Weighted ESPRIT-based DOA estimation method assisted by semi-passive intelligent reflection surface

Abstract

The invention discloses a weighted ESPRIT-based DOA estimation method under the assistance of a semi-passive intelligent reflection surface, and the method comprises the steps: constructing a DOA estimation model under the assistance of the semi-passive intelligent reflection surface, and obtaining a receiving signal; thirdly, calculating a covariance matrix of a received signal, decomposing the characteristics of the covariance matrix, and extracting a signal subspace and a characteristic value; secondly, constructing a Gram matrix, performing characteristic decomposition on the Gram matrix, and extracting a signal subspace and a characteristic value; then, calculating a weight parameter according to the characteristic value; and finally, weighting and fusing the signal subspace information to obtain DOA estimation. According to the method, the target can be effectively sensed under the condition that the base station is shielded, and DOA estimation is carried out. In addition, the characteristics of the intelligent reflection surface are fully utilized, an adaptive weighting mechanism is introduced, and the DOA can be estimated robustly. Simulation results show that the DOA estimation precision of the algorithm provided by the invention is superior to that of a traditional multiple signal classification method, and is close to and approaches the Cramer-Rao bound with an atomic norm minimization algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless positioning, and in particular, to a DOA estimation method assisted by a semi-passive intelligent reflecting surface based on weighted ESPRIT. Background Art

[0002] With the evolution of 5G and B5G wireless communication technologies, high-precision target positioning and direction-of-arrival (DOA) estimation have become core enabling technologies to support frontier applications such as intelligent connected vehicles and millimeter-wave radar sensing. Although the traditional subspace decomposition-based MUSIC algorithm has super-resolution characteristics under ideal array conditions, it faces multi-dimensional challenges in actual complex propagation environments: on the one hand, the multipath scattering effect in dense urban environments will destroy the structural characteristics of the received signal covariance matrix; on the other hand, signal energy attenuation in non-line-of-sight scenarios leads to angle ambiguity phenomena in traditional methods.

[0003] To address the above challenges, the semi-passive intelligent reflecting surface technology has opened up a new dimension for wireless channel regulation. Compared with traditional passive reflecting surfaces, the semi-passive intelligent reflecting surface can not only dynamically reconstruct the electromagnetic environment but also achieve joint sensing of incident angles by introducing a limited number of active sensors. However, the existing DOA estimation system still has double limitations: the traditional MUSIC algorithm fails to effectively integrate the spatial phase information of the passive reflecting elements of the intelligent reflecting surface, and although the sparse reconstruction method based on ANM can improve the estimation accuracy, its computational complexity is difficult to meet the real-time processing requirements of large-scale arrays.

[0004] This technical bottleneck drives the research breakthrough of new DOA estimation algorithms. The weighted ESPRIT fusion architecture proposed by the present invention innovatively transforms the geometric constraints of passive reflecting elements into a spatial weighting matrix, which not only inherits the computational efficiency advantages of subspace-based algorithms but also maximizes the utilization of semi-passive intelligent reflecting surface-assisted information through weighted optimization. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a DOA estimation method assisted by a semi-passive intelligent reflecting surface based on weighted ESPRIT for the defects involved in the background art.

[0006] The present invention adopts the following technical solutions to solve the above technical problems:

[0007] A DOA estimation method assisted by a semi-passive intelligent reflecting surface based on weighted ESPRIT, characterized by comprising the following steps:

[0008] Step 1), construct a DOA estimation model assisted by a semi-passive intelligent reflecting surface to obtain the echo signal x(t) received at the t-th snapshot moment;

[0009] In the described DOA estimation model assisted by a semi-passive intelligent reflecting surface, there are K far-field targets and a base station equipped with a uniform linear array of M B antennas. The line-of-sight path between the base station and the far-field targets is blocked. The passive reflecting array and the active sensing array of the semi-passive intelligent reflecting surface used to construct the non-line-of-sight path are both uniform linear arrays, and the number of array elements is M R and M S respectively;

[0010] The channel between the base station and the passive reflecting array where the superscript H represents conjugate transpose, represents the channel loss, λ is the signal wavelength, d B2R is the distance between the base station and the semi-passive intelligent reflecting surface, θ B2R and respectively represent the DOA on the passive reflecting array and the angle of departure of the base station, a R and a B respectively represent the array response vectors of the passive reflecting array and the base station; for a uniform linear array with an element spacing of half a wavelength, at a given angle θ, the i-th element of its array response vector is e j(i-1)πsinθ , where j represents the imaginary unit;

[0011] The channel from the passive reflecting array to target k and then to the active sensing array where, θ k represents the DOA of the k-th target on the active sensing array, a S represents the array response vector of the active sensing array, represents the channel loss from the passive reflecting array to target k and then to the active sensing array, d k represents the distance between the intelligent reflecting surface and target k, γ k is the radar cross section of target k;

[0012] Assume that the targets are all in the far field of the intelligent reflecting surface, and the element spacings of the arrays on the passive reflecting array, the active sensing array, and the base station are all half a wavelength. The intelligent reflecting surface adjusts its beam once per snapshot, for a total of T snapshots; the phase shift vector of the passive reflecting array at the t-th snapshot is where the superscript T represents transpose, represents the phase shift of the i-th element of the passive reflecting array;

[0013] The echo signal received by the active sensing array at the t-th snapshot where, represents the diagonal matrix composed of , Denote the transmit power of the base station, \(s(t)\) as the transmit signal with unit power, and \(n(t)\) as the circularly additive white Gaussian noise vector, where each element follows a complex normal distribution. Denote the noise power, \(w\) as the beamforming vector, and it satisfies \(\|w\| 2 = 1, and \(\|\cdot\|\) represents the \(l_2\) norm;

[0014] To maximize the received signal power of the active sensing array, the base station aligns the beam towards the intelligent reflecting surface, that is Then we have where

[0015] Step 2), Stack up the received echo signals of \(T\) snapshots to obtain the received signal where

[0016]

[0017] Step 3), Calculate the covariance matrix \(R\) of the received signal x , perform eigenvalue decomposition on it, and extract the signal subspace and eigenvalues

[0018] Step 3.1), Calculate the covariance matrix of the received signal \(X\) where represents the mathematical expectation, represents the \(M\) S dimensional identity matrix,

[0019] Step 3.2), Perform eigenvalue decomposition on \(R\) x to obtain where the eigenvalues \(\lambda\) i satisfy The corresponding eigenvectors form the signal subspace \(U\) s and the noise subspace \(U\) n ;

[0020] Step 4), Construct the Gram matrix Perform eigenvalue decomposition on it, and extract the signal subspace eigenvalues

[0021] Step 4.1), Define the Gram matrix When \(M\) S tends to infinity, we have where \(I\) T represents the \(T\) dimensional identity matrix;

[0022] Step 4.2), perform eigenvalue decomposition on G x to obtain where the eigenvalues μ i satisfy The corresponding eigenvectors form the signal subspace V s and the noise subspace V n ;

[0023] Step 5), calculate the weight parameters and according to the eigenvalues and

[0024] When M R >> K, the covariance matrix where represents the eigenvalues of R s ;

[0025] Since the eigenvalues x of R then the estimated value of the noise power The estimated value of the signal power

[0026] According to the water-filling principle, the higher the signal-to-noise ratio, the greater the allocated weight. The weight value x of R G x The weight value where

[0027] In practical applications, the estimated values of the weight parameters and are obtained from the corresponding estimated values of the eigenvalues and ;

[0028] Step 6), weighted fusion of the signal subspace information to obtain the DOA estimate value

[0029] Step 6.1), since the space spanned by the columns of the signal subspace U s is the same as the space spanned by the columns of A S , there exists an invertible matrix T1 such that U s = A S T1;

[0030] Define the row permutation matrices and where represents the zero matrix, then where the superscript + represents the generalized inverse, and the diagonal matrix

[0031] Step 6.2), there exists an invertible matrix T2 such that where

[0032] Respectively perform eigenvalue decomposition on (J2U s ) + J1U, to obtain eigenvalues

[0033] Step 6.3), weight and fuse the information in the signal subspace to obtain the DOA estimation value where are respectively eigenvalues.

[0034] As a further optimized solution of the DOA estimation method based on weighted ESPRIT assisted by a semi-passive intelligent reflecting surface in the present invention, step 3) is replaced by the following steps:

[0035] Calculate the covariance matrix R x estimate After that, perform eigenvalue decomposition on to obtain eigenvalues λ i and the estimated value of the signal subspace U s estimate

[0036] As a further optimized solution of the DOA estimation method based on weighted ESPRIT assisted by a semi-passive intelligent reflecting surface in the present invention, step 4) is replaced by the following steps:

[0037] Define the Gram matrix Perform eigenvalue decomposition on to obtain eigenvalues μ i and the estimated value of the signal subspace V s estimate

[0038] Compared with the prior art, the present invention adopts the above technical solutions and has the following technical effects:

[0039] 1. Make full use of the angle information contained in the semi-passive intelligent reflecting surface assisted system, and improve the DOA estimation accuracy compared with the traditional MUSIC algorithm;

[0040] 2. By designing an adaptive weighting framework, the algorithm proposed in the present invention realizes more efficient information extraction;

[0041] 3. Compared with the ANM method, the algorithm proposed in the present invention achieves performance close to the CRB while maintaining a low computational complexity;

[0042] 4. Under various transmit powers and numbers of snapshots, the algorithm proposed in the present invention exhibits robust direction estimation capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flowchart of the present invention;

[0044] Figure 2 is a scene diagram of DOA estimation assisted by a semi-passive intelligent reflecting surface;

[0045] Figure 3 is a schematic diagram of the computational complexity of the present invention and traditional DOA estimation methods under different numbers of passive reflecting elements;

[0046] Figure 4 is a schematic diagram of the root mean square error performance of the present invention and traditional DOA estimation methods under different transmit powers;

[0047] Figure 5 is a schematic diagram of the root mean square error performance of the present invention and traditional DOA estimation methods under different numbers of snapshots. DETAILED DESCRIPTION OF THE INVENTION

[0048] The technical solution of the present invention will be further described in detail below with reference to the drawings:

[0049] The present invention can be implemented in many different forms and should not be considered limited to the embodiments described herein. On the contrary, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present invention to those skilled in the art. In the drawings, components are enlarged for clarity.

[0050] The present invention discloses a DOA (direction-of-arrival) estimation method assisted by a semi-passive intelligent reflecting surface based on weighted ESPRIT (estimation of signal parameters via rotational invariance techniques). First, a DOA estimation model assisted by a semi-passive intelligent reflecting surface is constructed to obtain the received signal. Then, the covariance matrix of the received signal is calculated, eigen-decomposed, and the signal subspace and eigenvalues are extracted. Next, a Gram matrix is constructed, eigen-decomposed, and the signal subspace and eigenvalues are extracted. Then, the weight parameters are calculated based on the eigenvalues. Finally, the signal subspace information is weighted and fused to obtain the DOA estimation. The DOA estimation method assisted by a semi-passive intelligent reflecting surface based on weighted ESPRIT designed by the present invention can effectively sense the target and perform DOA estimation when the base station is blocked. In addition, this method makes full use of the characteristics of the intelligent reflecting surface and introduces an adaptive weighting mechanism, which can robustly estimate the DOA. The simulation results show that the DOA estimation accuracy of the algorithm proposed by the present invention is better than that of the traditional multiple signal classification (MUSIC) method, and is close to and approaches the Cramér-Rao bound (CRB) compared with the atomic norm minimization (ANM) algorithm.

[0051] As Figure 1 shown, the specific steps of the present invention are as follows:

[0052] Step 1), construct a DOA estimation model assisted by a semi-passive intelligent reflecting surface to obtain the echo signal x(t) received at the t-th snapshot moment;

[0053] As Figure 2 shown, in the DOA estimation model assisted by the semi-passive intelligent reflecting surface, it includes K far-field targets and a base station equipped with a uniform linear array with M B antennas. The line-of-sight path between the base station and the far-field targets is blocked. The passive reflection array and the active sensing array equipped on the semi-passive intelligent reflecting surface for constructing the non-line-of-sight path are both uniform linear arrays, and the number of array elements is M R and M S respectively;

[0054] The channel between the base station and the passive reflection array where the superscript H represents conjugate transpose, represents the channel loss, λ is the signal wavelength, d B2R is the distance between the base station and the semi-passive intelligent reflecting surface, θ B2R and respectively represent the DOA on the passive reflection array and the angle of departure of the base station, a R and a B respectively represent the array response vectors of the passive reflection array and the base station; for a uniform linear array with an element spacing of half a wavelength, at a given angle θ, the i-th element of its array response vector is e j(i-1)πsinθ , where j represents the imaginary unit;

[0055] The channel from the passive reflection array to target k and then to the active sensing array where, θ k represents the DOA of the k-th target on the active sensing array, a S represents the array response vector of the active sensing array, represents the channel loss from the passive reflection array to target k and then to the active sensing array, d k represents the distance between the intelligent reflecting surface and target k, γ k is the radar cross section of target k;

[0056] Assume that the targets are all in the far field of the intelligent reflecting surface, and the element spacings of the arrays on the passive reflection array, the active sensing array, and the base station are all half a wavelength, The intelligent reflecting surface adjusts its beam once per snapshot, for a total of T snapshots; the phase shift vector of the passive reflection array at the t-th snapshot is where, the superscript T represents the transpose, represents the phase shift of the i-th element of the passive reflection array;

[0057] The echo signal received by the active sensing array at the t-th snapshot where, represents the diagonal matrix composed of , represents the transmit power of the base station, s(t) is the transmit signal with unit power, and n(t) is a circularly additive Gaussian white noise vector, each of whose elements follows a complex normal distribution represents the noise power, w is the beamforming vector, and satisfies ||w|| 2 = 1, ||·|| represents the l2 norm;

[0058] To maximize the received signal power of the active sensing array, the base station aligns the beam towards the intelligent reflecting surface, that is Then there is where,

[0059] Step 2), stack the echo signals of the received T snapshots to obtain the received signal where,

[0060]

[0061] Step 3), calculate the covariance matrix R of the received signal x , perform eigen-decomposition on it, and extract the signal subspace and eigenvalues

[0062] Step 3.1), calculate the covariance matrix of the received signal X where represents the mathematical expectation, represents the M S dimensional identity matrix,

[0063] Step 3.2), perform eigen-decomposition on R x to obtain where the eigenvalue λ i satisfies The corresponding eigenvectors form the signal subspace U s and the noise subspace U n .

[0064] In practical applications, due to the limited number of snapshots, after calculating the estimated value x of the covariance matrix R , perform eigen-decomposition on to obtain the eigenvalue λ i and the estimated value s of the signal subspace U

[0065] Step 4), construct the Gram matrix Perform eigen-decomposition on it, and extract the signal subspace eigenvalues

[0066] Step 4.1), define the Gram matrix When M S tends to infinity, there is where, I T represents the T-dimensional identity matrix;

[0067] Step 4.2), perform eigen-decomposition on G x to obtain where the eigenvalue μ i satisfies The corresponding eigenvectors form the signal subspace V s and the noise subspace V n .

[0068] In practical applications, due to the finiteness of M S , for The eigenvalue μ is obtained by eigenvalue decomposition i and the signal subspace V s of the estimated value

[0069] Step 5), according to the eigenvalues and calculate the weight parameters and

[0070] When M R >> K, the covariance matrix where represents the eigenvalue of R s ;

[0071] Since the eigenvalues of R x then the estimated value of the noise power The estimated value of the signal power

[0072] According to the water filling principle, the higher the signal-to-noise ratio, the greater the allocated weight. The weight of R x The weight of G x G x The weight of where

[0073] In practical applications, the estimated values of the weight parameters and are obtained from the corresponding estimated eigenvalues and .

[0074] Step 6), weighted fusion of the signal subspace information to obtain the DOA estimated value

[0075] Step 6.1), since the space spanned by the columns of the signal subspace U s is the same as the space spanned by the columns of A S , there exists an invertible matrix T1 such that U s = A S T1;

[0076] Define the row permutation matrices and where represents the zero matrix, then where the superscript + represents the generalized inverse, and the diagonal matrix

[0077] Step 6.2), there exists an invertible matrix T2 such that where

[0078] For (J2U s ) + J1U, Performing eigenvalue decomposition to obtain eigenvalues

[0079] Step 6.3), weighted fusion of the information in the signal subspace to obtain the DOA estimation value where, are respectively eigenvalues.

[0080] Figure 3 This is a schematic diagram of the computational complexity (number of complex multiplications) of the method of the present invention and traditional DOA estimation methods varying with the number of passive reflecting array elements. The simulation conditions are as follows: 6 targets, the base station, the passive reflecting array, and the active sensor array are all equipped with uniform linear arrays. Among them, the number of array elements of the base station and the passive reflecting array is the same, the active sensor array is equipped with 10 array elements, the number of snapshots is 50, the grid search range is from -90° to 90°, and the search step is 0.01°. From Figure 3 it can be seen that the computational complexity of the method of the present invention is lower than that of the traditional MUSIC method and ANM method.

[0081] The performance estimation criterion of the present invention is that the root mean square error (RMSE) is defined as:

[0082]

[0083] where M c is the number of Monte Carlo experiments, represents the DOA estimation value of target k in the i-th experiment, and θ k represents the true value of the DOA of target k.

[0084] Figure 4 This is a performance curve graph of the root mean square error of the method of the present invention, the MUSIC method, and the ANM method varying with the transmit power. The simulation conditions are as follows: the signal s(t)=1, the carrier frequency is 28 GHz, M B =M R =16, M S =6, K = 3, d B2R = 40 m, γ k = 10 dBsm, the target DOAs are θ1 = 10°, θ2 = 30°, θ3 = 50°, d k = 5 m, the error tolerance in the ANM algorithm is set to 1000, T = 200, the grid search range is from -90° to 90°, the search step size is 0.01°, and the simulation is performed 500 times. From Figure 4 It can be seen that the DOA estimation accuracy of the present invention is always better than that of the traditional MUSIC method, is close to the ANM method and can approach the CRB.

[0085] Figure 5 This is the performance curve graph of the root mean square error of the method of the present invention, the MUSIC method, and the ANM method with the change of the number of snapshots. The simulation conditions are set as follows: the signal s(t) = 1, the carrier frequency is 28 GHz, M B = M R = 16, M S = 6, K = 3, d B2R = 40m, γ k = 10dBsm, The target DOAs are θ1 = 10 =, θ2 = 30°, θ3 = 50°, d k = 5m, the error tolerance in the ANM algorithm is set to 1000, The grid search range is from -90° to 90°, the search step size is 0.01°, and the simulation is performed 500 times. From Figure 5 It can be seen that the DOA estimation accuracy of the present invention is always better than that of the traditional MUSIC method, is close to the ANM method and can approach the CRB.

[0086] In summary, from the analysis of the simulation effect diagram, it can be seen that a DOA estimation method based on weighted ESPRIT assisted by a semi-passive intelligent reflecting surface proposed by the present invention can effectively estimate the target DOA. In addition, this method can make full use of the characteristics of the semi-passive intelligent reflecting surface and improve the DOA estimation accuracy compared with the traditional MUSIC algorithm. Compared with the ANM algorithm, the algorithm proposed by the present invention achieves a performance close to the CRB while maintaining a low computational complexity.

[0087] Those skilled in the art of the present technology can understand that, unless otherwise defined, all terms (including technical terms and scientific terms) used here have the same meaning as the general understanding of those of ordinary skill in the technical field to which the present invention belongs. It should also be understood that terms defined in a general dictionary such as those should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless defined as such here.

[0088] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only for the specific embodiments of the present invention and is not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A DOA estimation method assisted by a semi-passive intelligent reflecting surface based on weighted ESPRIT, characterized by comprising the following steps: Step 1), construct a DOA estimation model assisted by a semi-passive intelligent reflecting surface to obtain the echo signal x(t) received at the t-th snapshot moment; In the described DOA estimation model assisted by a semi-passive intelligent reflecting surface, there are K far-field targets and a base station equipped with a uniform linear array of M B antennas. The line-of-sight path between the base station and the far-field targets is blocked; The passive reflection array and the active sensing array equipped in the semi-passive intelligent reflecting surface for constructing non-line-of-sight paths are both uniform linear arrays, and the number of array elements is M R and M S ; Channel between the base station and the passive reflecting array where the superscript H represents the conjugate transpose, represents the channel loss, λ is the signal wavelength, d B2R is the distance between the base station and the semi-passive intelligent reflecting surface, θ B2R and respectively represent the DOA on the passive reflecting array and the departure angle of the base station, a R and a B respectively represent the array response vectors of the passive reflecting array and the base station; for a uniform linear array with an element spacing of half a wavelength, at a given angle θ, the i-th element of its array response vector is e j(i-1)πsinθ , where j represents the imaginary unit; Channel from the passive reflecting array to target k and then to the active sensing array where θ k represents the DOA of the k-th target on the active sensing array, a S represents the array response vector of the active sensing array, represents the channel loss from the passive reflecting array to target k and then to the active sensing array, d k represents the distance between the intelligent reflecting surface and target k, γ k is the radar cross-section area of target k; Let the targets be in the far field of the intelligent reflecting surface. The element spacing of the passive reflecting array, the active sensing array, and the array on the base station is all half-wavelength. The intelligent reflecting surface adjusts its beam each snapshot, with a total of T snapshots. The phase shift vector of the passive reflecting array at the t-th snapshot is where the superscript T represents transpose. denotes the phase shift of the i-th element of the passive reflecting array. The echo signal received by the active sensing array at the \(t\)-th snapshot where denotes a diagonal matrix composed of and denotes the transmit power of the base station, \(s(t)\) is the transmit signal with unit power, and \(n(t)\) is a circularly additive Gaussian white noise vector, each element of which follows a complex normal distribution denotes the noise power, \(w\) is the beamforming vector, and satisfies \(\|w\|\) 2 = 1, \(\|\cdot\|\) represents the \(l_2\) norm; To maximize the received signal power of the active sensing array, the base station aligns the beam towards the intelligent reflecting surface, i.e., Then there is where Step 2), stacking the echo signals of the received T snapshots to obtain a received signal wherein Step 3), calculate the covariance matrix R of the received signal x , perform eigen-decomposition on it, and extract the signal subspace and eigenvalues Step 3.1), calculate the covariance matrix of the received signal X where denotes the mathematical expectation, and I MS denotes an M S -dimensional identity matrix, Step 3.2), perform eigen - decomposition on R x to obtain where the eigenvalues λ i satisfy The corresponding eigen - vectors form the signal subspace U s and the noise subspace U n ; Step 4), construct the Gram matrix Perform its eigen - decomposition and extract the signal subspace Eigenvalue Step 4.1), define the Gram matrix When M S tends to infinity, there is where, I T represents the T-dimensional identity matrix; Step 4.2), perform eigen - decomposition on G x to obtain where the eigenvalues μ i satisfy The corresponding eigen - vectors form the signal subspace V s and the noise subspace V n ; Step 5), according to the eigenvalue and calculate the weight parameters and When M R >> K, the covariance matrix wherein represents the eigenvalue of R s ; Since R x has eigenvalues the estimated value of the noise power the estimated value of the signal power According to the water injection principle, the higher the signal-to-noise ratio, the greater the allocated weight, R x weight value of G x weight value of Among them, In practical applications, the estimated values of the weight parameters and are obtained from the corresponding estimated eigenvalue and ; Step 6), weighted fusion of signal subspace information to obtain the DOA estimation value Step 6.1), since the space spanned by the columns of the signal subspace U s is the same as the space spanned by the columns of A S , there exists an invertible matrix T1 such that U s = A S T1; Define the row exchange matrix and where denotes the zero matrix, then where the superscript + denotes the generalized inverse, and the diagonal matrix Step 6.2), there exists an invertible matrix T2 such that where Respectively for (J2U s ) + J1U, Performing eigenvalue decomposition to obtain eigenvalues Step 6.3), weighted fusion of the information in the signal subspace to obtain the DOA estimation value wherein are respectively eigenvalues of 2. The DOA estimation method based on weighted ESPRIT assisted by a semi-passive intelligent reflecting surface according to claim 1, characterized in that The said step 3) is replaced by the following steps: Calculate the covariance matrix R x Estimate of After that, for Perform eigen - decomposition to obtain the eigenvalues λ i And the estimate of the signal subspace U s Estimate of 3. The DOA estimation method assisted by a semi-passive intelligent reflecting surface based on weighted ESPRIT according to claim 1, characterized in that, The said step 4) is replaced by the following steps: Define the Gram matrix For Perform eigenvalue decomposition to obtain the eigenvalue μ i And the signal subspace V s The estimated value of

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