Robust beam convergence and nulling method for communication and perception integrated system

Abstract

The invention provides a robust beam convergence and nulling method for a communication and perception integrated system, which relates to the technical field of wireless communication, and comprises the following steps: firstly, constructing the communication and perception integrated system comprising a base station, a communication user and a jammer, and receiving a communication signal sent by the communication user and an interference signal sent by the jammer by the base station; the method comprises the following steps: respectively calculating receiving steering vectors of a communication signal and an interference signal, calculating respective covariance matrixes according to the receiving steering vectors, constructing a receiving beam model based on the covariance matrixes, enabling the signal to interference plus noise ratio of the received communication signal sent by a communication user to be maximum, and in the direction in which a jammer sends the interference signal, considering an interference suppression constraint condition, according to the method, parameters are designed to suppress interference, the tolerance of direction error estimation is improved, the beam stability is improved, a receiving beam model is subjected to multiple times of random optimization solution, an optimal receiving beam vector is obtained, a robust beam forming direction result is obtained, and the calculation complexity is reduced.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and more particularly to a robust beam focusing and nulling method for a communication and perception integrated system. Background Art

[0002] With the rapid development of wireless communication technology, the complexity and diversity of communication networks are constantly increasing, and the requirements for communication system performance and reliability are also increasing. However, modern communication systems often face a variety of complex interference sources, such as adjacent-channel interference, co-channel interference, and other non-ideal signals. These interference signals seriously affect communication quality, degrading system stability and user experience. To effectively suppress interference and improve channel capacity and communication quality, beamforming technology, due to its efficient directional transmission capabilities, has been widely used.

[0003] Beamforming technology is a technique that forms a beam with enhanced signals in a specific direction by manipulating the signal parameters (such as phase and amplitude) of multiple sensors or antenna arrays. Its core goal is to suppress interference signals from non-target directions and improve system efficiency by transmitting or receiving signals in a directional manner. Existing beamforming methods rely primarily on accurately estimating the target and interference directions to achieve optimized signal processing. However, in complex environments, due to channel uncertainty, device noise, and hardware errors, direction estimation often contains errors, resulting in a performance degradation of traditional beamforming technology.

[0004] Robust beamforming technology optimizes the robustness of the beam based on traditional beamforming technology. The diagonal loading method is one of the most widely used robust beamforming algorithms. By adding a proportional unit matrix to the array signal covariance matrix, it can effectively suppress steering deviation, but the diagonal loading factor is difficult to determine. The eigenspace method projects the assumed steering vector into the signal and interference subspace to estimate the true steering vector. However, it requires prior information such as the assumed desired signal steering vector and the number of interfering signals, and its performance degrades severely in low signal-to-noise ratio environments.

[0005] Prior art discloses a robust beamforming method for integrated radar and communication, taking into account channel error and angle error estimation. An objective function for maximizing the SINR of the radar output signal is constructed to solve the maximization beamforming optimization problem. Robust user quality of service constraints are also given. The robustness constraints in the beamforming optimization problem are simplified by applying existing criteria for solving the residual modulus minimax problem and existing linear fractional programming principles, thereby improving the robustness of the beamforming. However, this solution does not impose range constraints on the uncertainty of the target and interference directions, resulting in a significant impact of the direction estimation error on the received signal-to-noise ratio. An alternating optimization method is used to solve the transmit and receive beamforming vectors, and second-order cone programming is combined to transform the beamforming optimization problem into a convex constraint problem. This results in high computational complexity and lacks constraints on beam flatness, resulting in poor stability of the formed beam. Summary of the Invention

[0006] To address the problems of current robust beamforming methods being sensitive to directional estimation errors, having high computational complexity, and having poor beam stability, the present invention proposes a robust beam focusing and nulling method for integrated communication and perception systems, which improves the tolerance of directional error estimation, improves beam stability by utilizing flatness constraints, and improves computational efficiency by utilizing semidefinite relaxation and convex optimization tools, thereby reducing technical complexity.

[0007] In order to achieve the above technical effects, the technical solutions of the present invention are as follows:

[0008] A robust beam focusing and null steering method for a communication and perception integrated system, the method comprising the following steps:

[0009] S1: Build an integrated communication and perception system, including base stations, communication users, and jammers. The base stations receive communication signals from communication users and interference signals from jammers.

[0010] S2: Calculate the reception steering vector of the communication signal and the reception steering vector of the interference signal respectively;

[0011] S3: Calculate the signal covariance matrix based on the received steering vector of the communication signal, and calculate the interference noise covariance matrix based on the received steering vector of the interference signal;

[0012] S4: Construct a receive beam model based on the signal covariance matrix and the interference noise covariance matrix to maximize the signal-to-interference-noise ratio of the communication signal sent by the received communication user. In the direction where the jammer sends the interference signal, consider the interference suppression constraints and design parameters to suppress interference.

[0013] S5: Perform multiple random optimization solutions on the receive beam model to obtain the optimal receive beam vector and the robust beamforming direction result.

[0014] In this technical solution, a communication and perception integrated system including a base station, a communication user and a jammer is first constructed. The base station receives the communication signal sent by the communication user and the interference signal sent by the jammer, calculates the reception steering vectors of the communication signal and the interference signal respectively, calculates their respective covariance matrices according to the reception steering vectors, and constructs a reception beam model based on the covariance matrix to maximize the signal-to-interference-noise ratio of the communication signal sent by the received communication user. In the direction where the jammer sends the interference signal, the interference suppression constraint is considered and the parameters are designed to suppress the interference, thereby improving the tolerance of the directional error estimation and the beam stability. The reception beam model is subjected to multiple random optimization solutions to obtain the optimal reception beam vector, obtain the robust beamforming direction result, and reduce the complexity of the calculation.

[0015] Preferably, in the communication and perception integrated system, the communication user transmits an uplink signal to the base station, and the jammer transmits an interference signal to the base station, and the base station and the communication user are respectively equipped with N R and N T A uniform linear array of receiving antennas;

[0016] Assume that the direction of the communication user relative to the base station antenna is θ C , the jammer's direction relative to the base station antenna is θ I , then the total signal y received by the base station is the superposition of the uplink signal transmitted by the communication user to the base station and the interference signal transmitted by the jammer to the base station, and the expression is:

[0017]

[0018] Among them, P C is the transmission power of the communication user, P I is the transmit power of the jammer, s∈ is the transmit symbol of the communication user, x∈ is the transmit symbol of the jammer, f is the beamforming vector of the communication user, a(θ C ) is the receiving steering vector of the communication signal, a(θ I )∈ is the receiving steering vector of the interference signal, h c represents the wireless channel response vector from the communication user to the base station, h I Represents the wireless channel response vector from the jammer to the base station, Z∈ has a mean of 0 and a variance of complex Gaussian white noise, w represents the receiving beam, and H represents the matrix transpose operation.

[0019] Preferably, the receiving steering vector a(θ C ) and the receiving steering vector a(θI The calculation expressions of )∈ are:

[0020]

[0021] Where vec[] is a column vector and r is the uniform linear array order of the receiving antenna.

[0022] Preferably, in step S3, the communication signal covariance matrix R S and the interference noise covariance matrix R IN The expressions are:

[0023] R S =P C a(θ C )a H (θ C )

[0024]

[0025] Among them, a(θ C )∈ is the receiving steering vector of the communication signal, a(θ I ) is the receiving steering vector of the interference signal, is the variance of complex Gaussian white noise, I is the unit matrix, and the communication signal covariance matrix R s and the interference noise covariance matrix R IN The dimensions are all N R ×N R .

[0026] Preferably, the receiving beam model constructed in step S4 maximizes the signal-to-interference-noise ratio of the received communication signal, and the receiving beam model is constructed based on the communication signal covariance matrix and the interference noise covariance matrix. In the receiving beam model, the objective function expression is:

[0027]

[0028] Where w represents the receiving beam;

[0029] The constraint expression is:

[0030]

[0031] Among them, Γ C To enhance the required degree of communication signal enhancement, Γ I the desired degree of suppression to suppress interfering signals;

[0032] The design parameters for suppressing interference include: the estimated error range θ of the communication signal receiving angle C , that is, the main beam of the receiving beamwidth is in Θ CThe angle range can be aligned with the communication user; the estimated error range of the interference signal suppression angle Θ I , that is, the receiving beam is in Θ I The interference signal is suppressed within the angular range, and the required suppression degree of the interference signal is Γ I .

[0033] Preferably, after constructing the receiving beam model, the process further includes equivalently reconstructing the receiving beam model, and the process is as follows:

[0034] The receiving beam model is equivalent, where the objective function is equivalent to:

[0035]

[0036] The constraints are equivalent to:

[0037]

[0038] w H R IN w=1

[0039] By introducing the auxiliary variable ω, the objective function expression (1) of the receiving beam model is reconstructed into expression (2):

[0040] Objective function:

[0041]

[0042] The constraints are refactored as:

[0043]

[0044] w H R S w≥ω

[0045] w H R IN w=1

[0046] Let W = ww H , reconstruct the objective function expression (2) of the receiving beam model into expression (3):

[0047] Objective function:

[0048]

[0049] The constraints are refactored as:

[0050] Tr(a(θ I )a H (θ I )W)≤Γ I ,θ I ∈ΘI

[0051] Tr(a(θ C )a H (θ C )W)≥ω,θ C ∈Θ C

[0052] Tr(R IN W)=1

[0053] rank(W)=1

[0054] W±0

[0055] Where Tr() represents the trace of the matrix, rank() represents the rank of the matrix, and W±0 represents that the matrix W is a semi-positive definite matrix;

[0056] By using semidefinite relaxation and ignoring the constraint condition rank(W)=1, the receiving beam model expression (3) is transformed into a receiving beam model that conforms to semidefinite programming, which is expressed as:

[0057] The objective function is:

[0058]

[0059] The constraints are:

[0060]

[0061] Preferably, in step S4, the design parameter for suppressing interference further includes a flatness coefficient, and the flatness coefficient is used to constrain the receiving beam model, and the expression is:

[0062] αmax(Tr(a(θ C )a H (θ C )W))-min(Tr(a(θ C )a H (θ C )W))≤0,θ C ∈Θ C

[0063] βmax(Tr(a(θ I )a H (θ I )W))-min(Tr(a(θ I )a H (θ I )W))≤0,θ I ∈Θ I

[0064] Among them, α∈[0,1), β∈[0,1) are flat proportional coefficients, max(Tr(a(θ C )a H (θ C )W)),θ C ∈Θ C represents θ C ∈Θ C Within the range Tr(a(θ C )a H (θ C )W),min(Tr(a(θ C )a H (θ C )W)),θ C ∈Θ C represents θ C ∈Θ C Within the range Tr(a(θ C )a H (θ C )W) minimum value;

[0065] The expression of the final receiving beam model is:

[0066] The objective function is:

[0067]

[0068] The constraints are:

[0069] Tr(a(θ I )a H (θ I )W)≤Γ I ,θ I ∈Θ I

[0070] Tr(a(θ C )a H (θ C )W)≥ω,θ C ∈Θ C

[0071] Tr(R IN W)=1

[0072] W±0

[0073] αmax(Tr(a(θ C )a H (θ C )W))-min(Tr(a(θ C )a H (θ C )W))≤0,θ C ∈ΘC

[0074] βmax(Tr(a(θ I )a H (θ I )W))-min(Tr(a(θ I )a H (θ I )W))≤0,θ I ∈Θ I

[0075] The flat coefficient is used to constrain the receiving beam model, which improves the stability of the beam and ensures that the received signal can still have good quality when there are errors in the system.

[0076] Preferably, solving the receiving beam model includes: solving the receiving beam model using convex optimization, the process being:

[0077] The convex optimization toolkit CVX is used to solve the receiving beam model and obtain the diagonal matrix W of the optimal receiving beam vector. opt .

[0078] Preferably, if the diagonal matrix W of the optimal acceptance beam vector opt The rank of is 1, then directly W opt Decompose to obtain the optimal receiving beam vector w opt , the expression is:

[0079]

[0080] If W opt If the rank is not 1, then Gaussian randomization is used to obtain the optimal receiving beam vector w opt .

[0081] Preferably, the Gaussian randomization process is:

[0082] To W opt Perform eigenvalue decomposition, the expression is:

[0083] W opt =UΣU H

[0084] Where U is the eigenvector matrix and Σ is the eigenvalue diagonal matrix;

[0085] The receiving beam vector wopt is generated randomly multiple times, and the expression is:

[0086]

[0087] Among them, v is an element with a mean of 0, a variance of 1, and a dimension of N R×1 complex Gaussian random variable;

[0088] Among the randomly generated multiple receiving beam vectors wopt, select With W opt The one with the smallest error between them is taken as the optimal receiving beam vector w opt .

[0089] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0090] The present invention proposes a robust beam focusing and nulling method for a communication and perception integrated system. First, a communication and perception integrated system including a base station, a communication user and a jammer is constructed. The base station receives a communication signal sent by the communication user and an interference signal sent by the jammer, calculates reception steering vectors of the communication signal and the interference signal respectively, calculates their respective covariance matrices according to the reception steering vectors, and constructs a reception beam model based on the covariance matrix to maximize the signal-to-interference-noise ratio of the communication signal sent by the received communication user. In the direction where the jammer sends the interference signal, the interference suppression constraint is considered, and parameters are designed to suppress interference, thereby improving the tolerance of the directional error estimation and the beam stability. The reception beam model is subjected to multiple random optimization solutions to obtain the optimal reception beam vector, obtain the robust beamforming direction result, and reduce the complexity of the calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 A schematic diagram illustrating a flow chart of a robust beam focusing and null steering method for a communication and perception integrated system proposed in Embodiment 1 of the present invention;

[0092] Figure 2 A schematic diagram showing the structure of the communication and perception integration system proposed in Example 1 of the present invention;

[0093] Figure 3 : represents a robust beamforming pattern drawn using the robust beam focusing and null steering method proposed in embodiment 3 of the present invention;

[0094] Figure 4 1 represents the beamforming pattern drawn using the existing background technology proposed in Example 3 of the present invention;

[0095] Figure 5 The figure shows a performance comparison of a beamforming pattern obtained by using the robust beam focusing and null steering method proposed in Example 4 of the present invention and a beamforming pattern obtained by using the background technology in 100 random experiments. DETAILED DESCRIPTION

[0096] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0097] In order to better illustrate this embodiment, some parts of the drawings may be omitted, enlarged, or reduced, and do not represent the actual size;

[0098] It is understandable to those skilled in the art that descriptions of certain well-known contents may be omitted in the drawings.

[0099] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0100] The positional relationships described in the drawings are for illustrative purposes only and should not be construed as limiting this patent;

[0101] Example 1

[0102] This embodiment proposes a robust beam focusing and null steering method for a communication and perception integrated system. The flowchart of this method is shown in FIG. Figure 1 , including the following steps:

[0103] S1: Constructing a communication and perception integration system, including: a base station, a communication user and a jammer, the base station receives the communication signal sent by the communication user and receives the interference signal sent by the jammer; in this embodiment, the structural diagram of the constructed communication and perception integration system is as follows Figure 2 shown.

[0104] S2: Calculate the reception steering vector of the communication signal and the reception steering vector of the interference signal respectively;

[0105] S3: Calculate the signal covariance matrix based on the received steering vector of the communication signal, and calculate the interference noise covariance matrix based on the received steering vector of the interference signal;

[0106] S4: Construct a receive beam model based on the signal covariance matrix and the interference noise covariance matrix to maximize the signal-to-interference-noise ratio of the communication signal sent by the received communication user. In the direction where the jammer sends the interference signal, consider the interference suppression constraints and design parameters to suppress interference.

[0107] S5: Perform multiple random optimization solutions on the receive beam model to obtain the optimal receive beam vector and the robust beamforming direction result.

[0108] Example 2

[0109] In this embodiment, in the communication and perception integrated system, the communication user transmits an uplink signal to the base station, and the jammer transmits an interference signal to the base station. The base station and the communication user are respectively equipped with N R and N T A uniform linear array of receiving antennas;

[0110] Assume that the direction of the communication user relative to the base station antenna is θC , the jammer's direction relative to the base station antenna is θ I , then the total signal y received by the base station is the superposition of the uplink signal transmitted by the communication user to the base station and the interference signal transmitted by the jammer to the base station, and the expression is:

[0111]

[0112] Among them, P C is the transmission power of the communication user, P I is the transmit power of the jammer, s∈ is the transmit symbol of the communication user, x∈ is the transmit symbol of the jammer, f is the beamforming vector of the communication user, a(θ C ) is the receiving steering vector of the communication signal, a(θ I )∈ is the receiving steering vector of the interference signal, h c represents the wireless channel response vector from the communication user to the base station, h I Represents the wireless channel response vector from the jammer to the base station, Z∈ has a mean of 0 and a variance of complex Gaussian white noise, w represents the receiving beam, and H represents the matrix transpose operation.

[0113] In this embodiment, the receiving steering vector a(θ C ) and the receiving steering vector a(θ I The calculation expressions of )∈ are:

[0114]

[0115] Where vec[] is a column vector and r is the uniform linear array order of the receiving antenna.

[0116] In this embodiment, in step S3, the communication signal covariance matrix R S and the interference noise covariance matrix R IN The expressions are:

[0117] R S =P C a(θ C )a H (θ C )

[0118]

[0119] Among them, a(θ C )∈ is the receiving steering vector of the communication signal, a(θ I ) is the receiving steering vector of the interference signal, is the variance of complex Gaussian white noise, I is the unit matrix, and the communication signal covariance matrix R sand the interference noise covariance matrix R IN The dimensions are all N R ×N R .

[0120] In this embodiment, the receiving beam model described in step S4 is constructed to maximize the signal-to-interference-noise ratio of the received communication signal. The receiving beam model is constructed based on the communication signal covariance matrix and the interference noise covariance matrix. In the receiving beam model, the objective function expression is:

[0121]

[0122] Where w represents the receiving beam;

[0123] The constraint expression is:

[0124]

[0125] Among them, Γ C To enhance the required degree of communication signal enhancement, Γ I the desired degree of suppression to suppress interfering signals;

[0126] The design parameters for suppressing interference include: the estimated error range θ of the communication signal receiving angle C , that is, the main beam of the receiving beamwidth is in Θ C The angle range can be aligned with the communication user; the estimated error range of the interference signal suppression angle Θ I , that is, the receiving beam is in Θ I The interference signal is suppressed within the angular range, and the required suppression degree of the interference signal is Γ I .

[0127] In this embodiment, after constructing the receiving beam model, the receiving beam model is further reconstructed equivalently. The process is as follows:

[0128] The receiving beam model is equivalent, where the objective function is equivalent to:

[0129]

[0130] The constraints are equivalent to:

[0131]

[0132] w H R IN w=1

[0133] By introducing the auxiliary variable ω, the objective function expression (1) of the receiving beam model is reconstructed into expression (2):

[0134] Objective function:

[0135]

[0136] The constraints are refactored as:

[0137]

[0138] w H R S w≥ω

[0139] w H R IN w=1

[0140] Let W = ww H , reconstruct the objective function expression (2) of the receiving beam model into expression (3):

[0141] Objective function:

[0142]

[0143] The constraints are refactored as:

[0144] Tr(a(θ I )a H (θ I )W)≤Γ I ,θ I ∈Θ I

[0145] Tr(a(θ C )a H (θ C )W)≥ω,θ C ∈Θ C

[0146] Tr(R IN W)=1

[0147] rank(W)=1

[0148] W±0

[0149] Where Tr() represents the trace of the matrix, rank() represents the rank of the matrix, and W±0 represents that the matrix W is a semi-positive definite matrix;

[0150] By using semidefinite relaxation and ignoring the constraint condition rank(W)=1, the receiving beam model expression (3) is transformed into a receiving beam model that conforms to semidefinite programming, which is expressed as:

[0151] The objective function is:

[0152]

[0153] The constraints are:

[0154]

[0155] In this embodiment, in step S4, the design parameters for suppressing interference also include a flatness coefficient, which is used to constrain the receiving beam model. The expression is:

[0156] αmax(Tr(a(θ C )a H (θ C )W))-min(Tr(a(θ C )a H (θ C )W))≤0,θ C ∈Θ C

[0157] βmax(Tr(a(θ I )a H (θ I )W))-min(Tr(a(θ I )a H (θ I )W))≤0,θ I ∈Θ I

[0158] Among them, α∈[0,1), β∈[0,1) are flat proportional coefficients, max(Tr(a(θ C )a H (θ C )W)),θ C ∈Θ C represents θ C ∈Θ C Within the range Tr(a(θ C )a H (θ C )W),min(Tr(a(θ C )a H (θ C )W)),θ C ∈Θ C represents θ C ∈Θ C Within the range Tr(a(θ C )a H (θ C )W) minimum value;

[0159] The expression of the final receiving beam model is:

[0160] The objective function is:

[0161]

[0162] The constraints are:

[0163]

[0164] In this embodiment, solving the receiving beam model includes: solving the receiving beam model using convex optimization, and the process is:

[0165] The convex optimization toolkit CVX is used to solve the receiving beam model and obtain the diagonal matrix W of the optimal receiving beam vector. opt .

[0166] In this embodiment, if the diagonal matrix W of the optimal receiving beam vector is opt The rank of is 1, then directly W opt The optimal receiving beam vector wopt is decomposed and expressed as:

[0167]

[0168] If W opt If the rank is not 1, then Gaussian randomization is used to obtain the optimal receiving beam vector w opt .

[0169] Preferably, the Gaussian randomization process is:

[0170] To W opt Perform eigenvalue decomposition, the expression is:

[0171] W opt =UΣU H

[0172] Where U is the eigenvector matrix and Σ is the eigenvalue diagonal matrix;

[0173] The receiving beam vector wopt is generated randomly multiple times, and the expression is:

[0174]

[0175] Among them, v is an element with a mean of 0, a variance of 1, and a dimension of N R ×1 complex Gaussian random variable;

[0176] Among the randomly generated multiple receiving beam vectors wopt, select With W opt The one with the smallest error between them is taken as the optimal receiving beam vector w opt .

[0177] Example 3

[0178] The base station of the integrated communication and perception system is equipped with 128 antennas, and the direction of the communication user relative to the base station antenna is θ c=0°, the jammer's direction relative to the base station antenna is θ I =60°, the required enhancement degree of the enhanced communication signal is Γ C = 0dBW, the required suppression degree of the interference signal is Γ I =-70dBW, since the estimated information is imperfect, the communication angle range and interference angle range are taken as Θ C =[-32°,-28°], Θ I = [59°, 61°], the discretization angle interval is 0.1°, α = 0.9, β = 0.9, and the robust beamforming pattern is drawn using the robust beam focusing and nulling method proposed in this invention. Figure 3 , we can see that the beam pattern drawn by this solution is flat within the communication direction, but has a flat "dent" in the interference direction. This ensures that even when errors exist in the system, the received signal can still have good quality.

[0179] The beamforming pattern drawn using the existing background technology is as follows: Figure 4 As shown, it can be seen that it cannot achieve a flat range in the communication direction and the interference direction, so when there is an error in the system, it does not have good communication quality.

[0180] Example 4

[0181] The base station of the integrated communication and perception system is equipped with 128 antennas, and the interference and noise power are set to Γ C =0dBW, Γ I =-70dBW, the communication angle range and interference angle range are Θ C =[-32°,-28°], Θ I =[59°,61°], the discretization angle interval is 0.1°, α=0.9, β=0.9; the real communication direction is randomly set to have a mean of -30° and a variance of 2°, and the real interference direction is set to have a mean of 60° and a variance of 1°, and 100 random experiments are performed. A comparison chart of the beamforming pattern performance of the robust beam focusing and nulling method proposed in the present invention and the beamforming pattern performance obtained by using the background technology in the 100 random experiments is drawn, as shown in FIG. Figure 5 shown.

[0182] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A robust beam focusing and nulling method for an integrated communication and perception system, characterized in that: The following steps are involved: S1: Build an integrated communication and perception system, including base stations, communication users, and jammers. The base stations receive communication signals from communication users and interference signals from jammers. S2: Calculate the reception steering vector of the communication signal and the reception steering vector of the interference signal respectively; S3: Calculate the signal covariance matrix based on the received steering vector of the communication signal, and calculate the interference noise covariance matrix based on the received steering vector of the interference signal; S4: Construct a receiving beam model based on the communication signal covariance matrix and the interference noise covariance matrix to maximize the signal-to-interference-noise ratio of the communication signal sent by the received communication user. In the direction where the jammer sends the interference signal, consider the interference suppression constraints and design parameters to suppress interference. S5: Perform multiple random optimization solutions on the receive beam model to obtain the optimal receive beam vector and the robust beamforming direction result.

2. The robust beam focusing and null steering method for a communication and perception integrated system according to claim 1, characterized in that: In the communication and perception integrated system, the communication user transmits an uplink signal to the base station, and the jammer transmits an interference signal to the base station. The base station and the communication user are respectively equipped with N R and N T A uniform linear array of receiving antennas; Assume that the direction of the communication user relative to the base station antenna is θ C , the jammer's direction relative to the base station antenna is θ I , then the total signal y received by the base station is the superposition of the uplink signal transmitted by the communication user to the base station and the interference signal transmitted by the jammer to the base station, and the expression is: Among them, P C is the transmission power of the communication user, P I is the transmit power of the jammer, s∈ is the transmit symbol of the communication user, x∈ is the transmit symbol of the jammer, f is the beamforming vector of the communication user, a(θ C ) is the receiving steering vector of the communication signal, a(θ I )∈ is the receiving steering vector of the interference signal, h c represents the wireless channel response vector from the communication user to the base station, h I Represents the wireless channel response vector from the jammer to the base station, Z∈ has a mean of 0 and a variance of complex Gaussian white noise, w represents the receiving beam, and H represents the matrix transpose operation.

3. The robust beam focusing and null steering method for a communication and perception integrated system according to claim 2, characterized in that: Step S2: The receiving steering vector a(θ C ) and the receiving steering vector a(θ I The calculation expressions of )∈ are: Where vec[] is a column vector and r is the uniform linear array order of the receiving antenna.

4. The robust beam focusing and null steering method for a communication and perception integrated system according to claim 3, characterized in that: In step S3, the communication signal covariance matrix R S and the interference noise covariance matrix R IN The expressions are: R S =P C a(θ C )a H (i C ) Among them, a(θ C )∈ is the receiving steering vector of the communication signal, a(θ I ) is the receiving steering vector of the interference signal, is the variance of complex Gaussian white noise, I is the unit matrix, and the communication signal covariance matrix R s and the interference noise covariance matrix R IN The dimensions are all N R ×N R .

5. The robust beam focusing and null steering method for a communication and perception integrated system according to claim 4, characterized in that: The receiving beam model described in step S4 is constructed to maximize the signal-to-interference-noise ratio of the received communication signal. The receiving beam model is constructed based on the communication signal covariance matrix and the interference noise covariance matrix. In the receiving beam model, the objective function expression is: Where w represents the receiving beam; The constraint expression is: Among them, Γ C To enhance the required degree of communication signal enhancement, Γ I the desired degree of suppression to suppress interfering signals; The design parameters for suppressing interference include: the estimated error range θ of the communication signal receiving angle C , that is, the main beam of the receiving beamwidth is in Θ C The angle range can be aligned with the communication user; the estimated error range of the interference signal suppression angle Θ I , that is, the receiving beam is in Θ I The interference signal is suppressed within the angular range, and the required suppression degree of the interference signal is Γ I .

6. The robust beam focusing and null steering method for a communication and perception integrated system according to claim 5, characterized in that: After the receiving beam model is constructed, the process further includes: equivalently reconstructing the receiving beam model, the process being: The receiving beam model is equivalent, where the objective function is equivalent to: The constraints are equivalent to: w H R IN w=1 By introducing the auxiliary variable ω, the objective function expression (1) of the receiving beam model is reconstructed into expression (2): Objective function: The constraints are refactored as: w H R S w≥ω w H R IN w=1 Let W = ww H , reconstruct the objective function expression (2) of the receiving beam model into expression (3): Objective function: The constraints are refactored as: Tr(a(θ I )a H (i I )W)≤Γ I ,i I ∈Θ I Tr(a(θ C )a H (i C )W)≥ω,θ C ∈Θ C Tr(R IN W)=1 rank(W)=1 W±0 Where Tr() represents the trace of the matrix, rank() represents the rank of the matrix, and W±0 represents that the matrix W is a semi-positive definite matrix; By using semidefinite relaxation and ignoring the constraint condition rank(W)=1, the receiving beam model expression (3) is transformed into a receiving beam model that conforms to semidefinite programming, which is expressed as: The objective function is: The constraints are:

7. The robust beam focusing and null steering method for a communication and perception integrated system according to claim 6, characterized in that: In step S4, the design parameters for suppressing interference also include a flatness coefficient, which is used to constrain the receiving beam model. The expression is: αmax(Tr(a(θ C )a H (i C )W))-min(Tr(a(θ C )a H (i C )W))≤0,θ C ∈Θ C βmax(Tr(a(θ I )a H (i I )W))-min(Tr(a(θ I )a H (i I )W))≤0,θ I ∈Θ I Among them, α∈[0,1), β∈[0,1) are flatness coefficients, max(Tr(a(θ C )a H (θ C )W)),θ C ∈Θ C represents θ C ∈Θ C Within the range Tr(a(θ C )a H (θ C )W),min(Tr(a(θ C )a H (θ C )W)),θ C ∈Θ C represents θ C ∈Θ C Within the range Tr(a(θ C )a H (θ C )W) minimum value; The expression of the final receiving beam model is obtained: The objective function is: The constraints are:

8. The robust beam focusing and null steering method for a communication and perception integrated system according to claim 7, characterized in that: Solving the receiving beam model includes: solving the receiving beam model using convex optimization, the process is: The convex optimization toolkit CVX is used to solve the receiving beam model and obtain the diagonal matrix W of the optimal receiving beam vector. opt .

9. The robust beam focusing and null steering method for a communication and perception integrated system according to claim 8, characterized in that: If the diagonal matrix W of the optimal acceptance beam vector opt The rank of is 1, then directly W opt Decompose to obtain the optimal receiving beam vector w opt , the expression is: If W opt If the rank is not 1, then Gaussian randomization is used to obtain the optimal receiving beam vector w opt .

10. The robust beam focusing and null steering method for a communication and perception integrated system according to claim 9, characterized in that: The Gaussian randomization process is: To W opt Perform eigenvalue decomposition, the expression is: IN opt =UΣU H Where U is the eigenvector matrix and Σ is the eigenvalue diagonal matrix; Randomly generate the receiving beam vector w multiple times opt , the expression is: Among them, v is an element with a mean of 0, a variance of 1, and a dimension of N R ×1 complex Gaussian random variable; In the randomly generated receiving beam vector w opt , select With W opt The one with the smallest error between them is taken as the optimal receiving beam vector w opt .

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