A high-gain phased array antenna unit layout method and system

Abstract

The application belongs to the field of communication and electronic information technology, and particularly relates to a high-gain phased array antenna unit layout method and system, which mainly comprises the following steps: step one: generating initial parameters, generating initial unit layout parameters based on core gain area and edge scanning area dynamic clustering topology, synchronously inputting unit radiation characteristics, feeding parameters and hardware installation datum, and establishing a correlation mapping table of layout parameters and hardware performance; step five: hardware fault tolerance identification and adaptation, identifying unit damage and feeding link fault problems through a unit fault detection module, marking fault unit positions and fault types; and step seven: closed-loop iterative optimization, continuously collecting calibration data and performance data after fault tolerance adaptation, feeding back to a reinforcement learning optimization model, and iteratively optimizing self-calibration thresholds, compensation priority rules, fault tolerance adaptation strategies and feeding matching parameters.

Description

Technical Field

[0001] This invention belongs to the field of communication and electronic information technology, specifically a high-gain phased array antenna element layout method and system. Background Technology

[0002] As a core component of next-generation wireless communication and detection systems such as satellite communication, radar detection, and millimeter-wave communication, the performance of phased array antennas directly determines the operating range, detection accuracy, and long-term stable operation capability of the entire system.

[0003] Antenna element layout is a key design element for phased array antennas to achieve high gain, wide-angle scanning, low grating lobes, and low sidelobes, directly affecting array radiation efficiency, inter-element mutual coupling suppression, and engineering feasibility. Currently, most phased array antennas adopt a regular, uniform periodic layout of rectangles or triangles.

[0004] However, in existing technologies, most calibration methods are static calibration, which cannot correct parameter deviations in real time throughout the entire antenna operation process. This can easily lead to performance drift, and manual intervention is required after a failure occurs, affecting the continuous operation of the system. At the same time, layout parameters and hardware performance data are isolated from each other and no effective correlation support mechanism has been established. This results in the inability of self-calibration, fault tolerance adaptation and layout topology adjustment to work in tandem, further exacerbating the disconnect between theoretical design and engineering implementation, and making it difficult to meet the long-term stable operation requirements in complex scenarios.

[0005] Therefore, the present invention provides a method and system for arranging high-gain phased array antenna elements. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by the present invention to solve its technical problem is as follows: On the one hand, the present invention provides a high-gain phased array antenna element layout method and system, which mainly includes the following steps: Step 1: Initial layout parameter generation. Based on the dynamic clustering topology of the core gain region and the edge scanning region, the initial cell layout parameters are generated. The cell radiation characteristics, power supply parameters and hardware installation baseline data are entered simultaneously, and a mapping table between layout parameters and hardware performance is established. Step 2: Real-time self-calibration is performed. Throughout the entire antenna operation process, the built-in electromagnetic induction sensor and phase detection module collect data on unit position offset, phase deviation, and gain attenuation in real time. By comparing the collected multi-dimensional errors with the initial reference data, calibration compensation coefficients are generated. Step 3: Compensation coefficient verification and priority ranking. Input the calibration compensation coefficients generated in Step 2 into the preset performance impact assessment model to verify the validity of the coefficients and eliminate invalid compensation caused by abnormal data. At the same time, prioritize the compensation needs of each unit based on the gain contribution. Prioritize the allocation of compensation resources to core gain area units and edge units in the beam scanning direction, and postpone the compensation of non-critical area units. Step 4: Dynamic parameter correction. Based on the calibration compensation coefficient, adjust the position coordinates, feed phase, and amplitude of the corresponding unit. Step 5: Hardware fault tolerance identification and adaptation. Through the unit fault detection module, the working status of each antenna unit is monitored in real time, the unit damage and power supply link failure are identified, and the location and type of faulty unit are marked. Step 6: Fault-tolerant reconstruction and performance compensation. For the faulty unit, the layout reconstruction logic is triggered to adjust the clustering distribution and excitation weight of the remaining units. The layout parameters are matched with the reconstructed layout parameters through the power supply network adaptive adjustment module. Step 7: Closed-loop iterative optimization. Continuously collect calibration data and performance data after fault tolerance adaptation, and feed them back to the reinforcement learning optimization model to iteratively optimize the self-calibration threshold, compensation priority rules, fault tolerance adaptation strategy and power supply matching parameters.

[0008] Preferably, in step one, the establishment of the association mapping table includes: based on the initial unit layout parameters generated by dynamic clustering topology of the core gain region and the edge scanning region, combined with the antenna unit radiation characteristics, feed parameters and hardware installation reference data, constructing an association mapping table between the layout parameters and hardware performance, which is used to store and call the initial reference data, and provide data support for subsequent real-time self-calibration and parameter correction.

[0009] Preferably, in step two, the multi-dimensional error acquisition includes: real-time acquisition of three types of core error data throughout the entire antenna operation process using a built-in electromagnetic induction sensor and phase detection module, including real-time data on the unit position offset caused by processing error, temperature deformation and vibration, feed phase deviation, and unit gain attenuation data.

[0010] Preferably, in step three, the priority ranking of compensation requirements includes: inputting the generated calibration compensation coefficients into a preset performance impact assessment model, and after verifying the validity of the coefficients, prioritizing the compensation requirements of all units according to the contribution of each antenna unit to the overall radiation gain, and giving priority to ensuring the compensation resources of the core gain area units and the edge units of the beam scanning direction.

[0011] Preferably, in step four, the parameter adjustment of the electronic control mechanism includes: according to the calibration compensation coefficient and priority ranking results, the physical position coordinates of the antenna units of the corresponding priority are finely adjusted through the integrated micro electronic control adjustment mechanism, and the feed phase and amplitude parameters of the units are adjusted synchronously to ensure the stability of the layout topology and avoid antenna performance drift caused by parameter deviation.

[0012] Preferably, in step five, the fault type is labeled as follows: the working status of each antenna unit is monitored in real time by the unit status monitoring unit and the fault identification algorithm unit. After identifying unit damage or abnormal power supply link faults, the specific location of the fault unit is accurately marked, and the fault type and fault level are clearly labeled, providing an accurate basis for subsequent fault-tolerant reconstruction.

[0013] Preferably, in step six, the reassignment of the incentive weights is as follows: for the identified faulty units, the layout reconstruction logic is triggered to adjust the clustering distribution of the remaining normal units, and at the same time, the power supply incentive weights of the remaining normal units are redistributed in conjunction with the adaptive adjustment of the power supply network.

[0014] Preferably, in step seven, the strategy threshold iteration includes: continuously collecting antenna performance data after self-calibration correction and fault tolerance adaptation, feeding the data back to the reinforcement learning optimization model, and optimizing the self-calibration threshold, compensation priority division rules, fault tolerance adaptation strategy, and feed matching parameters through model iteration.

[0015] On the other hand, a high-gain phased array antenna element layout system, which is mainly applicable to any of the above-mentioned high-gain phased array antenna element layout methods, mainly includes: Layout parameter management module: responsible for the input, storage and retrieval of initial layout parameters, establishing a database linking layout parameters and hardware performance, supporting real-time parameter updates and traceability, and providing data support for self-calibration and fault-tolerant adaptation; Real-time self-calibration module: includes an electromagnetic induction sensor, a phase detection unit and a calibration algorithm unit. Its core function is to collect unit position, phase and gain data in real time, calculate compensation coefficients and output parameter correction instructions. Unlike the static calibration of existing patents, it realizes dynamic real-time calibration during the working process. Hardware fault-tolerant detection module: Composed of unit status monitoring unit and fault identification algorithm unit, it can quickly identify faulty units by monitoring unit radiation signals and feed current parameters, mark fault level and type, and provide a basis for fault-tolerant reconstruction without the need for additional detection hardware, thus reducing system cost; Adaptive module: integrates micro electronic control adjustment mechanism and power supply network adaptive adjustment unit, receives calibration correction command and fault-tolerant reconstruction command, completes unit position adjustment and power supply parameter matching, realizes dynamic adaptation of layout and hardware, and does not require additional design of adaptation structure; Closed-loop control module: As the core of the system, it connects all the above modules, realizes closed-loop control of data acquisition, command issuance, performance feedback and iterative optimization, coordinates the collaborative work of each module, and ensures the synchronization of self-calibration, fault tolerance adaptation and dynamic changes in layout topology.

[0016] Preferably, the construction of the associated database refers to the process performed by the layout parameter management module, which associates and binds the initial unit layout parameters generated based on dynamic clustering topology with antenna unit radiation characteristic data, feed parameter data, and hardware installation reference data to construct an as-updable and traceable associated database. This database is used to store the above-mentioned data and supports on-demand access, providing a core data foundation for error acquisition and calibration compensation coefficient calculation of the real-time self-calibration module, as well as fault identification of the hardware fault tolerance detection module and parameter adjustment of the adaptive adaptation module.

[0017] The beneficial effects of this invention are as follows: 1. The high-gain phased array antenna element layout method and system described in this invention accurately corrects parameter offsets caused by processing errors, temperature deformation, and vibration by prioritizing real-time multi-dimensional error acquisition and gain contribution compensation requirements throughout the entire process. This reduces performance drift at its source. Combined with dynamic clustering topology of the core gain region and edge scanning region, it achieves wide-angle scanning grating lobe suppression while ensuring high gain, taking into account both radiation performance and environmental adaptability. This effectively addresses the pain points of traditional layouts being susceptible to external interference that leads to gain reduction and sidelobe rise.

[0018] 2. The high-gain phased array antenna element layout method and system described in this invention addresses hardware issues such as element damage and power supply failure by achieving automatic fault identification, accurate calibration, and adaptive layout reconstruction. Performance compensation is achieved through excitation weight reconfiguration, maintaining the core performance of high gain and low grating lobe of the antenna without manual intervention. Compared with traditional modular replacement schemes, this significantly improves the robustness of the system, reduces maintenance costs and downtime losses, and adapts to the long-term continuous operation requirements in complex scenarios.

[0019] 3. The high-gain phased array antenna element layout method and system described in this invention achieves data linkage by relying on an associated database and completes iterative optimization of policy thresholds by combining reinforcement learning. This allows the layout parameters, calibration rules, and fault tolerance strategies to continuously adapt to changes in operating conditions. The modular system realizes closed-loop control of the entire process of layout design, calibration, fault tolerance, and adaptation, breaking the barrier between traditional layout theoretical design and engineering implementation. It does not require additional hardware structures and significantly improves system adaptability and long-term operational reliability under the premise of miniaturization and low cost. Attached Figure Description

[0020] The invention will now be further described with reference to the accompanying drawings.

[0021] Figure 1 This is a schematic diagram of the layout method in this invention; Figure 2 This is a schematic diagram of the layout system process in this invention. Detailed Implementation

[0022] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0023] like Figure 1 As shown in the embodiment of the present invention, a high-gain phased array antenna element layout method and system mainly includes the following steps: Step 1: Initial layout parameter generation. Based on the dynamic clustering topology of the core gain region and the edge scanning region, the initial cell layout parameters are generated. The cell radiation characteristics, power supply parameters and hardware installation baseline data are entered simultaneously, and a mapping table between layout parameters and hardware performance is established. First, based on the operating wavelength, maximum scanning angle, and gain requirements of the phased array antenna, a dual-region dynamic clustering topology is defined, consisting of a core gain region and an edge scanning region. The core gain region ensures the basic high-gain performance of the antenna, while the edge scanning region adapts to wide-angle scanning requirements and suppresses grating lobe generation. Based on this topology, initial unit layout parameters are generated using electromagnetic simulation tools, including the three-dimensional coordinates and radiation pattern parameters of each antenna element. Simultaneously, the inherent radiation characteristics, feed parameters, and hardware installation reference data of each antenna element are entered. The inherent radiation characteristics refer to radiation efficiency and polarization, the feed parameters refer to feed phase and amplitude reference values, and the hardware installation reference data refers to the allowable range of installation errors. A mapping table between layout parameters and hardware performance is established. This mapping table can intuitively reflect the antenna radiation performance and hardware adaptation status corresponding to different layout parameters, providing basic data support for all subsequent calibration and correction steps, and ensuring that the data at each stage is traceable and retrievable. Step 2: Real-time self-calibration is performed. Throughout the entire antenna operation process, the built-in electromagnetic induction sensor and phase detection module collect data on unit position offset, phase deviation, and gain attenuation in real time. By comparing the collected multi-dimensional errors with the initial reference data, calibration compensation coefficients are generated. The antenna's built-in electromagnetic induction sensor collects real-time position data of each unit, and the phase detection module collects feed phase deviation and gain attenuation data of each unit. The collected real-time data is compared with the initial reference data generated in step one, and a calibration compensation coefficient is generated through a preset algorithm for subsequent parameter correction. This achieves dynamic linkage of real-time monitoring, deviation identification, and compensation calculation, which can effectively avoid performance drift. Step 3: Compensation coefficient verification and priority ranking. Input the calibration compensation coefficients generated in Step 2 into the preset performance impact assessment model to verify the validity of the coefficients and eliminate invalid compensation caused by abnormal data. At the same time, prioritize the compensation needs of each unit based on the gain contribution. Prioritize the allocation of compensation resources to core gain area units and edge units in the beam scanning direction, and postpone the compensation of non-critical area units. To avoid generating invalid compensation from abnormal data, such as erroneous data acquisition caused by sensor malfunctions, and to ensure calibration accuracy, the calibration compensation coefficients generated in step two are input into a preset performance impact assessment model. The effectiveness of the coefficients is verified through the model, and invalid compensation data is eliminated. At the same time, to optimize the allocation of compensation resources and avoid resource waste caused by indiscriminate correction, the compensation requirements of all antenna elements are prioritized based on their contribution to the overall radiation gain. Among them, the core gain area elements directly determine the overall gain level of the antenna, and the edge elements in the beam scanning direction directly affect the grating lobe suppression effect during wide-angle scanning. Therefore, compensation resources are allocated to these two types of elements first, and compensation for non-critical area elements is delayed to ensure that the core performance is not affected. Step 4: Dynamic parameter correction. Based on the calibration compensation coefficient, the position coordinates, feed phase, and amplitude of the corresponding unit are automatically adjusted. Based on the calibration compensation coefficients and priority ranking results verified in step three, the physical position coordinates of the antenna elements of the corresponding priority are finely adjusted through the integrated micro-electronic control adjustment mechanism. At the same time, the feed phase and amplitude parameters of the elements are adjusted synchronously through the feed network, so that the working state of each element returns to the reference level, ensuring the stability of the layout topology and avoiding the problems of antenna gain reduction, sidelobe level rise and performance drift caused by parameter deviation, thus achieving accurate implementation of calibration compensation. Step 5: Hardware fault tolerance identification and adaptation. Through the unit fault detection module, the working status of each antenna unit is monitored in real time, the unit damage and power supply link failure are identified, and the location and type of faulty unit are marked. The unit fault detection module monitors the radiation signal strength and feed current of each antenna unit in real time, compares them with the normal operating parameter thresholds, identifies unit damage and feed link faults, and accurately marks the specific location of the faulty unit. At the same time, it clearly defines the fault type and fault level, namely complete damage and partial performance degradation, providing accurate fault information for subsequent fault-tolerant reconstruction and ensuring the targeted nature of fault-tolerant operations. Step 6: Fault-tolerant reconstruction and performance compensation. For faulty units, the layout reconstruction logic is automatically triggered to adjust the clustering distribution and excitation weights of the remaining units. At the same time, the layout parameters are matched after reconstruction through the power supply network adaptive adjustment module. For the faulty units identified in step five, the layout reconstruction logic is automatically triggered without manual intervention to adjust the clustering distribution of the remaining normal units, so that the remaining units can re-form a reasonable topology to make up for the performance loss caused by the absence of faulty units. At the same time, the feed excitation weights of the remaining normal units are redistributed, and in conjunction with the feed network adaptive adjustment module, the layout parameters after reconstruction are matched in real time to ensure that the antenna as a whole can still maintain the core performance of high gain and low grating lobe, thus solving the defects of needing manual replacement after failure and being unable to work continuously. Step 7: Closed-loop iterative optimization. Continuously collect calibration data and performance data after fault tolerance adaptation, and feed them back to the reinforcement learning optimization model to iteratively optimize the self-calibration threshold, compensation priority rules, fault tolerance adaptation strategy and power supply matching parameters. To continuously improve the robustness of the system, antenna performance data, such as gain, sidelobe level, and scanning range, are continuously collected after self-calibration and fault-tolerant adaptation. This data is then fed back to the reinforcement learning optimization model. Through iterative optimization of the model, the self-calibration threshold, compensation priority division rules, fault-tolerant adaptation strategy, and feed matching parameters are continuously optimized. This process continuously improves the operational accuracy of each component, enabling the antenna to operate stably for a long time in complex working environments and further enhancing the reliability of engineering implementation.

[0024] like Figure 1 As shown, in step one, the establishment of the association mapping table includes: the initial unit layout parameters generated based on the dynamic clustering topology of the core gain region and the edge scanning region, combined with the antenna unit radiation characteristics, feed parameters and hardware installation reference data, to construct an association mapping table between the layout parameters and hardware performance, which is used to store and call the initial reference data, and to provide data support for subsequent real-time self-calibration and parameter correction; This is the foundational data support component of the entire layout methodology. Its core function is to address the shortcomings of layout parameters and hardware performance data being isolated and unable to coordinate effectively. The specific implementation method is as follows: The initial cell layout parameters, including the coordinate parameters of each cell, are generated based on the dynamic clustering topology of the core gain region and the edge scan region. ,in , This refers to the total number of antenna elements, combined with antenna element radiation characteristic data (such as radiation efficiency). Feeder parameter data (such as reference feeder phase) Reference feed amplitude ) and hardware installation baseline data (such as installation error thresholds) ), construct a mapping table between layout parameters and hardware performance; This association mapping table adopts a two-dimensional data structure, with the horizontal axis representing the layout parameter types and the vertical axis representing the corresponding hardware performance parameters. Its mathematical expression can be simplified as follows: in, For the association mapping table, For the first The radiated power of each unit For the first The gain contribution value of each unit. For the first The sidelobe level corresponding to each unit For the first The hardware adaptation coefficients of each unit are mapped in a table that stores and retrieves all initial reference data. This table provides a core data base for error comparison, calibration compensation coefficient calculation, and precise adjustment during parameter correction in the subsequent real-time self-calibration process. It ensures that the data in each step is consistent and traceable, and avoids calibration deviations and correction errors caused by isolated data.

[0025] like Figure 1 As shown, in step two, the multi-dimensional error acquisition includes: real-time acquisition of three types of core error data throughout the entire antenna operation process using the built-in electromagnetic induction sensor and phase detection module, including processing error, real-time data of unit position offset caused by temperature deformation and vibration, feed phase deviation, and unit gain attenuation data. The core premise for achieving real-time self-calibration is to comprehensively and accurately collect various error data during antenna operation, providing a reliable basis for generating calibration compensation coefficients and solving the problem of insufficient calibration accuracy caused by single or incomplete error collection. The specific implementation method is as follows: By using the built-in electromagnetic induction sensor and phase detection module of the antenna, three types of core error data are collected in real time throughout the entire antenna operation process, from startup to stable operation, to achieve comprehensive error monitoring. The first type of error data is the cell position offset. This offset is caused by a combination of machining errors, temperature deformation, and vibration factors, and its calculation method is as follows: in, For the first Real-time position coordinates of each unit, For the first The initial reference position coordinates of each unit, For the first The actual position offset of each unit; The second type of error data is the feed phase deviation. The calculation method is as follows: in, For the first Real-time power supply phase of each unit, For the first The reference feed phase of each unit; The third type of error data is unit gain attenuation data. The calculation method is as follows: in, For the first The reference gain value of each unit, For the first The real-time gain value of each unit, by synchronously collecting the above three types of error data, can comprehensively reflect the working state deviation of the antenna unit, providing a solid experimental basis for the accurate calculation of subsequent calibration compensation coefficients.

[0026] like Figure 1 As shown, in step three, the priority ranking of compensation requirements includes: inputting the generated calibration compensation coefficients into a preset performance impact assessment model, and after verifying the validity of the coefficients, prioritizing the compensation requirements of all units according to the contribution of each antenna unit to the overall radiation gain, and giving priority to ensuring the compensation resources of the core gain area units and the edge units of the beam scanning direction. First, use the calibration compensation coefficients generated in step two. (corresponding to the first) (Each unit) inputs a preset performance impact assessment model. This model verifies the validity of the coefficients by comparing the matching degree between the compensation coefficients and the error threshold, and eliminates invalid compensation coefficients caused by abnormal data, such as compensation coefficients that exceed the reasonable range due to sensor mis-collection. Subsequently, based on the contribution of each antenna element to the overall radiation gain, the gain contribution of each element is calculated. The calculation formula is as follows: in, For the first The real-time gain value of each unit. For the first The percentage of radiation area per unit. This represents the total number of antenna elements. The range of values ​​is , The larger the value, the higher the contribution of the unit to the overall radiation gain; Based on gain contribution The size of the compensation requirement is used to prioritize the compensation needs of all units and set priority thresholds. It can be adjusted according to the actual gain requirements, and is usually taken as 0.6. When a unit is identified as a high-priority unit, it will be given priority in the allocation of compensation resources; when When a unit is identified as a low-priority unit, compensation is delayed. This includes core gain region units and edge units along the beam scanning direction. All values ​​are greater than or equal to Therefore, compensation resources are prioritized to ensure that the core performance of the antenna is not affected by errors.

[0027] like Figure 1 As shown, in step four, the parameter adjustment of the electronic control mechanism includes: according to the calibration compensation coefficient and priority ranking results, the physical position coordinates of the antenna units of the corresponding priority are finely adjusted through the integrated micro electronic control adjustment mechanism, and the feed phase and amplitude parameters of the units are adjusted synchronously to ensure the stability of the layout topology and avoid antenna performance drift caused by parameter deviation. As a core execution step in calibration and compensation, parameter adjustment of the electronic control mechanism aims to restore the antenna unit's operating state to a baseline level through precise physical adjustments and parameter tuning. This addresses the issues of low parameter correction accuracy and complex operation. The specific implementation method is as follows: Based on the calibration compensation coefficient verified in step three Based on the priority ranking results, parameter correction is performed on high-priority units first, namely core gain area units and edge units in the beam scanning direction. By integrating a miniature electronically controlled adjustment mechanism at the bottom of each antenna element, the physical position coordinates of the element are finely adjusted, with the position adjustment amount... With calibration compensation coefficient The relationship is: in, For the first The initial reference position coordinates of each unit, For real-time location coordinates, For calibration compensation coefficients, the range of values ​​is as follows: This ensures precise and controllable position adjustments, avoiding new errors caused by over-adjustment; Simultaneously, the feed phase and amplitude parameters of the feed network synchronous adjustment unit are adjusted, and the feed phase adjustment amount is... Power supply amplitude adjustment amount They are respectively: in, For feed phase deviation, For gain attenuation data, Using the reference gain value, the stability of the layout topology is ensured by synchronously fine-tuning the above physical location and feeding parameters, avoiding antenna performance drift caused by parameter deviation, and achieving accurate calibration compensation.

[0028] like Figure 1As shown, in step five, the fault type is marked as follows: the working status of each antenna unit is monitored in real time through the unit status monitoring unit and the fault identification algorithm unit. After identifying the unit damage and abnormal power supply link fault, the specific location of the fault unit is accurately marked, and the fault type and fault level are clearly marked, providing an accurate basis for subsequent fault-tolerant reconstruction. Fault type identification is a prerequisite for achieving fault-tolerant reconfiguration. Its core purpose is to accurately identify faulty units and clarify fault information, thereby solving the problems of ambiguous fault identification and the inability to perform targeted fault-tolerant processing. The specific implementation method is as follows: The unit status monitoring unit in the hardware fault-tolerant detection module collects the operating parameters of each antenna unit in real time, including the radiated signal strength. Feed current And compare it with the preset normal working parameter threshold; Set the normal threshold range for radiation signal intensity as follows: The normal threshold range of the feed current is ,when or or At that time, the unit is determined to be abnormal; subsequently, the fault identification algorithm unit determines the fault type of the abnormal unit, which is divided into two categories: complete damage fault (marked as...). ) and some performance degradation faults (marked as The determination formula is: At the same time, the specific location coordinates of the faulty unit are accurately marked. Based on the degree of impact of the fault on performance, the fault level is clearly defined, divided into Level 1 faults, which affect core performance, and Level 2 faults, which do not affect core performance. This provides accurate fault information for subsequent fault-tolerant refactoring, ensuring that fault-tolerant operations can be carried out in a targeted manner and improving the efficiency and effectiveness of fault tolerance.

[0029] like Figure 1 As shown, in step six, the reconfiguration of the incentive weights is as follows: for the identified faulty units, the layout reconstruction logic is triggered to adjust the clustering distribution of the remaining normal units, and the power supply incentive weights of the remaining normal units are redistributed in conjunction with the adaptive adjustment of the power supply network. The core technical means of achieving fault-tolerant performance compensation aims to compensate for the performance loss caused by the absence of faulty units by adjusting the excitation weights of the remaining normal units, thereby solving the problem of significant performance degradation and inability to continue working after a fault. The specific implementation method is as follows: For the faulty units identified in step five, whether they are completely damaged or have partially degraded performance, the layout reconstruction logic is automatically triggered to adjust the clustering distribution of the remaining normal units, so that the remaining units can re-form a reasonable core gain region and edge scan region topology to ensure the rationality of the overall layout. Simultaneously, the power supply excitation weights of the remaining normal units are redistributed, assuming the number of faulty units is... The number of remaining normal units is The initial incentive weights are ( ), Reconfigured incentive weights The calculation formula is: in, For the first The formula for the gain contribution of each normal unit ensures that units with high gain contribution receive higher excitation weights, further enhancing core performance. At the same time, in conjunction with the adaptive adjustment module of the feed network, the layout parameters after reconstruction are matched in real time, and the feed phase and amplitude are adjusted to ensure that the antenna as a whole can still maintain the core performance of high gain and low grating lobe, and performance compensation after failure can be achieved without manual intervention.

[0030] like Figure 1 As shown, in step seven, the strategy threshold iteration includes: continuously collecting antenna performance data after self-calibration correction and fault-tolerant adaptation, feeding this data back to the reinforcement learning optimization model, and iteratively optimizing the self-calibration threshold, compensation priority division rules, fault-tolerant adaptation strategy, and feed matching parameters through model iteration; this is key to continuously improving the robustness of the system. Its core purpose is to solve the problem of fixed parameters that cannot adapt to changes in complex working environments through continuous iterative optimization. The specific implementation method is as follows: During antenna operation, antenna performance data, including overall gain, is continuously collected after self-calibration correction and fault-tolerant adaptation. Sidelobe level Scan range Core performance parameters; The above performance data is fed back into the reinforcement learning optimization model, and the model uses... maximize, minimize, Meeting design requirements is the optimization objective. The core parameters, including the self-calibration threshold, are iteratively optimized using the gradient descent algorithm. (Phase deviation threshold) (Position offset threshold), the threshold in the compensation priority division rule. Fault determination threshold in fault tolerance adaptation strategy and power supply matching parameters The feed amplitude adjustment coefficient, and the objective function for iterative optimization are: in, As a baseline overall gain, As the reference sidelobe level, To design the scanning range, The weighting coefficients are set according to actual needs to meet the requirements. Through continuous iterative optimization, the system can adapt to changes in complex working environments, continuously improve its robustness, and ensure long-term stable operation of the antenna.

[0031] like Figure 2 As shown, a high-gain phased array antenna element layout system is provided. This layout system is mainly applicable to any of the high-gain phased array antenna element layout methods mentioned above, and mainly includes: Layout parameter management module: responsible for the input, storage and retrieval of initial layout parameters, establishing a database linking layout parameters and hardware performance, supporting real-time parameter updates and traceability, and providing data support for self-calibration and fault-tolerant adaptation; Specifically, this includes initial cell layout parameters, cell radiation characteristic data, power supply parameter data, and hardware installation baseline data generated based on dynamic clustering topology. Simultaneously, this module establishes a database linking layout parameters and hardware performance, supporting real-time parameter updates and traceability. When layout parameters or hardware performance data change, the database is updated synchronously, ensuring data accuracy and timeliness. This provides core data support for subsequent error acquisition and calibration compensation coefficient calculation in the real-time self-calibration module, as well as fault identification in the hardware fault tolerance detection module and parameter adjustment in the adaptive module, ensuring data continuity and collaborative operation among all modules. Real-time self-calibration module: includes an electromagnetic induction sensor, a phase detection unit and a calibration algorithm unit. Its core function is to collect unit position, phase and gain data in real time, calculate compensation coefficients and output parameter correction instructions. Unlike the static calibration of existing patents, it realizes dynamic real-time calibration during the working process. This module is the core execution module for real-time calibration, comprising three sub-units: an electromagnetic induction sensor, a phase detection unit, and a calibration algorithm unit. The electromagnetic induction sensor is used to collect the position data of each antenna element in real time, the phase detection unit is used to collect the feed phase deviation and gain attenuation data of each element in real time, and the calibration algorithm unit is used to compare the collected real-time data with the initial reference data, generate calibration compensation coefficients through a preset algorithm, and output parameter correction instructions. It can continuously perform self-calibration operations throughout the entire antenna operation process, realize dynamic real-time calibration during operation, and effectively avoid performance drift. Hardware fault-tolerant detection module: Composed of unit status monitoring unit and fault identification algorithm unit, it can quickly identify faulty units by monitoring unit radiation signals and feed current parameters, mark fault level and type, and provide a basis for fault-tolerant reconstruction without the need for additional detection hardware, thus reducing system cost; The unit status monitoring unit monitors the radiated signal intensity and feed current operating parameters of each antenna unit in real time. The fault identification algorithm unit compares the real-time parameters with the normal threshold to quickly identify the faulty unit and mark the fault level and type, providing an accurate basis for subsequent fault-tolerant reconstruction. This module does not require additional detection hardware and can directly use existing monitoring resources to achieve fault detection, effectively reducing system costs while improving the efficiency and accuracy of fault identification. Adaptive module: integrates micro electronic control adjustment mechanism and power supply network adaptive adjustment unit, receives calibration correction command and fault-tolerant reconstruction command, completes unit position adjustment and power supply parameter matching, realizes dynamic adaptation of layout and hardware, and does not require additional design of adaptation structure; This module is the core execution module for parameter correction and fault-tolerant adaptation. It integrates two sub-units: a micro-electronic control adjustment mechanism and a feed network adaptive adjustment unit. The micro-electronic control adjustment mechanism is used to receive calibration correction commands and fault-tolerant reconstruction commands to fine-tune the physical position coordinates of the antenna element. The feed network adaptive adjustment unit is used to synchronously adjust the feed phase and amplitude parameters of the element, completing the dynamic adaptation of the layout and hardware. No additional adaptation structure design is required, which simplifies the system complexity and improves the engineering feasibility. Closed-loop control module: As the core of the system, it connects all the above modules to realize closed-loop control of data acquisition, command issuance, performance feedback and iterative optimization. It coordinates the collaborative work of each module to ensure the synchronization of self-calibration, fault tolerance adaptation and dynamic changes in layout topology, and improves the stability and reliability of system engineering implementation. Specifically, this module receives error data from the real-time self-calibration module and fault data from the hardware fault tolerance detection module, and sends parameter correction instructions and fault tolerance reconstruction instructions to the adaptive adaptation module. At the same time, it collects the performance data after adaptive adaptation and feeds it back to the reinforcement learning optimization model to iteratively optimize the parameters of each module and coordinate the collaborative work of each module to ensure the synchronization of self-calibration, fault tolerance adaptation and dynamic changes in layout topology, thereby greatly improving the stability and reliability of the system engineering implementation.

[0032] like Figure 2As shown, the construction of the associated database refers to the process performed by the layout parameter management module. This module associates and binds the initial unit layout parameters generated based on dynamic clustering topology with antenna unit radiation characteristic data, feed parameter data, and hardware installation reference data to build a real-time updated and traceable associated database. This database stores the aforementioned data types and supports on-demand retrieval. It provides the core data foundation for error acquisition and calibration compensation coefficient calculation in the real-time self-calibration module, as well as fault identification in the hardware fault tolerance detection module and parameter adjustment in the adaptive adaptation module. The layout parameter management module's core function is also the foundation for the entire system to achieve collaborative operation. Its core purpose is to address the shortcomings of isolated system data that cannot support the collaborative operation of various modules. The specific implementation method is as follows: The construction of the relational database is performed by the layout parameter management module. First, it obtains the initial cell layout parameters generated based on the dynamic clustering topology of the core gain region and the edge scan region, including the three-dimensional coordinates of each cell. First, the element number is determined. Then, the initial element layout parameters are compared with the antenna element radiation characteristic data (such as radiation efficiency). Polarization mode), feed parameter data (such as reference feed phase) Reference feed amplitude ) and hardware installation baseline data (such as installation error thresholds) Hardware compatibility coefficient To establish associations and bind data, a real-time updated and traceable association database is constructed. The storage structure of this relational database adopts a relational database structure, and its data relationships can be represented as follows: in, For the first The numbering of each antenna element, For the first The set of layout parameters for each unit. For the first The set of radiation characteristics and feed parameters of each unit. For the first A set of hardware installation baseline data for each unit; This database is used to store the above-mentioned types of data and supports on-demand access. When the real-time self-calibration module needs to collect error data, it can retrieve the initial benchmark data from the database for comparison. When the hardware fault tolerance detection module needs to identify faults, it can retrieve the normal operating parameter thresholds from the database. When the adaptive adaptation module needs to adjust parameters, it can retrieve the layout parameters and hardware adaptation data from the database, providing a core data foundation for the work of each module, ensuring that the entire system can operate collaboratively and efficiently, and improving the engineering feasibility and reliability of the system.

[0033] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for arranging high-gain phased array antenna elements, characterized in that, This layout method mainly includes the following steps: Step 1: Initial layout parameter generation. Based on the dynamic clustering topology of the core gain region and the edge scanning region, the initial cell layout parameters are generated. The cell radiation characteristics, power supply parameters and hardware installation baseline data are entered simultaneously, and a mapping table between layout parameters and hardware performance is established. Step 2: Real-time self-calibration is performed. Throughout the entire antenna operation process, the built-in electromagnetic induction sensor and phase detection module collect data on unit position offset, phase deviation, and gain attenuation in real time. By comparing the collected multi-dimensional errors with the initial reference data, calibration compensation coefficients are generated. Step 3: Compensation coefficient verification and priority ranking. Input the calibration compensation coefficients generated in Step 2 into the preset performance impact assessment model to verify the validity of the coefficients and eliminate invalid compensation caused by abnormal data. At the same time, prioritize the compensation needs of each unit based on the gain contribution. Prioritize the allocation of compensation resources to core gain area units and edge units in the beam scanning direction, and postpone the compensation of non-critical area units. Step 4: Dynamic parameter correction. Based on the calibration compensation coefficient, adjust the position coordinates, feed phase, and amplitude of the corresponding unit. Step 5: Hardware fault tolerance identification and adaptation. Through the unit fault detection module, the working status of each antenna unit is monitored in real time, the unit damage and power supply link failure are identified, and the location and type of faulty unit are marked. Step 6: Fault-tolerant reconstruction and performance compensation. For the faulty unit, the layout reconstruction logic is triggered to adjust the clustering distribution and excitation weight of the remaining units. The layout parameters are matched with the reconstructed layout parameters through the power supply network adaptive adjustment module. Step 7: Closed-loop iterative optimization. Continuously collect calibration data and performance data after fault tolerance adaptation, and feed them back to the reinforcement learning optimization model to iteratively optimize the self-calibration threshold, compensation priority rules, fault tolerance adaptation strategy and power supply matching parameters.

2. The high-gain phased array antenna element layout method according to claim 1, characterized in that: In step one, the establishment of the association mapping table includes: based on the initial unit layout parameters generated by the dynamic clustering topology of the core gain region and the edge scanning region, combined with the antenna unit radiation characteristics, feed parameters and hardware installation reference data, constructing an association mapping table between the layout parameters and hardware performance, which is used to store and call the initial reference data, and provide data support for subsequent real-time self-calibration and parameter correction.

3. The high-gain phased array antenna element layout method according to claim 2, characterized in that: In step two, multi-dimensional error acquisition includes: real-time acquisition of three types of core error data throughout the entire antenna operation process using a built-in electromagnetic induction sensor and phase detection module, including real-time data on unit position offset caused by processing error, temperature deformation and vibration, feed phase deviation, and unit gain attenuation data.

4. The high-gain phased array antenna element layout method according to claim 3, characterized in that: In step three, the priority ranking of compensation requirements includes: inputting the generated calibration compensation coefficients into a preset performance impact assessment model, and after verifying the validity of the coefficients, prioritizing the compensation requirements of all units based on the contribution of each antenna unit to the overall radiation gain, and giving priority to ensuring compensation resources for core gain area units and edge units in the beam scanning direction.

5. The high-gain phased array antenna element layout method according to claim 4, characterized in that: In step four, the parameter adjustment of the electronic control mechanism includes: according to the calibration compensation coefficient and priority ranking results, the physical position coordinates of the antenna elements of the corresponding priority are finely adjusted through the integrated micro electronic control adjustment mechanism, and the feed phase and amplitude parameters of the elements are adjusted synchronously to ensure the stability of the layout topology and avoid antenna performance drift caused by parameter deviation.

6. The high-gain phased array antenna element layout method according to claim 5, characterized in that: In step five, the fault type is labeled as follows: the working status of each antenna unit is monitored in real time through the unit status monitoring unit and the fault identification algorithm unit. After identifying unit damage or abnormal power supply link faults, the specific location of the fault unit is accurately marked, and the fault type and fault level are clearly labeled, providing an accurate basis for subsequent fault-tolerant reconstruction.

7. A high-gain phased array antenna element layout method according to claim 6, characterized in that: In step six, the reassignment of incentive weights is as follows: for the identified faulty units, the layout reconstruction logic is triggered to adjust the clustering distribution of the remaining normal units, and the power supply incentive weights of the remaining normal units are redistributed in conjunction with the adaptive adjustment of the power supply network.

8. A high-gain phased array antenna element layout method according to claim 7, characterized in that: In step seven, the strategy threshold iteration includes: continuously collecting antenna performance data after self-calibration correction and fault tolerance adaptation, feeding the data back to the reinforcement learning optimization model, and optimizing the self-calibration threshold, compensation priority division rules, fault tolerance adaptation strategy and feed matching parameters through model iteration.

9. A high-gain phased array antenna element layout system, characterized in that, A high-gain phased array antenna element layout method applicable to any one of claims 1-8, the system comprising: Layout parameter management module: responsible for the input, storage and retrieval of initial layout parameters, establishing a database linking layout parameters and hardware performance, supporting real-time parameter updates and traceability, and providing data support for self-calibration and fault-tolerant adaptation; Real-time self-calibration module: includes an electromagnetic induction sensor, a phase detection unit and a calibration algorithm unit. Its core function is to collect unit position, phase and gain data in real time, calculate compensation coefficients and output parameter correction instructions. Unlike the static calibration of existing patents, it realizes dynamic real-time calibration during the working process. Hardware fault-tolerant detection module: includes a unit status monitoring unit and a fault identification algorithm unit; the unit status monitoring unit is used to collect unit radiation signals and feed current parameters, and the fault identification algorithm unit is used to identify faulty units based on the parameters collected by the unit status monitoring unit, mark the fault level and fault type of the faulty units, and output the fault identification results as the basis for fault-tolerant reconstruction. Adaptive module: integrates micro electronic control adjustment mechanism and power supply network adaptive adjustment unit, receives calibration correction command and fault-tolerant reconstruction command, completes unit position adjustment and power supply parameter matching, and realizes dynamic adaptation of layout and hardware; Closed-loop control module: Connects all the above modules to realize closed-loop control of data acquisition, command issuance, performance feedback and iterative optimization, coordinates the collaborative work of each module, and ensures the synchronization of self-calibration, fault tolerance adaptation and dynamic changes in layout topology.

10. A high-gain phased array antenna element layout system according to claim 9, characterized in that: The construction of the associated database refers to the process executed by the layout parameter management module, which associates and binds the initial unit layout parameters generated based on dynamic clustering topology with antenna unit radiation characteristic data, feed parameter data, and hardware installation reference data to build an as-updable and traceable associated database. This database is used to store the above-mentioned data and supports on-demand access, providing a core data foundation for error acquisition and calibration compensation coefficient calculation of the real-time self-calibration module, as well as fault identification of the hardware fault tolerance detection module and parameter adjustment of the adaptive adaptation module.

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