Anti-interference receiving beam optimization method for communication and perception fusion system

Abstract

The invention discloses an anti-interference receiving beam optimization method for a communication and perception fusion system. The method comprises the following steps: estimating an arrival angle of a receiving signal of the communication and perception fusion system by using a spatial spectrum estimation algorithm; according to the angle of arrival of the received signal and the mathematical model of the received signal, carrying out inversion to obtain snapshot information of the received signal; according to the snapshot information of the receiving signal, a receiving beam vector optimization problem is constructed, the receiving beam vector optimization problem aims at maximizing the uplink communication channel capacity, interference suppression constraint is introduced at the same time, and an optimal receiving beam vector is designed; and obtaining an optimal receiving beam vector by solving the receiving beam vector optimization problem. According to the invention, external electromagnetic interference signals can be effectively suppressed, the communication quality and sensing performance of an ISAC system are improved, the bit error rate is reduced, and the accuracy of functions of target detection, positioning, tracking and the like is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of anti-interference technology in a wireless communication and positioning perception fusion system, and more specifically, to an anti-interference receiving beam optimization method for a communication and perception fusion system. Background Art

[0002] With the rapid development of wireless communication technology, spectrum resources have become increasingly scarce. Integrated communication and perception (ISAC) technology has emerged. It integrates communication and perception functions on the same platform and improves spectrum efficiency and system performance by sharing spectrum and hardware resources. The core idea of ​​ISAC technology is to jointly design communication and perception tasks, share hardware resources and signal processing algorithms, and thus improve spectrum efficiency and system performance. For example, the ISAC system can use communication signals for environmental perception to achieve functions such as target detection, positioning and tracking; at the same time, the ISAC system can also use perception signals for communication to achieve functions such as data transmission and communication enhancement. The advantages of ISAC technology are mainly reflected in the following aspects: (1) Spectrum efficiency improvement: The ISAC system can effectively improve spectrum utilization and alleviate the problem of spectrum resource shortage by sharing spectrum resources. (2) System performance improvement: The ISAC system can use perception information for communication, thereby improving communication quality and reliability; at the same time, the ISAC system can also use communication information for perception, thereby improving perception accuracy and efficiency. (3) Cost reduction: The ISAC system can reduce system costs and improve the economic benefits of the system by sharing hardware resources. However, in practical applications, ISAC systems face challenges from external electromagnetic interference signals. These interference signals may come from other wireless communication systems, wireless sensor networks, radar systems, etc., which will have a negative impact on the communication quality and perception performance of the ISAC system. For example, they will reduce the communication signal-to-noise ratio, resulting in an increase in the bit error rate, affecting communication quality; and interfere with the detection and recognition of perception signals, reducing perception performance.

[0003] Existing interference suppression methods, such as linearly constrained minimum variance (LCMV) beamforming, minimum mean square error (MMSE) beamforming, and angle-guided receive beamforming, can suppress interference signals to a certain extent, but they all have limitations. For example, the LCMV method requires prior knowledge of the interference source's direction, which is often difficult to accurately determine in practical applications. The MMSE method is also sensitive to noise, leading to false detections and missed detections, which can cause target signal loss. Summary of the Invention

[0004] One of the objectives of the present invention is to provide an anti-interference receiving beam optimization method for a communication and perception fusion system to solve the external electromagnetic interference problem faced by the integrated communication and perception (ISAC) system.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] An embodiment of the present invention provides a method for optimizing an anti-interference receiving beam in a communication and perception fusion system, comprising the following steps:

[0007] The spatial spectrum estimation algorithm is used to estimate the arrival angle of the received signal of the communication and perception fusion system;

[0008] Inverting to obtain snapshot information of the received signal according to the arrival angle of the received signal and the mathematical model of the received signal;

[0009] Constructing a receive beam vector optimization problem based on the snapshot information of the received signal, wherein the receive beam vector optimization problem aims to maximize uplink communication channel capacity while introducing interference suppression constraints to design an optimal receive beam vector;

[0010] By solving the receiving beam vector optimization problem, an optimal receiving beam vector is obtained.

[0011] In the above-mentioned technical means, spatial spectrum estimation algorithms and matrix operation inverse solution are used to obtain signal snapshot information, providing accurate basic data for beam optimization. Interference suppression constraints are introduced when constructing a beam optimization model based on channel capacity. This can effectively balance the relationship between improving communication performance and suppressing interference. It comprehensively considers the needs of maximizing channel capacity and suppressing interference. By optimizing the receiving beam vector, it is possible to achieve effective suppression of interference signals in specific directions while improving communication performance.

[0012] Furthermore, the signal snapshot information is the sampling value of the signal at different time snapshots, including the amplitude and phase of the signal.

[0013] Furthermore, the mathematical model of the received signal is:

[0014]

[0015] Where, Y represents the received signal, a R (θ c ) represents the receiving steering vector of the uplink communication signal, θ c is the arrival angle of the uplink communication signal, Indicates the signal snapshot information of the uplink communication signal, a R (θ i ) represents the receiving steering vector of the i-th interference source signal, θ i represents the arrival angle of the i-th interference source signal, represents the signal snapshot information of the i-th interference source signal, K Irepresents the number of interference source signals, N represents the complex-valued zero-mean Gaussian noise matrix at the receiving end;

[0016] The matrix expression of the mathematical model of the received signal is:

[0017] Y=A R (Θ)S+N

[0018] A R (Θ)=[a R (θ c ),A R (θ I )]

[0019]

[0020] Furthermore, the spatial spectrum estimation algorithm is used to estimate the arrival angle of the received signal of the communication and perception fusion system, including:

[0021] make is the covariance matrix of the received signal snapshot;

[0022] R Y Perform singular value decomposition and get:

[0023]

[0024] Where U S represents the signal subspace matrix, Λ S Represents the eigenvalue submatrix of the signal subspace, U N represents the noise subspace matrix, Λ N represents the eigenvalue submatrix of the noise subspace matrix;

[0025] The angle spectrum function is constructed using the receiving steering vector noise space at the receiving end to estimate the signal arrival angle in turn:

[0026]

[0027] Where, Represents uplink communication signal, 1st to Kth I The estimated angle of arrival of the interference source signal.

[0028] Further, the snapshot information of the received signal is obtained by inversion, including the uplink communication signal, the 1st to Kth I The estimated arrival angle of the interference source signal is substituted into the steering vector model:

[0029]

[0030] Furthermore, the receive beam vector optimization problem is:

[0031]

[0032] Where w c represents the receiving beam vector, cap(w c ) represents the uplink communication channel capacity, Γ is the interference suppression requirement factor, which represents the maximum tolerable interference power level.

[0033] Furthermore, solving the receive beam vector optimization problem includes:

[0034] S1: Simplify the receive beam vector optimization problem using Lagrangian dual optimization and fractional programming;

[0035] S2: Based on the simplified receive beam vector optimization problem, the conditions satisfied by the optimal receive beam vector are obtained;

[0036] S3: Using a binary search on the conditions satisfied by the optimal receiving beam vector to obtain the current optimal receiving beam vector;

[0037] S4: Repeat steps S1-S3 until the receive beam vector optimization problem converges and the final optimal receive beam vector is obtained.

[0038] Furthermore, the receive beam vector optimization problem is simplified by using Lagrangian dual optimization and fractional programming, including:

[0039] Introduction of cofactors , convert the receive beam vector optimization problem into:

[0040]

[0041] Introducing Lagrange multipliers to handle constraints, we get the Lagrange function:

[0042]

[0043] The receive beam vector optimization problem is converted to:

[0044]

[0045] make Optimal satisfy Determine the Lagrange multiplier as:

[0046]

[0047] Substituting the Lagrange multiplier into the Lagrange function, we get:

[0048]

[0049] Introducing auxiliary variable ρ c , using fractional transformation, construct the quadratic lower bound of the above Lagrangian function:

[0050]

[0051] Among them, ρ c and w c The above function is convex, so in the previous solution Under the condition of The optimal auxiliary parameter expression is obtained:

[0052]

[0053] Introducing the Lagrange multiplier λ w Get the new Lagrangian function:

[0054]

[0055] Furthermore, the conditions satisfied by the optimal receiving beam vector are obtained:

[0056] make The optimal receiving beam vector expression is obtained:

[0057]

[0058]

[0059] By order Get the optimal Lagrange multiplier Producing a solution So that it satisfies:

[0060]

[0061] Furthermore, step S3 further includes:

[0062] Normalize the current optimal receive beam vector.

[0063] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0064] 1. This invention effectively suppresses external electromagnetic interference signals, improving the ISAC system's communication quality and perception performance, reducing bit error rates, and enhancing the accuracy of target detection, positioning, and tracking. In complex electromagnetic environments, this invention significantly improves the ISAC system's anti-interference capabilities and ensures stable system operation.

[0065] 2. By optimizing the model and algorithm design, this invention maximizes the communication performance of the ISAC system in complex electromagnetic environments, maintaining excellent communication performance and providing reliable technical support for the practical application of the ISAC system. This invention not only considers the improvement of communication performance but also takes into account the need for interference suppression, thus possessing high practical value.

[0066] 3. Compared to traditional interference suppression methods such as LCMV and MMSE beamforming, this invention does not require precise prior knowledge of the interference source's direction, is insensitive to noise, and has greater adaptability and robustness. In practical applications, the direction of the interference source is often difficult to accurately determine. However, this invention can effectively suppress interference signals in such situations, demonstrating its high engineering application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A flowchart of a method for optimizing an anti-interference receiving beam in a communication and perception fusion system provided by an embodiment of the present invention;

[0068] Figure 2 A schematic diagram of an uplink communication / perception scenario of a communication and perception fusion system provided in an embodiment of the present invention;

[0069] Figure 3 A flowchart of a binary search process according to an embodiment of the present invention;

[0070] Figure 4 A schematic diagram of an uplink communication rate convergence curve provided by an embodiment of the present invention;

[0071] Figure 5 The receive beam pattern provided by the embodiment of the present invention;

[0072] Figure 6 A schematic diagram illustrating the effect of the interference power threshold on the residual interference power according to the method of the embodiment of the present invention;

[0073] Figure 7 A schematic diagram illustrating the impact of interference suppression requirements on system performance according to the method of the embodiment of the present invention;

[0074] Figure 8 This is a schematic diagram of the impact of external interference power on receiving performance when using the method provided in this embodiment of the present invention. DETAILED DESCRIPTION

[0075] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0076] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0077] It is understandable to those skilled in the art that some well-known structures and descriptions thereof may be omitted in the drawings.

[0078] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0079] Example 1

[0080] The embodiment of the present invention provides a method for optimizing the anti-interference receiving beam of a communication and perception fusion system. Figure 1 As shown, the following steps are included:

[0081] The spatial spectrum estimation algorithm is used to estimate the arrival angle of the received signal of the communication and perception fusion system;

[0082] Inverting to obtain snapshot information of the received signal according to the arrival angle of the received signal and the mathematical model of the received signal;

[0083] Constructing a receive beam vector optimization problem based on the snapshot information of the received signal, wherein the receive beam vector optimization problem aims to maximize uplink communication channel capacity while introducing interference suppression constraints to design an optimal receive beam vector;

[0084] By solving the receiving beam vector optimization problem, an optimal receiving beam vector is obtained.

[0085] In an embodiment of the present invention, in order to solve the external electromagnetic interference problem faced by the integrated communication and perception (ISAC) system, a beam nulling-based interference suppression method is adopted to fully utilize the perception function of the ISAC system to effectively suppress interference signals in specific directions, thereby maximizing uplink communication performance and maintaining good communication performance, providing theoretical and technical support for the application of the ISAC system in complex electromagnetic environments.

[0086] Example 2

[0087] This embodiment further describes the signal estimation stage, in which the ISAC system's perception function is utilized to estimate the angle of arrival of the received signal using the MUSIC (Multiple Signal Classification) algorithm. The MUSIC algorithm is a high-resolution spectrum estimation method that can use the covariance matrix of the received signal to estimate the direction of the signal source through operations such as eigendecomposition. Specifically, the covariance matrix of the received signal is first constructed, and then singular value decomposition is performed on it to obtain the signal subspace and noise subspace. Utilizing the orthogonality of the signal subspace and the noise subspace, an angle spectrum function is constructed, and the angle of arrival of the received signal is estimated by searching for the peak of the angle spectrum function.

[0088] Signal snapshot information is obtained through matrix operations. After estimating the arrival angle of the interference signal, this angle information can be combined with the received signal model to obtain signal snapshot information through matrix operations. Signal snapshot information is the sampled values ​​of the received signal at different time snapshots. It contains information such as signal amplitude and phase, providing basic data for subsequent beam optimization.

[0089] In a specific embodiment, consider a system equipped with N R Uniform Linear Array (ULA) single-base station ISAC system with multiple antennas, such as Figure 2 As shown. The ISAC base station communicates with the uplink terminal and receives K I Interference source signal, set It is a snapshot of the communication signal transmitted by the uplink communication. is the signal snapshot emitted by the i-th interference source, satisfying Therefore, the base station receiving signal model is:

[0090]

[0091] in, θ c is the arrival angle of the uplink communication signal received by the base station, θ i is the arrival angle of the i-th interference source signal. And is the complex-valued zero-mean Gaussian noise matrix at the receiving end, and its covariance matrix is also, is the base station receive steering vector, given by:

[0092]

[0093] According to formula (1), one-dimensional N R K I The signal model of +1 signal source is organized into a matrix expression:

[0094] Y=A R (Θ)S+N (3)

[0095] in, and

[0096] make Get the covariance matrix of the received signal snapshot. Y Perform singular value decomposition and obtain the following expression:

[0097]

[0098] in, For K I +1 maximum eigenvalue submatrix, and is the signal subspace matrix spanned by the corresponding eigenvectors. In addition, and are the eigenvalue submatrix and eigenvector submatrix associated with the noise subspace.

[0099] Then, based on the orthogonality of the signal subspace and the noise subspace, the receiving steering vector a at the receiving end is used R (θ) and the noise space to construct the angle spectrum function, and estimate the signal arrival angle parameters in turn, namely:

[0100]

[0101] By using the estimated angle parameters and substituting them into the steering vector model (2), we can inversely solve the received signal snapshot information. The specific results are as follows:

[0102]

[0103] The signal direction angle is estimated by using the MUSIC method, and the estimated signal snapshot information is obtained by using matrix operation inverse solution.

[0104] Example 3

[0105] This embodiment specifically describes the beam optimization modeling phase, which is based on an optimization model for channel capacity and introduces interference suppression constraints. Channel capacity is an important indicator of communication system performance, representing the maximum amount of information that can be transmitted per unit time under given channel conditions. The present invention constructs an optimization model based on channel capacity to maximize uplink communication performance. Furthermore, to suppress interference signals from specific directions, interference suppression constraints are introduced. These constraints require that during the optimization process, the gain of the receive beam in the direction of the interference signal be as small as possible, thereby suppressing the interference signal.

[0106] The optimization model's objective function is an expression of the uplink communication channel capacity, which is dependent on factors such as the receive beam vector, channel state information, and noise power. Constraints include interference suppression and a normalization constraint on the receive beam vector. Normalization constraints ensure that the receive beam vector power does not exceed a certain limit, thus avoiding excessive burden on the receiving system.

[0107] In a specific embodiment, an optimization problem is constructed based on the estimated data to design the receiving beam vector. In order to better meet the communication performance, the signal energy of the interference source is suppressed. Based on formula (1), the uplink communication channel capacity expression is:

[0108]

[0109] The receive beam vector optimization problem for beam nulling aims to maximize the uplink communication channel capacity and can be expressed as:

[0110]

[0111] Where Γ is the interference suppression requirement factor, which represents the maximum tolerable interference power level.

[0112] Example 4

[0113] This embodiment further describes the beam optimization problem-solving stage, which uses dual transformation and fractional transformation to simplify the model in response to the non-convexity of the channel capacity optimization model. The channel capacity optimization model usually has a complex logarithmic-fractional structure and is a non-convex optimization problem that is difficult to solve directly. To simplify the model, the present invention uses the methods of dual transformation and fractional transformation. Dual transformation converts the original problem into a dual problem, and by solving the dual problem, the solution to the original problem is obtained. Fractional transformation transforms the fractional terms in the optimization model into a form that is easier to handle.

[0114] An alternating optimization algorithm is designed to solve for the optimal receive beam vector. Based on the simplified model, the present invention devised an alternating optimization algorithm. This algorithm is an iterative algorithm that progressively approaches the optimal solution by alternating between updating different variables. In each iteration, one variable is optimized while the other variables are fixed. Then, the newly obtained variable value is fixed and another variable is optimized, and this cycle repeats until convergence conditions are met. This alternating optimization algorithm can solve for the optimal receive beam vector, effectively suppressing interference signals from specific directions.

[0115] In a specific embodiment, in order to solve the complex logarithmic-fraction structure of the channel capacity, the embodiment of the present invention proposes a method of decoupling the complex problem into a multi-variable alternating optimization method. From formula (8), it can be seen that the objective function is about w c The embodiment of the present invention uses Lagrange dual optimization and fractional programming methods to simplify the design of related algorithms.

[0116] First, by introducing the auxiliary factor The optimization problem can be transformed as follows:

[0117]

[0118]

[0119] Since the objective function of (11) is about is an increasing function of , and (12) is about The right-hand side range constraint, therefore, the expression for the auxiliary variable is given by:

[0120]

[0121] Among them, the previous solution obtained by the j-th iteration Determine, therefore, here it can be used as a constant for the current j+1 iteration w c Secondly, the Lagrange multiplier is introduced to deal with the constraints of (12), thus obtaining the Lagrange function:

[0122]

[0123] Therefore, through the Lagrange dual transformation, the subproblem of the receive beam vector based on uplink communication is transformed into:

[0124]

[0125] make Optimal satisfy Then, combined with formula (13), the Lagrange multiplier is finally determined as:

[0126]

[0127] Substituting (16) into (14) yields the Lagrangian function:

[0128]

[0129] However, the function is c The variable is still non-convex because the above Lagrangian function contains a fractional term, as shown in (17). In order to solve the fractional optimization problem, the auxiliary variable ρ is introduced again c , using fractional transformation, we can construct the quadratic lower bound of the above Lagrangian function, which is given by the following formula:

[0130]

[0131] Among them, ρ c and w c The above function is convex, so in the previous solution Under the condition of The optimal auxiliary parameter expression is obtained:

[0132]

[0133] In order to deal with the constraint of maximum energy suppression of beam nulling, the Lagrange multiplier λ is introduced. w The new Lagrangian function is obtained and expressed as:

[0134]

[0135] Currently, given other variables, the objective function only has w c and λ w is unknown, so The optimal receiving beam vector expression is obtained:

[0136]

[0137] By order Get the optimal Lagrange multiplier Should produce a solution So that it satisfies:

[0138]

[0139] It is worth noting that the optimal solution of the receiving beam vector depends on the auxiliary variables satisfying equation (23), and equation (23) is a function of the auxiliary variables, and the function satisfies monotonically decreasing conditions. Therefore, binary search can be used to obtain the auxiliary variable solution that meets the conditions, and then obtain the optimal receiving beamforming solution. The binary search program flowchart is as follows: Figure 3 shown.

[0140] Finally, after the base station receiving beam vector is determined by (21), the following normalization process is performed.

[0141]

[0142] This embodiment uses clever dual and fractional transformations to simplify the non-convex channel capacity optimization model, efficiently solving for the optimal receive beam vector and achieving precise suppression of interference signals from specific directions. The alternating optimization algorithm can quickly converge to the optimal solution under complex optimization models, demonstrating both efficiency and practicality.

[0143] Considering a given received signal Y, the signal angle is estimated by the MUSIC algorithm and the signal snapshot information is obtained by matrix inversion operation. The beam nulling optimization problem is constructed to determine the initial solution. Alternately update the auxiliary variables and Until convergence. The specific operation process is as follows:

[0144] 1. Use the received signal Y to construct the autocorrelation matrix

[0145] 2. Determine the noise subspace U according to formula (4) N ;

[0146] 3. Determine the signal angle according to formula (5)

[0147] 4. Determine the signal snapshot information according to formula (6)

[0148] 5. Randomly initialize variables

[0149] 6. Execute when the objective function does not converge (j = 1, 2, 3, ...);

[0150] 7. Determine according to formula (13)

[0151] 8. Determine according to formula (19)

[0152] 9. According to formula (21) and (23), use binary search to determine

[0153] 10. Update according to formula (21)

[0154] 11. Normalize according to formula (24)

[0155] 12. Repeat steps 7-11 until the objective function converges.

[0156] Example 5

[0157] This embodiment provides a verification description of the beam suppression algorithm of the present invention.

[0158] Figure 4 The convergence behavior of the uplink communication rate is given. As can be seen from the figure, the algorithm of the present invention can converge to the maximum value in a relatively small number of iterations. Thanks to the integrity of the solution of the present invention, the base station sensing algorithm is used for signal processing to accurately grasp the interference suppression direction and the communication user direction, providing a simple solution for perception-assisted communication anti-interference technology.

[0159] like Figure 5 As shown in the figure, the effect of the notched receive beam vector is demonstrated through the beam pattern. From the results shown, it can be seen that the main lobe gain of the target signal has a 26dB gain compared to the side lobe, and the notch can achieve -350dB of energy suppression.

[0160] like Figure 6 The results show the changing trends of residual interference power under different initial external interference power conditions. The experimental results show that as the initial external interference power increases, the residual interference power also increases accordingly, but remains below the set maximum interference power threshold. This phenomenon indicates that the interference signal is effectively controlled during uplink communication, thus proving the effectiveness of the proposed algorithm in suppressing interference.

[0161] Figure 7(a) The effect of the change of the interference suppression demand factor on the residual interference power was investigated. The results show that the residual interference power closely follows the suppression demand and shows a linear growth trend as the suppression demand decreases. This shows that the equality constraint of the present invention can control the anti-interference ability of the system according to the demand, and its controllability has been verified. Figure 7 (b) The impact of varying the interference suppression requirement factor on the uplink communication rate was investigated. Three different system noise levels were considered. When the interference suppression requirement was significantly lower than the background noise power, the interference signal had no significant impact on the communication rate. However, when the interference suppression requirement was higher than the background noise power, system performance was primarily affected by the external interference signal, resulting in a sharp decline in performance.

[0162] The present invention uses two baselines as a comparison for the interference suppression method of the present invention. Baseline 1 is a beamforming algorithm based on the linear constrained minimum variance criterion (LCMV), and baseline 2 is an unoptimized one that only uses the target angle direction as a guide to form the receiving beam vector in the target direction. Figure 8 As shown, in the baseline 1 method, the residual interference power increases with the increase of the external interference power. Although the residual power can still be stabilized below the background noise power, when the external interference power increases, different receive beam vector solutions will be obtained, and the suppression performance will be significantly affected by the interference power, which may cause the deviation of the beamforming direction or the decrease of gain, thereby affecting the actual application effect of the algorithm. Under higher interference power conditions, baseline 2 cannot be suppressed to below the background noise power, which has a serious impact on the performance of communication. However, the method of the present invention always controls the interference suppression threshold below the threshold value when the interference suppression threshold is set at -150dBW. Within the range of this interference, there is no obvious loss in the performance of communication, which shows the advantage of the method of the present invention in suppressing interference.

[0163] The same or similar reference numerals correspond to the same or similar components;

[0164] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0165] 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 method for optimizing anti-interference receiving beams in a communication and perception fusion system, characterized in that: The following steps are involved: The spatial spectrum estimation algorithm is used to estimate the arrival angle of the received signal of the communication and perception fusion system; Inverting to obtain snapshot information of the received signal according to the arrival angle of the received signal and the mathematical model of the received signal; Constructing a receive beam vector optimization problem based on the snapshot information of the received signal, wherein the receive beam vector optimization problem aims to maximize uplink communication channel capacity while introducing interference suppression constraints to design an optimal receive beam vector; By solving the receiving beam vector optimization problem, an optimal receiving beam vector is obtained.

2. The anti-interference receiving beam optimization method for a communication and perception fusion system according to claim 1, characterized in that: The signal snapshot information is the sampling value of the signal at different time snapshots, including the amplitude and phase of the signal.

3. The anti-interference receiving beam optimization method for a communication and perception fusion system according to claim 2, characterized in that: The mathematical model of the received signal is: Where, Y represents the received signal, a R (θ c ) represents the receiving steering vector of the uplink communication signal, θ c is the arrival angle of the uplink communication signal, Indicates the signal snapshot information of the uplink communication signal, a R (θ i ) represents the receiving steering vector of the i-th interference source signal, θ i represents the arrival angle of the i-th interference source signal, represents the signal snapshot information of the i-th interference source signal, K I represents the number of interference source signals, N represents the complex-valued zero-mean Gaussian noise matrix at the receiving end; The matrix expression of the mathematical model of the received signal is: Y=A R (Θ)S+N A R (Θ)=[a R (i c ),A R (i I )] 4. The anti-interference receiving beam optimization method for a communication and perception fusion system according to claim 3, characterized in that: The spatial spectrum estimation algorithm is used to estimate the arrival angle of the received signal of the communication and perception fusion system, including: make is the covariance matrix of the received signal snapshot; R Y Perform singular value decomposition and get: Where U S represents the signal subspace matrix, Λ S Represents the eigenvalue submatrix of the signal subspace, U N represents the noise subspace matrix, Λ N represents the eigenvalue submatrix of the noise subspace matrix; The angle spectrum function is constructed using the receiving steering vector noise space at the receiving end to estimate the signal arrival angle in turn: Where, Represents uplink communication signal, 1st to Kth I The estimated angle of arrival of the interference source signal.

5. The anti-interference receiving beam optimization method for a communication and perception fusion system according to claim 4, characterized in that: Inversion obtains the snapshot information of the received signal, including uplink communication signal, 1 to K I The estimated arrival angle of the interference source signal is substituted into the steering vector model:

6. The anti-interference receiving beam optimization method for the communication and perception fusion system according to claim 5, characterized in that: The receive beam vector optimization problem is: Where w c represents the receiving beam vector, cap(w c ) represents the uplink communication channel capacity, Γ is the interference suppression requirement factor, which represents the maximum tolerable interference power level.

7. The anti-interference receiving beam optimization method for a communication and perception fusion system according to claim 6, characterized in that: Solving the receive beam vector optimization problem includes: S1: Simplify the receive beam vector optimization problem using Lagrangian dual optimization and fractional programming; S2: Based on the simplified receive beam vector optimization problem, the conditions satisfied by the optimal receive beam vector are obtained; S3: Using a binary search on the conditions satisfied by the optimal receiving beam vector to obtain the current optimal receiving beam vector; S4: Repeat steps S1-S3 until the receive beam vector optimization problem converges and the final optimal receive beam vector is obtained.

8. The anti-interference receiving beam optimization method for a communication and perception fusion system according to claim 7, characterized in that: The receive beam vector optimization problem is simplified by using Lagrangian dual optimization and fractional programming, including: Introduction of cofactors The receive beam vector optimization problem is converted into: Introducing Lagrange multipliers to handle constraints, we get the Lagrange function: The receive beam vector optimization problem is converted to: make Optimal satisfy Determine the Lagrange multiplier as: Substituting the Lagrange multiplier into the Lagrange function, we get: Introducing auxiliary variable ρ c , using fractional transformation, construct the quadratic lower bound of the above Lagrangian function: Among them, ρ c and w c The above function is convex, so in the previous solution Under the condition of The optimal auxiliary parameter expression is obtained: Introducing the Lagrange multiplier λ w Get the new Lagrangian function:

9. The anti-interference receiving beam optimization method for a communication and perception fusion system according to claim 8, characterized in that: The conditions for obtaining the optimal receiving beam vector are: make The optimal receiving beam vector expression is obtained: By order Get the optimal Lagrange multiplier Producing a solution So that it satisfies:

10. The anti-interference receiving beam optimization method for the communication and perception fusion system according to claim 9, characterized in that: Step S3 further includes: Normalize the current optimal receive beam vector.

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