Non-uniform phased-array antenna arrangement method and system of ku frequency band

Abstract

The invention discloses a non-uniform phased-array antenna arrangement method and system of a ku frequency band, and relates to the technical field of array antenna design, and the method comprises the steps: building a target function with a sidelobe level, a beam width and a scanning range as a joint optimization target; in the ku frequency band, a non-uniform spacing distribution scheme of the array antennas is searched based on the adaptive crossover and mutation probability, and the distribution scheme is determined according to an objective function evaluation result; and performing error compensation optimization on the initial distribution by combining the online monitoring data and the target parameter difference, and generating a final antenna arrangement scheme. The problems that in the prior art, the arrangement optimization target is single, and the performance and cost of the phased-array antenna are affected are solved, and the technical effects of online monitoring, complete monitoring and good early warning capacity are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of array antenna design, and in particular to a method and system for arranging non-uniform phased array antennas in the Ku frequency band. Background Art

[0002] Phased array antennas, as a high-performance antenna technology, are widely used in radar, communications, and other fields. Traditional phased array antenna designs typically prioritize uniform layouts and focus on optimizing a single performance metric, such as reducing sidelobe levels or beamwidth, or simply expanding scanning range. This makes it difficult to balance and coordinate multiple key performance indicators, limiting overall antenna performance and failing to meet the demand for both cost and high performance. Summary of the Invention

[0003] The present invention provides a Ku-band non-uniform phased array antenna arrangement method and system to solve the technical problem in the prior art that the arrangement optimization target is single and affects the performance and cost of the phased array antenna, and achieves the technical effect of providing multi-objective comprehensive optimization, improving antenna performance and reliability, and reducing antenna cost.

[0004] In a first aspect, the present invention provides a method for arranging a non-uniform phased array antenna in the Ku band, wherein the method for arranging a non-uniform phased array antenna in the Ku band includes: An objective function with sidelobe level, beamwidth and scanning range as joint optimization targets is established.

[0005] Based on the objective function, adaptive crossover probability and mutation probability are used to search for a non-uniform spacing distribution scheme of array antennas in the Ku band, and the distribution scheme is determined based on the evaluation result of the objective function to generate a non-uniform unit spacing distribution.

[0006] The antenna operating parameters are monitored online by monitoring equipment, and the non-uniform unit spacing distribution is compensated for errors based on the difference between the online monitoring data and the target parameters, so as to optimize the non-uniform unit spacing distribution and obtain an antenna arrangement scheme.

[0007] In a feasible implementation, error compensation is performed on the non-uniform cell spacing distribution based on the difference between the online monitoring data and the target parameter to optimize the non-uniform cell spacing distribution, including: Analyze requirements from multiple dimensions, including sidelobe suppression, array antenna lightweighting, and power consumption, to obtain layout constraints and reward conditions.

[0008] According to the arrangement constraints and arrangement reward conditions, the non-uniform unit spacing distribution is error compensated and optimized with the target parameter difference as the compensation amount to obtain the antenna arrangement scheme. The antenna arrangement scheme is an antenna arrangement strategy that meets the target parameter difference and the arrangement constraints and arrangement reward conditions.

[0009] In a feasible implementation, an objective function is established with the sidelobe level, beamwidth, and scanning range as joint optimization targets, including: According to the application requirements of the array antenna, the sidelobe level, beam width and scanning range parameters are analyzed to obtain the expected values.

[0010] Based on the tolerance range and parameter influence of application requirements, the realization evaluation relationship of the expected value of the requirements is converted into a mathematical expression, and the objective functions of the expected value of the sidelobe level, beam width and scanning range requirements are established respectively.

[0011] The objective functions of the sidelobe level, beam width, and scanning range are combined to construct an objective function of a joint optimization target.

[0012] In a feasible implementation, based on the objective function, adaptive crossover probability and mutation probability are used to search for a non-uniform spacing distribution scheme for array antennas in the Ku band, including: Based on historical sample data, array distribution rules are constructed.

[0013] The application requirements of the array antenna are distributed and analyzed according to the array distribution rule to construct an initial generation population.

[0014] In the ku frequency band, a population crossover is performed on the primary population to generate new offspring individuals, and a mutation operation is performed on the new offspring individuals to obtain mutant offspring. All distribution schemes of the crossover and mutation are summarized to construct a distribution scheme set.

[0015] The objective function is used to evaluate each scheme in the distribution scheme set, and a distribution scheme that meets the expected value of the demand and has the maximum evaluation result of the objective function is found to obtain a non-uniform unit spacing distribution.

[0016] In a feasible implementation, array distribution rules are constructed based on historical sample data, including: Based on the historical sample data, the influence relationship of the array arrangement parameters on the sidelobe level, beam width, and scanning range in the Ku band is analyzed.

[0017] The array distribution rule is constructed according to the influence relationship of the arrangement parameters.

[0018] In a feasible implementation, constructing array distribution rules further includes: According to the sidelobe level, beam width, and scanning range, the array distribution characteristics of historical sample data are analyzed to obtain the synchronous change relationship of the array distribution characteristics. The array distribution characteristics describe the overall distribution relationship of the array, including the sparseness of the distribution edge, the density of the center, and the smoothness of the transition zone.

[0019] The array distribution rule is constructed according to the synchronous change relationship of the array distribution characteristics.

[0020] In a feasible implementation, constructing the initial population includes: According to the synchronous change relationship of the array distribution characteristics and the application requirements of the array antenna, the overall distribution relationship matching and screening are performed according to the sidelobe level, beam width and scanning range to obtain the initial generation population.

[0021] In a feasible implementation, a population crossover is performed on the primary population to generate new offspring individuals, and a mutation operation is performed on the new offspring individuals to obtain mutant offspring. All distribution schemes of the crossover and mutation are summarized to construct a distribution scheme set, including: Identify the array deviation distribution characteristics of the primary population, cross-combine the array deviation distribution characteristics based on the synchronous change relationship, and construct new offspring individuals, wherein the objective function is used to perform cross-evaluation, and when the cross-evaluation does not meet the evolutionary requirements, it is eliminated, and when it meets the evolutionary requirements, it is retained to obtain a cross-distribution scheme.

[0022] Based on the arrangement parameter influence relationship, the arrangement parameters of the new offspring individuals are mutated, and the mutated individuals are screened using the objective function, and those that meet the variation evaluation are retained to obtain a variation distribution scheme.

[0023] The crossover distribution scheme and the variation distribution scheme are aggregated to construct the distribution scheme set.

[0024] In one feasible implementation, obtaining the antenna arrangement solution includes: According to the target parameter difference, the distribution relationship is adjusted based on gradient descent until the target parameter difference and the arrangement constraints and arrangement reward conditions are met.

[0025] When the number of iterations still cannot meet the requirement, the auxiliary compensation component library is searched and the antenna arrangement solution search and optimization is performed using the compensation component library.

[0026] In a second aspect, the present invention further provides a Ku-band non-uniform phased array antenna arrangement system, wherein the Ku-band non-uniform phased array antenna arrangement system includes: The objective function establishment module is used to establish an objective function with sidelobe level, beam width and scanning range as joint optimization targets.

[0027] The distribution scheme search module is used to search for a non-uniform spacing distribution scheme for array antennas in the Ku band based on the objective function using adaptive crossover probability and mutation probability, determine the distribution scheme based on the evaluation result of the objective function, and generate a non-uniform unit spacing distribution.

[0028] The online monitoring and error compensation module is used to monitor the antenna operating parameters online through monitoring equipment, perform error compensation on the non-uniform unit spacing distribution based on the difference between the online monitoring data and the target parameters, optimize the non-uniform unit spacing distribution, and obtain an antenna arrangement plan.

[0029] The present invention discloses a Ku-band non-uniform phased array antenna arrangement method and system, comprising: establishing an objective function that uses sidelobe level, beam width, and scanning range as optimization indicators; based on the objective function, using an adaptive crossover probability and mutation probability algorithm within the Ku-band to search and evaluate various array antenna non-uniform unit spacing distribution schemes, and ultimately determining the optimal distribution; using monitoring equipment to collect antenna operating parameters online, feeding back the deviation between the real-time monitoring value and the target parameter into the non-uniform unit spacing distribution scheme, performing error compensation and re-optimization, and generating a final antenna arrangement scheme. The Ku-band non-uniform phased array antenna arrangement method and system disclosed by the present invention solve the technical problem of a single arrangement optimization target affecting the performance and cost of the phased array antenna, and achieves the technical effect of providing multi-objective comprehensive optimization, improving antenna performance and reliability, and reducing antenna cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The present invention is a flowchart of a method for arranging non-uniform phased array antennas in the Ku band.

[0031] Figure 2 This is a structural schematic diagram of a Ku-band non-uniform phased array antenna arrangement system according to the present invention.

[0032] Description of the accompanying drawings: objective function establishment module 11, distribution solution search module 12, online monitoring and error compensation module 13. DETAILED DESCRIPTION

[0033] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the drawings.

[0034] Example 1, as Figure 1 This is a flow chart of a method for arranging a non-uniform phased array antenna in the Ku band according to the present invention, wherein the method for arranging a non-uniform phased array antenna in the Ku band includes: S100: Establishing an objective function with sidelobe level, beamwidth and scanning range as joint optimization targets.

[0035] Specifically, for phased array antennas, the spatial arrangement of antenna elements has a decisive influence on beam performance. Although the uniform array has a simple structure, it has bottlenecks in wide scanning angles and high directional pattern quality. Therefore, non-uniform arrays (such as sparse arrays and non-periodic arrays) can be used. By optimizing the arrangement, while maintaining the main lobe performance, the side lobes can be suppressed and the scanning range can be expanded.

[0036] Specifically, to achieve optimal overall performance, a joint optimization function is constructed with the goals of minimizing sidelobe level (SLL), minimizing beamwidth (BW), and maximizing scan range (SR). Sidelobe level refers to the level of the lobes in the antenna radiation pattern, excluding the main lobe, and reflects the antenna's directivity. Lower sidelobe levels indicate stronger antenna directivity and better anti-interference capabilities. Beamwidth is the angle between the two half-power points in the antenna radiation pattern, which determines the antenna's pointing accuracy. A narrower beamwidth indicates higher pointing accuracy. Scan range refers to the range over which the phased array antenna's beam can scan in space. A larger scan range increases the antenna's application flexibility.

[0037] By establishing an objective function with sidelobe level, beamwidth and scanning range as the joint optimization targets, these three interrelated and mutually constrained performance indicators are integrated, and a function that can simultaneously measure the quality of these three indicators is constructed through mathematical modeling, thereby providing a quantifiable and evaluable standard for subsequent optimization design.

[0038] For example, the antenna array is composed of N The array elements are composed of array elements with non-uniform spacing, and their arrangement position is { x 1, x2,…, x N}. The following joint optimization objective function can be constructed accordingly: ; in, is the total objective function value; is the array element position variable; is the maximum sidelobe level in the array pattern (unit: dB); is the main lobe beamwidth (half power point width can be used); is the effective scanning range (unit: degree), which is defined as the maximum scanning angle range when the directional pattern performance meets specific indicators (such as SLL < -13dB, BW < a certain threshold); w1, w2, w3 are the weight coefficients corresponding to each optimization target item, which are used to adjust the trade-off relationship in the objective function, and their sum is 1; among them, SLL, BW, and SR need to be normalized or processed for unit consistency to unify the dimensions.

[0039] Preferably, in order to ensure physical feasibility and engineering feasibility, corresponding constraints are introduced in the optimization process, which include, for example: minimum spacing constraint between array elements, total array length restriction, fixed or upper limit constraint on the number of array elements (such as sparse array design), symmetry constraint (optional), etc.

[0040] The above method steps can achieve comprehensive optimization of antenna performance by combining sidelobe level, beamwidth and scanning range, avoiding the performance shortcomings that may occur in traditional single-objective optimization methods, and improving the overall performance and application reliability of the antenna. At the same time, the clear objective function provides a solid foundation for the subsequent application of the optimization algorithm, making the optimization process more efficient and targeted, and able to quickly search for the optimal solution among many possible antenna arrangement schemes, thereby saving design time and computing resources. In some embodiments, establishing an objective function with sidelobe level, beamwidth, and scanning range as joint optimization targets includes: According to the application requirements of the array antenna, the sidelobe level, beam width and scanning range parameters are analyzed to obtain the expected values of the requirements; based on the tolerance range of the application requirements and the influence of the parameters, the realization evaluation relationship of the expected values of the requirements is converted into a mathematical expression, and the objective functions of the expected values of the sidelobe level, beam width and scanning range requirements are established respectively; the objective functions of the sidelobe level, beam width and scanning range are combined to construct an objective function of the joint optimization target.

[0041] Specifically, the application requirements of an array antenna refer to the functional requirements of the antenna in actual use scenarios, such as the required signal coverage range and the detection accuracy required in radar systems. By analyzing the application requirements of the array antenna, the corresponding sidelobe level, beamwidth, and scanning range parameters can be extracted as the desired values (i.e., optimization targets). For example, the desired maximum sidelobe level (≤-13dB); the desired beamwidth (≤3°); and the desired scanning range (≥±60°) may be required.

[0042] Specifically, the tolerance range is the acceptable fluctuation range of the three parameters mentioned above in actual applications to ensure the expected performance can be achieved in actual manufacturing and use. Parameter influence refers to the degree to which each parameter affects the overall performance of the antenna, which determines the weight of each parameter when constructing the objective function.

[0043] Furthermore, the actual performance requirements in antenna design are converted into expressions that can be processed by mathematical methods through mathematical modeling, thereby constructing the objective function and providing an operational basis for subsequent optimization algorithms.

[0044] For example, the above expected values are converted into mathematical expressions in the objective function: Sidelobe level objective function: ; Beamwidth objective function: ; Scan range objective function (since the scan range is maximized, it can be constructed as a negative value minimization): ; in, The position variables representing each antenna element in the array; 、 、 They represent the sidelobe level, beamwidth and scanning range of the array under this arrangement respectively; 、 、 They represent the expected maximum sidelobe level, expected beamwidth and expected scanning range of the array after combining the tolerance range.

[0045] At this time, the above-mentioned objective functions of sidelobe level, beam width, and scanning range can be combined, for example, by weighted combination to obtain a joint optimization objective function: ; in, w 1 ,w 2 ,w3 is the weight coefficient corresponding to each objective function, which is used to adjust the trade-off relationship in the objective function, and the sum is 1. By optimizing and solving this joint objective function, an optimal or near-optimal non-uniform array arrangement scheme that meets multiple performance indicators can be obtained.

[0046] The above-mentioned method of establishing a joint optimization objective function can accurately convert actual engineering requirements into quantifiable mathematical objectives through analysis of application requirements and mathematical modeling of parameters, thereby helping to improve the scientificity and accuracy of antenna design.

[0047] S200: Based on the objective function, adaptive crossover probability and mutation probability are used to search for a non-uniform spacing distribution scheme for array antennas in the Ku band, and a distribution scheme is determined based on an evaluation result of the objective function to generate a non-uniform unit spacing distribution.

[0048] Specifically, based on the aforementioned joint optimization objective function, an improved genetic algorithm (GA) can be used to optimize the non-uniform spacing arrangement of array antennas, wherein the introduction of adaptive crossover probability and mutation probability is used to improve the optimization efficiency and convergence quality.

[0049] Specifically, during the genetic operation process, through adaptive crossover probability and adaptive mutation probability, the crossover and mutation strategies in the distribution scheme search can be dynamically adjusted according to individual fitness, achieving a balance between exploration and development.

[0050] In some embodiments, based on the objective function, adaptive crossover probability and mutation probability are used to search for a non-uniform spacing distribution scheme for array antennas in the Ku band, including: Based on historical sample data, an array distribution rule is constructed; the application requirements of the array antenna are distributed and analyzed according to the array distribution rule to construct an initial population; within the Ku frequency band, a population crossover is performed on the initial population to generate new offspring individuals, and the new offspring individuals are mutated to obtain mutated offspring. All crossover and mutation distribution schemes are summarized to construct a distribution scheme set; the objective function is used to evaluate each scheme in the distribution scheme set, and a distribution scheme that meets the expected value of the requirements and has the maximum evaluation result of the objective function is found to obtain a non-uniform unit spacing distribution.

[0051] Specifically, historical sample data refers to the vast amount of data accumulated from past antenna designs and applications, including antenna placement schemes and performance metrics for various scenarios. This data provides a reference and basis for current antenna design. Array distribution rules, derived through analysis and summarization of historical sample data, reflect the placement characteristics and trends of array antennas for different application requirements, serving as a bridge between historical data and current design requirements.

[0052] Specifically, the primary population is the initial population in the genetic algorithm optimization process, consisting of multiple possible antenna layouts, with each individual in the population representing a possible layout. The distribution plan set is a collection of all distribution plans obtained after crossover and mutation, encompassing multiple possible antenna layouts.

[0053] Specifically, non-uniform unit spacing distribution means that the spacing between antenna units in the antenna array is not arranged equidistantly, but presents different spacing distribution patterns according to performance optimization requirements to improve the main lobe directional gain, suppress side lobes, and reduce weight and array costs.

[0054] Specifically, by mining and summarizing historical sample data, array distribution rules are constructed to provide guidance for current application requirements, allowing for rapid determination of possible placement directions. Next, based on the array distribution rules, the application requirements of the target array antenna (including but not limited to sidelobe level, beamwidth, scanning range, etc.) are combined for distribution analysis, generating an initial population that meets the rule constraints. Each individual represents a non-uniform unit spacing arrangement scheme. This initial population can be considered the starting point of the entire search process.

[0055] Furthermore, within the Ku-band (i.e., using the Ku-band as the constraint space for crossover and mutation), a crossover operation is performed on the initial population to generate new offspring individuals (new arrangements). Subsequently, mutation is performed on these new offspring individuals to further increase population diversity and prevent the optimization process from falling into local optima. All distribution plans after crossover and mutation are then aggregated to form a distribution plan set, which contains a variety of possible antenna arrangements, providing a rich selection for subsequent evaluation and screening. Finally, an objective function is used to evaluate each plan in the distribution plan set, calculating the corresponding objective function value. By comparing the objective function values, plans that meet the preset performance requirements are selected. Based on this, the plan with the largest objective function value is selected as the optimal non-uniform unit spacing distribution for the current iteration.

[0056] Through the above-mentioned method steps, historical samples are used to construct array distribution rules, thereby improving the quality of the initial population; the adaptive crossover and mutation mechanism is introduced to enhance the global search capability and local convergence ability of the genetic algorithm; thus, the optimal non-uniform element spacing distribution scheme can be accurately screened to meet the comprehensive optimization requirements of the antenna in terms of sidelobe level, beamwidth and scanning range.

[0057] In some implementations, constructing array distribution rules based on historical sample data includes: According to the historical sample data, the influence relationship of the array arrangement parameters on the sidelobe level, beam width, and scanning range in the Ku band is analyzed; and according to the arrangement parameter influence relationship, the array distribution rule is constructed.

[0058] Specifically, based on the acquired historical sample data, typical array configurations in the Ku band are first extracted, including non-uniform configurations with varying array element spacing, corresponding antenna performance metrics (including sidelobe level (SLL), beamwidth (BW), and scan range (SR)), and simulated or measured pattern data. Statistical analysis and modeling are then performed on the historical sample data to extract the influencing relationships between array configuration parameters (such as adjacent element spacing, spacing gradient, and symmetry) and antenna performance metrics. Exemplary methods include analyzing the impact trends of individual configuration parameters on SLL, beamwidth, and scan range, and establishing one-dimensional mapping relationships or response curves between parameters and performance metrics. Optionally, regression analysis, sensitivity analysis, or information entropy analysis are used to assess the importance of each parameter and the impact of its changes on performance.

[0059] Furthermore, based on the aforementioned influence relationships of the arrangement parameters, array distribution rules are constructed to guide subsequent population generation and optimization searches. These rules may include: the range and distribution density of unit spacing; the changing trends of arrangement parameters (e.g., dense center, sparse edge, gradient arrangement); symmetry, periodicity, or random perturbation characteristics; and the adaptability of various arrangement patterns under different performance indicators. Optionally, array distribution rules can be stored and accessed in the form of a rule library or parameterized template.

[0060] Through the above-mentioned method of constructing distribution rules based on historical samples, it is possible to fully utilize existing data resources to narrow the search space, thereby improving optimization efficiency; quantifying the impact of arrangement parameters on performance in advance helps guide the genetic algorithm to converge to a high-quality solution; at the same time, by constructing a non-uniform arrangement template with engineering feasibility, it also helps to improve the manufacturability and practicality of the final arrangement scheme.

[0061] In some implementations, establishing the array distribution rule further includes: The array distribution characteristics of historical sample data are analyzed according to the sidelobe level, beam width, and scanning range to obtain the synchronous change relationship of the array distribution characteristics. The array distribution characteristics describe the overall distribution relationship of the array, including the sparsity of the distribution edge, the center density, and the smoothness of the transition zone. Based on the synchronous change relationship of the array distribution characteristics, the array distribution rules are constructed.

[0062] Specifically, the array distribution characteristics of historical sample data were analyzed based on multiple antenna performance indicators (including sidelobe level, beamwidth, and scanning range) to extract key distribution features that affect performance. These array distribution features are used to describe the non-uniform arrangement of the entire array; for example, they include edge sparsity (the degree of variation in the spacing between elements at the ends of the array), center density (the degree of compression of the spacing between elements in the center of the array), and transition smoothness (the continuity and smoothness of the spacing change from the center to the edge).

[0063] Specifically, a comprehensive analysis of the distribution features extracted under the aforementioned multiple performance indicators was conducted to establish a synchronous variation relationship model. This model reflects the synergistic variation trends among array distribution features under different performance optimization objectives. For example, increasing the mainlobe gain generally increases the center density; suppressing grating lobes requires a moderate increase in edge sparsity; and extending the scanning range requires higher transition zone smoothness to reduce pattern distortion.

[0064] Furthermore, based on the above-mentioned synchronous change relationship, a distribution rule that can be used to guide the optimization of array arrangement is constructed. Preferably, the distribution rule may include: Edge thinning rule: The element spacing in the edge area of the array is moderately increased to 0.8λ∼1.2λ, where λ is the wavelength corresponding to the Ku band, to effectively suppress the generation of grating lobes.

[0065] Center densification rule: Compress the element spacing in the center area of the array to 0.4λ∼0.6λ to enhance the gain in the main lobe direction.

[0066] Transition zone smoothing rule: A buffer transition zone is set between the center and edge areas, and a continuously changing function (such as cosine function, exponential function or Gaussian function) is used to smoothly transition the unit spacing to avoid impedance mismatch or pattern distortion caused by sudden changes.

[0067] Finally, the above distribution rules are expressed and stored in the form of parameterized templates or piecewise function models, which can be directly called in the subsequent genetic algorithm population initialization and mutation operations.

[0068] The above-mentioned rule-building method based on array distribution characteristics can achieve the following technical effects: extracting the synergistic relationship between distribution characteristics under multiple performance indicators to realize global modeling of array arrangement; constructing arrangement rules with engineering feasibility to improve the convergence speed and solution quality of the genetic algorithm; and facilitating the rapid generation of high-quality individuals during the optimization process through the templated expression of distribution rules.

[0069] In some implementations, constructing the initial population includes: According to the synchronous change relationship of the array distribution characteristics and the application requirements of the array antenna, the overall distribution relationship matching and screening are performed according to the sidelobe level, beam width and scanning range to obtain the initial generation population.

[0070] Specifically, the construction of the initial population includes taking the application requirements of the array antenna as the first constraint, traversing the indicators of three dimensions such as sidelobe level, beam width, and scanning range to match the overall distribution relationship of possible antenna arrangement schemes, such as screening and matching suitable array distributions from historical array distributions or preset array distribution libraries as initial candidate schemes; then, taking the synchronous change relationship of array distribution characteristics as the second constraint, the obtained initial candidate schemes are screened and adjusted to ensure that each scheme does not significantly deteriorate other performance indicators while meeting one performance indicator; further, the schemes that pass the above two layers of selection and matching are combined as individuals into the initial population, providing a diverse initial group with potentially excellent characteristics for subsequent genetic algorithm optimization.

[0071] The above method steps can ensure that the individuals in the initial population have reasonable parameter correlation by analyzing the synchronous change relationship of the array distribution characteristics, thus avoiding conflicts between parameters. At the same time, the overall distribution relationship matching and screening of the sidelobe level, beam width, and scanning range are performed according to application requirements, which can ensure that the individuals in the initial population have good performance in multiple key performance indicators, providing a high-quality and comprehensive starting point for the subsequent optimization algorithm, helping to accelerate convergence and improve optimization efficiency.

[0072] In some implementations, a population crossover is performed on the primary population to generate new offspring individuals, a mutation operation is performed on the new offspring individuals to obtain mutant offspring, and all distribution schemes of the crossover and mutation are summarized to construct a distribution scheme set, including: Identify the array deviation distribution characteristics of the primary population, cross-combine the array deviation distribution characteristics based on the synchronous change relationship, and construct new offspring individuals, wherein the objective function is used to perform cross-evaluation, and when the cross-evaluation does not meet the evolutionary requirements, the individuals are eliminated, and when the evolutionary requirements are met, the individuals are retained to obtain a cross-distribution scheme; based on the arrangement parameter influence relationship, the arrangement parameters of the new offspring individuals are mutated, and the mutated individuals are screened using the objective function, and those that meet the mutation evaluation are retained to obtain a mutation distribution scheme; the cross-distribution scheme and the mutation distribution scheme are aggregated to construct the distribution scheme set.

[0073] Specifically, based on the existing array distribution rules and arrangement characteristics, crossover and mutation operations are performed on the population, and then screened using the objective function to construct a new set of distribution schemes. Array deviation distribution characteristics refer to the differences between each individual in the initial population (i.e., multiple antenna arrangements) and the ideal arrangement. This can be reflected in deviations in unit spacing and distortion in the array shape, reflecting the gap between the current population and the optimal solution.

[0074] Specifically, first, the array arrangement feature deviations of each individual in the primary population are extracted (such as differences in edge sparsity, center density, and transition zone smoothness), and combined with the aforementioned synchronous change relationship of the array distribution features, the deviation features of different individuals are combined and reconstructed to generate new offspring individuals with new feature combination patterns. For example, if the parent individual A has high center density and the parent individual B has high edge sparsity, then a offspring individual can be constructed, whose arrangement features are: the central area inherits the dense arrangement of A; the edge area inherits the sparse arrangement of B; the middle area is transitioned through a smoothing function to achieve the continuity of the overall structure and the complementary optimization of performance.

[0075] Specifically, the performance of each new individual generated by the crossover is then evaluated using an objective function. If the objective function value of the crossover individual does not meet the set evolutionary threshold, it is eliminated; if it meets the evolutionary requirements, it is retained as the crossover distribution scheme. Furthermore, a mutation operation is performed on the retained crossover offspring individuals to enhance population diversity. For example, referring to the influence of the aforementioned arrangement parameters on performance indicators, one or more arrangement parameters (such as unit spacing and distribution gradient) in the individual are perturbed or replaced to ensure the effectiveness of the optimization of the arrangement parameters.

[0076] Furthermore, the crossover distribution scheme and the mutation distribution scheme obtained above are summarized to construct a new distribution scheme set as the candidate solution set of the current evolution generation for subsequent selection operations or continued iterative optimization.

[0077] Through the above-mentioned crossover and mutation mechanism based on the influence relationship between distribution characteristics and arrangement parameters, the existing high-quality arrangement characteristics are integrated, which helps to improve the initial quality of new offspring individuals; multiple rounds of screening are carried out in combination with the objective function to ensure that the evolutionary direction is consistent with the performance optimization goal; and a high-quality and high-diversity distribution solution set is constructed to provide rich candidate solutions for subsequent selection operations.

[0078] S300: Online monitoring of antenna operating parameters by monitoring equipment, performing error compensation on the non-uniform unit spacing distribution based on the difference between the online monitoring data and the target parameters, optimizing the non-uniform unit spacing distribution, and obtaining an antenna arrangement solution.

[0079] Specifically, by online monitoring of the antenna system's operating status, the difference information between the actual operating parameters and the preset target parameters is obtained, and the arrangement correction is performed based on the difference, which can further optimize the distribution of non-uniform antenna unit spacing to meet the comprehensive requirements of antenna array performance.

[0080] Specifically, the target parameter difference is the error range between the parameters obtained by online monitoring (such as the main lobe gain of the radiation pattern, the sidelobe level, the electromagnetic distribution uniformity, the array power consumption, etc.) and the expected target value. It is used to guide the direction and intensity of error compensation. This deviation may be caused by the limitations of actual array production or optimization function settings (that is, it cannot fully reflect the actual beamforming situation and environment).

[0081] In some embodiments, performing error compensation on the non-uniform cell spacing distribution based on the difference between the online monitoring data and the target parameter to optimize the non-uniform cell spacing distribution includes: Demands are analyzed from multiple dimensions, including sidelobe suppression, lightweight array antenna, and power consumption, to obtain arrangement constraints and arrangement reward conditions. Based on the arrangement constraints and arrangement reward conditions, the target parameter difference is used as the compensation amount to perform error compensation optimization on the non-uniform unit spacing distribution to obtain the antenna arrangement scheme. The antenna arrangement scheme is an antenna arrangement strategy that satisfies the target parameter difference and the arrangement constraints and arrangement reward conditions.

[0082] Specifically, arrangement constraints refer to boundary conditions that must be met, such as maximum array size, structural strength, upper limit of system power consumption, maximum number of array elements, etc.; arrangement reward conditions refer to characteristics that are expected to be optimized first or have a greater impact on antenna system performance, such as minimizing side lobes, reducing weight, and correcting main lobe offsets.

[0083] Specifically, the antenna array is first functionally analyzed in multiple dimensions, including sidelobe suppression performance, lightweight structure requirements, and multi-dimensional power consumption constraints. This allows the antenna arrangement constraints (such as minimum spacing and weight restrictions) and arrangement reward functions (such as power efficiency scores and pattern purity) to be extracted as optimization boundaries. Then, based on the online monitoring results, the corresponding target parameter difference is calculated. The optimization direction and error weight in the gradient descent process are defined based on this difference, and the existing non-uniform unit spacing is adjusted to gradually approach the optimal solution that meets all arrangement constraints and performance objectives. For example, the larger the target parameter difference, the longer the optimization step size. The optimization step size can be adaptive, meaning that the step size gradually decreases with each optimization iteration, thereby improving optimization efficiency while avoiding missing the optimal solution.

[0084] In some implementations, obtaining the antenna arrangement scheme includes: According to the target parameter difference, the distribution relationship is adjusted based on gradient descent until the target parameter difference and the arrangement constraints and arrangement reward conditions are met; when the number of iterations still cannot meet the requirements, the auxiliary compensation component library is searched and the antenna arrangement scheme search and optimization is performed using the compensation component library.

[0085] Optionally, if the optimization process fails to reach the expected convergence requirement within the set iteration rounds, that is, the simple adjustment of the array element distribution cannot meet the performance expectations of the phased array antenna, the compensation component library (such as fine-tuning reflectors, phase modulators, camouflage units, etc.) is enabled to search and fusion optimize the auxiliary compensation strategy for the current antenna arrangement structure, thereby further improving the overall arrangement performance and ensuring the feasibility of the system.

[0086] Through the above-mentioned method and steps, online monitoring and error compensation of antenna operating parameters can timely detect and correct deviations between antenna performance and design targets, improve the actual performance and reliability of the antenna, and ensure that the antenna can operate stably in various complex environments to meet application requirements.

[0087] In summary, the Ku-band non-uniform phased array antenna arrangement method provided by the present invention has the following technical effects: By establishing an objective function that takes sidelobe level, beamwidth, and scanning range as optimization indicators, and based on this objective function, using adaptive crossover probability and mutation probability algorithms in the Ku-band, various array antenna non-uniform unit spacing distribution schemes are searched and evaluated to ultimately determine the optimal distribution. Antenna operating parameters are collected online using monitoring equipment, and the deviation between the real-time monitoring value and the target parameter is fed back into the non-uniform unit spacing distribution scheme for error compensation and re-optimization to generate the final antenna arrangement scheme, thereby achieving the technical effect of providing multi-objective comprehensive optimization, improving antenna performance and reliability, and reducing antenna costs.

[0088] Example 2, as Figure 2 This is a schematic diagram of the structure of a non-uniform phased array antenna arrangement system in the Ku band of the present invention. For example, Figure 1 The flow chart of the non-uniform phased array antenna arrangement method of the ku band of the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0089] Based on the same concept as the Ku-band non-uniform phased array antenna arrangement method in the above embodiment, the present invention further provides a Ku-band non-uniform phased array antenna arrangement system comprising: The objective function establishing module 11 is used to establish an objective function with sidelobe level, beam width and scanning range as joint optimization targets.

[0090] The distribution scheme search module 12 is used to search for a non-uniform spacing distribution scheme of array antennas in the Ku band based on the objective function using adaptive crossover probability and mutation probability, determine the distribution scheme based on the evaluation result of the objective function, and generate a non-uniform unit spacing distribution.

[0091] The online monitoring and error compensation module 13 is used to monitor the antenna operating parameters online through monitoring equipment, perform error compensation on the non-uniform unit spacing distribution based on the difference between the online monitoring data and the target parameters, optimize the non-uniform unit spacing distribution, and obtain an antenna arrangement plan.

[0092] In some embodiments, the online monitoring and error compensation module 13 includes: The demand analysis and arrangement condition acquisition unit is used to analyze demand from multiple dimensions such as sidelobe suppression, array antenna lightweighting, and power consumption, and obtain arrangement constraints and arrangement reward conditions.

[0093] The error compensation optimization and arrangement scheme determination unit is used to perform error compensation optimization on the non-uniform unit spacing distribution based on the arrangement constraints and arrangement reward conditions, with the target parameter difference as the compensation amount, to obtain the antenna arrangement scheme. The antenna arrangement scheme is an antenna arrangement strategy that meets the target parameter difference and the arrangement constraints and arrangement reward conditions.

[0094] In some embodiments, the objective function establishment module 11 includes: The parameter parsing and demand expected value acquisition unit is used to perform sidelobe level, beam width and scanning range parameter parsing according to the application requirements of the array antenna to obtain the demand expected values.

[0095] The objective function establishment unit is used to convert the realization evaluation relationship of the expected value of the requirement into a mathematical expression based on the tolerance range and parameter influence of the application requirement, and establish the objective functions of the expected value of the sidelobe level, beam width and scanning range requirement respectively.

[0096] The joint optimization objective function construction unit is used to combine the objective functions of the sidelobe level, beam width, and scanning range to construct an objective function of the joint optimization target.

[0097] In some embodiments, the distribution solution search module 12 includes: The array distribution rule building unit is used to build array distribution rules based on historical sample data.

[0098] The primary population construction unit is configured to perform distribution analysis on the application requirements of the array antenna according to the array distribution rule, and construct the primary population.

[0099] The distribution scheme set construction unit is used to perform population crossover on the primary population within the ku frequency band to generate new offspring individuals, perform mutation operations on the new offspring individuals to obtain mutant offspring, summarize all distribution schemes of crossover and mutation, and construct a distribution scheme set.

[0100] The non-uniform unit spacing distribution acquisition unit is used to evaluate each scheme in the distribution scheme set using the objective function, find a distribution scheme that meets the expected value of the demand and has the maximum evaluation result of the objective function, and obtain the non-uniform unit spacing distribution.

[0101] In some implementations, the array distribution rule construction unit in the distribution scheme search module 12 includes: The arrangement parameter influence relationship analysis unit is used to analyze the arrangement parameter influence relationship of the array arrangement parameters on the sidelobe level, beam width and scanning range in the Ku band based on the historical sample data.

[0102] The array distribution rule construction unit is used to construct the array distribution rule according to the arrangement parameter influence relationship.

[0103] In some implementations, the primary population construction unit in the distribution scheme search module 12 includes: The primary population screening unit is used to perform overall distribution relationship matching screening according to the synchronous change relationship of the array distribution characteristics, the sidelobe level, the beam width, and the scanning range according to the application requirements of the array antenna to obtain the primary population.

[0104] In some implementations, the distribution scheme set construction unit in the distribution scheme search module 12 includes: A new offspring individual construction and crossover distribution scheme acquisition unit is used to identify the array deviation distribution characteristics of the initial population, cross-combine the array deviation distribution characteristics based on the synchronous change relationship, and construct new offspring individuals, wherein the objective function is used to perform cross-evaluation, and when the cross-evaluation does not meet the evolutionary requirements, it is eliminated, and when it meets the evolutionary requirements, it is retained to obtain a crossover distribution scheme.

[0105] A variation distribution scheme acquisition unit is used to mutate the arrangement parameters of the new offspring individuals based on the arrangement parameter influence relationship, screen the mutated individuals using the objective function, retain those that meet the variation evaluation, and obtain a variation distribution scheme.

[0106] The distribution scheme set construction unit is used to aggregate the cross distribution scheme and the variation distribution scheme to construct the distribution scheme set.

[0107] In some implementations, the error compensation optimization and arrangement scheme determination unit in the online monitoring and error compensation module 13 includes: The distribution relationship adjustment unit is used to adjust the distribution relationship based on gradient descent according to the target parameter difference until the target parameter difference and the arrangement constraints and arrangement reward conditions are met.

[0108] The auxiliary compensation component library search and optimization unit is used to search the auxiliary compensation component library when the number of iterations still cannot meet the requirements, and use the compensation component library to search and optimize the antenna arrangement solution.

[0109] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment 1 are also applicable to the non-uniform phased array antenna arrangement system in the Ku band described in embodiment 2. For the sake of brevity of the specification, no further elaboration is given here.

[0110] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the embodiments mentioned above. It should be understood that those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention and are all included in the scope of protection of the present invention.

Claims

1. A method for arranging non-uniform phased array antennas in the Ku band, characterized in that: include: Establish an objective function with sidelobe level, beamwidth and scanning range as joint optimization targets; Based on the objective function, adaptive crossover probability and mutation probability are used to search for a non-uniform spacing distribution scheme for array antennas in the Ku band, and the distribution scheme is determined based on the evaluation result of the objective function to generate a non-uniform unit spacing distribution; The antenna operating parameters are monitored online by monitoring equipment, and the non-uniform unit spacing distribution is compensated for errors based on the difference between the online monitoring data and the target parameters, so as to optimize the non-uniform unit spacing distribution and obtain an antenna arrangement scheme.

2. The Ku-band non-uniform phased array antenna arrangement method according to claim 1, characterized in that: The non-uniform cell spacing distribution is compensated for errors based on the difference between the online monitoring data and the target parameter, and the non-uniform cell spacing distribution is optimized, including: Analyze requirements from multiple dimensions, including sidelobe suppression, array antenna lightweighting, and power consumption, to obtain layout constraints and reward conditions; According to the arrangement constraints and arrangement reward conditions, the non-uniform unit spacing distribution is error compensated and optimized with the target parameter difference as the compensation amount to obtain the antenna arrangement scheme. The antenna arrangement scheme is an antenna arrangement strategy that meets the target parameter difference and the arrangement constraints and arrangement reward conditions.

3. The Ku-band non-uniform phased array antenna arrangement method according to claim 1, characterized in that: Establish an objective function with sidelobe level, beamwidth and scanning range as joint optimization targets, including: According to the application requirements of the array antenna, analyze the sidelobe level, beam width and scanning range parameters to obtain the expected values; Based on the tolerance range and parameter influence of application requirements, the realization evaluation relationship of the expected value of the requirements is converted into a mathematical expression, and the objective functions of the expected value of the sidelobe level, beam width and scanning range requirements are established respectively; The objective functions of the sidelobe level, beam width, and scanning range are combined to construct an objective function of a joint optimization target.

4. The Ku-band non-uniform phased array antenna arrangement method according to claim 3, characterized in that: Based on the objective function, adaptive crossover probability and mutation probability are used to search for a non-uniform spacing distribution scheme for array antennas in the Ku band, including: Construct array distribution rules based on historical sample data; Performing distribution analysis on the application requirements of the array antenna according to the array distribution rule to construct an initial generation population; In the ku frequency band, the primary population is subjected to population crossover to generate new offspring individuals, the new offspring individuals are subjected to mutation operations to obtain mutant offspring, and all distribution schemes of crossover and mutation are summarized to construct a distribution scheme set; The objective function is used to evaluate each scheme in the distribution scheme set, and a distribution scheme that meets the expected value of the demand and has the maximum evaluation result of the objective function is found to obtain a non-uniform unit spacing distribution.

5. The Ku-band non-uniform phased array antenna arrangement method according to claim 4, characterized in that: Based on historical sample data, array distribution rules are constructed, including: Analyze the influence of array arrangement parameters on sidelobe level, beamwidth, and scanning range in the Ku band based on the historical sample data; The array distribution rule is constructed according to the influence relationship of the arrangement parameters.

6. The Ku-band non-uniform phased array antenna arrangement method according to claim 5, characterized in that: Constructing array distribution rules also includes: Analyze the array distribution characteristics of historical sample data according to sidelobe level, beamwidth, and scanning range to obtain the synchronous change relationship of the array distribution characteristics. The array distribution characteristics describe the overall distribution relationship of the array, including the sparseness of the distribution edge, the density of the center, and the smoothness of the transition zone; The array distribution rule is constructed according to the synchronous change relationship of the array distribution characteristics.

7. The Ku-band non-uniform phased array antenna arrangement method according to claim 6, characterized in that: Constructing the initial population, including: According to the synchronous change relationship of the array distribution characteristics and the application requirements of the array antenna, the overall distribution relationship matching and screening are performed according to the sidelobe level, beam width and scanning range to obtain the initial generation population.

8. The Ku-band non-uniform phased array antenna arrangement method according to claim 7, characterized in that: Perform a population crossover on the primary population to generate new offspring individuals, perform a mutation operation on the new offspring individuals to obtain mutant offspring, summarize all the distribution schemes of crossover and mutation, and construct a distribution scheme set, including: Identifying array deviation distribution characteristics of the primary population, cross-combining the array deviation distribution characteristics based on the synchronous change relationship, and constructing new offspring individuals, wherein cross-evaluation is performed using the objective function, and individuals are eliminated when the cross-evaluation does not meet the evolutionary requirements, and retained when the cross-evaluation meets the evolutionary requirements, to obtain a cross-distribution scheme; Based on the arrangement parameter influence relationship, the arrangement parameters of the new offspring individuals are mutated, the mutated individuals are screened using the objective function, and those that meet the variation evaluation are retained to obtain a variation distribution scheme; The crossover distribution scheme and the variation distribution scheme are aggregated to construct the distribution scheme set.

9. The Ku-band non-uniform phased array antenna arrangement method according to claim 2, characterized in that: Obtaining the antenna arrangement scheme includes: According to the target parameter difference, the distribution relationship is adjusted based on gradient descent until the target parameter difference and the arrangement constraints and arrangement reward conditions are met; When the number of iterations still cannot meet the requirement, the auxiliary compensation component library is searched and the antenna arrangement solution search and optimization is performed using the compensation component library.

10. A Ku-band non-uniform phased array antenna arrangement system, characterized in that: A method for arranging a non-uniform phased array antenna in the Ku band according to any one of claims 1 to 9, comprising: An objective function establishment module is used to establish an objective function with sidelobe level, beam width and scanning range as joint optimization targets; A distribution scheme search module is used to search for a non-uniform spacing distribution scheme for array antennas in the Ku band based on the objective function using adaptive crossover probability and mutation probability, determine the distribution scheme based on the evaluation result of the objective function, and generate a non-uniform unit spacing distribution; The online monitoring and error compensation module is used to monitor the antenna operating parameters online through monitoring equipment, perform error compensation on the non-uniform unit spacing distribution based on the difference between the online monitoring data and the target parameters, optimize the non-uniform unit spacing distribution, and obtain an antenna arrangement plan.

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