Random uniform sparse algorithm for phased-array antenna
By optimizing the sparse array of spaceborne phased array antennas using a random uniform sparse algorithm, the problems of high sidelobe level, slow generation speed, and poor economy are solved, achieving more efficient and stable sparse array generation, which is suitable for a variety of application scenarios.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-27
- Publication Date
- 2026-03-27
AI Technical Summary
The sparse array design of spaceborne phased array antennas suffers from problems such as high sidelobe levels, slow generation speed, poor economy, and poor beam robustness.
A random uniform sparse algorithm for phased array antennas is adopted. By constructing subarrays step by step and using a random sparse strategy, combined with matrix analysis and a random number generator, the sparse structure is optimized, the sidelobe level is reduced, the generation speed and economy are improved, and the beam robustness is ensured.
It effectively reduces sidelobe levels, improves generation speed and economy, enhances array noise immunity, ensures beam robustness, and adapts to different application scenarios and optimization needs.
Smart Images
Figure CN121744582A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of phased array antenna technology, and in particular to a random uniform sparse algorithm for phased array antennas. Background Technology
[0002] Low Earth Orbit (LEO) satellite communication, as a new type of constellation network, utilizes antennas deployed in low Earth orbit to provide broadband communication services to ground and air users, featuring low cost, wide coverage, broadband speeds, and low latency. With the decline in satellite manufacturing and rocket launch costs, LEO broadband satellites have demonstrated strong competitiveness in providing broadband access services, driving the rapid development of LEO constellation construction. As a crucial node on the satellite platform, onboard antennas typically need to communicate with multiple user targets simultaneously to improve aperture utilization efficiency, reduce the number of devices, and lower costs. Therefore, onboard antennas often need to form multiple beams simultaneously to meet the requirements of wide coverage and high gain.
[0003] Digital multi-beam antenna architectures are based on the DBF (Digital Beamforming) architecture. This architecture achieves rapid beam pointing switching through digital signal processing and beamforming networks, and theoretically, the number of beams can be infinitely expanded according to the algorithm. However, this architecture suffers from high power consumption, high integration density, and increased cost. In addition, the dense arrangement of active antenna array elements leads to high heat flux density, making heat dissipation difficult and, in severe cases, even affecting antenna integration.
[0004] To address these issues, array sparsity technology has emerged. This technology improves the overall system efficiency, particularly in spaceborne phased array antennas, by reducing the radiation participation of a portion of the array elements in a uniformly distributed array, while maintaining the same antenna aperture size. The application of sparse arrays has been validated for a long time in large phased array radar systems used by the US military. However, the sidelobe levels of the diffuse pattern after sparsening are often higher than those of a full array, and quantization lobes or even grating lobes are prone to appear at certain scanning angles. Therefore, appropriate methods are needed to suppress PSLL and quantization lobes.
[0005] The design of spaceborne phased array antennas also needs to consider the special structure of coplanar transmit and receive configurations, where the number of transmit antennas is typically several times that of receive antennas. When performing sparse optimization on phased array antennas with coplanar transmit and receive configurations, it is necessary to ensure that the sparse distribution of the transmit array is both random to reduce sidelobe levels and uniform to ensure consistent distribution of receive antennas. Furthermore, the generation speed and cost-effectiveness of the sparse array, as well as beam robustness, are also critical issues that urgently need to be addressed. Summary of the Invention
[0006] To address the problems of high sidelobe levels, slow generation speed, poor economy, and poor beam robustness in the sparse array design of existing spaceborne phased array antennas, this invention provides a random uniform sparse algorithm for phased array antennas, which reduces sidelobe levels, improves generation speed and economy, and ensures beam robustness.
[0007] To achieve the above-mentioned technical objectives, the present invention provides a technical solution: a random uniform sparse algorithm for phased array antennas, comprising the following steps: S1, divide the operating characteristics of the target array antenna into the smallest unit subarray grid, and perform uniform grid partitioning according to the subarray; S2, using the subarray as the basic unit, obtains the possible sparse structures of all basic unit antennas inside the antenna subarray based on the sparse target and the working characteristics of the array; S3, based on the preset antenna performance parameters and screening criteria, the sparse structure is combined step by step to obtain all possible sparse structures of the k-th level antenna subarray. S4, taking the k-th level subarray as a unit, and randomly combining the antenna arrays in sequence according to the selection criteria; S5, Select randomly combined antenna arrays according to the selection criteria; S6 performs array pattern traversal simulation on the selected arrays to select the optimal sparse structure.
[0008] In this technical solution, the sidelobe level is effectively reduced by employing a hierarchical subarray construction and random uniform sparse method. During antenna scanning, sparse arrays can easily lead to deterioration of the sidelobe level, but this algorithm, through a random sparse strategy, minimizes the generation of sidelobes, ensuring the absence of quantized lobes and grating lobes throughout the entire operating frequency band, thereby improving the purity of the antenna pattern and the accuracy of beam pointing. Through reasonable subarray partitioning and a hierarchical combination strategy, the search space is greatly reduced, significantly improving the generation speed of the sparse array. Compared to the traditional exhaustive method, this hierarchical combination method can significantly reduce computation time and resource consumption while maintaining performance, thus improving economic efficiency. By setting reasonable screening criteria and performance parameters, the algorithm can quickly eliminate unsuitable sparse structures, further accelerating the generation process of effective sparse arrays. By controlling the PSLL value, the array's ability to resist external noise interference is enhanced, ensuring beam robustness. This means that in practical applications, the antenna system can operate more stably and resist signal interference in various complex environments.
[0009] The present invention is further configured such that, in step S3, the stepwise combination of sparse structures includes: S31, using subarrays as basic units, obtains all possible sparse structures of the first-level antenna subarray based on the sparse target and array operating characteristics; S32, based on the set performance parameters and screening criteria, combines the possible sparse structures of the basic unit antenna and the possible sparse structures of the first-stage antenna subarray to obtain all the sparse structures of the second-stage antenna subarray. S33, according to the selection criteria, the second-level antenna subarrays are combined to obtain all possible sparse structures of the third-level subarrays. S34. Based on the sparse structure of the obtained third-level subarray, combine them to obtain all possible sparse structures of the fourth-level subarray.
[0010] In this technical solution, by subdividing the entire sparse optimization process into multiple levels, each step generates and filters sparse structures using subarrays as basic units. This not only makes the execution of the entire algorithm more systematic and logical but also significantly reduces computational complexity and required resources. During the step-by-step combination process, by introducing performance parameters and screening criteria, the sparse structures generated at each step can be rigorously screened, ensuring that the sparse structures of each subarray level meet predetermined performance indicators and design requirements. Through multi-level combination and screening, the algorithm can generate a large number of candidate sparse structures and select the optimal sparse configuration. This multi-level combination approach not only increases the diversity of sparse structures but also improves the probability of finding the global optimum. Compared to the traditional exhaustive search method, this method significantly improves the generation efficiency of sparse arrays by progressively narrowing the search range. Simultaneously, because each step undergoes rigorous screening, unnecessary computational resource waste is reduced, thereby improving the algorithm's economy. By controlling the PSLL value and ensuring the random uniform distribution of the array, the sparse array generated in this process not only reduces the sidelobe level but also enhances the array's resistance to external noise, thus improving beam robustness.
[0011] The present invention is further configured to: divide the array into sub-arrays with transceiver chips as control units according to the working mode of the target array antenna, wherein each sub-array contains one or more T / R components, and the T / R component includes a transceiver chip and a corresponding transceiver antenna; In the sparse algorithm, optimization is performed using T / R components as the basic unit.
[0012] In this technical solution, the array is divided into sub-arrays with transceiver chips as control units, allowing each sub-array to be processed as an independent optimization unit. This division method fully considers the control methods of actual antenna systems, ensuring the rationality and operability of the sparse optimization process. Optimization is proposed using T / R components as the basic unit, rather than individual antennas, which can more effectively reduce the number of components after sparsification while maintaining the overall performance and stability of the antenna system. Sparsification based on T / R components also avoids unnecessary complexity and interference to the system during the sparsification process. Compared to optimization based on individual antennas, optimization based on T / R components significantly reduces the computational load and complexity during the sparsification process, helping to improve the execution efficiency of the sparsity algorithm while reducing the demand for computing resources. Since T / R components typically contain multiple antennas, sparsification based on T / R components can reduce the number of components while maintaining the relative uniformity of antenna distribution, thereby reducing performance loss caused by sparsity and contributing to enhancing the overall stability and reliability of the antenna system.
[0013] The present invention is further configured such that, in step S1, the operating characteristic is that one or more channel transmitting antennas are turned off simultaneously, and it is not possible to turn off only the smallest unit.
[0014] In this technical solution, in practical applications, it may not be possible to shut down the transmission channel of the smallest unit individually, but rather to shut down the transmission antennas of one or more channels simultaneously. This increases the difficulty and complexity of sparse optimization. By considering this characteristic in practical work, the random uniform sparse algorithm becomes closer to real-world application scenarios, improving the algorithm's applicability and practicality. This requires that the algorithm's design process take this constraint into account, thereby ensuring the algorithm's effectiveness and reliability in practical applications.
[0015] The present invention is further configured such that, in step S3, the performance parameters of the antenna are the directivity coefficient and PSLL of the array antenna.
[0016] In this technical solution, the performance parameters in step S3 of the sparse algorithm are the directivity coefficient and PSLL of the array antenna, which provides a clear direction for the design and implementation of the algorithm, enabling the optimization process to focus on improving these two key indicators, thereby improving the overall performance of the antenna.
[0017] The present invention is further configured such that, in step S6, the optimal sparse structure is the sparse structure with the highest directional coefficient and the smallest PSLL.
[0018] This technical solution clearly defines the main evaluation criteria for the sparse algorithm in the final selection of sparse structures: the highest directivity and the lowest PSLL (Peak Sidelobe Level). These two criteria are directly related to the antenna's radiation efficiency and the quality of its radiation modes, and are important indicators for evaluating antenna performance. By pursuing the highest directivity and the lowest PSLL, the optimized sparse algorithm ensures that it achieves optimal performance in practical applications. A high directivity means that the antenna can concentrate more energy in the main lobe direction, improving radiation efficiency; while a low PSLL indicates good sidelobe suppression, reducing unnecessary energy leakage and interference.
[0019] The present invention is further configured such that: in step S6, during the screening process, a matrix analysis method is used to check the sparse state of adjacent antenna elements by traversing all possible sparse structures, and the sparsed elements are determined according to the random number generator.
[0020] In this technical solution, the matrix analysis method allows the algorithm to systematically traverse all possible sparse structures, avoiding blind attempts and improving selection efficiency. This enables the algorithm to maintain high operating efficiency even when processing large-scale arrays. Checking the sparsity state of adjacent antenna elements is a key step in ensuring the uniformity of the sparse array. This method avoids local over-density or under-density during the sparsification process, thus maintaining a uniform distribution throughout the array, which is crucial for improving the overall performance of the array. Using a random number generator to determine the elements to be sparsified adds randomness to the algorithm. This randomness helps to find a better solution among multiple feasible sparse schemes, especially when dealing with complex scenarios and multi-objective optimization problems. Randomness can increase the exploration range of the solution space and increase the probability of finding the global optimum. The combination of the matrix analysis method and the random number generator makes this sparse algorithm not only applicable to specific array structures and optimization objectives, but also flexibly adjustable to adapt to different application scenarios and optimization needs. This flexibility enhances the universality and scalability of the algorithm. Through precise screening and random optimization, the algorithm can minimize the number of antenna elements while meeting performance requirements, thereby reducing the complexity and cost of the system. This is of great significance for improving the effective utilization of resources and reducing the total cost of the system.
[0021] The present invention is further configured such that, in the process of combining the sparse structures of the fourth-level subarray, the combination methods include pairwise combination and geometric shape combination.
[0022] In this technical solution, by explicitly stating that pairwise combinations and geometrical combinations can be used in the combination process of the fourth-level subarray sparse structure, the algorithm can more efficiently construct more candidate sparse structures during execution. This diverse combination method can significantly expand the search space and increase the probability of finding the optimal sparse structure. The application of geometrical combination methods means that the algorithm is not limited to simple pairwise combinations, but can also be optimized for specific combinations according to the specific shape and requirements of the array. This flexibility allows the algorithm to adapt to arrays of different shapes and sizes, improving its practicality and applicability.
[0023] The present invention is further configured such that the geometric shape combination includes: identifying and combining subarrays into triangular or quadrilateral geometric shapes, optimizing their arrangement according to geometric characteristics, implementing the geometric shape combination logic through programming, and dynamically adjusting the position and orientation of the subarrays.
[0024] In this technical solution, by identifying and combining subarrays into triangular or quadrilateral geometric shapes, the algorithm can more flexibly adapt to different array layout requirements. This combination of geometric shapes not only enriches the arrangement of subarrays but also provides more possibilities for optimizing the overall performance of the antenna array. Optimizing the arrangement of subarrays based on geometric characteristics helps reduce unnecessary space waste and signal interference, improving the overall performance of the antenna array. By rationally arranging subarrays with geometric shapes, sidelobe levels can be reduced and directivity coefficients improved, thus enabling the antenna array to perform better in communication or radar applications. By programming the combination logic of geometric shapes, the algorithm can dynamically adjust the position and orientation of the subarrays to adapt to different working environments and task requirements. This adaptability makes the algorithm more flexible and versatile in practical applications, meeting the performance requirements of various complex scenarios. The combination of geometric shapes needs to consider multiple factors, such as the distance, angle, and phase between subarrays. These factors have a crucial impact on the performance of the antenna array. Therefore, introducing geometric characteristics and optimizing their arrangement during the combination process can significantly improve the accuracy of the algorithm, ensuring that the final sparse structure meets stringent performance requirements.
[0025] The present invention is further configured to: when dividing the subarray, establish a rule set according to the antenna type and layout constraints, evaluate the performance of the division scheme in real time through an intelligent division algorithm, and select the optimal division scheme.
[0026] This technical solution introduces an intelligent partitioning algorithm, combined with a rule set established based on antenna type and layout constraints. This algorithm can evaluate the performance of multiple partitioning schemes in real time. This approach transforms subarray partitioning from a simple, fixed pattern into a dynamic adjustment and optimization based on specific needs and constraints, significantly improving the accuracy and applicability of the partitioning. By considering antenna type and layout constraints, the intelligent partitioning algorithm generates subarray partitioning schemes that better meet actual needs, helping to reduce unnecessary space waste and signal interference, and improving the overall performance of the antenna array, such as directivity coefficient and PSLL key indicators. By defining a rule set and employing an intelligent partitioning algorithm, it can adapt to different types and layouts of antenna arrays, thereby improving the algorithm's flexibility and versatility. It can be applied to a wider range of scenarios and fields, meeting the specific needs of different users. By evaluating the performance of partitioning schemes in real time, the intelligent partitioning algorithm can quickly select the optimal scheme, avoiding the time and resource waste caused by blindly trying multiple partitioning methods, thus improving the algorithm's execution efficiency.
[0027] The beneficial effects of this invention are: (1) reducing sidelobe levels, improving generation speed and economy, and ensuring beam robustness; (2) adopting a hierarchical subarray construction method reduces the blindness of target objects, and at the same time, designing reasonable screening conditions improves the search speed and enhances the targeting; (3) based on the setting that adjacent subarray units do not repeat, different subarray positions are unevenly distributed, and non-sparse subarray units in the same area are unevenly distributed, the generation speed of random candidate sparse arrays is greatly improved, the array sparsity is enhanced, and its economy is improved; (4) by setting the array performance parameters directivity coefficient and sidelobe suppression ratio, random sparse matrices that meet the performance requirements can be quickly found in the candidate random sparse matrices, which greatly improves the generation speed and efficiency of sparse matrices; (5) the nine-step generation method is interconnected yet independent of each other, which facilitates the precise control of optimization conditions; at the same time, the corresponding constraints and parameters can be changed for different antenna types, which improves the universality and applicability of the method. Attached Figure Description
[0028] Figure 1 This is a flowchart illustrating the random uniform sparse algorithm for phased array antennas according to the present invention. Figure 2 (a) The smallest basic unit subarray in the embodiment; (b) Six sparse methods of the smallest basic unit subarray; Figure 3 A schematic diagram for obtaining a sparse structure of the smallest basic unit; Figure 4 A schematic diagram of the combination structure in Case 1 that does not meet the screening criteria; Figure 5 A schematic diagram of the final optimized sparse antenna distribution; Figure 6 This is a schematic diagram of the simulation results for the final optimized antenna pattern. Detailed Implementation
[0029] 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 only one preferred embodiment of this invention and are only used to explain this invention. They do not limit the scope of protection of this invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0030] like Figure 1 As shown in the figure, as an embodiment of the present invention, a random uniform sparse algorithm for phased array antennas is characterized by comprising the following steps: S1, divide the operating characteristics of the target array antenna into the smallest unit subarray grid, and perform uniform grid partitioning according to the subarray; S2, using the subarray as the basic unit, obtains the possible sparse structures of all basic unit antennas inside the antenna subarray based on the sparse target and the working characteristics of the array; S3, based on the preset antenna performance parameters and screening criteria, the sparse structure is combined step by step to obtain all possible sparse structures of the k-th level antenna subarray. S4, taking the k-th level subarray as a unit, and randomly combining the antenna arrays in sequence according to the selection criteria; S5, Select randomly combined antenna arrays according to the selection criteria; S6 performs array pattern traversal simulation on the selected arrays to select the optimal sparse structure.
[0031] In this embodiment, by employing a hierarchical subarray construction and a random uniform sparse method, the sidelobe level can be effectively reduced. During antenna scanning, sparse arrays can easily lead to deterioration of the sidelobe level, but this algorithm, through a random sparse strategy, minimizes the generation of sidelobes, ensuring the absence of quantized lobes and grating lobes throughout the entire operating frequency band, thereby improving the purity of the antenna pattern and the accuracy of beam pointing. Through reasonable subarray partitioning and a hierarchical combination strategy, the search space is greatly reduced, significantly improving the generation speed of the sparse array. Compared to the traditional exhaustive method, this hierarchical combination method can significantly reduce computation time and resource consumption while maintaining performance, thus improving economic efficiency. By setting reasonable screening criteria and performance parameters, the algorithm can quickly eliminate unsuitable sparse structures, further accelerating the generation process of effective sparse arrays. By controlling the PSLL value, the array's ability to resist external noise interference is enhanced, ensuring beam robustness. This means that in practical applications, the antenna system can operate more stably and resist signal interference in various complex environments.
[0032] Understandably, the working characteristics of an array refer to the correspondence between TR chips and channels. For example, one chip supports two transmit channels, or one chip supports eight transmit channels.
[0033] In one embodiment of the present invention, in step S3, the stepwise combination of sparse structures includes: S31, using subarrays as basic units, obtains all possible sparse structures of the first-level antenna subarray based on the sparse target and array operating characteristics; S32, based on the set performance parameters and screening criteria, combines the possible sparse structures of the basic unit antenna and the possible sparse structures of the first-stage antenna subarray to obtain all the sparse structures of the second-stage antenna subarray. S33, according to the selection criteria, the second-level antenna subarrays are combined to obtain all possible sparse structures of the third-level subarrays. S34. Based on the sparse structure of the obtained third-level subarray, combine them to obtain all possible sparse structures of the fourth-level subarray.
[0034] In this technical solution, the entire sparse optimization process is subdivided into multiple levels. Each step generates and filters sparse structures using subarrays as basic units. This not only makes the execution of the entire algorithm more systematic and logical but also significantly reduces computational complexity and resource requirements. During the step-by-step combination process, performance parameters and screening criteria are introduced to rigorously screen the sparse structures generated at each step, ensuring that the sparse structures of each subarray meet predetermined performance indicators and design requirements. Through multi-level combination and screening, the algorithm can generate a large number of candidate sparse structures and select the optimal sparse configuration. This multi-level combination approach not only increases the diversity of sparse structures but also improves the probability of finding the global optimum. Compared to the traditional exhaustive search method, this method significantly improves the generation efficiency of sparse arrays by progressively narrowing the search range. Simultaneously, the rigorous screening at each step reduces unnecessary computational resource waste, thereby improving the algorithm's economic efficiency. By controlling the PSLL value and ensuring the random uniform distribution of the array, the sparse array generated in this process not only reduces the sidelobe level but also enhances the array's resistance to external noise, thus improving beam robustness.
[0035] Preferably, step S1: Based on the operating characteristics of the target array antenna, the array is divided into minimum unit subarray grids, and the subarrays are uniformly partitioned. Since one transceiver chip can implement 2 transmit channels and 2 receive channels, with a transmit to receive channel ratio of 4:1, a minimum unit subarray can be composed of 2 transceiver chips, including 4 transmit channels and 1 receive channel, as shown below. Figure 2 As shown in (a), the yellow cross is the receiving antenna, and the green circle is the receiving antenna.
[0036] Step S2: Using the smallest unit subarray as the basic unit, and based on the array's operating characteristics, with the goal of optimizing the transceiver chips, each subarray unit must have its transmit channels sparsified in units of two, while the receive channels remain unchanged. This yields the possible sparse structure n0 of all basic antenna units within the antenna subarray, such as... Figure 2 As shown in (b), 1-6 represent six different types of sparsity. The black circle represents the sparse receiving antenna, which is represented by "0" in the matrix. The green circle represents the reserved circular antenna, which is represented by "1" in the matrix.
[0037] Step S31: Using the smallest element subarray as the basic unit, based on the sparse target and array operating characteristics, in this example, the sparse target of the transmitting antenna is 1 / 3, and the antenna sparsity rate inside the smallest element subarray is 1 / 2, such as... Figure 1As shown, the sparsity rate of using subarrays (smallest basic units) as basic units is 1 / 3 divided by 1 / 2 = 2 / 3. That is, in every combination of three small basic units, one small basic unit is retained and two small basic units are sparsed, thus obtaining all possible sparse structures n1, such as... Figure 3 As shown, in each type of sparse structure, the red outline unit is the smallest basic unit that is retained, and the blue outline unit is the smallest basic unit to be sparsified. There are three types of structures in total.
[0038] Step S32: Based on the set performance parameters and screening criteria, combine the sparse structures obtained in steps S2 and S31 to obtain all possible sparse structures of the second-level antenna subarray. The screening criteria must ensure uniform distribution, guaranteeing that the antenna density remains uniform in any region after the transmit antenna is "off". In this example, the screening criteria can be calculated based on the array antenna amplitude matrix. Typically, an element excitation of "1" indicates an active state, while "0" indicates it is off, i.e., it is sparsed (optimized). To meet the uniform distribution requirement, it can be set that no three adjacent antenna elements in the matrix can be simultaneously zero. Adjacent antenna elements can be three horizontal adjacent elements, three vertical adjacent elements, three right diagonal adjacent elements, three left diagonal adjacent elements, or any three elements in a 2×2 matrix. Figure 4 The combinatorial structures in Case 1 that do not meet the screening criteria are given. Steps S2 and S31 yielded a total of 3*6*6 = 108 sparse structure combinations. After adding screening criteria, from... Figure 4 As can be seen, in Case 1, there are 15 combinations that do not meet the screening criteria, and 21 combinations that do meet the screening criteria. Similarly, in Case 2, there are 36 combinations that meet the screening criteria, and 0 combinations that do not meet the criteria. In Case 3, there are 13 combinations that do not meet the screening criteria, and 21 combinations that do meet the screening criteria. Among the 108 structures of the first-level antenna subarray, 82 second-level antenna subarrays that meet the screening criteria are selected, and the antenna scale of the second-level antenna subarray reaches 12 elements.
[0039] Step S33: The candidate secondary antenna subarrays obtained in Step S1 are combined in pairs, iterating through all possibilities. The combination can be done by placing the candidate secondary antenna subarrays side-by-side or by placing two subarrays vertically. In this example, two candidate secondary antenna subarrays are placed vertically, and they belong to different Case combinations; that is, subarrays with the same Case structure are not selected. Based on the screening criteria, all possible sparse structures of the third-level subarray are obtained. The screening criteria aim for a uniform random distribution across the entire array. In this example, the number of third-level antenna subarray structures obtained from the above combinations of candidate secondary antenna subarrays before screening is 36*21 + 36*21 + 21*21 = 1953. 1474 third-level antenna subarrays that meet the screening criteria are selected, and the antenna size of the third-level antenna subarray has reached 24 elements.
[0040] Step S34: Repeat step S33, and combine the sparse structures of the obtained candidate third-level subarrays in pairs to obtain all possible sparse structures n4 of the fourth-level subarray. In this example, the number of fourth-level antenna subarray structures obtained by combining the above candidate third-level antenna subarrays was 1,407,448 before screening. 379,312 fourth-level antenna subarrays that meet the screening criteria were selected, and the antenna scale of the fourth-level antenna subarray has reached 48 elements.
[0041] Step S4: Using the candidate fourth-level subarrays as units, the entire large-scale antenna array is randomly combined sequentially according to the set screening criteria.
[0042] Step S5: Based on the selection criteria, screen all randomly combined candidate arrays to ensure that the antennas of the entire array are randomly and uniformly distributed. Figure 5 The final optimized sparse antenna distribution is presented, where the filled regions are the retained antenna elements and the boxed regions are the sparse antenna channels.
[0043] Step S6: Perform array pattern traversal simulation on the finally selected arrays to select the sparse structure with the highest directivity coefficient and the smallest PSLL. Figure 6 Simulation results for the final optimized antenna pattern are presented, with a beam pointing off-axis angle of 30° and an azimuth angle of 135°. The simulation results show that, across all beam pointing angles, there is no quantization lobe, and the PSLL is better than -14dB.
[0044] In one embodiment of the present invention, step S1 includes: Based on the operating mode of the target array antenna, the array is divided into sub-arrays with transceiver chips as control units. Each sub-array contains one or more T / R components, and each T / R component includes a transceiver chip and a corresponding transceiver antenna. In the sparse algorithm, optimization is performed using T / R components as the basic unit.
[0045] In this technical solution, the array is divided into sub-arrays with transceiver chips as control units, allowing each sub-array to be processed as an independent optimization unit. This division method fully considers the control methods of actual antenna systems, ensuring the rationality and operability of the sparse optimization process. Optimization is proposed using T / R components as the basic unit, rather than individual antennas, which can more effectively reduce the number of components after sparsification while maintaining the overall performance and stability of the antenna system. Sparsification based on T / R components also avoids unnecessary complexity and interference to the system during the sparsification process. Compared to optimization based on individual antennas, optimization based on T / R components significantly reduces the computational load and complexity during the sparsification process, helping to improve the execution efficiency of the sparse algorithm while reducing the demand for computing resources. Since T / R components typically contain multiple antennas, sparsification based on T / R components can reduce the number of components while maintaining the relative uniformity of antenna distribution, thereby reducing performance loss caused by sparsity and contributing to enhancing the overall stability and reliability of the antenna system.
[0046] Understandably, depending on the operating mode of the target array antenna, generally one transceiver chip can control multiple transmit and receive antennas, and subarray units are divided according to the transceiver chip. A transceiver chip and its corresponding transceiver antenna are represented by a T / R component. The number of transmit channels and receive channels contained in a T / R component is not necessarily equal. Therefore, a subarray may include multiple T / R components. In current conventional transceiver coplanar antenna designs, the number of transmitting antennas is often 2 or 4 times that of receiving antennas. The number of transceiver chips is determined by the number of transmitting antennas. Therefore, one transceiver chip typically controls 2 transmitting antennas and 1 receiving antenna, or 2 transceiver chips control 4 transmitting antennas and 1 receiving antenna. Based on the goal of array sparsity, the number of devices needs to be reduced after sparsity. Therefore, in the sparsity algorithm, sparsity should not be targeted at a single antenna, but rather at a T / R component. If the number of transmitting antennas in the antenna array is 4 times that of receiving antennas, one transceiver chip can control 2 transmitting antennas and 1 receiving antenna. A subarray can consist of 2 T / R components, including 2 transceiver chips, 4 transmitting antennas, and 1 receiving antenna.
[0047] In one embodiment of the present invention, in step S1, the operating characteristic is that one or more channel transmitting antennas are turned off simultaneously, and it is not possible to turn off only the smallest unit.
[0048] In practical applications, it may not be possible to shut down the transmission channel of the smallest unit individually, but rather to shut down the transmission antennas of one or more channels simultaneously. This increases the difficulty and complexity of sparse optimization. By considering this characteristic in real-world applications, the random uniform sparse algorithm becomes closer to practical application scenarios, improving its applicability and practicality. This limitation must be taken into account during the algorithm's design process to ensure its effectiveness and reliability in practical applications.
[0049] In step S3, the performance parameters of the antenna are the directivity coefficient and PSLL of the array antenna.
[0050] In this technical solution, the performance parameters in step S3 of the sparse algorithm are the directivity coefficient and PSLL of the array antenna, which provides a clear direction for the design and implementation of the algorithm, enabling the optimization process to focus on improving these two key indicators, thereby improving the overall performance of the antenna.
[0051] Preferably, in step S6, the optimal sparse structure is the sparse structure with the highest directionality coefficient and the smallest PSLL.
[0052] This technical solution clearly defines the main evaluation criteria for the sparse algorithm in the final selection of sparse structures: the highest directivity and the lowest PSLL (Peak Sidelobe Level). These two criteria are directly related to the antenna's radiation efficiency and the quality of its radiation modes, and are important indicators for evaluating antenna performance. By pursuing the highest directivity and the lowest PSLL, the optimized sparse algorithm ensures that it achieves optimal performance in practical applications. A high directivity means that the antenna can concentrate more energy in the main lobe direction, improving radiation efficiency; while a low PSLL indicates good sidelobe suppression, reducing unnecessary energy leakage and interference.
[0053] In the screening process, a matrix analysis method is used to check the sparsity state of adjacent antenna elements by traversing all possible sparse structures, and the element to be sparsed is determined according to the random number generator.
[0054] In this technical solution, the matrix analysis method allows the algorithm to systematically traverse all possible sparse structures, avoiding blind attempts and improving selection efficiency. This enables the algorithm to maintain high operating efficiency even when processing large-scale arrays. Checking the sparsity state of adjacent antenna elements is a key step in ensuring the uniformity of the sparse array. This method avoids local over-density or under-density during the sparsification process, thus maintaining a uniform distribution throughout the array, which is crucial for improving the overall performance of the array. Using a random number generator to determine the elements to be sparsified adds randomness to the algorithm. This randomness helps to find a better solution among multiple feasible sparse schemes, especially when dealing with complex scenarios and multi-objective optimization problems. Randomness can increase the exploration range of the solution space and increase the probability of finding the global optimum. The combination of the matrix analysis method and the random number generator makes this sparse algorithm not only applicable to specific array structures and optimization objectives, but also flexibly adjustable to adapt to different application scenarios and optimization needs. This flexibility enhances the universality and scalability of the algorithm. Through precise screening and random optimization, the algorithm can minimize the number of antenna elements while meeting performance requirements, thereby reducing the complexity and cost of the system. This is of great significance for improving the effective utilization of resources and reducing the total cost of the system.
[0055] In the combination of sparse structures in the fourth-level subarray, the combination methods include pairwise combinations and geometrical combinations. By explicitly stating that pairwise and geometrical combinations can be used in the combination of sparse structures in the fourth-level subarray, the algorithm can more efficiently construct more candidate sparse structures during execution. This diverse combination method significantly expands the search space and increases the probability of finding the optimal sparse structure. The application of geometrical combinations means that the algorithm is not limited to simple pairwise combinations; it can also perform targeted combination optimizations based on the specific shape and requirements of the array. This flexibility allows the algorithm to adapt to arrays of different shapes and sizes, improving its practicality and applicability.
[0056] Preferably, the geometric shape combination includes: identifying and combining subarrays into triangular or quadrilateral geometric shapes, optimizing their arrangement based on geometric characteristics, implementing the geometric shape combination logic through programming, and dynamically adjusting the position and orientation of the subarrays. By identifying and combining subarrays into triangular or quadrilateral geometric shapes, the algorithm can more flexibly adapt to different array layout requirements. This geometric shape combination method not only enriches the arrangement forms of subarrays but also provides more possibilities for optimizing the overall performance of the antenna array. Optimizing the arrangement of subarrays based on geometric characteristics helps reduce unnecessary space waste and signal interference, improving the overall performance of the antenna array. By rationally arranging subarrays with geometric shapes, sidelobe levels can be reduced and directivity coefficients can be improved, thus enabling the antenna array to perform better in communication or radar applications. By implementing the geometric shape combination logic through programming, the algorithm can dynamically adjust the position and orientation of the subarrays to adapt to different working environments and task requirements. This adaptability makes the algorithm more flexible and versatile in practical applications, meeting the performance requirements of various complex scenarios. Combining geometric shapes requires consideration of multiple factors, such as the distance, angle, and phase between subarrays. These factors have a crucial impact on the performance of the antenna array. Therefore, introducing geometric characteristics and optimizing their arrangement during the combination process can significantly improve the accuracy of the algorithm and ensure that the final sparse structure meets stringent performance requirements.
[0057] Preferably, when partitioning the subarray, a rule set is established based on antenna type and layout constraints. An intelligent partitioning algorithm is then used to evaluate the performance of each partitioning scheme in real time and select the optimal one. By introducing an intelligent partitioning algorithm and combining it with the rule set established based on antenna type and layout constraints, the algorithm can evaluate the performance of multiple partitioning schemes in real time. This approach makes subarray partitioning no longer a simple, fixed pattern, but rather dynamically adjusted and optimized according to specific needs and constraints, thus greatly improving the accuracy and applicability of the partitioning. Because it considers antenna type and layout constraints, the intelligent partitioning algorithm can generate subarray partitioning schemes that better meet actual needs, helping to reduce unnecessary space waste and signal interference, and improving the overall performance of the antenna array, such as directivity coefficient and PSLL key indicators. By defining a rule set and adopting an intelligent partitioning algorithm, it can adapt to different types and layouts of antenna arrays, thereby improving the algorithm's flexibility and universality. It can be applied to a wider range of scenarios and fields to meet the specific needs of different users. By evaluating the performance of partitioning schemes in real time, the intelligent partitioning algorithm can quickly select the optimal scheme, avoiding the time consumption and resource waste caused by blindly trying multiple partitioning methods, thus improving the algorithm's execution efficiency.
[0058] Understandably, the rule set includes the physical dimensions, operating frequency bands, polarization methods, and mutual interference characteristics of different types of antennas.
[0059] Optionally, to save computation time and resources, the entire large-scale antenna array can be randomly combined based on the third-level subarrays according to the set selection criteria. In this case, the randomness of the array antennas is relatively low. The relatively low randomness means that there are more repetitive antenna distribution structures and relatively fewer random structures.
[0060] The above embodiments, which describe the specific features of the present invention, are only used to further illustrate the present invention and should not be construed as limiting the scope of protection of the present invention. Any non-essential improvements and adjustments made to the present invention by those skilled in the art based on the above description of the invention shall fall within the scope of protection of the present invention.
Claims
1. A random uniform sparse algorithm for phased array antennas, characterized in that, Includes the following steps: S1, divide the operating characteristics of the target array antenna into the smallest unit subarray grid, and perform uniform grid partitioning according to the subarray; S2, using the subarray as the basic unit, obtains the possible sparse structures of all basic unit antennas inside the antenna subarray based on the sparse target and the working characteristics of the array; S3, based on the preset antenna performance parameters and screening criteria, the sparse structure is combined step by step to obtain all possible sparse structures of the k-th level antenna subarray. S4, taking the k-th level subarray as a unit, and randomly combining the antenna arrays in sequence according to the selection criteria; S5, Select randomly combined antenna arrays according to the selection criteria; S6 performs array pattern traversal simulation on the selected arrays to select the optimal sparse structure.
2. The random uniform sparse algorithm for phased array antennas according to claim 1, characterized in that, In step S3, the stepwise combination of sparse structures includes: S31, using subarrays as basic units, obtains all possible sparse structures of the first-level antenna subarray based on the sparse target and array operating characteristics; S32, based on the set performance parameters and screening criteria, combines the possible sparse structures of the basic unit antenna and the possible sparse structures of the first-stage antenna subarray to obtain all the sparse structures of the second-stage antenna subarray. S33, according to the selection criteria, the second-level antenna subarrays are combined to obtain all possible sparse structures of the third-level subarrays. S34. Based on the sparse structure of the obtained third-level subarray, combine them to obtain all possible sparse structures of the fourth-level subarray.
3. The random uniform sparse algorithm for phased array antennas according to claim 2, characterized in that, Step S1 includes: Based on the operating mode of the target array antenna, the array is divided into sub-arrays with transceiver chips as control units. Each sub-array contains one or more T / R components, and each T / R component includes a transceiver chip and a corresponding transceiver antenna. In the sparse algorithm, optimization is performed using T / R components as the basic unit.
4. The random uniform sparse algorithm for phased array antennas according to claim 1, characterized in that, In step S1, the operating characteristic is that one or more channel transmitting antennas are turned off simultaneously, and it is not possible to turn off only the smallest unit.
5. A random uniform sparse algorithm for phased array antennas according to claim 1, 2, 3, or 4, characterized in that, In step S3, the performance parameters of the antenna are the directivity coefficient and PSLL of the array antenna.
6. A random uniform sparse algorithm for phased array antennas according to claim 1, 2, 3, or 4, characterized in that, In step S6, the optimal sparse structure is the sparse structure with the highest directional coefficient and the smallest PSLL.
7. The random uniform sparse algorithm for phased array antennas according to claim 6, characterized in that, In step S6, during the screening process, a matrix analysis method is used to check the sparsity state of adjacent antenna elements by traversing all possible sparse structures, and the element to be sparsed is determined based on the random number generator.
8. The random uniform sparse algorithm for phased array antennas according to claim 2, characterized in that, In the process of combining sparse structures of the fourth-level subarray, the combination methods include pairwise combination and geometric combination.
9. A random uniform sparse algorithm for phased array antennas according to claim 8, characterized in that, The geometric shape combination includes: identifying and combining subarrays into triangular or quadrilateral geometric shapes, optimizing their arrangement according to geometric characteristics, implementing the geometric shape combination logic through programming, and dynamically adjusting the position and orientation of the subarrays.
10. The random uniform sparse algorithm for phased array antennas according to claim 3, characterized in that, When dividing the subarray, a rule set is established based on antenna type and layout constraints. The performance of the division scheme is evaluated in real time through an intelligent division algorithm, and the optimal division scheme is selected.