A quadrant amplitude mapping array sparsification method and related apparatus

Abstract

This invention discloses a sparse method and related equipment for quadrant amplitude mapping arrays. The method, through recursive partitioning and a deterministic amplitude mapping mechanism, eliminates the randomness of traditional probabilistic methods, ensuring the uniqueness and stability of the output layout under the same input conditions, thereby significantly improving the repeatability and engineering reliability of the design results. The method in this embodiment achieves high-precision matching between amplitude distribution and cell position through sparse cell allocation based on recursive quadrant partitioning, significantly reducing sidelobe levels while lowering the standard deviation of performance fluctuations. Furthermore, the method in this embodiment is independent of specific array geometry and naturally supports rectangular, circular, and irregular arrays with internal obstacles through its recursive processing mechanism, demonstrating excellent versatility and flexibility, and can be widely applied in the field of data processing technology.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a sparse quadrant amplitude mapping array method and related equipment. Background Technology

[0002] Large-scale sparse array design has significant application value in fields such as radar and communications. Traditional sparse methods, such as density-weighted methods and probabilistic mapping methods, while computationally efficient, rely on random sampling, leading to strong randomness and poor repeatability in the layout results. This results in significant performance fluctuations between different designs and insufficient engineering reliability. Furthermore, existing methods are typically designed for modeling regular arrays and are difficult to adapt flexibly to complex array geometries such as circular arrays or arrays with obstacles, limiting their practical application scope. Summary of the Invention

[0003] The main objective of this invention is to provide a quadrant amplitude mapping array sparse method, apparatus, electronic device, storage medium, and program product, which aims to solve at least one problem of the prior art.

[0004] To achieve the above objectives, one aspect of this invention proposes a sparse quadrant amplitude mapping array method, the method comprising: The array information of the antenna array and the number of sparse targets are obtained. The antenna array is taken as the target region, and the number of sparse targets is taken as the first sparse count. The array information includes the amplitude weighting and the coordinates of each array element. The first amplitude-weighted distribution and the first set of unit coordinates of the target region are determined based on the array information. Based on the first amplitude-weighted distribution and the first unit coordinate set, the target region is quadrant-partitioned by minimizing the partitioning factor to obtain multiple sparse units; The second amplitude-weighted distribution and the second unit coordinate set of each sparse unit are determined based on the array information. The number of units is allocated based on the first sparse number and the second amplitude weighted distribution to obtain the second sparse number of each sparse unit; If the second sparse number is 1, the cell distribution of the corresponding sparse cell is determined according to a random cell coordinate from the second cell coordinate set; If the second sparse number is not 1, take the sparse unit as the target region, take the second magnitude weighted distribution as the first magnitude weighted distribution, take the second unit coordinate set as the first unit coordinate set, and return to execute the step of partitioning the target region into quadrants based on the first magnitude weighted distribution and the first unit coordinate set by minimizing the partitioning factor, until the unit distribution of all sparse units is obtained recursively, and the target sparse array is obtained by summarizing.

[0005] In some embodiments, based on a first magnitude-weighted distribution and a first set of unit coordinates, the target region is quadrant-partitioned by minimizing a partitioning factor to obtain multiple sparse units, including the following steps: The traversal step size is obtained by transforming the coordinate range of the first unit's coordinate set; Several candidate points are extracted from the target region based on the traversal step size and used as a candidate point set. The first candidate point in the candidate point set is taken as the target point; The target area is divided into multiple candidate quadrants, with the target point as the center point. The magnitude-weighted cumulative value of all array elements in each candidate quadrant partition is obtained based on the first magnitude-weighted distribution quantization. The first sparse number is proportionally allocated using the magnitude-weighted cumulative value to determine the number of candidate units in each candidate quadrant partition; The partitioning factor corresponding to the target point is obtained by processing the magnitude-weighted cumulative value and the number of candidate units; Take the next candidate point in the candidate point set as the target point, and return to execute the step of dividing the target region into multiple candidate quadrant partitions with the target point as the center point, until the partitioning factor corresponding to all candidate points is obtained. The candidate point corresponding to the smallest partitioning factor is taken as the partition center point, and the candidate quadrant partition corresponding to the partition center point is taken as the sparse unit.

[0006] In some embodiments, the traversal step size is obtained by transforming the coordinate range of the first unit coordinate set, including the following steps: The range of the horizontal coordinate and the range of the vertical coordinate are determined based on the coordinate set of the first unit; wherein, the range of the horizontal coordinate includes the maximum value and the minimum value of the horizontal coordinate, and the range of the vertical coordinate includes the maximum value and the minimum value of the vertical coordinate; Based on the range of the horizontal and vertical axes, the traversal step size is obtained by quantizing the minimum value using the baseline step size parameter. The expression for the traversal step size is: ; In the formula, Indicates the step size during traversal; Indicates the minimum value; Indicates the reference step size parameter; This represents the maximum value of the x-coordinate; This represents the minimum value of the x-axis; This represents the maximum value of the y-axis; This represents the minimum value of the ordinate; This indicates rounding up to the nearest integer.

[0007] In some embodiments, the process of obtaining the partitioning factor corresponding to the target point based on the magnitude-weighted cumulative value and the number of candidate units includes the following steps: The partitioning factor corresponding to the target point is obtained by summing the absolute values ​​of the difference between the magnitude-weighted cumulative value and the number of candidate units in all candidate quadrant partitions corresponding to the target point. The expression for the dividing factor is: ; In the formula, Indicates the dividing factor; Indicates the first One candidate quadrant partition; Indicates the first The magnitude-weighted cumulative value of each candidate quadrant partition; Indicates the first The number of candidate units in each candidate quadrant partition; It represents the absolute value.

[0008] In some embodiments, the number of units is allocated based on a first sparse number and a second magnitude-weighted distribution to obtain a second sparse number for each sparse unit, including the following steps: Based on the cumulative sum of all magnitude weights in the second magnitude weighted distribution, the target magnitude cumulative value corresponding to each sparse unit is obtained; The target amplitude accumulation value is rounded to obtain the initial allocation quantity for each sparse unit. The initial allocation quantity will be used as the candidate allocation quantity; The allocation error of each sparse unit is determined based on the difference between the cumulative target magnitude and the number of candidate allocations; The total number of candidate assignments for all sparse units is obtained by summing up the number of candidate assignments. If the total number of candidate allocations is not equal to the number of the first sparse allocations, adjust and update the number of candidate allocations based on the allocation error. Return to the step of determining the allocation error of each sparse unit based on the difference between the target magnitude accumulation value and the number of candidate allocations, until the total number of candidate allocations equals the first sparse number, and determine the second sparse number of each sparse unit based on the final number of candidate allocations.

[0009] In some embodiments, if the total number of candidate allocations is not equal to the first sparse number, the number of candidate allocations is adjusted and updated based on the allocation error, including the following steps: If the total number of candidate allocations is greater than the first sparse number, the number of candidate allocations corresponding to the sparse unit with the largest allocation error is reduced. If the total number of candidate allocations is less than the first sparse number, the number of candidate allocations corresponding to the sparse unit with the minimum allocation error is incremented.

[0010] In some embodiments, if the antenna array is a three-dimensional array, before the step of using the antenna array as the target area, the method further includes the following steps: The three-dimensional array is projected onto a two-dimensional plane through a three-dimensional parallel projection, so that the target sparse array is obtained by processing to obtain a two-dimensional sparse result.

[0011] In some embodiments, after the step of summarizing to obtain the target sparse array, the method further includes the following steps: The two-dimensional sparse result is projected in parallel to the three-dimensional space, and then combined with adaptive meshing to obtain the three-dimensional spatial amplitude mapping result.

[0012] To achieve the above objectives, another aspect of the present invention provides a quadrant amplitude mapping array sparse device, the device comprising: The first module is used to obtain the array information of the antenna array and the target sparse number, taking the antenna array as the target region and the target sparse number as the first sparse number; wherein, the array information includes the amplitude weighting and the unit coordinates of each array element; The second module is used to determine the first amplitude-weighted distribution and the first set of unit coordinates of the target area based on array information. The third module is used to partition the target region into quadrants based on the first amplitude weighted distribution and the first unit coordinate set by minimizing the partitioning factor, thus obtaining multiple sparse units. The fourth module is used to determine the second amplitude-weighted distribution and the second set of coordinates for each sparse unit based on the array information. The fifth module is used to allocate the number of units based on the first sparse number and the second amplitude weighted distribution, so as to obtain the second sparse number of units for each sparse unit; The sixth module is used to determine the cell distribution of the corresponding sparse cell based on a random cell coordinate from the second cell coordinate set if the second sparse number is 1. The seventh module is used to, if the second sparse number is not 1, take the sparse unit as the target region, take the second amplitude weighted distribution as the first amplitude weighted distribution, take the second unit coordinate set as the first unit coordinate set, return to execute the operation of the third module, until the unit distribution of all sparse units is obtained recursively, and finally obtain the target sparse array.

[0013] In some embodiments, if the antenna array is a three-dimensional array, the apparatus further includes the following before performing the operation of using the antenna array as a target area: The eighth module is used to project a three-dimensional array onto a two-dimensional plane through a three-dimensional parallel projection, so that the target sparse array is obtained by processing to obtain a two-dimensional sparse result.

[0014] In some embodiments, after performing the step of summarizing to obtain the target sparse array, the apparatus further includes: The ninth module is used to project the two-dimensional sparse result into three-dimensional space in parallel, and then combine it with adaptive meshing to obtain the three-dimensional spatial amplitude mapping result.

[0015] To achieve the above objectives, another aspect of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method.

[0016] To achieve the above objectives, another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method.

[0017] To achieve the above objectives, another aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0018] The embodiments of the present invention include at least the following beneficial effects: The present invention provides a quadrant amplitude mapping array sparse method, apparatus, electronic device, storage medium, and program product. This scheme obtains the array information of an antenna array and the target sparse number, taking the antenna array as the target region and the target sparse number as the first sparse number. The array information includes the amplitude weighting and unit coordinates of each array element. Based on the array information, a first amplitude weighting distribution and a first set of unit coordinates of the target region are determined. Based on the first amplitude weighting distribution and the first set of unit coordinates, the target region is quadrant-partitioned by minimizing the partitioning factor to obtain multiple sparse units. Based on the array information, a second amplitude weighting distribution of each sparse unit is determined. The invention establishes a second set of unit coordinates; it allocates the number of units based on a first sparse number and a second amplitude-weighted distribution to obtain a second sparse number for each sparse unit; if the second sparse number is 1, it determines the unit distribution of the corresponding sparse unit based on a random unit coordinate from the second set of unit coordinates; if the second sparse number is not 1, it uses the sparse unit as the target region, the second amplitude-weighted distribution as the first amplitude-weighted distribution, and the second set of unit coordinates as the first set of unit coordinates, and returns to execute the step of quadrant partitioning the target region based on the first amplitude-weighted distribution and the first set of unit coordinates by minimizing the partitioning factor, until the unit distribution of all sparse units is obtained recursively, and finally summarizes to obtain the target sparse array. This embodiment of the invention, through recursive partitioning and deterministic amplitude mapping mechanism, can reduce the randomness of traditional probabilistic methods, ensure the uniqueness and stability of the output layout under the same input conditions, and thus significantly improve the repeatability and engineering reliability of the design results. This embodiment of the invention, through unit sparse allocation based on recursive quadrant partitioning, can achieve high-precision matching between amplitude distribution and unit position, significantly reducing sidelobe levels while reducing the standard deviation of performance fluctuations. Furthermore, the method of this invention does not depend on a specific array geometry and naturally supports rectangular, circular, and irregular arrays with internal obstacles through a recursive processing mechanism, demonstrating excellent versatility and flexibility. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of an implementation environment for the quadrant amplitude mapping array sparse method provided in this embodiment of the invention; Figure 2 This is a flowchart illustrating a quadrant amplitude mapping array sparse method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the overall process of the quadrant amplitude mapping array sparse method provided in the embodiments of the present invention; Figure 4 This is a schematic diagram of an extended example of a three-dimensional array provided in an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating an example of sparse QAM results for a rectangular array provided in an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating an example of sparse QAM results for irregular arrays provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of an example of a full-wave simulation unit provided in an embodiment of the present invention; Figure 8 This is a schematic diagram illustrating an example of full-wave simulation array results provided in an embodiment of the present invention; Figure 9 This is a schematic diagram illustrating a comparison between a full-wave simulation sparse array and an amplitude-weighted array provided in an embodiment of the present invention. Figure 10 This is a schematic diagram of a density-weighted and QAM comparison example provided in an embodiment of the present invention; Figure 11 This is a schematic diagram of the structure of a quadrant amplitude mapping array sparse device provided in an embodiment of the present invention; Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0021] It is understood that the terms “first,” “second,” etc., used in this invention may be used herein to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are used only to distinguish one concept from another. For example, first information may also be referred to as second information without departing from the scope of embodiments of the invention, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to determination” as used herein may be interpreted as “when…” or “when…” or “in response to determination.”

[0022] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0024] In related technologies, there is an urgent need for a deterministic sparse design method that can guarantee consistent results and is applicable to arbitrary array shapes.

[0025] In view of this, this invention provides a quadrant amplitude mapping array sparsity method and related equipment. This method obtains the array information of an antenna array and the target sparse number, using the antenna array as the target region and the target sparse number as the first sparse number. The array information includes the amplitude weighting and unit coordinates of each array element. Based on the array information, a first amplitude weighting distribution and a first set of unit coordinates for the target region are determined. Based on the first amplitude weighting distribution and the first set of unit coordinates, the target region is quadrant-partitioned by minimizing the partitioning factor to obtain multiple sparse units. Based on the array information, a second amplitude weighting distribution and a second set of unit coordinates for each sparse unit are determined. The number of units is allocated based on the first sparse number and the second amplitude-weighted distribution, resulting in the second sparse number for each sparse unit. If the second sparse number is 1, the unit distribution of the corresponding sparse unit is determined by randomly selecting a unit coordinate from the second unit coordinate set. If the second sparse number is not 1, the sparse unit is taken as the target region, the second amplitude-weighted distribution is taken as the first amplitude-weighted distribution, and the second unit coordinate set is taken as the first unit coordinate set. The process of quadrant partitioning the target region based on the first amplitude-weighted distribution and the first unit coordinate set by minimizing the partitioning factor is repeated until the unit distribution of all sparse units is obtained recursively, and the target sparse array is obtained by summing them up. This embodiment of the invention reduces the randomness of traditional probabilistic methods through recursive partitioning and deterministic amplitude mapping mechanisms, ensuring the uniqueness and stability of the output layout under the same input conditions, thereby significantly improving the repeatability and engineering reliability of the design results. The method of this embodiment of the invention achieves high-precision matching between amplitude distribution and unit position through unit sparse allocation based on recursive quadrant partitioning, significantly reducing the sidelobe level while reducing the standard deviation of performance fluctuation. Furthermore, the method of this invention does not depend on a specific array geometry and naturally supports rectangular, circular, and irregular arrays with internal obstacles through a recursive processing mechanism, demonstrating excellent versatility and flexibility.

[0026] It is understood that the quadrant amplitude mapping array sparse method provided by this invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal can be a smartphone, tablet, laptop, or desktop computer, but it is not limited to these.

[0027] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided by an embodiment of the present invention. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0028] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0029] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0030] Terminal 102 can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the invention does not impose any limitations.

[0031] For example, based on Figure 1 The implementation environment shown in this embodiment of the invention provides a quadrant amplitude mapping array sparse method. The following description uses the application of this quadrant amplitude mapping array sparse method in server 101 as an example. It can be understood that this quadrant amplitude mapping array sparse method can also be applied to terminal 102.

[0032] Reference Figure 2 , Figure 2 This is an optional flowchart of the quadrant amplitude mapping array sparse method provided in the embodiments of the present invention. The execution subject of the quadrant amplitude mapping array sparse method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 The method may include, but is not limited to, steps S100 to S700.

[0033] Step S100: Obtain the array information of the antenna array and the target sparse number, take the antenna array as the target area, and take the target sparse number as the first sparse number; The array information includes the magnitude weighting and cell coordinates of each array element; For example, in some specific implementations, it is assumed that for a rectangular planar array with a design frequency of 93 GHz, its array size is determined to be 75 × 75 grid points (cell spacing of half a wavelength 0.2 mm). For example, the target sparsity is set to 15%, that is, 844 cells are retained from a full array of 5625 cells. =844), the array information includes the coordinates (X,Y) of each grid point (element) and its amplitude weighting value calculated according to the Taylor weighting function (target sidelobe level -17.21dB). .

[0034] Step S200: Determine the first amplitude-weighted distribution and the first set of cell coordinates of the target region based on the array information; For example, in some specific implementations, at the initial recursive level, the entire 75×75 rectangular array can be considered as a "target region". The first amplitude-weighted distribution of this region is the Taylor-weighted distribution of the entire array. The first unit coordinate set contains the coordinates (X,Y) of all 5625 units on the array surface.

[0035] Step S300: Based on the first amplitude weighted distribution and the first unit coordinate set, the target region is quadrant partitioned by minimizing the partitioning factor to obtain multiple sparse units; It should be noted that in some embodiments, step S300 may include the following steps: transforming the coordinate range of the first unit coordinate set to obtain a traversal step size; traversing the target region based on the traversal step size to extract several candidate points as a candidate point set; taking the first candidate point in the candidate point set as the target point; dividing the target region into multiple candidate quadrant partitions with the target point as the center point; obtaining the amplitude weighted cumulative value of all array units in each candidate quadrant partition based on the first amplitude weighted distribution quantization; proportionally allocating the first sparse number using the amplitude weighted cumulative value to determine the number of candidate units in each candidate quadrant partition; processing based on the amplitude weighted cumulative value and the number of candidate units to obtain the partitioning factor corresponding to the target point; taking the next candidate point in the candidate point set as the target point, returning to execute the step of dividing the target region into multiple candidate quadrant partitions with the target point as the center point, until the partitioning factors corresponding to all candidate points are obtained; taking the candidate point corresponding to the smallest partitioning factor as the partition center point, and taking the candidate quadrant partition corresponding to the partition center point as a sparse unit.

[0036] For example, in some specific implementations, the specific process of quadrant partitioning by minimizing the partitioning factor is as follows: First, based on the coordinate range of the current target region [ ]and[ The adaptive traversal step size is calculated using the formula. Then, using this step size, traverse from the lower left corner to the upper right corner of the region to generate a series of candidate center points. For each candidate center point, perform the following: divide the region into four candidate quadrants centered on that point; calculate the magnitude-weighted cumulative value within each quadrant. ,according to The proportion is obtained by distributing the total sparse number. Then calculate the splitting factor. After the traversal is complete, select the one that makes... The smallest candidate point is used as the final partition center point, and the corresponding quadrant division is the result of this partitioning (multiple sparse units).

[0037] It should be noted that in some embodiments, the traversal step size is obtained by transforming the coordinate range of the first unit coordinate set, which may include the following steps: determining the horizontal coordinate range and the vertical coordinate range based on the first unit coordinate set; wherein, the horizontal coordinate range includes the maximum and minimum values ​​of the horizontal coordinates, and the vertical coordinate range includes the maximum and minimum values ​​of the vertical coordinates; based on the horizontal coordinate range and the vertical coordinate range, the traversal step size is obtained by quantizing the minimum value using the reference step size parameter; wherein, the expression for the traversal step size is: ; In the formula, Indicates the step size during traversal; Indicates the minimum value; Indicates the reference step size parameter; This represents the maximum value of the x-coordinate; This represents the minimum value of the x-axis; This represents the maximum value of the y-axis; This represents the minimum value of the ordinate; This indicates rounding up to the nearest integer.

[0038] For example, in some specific implementations, the traversal step size is... The calculation example is as follows: For a given target region, its cell coordinates are in The maximum difference in direction is ,exist The maximum difference in direction is Take these two differences and divide them by... ( The reference step size parameter (with a size equal to the full array cell spacing) is rounded up, then the smaller of the two values ​​is taken, and finally multiplied by the reference step size. To obtain the adaptive traversal step size For example, if the region The directional span is 70 unit spacings. The direction is 50 unit spacing. =0.2mm, then the calculated value is... =min( , ) 0.2 = min(7, 5) 0.2 = 1.0 mm; This step size can be dynamically adjusted according to the size of the region to optimize search efficiency.

[0039] It should be noted that, in some embodiments, obtaining the partitioning factor corresponding to the target point based on the magnitude-weighted cumulative value and the number of candidate units may include the following steps: obtaining the partitioning factor corresponding to the target point based on the cumulative absolute value of the difference between the magnitude-weighted cumulative value and the number of candidate units in all candidate quadrant partitions corresponding to the target point; wherein, the expression for the partitioning factor is: ; In the formula, Indicates the dividing factor; Indicates the first One candidate quadrant partition; Indicates the first The magnitude-weighted cumulative value of each candidate quadrant partition; Indicates the first The number of candidate units in each candidate quadrant partition; It represents the absolute value.

[0040] For example, in some specific implementations, for a candidate partition center point, four quadrants are generated. Assume the calculated magnitude-weighted cumulative values ​​for the four quadrants are as follows: =210.5, =180.2, =195.7, =257.6 (total 844). The initial number of units allocated by rounding might be... =211, =180, =196, =257 (total 844). Therefore, the dividing factor is... = |210.5-211| + |180.2-180| + |195.7-196| + |257.6-257| = 0.5+0.2+0.3+0.6 = 1.6. This value characterizes the overall mismatch between magnitude weights and the number of units allocated under this partitioning scheme.

[0041] Step S400: Determine the second amplitude-weighted distribution and the second unit coordinate set for each sparse unit based on the array information; For example, in some specific implementations, for each quadrant (sub-region, i.e., a "sparse unit") that has been divided, the amplitude weighting values ​​of all array elements falling within that quadrant can be extracted from the original array information, starting from its corresponding physical coordinate range, to form the second amplitude weighting distribution of the sparse unit. At the same time, the coordinates of these array elements are extracted to form the second unit coordinate set of the sparse unit.

[0042] Step S500: Allocate the number of units based on the first sparse number and the second amplitude weighted distribution to obtain the second sparse number of each sparse unit; It should be noted that in some embodiments, step S500 may include the following steps: obtaining a target amplitude accumulation value corresponding to each sparse unit based on the cumulative value of all amplitude weights in the second amplitude weighted distribution; rounding the target amplitude accumulation value to obtain the initial allocation quantity corresponding to each sparse unit; using the initial allocation quantity as the candidate allocation quantity; determining the allocation error of each sparse unit based on the difference between the target amplitude accumulation value and the candidate allocation quantity; accumulating the candidate allocation quantities of all sparse units to obtain the total number of candidate allocations; if the total number of candidate allocations is not equal to the first sparse number, adjusting and updating the candidate allocation quantity based on the allocation error; returning to the step of determining the allocation error of each sparse unit based on the difference between the target amplitude accumulation value and the candidate allocation quantity, until the total number of candidate allocations is equal to the first sparse number, and determining the second sparse number of each sparse unit based on the final candidate allocation quantity.

[0043] For example, in some specific implementations, suppose there are currently four sparse units (quadrants), and their magnitude-weighted cumulative value (target magnitude cumulative value) is: =[210.5, 180.2, 195.7, 257.6], the first sparse count. =844. First, regarding... The initial allocation quantity is obtained by rounding each value to the nearest integer. =[211, 180, 196, 257], the sum is 844, which is exactly equal to Calculate the allocation error. = = [-0.5, 0.2, -0.3, 0.6]. Since the sum is already matched, no adjustment is needed. If the sum after rounding is 843 (less than...), then... Then you can choose the error. The quadrant with the smallest (largest negative value, i.e., -0.5) has the following unit count. The error is increased from 211 to 212, then recalculated and judged, until the sum is 844. This adjustment mechanism ensures that the allocation result is closest to the amplitude distribution while satisfying the total number constraint.

[0044] It should be noted that in some embodiments, if the total number of candidate allocations is not equal to the first sparse number, adjusting and updating the number of candidate allocations based on the allocation error may include the following steps: if the total number of candidate allocations is greater than the first sparse number, the number of candidate allocations corresponding to the sparse unit with the largest allocation error is reduced; if the total number of candidate allocations is less than the first sparse number, the number of candidate allocations corresponding to the sparse unit with the smallest allocation error is increased.

[0045] For example, in some specific implementations, when the total number of candidate allocations =845> When the value is 844, check the allocation error of each quadrant. .Discover =0.6 is the maximum (the largest positive error, meaning the actual amplitude weight of this quadrant). Compared to the number of units already allocated If there is the largest "surplus", then... Decrease by 1 (from 257 to 256), thus reducing the total to 844. Conversely, if =843<844, then the search error The smallest (the largest negative value, such as...) The quadrant of (-0.5) will Increase by 1 (from 211 to 212), bringing the total to 844.

[0046] Step S600: If the second sparse number is 1, determine the cell distribution of the corresponding sparse cell based on a random cell coordinate from the second cell coordinate set. Step S700: If the second sparse number is not 1, take the sparse unit as the target region, take the second amplitude weighted distribution as the first amplitude weighted distribution, take the second unit coordinate set as the first unit coordinate set, return to execute the step of quadrant partitioning the target region based on the first amplitude weighted distribution and the first unit coordinate set by minimizing the partitioning factor, until the unit distribution of all sparse units is obtained recursively, and the target sparse array is obtained by summarizing. For example, in some specific implementations, after obtaining the second sparse number in each quadrant, a judgment is made: If the second sparse number is 1: If only 1 unit is allocated to a certain quadrant, then randomly select a coordinate position from the coordinate set of the second unit of that quadrant to place the unit. The layout of the sparse unit is determined, and the recursion of this branch terminates.

[0047] If the second sparsity count is not 1: For example, if a quadrant is allocated 50 units, then that quadrant is taken as the new "target region," and its amplitude distribution and coordinate set are updated to the corresponding second amplitude-weighted distribution and second unit coordinate set. Its target sparsity count is updated to 50 (the new first sparsity count). Then, this new target region is divided into four quadrants and allocated units again. This process is repeated recursively until all branches reach the termination condition of "sparse count is 1."

[0048] It should be noted that in some embodiments, if the antenna array is a three-dimensional array, before the step of using the antenna array as the target area, the method may further include the following steps: projecting the three-dimensional array onto a two-dimensional plane through three-dimensional parallelism, so that the target sparse array that is processed to obtain a two-dimensional sparse result is obtained.

[0049] For example, in some specific implementations, when the antenna array is a three-dimensional curved surface array, before starting the recursive sparsity process, all array elements and their amplitude weights on the three-dimensional array are first mapped to a two-dimensional plane using a three-dimensional parallel projection method. On this two-dimensional projection plane, the positions of the array elements are represented by projected coordinates, while the amplitude weights remain unchanged. Subsequently, the aforementioned method flow is applied to this two-dimensional plane to achieve quadrant amplitude mapping sparsity, generating a two-dimensional sparse layout (i.e., a two-dimensional sparse result).

[0050] It should be noted that, in some embodiments, after summarizing the steps to obtain the target sparse array, the method may further include the following steps: projecting the two-dimensional sparse result in parallel to the three-dimensional space, and then combining the adaptive mesh partitioning to obtain the three-dimensional space amplitude mapping result.

[0051] For example, in some specific implementations, after obtaining the sparse layout result of the two-dimensional plane, the positions of the sparse units on this two-dimensional layout are mapped back to the original three-dimensional curved surface array through parallel projection to determine which array elements are retained in the three-dimensional space. Then, adaptive mesh generation technology can be combined to perform finer mesh generation and mapping optimization on the surface of the three-dimensional array, ultimately achieving a precise mapping of the amplitude weight distribution in the three-dimensional space to the positions of the sparse array elements, thus completing the design of the three-dimensional sparse array.

[0052] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0053] First, it should be noted that traditional large-scale sparse array design methods, such as density-weighted methods and other probabilistic mapping methods, while computationally efficient, suffer from strong layout randomness and poor stability. Significant differences exist between different runs, leading to insufficient consistency and reliability in engineering applications. Furthermore, existing technologies lack a deterministic mapping mechanism that can guarantee layout consistency, making it difficult to meet the requirements of high-precision antenna arrays for sidelobe level control and array geometry adaptability.

[0054] In view of this, the present invention aims to overcome the aforementioned technical difficulties and provide a quadrant amplitude mapping (QAM) method for sparse arrays. This method achieves a deterministic mapping of amplitude distribution to a sparse layout through recursive partitioning and amplitude-weighted allocation, thereby significantly reducing layout randomness and improving the stability and efficiency of array design. The present invention also aims to support arbitrary array geometries (such as rectangular, circular, or irregular arrays) and can be extended to three-dimensional array design, providing an efficient and reliable sparse solution for large-scale antenna arrays.

[0055] The core of this invention lies in a recursive partitioning mechanism, which divides the array aperture into four quadrants and allocates the number of sparse cells according to the amplitude distribution, thereby achieving a precise conversion from amplitude weight to cell position. For example... Figure 3 As shown, the technical solution of this embodiment of the invention specifically includes the following steps: 1. Input parameter processing: Received amplitude weighted distribution Unit coordinates and the number of target sparse units These parameters serve as initial conditions for the sparse process, ensuring that the method is applicable to various array configurations.

[0056] 2. Recursive Partitioning Mechanism: A four-quadrant recursive partitioning strategy is adopted to progressively refine the amplitude matching precision. The partitioning process determines the partition center point through an adaptive traversal strategy. traversal step size The step size is dynamically adjusted based on the array size to minimize partitioning error. Specifically, the step size adjustment formula is:

[0057] in This is the baseline step size parameter. After partitioning, the array is divided into four quadrants:

[0058]

[0059]

[0060]

[0061] 3. Unit quantity allocation: based on the amplitude weighting value of each quadrant. Distribute the number of sparse units First, a preliminary allocation will be made: Then calculate the error. The adjustment mechanism ensures that the total number of units equals... ,when At that time, by increasing or decreasing The value makes the total number of units equal to The allocation process involves minimizing the partitioning factor. Determine the optimal partitioning method.

[0062] 4. Recursive Execution and Termination: Recursively execute the above partitioning and allocation process for each quadrant until... Individual cells are placed randomly at random. This recursive mechanism ensures that amplitude matching is refined at multiple levels, improving layout accuracy.

[0063] In some specific embodiments, the present invention can be implemented through the following process: Input amplitude weighted distribution Unit coordinates and the number of target sparse units With the division of quadrants , , , All are in a dynamic state of change. That is, the initial input is the total array distribution and the number of targets. During the continuous division process, each parameter represents the array distribution and the number of targets in the current region. Weighted by magnitude The number of sparse units in each quadrant is assigned. ,satisfy , To indicate different quadrants, take values ​​from 1 to 4; By minimizing the partitioning factor Determine the optimal partitioning method; The above partitioning and allocation process is recursively performed on each quadrant until the current target sparse number is [value missing]. A single unit is randomly placed at a time, ending the current partitioning. Then, the process continues to traverse the next region until all regions of the array have been partitioned and traversed, reaching the termination condition.

[0064] In some optional implementations, the partitioning process employs an adaptive traversal strategy to determine the partition center point. That is, starting from the center point, traverse from the lower left corner to the upper right corner of the array, until the minimum partition factor is reached. When the value is minimum, select that point as the partition center. Iterate through the step sizes. The step size is dynamically adjusted based on the array size; that is, when the array size is large, the step size is large, and when the array size is small, the step size is small, achieving adaptive partitioning based on the array size and saving partitioning time.

[0065] in, Represents the step size during traversal; The reference step size parameter is the spacing between the full array cells; , Represents the coordinates of all array elements within the region; To round up In some optional implementations, the cell number allocation process includes: Preliminary allocation: , Indicates the rounding operation; Error calculation: ; Adjustment mechanism: When At that time, by increasing or decreasing The value makes the total number of units equal to .when When selecting error Large areas, making ;when When selecting error Small, make This adjustment consistently ensures consistency between the amplitude and the number of units within the region, as well as minimizes errors.

[0066] In some alternative implementations, the method of this invention supports arbitrary array geometries, including rectangular arrays, circular arrays, and irregular arrays with internal barriers.

[0067] Some alternative implementations, such as Figure 4 As shown, the method of this embodiment can be extended to three-dimensional array design. It performs sparse mapping under region quartic by parallel projection of three-dimensional data onto a two-dimensional plane, and then projects the two-dimensional sparse result back onto three-dimensional data in parallel, and achieves three-dimensional spatial amplitude mapping by adaptive mesh partitioning.

[0068] The technical effects of the embodiments of the present invention will be explained in detail below with reference to specific application scenarios: Example 1: Sparse design of rectangular array; such as Figure 5 As shown, taking a 75×75 rectangular array as an example, with Taylor weighted distribution (n=6, sidelobe=-17.21dB), target sparsity of 15% (retaining 844 units), sparse array sidelobe=-17.33dB, and computation time of 4.56 seconds for 30 iterations.

[0069] Example 2: Irregular array application; such as Figure 6 As shown, for an irregular array with circular notches (radius 60 mm, spacing half a wavelength), the amplitude-weighted sidelobes are -15.78 dB. Using the same recursive partitioning strategy with a sparsity of 12% (400 elements), the sidelobes are -15.92 dB. The results demonstrate that the sparse array generated by the proposed QAM is close to a full amplitude-weighted array even in irregular configurations. The sparsified sidelobe levels remain well controlled, indicating that the proposed QAM is applicable to arrays of arbitrary geometries.

[0070] Example 3: Full-wave electromagnetic simulation verification; To verify the engineering feasibility of the design scheme, full-wave electromagnetic simulation was performed using CST software. A 93GHz microstrip patch antenna element was used, with the structure as follows: Figure 7 As shown. Dielectric constant. r=2.2 (Rogers substrate), patch length =0.98mm, width =1.6mm, substrate thickness =0.127mm, metal thickness =0.035mm, unit spacing =0.2mm. Full-wave simulation was performed on the sparsely arranged array to obtain the radiation pattern and sidelobe levels. The CST full-wave simulation results are highly consistent with the theoretical array factor results. Figure 8 Display the full-wave simulation array results; Figure 9 The results show a comparison between a full-wave simulated sparse array and an amplitude-weighted array, with sidelobe levels of -17.17 dB (CST) and -17.21 dB (theoretical), respectively, with a deviation of only 0.04 dB, demonstrating the effectiveness of the method of this invention.

[0071] Example 4: Performance Comparison; Compared with the density-weighted method, the QAM method showed a sidelobe level standard deviation of 0.2dB vs 0.89dB (a reduction of 77.5%) in 30 trials, and an optimal sidelobe level of -17.73dB vs -15.17dB (an improvement of 2.56dB). Figure 10 This paper presents the sidelobe levels obtained by running QAM and density-weighted algorithms through a single numerical simulation experiment, using a Taylor-weighted full array (sidelobe of -17.21 dB) as a performance benchmark at different sparsity ratios. The results show that for the same number of elements, QAM consistently achieves lower sidelobes than density-weighted algorithms, demonstrating its superior fitting performance. Notably, at certain sparsity ratios, the sidelobes obtained by QAM are slightly lower than those of the original amplitude-weighted full array. This phenomenon is attributed to the spatial constraints at the array edges—regions allocated high amplitude values ​​but lacking sufficient space to accommodate a corresponding number of elements. Therefore, fewer elements are placed in these regions, resulting in a more sparse overall configuration and reduced sidelobes. Furthermore, since the number of elements in each sub-region is determined by the physical area, the resulting array exhibits higher structural accuracy. This enhanced positional determinism provides greater flexibility and reliability in practical engineering applications.

[0072] This invention verifies the impact of key parameters on algorithm performance through systematic ablation experiments, ensuring the robustness of the method in different scenarios. The default approach employs adaptive center selection, a four-partition strategy, and a recursion depth of -1 layer. For extremely low sidelobe requirements, the recursion depth can be increased; for strongly coupled environments, a minimum cell spacing constraint can be introduced. This method can be extended to 3D array design, achieving 3D spatial amplitude mapping through surface projection and adaptive mesh generation.

[0073] In summary, this invention solves the problem of high layout randomness in traditional methods by eliminating probabilistic random factors and ensuring consistent output for the same input. It minimizes the matching error between amplitude and cell number by employing optimized partition center points and dynamic step size adjustment, thus improving sparsity accuracy. A four-quadrant recursive strategy achieves multi-level amplitude matching, making it suitable for efficient processing of large-scale arrays.

[0074] The method of this invention does not depend on a specific array shape and can handle irregular configurations. Through the above technical solution, this invention achieves the following beneficial effects: Improved layout stability: Compared with the traditional density-weighted method, the QAM method reduced the standard deviation of sidelobe level from 0.89dB to 0.2dB (a reduction of 77.5%) in 30 trials, and improved the optimal sidelobe from -15.17dB to -17.73dB (an improvement of 2.56dB), significantly enhancing the consistency of the results.

[0075] Computational efficiency optimization: The recursive partitioning mechanism reduces redundant computation. For example, in the sparse design of a 75×75 rectangular array (target sparsity of 15%), the computation time for 30 iterations is only 4.56 seconds.

[0076] Engineering feasibility verification: Through full-wave electromagnetic simulation (such as CST software), the deviation of the sparse array sidelobe level from the theoretical value is only 0.04dB, proving the reliability of the method in actual engineering.

[0077] Flexibility and reliability: The method is applicable to different sparsity rates and array configurations, and can still maintain low sidelobe levels (such as -15.92dB in Example 2) in irregular arrays, providing high structural accuracy for antenna design.

[0078] like Figure 11 As shown, this embodiment of the invention also provides a quadrant amplitude mapping array sparse device 900, which can implement the above-described method. This device may include: The first module 910 is used to obtain the array information of the antenna array and the target sparse number, taking the antenna array as the target region and the target sparse number as the first sparse number; wherein, the array information includes the amplitude weighting and unit coordinates of each array element; The second module 920 is used to determine the first amplitude-weighted distribution and the first set of unit coordinates of the target area based on array information; The third module 930 is used to partition the target region into quadrants based on the first amplitude weighted distribution and the first unit coordinate set by minimizing the partitioning factor, and obtain multiple sparse units. The fourth module 940 is used to determine the second amplitude-weighted distribution and the second set of coordinates for each sparse unit based on array information; The fifth module 950 is used to allocate the number of units based on the first sparse number and the second amplitude weighted distribution, so as to obtain the second sparse number of each sparse unit; The sixth module 960 is used to determine the cell distribution of the corresponding sparse cell based on a random cell coordinate from the second cell coordinate set if the second sparse number is 1. The seventh module 970 is used to take the sparse cells as the target region, take the second amplitude-weighted distribution as the first amplitude-weighted distribution, take the second cell coordinate set as the first cell coordinate set, and return to execute the operation of the third module if the second sparse number is not 1, until the cell distribution of all sparse cells is obtained recursively, and the target sparse array is obtained by summarizing.

[0079] In some embodiments, if the antenna array is a three-dimensional array, the apparatus further includes the following before performing the operation of using the antenna array as a target area: The eighth module is used to project a three-dimensional array onto a two-dimensional plane through a three-dimensional parallel projection, so that the target sparse array is obtained by processing to obtain a two-dimensional sparse result.

[0080] In some embodiments, after performing the step of summarizing to obtain the target sparse array, the apparatus further includes: The ninth module is used to project the two-dimensional sparse result into three-dimensional space in parallel, and then combine it with adaptive meshing to obtain the three-dimensional spatial amplitude mapping result.

[0081] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0082] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0083] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0084] like Figure 12 As shown, Figure 12 The hardware structure of an electronic device 1000 according to another embodiment is illustrated. The electronic device 1000 includes: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (aSIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention. The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RaM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004); The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0085] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0086] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0087] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0088] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0089] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0090] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0091] The quadrant amplitude mapping array sparse method, apparatus, electronic device, storage medium, and program product provided in this invention obtains the array information of an antenna array and the target sparse number, taking the antenna array as the target region and the target sparse number as the first sparse number. The array information includes the amplitude weighting and unit coordinates of each array element. Based on the array information, a first amplitude weighting distribution and a first set of unit coordinates for the target region are determined. Based on the first amplitude weighting distribution and the first set of unit coordinates, the target region is quadrant-partitioned by minimizing a partitioning factor to obtain multiple sparse units. Based on the array information, a second amplitude weighting distribution and a second set of unit coordinates for each sparse unit are determined. The method involves allocating the number of sparse units based on the first sparse number and the second amplitude-weighted distribution, resulting in the second sparse number for each sparse unit. If the second sparse number is 1, the unit distribution of the corresponding sparse unit is determined by randomly selecting a unit coordinate from the second unit coordinate set. If the second sparse number is not 1, the sparse unit is used as the target region, the second amplitude-weighted distribution is used as the first amplitude-weighted distribution, and the second unit coordinate set is used as the first unit coordinate set. The process then returns to the previous step of quadrant partitioning the target region based on the first amplitude-weighted distribution and the first unit coordinate set by minimizing the partitioning factor, until the unit distribution of all sparse units is recursively obtained, and the target sparse array is obtained by summing the results. This embodiment of the invention, through recursive partitioning and deterministic amplitude mapping mechanisms, reduces the randomness of traditional probabilistic methods, ensures the uniqueness and stability of the output layout under the same input conditions, and thus significantly improves the repeatability and engineering reliability of the design results. The method of this embodiment, through unit sparse allocation based on recursive quadrant partitioning, can achieve high-precision matching between amplitude distribution and unit position, significantly reducing sidelobe levels while reducing the standard deviation of performance fluctuations. Furthermore, the method of this invention does not depend on a specific array geometry and naturally supports rectangular, circular, and irregular arrays with internal obstacles through a recursive processing mechanism, demonstrating excellent versatility and flexibility.

[0092] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0093] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present invention, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0094] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0095] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0096] The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present invention should be within the scope of the claims of the present invention.

Claims

1. A sparse method for quadrant amplitude mapping arrays, characterized in that, The method includes the following steps: The array information of the antenna array and the target sparse number are obtained, the antenna array is taken as the target region, and the target sparse number is taken as the first sparse number; wherein, the array information includes the amplitude weighting and the unit coordinates of each array element; Based on the array information, determine the first amplitude-weighted distribution and the first set of cell coordinates for the target region; Based on the first amplitude-weighted distribution and the first unit coordinate set, the target region is quadrant-partitioned by minimizing the partitioning factor to obtain multiple sparse units; Based on the array information, determine the second amplitude-weighted distribution and the second unit coordinate set for each sparse unit; The number of units is allocated based on the first sparse number and the second amplitude-weighted distribution to obtain the second sparse number of each sparse unit; If the second sparse number is 1, the cell distribution corresponding to the sparse cell is determined according to a random cell coordinate from the second cell coordinate set; If the second sparse number is not 1, the sparse unit is taken as the target region, the second amplitude-weighted distribution is taken as the first amplitude-weighted distribution, the second unit coordinate set is taken as the first unit coordinate set, and the step of performing quadrant partitioning of the target region by minimizing the partitioning factor based on the first amplitude-weighted distribution and the first unit coordinate set is returned to be executed until the unit distribution of all the sparse units is obtained recursively, and the target sparse array is obtained by summarizing.

2. The method according to claim 1, characterized in that, The step of partitioning the target region into quadrants based on the first amplitude-weighted distribution and the first unit coordinate set to obtain multiple sparse units by minimizing the partitioning factor includes the following steps: The traversal step size is obtained by transforming the coordinate range of the first unit coordinate set; Based on the traversal step size, the target region is traversed to extract several candidate points as a candidate point set; The first candidate point in the candidate point set is taken as the target point; Using the target point as the center point, the target area is divided into multiple candidate quadrant partitions; The magnitude-weighted cumulative value of all array elements in each candidate quadrant partition is obtained based on the first magnitude-weighted distribution quantization. The first sparse number is proportionally allocated using the magnitude-weighted cumulative value to determine the number of candidate units in each candidate quadrant partition; The partitioning factor corresponding to the target point is obtained by processing the magnitude-weighted cumulative value and the number of candidate units; Take the next candidate point in the candidate point set as the target point, and return to execute the step of dividing the target region into multiple candidate quadrant partitions with the target point as the center point, until the partitioning factor corresponding to all the candidate points is obtained; The candidate point corresponding to the smallest partitioning factor is taken as the partition center point, and the candidate quadrant partition corresponding to the partition center point is taken as the sparse unit.

3. The method according to claim 2, characterized in that, The step of obtaining the traversal step size based on the coordinate range of the first unit coordinate set includes the following steps: The range of the horizontal coordinate and the range of the vertical coordinate are determined based on the first unit coordinate set; wherein, the range of the horizontal coordinate includes the maximum value and the minimum value of the horizontal coordinate, and the range of the vertical coordinate includes the maximum value and the minimum value of the vertical coordinate; Based on the range of the horizontal coordinate and the range of the vertical coordinate, the traversal step size is obtained by quantizing the minimum value using the reference step size parameter. The expression for the traversal step size is: ； In the formula, Indicates the step size during traversal; Indicates the minimum value; Indicates the reference step size parameter; This represents the maximum value of the x-coordinate; This represents the minimum value of the x-axis; This represents the maximum value of the y-axis; This represents the minimum value of the ordinate; This indicates rounding up to the nearest integer.

4. The method according to claim 2, characterized in that, The process of obtaining the partitioning factor corresponding to the target point based on the magnitude-weighted cumulative value and the number of candidate units includes the following steps: The partitioning factor corresponding to the target point is obtained by summing the absolute values ​​of the difference between the magnitude-weighted cumulative value and the number of candidate units in all the candidate quadrant partitions corresponding to the target point. The expression for the partitioning factor is as follows: ； In the formula, Indicates the dividing factor; Indicates the first One candidate quadrant partition; Indicates the first The magnitude-weighted cumulative value of each candidate quadrant partition; Indicates the first The number of candidate units in each candidate quadrant partition; It represents the absolute value.

5. The method according to claim 1, characterized in that, The step of allocating the number of units based on the first sparse number and the second amplitude-weighted distribution to obtain the second sparse number of each sparse unit includes the following steps: Based on the cumulative sum of all the magnitude weights in the second magnitude weighted distribution, the target magnitude cumulative value corresponding to each sparse unit is obtained; The target amplitude accumulation value is rounded down to obtain the initial allocation quantity corresponding to each sparse unit; The initial allocation quantity is used as the candidate allocation quantity; The allocation error of each sparse unit is determined based on the difference between the target amplitude accumulation value and the number of candidate allocations; The total number of candidate allocations for all the sparse units is obtained by summing the number of candidate allocations. If the total number of candidate allocations is not equal to the first sparse number, the number of candidate allocations is adjusted and updated based on the allocation error; Return to the step of determining the allocation error of each sparse unit based on the difference between the target amplitude accumulation value and the number of candidate allocations, until the total number of candidate allocations equals the first number of sparse units, and determine the second number of sparse units for each sparse unit based on the final number of candidate allocations.

6. The method according to claim 5, characterized in that, If the total number of candidate allocations is not equal to the first sparse number, the number of candidate allocations is adjusted and updated based on the allocation error, including the following steps: If the total number of candidate allocations is greater than the first number of sparse units, the number of candidate allocations corresponding to the sparse unit with the largest allocation error is reduced. If the total number of candidate allocations is less than the first number of sparse units, the number of candidate allocations corresponding to the sparse unit with the smallest allocation error is incrementally processed.

7. The method according to claim 1, characterized in that, If the antenna array is a three-dimensional array, before the step of using the antenna array as the target area, the method further includes the following steps: The three-dimensional array is projected onto a two-dimensional plane through a three-dimensional parallel projection, so that the target sparse array is obtained by processing to obtain a two-dimensional sparse result.

8. The method according to claim 7, characterized in that, After the step of summarizing to obtain the target sparse array, the method further includes the following steps: The two-dimensional sparse result is projected in parallel to the three-dimensional space, and then combined with adaptive mesh partitioning to obtain the three-dimensional spatial amplitude mapping result.

9. A quadrant amplitude mapping array sparse device, characterized in that, The device includes: The first module is used to obtain the array information of the antenna array and the target sparse number, taking the antenna array as the target region and the target sparse number as the first sparse number; wherein, the array information includes the amplitude weighting and unit coordinates of each array element; The second module is used to determine the first amplitude-weighted distribution and the first set of cell coordinates of the target region based on the array information. The third module is used to partition the target region into quadrants based on the first amplitude-weighted distribution and the first unit coordinate set by minimizing the partitioning factor to obtain multiple sparse units. The fourth module is used to determine the second amplitude-weighted distribution and the second unit coordinate set for each sparse unit based on the array information. The fifth module is used to allocate the number of units based on the first sparse number and the second amplitude-weighted distribution to obtain the second sparse number of each sparse unit; The sixth module is used to determine the cell distribution of the corresponding sparse cell based on a random cell coordinate from the second cell coordinate set if the second sparse number is 1. The seventh module is used to, if the second sparse number is not 1, take the sparse unit as the target region, take the second amplitude-weighted distribution as the first amplitude-weighted distribution, take the second unit coordinate set as the first unit coordinate set, return to execute the operation of the third module, until the unit distribution of all the sparse units is obtained recursively, and summarize to obtain the target sparse array.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method described in any one of claims 1 to 8.

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