Anti-interference array antenna layout optimization method in limited space

Abstract

The invention belongs to the technical field of satellite navigation and wireless communication, and particularly relates to an anti-interference array antenna layout optimization method in a limited space. According to the method, an antenna layout problem is converted into a structured space filling problem, and automatic solving is realized through parametric modeling and numerical calculation. The method specifically comprises the steps of limited space digital definition, special-shaped polyhedron parameterization generation and optimization, and array element automatic layout and performance evaluation. By adopting the special-shaped polyhedron array layout, a larger effective array layout area is realized in a limited space, enough array element spacing is fundamentally ensured, a mutual coupling effect is reduced, and meanwhile, an internal wide-beam receiving system is formed by the natural multi-orientation characteristic of the special-shaped polyhedron; and the signal receiving robustness in a dynamic attitude is greatly enhanced. The method has the characteristics of high automation degree and high universality, and can be suitable for limited space antenna layout design of various carrier platforms.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation and wireless communication technology, and specifically to an automated design and optimization method for the irregular polyhedral array layout of satellite navigation anti-interference antennas under the constraints of limited space on a carrier platform. Background Technology

[0002] Anti-jamming array antennas are key components for improving the anti-jamming capabilities of satellite navigation terminals. Traditional anti-jamming array antennas mostly adopt a regular planar array form, which is theoretically mature and easy to manufacture. However, on carrier platforms such as aircraft and unmanned vehicles with strict requirements on size, weight, and shape, the installation space for antennas is usually an irregular and restricted area. In this context, the inherent defects of planar arrays become apparent: First, in order to accommodate enough array elements within a limited projected area (e.g., at least N+1 array elements are needed to counter N interferences), the element spacing is forced to shrink, leading to severe mutual coupling effects, manifested as pattern distortion, wider and shallower interference nulls, and a significant decrease in anti-jamming performance. Second, the beam coverage of planar arrays is limited, and when the carrier platform undergoes drastic attitude changes, it is prone to satellite signal loss.

[0003] To address these issues, existing technologies employ conformal designs between the anti-interference array antenna and the carrier platform surface. However, these designs are often tailored to specific shapes and lack universality; moreover, the design process heavily relies on human experience, involving a trial-and-error cycle of "design-simulation-debugging," which is inefficient and makes it difficult to quickly and automatically find the optimal layout scheme that can both ensure the element spacing and maximize space utilization to improve full-space coverage performance in any given complex and constrained space. Summary of the Invention

[0004] In view of this, the present invention aims to provide an automated and optimized method for the layout of irregular polyhedral array antennas. This method aims to transform the abstract problem of "finding the best layout in a confined space" into a computable problem of "three-dimensional space filling and geometric optimization", thereby achieving comprehensive optimization of element spacing, array area and full spatial coverage performance.

[0005] This application provides a method for optimizing the layout of irregular polyhedral array antennas in confined space, including the following steps:

[0006] Step 1: Digital definition of confined space: Using the 3D CAD model or geometric parameters of the carrier platform, define the spatial boundary constraints of the antenna array element installation area to form a confined space, and abstract and discretize this confined space into a 3D mathematical model for numerical calculation.

[0007] Step 2, Polyhedron parameterization and initialization: Within a confined space, a three-dimensional polyhedron structure is constructed, and the generation parameters are adjusted to control the morphological evolution of the polyhedron, serving as the polyhedron topology basis for deploying anti-interference array antennas;

[0008] Step 3, Automatic Cutting and Fitting Optimization: The deformed three-dimensional polyhedron structure is iteratively trimmed so that all facets are located inside the confined space, thereby generating an irregular array surface with the largest surface area that fits the inner wall of the confined space.

[0009] Step 4: Automatic Array Element Deployment and Evaluation: On the irregular array surface, automatically find the optimal deployment position for N array elements.

[0010] Furthermore, the confined space Ω is defined as The bounded closed set in Ω is used as the feasible region for subsequent layout optimization:

[0011] ;

[0012] F(x,y,z)<0 indicates that the point is inside Ω, and F(x,y,z)>0 indicates that the point is outside Ω. By discretizing the continuous boundary surface of the carrier platform CAD model into a polygonal mesh using a mesh generation algorithm, a model for numerical calculation is generated.

[0013] Furthermore, the specific steps of step 2 are as follows:

[0014] Step 2.1: Establish initial topology seed: Based on the available space shape of the carrier platform and the number of antenna array elements required, select a simple basic polyhedron as the topology seed and define the initial relationship between its vertices, edges and faces;

[0015] Step 2.2, Parametric Deformation Control: To drive the topological seed to undergo morphological changes to fill the space, a numerical modeling method based on vertex encoding is adopted, and the morphological evolution of the polyhedron is controlled by adjusting the generation parameters.

[0016] Furthermore, in step 2.2, the deformation control of the polyhedron is achieved by performing translation and rotation transformations on the polyhedron within the confined space.

[0017] Furthermore, the optimization steps in step 3 are as follows:

[0018] Step 3.1, Initialize Traversal: Set the initial state of the algorithm to prepare for traversing all polygonal faces of the initial polyhedron; establish a global point set. and global patch set This is used to store the final optimized geometric model data;

[0019] Step 3.2, Patch Selection and Loop Control: Select a polygon patch to be processed sequentially. If all the dough pieces have been processed, proceed to step 3.6; otherwise, continue to step 3.3.

[0020] Step 3.3, Determining the positional relationship between the surface and the spatial boundary: This is done by calculating the surface... The signed distance of each vertex relative to the boundary function of the restricted space is used to determine the positional relationship between the patch and the spatial boundary.

[0021] Step 3.4, Intersecting Patch Cutting and Reconstruction: For patches intersecting the boundary, perform geometric cutting calculations;

[0022] Step 3.5 Iterative Judgment: After completing the processing of the current facet, return to step 3.2 and continue to select and process the next facet until all initial facets have been processed;

[0023] Step 3.6: Generate a polyhedron for array arrangement: When the algorithm loop ends, the global point set is used. and global patch set The defined three-dimensional geometric model is the optimized antenna array surface used for anti-jamming arrays. Furthermore, it is completely located within the confined space Ω, achieving maximum fit and filling of the space.

[0024] Furthermore, in step 3.3, for the dough sheet... For each vertex v, calculate the scalar value d(v) of the symbolic distance. Based on the symbolic consistency of all vertices d(v), perform judgment and flow splitting:

[0025] If all vertices satisfy d(v)>0: the face is completely outside the confined space Ω, then discard the face.

[0026] If all vertices satisfy d(v) < 0, then the face is completely inside the restricted space Ω, and its coordinates are stored in the global point set. The global indices of the vertices are assigned; subsequently, patches are constructed using these global indices and stored in the global patch set. After processing is complete, return to step 3.2;

[0027] If all vertices satisfy d(v)=0): the patch intersects the boundary of the restricted space Ω, proceed to step 3.4.

[0028] Furthermore, in step 3.4, the geometric cutting calculation includes calculating the mathematical intersections of the edge lines and the boundary surfaces, and reconstructing new polygonal patches using the intersections and internal vertices;

[0029] The cutting plane is defined by a general equation:

[0030]

[0031] Where A, B, C, and D are real constants, and A, B, and C are not all zero at the same time;

[0032] Let function Indicates the distance from point P to the plane. Given a signed distance, suppose the coordinates of two adjacent vertices of a polygon face are P1(x1,y1,z1) and P2(x2,y2,z2), and that... ,

[0033] The coordinates of the intersection point Q of the virtual edge formed by these two points and the cutting plane are:

[0034]

[0035] Where t is the linear interpolation parameter. ;

[0036] Similarly, the intersection of another virtual edge line within the polygonal face and the cutting plane can be obtained. Connecting the two lines forms a new chord in the polyhedral spatial structure.

[0037] Remove the vertices of the cutting plane that are outside the structural space, and globally encode the endpoints of the chord. The remaining vertices and the endpoints of the chord then form a new face. Encode and store this face in the face set to complete one cutting process.

[0038] Furthermore, in step 4, the core optimization objective is to maximize the minimum Euclidean distance between any two array elements. The objective function is:

[0039]

[0040] Where, p i Let p be the three-dimensional spatial position of the i-th element. j To determine the three-dimensional spatial position of the j-th array element, the above model is solved using a numerical optimization algorithm to obtain the set of array element positions {p}. i}, The antenna array is defined as the anti-interference array antenna layout, where N is the total number of antenna elements.

[0041] Furthermore, it also includes step 5, making optimization decisions based on the performance evaluation results: if the performance evaluation results of the anti-interference array antenna layout model obtained in step 4 do not meet the predetermined indicators, then the generation parameters in step 2 are adjusted in reverse, and steps 2 to 4 are executed again; this process is iterated until the optimal anti-interference array antenna layout scheme that simultaneously meets the spatial constraints and electrical performance requirements is obtained.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] (1) Methodological innovation: This invention is the first to clearly define the antenna layout problem and transform it into a "structured space filling" problem. It solves the problem through computational geometry, thereby automating and optimizing the design process and eliminating the reliance on human experience.

[0044] (2) Core algorithm advantages: The numerical modeling and cutting algorithm based on vertices and patches adopted in this invention avoids the complex and time-consuming Boolean operations of entities in traditional CAD software, greatly improves the modeling and optimization speed, and makes rapid iteration possible.

[0045] (3) Excellent electrical performance: By constructing an initial, scalable three-dimensional polyhedron structure, a larger effective array area is obtained under the same projected area, which fundamentally ensures sufficient array element spacing and reduces mutual coupling.

[0046] (4) Strong universality: This method does not depend on a specific carrier shape and can be applied to any constrained space defined by mathematical models or grid data, thus having wide applicability. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of the workflow of the present invention; Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] This invention proposes a method for optimizing the layout of anti-interference array antennas in confined spaces. This embodiment achieves automated optimization of antenna layout in confined spaces through rigorous mathematical derivation and numerical calculation methods, avoiding the reliance on specific software platforms and manual experience in traditional designs, and exhibiting better versatility and repeatability.

[0051] This embodiment employs a layered processing architecture, comprising a space definition layer, a geometry generation layer, an optimization analysis layer, and a performance evaluation layer. The methodology is based on computational geometry theory, achieving automated layout optimization through numerical computation.

[0052] like Figure 1 As shown, this invention proposes a method for optimizing the layout of anti-interference array antennas in confined spaces, comprising the following steps:

[0053] Step 1: Digital definition of confined space: Using the 3D CAD model or geometric parameters of the carrier platform, define the spatial boundary constraints of the antenna array element installation area to form a confined space, and abstract and discretize this confined space into a 3D mathematical model that can be used for numerical calculation.

[0054] Define the confined space (i.e., the available installation area for the anti-jamming array antenna) Ω as The bounded closed set in Ω is used as the feasible region for subsequent layout optimization. Ω can be described in the following mathematical form:

[0055] ;

[0056] F(x,y,z)<0 indicates that the point is inside Ω, and F(x,y,z)>0 indicates that the point is outside Ω. By using mesh generation algorithms (such as Delaunay triangulation and leading edge method) on the CAD model of the carrier platform, the continuous boundary surface is discretized into a polygonal mesh, generating a model that can be used for numerical calculation.

[0057] N antenna elements are deployed within the confined space Ω. The three-dimensional spatial position of the i-th element is denoted as pᵢ = (xᵢ, yᵢ, zᵢ). To ensure proper installation conditions, sufficient space must be provided for installation and heat dissipation. Each element's position must satisfy the boundary safety distance constraint, meaning the shortest distance from the element to the boundary ∂Ω must not be less than a preset safety value d. safe .

[0058] Step 2, Polyhedron Parameterization and Initialization: Within the confined space Ω obtained in Step 1, an initial, scalable three-dimensional polyhedron structure is constructed as the polyhedron topology basis for deploying anti-interference array antennas.

[0059] Step 2.1: Establish Initial Topology Seed: Based on the approximate shape of the available space on the carrier platform and the required number of antenna elements, select a simple basic polyhedron (such as a cube, regular hexahedron, etc.) as the topology seed, and define the initial relationships of its vertices, edges, and faces. Assuming the anti-interference array antenna and the carrier platform have three mounting surfaces and four antenna elements, the basic polyhedron used as the topology seed must be at least a heptahedron. Through the mathematical representation of the vertex set, edge set, and face set, establish a complete topological description of the polyhedron, laying the foundation for subsequent parameterized expansion.

[0060] Step 2.2, Parametric Deformation Control: To drive the topological seed to undergo morphological changes to fill the space, a vertex-encoding-based numerical modeling method is adopted. The morphological evolution of the polyhedron is controlled by adjusting the generation parameters. The generation parameters include geometric transformation parameters (translation vector, rotation matrix), deformation parameters (scaling factor, subdivision number), and topological parameters (target value for the number of facets).

[0061] Precise deformation control of polyhedra can be achieved by performing spatial transformations (mainly translation and selection transformations) on polyhedra within a confined space.

[0062] (Translation Transformation Formula) Let the coordinates of any point on a polyhedron (any point on what) be (x, y, z). Then, translate along the x, y, and z axes respectively. After the distance is calculated, the coordinates of the transformed point (x', y', z') are:

[0063] ;

[0064] (Rotation Transformation Formula) To perform a rotation transformation on any point of a polyhedron around any axis at a certain angle, the vector axis passing through the origin should generally be chosen as the axis of rotation. Similarly, let the coordinates of the point be (x, y, z), and let the rotation vector axis passing through the origin be... The rotation angle is θ, and the transformation matrix R can be written as:

[0065]

[0066] The coordinates of the transformed point (x', y', z') are:

[0067] .

[0068] Step 3, Automatic Cutting and Fitting Optimization: Iteratively trim the polyhedron generated in Step 2 so that all its faces are located inside the confined space Ω, thereby generating an irregular array surface with the largest surface area that fits the inner wall of the confined space Ω.

[0069] The specific steps for optimization are as follows:

[0070] Step 3.1, Initialize Traversal: Set the initial state of the algorithm to prepare for traversing all polygonal faces of the initial polyhedron. Establish a global point set. and global patch set This is used to store the final optimized geometric model data;

[0071] Step 3.2, Patch Selection and Loop Control: Select a polygon patch to be processed sequentially. If all face pieces have been processed, proceed to step 3.6; otherwise, continue to step 3.3.

[0072] Step 3.3, Determining the positional relationship between the surface and the spatial boundary: This is done by calculating the surface... The signed distances of each vertex relative to the bounded space boundary function F(x,y,z)=0 are used to determine the positional relationship between the face and the space boundary. For a face... For each vertex v(x, y, z), (i.e., the signed distance), calculate the scalar value d(v) = F(v). Based on the sign consistency of all vertices d(v), perform decision-making and flow splitting:

[0073] If all vertices satisfy (d(v)>0): the face is completely outside the confined space Ω, then discard the face;

[0074] If all vertices satisfy (d(v) < 0): the face is completely inside the confined space Ω, store its coordinates. Then, global indices are assigned. Subsequently, facets are constructed using these global indices of vertices and stored in the global facet set. After processing is complete, return to step 3.2;

[0075] If all vertices satisfy (d(v)=0): the patch intersects the boundary of the restricted space Ω, proceed to step 3.4;

[0076] Step 3.4, Intersecting Patch Cutting and Reconstruction: For patches that intersect with the boundary, perform precise geometric cutting calculations; the geometric cutting calculations include calculating the mathematical intersections of the edge lines and the boundary surfaces, and reconstructing new polygonal patches using the intersections and internal vertices.

[0077] The cutting plane is defined by a general equation:

[0078]

[0079] Where A, B, C, and D are real constants, and A, B, and C are not all zero at the same time.

[0080] Let function Indicates the distance from point P to the plane. Given a signed distance, suppose the coordinates of two adjacent vertices of a polygon face are P1(x1,y1,z1) and P2(x2,y2,z2), and that... ,

[0081] The coordinates of the intersection point Q of the virtual edge formed by these two points and the cutting plane are:

[0082]

[0083] Where t is the linear interpolation parameter. .

[0084] Similarly, the intersection point of another virtual edge within the polygonal face and the cutting plane can be obtained. Connecting the two points forms a new chord in the polyhedral spatial structure. The characteristic equation of the cutting plane can also be calculated using a similar analytical method when it takes other forms. By removing the vertices of the face outside the structural space and globally encoding the endpoints of the chord, the remaining vertices and the endpoints of the chord form a new face. Encoding and storing this face in a face set completes one cutting operation. The cutting process is entirely performed through the calculation and encoding adjustment of point coordinates, avoiding manipulation of the solid, especially complex Boolean operations, thus greatly improving the computational speed of cutting modeling.

[0085] Step 3.5 Iterative Judgment: After completing the processing of the current facet, return to step 3.2 and continue to select and process the next facet until all initial facets have been processed;

[0086] Step 3.6: Generate a polyhedron for array arrangement: When the algorithm loop ends (all initial facets have been processed), the global point set is used. and global patch set The defined three-dimensional geometric model is the optimized antenna array surface S used for anti-jamming arrays. f The surface lies entirely within the confined space Ω, achieving maximum fit and filling of the space.

[0087] Step 4, Automatic Element Deployment and Evaluation: On the generated deployment surface S f The above method automatically finds the optimal placement for N array elements. The core optimization objective is to maximize the minimum Euclidean distance between any two array elements; the objective function is:

[0088]

[0089] Where p i p j To determine the positions of the array elements, the above model is solved using a numerical optimization algorithm, resulting in the set of array element positions {p}. i}

[0090] Then, after importing the layout scheme into professional electromagnetic simulation software, the key performance indicators are evaluated: Mutual coupling matrix: [C] = {c_ij}, where c_ij represents the coupling coefficient between array elements i and j; Anti-interference indicators: null depth D_n, null width W_n; Coverage space range: spatial gain distribution G(θ,φ), number of visible satellites N_visible.

[0091] Step 5: Optimize based on performance evaluation results: If the performance evaluation results of the anti-interference array antenna layout model obtained in Step 4 do not meet the predetermined targets, then adjust the generation parameters (such as transformation parameters and deformation parameters) in Step 2 in reverse, and re-execute Steps 2 to 4. Iterate in this way until the optimal anti-interference array antenna layout scheme that simultaneously satisfies spatial constraints and electrical performance requirements is obtained.

[0092] In this specification, the terms "an embodiment," "example," "specific example," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0093] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for optimizing an anti-interference array antenna layout in a confined space, characterized in that, The method comprises the following steps: Step 1: restricted space digitization definition: using the three-dimensional CAD model or geometric parameters of the carrier platform, the spatial boundary constraint of the antenna element installation area is defined to form a restricted space, and the restricted space is abstracted and discretized into a three-dimensional mathematical model for numerical calculation; Step 2, polyhedral parameterization generation and initialization: a three-dimensional polyhedral structure is constructed inside the restricted space, and the generated parameters are adjusted to control the morphological evolution of the polyhedron, which serves as the polyhedral topology basis for laying the anti-interference array antenna; Step 3, automatic cutting and fitting optimization: the three-dimensional polyhedral structure after morphological deformation is iteratively pruned so that all face sheets are located inside the restricted space, thereby generating a special-shaped array layout surface that is attached to the inner wall of the restricted space and has the maximum surface area; Step 4, automatic array element layout and automatic evaluation: the optimal layout position for N array elements is automatically found on the special-shaped array layout surface.

2. The anti-jamming array antenna layout optimization method of claim 1, wherein, Let Ω be a bounded closed set in R3, and let Ω be the feasible region for the subsequent layout optimization: Let Ω be a bounded closed set in R3, and let Ω be the feasible region for the subsequent layout optimization: ； F(x, y, z) < 0 means that the point is located inside Ω, F(x, y, z) > 0 means that the point is located outside Ω; by using a mesh generation algorithm on the carrier platform CAD model, the continuous boundary surface is discretized into a polygonal mesh, and a model for numerical calculation is generated.

3. The method of claim 1, wherein, The specific steps of step 2 are as follows: Step 2.1, establishing an initialization topology seed: according to the available space form of the carrier platform and the number requirement of the antenna array elements, a simple basic polyhedron is selected as the topology seed, and the initial relationship of its vertices, edges and faces is defined; Step 2.2, parameterized morphological deformation control: in order to drive the topology seed to change its form to fill the space, a numerical modeling method based on vertex coding is adopted to control the morphological evolution of the polyhedron by adjusting the generated parameters.

4. The method of claim 3, wherein, In step 2.2, the morphological deformation control of the polyhedron is realized by performing translation transformation and rotation change on the polyhedron inside the restricted space.

5. The method of claim 1, wherein, The optimization steps in step 3 are as follows: Step 3.1, initialization of traversal: set the initial state of the algorithm, prepare to traverse all polygonal facets of the initial polyhedron; build global point set and global facet set for storing the final optimized geometry model data; Step 3.2, selection of a polygonal face and loop control: select a polygonal face to be processed in sequence ; if all the polygonal faces have been processed, jump to step 3.6; otherwise, continue to step 3.3; Step 3.3, judging the position relationship between the face and the space boundary: judging the position relationship between the face and the space boundary by calculating the signed distance of each vertex relative to the limited space boundary function judging the position relationship between the face and the space boundary by calculating the signed distance of each vertex relative to the limited space boundary function Step 3.4, intersecting face sheet cutting and reconstruction: for the face sheet intersecting with the boundary, geometric cutting calculation is performed; Step 3.5, iterative judgment: after completing the processing of the current face sheet, return to step 3.2 to continue selecting and processing the next face sheet until all initial face sheets are processed; Step 3.6, generating the polyhedron available for arraying: when the algorithm loop ends, the three-dimensional geometric model defined by the global point set and the global patch set is the optimized arraying surface for the anti-interference array antenna ; and is completely located inside the limited space Ω and realizes the maximum fitting and filling of the space.

6. The method of optimizing an anti-jamming array antenna layout of claim 5, wherein, In step 3.3, for each vertex v of the patch a signed distance scalar value d(v) is computed, and based on the signed consistency of all vertices d(v), a decision and split handling is performed: If all vertices satisfy d(v)>0: the face sheet is completely outside the restricted space Omega, and the face sheet is discarded; If all vertices satisfy d(v) < 0: the patch is completely inside the restricted space Ω, store its coordinates into the global point set and assign a global index; Subsequently, the global indices of these vertices are used to build the facets and store them in the global facet set ; after the processing is completed, return to step 3.2; If all vertices satisfy d(v)=0: the face sheet intersects with the boundary of the restricted space Omega, and step 3.4 is entered.

7. The method of claim 5, wherein, In step 3.4, the geometric cutting calculation includes calculating the mathematical intersection point of the edge line and the boundary surface, and reconstructing a new polygonal face sheet with the intersection point and the internal vertex; The cutting plane is defined by a general equation: ； Where A, B, C, and D are real constants, and A, B, and C are not zero at the same time; Let the function denote the signed distance of a point P to the plane , assuming that the coordinates of two adjacent vertices of the polygonal face are P1(x1,y1,z1) and P2(x2,y2,z2) respectively, and have ; Then the coordinates of the intersection point Q of the virtual edge line connected by the two points and the cutting plane are: ； where t is a linear interpolation parameter, ; Similarly, the intersection point of another virtual edge line in the polygonal face and the cutting plane can be obtained, and the two are connected to form a new chord of the polyhedral space structure; Remove the vertices outside the structure space of the cutting plane, and globally code the end points of the chord, then the remaining vertices and the end points of the chord constitute a new face, which is coded and stored in the face set, thereby completing a cutting process.

8. The method of claim 5, wherein, In step 4, the optimization objective is to maximize the minimum Euclidean distance between any two array elements, and the optimization objective function is: ； wherein, p i is the three-dimensional spatial position of the i-th array element, p j is the three-dimensional spatial position of the j-th array element, the above model is solved by a numerical optimization algorithm to obtain an array element position set {p i}, is an anti-interference array antenna layout surface, and N is the total number of antenna array elements.

9. The method of claim 1, wherein, Further comprising a step 5 of making optimization decision through the performance evaluation result: if the performance evaluation result of the anti-interference array antenna layout model obtained in the step 4 does not reach the predetermined index, the generating parameters in the step 2 are adjusted reversely, and the step 2 to the step 4 are executed again; the iteration is continued until the optimal anti-interference array antenna layout scheme satisfying the space constraint and the electrical performance requirement is obtained.

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