Alternating optimization-based analog-digital hybrid array airspace anti-interference method

Abstract

The invention discloses an analog-digital hybrid array airspace anti-interference method based on alternating optimization, which 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 formation vector and an analog formation vector; solving the first optimization problem by adopting 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 formation vector and the analog formation vector; and solving the second optimization problem by adopting an alternating optimization algorithm to obtain an optimal digital formation vector and an optimal analog formation vector, so as to solve the problem of low anti-interference capability of a high-maneuverability platform anti-interference scene with limited receiving antenna array elements and more interference quantity by adopting an analog-digital hybrid array anti-interference method at present.

Description

Technical Field

[0001] The invention belongs to the technical field of anti-interference, and in particular relates to a spatial domain anti-interference method of an analog-digital hybrid array based on alternating optimization. Background Art

[0002] In view of the difficulty in deploying the all-digital array structure, its volume, weight and cost are restricted by the RF chain, making it difficult to deploy on a large scale, and an analog-digital hybrid structure array antenna was proposed and implemented. Due to the reduction in the number of RF chains, the deployment difficulty of the analog-digital hybrid structure array antenna is greatly reduced, and it is especially suitable for deployment on high-mobility platforms with limited carrying capacity (such as aircraft, missiles, etc.). Regarding the airspace anti-interference of the analog-digital hybrid structure array antenna, the traditional airspace anti-interference algorithm points the analog beam in the direction of the communication signal and only uses digital beamforming for anti-interference. The freedom of analog beamforming is not used, and the anti-interference freedom is limited by digital beamforming. Therefore, in the case of multiple interferences, the anti-interference performance of the traditional hybrid array airspace anti-interference algorithm will be severely reduced, and the advantages of the analog-digital hybrid structure array antenna will not be brought into play; the analog-digital hybrid array receive beamforming design method based on subspace interference suppression proposed in 2022 is based on such as Figure 1 The beamforming design framework of the analog-digital hybrid array is shown in Figure 1. However, this method still does not fully utilize the anti-interference ability of analog beams, and has high requirements on the spatial freedom of the array, making it difficult to apply to anti-interference scenarios of high-mobility platforms with limited receiving antenna elements and a large number of interferences.

[0003] In summary, the existing receive beamforming design method of analog-digital hybrid structure array antenna is difficult to directly apply in the anti-interference scenario of a high-mobility platform with limited receiving antenna elements and a large number of interferences. The limited spatial freedom of the array has serious constraints on the anti-interference performance.

[0004] Therefore, the current anti-interference method using analog-digital hybrid arrays has the problem of low anti-interference capability for high-mobility platform anti-interference scenarios with limited receiving antenna elements and a large number of interferences. Summary of the invention

[0005] The purpose of the present invention is to provide an analog-digital hybrid array spatial anti-interference method based on alternating optimization, so as to solve the problem of low anti-interference ability of the current analog-digital hybrid array anti-interference method for high-mobility platform anti-interference scenarios with limited receiving antenna elements and a large number of interferences.

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

[0007] On the one hand, the present specification provides a spatial domain anti-interference method for an analog-digital hybrid array based on alternating optimization, comprising:

[0008] Step 102, constructing a first optimization problem based on the hybrid array antenna structure, the digitally formed vector, and the analogically formed vector;

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

[0010] Step 106, constructing a second optimization problem based on the optimal beam weight vector, the digital formation vector, and the analog formation vector;

[0011] Step 108, using an alternating optimization algorithm to solve the second optimization problem, and obtaining an optimal digital formation vector and an optimal analog formation vector.

[0012] On the other hand, the present specification provides an analog-digital hybrid array spatial anti-interference device based on alternating optimization, comprising:

[0013] A first optimization problem constructing module, used to construct a first optimization problem based on a hybrid array antenna structure, a digitally formed vector, and an analogly formed vector;

[0014] A first optimization problem solving module, used for solving the first optimization problem by using a quasi-Newton method to obtain an optimal beam weight vector;

[0015] A second optimization problem constructing module, configured to construct a second optimization problem based on the optimal beam weight vector, the digital formation vector, and the analog formation vector;

[0016] The second optimization problem solving module is used to solve the second optimization problem by adopting an alternating optimization algorithm to obtain an optimal digital formation vector and an optimal analog formation vector.

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

[0018] This method makes full use of the anti-interference capability of analog beams and makes greater use of the spatial freedom of analog-digital hybrid arrays. For proprietary application scenarios, it utilizes the anti-interference freedom of analog domain beam design, greatly improving the communication anti-interference performance in multiple interference scenarios in analog and digital domains. It is suitable for scenarios with limited receiving antenna elements and a large number of interferences. Therefore, it can provide strong guarantees for the communication capabilities of special equipment such as high-mobility platforms with limited deployment space for receiving antenna arrays and high anti-interference capability requirements, thereby solving the problem of low anti-interference capability of the current anti-interference method using analog-digital hybrid arrays for high-mobility platform anti-interference scenarios with limited receiving antenna elements and a large number of interferences.

[0019] 1. Different from the traditional hybrid array spatial domain anti-interference method, this hybrid array anti-interference method jointly designs digital beamforming and analog beamforming, uses the alternating optimization method to iteratively design the digital beam weight vector and the analog beam weight vector, realizes the collaborative anti-interference of analog beamforming and digital beamforming, and makes full use of the anti-interference freedom of analog beam design, which can improve the communication anti-interference performance in multiple interference scenarios;

[0020] 2. Aiming at the constraint of limited adjustable amplitude and phase of analog array elements of analog-to-digital hybrid array, this anti-interference method uses quasi-Newton method to relax non-convex constraints when designing analog beamforming, so as to find the optimal gradient direction within the limited set of analog beam weight vector values, and complete the optimal design of analog beam weight vector through the optimal gradient direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 FIG. 1 is a structural diagram of analog-digital hybrid receive beamforming in the background technology of the present invention.

[0022] Figure 2 The figure is a flow chart of a spatial domain anti-interference method of an analog-digital hybrid array based on alternating optimization in one embodiment of the present invention.

[0023] Figure 3 FIG. 4 is a structural diagram of an alternating optimization method in one embodiment of the present invention.

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

[0025] Figure 5 The figure is a schematic diagram of an electronic device according to the present invention. DETAILED DESCRIPTION

[0026] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The advantages and features of the present invention will become more apparent from the following description and claims. It should be noted that the drawings are all in very simplified form and are not in precise proportions, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0027] It should be noted that, in order to clearly explain the content of the present invention, the present invention specifically cites multiple embodiments to further illustrate different implementations of the present invention, wherein the multiple embodiments are enumerated rather than exhaustive. In addition, for the sake of brevity of explanation, the contents mentioned in the previous embodiments are often omitted in the subsequent embodiments. Therefore, the contents not mentioned in the subsequent embodiments can refer to the previous embodiments accordingly.

[0028] Example 1

[0029] Please refer to Figure 2 , Figure 2 The figure shows the spatial domain anti-interference method of analog-digital hybrid array based on alternating optimization provided by this embodiment. In this embodiment, the method includes:

[0030] Step 102, constructing a first optimization problem based on the hybrid array antenna structure, the digitally formed vector, and the analogically formed vector;

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

[0032] A first optimization problem is constructed based on a data matrix consisting of a digitally formed vector, an analog formed vector, and a number of snapshots; the digitally formed vector is related to the number of RF chains of the hybrid array antenna; the analog formed vector is related to the number of RF chains and the number of array elements of the hybrid array antenna.

[0033] In this embodiment, after step 102, the following steps are further included:

[0034] A first constraint condition is constructed based on a digitally formed vector, an analog formed vector, a channel vector of a channel signal, a channel information error at a receiving end, and a random error set.

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

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

[0037] Step 202, constructing a non-convex constraint based on the first restriction condition;

[0038] Step 204, using the quasi-Newton method to relax the non-convex constraints to solve the first optimization problem, and obtain an iteration direction and an iteration step;

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

[0040] Step 2042, based on the non-convex constraint, convert the first optimization problem into a convex second-order cone programming problem, and solve it to obtain an iteration direction;

[0041] Step 2044, based on the convex second-order cone programming problem, a one-dimensional linear search problem is obtained and solved to obtain an iterative step.

[0042] Step 206: Obtain an optimal beam weight vector based on the quasi-Newton direction, iteration step, and iteration threshold.

[0043] Step 106, constructing a second optimization problem based on the optimal beam weight vector, the digital formation vector, and the analog formation vector;

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

[0045] The method comprises constructing a second optimization problem so that the receiving performance of the digitally formed vector and the analog formed vector approaches the optimal beam weight vector.

[0046] Step 108, using an alternating optimization algorithm to solve the second optimization problem, and obtaining an optimal digital formation vector and an optimal analog formation vector.

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

[0048] Step 302: split the second optimization problem into a plurality of independent problems based on the number of radio frequency chains of the array antenna;

[0049] Step 304, using an alternating optimization algorithm to solve several independent problems to obtain an optimal digital formation vector and an optimal analog formation vector.

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

[0051] Step 3042, when the analog formation vector is fixed, a solution formula for the digital formation vector is obtained based on a number of independent problems;

[0052] Step 3044, when the digital formation vector is fixed, a solution formula for the analog formation vector is obtained based on a number of independent problems;

[0053] Step 3046, iteratively solve the digital vector formation solution formula and the analog vector formation solution formula to obtain the optimal digital vector formation and the optimal analog vector formation.

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

[0055] For the non-convex constraints introduced by the limited amplitude and phase of the simulated array element for the optimal design of the simulated weight vector, this patent uses the quasi-Newton method to relax the non-convex constraints to find the optimal gradient direction within the limited 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 1: according to the anti-interference requirements and the characteristics of the hybrid array antenna, for the number of array elements N r 、The number of radio frequency chains is N RF The first optimization problem is constructed for the hybrid antenna array:

[0057]

[0058] C 2 :W A ∈W.(1)

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

[0060] Step 2: Solve the first optimization problem:

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

[0062] definition Then the first optimization problem becomes:

[0063]

[0064] str H h 0 -κ||r|| 2 ≥1,

[0065]

[0066] Solve the optimization problem (2) in an iterative way, then we have r (k+1) =r (k) +λ (k) Δr (k) ,k=0,1,2,...,λ (k) and Δr (k) are the step and step direction of the kth iteration (which can be called the quasi-Newton direction). Perform a second-order Taylor expansion on F(r+Δr), and we have:

[0067]

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

[0069]

[0070] Based on expressions (3) and (4), the non-convex constraint r H h 0 -κ||r|| 2 ≥1 for analysis, if:

[0071] (r+Δr) H h 0 -κ||r+Δr|| 2 ≥1 (5)

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

[0073] (r+Δr) H h 0 -κ||r+Δr|| 2 ≥r H h 0 -κ||r|| 2 +Δr H h 0 -κ||Δr|| 2 (6)

[0074] Combined Constraints H h 0 -κ||r|| 2 ≥1, with:

[0075] Δr H h 0 -κ||Δr|| 2 ≥0 (7)

[0076] When is satisfied, inequality (5) holds. To determine the iteration direction Δr in the kth iteration (when r is known (k) ), based on expressions (3) and (7), the optimization problem (2) in the kth iteration is transformed into:

[0077]

[0078] stκ||Δr|| 2 ≤Δr H h 0 ,

[0079]

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

[0081]

[0082] Solve the iterative step in the kth iteration. The optimization problem (9) is a one-dimensional linear search problem.

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

[0084]

[0085] In the above algorithm, is the iteration threshold, which is used to judge the designed r (k) Whether to converge.

[0086] Step 2.2: Alternate optimization to design w D and W A

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

[0088]

[0089] wxya A ∈W(10)

[0090] because have:

[0091]

[0092] in, For r opt The nth subvector of n w D The nth element of , that is, the nth element of the digital shaping vector, ν n w A The nth element of is the nth element of the simulated forming vector. The specific interpretation is shown in formula (1). Combining (10) and (11), we have:

[0093]

[0094] in ν n A set of values.

[0095] The second optimization problem (12) can be split into N RF Separate questions:

[0096]

[0097] The alternating optimization algorithm is used to solve the second optimization problem (13) after splitting. When fixed, for:

[0098]

[0099] when When fixed, The solution satisfies:

[0100]

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

[0102] By using expressions (14) and (15) to iteratively solve {ν n} and {ω n}, we can get the optimal w D and W A Complete the receive beamforming design.

[0103] In summary, this method makes full use of the anti-interference capability of analog beams and makes greater use of the spatial freedom of analog-digital hybrid arrays. For proprietary application scenarios, the anti-interference freedom of analog domain beam design is utilized to greatly improve the communication anti-interference performance in multiple interference scenarios in analog and digital domains. It is suitable for scenarios with limited receiving antenna elements and a large number of interferences. Therefore, it can provide strong guarantees for the communication capabilities of special equipment such as high-mobility platforms with limited deployment space for receiving antenna arrays and high requirements for anti-interference capabilities, thereby solving the problem of low anti-interference capability of the current anti-interference method using analog-digital hybrid arrays for high-mobility platform anti-interference scenarios with limited receiving antenna elements and a large number of interferences.

[0104] 1. Different from the traditional hybrid array spatial domain anti-interference method, this hybrid array anti-interference method jointly designs digital beamforming and analog beamforming, uses the alternating optimization method to iteratively design the digital beam weight vector and the analog beam weight vector, realizes the collaborative anti-interference of analog beamforming and digital beamforming, and makes full use of the anti-interference freedom of analog beam design, which can improve the communication anti-interference performance in multiple interference scenarios;

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

[0106] Example 2

[0107] Please refer to Figure 4 , Figure 4The figure shows the spatial domain anti-interference method of analog-digital hybrid array based on alternating optimization provided by this embodiment. In this embodiment, the device includes:

[0108] A first optimization problem constructing module, used to construct a first optimization problem based on a hybrid array antenna structure, a digitally formed vector, and an analogly formed vector;

[0109] A first optimization problem solving module, used for solving the first optimization problem by using a quasi-Newton method to obtain an optimal beam weight vector;

[0110] A second optimization problem constructing module, configured to construct a second optimization problem based on the optimal beam weight vector, the digital formation vector, and the analog formation vector;

[0111] The second optimization problem solving module is used to solve the second optimization problem by adopting an alternating optimization algorithm to obtain an optimal digital formation vector and an optimal analog formation vector.

[0112] Optionally, a first optimization problem construction module is used to construct a first optimization problem based on a data matrix consisting of a digital formation vector, an analog formation vector, and a plurality of snapshots; the digital formation vector is related to the number of RF chains of the hybrid array antenna; the analog formation vector is related to the number of RF chains and the number of array elements of the hybrid array antenna.

[0113] Optionally, also include:

[0114] The first constraint condition building module is used to build the first constraint condition based on the digital formation vector, the analog formation vector, the channel vector of the channel signal, the channel information error of the receiving end, and the random error set.

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

[0116] A non-convex constraint construction unit, used for constructing a non-convex constraint based on the first restriction condition;

[0117] A non-convex constraint relaxation unit, used for solving the first optimization problem by relaxing the non-convex constraint using a quasi-Newton method, and obtaining an iteration direction and an 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 units include:

[0120] An iteration direction solving subunit, used for converting the first optimization problem into a convex second-order cone programming problem based on a non-convex constraint, and solving to obtain an iteration direction;

[0121] The iterative step solving subunit is used to obtain a one-dimensional linear search problem based on a convex second-order cone programming problem and solve it to obtain iterative steps.

[0122] Optionally, a second optimization problem constructing module is used to construct a second optimization problem so that the receiving performance of the digitally formed vector and the analog formed vector approaches the optimal beam weight vector.

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

[0124] A problem splitting unit, used for splitting the second optimization problem into a plurality of independent problems based on the number of radio frequency chains of the array antenna;

[0125] The alternating optimization unit is used to solve a number of independent problems by using an alternating optimization algorithm to obtain an optimal digital formation vector and an optimal analog formation vector.

[0126] Optionally, the alternate optimization unit includes:

[0127] The analog vector formula determination subunit is used to obtain a digital vector solution formula based on a number of independent problems when the analog vector is fixed;

[0128] A simulation vector formula determination subunit is used to obtain a simulation vector solution formula based on a number of independent problems when the digital vector is fixed;

[0129] The vector solving subunit is used to iteratively solve the digital vector solving formula and the analog vector solving formula to obtain the optimal digital vector and the optimal analog vector.

[0130] Based on this, this device makes full use of the anti-interference capability of analog beams and makes greater use of the spatial freedom of analog-digital hybrid arrays. For proprietary application scenarios, the anti-interference freedom of analog domain beam design is utilized to greatly improve the communication anti-interference performance in multiple interference scenarios in analog and digital domains. It is suitable for scenarios with limited receiving antenna elements and a large number of interferences. Therefore, it can provide strong guarantees for the communication capabilities of special equipment such as high-mobility platforms with limited deployment space for receiving antenna arrays and high anti-interference capabilities. This solves the problem of low anti-interference capability of the current anti-interference method using analog-digital hybrid arrays for high-mobility platform anti-interference scenarios with limited receiving antenna elements and a large number of interferences.

[0131] Example 3

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

[0133] The network interface, processor and memory can be connected to each other through a bus system. The above bus can be divided into an address bus, a data bus, a control bus, etc.

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

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

[0136] Step 102, constructing a first optimization problem based on the hybrid array antenna structure, the digitally formed vector, and the analogically formed vector;

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

[0138] Step 106, constructing a second optimization problem based on the optimal beam weight vector, the digital formation vector, and the analog formation vector;

[0139] Step 108, using an alternating optimization algorithm to solve the second optimization problem, and obtaining an optimal digital formation vector and an optimal analog formation vector.

[0140] The processor may be an integrated circuit chip with signal processing capability. In the implementation process, each step of the above method may be completed by an integrated logic circuit of the processor hardware or by instructions in the form of software.

[0141] Based on the same invention, the embodiment of this specification also provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple application programs, the electronic device executes Figure 2-Figure 3 The corresponding embodiment provides an analog-digital hybrid array spatial domain anti-interference method based on alternating optimization.

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

[0143] In addition, for the above-mentioned specific implementation of the system, since it is basically similar to the method implementation, the description is relatively simple, and the relevant parts can be referred to the partial description of the method implementation. Moreover, it should be noted that in each module of the system of the present application, the components therein are logically divided according to the functions to be implemented, but the present application is not limited thereto, and the components can be re-divided or combined as needed.

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

[0145] The above is a description of a specific embodiment of the present specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the specific order or sequential order shown in the process depicted in the drawings is not necessarily required to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0146] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A spatial domain anti-interference method for analog-digital hybrid arrays based on alternating optimization, characterized in that: include: Based on the hybrid array antenna structure, digital vector formation and analog vector formation, a first optimization problem is constructed; The quasi-Newton method is used to solve the first optimization problem and obtain the optimal beam weight vector; Constructing a second optimization problem based on the optimal beam weight vector, the digital formation vector, and the analog formation vector; The alternating optimization algorithm is used to solve the second optimization problem to obtain the optimal digital formation vector and the optimal analog formation vector.

2. The method according to claim 1, characterized in that The construction of the first optimization problem based on the hybrid array antenna structure, digitally formed vectors and analog formed vectors includes: constructing the first optimization problem based on a data matrix consisting of a digitally formed vector, an analog formed vector and a number of snapshots; the digitally formed vector is related to the number of RF chains of the hybrid array antenna; the analog formed vector is related to the number of RF 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, digitally formed vectors and analog formed vectors, it also includes: constructing a first constraint condition based on the digitally formed vectors, the analog formed vectors, the channel vectors of the channel signals, the channel information errors at the receiving end and a set of random errors.

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

5. The method according to claim 2, characterized in that: The method of using the quasi-Newton method to relax the non-convex constraints to solve the first optimization problem and obtain the iteration direction and the iteration step comprises: 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 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 constructing of the second optimization problem based on the optimal beam weight vector, the digital forming vector and the analog forming vector includes constructing the second optimization problem so that the receiving performance of the digital forming vector and the analog forming vector approaches the optimal beam weight vector.

7. The method according to claim 1, characterized in that The adopting of an alternating optimization algorithm to solve the second optimization problem and obtain an optimal digital formation vector and an optimal analog formation vector comprises: Based on the number of radio frequency chains of the array antenna, the second optimization problem is split into several independent problems; An alternating optimization algorithm is used to solve several independent problems to obtain the optimal digital formation vector and the optimal analog formation vector.

8. The method according to claim 5, characterized in that The method of using an alternating optimization algorithm to solve a plurality of independent problems to obtain an optimal digital formation vector and an optimal analog formation vector includes: When the analog formation vector is fixed, a solution formula for the digital formation vector is obtained based on several independent problems; When the digital formation vector is fixed, the solution formula for the analog formation vector is obtained based on several independent problems; The digital vector formation solution formula and the analog vector formation solution formula are iteratively solved to obtain the optimal digital vector formation and the optimal analog vector formation.

9. A spatial anti-interference device for analog-digital hybrid array based on alternating optimization, characterized in that: include: A first optimization problem constructing module, used to construct a first optimization problem based on a hybrid array antenna structure, a digitally formed vector, and an analogly formed vector; A first optimization problem solving module, used for solving the first optimization problem by using a quasi-Newton method to obtain an optimal beam weight vector; A second optimization problem constructing module, configured to construct a second optimization problem based on the optimal beam weight vector, the digital formation vector, and the analog formation vector; The second optimization problem solving module is used to solve the second optimization problem by adopting an alternating optimization algorithm to obtain an optimal digital formation vector and an optimal analog formation vector.

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.

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