Intelligent metasurface-assisted multi-target positioning system

Abstract

The invention discloses an intelligent metasurface-assisted multi-target positioning system, and belongs to the field of wireless communication. The system comprises an intelligent metasurface; a receiver; the plurality of wireless access points can periodically transmit detection signals, and the detection signals can reach the intelligent metasurface through a plurality of targets and reach the receiver after being reflected by the intelligent metasurface; the received signal model building module is used for solving expressions of signals received by the intelligent metasurface and the receiver under different time blocks and time slots; the virtual multi-dimensional signal construction module is used for calculating the multi-dimensional signal according to the signal received by the receiver to obtain a virtual steering vector received by the receiver; the spatial information positioning module is used for obtaining a spatial spectrum of the multi-signal classification algorithm; and searching the spatial spectrum to obtain steering vectors corresponding to a plurality of peak values in the spatial spectrum, and obtaining angles and distances of a plurality of targets.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, and in particular to an intelligent metasurface-assisted multi-target positioning system. Background Art

[0002] The prior art, "Intelligent Metasurface-Assisted Passive Target Localization Method Based on MUSIC and Maximum Parallelism (CN117075095A)," proposes a RIS-assisted single-target localization system. This system leverages the phase reconfigurability of RIS to construct a one-dimensional pseudo-MUSIC spatial spectrum based on the relationship between the characteristic matrix and the signal and noise spaces, thereby estimating the azimuth angle between the target and the base station. Furthermore, it utilizes the time-varying characteristics of the intelligent metasurface to construct a channel cross-term codebook. This codebook is then parallelized with the actual channel cross-terms to obtain a parallelism spatial spectrum, thereby estimating the azimuth angle between the target and the intelligent metasurface, and thus the position of the individual target, with high accuracy and low computational complexity. Another technology, "A Multi-target Positioning Method and System (CN110376548B)", provides a multi-target positioning method that deploys multiple anchor nodes in the monitoring area. When N targets enter the monitoring area, the signals received by the targets from the anchor nodes in the monitoring area are directly collected; the anchor nodes corresponding to the strongest N signals are used as cluster centers, and the monitoring area is divided into N grids using a grid clustering algorithm; within each grid, different methods can be used to calculate the positions of multiple targets based on the anchor node signals received by the targets.

[0003] The main drawbacks of existing technologies are: 1) Existing channel models are still based on the far-field plane wave assumption and fail to fully exploit the distance domain information contained in the propagation characteristics of near-field spherical waves. In 6G high-frequency bands and ultra-large-scale array scenarios, communication distances within tens of meters may be in the near-field region. However, traditional methods are limited by the far-field assumption, resulting in insufficient positioning information in the distance dimension. 2) Existing multi-target positioning systems mainly use multi-antenna receivers, which use the phase difference or time difference of the signal in space to calculate the signal's arrival angle. However, single-antenna receiving systems cannot directly obtain spatial phase differences, making it difficult to achieve angle resolution. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention proposes a multi-target positioning system assisted by an intelligent metasurface.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A first aspect of the present invention relates to a multi-target positioning system assisted by an intelligent metasurface, comprising:

[0007] a smart metasurface disposed in a near-field region of the target;

[0008] a receiver, located in the far-field region of the smart metasurface;

[0009] Multiple wireless access points can periodically transmit detection signals, and the detection signals can pass through multiple targets to reach the smart metasurface, and then reach the receiver after being reflected by the smart metasurface;

[0010] A received signal model construction module is used to divide the detection signal period into multiple time blocks, each time block includes multiple time slots, the reflection pattern of the smart metasurface is the same in each time block, and the reflection pattern is different between different time slots in the same time block, and the expression of the signal received by the smart metasurface and the receiver in different time blocks and time slots is obtained;

[0011] a virtual multi-dimensional signal construction module, configured to accumulate the signal received by the receiver in each time block consisting of a plurality of consecutive time slots to form a multi-dimensional signal, and calculate the multi-dimensional signal based on the signal received by the receiver to obtain a virtual steering vector received by the receiver;

[0012] Furthermore, the spatial information positioning module is used to jointly estimate the angle and distance parameters based on the orthogonality of the signal subspace and noise subspace of the receiver's received signal through a multi-signal classification algorithm, thereby obtaining a spatial spectrum of the multi-signal classification algorithm; and to search the spatial spectrum to obtain the guidance vectors corresponding to the multiple peaks in the spatial spectrum, thereby obtaining the angles and distances of multiple targets.

[0013] Optionally, both the wireless access point and the smart metasurface are configured with a uniform linear array, and the reflection unit of the smart metasurface is capable of regulating the phase of the incident signal.

[0014] Optionally, the near-field channel between the smart metasurface and the target can be modeled using a uniform spherical wave model, and the modeling method includes the following steps:

[0015] Assume r k ,θ k denote the distance and arrival angle of the kth (k=1,2,…,K) target to the RIS center reflection unit, d R represents the distance between two adjacent reflective units of RIS. Then the guidance vector from the position of target k to RIS is:

[0016]

[0017] in, represents the complex field, a m (r k ,θ k ), m=-M,…,M represents the mth element of the steering vector; let r k,m represents the distance from the kth target to the mth reflector unit of RIS. Then the phase difference between the kth target and the mth reflector unit of RIS and the central reflector unit of RIS is calculated as:

[0018]

[0019] to r k,m Using the second-order Taylor expansion:

[0020]

[0021] Based on the Fresnel approximation, the higher-order terms above the second power are ignored, so the phase difference can be approximated as:

[0022]

[0023] Considering that all targets are in the far field of AP, assuming d A Indicates the distance between adjacent units in the AP antenna array. represents the departure angle from the AP antenna array to the kth target, and the steering vector from the AP to the kth target is:

[0024]

[0025] Let the distance from AP to the kth target be d AT,k , the distance from RIS to Rx is d RR , the path loss constant from AP to the kth target and then to RIS is γ ATR,k , the path loss constant from RIS to Rx is γ RR The channel fading from AP to the kth target and then to RIS is:

[0026]

[0027] Where d0 is the reference distance. Therefore, the channel from AP to the kth target and then to RIS is modeled as:

[0028]

[0029] where ε k represents the radar cross section of the kth target.

[0030] Optionally, the steering vector from the smart metasurface to the receiver is:

[0031]

[0032] γ represents the departure angle from RIS to Rx;

[0033] Therefore, the channel from the smart metasurface to the receiver is modeled as:

[0034] h RR =δb(γ)

[0035] in Indicates the channel fading from RIS to Rx.

[0036] A second aspect of the present invention relates to a multi-target positioning method assisted by an intelligent metasurface, comprising the following steps:

[0037] Controlling the wireless access point to periodically transmit a detection signal, wherein the detection signal can pass through a plurality of targets to reach the smart metasurface, and then reach the receiver after being reflected by the smart metasurface;

[0038] The cycle includes multiple time blocks, each time block includes multiple time slots, the reflection pattern of the smart metasurface in each time block is the same, and the reflection patterns are different between different time slots in the same time block, and expressions of the signals received by the smart metasurface and the receiver in different time blocks and time slots are obtained;

[0039] Accumulating the signal received by the receiver in each time block consisting of a plurality of consecutive time slots to form a multidimensional signal, and calculating the multidimensional signal according to an expression of the signal received by the smart metasurface to obtain a virtual steering vector;

[0040] The multi-signal classification algorithm uses the orthogonality of the signal subspace and noise subspace of the receiver's received signal to jointly estimate angle and distance parameters, thereby obtaining a spatial spectrum of the multi-signal classification algorithm. The spatial spectrum is then searched to obtain steering vectors corresponding to several peaks in the spatial spectrum, thereby obtaining the angles and distances of several targets.

[0041] Optionally, the method for calculating the expression of the signal received by the smart metasurface includes the following steps:

[0042] Each time block consists of L time slots, and the estimated target (r k ,θ k ) The total number of time blocks required is Q; in the same time block q, q = 1, ..., Q, the wireless access point repeatedly sends the same sensing signal in each time slot l, l = 1, ..., L, set Its satisfaction n=1,…,2N+1, CN(0,1) represents a complex Gaussian distribution with mean 0 and variance 1.

[0043] make is the transmit beamforming matrix of the sensing signal, and W is set as a unitary matrix. Then the signal transmitted by the wireless access point in the lth time slot of the qth time block is expressed as:

[0044]

[0045] The signal received by the smart metasurface in the lth time slot of the qth time block is:

[0046]

[0047] The smart metasurface uses the same reflection pattern in different time blocks, and the reflection pattern is different between different time slots in the same time block, that is, the reflection phase shift matrix satisfies:

[0048] Φ ((q-1)L+1) ≠Φ (( q -1)L+2) ≠…≠Φ (qL)

[0049]

[0050] Where l=1,…,L,q=1,…,Q, is the constant smart metasurface reflection pattern used in the lth time slot of all time blocks;

[0051] Optionally, the method for calculating the expression of the signal received by the receiver includes the following steps: the signal received by the receiver in the lth time slot of the qth time block is expressed as:

[0052]

[0053] where n (( q-1)L+l ) ~CN(0,σ 2 ) represents additive Gaussian white noise, σ 2 represents the noise power;

[0054] The above formula can be further written as:

[0055]

[0056] Optionally, the signal received by the receiver meets the conditions for using a multiple signal classification algorithm, namely:

[0057] ①L>K

[0058] ②

[0059] ③ Does not change over time

[0060] in yes The covariance matrix of .

[0061] Optionally, in the multi-signal classification algorithm, the orthogonality of the signal subspace and the noise subspace is used to achieve joint estimation of the angle and distance parameters. First, the covariance matrix of the signals received over Q time blocks is obtained as:

[0062]

[0063] Then Perform eigenvalue decomposition to obtain:

[0064]

[0065] where Λ=diag(λ1,λ2,…,λ L ), the diagonal elements of the matrix Λ are the eigenvalues of R, U=[u1,…,u L ] consists of the corresponding eigenvectors, without loss of generality, assuming that λ1≥λ2≥…≥λ L ,definition is the noise subspace, so the spatial spectrum used in the multi-signal classification algorithm can be defined as:

[0066]

[0067] So the estimated value The K peaks of the above spatial spectrum are obtained by two-dimensional search, so that K estimated distances can be obtained. and angles

[0068] The third aspect of the present invention relates to a computer-readable storage medium storing instructions, which, when executed, can implement the above-mentioned intelligent metasurface-assisted multi-target positioning method.

[0069] The fourth aspect of the present invention relates to a spatial positioning device, comprising the above-mentioned computer-readable storage medium or the above-mentioned intelligent metasurface-assisted multi-target positioning system.

[0070] Beneficial effects of the present invention:

[0071] (1) Compared with the traditional far-field multi-target positioning system, the multi-target positioning system based on RIS proposed in the present invention can not only accurately estimate the distance-angle information of different targets when the multiple targets are at different positions, but also estimate the position information of different targets when the multiple targets are at the same angle.

[0072] (2) Compared with the traditional multi-antenna receiving system, the present invention proposes a joint parameter estimation method by rationally designing the time-varying RIS phase shift matrix. The created time domain multidimensional signal can be equivalent to the multi-channel receiving signal and simulate the spatial phase difference characteristics of the multi-antenna system. On this basis, the 2D-

[0073] The MUSIC algorithm can accurately estimate the range-angle information of multiple targets in a RIS-assisted target positioning system. The proposed scheme enables the system to achieve positioning accuracy comparable to traditional multi-antenna systems in a single-receive antenna architecture while significantly reducing receiver complexity and cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The present invention will be further described below with reference to the accompanying drawings.

[0075] Figure 1 This is the multi-target positioning system model based on intelligent metasurface of this application;

[0076] Figure 2 The spatial spectrum of the 2D-MUSIC algorithm in the multi-target positioning method based on intelligent metasurface of this application;

[0077] Figure 3 The distance spectrum obtained by the multi-target positioning method based on the smart metasurface of this application;

[0078] Figure 4 The angle spectrum obtained by the multi-target positioning method based on the intelligent metasurface of this application

[0079] Figure 5 Flowchart of the multi-target positioning method based on intelligent metasurface of this application DETAILED DESCRIPTION

[0080] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0081] In some embodiments of the present invention, a multi-target positioning method based on a smart metasurface is disclosed, comprising the following steps:

[0082] Step 1: Build a system model

[0083] In this embodiment, a multi-target positioning method based on intelligent metasurface is provided. Figure 1As shown in the figure, the multi-target positioning system based on intelligent metasurfaces consists of one wireless access point (AP), K targets to be detected (targets), one intelligent metasurface (RIS), and one receiver (Rx). The sensing signal transmitted by the AP passes through multiple detection targets before reaching the RIS, which then reflects it to reach the Rx. The AP is equipped with 2N+1 (2N+1>K) antennas, the Rx is equipped with one antenna, and the RIS has 2M+1 reflective elements. Both the AP and RIS are configured with uniform linear arrays (ULAs). Considering the urban environment, the direct link between the target and the Rx is blocked by obstacles. Therefore, a RIS is deployed between the target and the Rx to provide a reflective link, so that multiple targets are in the near field of the RIS.

[0084] (1) RIS array model

[0085] The system deploys a passive RIS consisting of 2M+1 reflective units. Each reflective unit can autonomously adjust the phase of the incident signal. -M ,…,φ M ] T is the phase shift matrix of RIS, where diag(x) represents a diagonal matrix. The diagonal elements are all elements in the vector x, that is, the reflection coefficients, and the off-diagonal elements are all 0.

[0086] (2) AP-Targets-RIS channel model

[0087] If RIS is deployed in the near-field area of K targets, the near-field channel between RIS and the targets can be modeled using the Uniform Spherical Wave (USW) model. k ,θ k denote the distance and arrival angle of the kth (k=1,2,…,K) target to the RIS center reflection unit, d R represents the distance between two adjacent reflective units of RIS. Then the guidance vector from the position of target k to RIS is:

[0088]

[0089] in, represents the complex field, a m (r k ,θ k ), m=-M,…,M represents the mth element of the steering vector. Let r k,mrepresents the distance from the kth target to the mth reflector unit of RIS. Then the phase difference between the kth target and the mth reflector unit of RIS and the central reflector unit of RIS is calculated as:

[0090]

[0091] to r k,m Using the second-order Taylor expansion:

[0092]

[0093] Based on the Fresnel approximation, the higher-order terms above the second power are ignored, so the phase difference can be approximated as:

[0094]

[0095] Considering that all targets are in the far field of AP, assuming d A Indicates the distance between adjacent units in the AP antenna array. represents the departure angle from the AP antenna array to the kth target, and the steering vector from the AP to the kth target is:

[0096]

[0097] Let the distance from AP to the kth target be d AT,k , the distance from RIS to Rx is d RR , the path loss constant from AP to the kth target and then to RIS is γ ATR,k , the path loss constant from RIS to Rx is γ RR In the USW model, when the propagation distance is greater than a certain threshold, i.e., the uniform power distance, it can be assumed that the receiving end power can remain relatively stable and will not fluctuate significantly due to slight changes in distance. In other words, the channel fading from the kth target to the RIS in 2M+1 units is approximately equal. Therefore, the channel fading from the AP to the kth target and then to the RIS is:

[0098]

[0099] Where d0 is the reference distance. Therefore, the channel from AP to the kth target and then to RIS is modeled as:

[0100]

[0101] where ε k Represents the radar cross section (RCS) of the k-th target.

[0102] (3)RIS-Rx far-field channel model

[0103] Rx is located in the far field of RIS. Let γ represent the departure angle from RIS to Rx. Then the steering vector from RIS to Rx is:

[0104]

[0105] Therefore, the channel from RIS to Rx can be modeled as:

[0106] h RR =δb(γ)

[0107] in Indicates the channel fading from RIS to Rx.

[0108] Step 2: Design AP detection signal and RIS dynamic phase encoding

[0109] Assuming that every L (L>K) time slots constitute a time block, the estimated target (r k ,θ k ) The total number of time blocks required is Q. In the same time block q,q=1,…,Q, the AP repeatedly sends the same sensing signal in each time slot l,l=1,…,L, let Its satisfaction n=1,…,2N+1, CN(0,1) represents a complex Gaussian distribution with mean 0 and variance 1. make is the transmit beamforming matrix of the sensing signal. To achieve omnidirectional sensing, W is set to a unitary matrix. The signal transmitted by the AP in the lth time slot of the qth time block can be expressed as:

[0110]

[0111] The signal received by RIS in the lth time slot of the qth time block is:

[0112]

[0113] The RIS is designed to use the same reflection pattern in different time blocks, and the reflection pattern is different in different time slots of the same time block, that is, the reflection phase shift matrix meets the following two conditions:

[0114] Φ ((q-1)L+1 )≠Φ ((q-1)L+2) ≠…≠Φ (qL)

[0115]

[0116] Where l=1,…,L,q=1,…,Q, is the constant RIS reflection pattern used in the lth time slot of all time blocks. Therefore, the signal received by Rx in the lth time slot of the qth time block is expressed as:

[0117]

[0118] where n (( q-1)L+l ) ~CN(0,σ 2 ) represents additive Gaussian white noise, σ 2 Represents the noise power.

[0119] The above formula can be further written as:

[0120]

[0121] Step 3: Establish a virtual multi-dimensional receiving signal model

[0122] Recombining the signal terms in the above formula, the equivalent channel from K targets to Rx is written as

[0123] (r,θ)=[(r1,θ1),(r2,θ2),…,(r K ,θ K )] T , the signals reflected from K targets are written as Therefore, the above formula can be further equivalent to:

[0124]

[0125] The received signal is accumulated in each time block consisting of L>K consecutive time slots to form a multi-dimensional signal. Represents the signal vector received by Rx at the qth time block, It can be expressed as:

[0126]

[0127] in satisfy I L represents the L×L dimensional unit matrix, and has:

[0128]

[0129] in

[0130] Considering the RIS reflection pattern explained in step 2: Φ (1) ≠Φ (2) ≠…≠Φ (L) , we can get Where rank(·) represents the matrix rank operation. Similarly, have to does not change with time q.

[0131] The system provided in this embodiment can be equivalent to a virtual multi-antenna receiving system, in which the detection signal transmitted by the AP passes through K targets and is received by the virtual Rx using L receiving antennas, and the virtual Rx is in the direction θ k The virtual steering vector on Among them, by designing different RIS reflection matrices on L time slots, the phase difference generated by L virtual receiving antennas is simulated for positioning.

[0132] Step 4: Multi-target positioning

[0133] Using the above signal model, the signal received by Rx meets the conditions for using the Multiple Signal Classification (MUSIC) algorithm, namely:

[0134] ①L>K

[0135] ②

[0136] ③ Does not change over time

[0137] in yes The covariance matrix of .

[0138] Next, we use the 2D-MUSIC algorithm to achieve joint estimation of angle and distance parameters through the orthogonality of the signal subspace and the noise subspace. First, we get the covariance matrix of the signal received over Q time blocks:

[0139]

[0140] Then perform eigenvalue decomposition (EVD) on R to obtain:

[0141]

[0142] where Λ=diag(λ1,λ2,…,λ L ), the diagonal elements of the matrix Λ are The eigenvalues of U=[u1,…,u L ] consists of the corresponding eigenvectors, without loss of generality, assuming that λ1≥λ2≥…≥λ L ,definition is the noise subspace, so the spatial spectrum used in the MUSIC algorithm can be defined as:

[0143]

[0144] So the estimated value The K peaks of the above spatial spectrum are obtained by two-dimensional search, so that K estimated distances can be obtained. and angles

[0145] In summary, this embodiment proposes a multi-target positioning system based on RIS. The RIS is deployed in the near field of multiple targets. A uniform spherical wave model is used to describe the near-field channel between the RIS and the targets, thereby establishing a signal propagation model. Compared to traditional plane wave-based models, this model not only accurately estimates the range-angle information of multiple targets when they are at different positions, but also estimates the position information of different targets when they are at the same angle.

[0146] This embodiment provides a time-domain multidimensional parameter joint estimation method based on a time-varying RIS. The method continuously transmits the same detection signal in multiple consecutive time slots. Simultaneously, the RIS dynamically adjusts its reflection phase shift matrix in each time slot. At the receiver Rx, the received signals from different time slots are synthesized to construct a virtual multidimensional received signal model. The created virtual steering vector contains the range-angle information of different targets, enabling the receiver to achieve angle-range joint positioning of multiple targets using only a single receiving antenna.

[0147] In some specific embodiments of the present invention, in order to further demonstrate the effect of the present invention, simulation experiments are performed using a Matlab platform to evaluate the positioning performance of the methods proposed in the above embodiments.

[0148] exist Figures 2 to 4 The distance and angle estimation spectra of the method proposed in the above embodiment are shown in Figure 1. The wavelength is set to 0.03 m, the number of AP transmit antennas is set to 2N + 1 = 65, the number of RIS reflectors is set to 2M + 1 = 201, the number of estimated time blocks is set to 12, the number of time slots per block is set to 6, the distance between the AP and the target is set to 100 m and 110 m, the distance between the target and the center of the RIS array is set to 5 m and 6 m, the angle of arrival from the target to the center of the RIS array is set to 30° and 30°, the distance from the center of the RIS array to Rx is set to 75 m, the angle of arrival from the RIS to Rx is 60°, and the AP and RIS are assumed to be co-level. Numerical results show that the estimated distances and angles of the paths from two users to the RIS are (5.01 m, 30°) and (5.99 m, 30°), respectively, which are very close to the true values and can estimate the positions of different targets from the same angle.

[0149] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0150] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. An intelligent metasurface-assisted multi-target positioning system, characterized in that: include: a smart metasurface disposed in a near-field region of the target; a receiver, located in the far-field region of the smart metasurface; Multiple wireless access points can periodically transmit detection signals, and the detection signals can pass through multiple targets to reach the smart metasurface, and then reach the receiver after being reflected by the smart metasurface; A received signal model construction module is used to divide the detection signal period into multiple time blocks, each time block includes multiple time slots, the reflection pattern of the smart metasurface is the same in each time block, and the reflection pattern is different between different time slots in the same time block, and the expression of the signal received by the smart metasurface and the receiver in different time blocks and time slots is obtained; a virtual multi-dimensional signal construction module, configured to accumulate the signal received by the receiver in each time block consisting of a plurality of consecutive time slots to form a multi-dimensional signal, and calculate the multi-dimensional signal based on the signal received by the receiver to obtain a virtual steering vector received by the receiver; and a spatial information positioning module for implementing a joint estimation of angle and distance parameters based on the orthogonality of the signal subspace and noise subspace of the signal received by the receiver through a multi-signal classification algorithm, thereby obtaining a spatial spectrum of the multi-signal classification algorithm; The spatial spectrum is searched to obtain steering vectors corresponding to a plurality of peaks in the spatial spectrum, and angles and distances of a plurality of targets are obtained.

2. The intelligent metasurface-assisted multi-target positioning system according to claim 1, characterized in that: The wireless access point and the smart metasurface are both configured with a uniform linear array, and the reflection unit of the smart metasurface is capable of regulating the phase of the incident signal.

3. The intelligent metasurface-assisted multi-target positioning system according to claim 1, characterized in that: The near-field channel between the intelligent metasurface and the target can be modeled using a uniform spherical wave model, and the modeling method includes the following steps: Assume r k ,θ k denote the distance and arrival angle from the kth target to the central reflection unit of the smart metasurface, d R represents the distance between two adjacent reflective units of the smart metasurface; then the guidance vector from the position of the kth target to the smart metasurface is: in, represents the complex field, a m (r k ,θ k ), m=-M,…,M represents the mth element of the steering vector; let r k,m represents the distance from the kth target to the mth reflective unit of the smart metasurface. The phase difference between the kth target and the mth reflective unit of the smart metasurface and the central reflective unit of the smart metasurface is calculated as: to r k,m Using the second-order Taylor expansion: Based on the Fresnel approximation, the higher-order terms above the second power are ignored, so the phase difference can be approximated as: Considering that all targets are in the far field area of the wireless access point, assuming that d A Indicates the distance between adjacent elements of the wireless access point antenna array. represents the departure angle from the wireless access point antenna array to the kth target, and the steering vector from the wireless access point to the kth target is: Let the distance from the wireless access point to the kth target be d AT,k , the distance from the smart metasurface to the receiver is d RR , the path loss constant from the wireless access point to the kth target and then to the smart metasurface is γ ATR,k , the path loss constant from the smart metasurface to the receiver is γ RR ; The channel fading from the wireless access point to the kth target and then to the smart metasurface is: Where d0 is the reference distance; the channel model from the wireless access point to the kth target and then to the smart metasurface is: where ε k represents the radar cross section of the kth target.

4. The intelligent metasurface-assisted multi-target positioning system according to claim 1, characterized in that: The steering vector from the smart metasurface to the receiver is: γ represents the departure angle from the smart metasurface to the receiver; Therefore, the channel from the smart metasurface to the receiver is modeled as: h RR =δb(γ) in represents the channel fading from the smart metasurface to the receiver.

5. A multi-target positioning method assisted by an intelligent metasurface, comprising the following steps: Controlling the wireless access point to periodically transmit a detection signal, wherein the detection signal can pass through a plurality of targets to reach the smart metasurface, and then reach the receiver after being reflected by the smart metasurface; The cycle includes multiple time blocks, each time block includes multiple time slots, the reflection pattern of the smart metasurface in each time block is the same, and the reflection patterns are different between different time slots in the same time block, and expressions of the signals received by the smart metasurface and the receiver in different time blocks and time slots are obtained; Accumulating the signal received by the receiver in each time block consisting of a plurality of consecutive time slots to form a multidimensional signal, and calculating the multidimensional signal according to an expression of the signal received by the smart metasurface to obtain a virtual steering vector; The multi-signal classification algorithm uses the orthogonality of the signal subspace and noise subspace of the receiver's received signal to jointly estimate angle and distance parameters, thereby obtaining a spatial spectrum of the multi-signal classification algorithm. The spatial spectrum is then searched to obtain steering vectors corresponding to several peaks in the spatial spectrum, thereby obtaining the angles and distances of several targets.

6. The multi-target positioning method assisted by the intelligent metasurface according to claim 5, characterized in that: The method for calculating the expression of the signal received by the intelligent metasurface comprises the following steps: Each time block consists of L time slots, and the estimated target (r k ,θ k ) The total number of time blocks required is Q; in the same time block q, q = 1, ..., Q, the wireless access point repeatedly sends the same sensing signal in each time slot l, l = 1, ..., L, set Its satisfaction CN(0,1) represents a complex Gaussian distribution with a mean of 0 and a variance of 1. make is the transmit beamforming matrix of the sensing signal, and W is set as a unitary matrix. Then the signal transmitted by the wireless access point in the lth time slot of the qth time block is expressed as: The signal received by the smart metasurface in the lth time slot of the qth time block is: The following two conditions are met: F ((q-1)L+1) ≠Φ ((q-1)L+2) ≠...≠Φ (qL) Where l=1,…,L,q=1,…,Q, is the constant smart metasurface reflection pattern used in the lth time slot of all time blocks; The method for calculating the expression of the signal received by the receiver comprises the following steps: the signal received by the receiver in the lth time slot of the qth time block is expressed as: where n ((q-1)L+l) ~CN(0,σ 2 ) represents additive Gaussian white noise, σ 2 represents the noise power; The above formula can be further written as:

7. The intelligent metasurface-assisted multi-target positioning method according to claim 5, characterized in that: The signal received by the receiver in the lth time slot of the qth time block is equivalent to: Accumulate the received signal in each time block consisting of L>K consecutive time slots to form a multi-dimensional signal; definition Represents the signal vector received by Rx at the qth time block, Expressed as: in satisfy I L represents the L×L dimensional unit matrix, and has: in The reflection phase shift matrix of the smart metasurface: Φ (1) ≠Φ (2) ≠…≠Φ (L) ,have to Where rank(·) represents the matrix rank operation; have to It does not change with time q.

8. The intelligent metasurface-assisted multi-target positioning method according to claim 5, characterized in that: The signal received by the receiver meets the conditions for using the multiple signal classification algorithm, namely: ①L>K ② ③ Does not change over time in yes The covariance matrix of In the multi-signal classification algorithm, the orthogonality of the signal subspace and the noise subspace is used to achieve joint estimation of the angle and distance parameters. First, the covariance matrix of the signals received over Q time blocks is obtained as: Then Perform eigenvalue decomposition to obtain: where Λ=diag(λ1,λ2,…,λ L ), the diagonal elements of the matrix Λ are The eigenvalues of U=[u1,…,u L ] consists of the corresponding eigenvectors, without loss of generality, assuming that λ1≥λ2≥…≥λ L ,definition is the noise subspace, so the spatial spectrum used in the multi-signal classification algorithm can be defined as: So the estimated value The K peaks of the above spatial spectrum are obtained by two-dimensional search, so that K estimated distances can be obtained. and angles 9. A computer-readable storage medium storing instructions, characterized in that: When the instruction is executed, the multi-target positioning method assisted by the intelligent metasurface according to any one of claims 5 to 8 can be implemented.

10. A spatial positioning device, characterized in that: A multi-target positioning system comprising the computer-readable storage medium of claim 9 or the intelligent metasurface-assisted multi-target positioning system of any one of claims 1 to 4.

Images