angle, j represents a complex number, k represents the subcarrier sequence number, and f represents the s represents bandwidth, K represents the number of subcarriers, represents the array response at RXq on the RIS-RXq path, represents the array response at RIS on the RIS-RXq path, express The transpose of and denote the elevation and azimuth of the departure angle at RIS on the RIS-RXq path, and denote the elevation and azimuth of the arrival angle at RXq respectively.

[0071] For example, a downlink training method is used, where the TX uses T different beamforming vectors for each subcarrier in T consecutive time frames, each time frame being divided into P time slots. In each time slot, the RIS uses a different phase shift matrix To reflect the incident signal, where v p represents the phase shift vector, The beamforming vector of the kth subcarrier in the tth time frame can be expressed as:

[0072] f k (t) = F RF (t)f BB,k (t)s k (t) (13)

[0073] Among them, s k (t) represents the pilot symbol of the kth subcarrier, represents the digital precoding vector, Indicates the common RF precoder for all subcarriers.

[0074] Example, reference Figure 3 As shown in , a block diagram of a MIMO-OFDM transceiver with digital precoding is given. The coding process includes:

[0075] Using the digital precoding vector f BB,k (t) For each pilot symbol s on a subcarrier k (t) performing precoding and converting the symbol block into the time domain using a K-point Inverse Discrete Fourier Transform (IDFT);

[0076] The cyclic prefix is ​​added to the symbol block and the general RF precoder F RF (t) applies to all subcarriers;

[0077] RIS passively reflects the signal through a programmable phase shift matrix, intelligently adjusts the phase distribution of the channel, and transmits the signal to the RX with an optimized phase and angle, ensuring that the signal strength at the RX is enhanced while reducing interference;

[0078] In each time frame, RXq uses the vector set of RF beamforming in turn To detect the transmitted signal, these RF beamforming vector sets are common across all subcarriers;

[0079] In each time slot, the received signal is initially combined in the RF domain, then the cyclic prefix is ​​removed and the symbols are converted back to the frequency domain via a discrete Fourier transform (DFT).

[0080] For simplicity, in this embodiment, it is assumed that all subcarriers have f BB,k (t) = f BB (t) and s k (t) = 1, so in this case, for all subcarriers:

[0081]

[0082] Where f(t) represents the beamforming vector, For all subcarriers;

[0083] In the p-th time slot and k-th subcarrier of the t-th time frame, the m-th subcarrier of the q-th RX r The received signal of the RF chain can be expressed as:

[0084]

[0085] in, represents additive Gaussian noise, represents the beamforming vector, express The transpose of G k,q represents the channel matrix on the RIS-RXq path, Φ p represents the phase shift matrix at RIS, H k represents the channel matrix on the TX-RIS path, f(t) represents the beamforming vector, Indicates the mth time slot of the pth time slot of the tth time frame, on the tth subcarrier, and on the qth RX r The received signal of the RF chain.

[0086] It should be understood that due to the complexity of target positioning, which mainly involves extracting multiple channel parameters and dealing with multipath interference, in this embodiment, in order to better handle the problem, target positioning is divided into two stages. In general, in the first stage, the CP decomposition algorithm is used to extract the delay parameters and angle parameters from the received signal obtained above to avoid the inability to accurately distinguish different channel information due to signal mixing; in the second stage, the extracted delay parameters and angle parameters are used to model the positioning problem based on geometric principles, and the straight line equation of TX-RIS is added to improve the traditional ellipsoid positioning algorithm to achieve precise positioning. The following describes these two stages in detail.

[0087] For example, in step S102, the received signal is converted into a tensor based on the wireless communication channel model, and the channel parameters are independently extracted from the tensor using the CP decomposition algorithm, which may include the following sub-steps:

[0088] Step S1021: for the receiver, the transmitter and the RIS, performing signal acquisition based on the receiver predefined radio frequency chain, time frame, time slot and subcarrier;

[0089] Step S1022: Modeling the received signal of the receiver as a fourth-order tensor based on the radio frequency chain, time frame, time slot and subcarrier;

[0090] Step S1023: Decompose the fourth-order tensor into a sum of multiple rank-one tensors using the CP decomposition algorithm to obtain a decomposition result, and estimate a factor matrix based on the decomposition result;

[0091] Step S1024: Estimate channel parameters according to the factor matrix to obtain delay parameters and angle parameters.

[0092] Specifically, in step S1021, taking the qth receiver as an example, each receiver, transmitter and RIS may perform signal acquisition based on the RXq predefined RF chain, time frame, time slot and subcarrier, wherein the predefined RF chain, time frame, time slot and subcarrier are respectively represented as For the convenience of description, the following matrices are further defined for the RF chain, time frame, and time slot:

[0093]

[0094] Among them, W q represents the matrix defined for the RF chain, w(q,M R ) indicates W q The Mth R elements, F represents the matrix defined for the time frame, f(T) represents the Tth element in the F matrix, V represents the matrix defined for the time slot, v P represents the Pth element in the V matrix;

[0095] At the same time, for the convenience of description, the following vectors are further defined:

[0096]

[0097] Where g represents the vector defined for the subcarrier, represents the Kth tr elements, γ q,l represents the random complex gain of the TX-RIS-RX cascade path,

[0098] Specifically, in step S1022, based on the matrices and vectors further defined for the RF chain, time frame, time slot and subcarrier, the received signal of each receiver is modeled as a fourth-order tensor by using different combinations of these parameters at TX, RIS and each receiver. Among them, the first (m r ,p,t,k) elements represent when RXq is applied RIS and TX apply v p , f(t) and g k When RXq receives the received signal, the received signal can be expressed as:

[0099]

[0100] Where L = L 1 L 2 is the total number of paths between TX and RXq, represents the cascade elevation angle at RIS, represents the cascade azimuth at RIS, is the delay of the TX-RIS-RXq cascade path.

[0101] definition Then, at the pth time slot of the tth time frame of the kth subcarrier, the received signal received by RXq can be expressed as:

[0102]

[0103] in,

[0104] definition Then, at the kth subcarrier and the tth time frame, the received signal received by RXq can be expressed as:

[0105]

[0106] in, f(t) T represents the transpose of the beamforming vector f(t), W q Transpose of the matrix, V T Represents the transpose of the V matrix.

[0107] It should be noted that, since each RX has signals from multiple time frames and multiple subcarriers, in step S1022, the signals of all subcarriers received in all time frames can be represented as a fourth-order tensor

[0108] From the above formula (21), we can know that the slice Y k,q (t) can be represented as two rank-one tensors and vector The weighted sum of the outer products, so in step S1023, the fourth-order tensor The following CP decomposition is allowed, converting the fourth-order tensor Decomposed into the sum of multiple rank-one tensors, it can be schematically represented as:

[0109]

[0110] in, A tensor representing noise, represents the outer product, Both represent rank-one tensors, L represents the total number of paths between TX and RXq, l represents the sequence number of the path, and r q The subscript representing the angle of the qth RX end, I q The subscript representing the angle at RIS, τ q The corner mark indicating the qth RX end delay. And:

[0111]

[0112] Among them, F T represents the transpose of the F matrix.

[0113] The above formula (4) can be regarded as the decomposition result of the fourth-order tensor. By defining Get the factor matrix, where {A, B, C, D} is a fourth-order tensor The factor matrix of .

[0114] It should be noted that it can be seen that the delay parameters and angle parameters of the channel can be easily estimated from the factor matrix. Therefore, in step S1024, this embodiment first estimates the delay parameters and angle parameters of the channel from the fourth-order tensor Four factor matrices are estimated in , and then the relevant channel parameters are further estimated based on these estimated factor matrices. According to formula (4), the fourth-order tensor The n (n = 1, 2, 3, 4) modal expansion can be expressed as:

[0115] Y q,(1) =A(D⊙C⊙B) T +N q,(1) (twenty four)

[0116] Y q,(2) =B(D⊙C⊙A) T +N q,(2) (25)

[0117] Y q,(3) =C(D⊙B⊙A) T +N q,(3) (26)

[0118] Y q,(4) =D(C⊙B⊙A) T +N q,(4) (27)

[0119] Among them, Y q,(1) and N q,(1) Respectively indicate and The 1-modal expansion matrix, Y q,(2) and N q,(2) Respectively and The 2-modal expanded matrix, Y q,(3) and N q,(3) Respectively and The 3-modal expanded matrix, Y q,(4) and N q,(4) Respectively and The matrix after the 4-modal expansion of , ⊙ represents the Hartree-Rao product.

[0120] Assuming that the number of paths L is known a priori, the fourth-order tensor in step S1023 above is The CP decomposition of can be solved by solving the following least squares problem:

[0121]

[0122] in, Represents a fourth-order tensor

[0123] It should be noted that the problem expressed in the above formula (28) can be effectively solved by the alternating least squares (ALS) method. The alternating least squares method alternately optimizes the four factor matrices to minimize the data fitting error of one factor matrix, while the other three factor matrices are fixed, which can be expressed as:

[0124]

[0125] Among them, A (u) , B (u) , C (u) , D (u) They represent the factor matrices at the u-th iteration, They represent the estimated values ​​of A, B, C, and D at the u+1th iteration respectively.

[0126] The above formula is solved by the alternating least squares method. When the difference between two iterations is less than a given threshold, the solution can be expressed as:

[0127] According to the above solution results, use Represents the estimated values ​​of factor matrices A, B, C, and D. Based on the correlation between the estimated factor matrix and the actual factor matrix, the estimated factor matrix can be expressed as Among them, Λ 1 ,Λ 2 ,Λ 3 ,Λ 4 is an unknown non-singular diagonal matrix that satisfies Λ 1 Λ 2 Λ 3 Λ 4 =I;Π 1 , Π 2 ,Π 3 , Π 4 represents the estimation error; Γ is the unknown permutation matrix and can be ignored.

[0128] Since each column of the factor matrices A, B, C, and D can be represented by the arrival angle of the associated RX RIS cascade angle (θ I,l ,φ I,l ), TX departure angle (θ t,l ,φ t,l ) and time delay τ q,l Therefore, a correlation-based method can be used to estimate the relevant parameters, and this correlation-based method can be regarded as a maximum likelihood estimation.

[0129] Then define the vector Then the cascade path delay parameter can be estimated as:

[0130]

[0131] in, Represents the estimated factor matrix Column l, express The conjugate transpose of represents the estimated delay of the cascade path, g(τ l ) represents a vector defined.

[0132] Then the elevation and azimuth angles of the arrival angle at the qth RX can be estimated as:

[0133]

[0134] in, for The lth column of

[0135] The elevation and azimuth angles of the cascade at the RIS can be estimated as:

[0136]

[0137] in, for The lth column of

[0138] The elevation and azimuth of the departure angle at TX can be estimated as:

[0139]

[0140] in, for The first column of .

[0141] According to the above estimates, the non-singular diagonal moment Λ 1 ,Λ 2 ,Λ 3 Then according to You can get Λ 4 The estimated value of .

[0142] Define the matrix G(γ q ) = diag[γ q,1 ,...,γ q,L ], ignoring the estimation error, the fading coefficient can be obtained by D = [g(τ 1 ),...,g(τ L )] T G(γ q )=G(τ l )G(γ q ) is calculated as:

[0143]

[0144] Among them, G(γ q ) represents a matrix defined, Denotes G(γ q ), G(τ l ) is expressed by [g(τ 1 ),...,g(τ L )] T The matrix formed by D represents the fading coefficient. Denote G(τ l ) Take the Moore-Penrose generalized inverse matrix.

[0145] In this embodiment, since the signal passes through different propagation paths during the propagation process, the receiver receives multiple signals with different delays and strengths. And the signal power of the non-line-of-sight (NLoS) propagation path is much lower than the signal power of the line-of-sight (LoS) propagation path, and is close to the noise power. In addition, when locating the target, it mainly depends on the LoS path between the transmitter and the receiver. Therefore, in order to improve the positioning accuracy, this embodiment is to find the path with the largest received signal power, which corresponds to the estimated parameters of the LoS path, which can be calculated as:

[0146]

[0147] in, represents the estimated value of the received signal of the lth path, which is composed of the estimated parameters represented by the above formulas (33) to (36), P max Indicates the maximum received signal power. The channel parameter corresponding to the path with the maximum received signal power can be expressed as

[0148] In step S103, according to the extracted channel parameters, a first straight line equation of the transmitter-RIS path and a second straight line equation of the RIS-receiver path are constructed based on geometric principles, which may include the following sub-steps:

[0149] Step S1031: obtaining a global Cartesian coordinate system, coordinate positions of a transmitter and a receiver in the global Cartesian coordinate system;

[0150] Step S1032: define the midpoint of the line connecting the coordinate position of the transmitter and the coordinate position of the receiver as the coordinate origin, locate the coordinate position from the coordinate origin to the receiver as the x-axis, translate and rotate the global Cartesian coordinate system to construct a local coordinate system, and obtain the ellipsoid equation in the local coordinate system;

[0151] Step S1033: determining an expression of the channel parameter in a local coordinate system based on the channel parameter extracted from the wireless communication channel model;

[0152] Step S1034: Determine the first straight line equation and the second straight line equation according to the expression of the channel parameter in the local coordinate system.

[0153] For example, in step S1031, since the positions of TX and RX are deployed in advance, the coordinate positions of TX and RX are known. Then TX, RIS and RXq are in the global Cartesian coordinate system C B The coordinates in are p t =[p t,x ,pt,y ,p t,z ] T =[0,0,0] T , p=[p x ,p y ,p z ] T and p q =[p q,x ,p q,y ,p q,z ] T .

[0154] It should be noted that, based on the departure angle on the TX-RIS path and the arrival angle on the RIS-RXq path obtained in step S103, the position of RIS at the transmitter is and the direction at the receiver is In step S103, the delay of the cascade path has also been obtained, so the distance of the entire LoS path can be given by Determine, where c is the speed of light. When the cascade path distance is fixed, an ellipsoid can be established with the positions of TX and RXq as the focus, and the position of the target is on the ellipsoid. By deriving the intersection of the corresponding ellipsoid and the straight line, the position of the target can be estimated as the center of the intersection.

[0155] The distance from TX to RXq can be expressed as d q =|p q |, so the lengths of the three semi-major axes associated with the qth ellipsoid can be calculated as Then in step S1032, refer to Figure 4 As shown in , construct a local coordinate system Define the midpoint of the line connecting TX and RXq as the local coordinate system The coordinate origin of the receiver is defined as the x-axis, and the global Cartesian coordinate system C is translated parallel to the receiver. B , and then rotate the global Cartesian coordinate system C around its three semi-major axes B , we can get the local coordinate system To simplify the coordinate system transformation, the rotation angle around the x-axis can be set to zero. Then the local coordinate system The ellipsoid equation can be expressed as:

[0156]

[0157] Among them, a q represents the length of the semi-major axis of the ellipsoid on the x-axis, b q Represents the length of the semi-major axis of the ellipsoid on the y-axis and z-axis.

[0158] For example, in step S1033, based on the channel parameters extracted from the wireless communication channel model, an expression of the channel parameters in the local coordinate system is determined, which is specifically as follows:

[0159] The direction vector from TX to RIS is: The direction vector from RIS to RXq is The direction vector m from TX to RIS is in the global Cartesian coordinate system C B The slope of the TX-RIS line on the In the above example, the x-axis, y-axis, and z-axis are defined from the global Cartesian coordinate system C B To local coordinate system The rotation angle is Similarly, the direction vector m from RIS to RXq q is the RIS-RX line in the local Cartesian coordinate system The slope also needs to be rotated to the local coordinate system In the local Cartesian coordinate system, the x-axis, y-axis, and z-axis are defined as To local coordinate system The rotation angle is The corresponding rotation matrices can be expressed as:

[0160]

[0161] in, It represents the rotation matrix corresponding to the x-axis when the TX-RIS line rotates. It represents the rotation matrix corresponding to the y-axis when the TX-RIS line rotates. It represents the rotation matrix corresponding to the z-axis when the TX-RIS line rotates;

[0162]

[0163] in, It represents the rotation matrix corresponding to the x-axis when the RIS-RX line rotates. It represents the rotation matrix corresponding to the y-axis when the RIS-RX line rotates. Represents the rotation matrix corresponding to the z-axis when the RIS-RX line rotates;

[0164] Among them, the relevant angles can be expressed as:

[0165]

[0166] in, and They are the global Cartesian coordinate system C B The unit vectors along the x-axis and z-axis in ; is the global Cartesian coordinate system C B The coordinates of RXq in;

[0167]

[0168] in, and The local Cartesian coordinate system is The unit vectors along the x-axis and z-axis in ; is the local Cartesian coordinate system The coordinates of TX in the local coordinate system. The direction vector m and the direction vector m q Can be expressed as and

[0169] For example, in step S1034, the first straight line equation and the second straight line equation are determined according to the expression of the channel parameter in the local coordinate system, as follows:

[0170] Based on the above obtained local coordinate system The direction vector and direction vector In the local coordinate system In the equation, the first straight line equation from the transmitter to the RIS path can be expressed as:

[0171]

[0172] in, represents the equation of the first straight line, It means taking the x-axis component, It means taking the component of y, Indicates the component of z, d q Indicates the distance from TX to RXq;

[0173] In the local coordinate system In the equation, the second straight line equation from RIS to the qth receiver path can be expressed as:

[0174]

[0175] in, represents the equation of the second line, represents the x-axis component, It means taking the component of y, Indicates the component of z, d q Indicates the distance from TX to RXq.

[0176] In step S104, based on the first straight line equation and the second straight line equation, an ellipsoid positioning algorithm is applied to solve the positioning result, which may include the following sub-steps:

[0177] Step S1041: defining a first intersection point between the first straight line equation and the ellipsoid equation in the local coordinate system in the local coordinate system, and solving the coordinates of the first intersection point based on the ellipsoid equation and the first straight line equation to obtain first solution coordinates;

[0178] Step S1042: defining a second intersection point between the second straight line equation and the ellipsoid equation in the local coordinate system in the local coordinate system, and solving the coordinates of the second intersection point based on the ellipsoid equation and the second straight line equation to obtain second solution coordinates;

[0179] Step S1043: Based on the first solution coordinates and the second solution coordinates, obtain the positioning result.

[0180] For example, in step S1041, since the first straight line equation and the equation of the second line Through the ellipsoid point and Point Therefore, the equation of the first line is and the equation of the second line It will certainly be the same as the ellipsoid Intersection, Ellipsoid This is the ellipsoid defined by the ellipsoid equation. In the equation of the first line, The intersection point with the ellipsoid equation is considered as the first intersection point, and the coordinates of the first intersection point are defined as The coordinates of the first intersection point can be defined as follows:

[0181]

[0182] By solving the above formula (44), we can get There are two first solution coordinates, expressed as and And it can be found that the target position should be on the right focus towards In the positive direction. So the coordinates of the first solution must satisfy:

[0183]

[0184] in, Represents an ellipsoid The intersection point with the equation of the first line, Representation and Represents an ellipsoid The two first intersection points with the equation of the first straight line, represents the direction vector, Represents the local coordinate system Direction vector of lower TX-RIS;

[0185] The coordinates of the first solution that does not satisfy formula (45) are discarded, and the coordinates of the first solution that satisfies formula (45) are marked as The distance from TX to RIS can be calculated as in, Then RIS is in the global Cartesian coordinate system C B The position in the coordinate system can be expressed as p I =d TI m.

[0186] For example, in step S1042, in the local coordinate system In the equation of the second line, The intersection point with the ellipsoid equation is regarded as the second intersection point, and the coordinates of the second intersection point are defined as The coordinates of the second intersection point can be defined as follows:

[0187]

[0188] By solving the above formula (46), we can get There are two second solution coordinates, expressed as and And it can be found that the position of RIS should be away from the left focal point towards The negative direction. So the coordinates of the second solution must satisfy:

[0189]

[0190] in, Represents an ellipsoid The intersection point with the equation of the second line, and Represents an ellipsoid The two second intersection points with the equation of the second line, represents the direction vector, Represents the local coordinate system The direction vector of RIS-RXq;

[0191] The coordinates of the second solution that does not satisfy formula (47) are discarded, and the coordinates of the second solution that satisfies formula (47) are marked as The distance from RIS to RXq can be calculated as in, Then RIS is in the local Cartesian coordinate system The position in the coordinate system can be expressed as In order to Transform to the global Cartesian coordinate system C B , you need to change m q Further rotate to in, You can refer to formulas (40)-(41) for similar definitions. When deploying RX, you can preset The rotation angle in the global Cartesian coordinate system C B The position below is Among them, p q Represents RXq in the global Cartesian coordinate system C B The coordinates in represents the distance of RIS-RXq, Indicates that m q Rotate to the global Cartesian coordinate system C B The direction vector obtained below.

[0192] For example, in step S1043, based on the first solution coordinates and the second solution coordinates, a positioning result is obtained, as follows:

[0193] The p I and Find the average value and get the positioning result of RIS positioning using the qth RX: By obtaining all the positioning results of a group of distributed receivers for RIS positioning according to the above method, and then averaging all the positioning results, the final positioning result of RIS is obtained. The final positioning result of RIS is the final positioning result of the target. The final positioning result of RIS can be derived as

[0194] In the embodiments of the present application, in order to evaluate the performance of the method proposed in the present application, the Cramér-Rao Bound (CRB) analysis process is given below.

[0195] CRB is the lower bound of the variance of any unbiased estimator and provides a benchmark for evaluating the performance of the method proposed in this application. In this embodiment, not only the CRB of the channel parameters is derived, but also the CRB of the target position is further estimated based on these parameters. In the indirect positioning process, the channel parameters are first estimated using the signal received by RX, and then the target position is determined using the estimated channel parameters. In order to cooperate with the indirect positioning method, the CRB calculation is divided into the following two parts.

[0196] The first part is to derive the CRB of angle parameters and delay parameters, specifically:

[0197] Define the delay combination of all paths of all receivers τ = [τ 1,1 ,...,τQ,L ] T , the combination of all elevation angles at the receiver Combination of all azimuths at the receiver The combination of all elevation angles at the transmitter is θ t =[θ t,1 ,...,θ t,L ] T , the combination of all azimuth angles at the transmitter φ t =[φ t,1 ,...,φ t,L ] T ,vector The vector g can represent the delay and angle vector containing the above combination, then CRB(g)=I -1 (g), where I(g) is the Fisher information matrix (FIM) of the vector g.

[0198] The second part is to derive the CRB of the target location based on the FIM of the parameters. Since the angle parameters and delay parameters of the path with the largest received signal power are used in the positioning process, only the corresponding FIM is considered here, denoted as I(g max ), then the FIM of the target position can be expressed as:

[0199]

[0200] in, is the vector g max Jacobian matrix of the target position; therefore, the CRB of the target position can be expressed as CRB = tr(I -1 (p)).

[0201] In the embodiments of the present application, in order to further verify the effectiveness of the method proposed in the present application in terms of accurate positioning, the results of simulation experiments are given below.

[0202] In the simulation experiment, the number of TX and RX antennas and the elements of RIS are set as: M x =8 and M y = 8. The number of RF chains for TX and each RX is set to M T =M R =2. The antenna spacings of TX, each RX and RIS are set to: d t =λ / 2, d r =λ / 2 and d I =λ / 4. The arrival angle and departure angle are randomly distributed in [0,2π], and the number of paths is set to: L 1 =2, L 2 =2, and the delay of each path is τq,l Uniformly distributed between 0 and 100 nanoseconds. Complex gain and Obey circularly symmetric Gaussian distribution and in, Indicates the lth time from TX to RIS 1 The length of the path, Indicates the first 2 The length of the path. It also sets f c =28GHz, the total number of subcarriers K = 128, from which K is selected tr subcarriers for training, and the sampling rate is set to f s =0.32GHz. Beamforming matrix F and combining matrix W q is randomly generated, and its components are uniformly selected on the unit circle. The number of RXs is Q = 2, and the positions of the two RXs are fixed at p 1 =[2,10,9] T and p 2 =[-5,8,6] T The signal-to-noise ratio (SNR) is defined as the ratio of the signal component to the noise component.

[0203] In the simulation experiment, the parameters related to TX, RIS and RX1 are first evaluated. The estimation accuracy of is measured by using mean square error (MSE), and the CRB results are also included, which provides a benchmark for evaluating the performance of the method proposed in this application. The mean square error of each group of parameters can be calculated as: in, from Figures 5 to 9 It can be seen from the figure that for the method proposed in this application, the CRB of each channel parameter decreases exponentially with the increase of the signal-to-noise ratio. Especially in the case of high signal-to-noise ratio, the MSE obtained by the tensor-based method is close to the CRB, which verifies the effectiveness of the method proposed in this application and obtains accurate channel parameter estimation. In addition, for the ellipsoid positioning method proposed in the prior art, Fig.10 The relationship between the MSE of the ellipsoid positioning method and the CRB of the target position and the signal-to-noise ratio is shown. Figures 5 to 9 The simulation results of the channel parameters are similar, and the obtained MSE decreases with the increase of the signal-to-noise ratio. However, the gap between MSE and CRB indicates that there is still room for improvement in the method proposed in this application.

[0205] Fig.11The three-dimensional mapping diagram of the target estimated position with a signal-to-noise ratio of 20 dB is shown, taking two RXs as an example, where P is set tr =8,T=8,K tr =8, Q = 2. Fig.11 It can be seen from the figure that the method proposed in this application has high reliability in positioning.

[0206] In addition, the performance of the two-stage positioning scheme proposed in this application is also studied and compared with the least squares algorithm and ellipsoid positioning algorithm in the prior art. In the least squares algorithm, the relationship between the estimated angle parameter and the target position is established, and the least squares algorithm is used to solve it, so as to locate the target. For the ellipsoid positioning algorithm, it is necessary to estimate the elevation and azimuth angles of the receiving end arrival angle and the cascade path delay, construct the linear equation of RIS-RX, and locate the target by calculating the intersection of the straight line and the ellipsoid.

[0207] Fig.12 The normalized mean square error (NMSE) results of the algorithm proposed in this application and the least squares algorithm and ellipsoid positioning algorithm in the prior art are shown as a function of the signal-to-noise ratio. The NMSE calculation formula is: from Fig.12 It can be seen that the algorithm proposed in this application has a significant performance improvement over the other two algorithms, mainly because the addition of the first straight line equation line of the TX-RIS path helps to locate the target more accurately. On the other hand, it can also be seen that the algorithm proposed in this application can also work well under low signal-to-noise ratio conditions, which shows that the algorithm proposed in this application has good denoising performance.

[0208] Fig.13 The relationship between the NMSE of the target position estimation and the number of RXs Q is shown, and the positions of the third to sixth RXs are set as follows: 3 =[21,15,18] T 、p 4 =[-21,-15,-18] T 、p 5 =[-15,-10,-9] T 、p 6 =[-7,-5,0] T .from Fig.13 As can be seen from the figure, as Q increases, the NMSE of the position estimate gradually decreases. This is because as the number of collected observations increases, the positioning parameters increase accordingly, which further improves the positioning accuracy.

[0209] Fig.14The relationship between the NMSE of the algorithm proposed in this application and the other two algorithms and the number of RIS elements is shown, where M x =M y =M 0 .from Fig.14 It can be seen from the figure that the mean square error of each algorithm increases with M 0 This is because increasing M 0 It can provide more reflection paths for the signal and enhance the diversity of signal propagation. Therefore, it can help the system estimate the signal parameters more accurately, thereby improving the performance of positioning estimation.

[0210] Fig.15 The comparison of NMSE and running time is shown. The results show that NMSE gradually decreases with the increase of running time. In addition, by comparing the other two algorithms, it can be found that the algorithm proposed in this application can achieve higher positioning accuracy with a shorter running time. This shows that the algorithm proposed in this application not only has higher accuracy, but also has better efficiency than the other two algorithms.

[0211] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A MIMO-OFDM system positioning method enabled by a reconfigurable intelligent metasurface, characterized in that: include: Establishing a wireless communication channel model of a MIMO-OFDM system assisted by RIS, and determining a channel matrix of the wireless communication channel model and a received signal of a receiver; Based on the wireless communication channel model, the received signal is converted into a tensor, and channel parameters are independently extracted from the tensor using a CP decomposition algorithm, wherein the channel parameters include a delay parameter and an angle parameter; According to the extracted channel parameters, constructing a first straight line equation of a transmitter-to-RIS path and a second straight line equation of a RIS-to-receiver path based on geometric principles; Based on the first straight line equation and the second straight line equation, an ellipsoid positioning algorithm is applied to solve a positioning result.

2. The target-carrying reconfigurable intelligent metasurface-enabled MIMO-OFDM system positioning method according to claim 1, characterized in that: The channel matrix includes a channel matrix from the transmitter to the RIS path and a channel matrix from the RIS to the receiver path, wherein the channel matrix on the transmitter to the RIS path is: Among them, L1 represents the total number of paths between TX-RIS, represents the random complex gain associated with the l1th path, j represents a complex number, k represents the sequence number of the subcarrier, and f s represents bandwidth, K represents the number of subcarriers, represents the delay associated with the l1th path, represents the array response at TX on the TX-RIS path, represents the array response at RIS on the TX-RIS path, express The transpose of and denote the elevation and azimuth of the departure angle at TX, respectively. and denote the elevation and azimuth of the arrival angle at RIS on the TX-RIS path, respectively; The channel matrix on the RIS to receiver path is: Where L2 represents the total number of paths between RIS-RXq, represents the random complex gain associated with the l2th path, represents the delay associated with the l2th path, The subscript representing the angle of the RIS end on the RIS-RXq path, r q The index of the qth RX end angle, k represents the subcarrier sequence number, and f s represents bandwidth, K represents the number of subcarriers, represents the array response at RXq on the RIS-RXq path, represents the array response at RIS on the RIS-RXq path, express The transpose of and denote the elevation and azimuth of the departure angle at RIS on the RIS-RXq path, and denote the elevation and azimuth of the arrival angle at RXq respectively.

3. The target-carrying reconfigurable intelligent metasurface-enabled MIMO-OFDM system positioning method according to claim 2, characterized in that: The received signal is: in, represents additive Gaussian noise, represents the beamforming vector, express The transpose of G k,q represents the channel matrix on the RIS-RXq path, Φ p represents the phase shift matrix at RIS, H k represents the channel matrix on the TX-RIS path, f(t) represents the beamforming vector, Indicates the mth time slot of the pth time slot of the tth time frame, on the tth subcarrier, and on the qth RX r The received signal of the RF chain.

4. The target-carrying reconfigurable intelligent metasurface-enabled MIMO-OFDM system positioning method according to claim 1, characterized in that: The step of converting the received signal into a tensor based on the wireless communication channel model, and independently extracting channel parameters from the tensor using a CP decomposition algorithm, includes: For the receiver, the transmitter and the RIS, performing signal acquisition based on the receiver predefined radio frequency chain, time frame, time slot and subcarrier; Modeling a received signal of the receiver as a fourth-order tensor based on the radio frequency chain, time frame, time slot and subcarrier; Decomposing the fourth-order tensor into a sum of multiple rank-one tensors using the CP decomposition algorithm to obtain a decomposition result, and estimating a factor matrix based on the decomposition result; The channel parameters are estimated according to the factor matrix to obtain the delay parameter and the angle parameter.

5. The target-carrying reconfigurable intelligent metasurface-enabled MIMO-OFDM system positioning method according to claim 4, characterized in that: In the step of modeling the received signal of the receiver as a fourth-order tensor, the obtained fourth-order tensor can be expressed as: in, A tensor representing noise, represents the outer product, Both represent rank-one tensors, L represents the total number of paths between TX and RXq, l represents the sequence number of the path, and r q The subscript representing the angle of the qth RX end, I q The subscript representing the angle at RIS, τ q The subscript indicating the qth RX-side delay.

6. The target-carrying reconfigurable intelligent metasurface-enabled MIMO-OFDM system positioning method according to claim 4, characterized in that: In the step of estimating the channel parameters according to the factor matrix to obtain the delay parameter and the angle parameter, the delay parameter and the angle parameter are the angle parameters and the delay parameters under the path where the power of the received signal is the maximum.

7. The target-carrying reconfigurable intelligent metasurface-enabled MIMO-OFDM system positioning method according to claim 1, characterized in that: The wireless communication channel model of the MIMO-OFDM system based on RIS assistance is established, including: establishing a global Cartesian coordinate system based on the transmitter; establishing a local Cartesian coordinate system based on the receiver; The transmitter and the receiver are both configured with a uniform planar array and a radio frequency chain.

8. The target-carrying reconfigurable intelligent metasurface-enabled MIMO-OFDM system positioning method according to claim 7, characterized in that: The step of constructing a first straight line equation from a transmitter to a RIS path and a second straight line equation from a RIS to a receiver path based on a geometric principle according to the extracted channel parameters comprises: Obtaining the coordinate positions of the global Cartesian coordinate system, the transmitter and the receiver in the global Cartesian coordinate system; The midpoint of the line connecting the coordinate position of the transmitter and the coordinate position of the receiver is defined as the coordinate origin, the coordinate position from the coordinate origin to the receiver is positioned as the x-axis, the global Cartesian coordinate system is translated and rotated to construct a local coordinate system, and the ellipsoid equation in the local coordinate system is obtained; Based on the channel parameters extracted from the wireless communication channel model, determining an expression of the channel parameters in the local coordinate system; The first straight line equation and the second straight line equation are determined according to expressions of channel parameters in the local coordinate system.

9. The target-mounted reconfigurable intelligent metasurface-enabled MIMO-OFDM system positioning method according to claim 8, characterized in that: The first straight line equation is expressed as: in, represents the equation of the first straight line, It means taking the x-axis component. It means taking the component of y, Indicates the component of z, d q Indicates the distance from TX to RXq; The second straight line equation is expressed as: in, represents the equation of the second line, represents the x-axis component, It means taking the component of y, Indicates the component of z, d q Indicates the distance from TX to RXq.

10. The target-carrying reconfigurable intelligent metasurface-enabled MIMO-OFDM system positioning method according to claim 9, characterized in that: The applying an ellipsoid positioning algorithm to solve a positioning result based on the first straight line equation and the second straight line equation comprises: In the local coordinate system, define a first intersection point between the first straight line equation and the ellipsoid equation in the local coordinate system, and solve the coordinates of the first intersection point based on the ellipsoid equation and the first straight line equation to obtain first solution coordinates; In the local coordinate system, define a second intersection point of the second straight line equation and the ellipsoid equation in the local coordinate system, and solve the coordinates of the second intersection point based on the ellipsoid equation and the second straight line equation to obtain second solution coordinates; The positioning result is obtained based on the first solution coordinates and the second solution coordinates.

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