Analog-digital hybrid array spatial anti-jamming method based on alternate optimization

Abstract

The specification discloses an analog-digital hybrid array space anti-interference method based on alternating optimization, relates to the technical field of anti-interference, and comprises the following steps: constructing a first optimization problem based on a hybrid array antenna structure, a digital formed vector and an analog formed vector; solving the first optimization problem by using a quasi-Newton method to obtain an optimal beam weight vector; constructing a second optimization problem based on the optimal beam weight vector, the digital formed vector and the analog formed vector; and solving the second optimization problem by using an alternating optimization algorithm to obtain an optimal digital formed vector and an optimal analog formed vector, so as to solve the problem that the current analog-digital hybrid array anti-interference method has low anti-interference capability in the anti-interference scene of a high-mobility platform with limited receiving antenna elements and a large number of interferences.

Description

Technical Field

[0001] This invention belongs to the field of anti-interference technology, specifically relating to a spatial anti-interference method for analog-digital hybrid arrays based on alternating optimization. Background Technology

[0002] Given the high deployment difficulty of all-digital array structures, whose size, weight, and cost are constrained by the radio frequency chain, making large-scale deployment difficult, hybrid analog-digital array antennas have been proposed and implemented. By reducing the number of radio frequency chains, the deployment difficulty of hybrid analog-digital array antennas is significantly reduced, making them particularly suitable for deployment on highly mobile platforms with limited load-bearing capacity (such as aircraft and missiles). Regarding spatial anti-interference for hybrid analog-digital array antennas, traditional spatial anti-interference algorithms point the analog beam towards the communication signal direction, utilizing only digital beamforming for anti-interference, without leveraging the degrees of freedom of analog beamforming. The degrees of freedom for anti-interference are limited by digital beamforming. Therefore, under multiple interference conditions, the anti-interference performance of traditional hybrid array spatial anti-interference algorithms deteriorates significantly, failing to leverage the advantages of hybrid analog-digital array antennas. A hybrid analog-digital array receiver beamforming design method based on subspace interference suppression, proposed in 2022, is based on... Figure 1 The diagram illustrates a beamforming design framework for a hybrid analog-digital array. However, this method still does not fully utilize the anti-interference capability of analog beams, requires a high degree of spatial freedom for the array, and is difficult to apply to anti-interference scenarios of highly mobile platforms with limited receiving antenna elements and a large number of interfering signals.

[0003] In summary, existing beamforming design methods for hybrid analog-digital array antennas are difficult to apply directly in high-mobility platform anti-interference scenarios where the number of receiving antenna elements is limited and the number of interferences is large. The limited spatial degrees of freedom of the array severely restrict its anti-interference performance.

[0004] Therefore, current methods for anti-interference using hybrid analog-digital arrays have low anti-interference capabilities for highly mobile platforms with limited receiving antenna array elements and a large number of interfering elements. Summary of the Invention

[0005] The purpose of this invention is to provide a spatial anti-interference method for analog-digital hybrid arrays based on alternating optimization, in order to solve the problem that current anti-interference methods using analog-digital hybrid arrays have low anti-interference capability in high-mobility platform anti-interference scenarios with limited receiving antenna array elements and a large number of interferences.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] On the one hand, this specification provides a spatial anti-interference method for analog-digital hybrid arrays based on alternating optimization, including:

[0008] Step 102: Based on the hybrid array antenna structure, digital forming vector, and analog forming vector, construct the first optimization problem;

[0009] Step 104: Solve the first optimization problem using the quasi-Newton method to obtain the optimal beam weight vector;

[0010] Step 106: Based on the optimal beam weight vector, digital forming vector, and analog forming vector, construct the second optimization problem;

[0011] Step 108: Use the alternating optimization algorithm to solve the second optimization problem and obtain the optimal digital forming vector and the optimal simulation forming vector.

[0012] On the other hand, this specification provides a spatial anti-interference device for a hybrid analog-digital array based on alternating optimization, including:

[0013] The first optimization problem construction module is used to construct the first optimization problem based on the hybrid array antenna structure, digital forming vector, and analog forming vector.

[0014] The first optimization problem solving module is used to solve the first optimization problem using the quasi-Newton method to obtain the optimal beam weight vector;

[0015] The second optimization problem construction module is used to construct the second optimization problem based on the optimal beam weight vector, digital forming vector, and analog forming vector.

[0016] The second optimization problem solving module is used to solve the second optimization problem using an alternating optimization algorithm to obtain the optimal digital forming vector and the optimal simulation forming vector.

[0017] Based on the above technical solution, this specification can achieve the following technical effects:

[0018] This method fully utilizes the anti-interference capability of analog beams and maximizes the spatial degrees of freedom of the analog-digital hybrid array. Targeting specific application scenarios, it leverages the anti-interference degrees of freedom in analog domain beam design, significantly improving communication anti-interference performance in multi-interference scenarios in both the analog and digital domains. It is suitable for scenarios with limited receiving antenna array elements and a large number of interferences. Therefore, it can provide strong support for the communication capabilities of special equipment such as highly mobile platforms with limited deployment space for receiving antenna arrays and high anti-interference requirements. This solves the problem of low anti-interference capability in current methods using analog-digital hybrid arrays for highly mobile platforms with limited receiving antenna array elements and a large number of interferences.

[0019] 1. Unlike traditional hybrid array spatial anti-interference methods, this hybrid array anti-interference method jointly designs digital beamforming and analog beamforming, and uses an alternating optimization method to iteratively design digital beam weight vectors and analog beam weight vectors to achieve collaborative anti-interference between analog beamforming and digital beamforming. It makes full use of the anti-interference degree of freedom of analog beam design, which can improve the communication anti-interference performance in multi-interference scenarios.

[0020] 2. To address the constraint that the amplitude and phase of the analog array elements in the analog-digital hybrid array are finitely adjustable, this anti-interference method uses a quasi-Newton method to relax the non-convex constraint when designing the analog beamforming, so as to find the optimal gradient direction within the finite set of analog beam weight vector values, and complete the optimal design of the analog beam weight vector through the optimal gradient direction. Attached Figure Description

[0021] Figure 1 This is a diagram of a hybrid analog-to-digital receiver beamforming structure in the background art of this invention.

[0022] Figure 2 This is a flowchart illustrating a spatial anti-interference method for a hybrid analog-digital array based on alternating optimization, according to an embodiment of the present invention.

[0023] Figure 3 This is a structural diagram of an alternating optimization method in one embodiment of the present invention.

[0024] Figure 4 This is a schematic diagram of the structure of a spatial anti-interference device for a hybrid analog-digital array based on alternating optimization in one embodiment of the present invention.

[0025] Figure 5 This is a schematic diagram of an electronic device according to the present invention. Detailed Implementation

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer from the following description and claims. It should be noted that the drawings are all in a very simplified form and are not to a precise scale, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.

[0027] It should be noted that, in order to clearly illustrate the content of this invention, several embodiments are provided to further explain different implementations of the invention. These embodiments are enumerated rather than exhaustive. Furthermore, for the sake of brevity, content mentioned in the preceding embodiments is often omitted in the following embodiments. Therefore, content not mentioned in the later embodiments can be referred to in the preceding embodiments.

[0028] Example 1

[0029] Please refer to Figure 2 , Figure 2 The diagram illustrates a spatial interference mitigation method for hybrid analog-digital arrays based on alternating optimization, provided in this embodiment. In this embodiment, the method includes:

[0030] Step 102: Based on the hybrid array antenna structure, digital forming vector, and analog forming vector, construct the first optimization problem;

[0031] In this embodiment, one implementation of step 102 is as follows:

[0032] The first optimization problem is constructed based on a data matrix consisting of digital forming vectors, analog forming vectors, and several snapshots; the digital forming vectors are related to the number of radio frequency chains in the hybrid array antenna; the analog forming vectors are related to the number of radio frequency chains and the number of array elements in the hybrid array antenna.

[0033] In this embodiment, the method further includes the following after step 102:

[0034] The first constraint condition is constructed based on the digital forming vector, the analog forming vector, the channel vector of the channel signal, the channel information error at the receiver, and the set of random errors.

[0035] Step 104: Solve the first optimization problem using the quasi-Newton method to obtain the optimal beam weight vector;

[0036] In this embodiment, one implementation of step 104 is as follows:

[0037] Step 202: Based on the first constraint, construct non-convex constraints;

[0038] Step 204: Use the quasi-Newton method to relax the non-convex constraints and solve the first optimization problem to obtain the iteration direction and iteration step.

[0039] In this embodiment, one implementation of step 204 is as follows:

[0040] Step 2042: Based on the non-convex constraints, the first optimization problem is transformed into a convex second-order cone programming problem, and the iteration direction is obtained by solving the problem.

[0041] Step 2044: Based on the convex second-order cone programming problem, obtain the one-dimensional linear search problem and solve it to obtain the iterative step.

[0042] Step 206: Based on the quasi-Newton direction, iterative step, and iterative threshold, obtain the optimal beam weight vector.

[0043] Step 106: Based on the optimal beam weight vector, digital forming vector, and analog forming vector, construct the second optimization problem;

[0044] In this embodiment, one implementation of step 106 is as follows:

[0045] This includes constructing a second optimization problem that makes the receiving performance of the digital and analog forming vectors approximate the optimal beam weight vector.

[0046] Step 108: Use the alternating optimization algorithm to solve the second optimization problem and obtain the optimal digital forming vector and the optimal simulation forming vector.

[0047] In this embodiment, one implementation of step 108 is as follows:

[0048] Step 302: Based on the number of radio frequency chains in the array antenna, the second optimization problem is broken down into several independent problems;

[0049] Step 304: Use an alternating optimization algorithm to solve several independent problems to obtain the optimal digital forming vector and the optimal simulation forming vector.

[0050] In this embodiment, one implementation of step 304 is as follows:

[0051] Step 3042: When the simulated forming vector is fixed, obtain the digital forming vector solution formula based on several independent problems;

[0052] Step 3044: When the digital forming vector is fixed, obtain the simulation forming vector solution formula based on several independent problems;

[0053] Step 3046: Iteratively solve the formulas for solving the digital forming vector and the simulated forming vector to obtain the optimal digital forming vector and the optimal simulated forming vector.

[0054] Specifically, the process of designing analog and digital beam weight vectors using the alternating optimization method is as follows: Figure 3 As shown.

[0055] For the non-convex constraints introduced by the finite amplitude and phase of the simulated array elements on the optimal design of the simulated weight vector, this patent uses the quasi-Newton method to relax the non-convex constraints, so as to find the optimal gradient direction within the finite set of simulated beam weight vector values, and complete the optimal design of the simulated beam weight vector through the optimal gradient direction.

[0056] For example, step one: Based on the anti-interference requirements and the characteristics of the hybrid array antenna, for an array with N elements... r The number of radio frequency chains is N RF Given a hybrid antenna array, construct the first optimization problem:

[0057]

[0058] C2:W A ∈W.(1)

[0059] in, For digital shaping vectors, To simulate the shaping vector, X = [x(1),...,x(T)] is a data matrix composed of multiple snapshots. This is the channel vector corresponding to the communication signal. θ (l) , These are the channel gain, signal incident angle, and steering vector, respectively. For the error of the known channel information at the receiver, Q(κ)={h e |||h e ||2≤κ} represents the set of random errors. W is W A The set of possible values ​​is limited by the hybrid antenna structure.

[0060] Step Two: Solving the First Optimization Problem:

[0061] Step 2.1: Relax non-convex constraints using quasi-Newton method

[0062] definition The first optimization problem then becomes:

[0063]

[0064] str H h0-κ||r||2≥1,

[0065]

[0066] Solving the optimization problem (2) iteratively, we have r (k+1) =r (k) +λ (k) Δr (k) k = 0, 1, 2, ..., λ (k) and Δr (k) Let F(r+Δr) be the step size and step direction (which can be called the quasi-Newton direction) for the k-th iteration, respectively. Performing a second-order Taylor expansion on F(r+Δr), we have:

[0067]

[0068] in, For the first step of F(r), Q(r) = diag{|y(1)| p-2 ,...,|y(T)| p-2}, y(t)=x H (t)r); Both are partial Hessian matrices, and It can be represented as:

[0069]

[0070] Based on expressions (3) and (4), the non-convex constraint r H Analyzing h0-κ||r||2≥1, we have:

[0071] (r+Δr) H h0-κ||r+Δr||2≥1 (5)

[0072] According to Jensen's inequality, we get:

[0073] (r+Δr) H h0-κ||r+Δr||2≥r H h0-κ||r||2+Δr H h0-κ||Δr||2 (6)

[0074] Combined with constraint r H h0-κ||r||2≥1, there are:

[0075] Δr H h0-κ||Δr||2≥0 (7)

[0076] When satisfied, inequality (5) holds. To determine the iteration direction Δr in the k-th iteration (where r is known at this point)... (k) Based on expressions (3) and (7), the optimization problem (2) in the k-th iteration is transformed into:

[0077]

[0078] stκ||Δr||2≤Δr H h0,

[0079]

[0080] The optimization problem (8) is a convex second-order cone programming problem, and the quasi-Newton direction Δr can be obtained by solving the optimization problem (8). (k) Then, by solving:

[0081]

[0082] Solve for the iteration step in the k-th iteration. Optimization problem (9) is a one-dimensional linear search problem.

[0083] The above is the complete process of solving r using the quasi-Newton method, which can be summarized as follows:

[0084]

[0085] In the above algorithm, This is the iteration threshold used to determine the designed r. (k) Whether it converges.

[0086] Step 2.2: Alternately optimize the design of w separately D and W A

[0087] Construct a second optimization problem such that w D and W A The receiving performance is close to r opt :

[0088]

[0089] stW A ∈W(10)

[0090] because have:

[0091]

[0092] in, For r opt The nth subvector, ω n For w D The nth element, i.e., the nth element of the digital shaping vector, ν n For w A The nth element, i.e., the nth element of the simulated forming vector, is explained in equation (1). Combining (10) and (11), we have:

[0093]

[0094] in For ν n The set of possible values ​​for .

[0095] The second optimization problem (12) can be decomposed into N RF One independent question:

[0096]

[0097] The second optimization problem after splitting is solved using an alternating optimization algorithm (13), when When fixed, for:

[0098]

[0099] when When fixed, The solution satisfies:

[0100]

[0101] The optimization problem (15) can be solved by searching.

[0102] By iteratively solving {ν using expressions (14) and (15) n} and {ω n}, and the optimal w can be obtained. D and W A Complete the receiving beamforming design.

[0103] In summary, this method fully utilizes the anti-interference capability of analog beams and maximizes the spatial degrees of freedom of the analog-digital hybrid array. For specific application scenarios, it leverages the anti-interference degrees of freedom in analog domain beam design, significantly improving communication anti-interference performance in multi-interference scenarios in both the analog and digital domains. It is suitable for scenarios with limited receiving antenna array elements and a large number of interference sources. Therefore, it can provide strong support for the communication capabilities of special equipment such as highly mobile platforms with limited deployment space for receiving antenna arrays and high anti-interference requirements. This solves the problem of low anti-interference capability in current methods using analog-digital hybrid arrays for highly mobile platforms with limited receiving antenna array elements and a large number of interference sources.

[0104] 1. Unlike traditional hybrid array spatial anti-interference methods, this hybrid array anti-interference method jointly designs digital beamforming and analog beamforming, and uses an alternating optimization method to iteratively design digital beam weight vectors and analog beam weight vectors to achieve collaborative anti-interference between analog beamforming and digital beamforming. It makes full use of the anti-interference degree of freedom of analog beam design, which can improve the communication anti-interference performance in multi-interference scenarios.

[0105] 2. To address the constraint that the amplitude and phase of the analog array elements in the analog-digital hybrid array are finitely adjustable, this anti-interference method uses a quasi-Newton method to relax the non-convex constraint when designing the analog beamforming, so as to find the optimal gradient direction within the finite set of analog beam weight vector values, and complete the optimal design of the analog beam weight vector through the optimal gradient direction.

[0106] Example 2

[0107] Please refer to Figure 4 , Figure 4 The diagram illustrates a spatial interference mitigation method for hybrid analog-digital arrays based on alternating optimization, provided in this embodiment. In this embodiment, the device includes:

[0108] The first optimization problem construction module is used to construct the first optimization problem based on the hybrid array antenna structure, digital forming vector, and analog forming vector.

[0109] The first optimization problem solving module is used to solve the first optimization problem using the quasi-Newton method to obtain the optimal beam weight vector;

[0110] The second optimization problem construction module is used to construct the second optimization problem based on the optimal beam weight vector, digital forming vector, and analog forming vector.

[0111] The second optimization problem solving module is used to solve the second optimization problem using an alternating optimization algorithm to obtain the optimal digital forming vector and the optimal simulation forming vector.

[0112] Optionally, a first optimization problem construction module is used to construct a first optimization problem based on a data matrix composed of digital forming vectors, analog forming vectors, and several snapshots; the digital forming vectors are related to the number of radio frequency chains of the hybrid array antenna; the analog forming vectors are related to the number of radio frequency chains and the number of array elements of the hybrid array antenna.

[0113] Optional, also includes:

[0114] The first constraint construction module is used to construct the first constraint based on the digital forming vector, the analog forming vector, the channel vector of the channel signal, the channel information error of the receiver, and the set of random errors.

[0115] Optionally, the first optimization problem solving module includes:

[0116] Non-convex constraint building element, used to construct non-convex constraints based on the first constraint condition;

[0117] Non-convex constraint relaxation unit is used to relax non-convex constraints using the quasi-Newton method to solve the first optimization problem and obtain the iteration direction and iteration step.

[0118] The optimal beam weight vector solving unit is used to obtain the optimal beam weight vector based on the quasi-Newton direction, iterative step, and iterative threshold.

[0119] Optional, non-convex constraint relaxation elements include:

[0120] The iterative direction solving sub-unit is used to transform the first optimization problem into a convex second-order cone programming problem based on non-convex constraints, and solve it to obtain the iterative direction;

[0121] Iterative step-solving sub-units are used for convex second-order cone programming problems to obtain a one-dimensional linear search problem and solve iterative steps.

[0122] Optionally, a second optimization problem construction module is provided to construct a second optimization problem such that the receiving performance of the digital forming vector and the analog forming vector approximates the optimal beam weight vector.

[0123] Optionally, the second optimization problem-solving module includes:

[0124] The problem splitting unit is used to split the second optimization problem into several independent problems based on the number of radio frequency chains of the array antenna;

[0125] Alternating optimization units are used to solve several independent problems using alternating optimization algorithms to obtain the optimal digital forming vector and the optimal simulation forming vector.

[0126] Optional, alternating optimization units include:

[0127] The simulation vector formula determines the sub-unit and is used to obtain the digital forming vector solution formula based on several independent problems when the simulation forming vector is fixed.

[0128] The analog vector formula determines the sub-unit and is used to obtain the analog vector solution formula based on several independent problems when the digital vector is fixed.

[0129] The vector solving sub-unit is used to iteratively solve the digital forming vector solution formula and the analog forming vector solution formula to obtain the optimal digital forming vector and the optimal analog forming vector.

[0130] Based on this, this device fully utilizes the anti-interference capability of analog beams and makes greater use of the spatial degrees of freedom of the analog-digital hybrid array. For specific application scenarios, it utilizes the anti-interference degrees of freedom of analog domain beam design to greatly improve the communication anti-interference performance in multi-interference scenarios in both analog and digital domains. It is suitable for scenarios with limited receiving antenna array elements and a large number of interferences. Therefore, it can provide strong support for the communication capabilities of special equipment such as highly mobile platforms with limited deployment space for receiving antenna arrays and high requirements for anti-interference capabilities. This solves the problem of low anti-interference capability of current anti-interference methods using analog-digital hybrid arrays in scenarios with limited receiving antenna array elements and a large number of interferences in highly mobile platforms.

[0131] Example 3

[0132] Please refer to Figure 5 This embodiment provides an electronic device including a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it, forming a spatial anti-interference method for a hybrid analog-digital array based on alternating optimization at the logical level. Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is, the execution subject of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.

[0133] Network interfaces, processors, and memory can be interconnected via a bus system. These buses can be categorized as address buses, data buses, control buses, etc.

[0134] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include read-only memory and random access memory, and provides instructions and data to the processor.

[0135] The processor is used to execute the program stored in the aforementioned memory, and specifically perform the following:

[0136] Step 102: Based on the hybrid array antenna structure, digital forming vector, and analog forming vector, construct the first optimization problem;

[0137] Step 104: Solve the first optimization problem using the quasi-Newton method to obtain the optimal beam weight vector;

[0138] Step 106: Based on the optimal beam weight vector, digital forming vector, and analog forming vector, construct the second optimization problem;

[0139] Step 108: Use the alternating optimization algorithm to solve the second optimization problem and obtain the optimal digital forming vector and the optimal simulation forming vector.

[0140] A processor may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method can be completed through the processor's integrated hardware logic circuits or software instructions.

[0141] Based on the same invention, embodiments of this specification also provide a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform... Figures 2-3 The corresponding implementation provides a spatial anti-interference method for analog-digital hybrid arrays based on alternating optimization.

[0142] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-readable storage media containing computer-usable program code.

[0143] Furthermore, the specific implementation of the above system is basically similar to the method implementation, so the description is relatively simple. For relevant details, please refer to the description of the method implementation. Moreover, it should be noted that in the various modules of the system of this application, the components are logically divided according to the functions they are to perform. However, this application is not limited to this and can re-divide or combine the components as needed.

[0144] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences between it and other embodiments.

[0145] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and still achieve the desired result. Furthermore, the specific order or sequential order shown in the drawings is not necessarily required to achieve the desired result; in some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0146] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A spatial anti-interference method for analog-digital hybrid arrays based on alternating optimization, characterized in that, include: Based on the hybrid array antenna structure, digital forming vector, and analog forming vector, the first optimization problem is constructed. The quasi-Newton method is used to solve the first optimization problem to obtain the optimal beam weight vector; Based on the optimal beam weight vector, digital forming vector, and analog forming vector, a second optimization problem is constructed. The second optimization problem is solved using an alternating optimization algorithm to obtain the optimal digital forming vector and the optimal simulation forming vector. Step 1: Based on the anti-interference requirements and the characteristics of the hybrid array antenna, for an array with a number of elements... RF chain number is Given a hybrid antenna array, construct the first optimization problem: (1) For digital shaping vectors, To simulate the forming vector, A data matrix composed of multiple snapshots. This is the channel vector corresponding to the communication signal. , , These are the channel gain, signal incident angle, and steering vector, respectively. The error is the channel information known to the receiver. Represents the set of random errors; for The set of possible values ​​is limited by the hybrid antenna structure.

2. The method according to claim 1, characterized in that, The construction of the first optimization problem based on the hybrid array antenna structure, digital forming vector, and analog forming vector includes: constructing the first optimization problem based on the digital forming vector, analog forming vector, and a data matrix composed of multiple snapshots; the digital forming vector is related to the number of radio frequency chains of the hybrid array antenna; the analog forming vector is related to the number of radio frequency chains and the number of array elements of the hybrid array antenna.

3. The method according to claim 2, characterized in that, After constructing the first optimization problem based on the hybrid array antenna structure, digital forming vector, and analog forming vector, the method further includes: constructing the first constraint condition based on the digital forming vector, analog forming vector, channel vector of the channel signal, channel information error of the receiver, and random error set.

4. The method according to claim 3, characterized in that, The step of solving the first optimization problem using the quasi-Newton method to obtain the optimal beam weight vector includes: Based on the first constraint, construct non-convex constraints; The quasi-Newton method is used to relax the non-convex constraints to solve the first optimization problem, and the iteration direction and iteration step are obtained. The optimal beam weight vector is obtained based on the quasi-Newton direction, iterative step, and iterative threshold.

5. The method according to claim 2, characterized in that, The step of using a quasi-Newton method to relax non-convex constraints and solve the first optimization problem to obtain the iteration direction and iteration step includes: Based on the non-convex constraint, the first optimization problem is transformed into a convex second-order cone programming problem, and the iteration direction is obtained by solving it. Based on the convex second-order cone programming problem, a one-dimensional linear search problem is obtained and solved to obtain the iterative step.

6. The method according to claim 1, characterized in that, The construction of the second optimization problem based on the optimal beam weight vector, digital forming vector, and analog forming vector includes constructing a second optimization problem that makes the receiving performance of the digital forming vector and analog forming vector approximate the optimal beam weight vector.

7. The method according to claim 1, characterized in that, The step of using an alternating optimization algorithm to solve the second optimization problem and obtain the optimal digital forming vector and the optimal simulation forming vector includes: Based on the number of radio frequency chains in the array antenna, the second optimization problem is decomposed into several independent problems; An alternating optimization algorithm is used to solve several independent problems to obtain the optimal digital forming vector and the optimal simulated forming vector.

8. The method according to claim 7, characterized in that, The method of using an alternating optimization algorithm to solve several independent problems to obtain the optimal digital forming vector and the optimal simulation forming vector includes: When the simulated forming vector is fixed, the formula for solving the digital forming vector is obtained based on several independent problems; When the numerical forming vector is fixed, the formula for solving the simulated forming vector is obtained based on several independent problems; The optimal digital forming vector and the optimal simulated forming vector are obtained by iteratively solving the formulas for solving the digital forming vector and the simulated forming vector.

9. A spatial anti-interference device for a hybrid analog-digital array based on alternating optimization, characterized in that, include: The first optimization problem construction module is used to construct the first optimization problem based on the hybrid array antenna structure, digital forming vector, and analog forming vector. The first optimization problem solving module is used to solve the first optimization problem using the quasi-Newton method to obtain the optimal beam weight vector; The second optimization problem construction module is used to construct the second optimization problem based on the optimal beam weight vector, digital forming vector, and analog forming vector. The second optimization problem solving module is used to solve the second optimization problem using an alternating optimization algorithm to obtain the optimal digital forming vector and the optimal simulation forming vector. Step 1: Based on the anti-interference requirements and the characteristics of the hybrid array antenna, for an array with a number of elements... RF chain number is Given a hybrid antenna array, construct the first optimization problem: (1) in, For digital shaping vectors, To simulate the forming vector, A data matrix composed of multiple snapshots. This is the channel vector corresponding to the communication signal. , , These are the channel gain, signal incident angle, and steering vector, respectively. The error is due to the known channel information at the receiving end. Represents the set of random errors; for The set of possible values ​​is limited by the hybrid antenna structure.

10. An electronic device, characterized in that, include: processor; And a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the method as claimed in any one of claims 1 to 8.

Images