Phased-array antenna dynamic control method and system

Abstract

The invention relates to a phased-array antenna dynamic control method and system. The method comprises the following steps: collecting high-dynamic scene real-time environment data and target movement track information, inputting a beam scanning model, performing classification processing, and outputting an initial step length range and a beam pointing precision constraint condition; taking the initial step length range and the precision constraint as input, quantitatively matching the scanning speed and the real-time requirement through a self-adaptive step length adjustment algorithm, and obtaining an optimized step length configuration scheme adapted to a scene; on the basis of the scheme, an optimized beam parameter is generated through beam shape optimization and pointing deviation fine adjustment, and an updated control instruction is formed by combining a side lobe suppression threshold value and anti-interference demand adjustment; and correcting parameters of the control instruction through an iterative adjustment algorithm, balancing the scanning speed and the pointing precision in combination with the real-time operation state of the antenna, and outputting a dynamic control result. According to the method, the response timeliness, the pointing accuracy and the environment adaptability of the antenna in a high-dynamic scene are remarkably improved, and the control performance is ensured to be matched with application requirements.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of electronic information engineering, and particularly relates to a phased array antenna dynamic control method and system. BACKGROUND

[0002] In the fields of radar detection, satellite communication, navigation and positioning, etc., the phased array antenna becomes a core key component due to the advantages of rapid beam scanning and flexible pointing. In particular, in high dynamic scenarios (such as high-speed moving target tracking and complex electromagnetic environment operation), the real-time performance, accuracy and anti-interference capability of the beam pointing are strictly required. The existing phased array antenna dynamic control method mainly adopts fixed step scanning or single parameter correction strategy, which has the problems of poor step configuration and scene adaptability, and the beam pointing accuracy and scanning speed are difficult to balance. In addition, in the complex interference environment, the collaborative optimization of sidelobe suppression and dynamic target tracking is insufficient, which leads to the delay of beam pointing response, low iteration efficiency of deviation correction, and cannot meet the high-precision, fast-response and strong-adaptation requirements of antenna control in high dynamic scenarios. SUMMARY

[0003] Therefore, it is necessary to provide a phased array antenna dynamic control method and system which can significantly improve the response and real-time performance, pointing accuracy and environmental adaptability of the phased array antenna in high dynamic scenarios, and ensure that the antenna control performance is highly matched with the actual application requirements.

[0004] In a first aspect, the application provides a phased array antenna dynamic control method, comprising:

[0005] Obtaining real-time environment data and target moving trajectory information of a high dynamic scenario, and classifying and processing the real-time environment data and target moving trajectory information by using a pre-established beam scanning model to obtain an initial step range and a beam pointing accuracy constraint condition.

[0006] Determining a beam scanning speed and real-time requirement by using an adaptive step adjustment algorithm based on the initial step range and the beam pointing accuracy constraint condition, and obtaining an optimized step configuration scheme adapted to the current scenario.

[0007] Optimizing and fine-tuning the beam shape based on the optimized step configuration scheme to obtain optimized beam parameters, and obtaining updated control instructions in combination with the sidelobe suppression requirement based on the optimized beam parameters.

[0008] Gradually correcting the updated control instructions by using an iterative adjustment algorithm, and comprehensively balancing the scanning speed and pointing accuracy in combination with the real-time running state to obtain a dynamic control output result of the phased array antenna.

[0009] In one of the embodiments, the real-time environment data and target moving trajectory information are classified and processed by using a pre-established beam scanning model to obtain initial step range and beam pointing accuracy constraint conditions, including:

[0010] The real-time environment data and target moving trajectory information are preprocessed to obtain adaptive data; the preprocessing includes denoising processing, space-time coordinate alignment, and data integrity verification.

[0011] The pre-established beam scanning model is called to combine the target motion state, environment interference level in the high dynamic scene to perform hierarchical classification processing on the adaptive data, and a hierarchical classification processing result is obtained.

[0012] Based on the hierarchical classification processing result, control step optimization related feature parameters are extracted, and after effectiveness screening and boundary constraint calculation, initial step range and beam pointing accuracy constraint conditions are obtained; the feature parameters include target dynamic change rate, environment interference intensity threshold, and beam scanning response delay threshold.

[0013] In one of the embodiments, based on the initial step range and beam pointing accuracy constraint conditions, an adaptive step adjustment algorithm is used to determine the beam scanning speed and real-time requirement to obtain an optimized step configuration scheme adapted to the current scene, including:

[0014] Based on the initial step range and beam pointing accuracy constraint conditions, the target dynamic characteristics and task priority of the current high dynamic scene are combined to obtain real-time requirement information of the current scene.

[0015] The initial step range, beam pointing accuracy constraint conditions, and real-time requirement information are fused by using an adaptive step adjustment algorithm to obtain preliminary step adjustment parameters.

[0016] Based on the preliminary step adjustment parameters, the beam scanning speed configuration is determined through a step-scan speed mapping model.

[0017] The beam scanning speed configuration is matched and verified with the beam pointing accuracy constraint conditions and real-time requirement information in multiple dimensions, and a matching degree index is obtained by weighted fusion of the adaptation degrees of each dimension.

[0018] If the matching degree index meets a preset threshold, the preliminary step adjustment parameters are output as an adaptive step configuration scheme.

[0019] If the matching degree index does not meet the preset threshold, the preliminary step adjustment parameters are adjusted based on the matching degree index and the core reason for not meeting the threshold to obtain an optimized step configuration scheme.

[0020] In one of the embodiments, the step-scan speed mapping model is represented by the following formula:

[0021]

[0022] wherein, represents the beam scanning speed, represents the scene adaptation coefficient, which is calibrated by the environmental interference level of high dynamic scene, and the value range is , represents the preliminary step adjustment parameter, represents the task priority weight, which is set by the current scene task demand, represents the maximum allowed deviation of the beam pointing accuracy constraint, represents the target pointing accuracy demand of the current scene, represents the accuracy sensitive factor, and the value range is , represents the real-time acceleration in the target dynamic characteristic, represents the preset target maximum acceleration threshold, represents the basic scanning speed threshold.

[0023] In one of the embodiments, the matching degree index is calculated using the following formula:

[0024]

[0025] wherein, represents the matching degree index, , , respectively represent the accuracy adaptation weight, the real-time adaptation weight, and the dynamic tracking adaptation weight, which are dynamically allocated based on the current scene task priority, represents the actual achievable pointing accuracy corresponding to the beam scanning speed configuration, represents the demand accuracy in the beam pointing accuracy constraint condition, represents the maximum allowed pointing deviation, represents the accuracy sensitive adjustment factor, and the value range is , represents the actual response delay corresponding to the beam scanning speed configuration, represents the demand response delay in the real-time requirement information, represents the real-time acceleration in the target dynamic characteristic, represents the preset target maximum acceleration threshold, represents the anti-zero offset coefficient, , represents the dynamic sensitive factor, and the value range is .

[0026] In one of the embodiments, the beam shape optimization and fine tuning are performed based on an optimization step configuration scheme to obtain optimized beam parameters, and an updated control instruction is obtained based on the optimized beam parameters and a sidelobe suppression requirement, including:

[0027] Obtaining phase and amplitude adjustment control parameters of the optimization step configuration scheme.

[0028] Generating an initial beam pointing based on the phase and amplitude adjustment control parameters, and calculating a deviation value of the initial beam pointing from a target pointing.

[0029] Comparing the deviation value with an allowed deviation threshold in a preset beam pointing accuracy constraint condition, and if the deviation value exceeds the allowed deviation threshold, fine tuning the phase and amplitude adjustment control parameters according to a deviation compensation algorithm until the beam pointing deviation meets the allowed deviation threshold to obtain optimized beam parameters.

[0030] Based on a beam coverage range corresponding to the optimized beam parameters, combining a preset sidelobe suppression requirement, and obtaining interference signal data in the beam coverage range through signal detection.

[0031] Analyzing the strength distribution and frequency domain characteristics of the interference signal data, comparing the sidelobe corresponding strength of the interference signal data with a preset sidelobe suppression threshold, determining a sidelobe over-threshold region and a suppression requirement strength, and obtaining a beam sidelobe distribution adjustment scheme.

[0032] Based on the beam sidelobe distribution adjustment scheme, real-time fusion of target position dynamic update information is performed through a timing synchronization algorithm to calculate an actual response delay and a tracking error of the beam pointing; the dynamic update information includes a target real-time coordinate, a position change rate, and an update frequency.

[0033] Comparing the actual response delay with a preset response delay threshold and the actual tracking error with a preset tracking error threshold, respectively, to determine whether the real-time response capability of the beam pointing meets the preset requirement.

[0034] If the actual response delay and the actual tracking error do not exceed the corresponding threshold, an updated control instruction is generated.

[0035] If the actual response delay or the actual tracking error exceeds the corresponding threshold, the beam sidelobe distribution adjustment scheme is corrected based on a deviation compensation strategy to generate an updated control instruction.

[0036] In one of the embodiments, the updated control instruction is corrected step by step through an iterative adjustment algorithm, and the scanning speed and pointing accuracy are comprehensively balanced in combination with a real-time running state to obtain a dynamic control output result of the phased array antenna, including:

[0037] Driving the beam pointing adjustment by executing the updated control instruction to obtain dynamic deviation data of the updated beam pointing and the target position.

[0038] Based on dynamic deviation data, a hierarchical iterative adjustment algorithm based on deviation threshold is used to calculate the pointing correction amount step by step to obtain preliminary correction parameters; the preliminary correction parameters include phase correction amount and scan speed correction coefficient.

[0039] The scanning speed deviation is calculated by combining the preliminary calibration parameters with the real-time operating status of the phased array antenna. The balance weight is dynamically allocated according to the ratio of the scanning speed deviation to the pointing accuracy deviation. The real-time operating status includes the current scanning speed, load power, and environmental interference intensity.

[0040] The initial calibration parameters are weighted and adjusted according to the balance weights to generate optimized control commands.

[0041] The optimized control command drives the beam pointing to make secondary adjustments, and determines the dynamic control output results. The dynamic control output results include the final phase and amplitude correction values, the real-time beam scanning speed parameters, and the sidelobe distribution optimization parameters.

[0042] Secondly, this application also provides a phased array antenna dynamic control system, the system comprising:

[0043] The data acquisition module is used to acquire real-time environmental data and target movement trajectory information in high-dynamic scenes. It uses a pre-established beam scanning model to classify and process the real-time environmental data and target movement trajectory information to obtain the initial step size range and beam pointing accuracy constraints.

[0044] The step size optimization module is used to determine the beam scanning speed and real-time requirements based on the initial step size range and beam pointing accuracy constraints using an adaptive step size adjustment algorithm, thereby obtaining an optimized step size configuration scheme that adapts to the current scenario.

[0045] The instruction update module is used to optimize and fine-tune the beam shape based on the optimized step size configuration scheme to obtain the optimized beam parameters. Based on the optimized beam parameters and the sidelobe suppression requirements, the updated control instructions are obtained.

[0046] The deviation correction module is used to correct the updated control commands step by step through an iterative adjustment algorithm. It combines the real-time operating status to comprehensively balance the scanning speed and pointing accuracy, and obtains the dynamic control output result of the phased array antenna.

[0047] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0048] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method.

[0049] The above-mentioned phased array antenna dynamic control method, system, computer device and storage medium first acquire real-time environment data and target moving track information in a high dynamic scene through a data acquisition unit, input the two types of data into a pre-established beam scanning model, extract key features through classification processing, and output an initial step range and a beam pointing accuracy constraint condition adapted to the scene; then, using the initial step range and the beam pointing accuracy constraint condition as input, an adaptive step adjustment algorithm is adopted to quantitatively calculate the matching degree of the beam scanning speed and the real-time requirement, dynamically optimize the step parameter, and obtain an optimized step configuration scheme precisely adapted to the current scene; subsequently, based on the optimized step configuration scheme, the main lobe pointing and beam width of the beam are optimized through a beam shape optimization algorithm, and fine-tuning is performed in combination with pointing deviation checking to generate optimized beam parameters, and then the optimized beam parameters are further adjusted according to a preset sidelobe suppression threshold and interference signal suppression requirement to form updated control instructions; finally, the updated control instructions are input into a phased array antenna control unit, the phase correction amount, scanning speed coefficient and other parameters in the instructions are checked and corrected step by step through an iterative adjustment algorithm, and at the same time, the running state data of the antenna is collected in real time, the weight distribution of the scanning speed and the pointing accuracy is balanced based on a quantitative model, and finally the phased array antenna dynamic control result meeting the scene requirement is output. This method realizes precise adaptation of phased array antenna control to a high dynamic scene: the optimized step configuration scheme dynamically matches the scene characteristics through an adaptive algorithm, solving the problem of poor adaptability of a fixed step; the combination of beam parameter optimization and sidelobe suppression requirement realizes collaborative improvement of target tracking and anti-interference capability; the iterative adjustment algorithm and real-time running state are comprehensively balanced, effectively taking into account the beam scanning speed and the pointing accuracy, and avoiding performance imbalance caused by single parameter optimization. The response timeliness, pointing accuracy and environmental adaptability of the phased array antenna in a high dynamic scene are significantly improved, ensuring that the antenna control performance is highly matched with the actual application requirement. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the embodiments or the related art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0051] Figure 1 A flowchart of a phased array antenna dynamic control method provided by an embodiment of the present application;

[0052] Figure 2 A structural block diagram of a phased array antenna dynamic control system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0054] In one embodiment, as shown in the accompanying drawings, the present application provides a phased array antenna dynamic control method, which can include the following steps: Figure 1

[0055] Step S101, real-time environment data and target moving trajectory information of a high dynamic scene are acquired, and the real-time environment data and the target moving trajectory information are classified and processed by using a pre-established beam scanning model to obtain an initial step range and a beam pointing accuracy constraint condition.

[0056] Firstly, two types of core data under a high dynamic scene are acquired: the real-time environment data specifically include quantifiable parameters such as electromagnetic interference intensity, environment complexity level, and transmission channel attenuation coefficient; and the target moving trajectory information specifically covers data such as target real-time three-dimensional coordinates, instantaneous motion speed, acceleration, and motion trajectory prediction error. Subsequently, the above two types of data are synchronously input into a pre-established beam scanning model, the model extracts features from the input data based on scene complexity grading rules (such as three levels of low, medium, and high) and target motion state classification rules (such as uniform speed, acceleration, and variable acceleration motion), and filters out key parameters related to beam scanning. Finally, through the built-in quantitative calculation logic of the model, an initial step range (represented by a step unit L, specifically [L_min, L_max], where L_min is the minimum scanning step and L_max is the maximum scanning step, determined by the scene complexity and target motion speed threshold) and a beam pointing accuracy constraint condition (specifically a pointing deviation allowed threshold θ_max, which is pre-set according to the accuracy requirement of the target tracking task) that adapt to the current scene are output.

[0057] Step S102, based on the initial step range and the beam pointing accuracy constraint condition, an adaptive step adjustment algorithm is used to determine the beam scanning speed and the real-time requirement, and an optimized step configuration scheme that adapts to the current scene is obtained.

[0058] ​Specifically, using initial constraints as input, the algorithm optimizes the step size to achieve precise adaptation to the scene. Specifically, the initial step size range [L_min, L_max] and the beam pointing accuracy constraint threshold θ_max are used as input parameters for the adaptive step size adjustment algorithm. The core logic of this algorithm is to establish a quantitative matching model between the beam scanning speed v and the real-time requirement T: v = T × k (where k is the scene adaptation coefficient, dynamically determined by the environmental interference intensity and the target's motion acceleration; the stronger the interference and the greater the acceleration, the larger the value of k). This model iteratively calculates the matching degree between the scanning speed and the real-time requirement under different step sizes, eliminating step size parameters that exceed the initial step size range or cannot meet the pointing accuracy constraint. Finally, a unique optimized step size value or dynamic step size adjustment rule is determined, forming an optimized step size configuration scheme adapted to the current scene (such as a high-speed moving target or a strongly interfering environment).

[0059] Step S103: Based on the optimized step size configuration scheme, the beam shape is optimized and fine-tuned to obtain the optimized beam parameters. Based on the optimized beam parameters and the sidelobe suppression requirements, the updated control command is obtained.

[0060] Using the optimized step size as the core input, a beam shape optimization algorithm adjusts key parameters such as the main lobe pointing angle and beamwidth of the beam to initially adapt the beam to the target position and scanning requirements. Subsequently, a pointing deviation verification process is initiated: the deviation value Δθ between the current actual beam pointing and the target pointing is calculated, and Δθ is compared with a preset accuracy constraint threshold θ_max. If Δθ > θ_max, the phase compensation of the beam is fine-tuned until Δθ ≤ θ_max, generating optimized beam parameters (including main lobe pointing angle, beamwidth, and initial phase compensation). Next, combined with preset sidelobe suppression requirements (specifically reflected in the sidelobe suppression threshold P_max), the optimized beam parameters are further adjusted: by analyzing the beam sidelobe distribution, the sidelobe suppression weights are adjusted to ensure that the sidelobe intensity does not exceed P_max, achieving synergy between target tracking and anti-interference. Finally, the adjusted beam parameters (including phase correction, scanning speed coefficient, and sidelobe suppression weights) are encapsulated into updated control commands.

[0061] Step S104: The updated control commands are corrected step by step through an iterative adjustment algorithm. The scanning speed and pointing accuracy are balanced by combining the real-time operating status to obtain the dynamic control output result of the phased array antenna.

[0062] The generated updated control commands are input into the phased array antenna control unit to initiate beam pointing adjustment. An iterative adjustment algorithm is used to progressively correct the core parameters (phase correction and scanning speed coefficient) in the commands: first, the execution accuracy of individual parameters is verified, then the beam pointing effect of the combined parameters is verified. If a parameter causes the beam pointing deviation to exceed a threshold or the scanning speed to be substandard, the parameter is corrected based on the deviation value until both individual and combined parameters meet the basic requirements. Simultaneously, the antenna status monitoring module collects real-time operational status data, including the antenna's current load power, beam pointing response delay, and real-time changes in environmental interference. Based on this data, a quantized weight allocation model is used (ω is the scanning speed weight, ω is the pointing accuracy weight, and ω1+ω2=1). The weight ratio is dynamically adjusted according to scenario requirements (e.g., ω1>ω2 in high real-time scenarios, ω2>ω1 in high-precision scenarios), comprehensively balancing scanning speed and pointing accuracy. Finally, the phased array antenna dynamic control results after correction and balance optimization are output. These results include the final phase / amplitude control parameters, scanning speed command, and sidelobe distribution adjustment parameters, which directly drive the antenna to complete target tracking and beam control in high dynamic scenarios.

[0063] The aforementioned dynamic control method for phased array antennas collects real-time environmental data and target trajectory information in high-dynamic scenarios. The input beam scanning model is classified and processed, outputting initial step size range and beam pointing accuracy constraints. Using the initial step size range and accuracy constraints as input, an adaptive step size adjustment algorithm quantifies and matches scanning speed and real-time requirements to obtain an optimized step size configuration scheme adapted to the scenario. Based on this scheme, optimized beam parameters are generated through beam shape optimization and pointing deviation fine-tuning. These parameters are then adjusted in conjunction with sidelobe suppression thresholds and anti-interference requirements to form updated control commands. These control commands are input to the antenna control unit, where parameters are corrected through an iterative adjustment algorithm. The scanning speed and pointing accuracy are balanced by considering the antenna's real-time operating status, resulting in a dynamic control output. This method addresses the poor adaptability of fixed step sizes through adaptive step size optimization, and enhances target tracking and anti-interference capabilities through coordinated beam parameter optimization and sidelobe suppression. It balances scanning speed and pointing accuracy, significantly improving antenna response timeliness, pointing accuracy, and environmental adaptability in high-dynamic scenarios, ensuring that control performance matches application requirements.

[0064] In one embodiment, the real-time environmental data and target trajectory information are classified and processed using a pre-established beam scanning model to obtain the initial step size range and beam pointing accuracy constraints, which may include the following steps:

[0065] Step S201: Preprocess the real-time environmental data and target movement trajectory information to obtain adaptive data; the preprocessing includes noise reduction, spatiotemporal coordinate alignment and data integrity verification.

[0066] Step S202: Call the pre-established beam scanning model, and combine the target motion state and environmental interference level under high dynamic scene to perform hierarchical classification processing on the adaptability data to obtain the hierarchical classification processing result.

[0067] Step S203: Based on the hierarchical classification processing results, extract relevant feature parameters for control step size optimization. After effectiveness screening and boundary constraint calculation, obtain the initial step size range and beam pointing accuracy constraints. The feature parameters include target dynamic change rate, environmental interference intensity threshold, and beam scanning response delay threshold.

[0068] Specifically, preprocessing operations are first performed on real-time environmental data and target movement trajectory information in high-dynamic scenarios. This includes using filtering algorithms for noise reduction to eliminate environmental noise and equipment acquisition errors, aligning the spatiotemporal coordinates of the two types of data through spatiotemporal coordinate transformation, and performing data integrity verification (completing missing data and removing invalid data) based on preset integrity judgment rules. Finally, adaptive data with uniform format and meeting accuracy standards is obtained. Subsequently, a pre-established beam scanning model is called, using the adaptive data as input. The adaptive data is classified and processed according to the target motion state (e.g., uniform speed, acceleration, variable acceleration) and environmental interference level (e.g., low, medium, high) in high-dynamic scenarios. The classification and processing results corresponding to the motion state and interference level are output. Based on the classification and processing results, feature parameters related to control step size optimization, such as target dynamic change rate, environmental interference intensity threshold, and beam scanning response delay threshold, are extracted. The extracted feature parameters are screened for effectiveness (outliers and redundant values ​​are removed), and boundary constraint calculations are performed according to the application requirements of high-dynamic scenarios (setting reasonable upper and lower limits for each parameter). Finally, the initial step size range and beam pointing accuracy constraints are obtained.

[0069] In this embodiment, the preprocessing stage ensures the accuracy, consistency, and integrity of the input data through noise reduction, spatiotemporal alignment, and integrity verification, avoiding interference from invalid or biased data in subsequent processing. Combined with hierarchical classification processing based on target motion state and environmental interference level, the data processing is more aligned with the differentiated characteristics of high-dynamic scenarios, improving the scene adaptability of the processing results. The effective screening of feature parameters and the calculation of boundary constraints ensure the reliability and rationality of core feature parameters, thereby making the initial step size range and beam pointing accuracy constraints of the output more precise.

[0070] In one embodiment, an adaptive step size adjustment algorithm is used to determine the beam scanning speed and real-time requirements based on the initial step size range and beam pointing accuracy constraints, resulting in an optimized step size configuration scheme adapted to the current scenario. This may include the following steps:

[0071] Step S301: Based on the initial step size range and beam pointing accuracy constraints, and combined with the target dynamic characteristics and task priority of the current high dynamic scene, obtain the real-time requirement information of the current scene.

[0072] Step S302: The adaptive step size adjustment algorithm is used to fuse the initial step size range, beam pointing accuracy constraints and real-time requirements to obtain the preliminary step size adjustment parameters.

[0073] Step S303: Based on the initial step size adjustment parameters, determine the beam scanning speed configuration through the step size-scanning speed mapping model.

[0074] Step S304: Perform multi-dimensional matching verification of beam scanning speed configuration with beam pointing accuracy constraints and real-time requirements, and calculate the weighted fusion of the adaptability of each dimension to obtain the matching index.

[0075] Step S305: If the matching degree index meets the preset threshold, the initial step size adjustment parameters are output as the adaptation step size configuration scheme.

[0076] Step S306: If the matching degree index does not meet the preset threshold, then based on the matching degree index and the core reasons for not meeting the threshold, the initial step size adjustment parameters are adjusted to obtain an optimized step size configuration scheme.

[0077] Based on the initial step size range and beam pointing accuracy constraints, and combined with the target's dynamic characteristics (including real-time velocity, acceleration, and trajectory change frequency) and task priorities (such as real-time priority or pointing accuracy priority) in the current high-dynamic scenario, the real-time requirements of the current scenario are determined through quantitative analysis. These requirements include quantifiable indicators such as the beam pointing response delay threshold and the upper limit of the step size adjustment cycle. Subsequently, an adaptive step size adjustment algorithm is employed, using the initial step size range, beam pointing accuracy constraints, and real-time requirements as core inputs. A multi-parameter fusion model is established to perform weighted calculations and iterative analysis on the three types of inputs, outputting preliminary step size adjustment parameters (including the basic step size value, dynamic step size adjustment amplitude, and adjustment trigger threshold). Based on these preliminary step size adjustment parameters, a pre-built step size adjustment algorithm is invoked. A scanning speed mapping model (trained with extensive scene data to establish a quantitative correspondence between step size and beam scanning speed) is used to calculate a beam scanning speed configuration that matches the initial step size. This beam scanning speed configuration is then further validated through multi-dimensional matching with beam pointing accuracy constraints and real-time requirements. The compatibility between scanning speed and real-time requirements, scanning speed and pointing accuracy constraints, and step size parameters and scene characteristics are calculated. Weights for each dimension of compatibility are assigned based on task priority, and a weighted summation is used to obtain a comprehensive matching index. If this comprehensive matching index is greater than or equal to a preset threshold, the initial step size adjustment parameters are deemed to meet the current scene requirements, and this is directly output as the adapted step size configuration scheme. If the comprehensive matching index is less than the preset threshold, the core reasons for not meeting the threshold are analyzed (e.g., scanning speed not meeting real-time requirements, excessive step size leading to excessive pointing accuracy, etc.). Based on the shortcomings in each dimension of the matching index, the basic value or dynamic adjustment range of the initial step size adjustment parameters is specifically corrected. After repeated validation, an optimized step size configuration scheme is obtained.

[0078] This embodiment combines the dynamic characteristics of the target and the priority of tasks to determine the real-time requirements, ensuring that the requirements are aligned with the actual application scenario. The adaptive step size adjustment algorithm integrates multiple constraints to avoid configuration deviations caused by a single parameter. The introduction of the step size-scan speed mapping model establishes a clear correlation between step size and scan speed, improving the quantification of the configuration. Multi-dimensional matching verification and weighted fusion calculation of matching degree indicators comprehensively verify the adaptability of the configuration scheme and reduce the limitations of single-dimensional judgment. The feedback adjustment mechanism corrects parameters for the core reasons for not meeting the threshold, further improving the rationality and reliability of the step size configuration.

[0079] In one embodiment, the step size-scan speed mapping model can be expressed by the following formula:

[0080]

[0081] in, Indicates beam scanning speed. The scene adaptation coefficient is determined by the environmental interference level of the high dynamic scene, and its value range is [missing information]. , This indicates the initial step size adjustment parameter. This indicates the task priority weight, which is set according to the task requirements of the current scenario. This indicates the maximum permissible deviation of the beam pointing accuracy constraint. This indicates the target pointing accuracy requirement for the current scenario. This represents the precision sensitivity factor, and its value range. , This represents the real-time acceleration in the target's dynamic characteristics. This indicates the preset target maximum acceleration threshold. This indicates the baseline scan speed threshold.

[0082] This embodiment's step-scan speed mapping model establishes a clear correlation between beam scanning speed and initial step-size adjustment parameters, scene adaptation coefficients, task priority weights, accuracy constraints, target dynamic characteristics, and basic scanning speed thresholds through quantization formulas. It also incorporates the environmental interference level of high-dynamic scenes (represented by k), task priority (represented by α), and beam pointing accuracy requirements (represented by k). (E, γ) and the target's real-time dynamics (through...) , By incorporating key elements from multiple dimensions such as the initial step size and beam scanning speed into the scanning speed calculation, a precise quantitative mapping from the initial step size to the beam scanning speed is achieved. This avoids the reliance on subjective experience in matching the step size and scanning speed, and ensures that the scanning speed configuration can synchronously adapt to the initial step size range, beam pointing accuracy constraints, and real-time requirements. This provides a precise and quantifiable speed benchmark for subsequent multi-dimensional matching verification, effectively improving the adaptability of the step size configuration scheme to high-dynamic scenarios, and laying a key model support for ensuring the balance between scanning speed, pointing accuracy, and real-time performance of phased array antennas.

[0083] In one embodiment, the matching degree index can be calculated using the following formula:

[0084]

[0085] in, This represents the matching degree metric. , , These represent the accuracy adaptation weight, real-time adaptation weight, and dynamic tracking adaptation weight, which are dynamically allocated based on the task priority in the current scenario. This indicates the actual achievable pointing accuracy corresponding to the beam scanning speed configuration. This represents the required accuracy in the beam pointing accuracy constraint. Indicates the maximum permissible pointing deviation. This represents the precision-sensitive adjustment factor, with a value range of... , This indicates the actual response delay corresponding to the beam scanning speed configuration. This indicates the delay in response to real-time requirements. This represents the real-time acceleration in the target's dynamic characteristics. This indicates the preset target maximum acceleration threshold. Indicates the zero-bias coefficient. , Represents the dynamic sensitivity factor, with a value range of .

[0086] This embodiment's matching index formula integrates the deviation between the actual achievable pointing accuracy and the required accuracy, the difference between the actual response delay and the required response delay, and the relationship between the target's real-time acceleration and the maximum acceleration threshold, through dynamically allocated precision adaptation weights, real-time adaptation weights, and dynamic tracking adaptation weights corresponding to the beam scanning speed configuration. It introduces a precision-sensitive adjustment factor λ, a dynamic sensitivity factor μ, and an anti-zero bias coefficient δ to achieve multi-dimensional quantitative fusion calculation of the precision adaptability, real-time adaptability, and dynamic tracking adaptability associated with the step size configuration. It dynamically matches the current scenario task priority through weights and ensures the sensitivity and stability of the calculation with the sensitivity factor and the anti-zero bias coefficient, avoiding the bias of single-dimensional judgment or subjective experience evaluation. It provides accurate and quantifiable judgment basis for the matching verification of the step size configuration scheme, further improving the adaptation accuracy of the step size configuration scheme to high dynamic scenarios, and providing key quantitative support for ensuring the balance of phased array antenna scanning speed, pointing accuracy, and dynamic tracking performance.

[0087] In one embodiment, beam shape optimization and fine-tuning are performed based on an optimized step size configuration scheme to obtain optimized beam parameters. Based on the optimized beam parameters and sidelobe suppression requirements, updated control commands are obtained, which may include the following steps:

[0088] Step S401: Obtain the phase amplitude adjustment control parameters of the optimized step size configuration scheme.

[0089] Step S402: Generate an initial beam pointing based on the phase amplitude adjustment control parameters, and calculate the deviation between the initial beam pointing and the target pointing.

[0090] Step S403: Compare the deviation value with the allowable deviation threshold in the preset beam pointing accuracy constraint. If the deviation value exceeds the allowable deviation threshold, fine-tune the phase amplitude adjustment control parameter according to the deviation compensation algorithm until the beam pointing deviation meets the allowable deviation threshold, and obtain the optimized beam parameters.

[0091] Step S404: Based on the beam coverage range corresponding to the optimized beam parameters and combined with the preset sidelobe suppression requirements, obtain interference signal data within the beam coverage range through signal detection.

[0092] Step S405: Analyze the intensity distribution and frequency domain characteristics of the interference signal data, compare the corresponding intensity of the sidelobes of the interference signal data with the preset sidelobe suppression threshold, determine the sidelobe over-threshold region and the required suppression intensity, and obtain the beam sidelobe distribution adjustment scheme.

[0093] Step S406: Based on the beam sidelobe distribution adjustment scheme, the target position dynamic update information is fused in real time using a timing synchronization algorithm to calculate the actual response delay and tracking error of the beam pointing; the dynamic update information includes the target's real-time coordinates, position change rate, and update frequency.

[0094] Step S407: Compare the actual response delay with the preset response delay threshold and the actual tracking error with the preset tracking error threshold to determine whether the real-time response capability of the beam pointing meets the preset requirements.

[0095] In step S408, if the actual response delay and the actual tracking error do not exceed the corresponding thresholds, an updated control command is generated.

[0096] Step S409: If the actual response delay or actual tracking error exceeds the corresponding threshold, the beam sidelobe distribution adjustment scheme is corrected based on the deviation compensation strategy, and an updated control command is generated.

[0097] Specifically, first, the phase amplitude adjustment control parameters corresponding to the optimized step size configuration scheme are obtained. Based on these parameters, an initial beam pointing is generated. The deviation between the initial beam pointing and the target pointing is calculated through positioning detection. This deviation is compared with the allowable deviation threshold in the preset beam pointing accuracy constraint. If the deviation exceeds the threshold, the deviation compensation algorithm is invoked to fine-tune the phase amplitude adjustment control parameters. This process of calculating the deviation and comparison is repeated until the beam pointing deviation meets the allowable deviation threshold, at which point the optimized beam parameters are output. Based on the beam coverage area corresponding to the optimized beam parameters, and combined with the preset sidelobe suppression requirements, interference signal data within the beam coverage area is collected through the signal detection module. Intensity distribution statistics and frequency domain feature analysis are performed on this data. The sidelobe intensity corresponding to the interference signal data is compared point-by-point with the preset sidelobe suppression threshold to determine the location, range, and suppression requirement intensity of the sidelobe exceeding the threshold region, thus forming... A beam sidelobe distribution adjustment scheme is proposed. Based on this scheme, a timing synchronization algorithm is used to fuse the target position dynamic update information (including the target's real-time coordinates, position change rate, and update frequency) in real time, and calculate the actual response delay and tracking error of the beam pointing. The actual response delay is compared with a preset response delay threshold, and the actual tracking error is compared with a preset tracking error threshold to determine whether the real-time response capability of the beam pointing meets the preset requirements. If neither the actual response delay nor the actual tracking error exceeds the corresponding threshold, an updated control command is directly generated based on the current beam parameters and the sidelobe adjustment scheme. If the actual response delay or the actual tracking error exceeds the corresponding threshold, the beam sidelobe distribution adjustment scheme is corrected based on the core reasons for exceeding the threshold (e.g., optimizing timing synchronization parameters if the response delay exceeds the standard, and adjusting the sidelobe suppression weight if the tracking error exceeds the standard) in conjunction with the deviation compensation strategy. Then, an updated control command is generated based on the corrected scheme.

[0098] This embodiment achieves precise matching of beam parameters with the requirements of high-dynamic scenarios: fine-tuning of phase amplitude parameters and deviation verification mechanisms ensure that beam pointing accuracy meets constraints; interference signal detection and sidelobe distribution adjustment effectively improve beam anti-interference capabilities and prevent sidelobe interference from affecting target tracking; a timing synchronization algorithm integrates target dynamic update information to ensure that beam pointing can keep up with target position changes in real time, reducing tracking errors and response delays; a two-way threshold comparison and deviation compensation correction mechanism can promptly identify and resolve shortcomings in real-time response and tracking accuracy, ensuring the reliability of control commands. The overall process achieves synergistic optimization of pointing accuracy, anti-interference capability, and real-time tracking performance, providing precise control support for the stable and efficient operation of phased array antennas in high-dynamic scenarios, and improving the adaptability and robustness of antenna control.

[0099] In one embodiment, the updated control commands are corrected step by step through an iterative adjustment algorithm. By combining the real-time operating status to comprehensively balance the scanning speed and pointing accuracy, the dynamic control output result of the phased array antenna is obtained, which may include the following steps:

[0100] Step S501: Execute the updated control command to drive beam pointing adjustment and obtain the updated dynamic deviation data between the beam pointing and the target position.

[0101] Step S502: Based on the dynamic deviation data, a hierarchical iterative adjustment algorithm based on the deviation threshold is used to calculate the pointing correction amount step by step to obtain the preliminary correction parameters; the preliminary correction parameters include the phase correction amount and the scan speed correction coefficient.

[0102] Step S503: Calculate the scanning speed deviation using the preliminary calibration parameters combined with the real-time operating status of the phased array antenna, and dynamically allocate the balancing weight according to the ratio of the scanning speed deviation to the pointing accuracy deviation; the real-time operating status includes the current scanning speed, load power, and environmental interference intensity.

[0103] Step S504: Adjust the preliminary correction parameters according to the balance weights to generate optimized control commands.

[0104] Step S505: Execute the optimization control command to drive the beam pointing secondary adjustment and determine the dynamic control output result; the dynamic control output result includes the final correction values ​​of phase and amplitude, beam real-time scanning speed parameters and sidelobe distribution optimization parameters.

[0105] Specifically, the dynamic deviation data between the adjusted beam pointing and the target position is obtained through the position detection module. Using this dynamic deviation data as input, a hierarchical iterative adjustment algorithm based on a deviation threshold is employed. The deviation is categorized into levels (e.g., small, medium, large) and the pointing correction is calculated step-by-step for different iteration step sizes, outputting preliminary correction parameters. These parameters include phase correction (used to correct beam pointing angle deviation) and scanning speed correction coefficient (used to adjust the beam scanning rate). Using these preliminary correction parameters, combined with the real-time operating status of the phased array antenna (including current scanning speed, load power, and environmental interference intensity), a quantization model is used to calculate the actual scanning speed and theoretical value. The deviation in scanning speed is dynamically weighted according to the ratio of this deviation to the beam pointing accuracy deviation (ω is the scanning speed weight and ω is the pointing accuracy weight, satisfying ω1+ω2=1). The phase correction and scanning speed correction coefficients in the initial correction parameters are weighted and adjusted according to this weighting to correct the parameter deviation and generate an optimized control command. The optimized control command is executed to drive a secondary beam pointing adjustment. Based on the adjusted beam operation data, the dynamic control output result is determined. This result specifically includes the final phase and amplitude correction values, real-time beam scanning speed parameters, and sidelobe distribution optimization parameters, providing direct parameter support for the subsequent stable operation of the antenna.

[0106] In this embodiment, real-time acquisition of dynamic deviation data provides accurate input for the calculation of correction parameters. The hierarchical iterative adjustment algorithm based on deviation thresholds improves the pertinence and accuracy of correction parameters, avoiding the low correction efficiency caused by a single iterative mode. By combining the real-time operation status of the antenna to calculate the scanning speed deviation and dynamically allocate balancing weights, the problem of difficulty in balancing scanning speed and pointing accuracy in traditional control is effectively solved, ensuring that parameter adjustments are adapted to the actual operating conditions of the antenna. Through secondary adjustment and optimization of control command generation, parameter deviations are further corrected, ensuring the reliability of dynamic control output results. The final output multi-dimensional parameters cover the core requirements of antenna operation, providing comprehensive support for the stable operation of phased array antennas in high dynamic scenarios, and significantly improving the adaptability, accuracy, and robustness of antenna control.

[0107] In one embodiment, such as Figure 2 As shown, this application also provides a phased array antenna dynamic control system, which may include:

[0108] The data acquisition module 601 is used to acquire real-time environmental data and target movement trajectory information of high dynamic scenes. It uses a pre-established beam scanning model to classify and process the real-time environmental data and target movement trajectory information to obtain the initial step size range and beam pointing accuracy constraints.

[0109] The step size optimization module 602 is used to determine the beam scanning speed and real-time requirements based on the initial step size range and beam pointing accuracy constraints using an adaptive step size adjustment algorithm, thereby obtaining an optimized step size configuration scheme that adapts to the current scenario.

[0110] The instruction update module 603 is used to optimize and fine-tune the beam shape based on the optimized step size configuration scheme to obtain the optimized beam parameters, and to obtain the updated control instructions based on the optimized beam parameters and the sidelobe suppression requirements.

[0111] The deviation correction module 604 is used to correct the updated control commands step by step through an iterative adjustment algorithm, and combine the real-time operating status to comprehensively balance the scanning speed and pointing accuracy to obtain the dynamic control output result of the phased array antenna.

[0112] The aforementioned phased array antenna dynamic control system achieves precise control through the closed-loop collaboration of four functional modules, with a clear data flow and logical progression: The data acquisition module first acquires real-time environmental data (including electromagnetic interference intensity and environmental complexity level) and target trajectory information (including target real-time coordinates, velocity, and acceleration) under high-dynamic scenarios through a preset acquisition unit. These two types of data are input into a pre-established beam scanning model. Key features are extracted through processing rules based on scene complexity classification and target motion state classification. The initial step size range and beam pointing accuracy constraints (pointing deviation allowable threshold) are output through quantization calculation, providing basic input for subsequent modules. The step size optimization module uses the initial step size range and beam pointing accuracy constraints as core inputs. It employs an adaptive step size adjustment algorithm to establish a quantized matching model between beam scanning speed and real-time requirements, dynamically iteratively optimizing the step size parameters to generate a model that matches the current... An optimized step size configuration scheme is precisely adapted to the scene (target motion state, environmental interference level). Based on this optimized step size configuration scheme, the command update module adjusts parameters such as main lobe pointing and beamwidth through a beam shape optimization algorithm. Combined with pointing deviation verification and fine-tuning, it generates optimized beam parameters. Then, according to the preset sidelobe suppression threshold and interference signal suppression requirements, it supplements and adjusts the phase compensation amount and sidelobe suppression weight in the beam parameters to form an updated control command. The deviation correction module inputs the updated control command into the antenna control unit. Through iterative adjustment algorithms, it verifies and corrects parameters such as phase correction amount and scanning speed coefficient in the command level by level. At the same time, it collects the real-time operating status data of the antenna (including current scanning speed, load power, and environmental interference intensity). Based on the quantized weight allocation model, it comprehensively balances the scanning speed and pointing accuracy, and finally outputs the dynamic control result of the phased array antenna that meets the scene requirements.

[0113] The classification processing of the data acquisition module in this embodiment ensures the accuracy of the initial constraints, providing a reliable foundation for subsequent optimization. The adaptive algorithm of the step size optimization module solves the problem of poor adaptability of traditional fixed step sizes to high-dynamic scenarios. The instruction update module achieves a synergistic improvement in target tracking accuracy and anti-interference capability by combining beam parameter optimization with sidelobe suppression requirements. The iterative adjustment and real-time state balancing of the deviation correction module effectively balance scanning speed and pointing accuracy, avoiding performance imbalance caused by single parameter optimization. The overall system significantly improves the response timeliness, pointing accuracy, and environmental adaptability of the phased array antenna in high-dynamic scenarios through quantization models, threshold constraints, and iterative optimization techniques. It ensures that the antenna control performance is precisely matched with actual application requirements, and the module division is clear and highly implementable, making it suitable for various high-dynamic target tracking and antenna control scenarios in complex electromagnetic environments.

[0114] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0115] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the phased array antenna dynamic control method as described above.

[0116] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0117] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0118] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A dynamic control method for a phased array antenna, characterized in that, The method includes: Real-time environmental data and target movement trajectory information of high dynamic scenes are acquired, and the real-time environmental data and target movement trajectory information are classified and processed using a pre-established beam scanning model to obtain the initial step size range and beam pointing accuracy constraints. Based on the initial step size range and beam pointing accuracy constraints, an adaptive step size adjustment algorithm is used to determine the beam scanning speed and real-time requirements, thereby obtaining an optimized step size configuration scheme that is suitable for the current scenario. Based on the optimized step size configuration scheme, the beam shape is optimized and fine-tuned to obtain the optimized beam parameters. Based on the optimized beam parameters and the sidelobe suppression requirements, the updated control command is obtained. The updated control commands are corrected step by step through an iterative adjustment algorithm. The scanning speed and pointing accuracy are balanced by combining the real-time operating status to obtain the dynamic control output result of the phased array antenna.

2. The method according to claim 1, characterized in that, The process of classifying and processing the real-time environmental data and target trajectory information using a pre-established beam scanning model to obtain initial step size range and beam pointing accuracy constraints includes: The real-time environmental data and target movement trajectory information are preprocessed to obtain adaptation data; the preprocessing includes noise reduction, spatiotemporal coordinate alignment, and data integrity verification. The pre-established beam scanning model is invoked, and the adaptability data is classified and processed according to the target motion state and environmental interference level in the high dynamic scene to obtain the classification and processing results. Based on the hierarchical classification processing results, relevant feature parameters for control step size optimization are extracted. After effectiveness screening and boundary constraint calculation, the initial step size range and beam pointing accuracy constraints are obtained. The feature parameters include the target dynamic change rate, environmental interference intensity threshold, and beam scanning response delay threshold.

3. The method according to claim 1, characterized in that, The adaptive step size adjustment algorithm, based on the initial step size range and beam pointing accuracy constraints, determines the beam scanning speed and real-time requirements, resulting in an optimized step size configuration scheme adapted to the current scenario, including: Based on the initial step size range and beam pointing accuracy constraints, and combined with the target dynamic characteristics and task priorities of the current high-dynamic scene, the real-time requirements information of the current scene are obtained. An adaptive step size adjustment algorithm is used to fuse the initial step size range, beam pointing accuracy constraints and real-time requirements to obtain preliminary step size adjustment parameters. Based on the initial step size adjustment parameters, the beam scanning speed configuration is determined through the step size-scanning speed mapping model; The beam scanning speed configuration is matched and verified with the beam pointing accuracy constraints and real-time requirements in multiple dimensions, and the matching degree index is obtained by weighted fusion of the adaptability of each dimension. If the matching degree index meets the preset threshold, the preliminary step size adjustment parameters are output as the adaptation step size configuration scheme. If the matching degree index does not meet the preset threshold, then the initial step size adjustment parameter is adjusted based on the matching degree index and the core reason for not meeting the threshold, so as to obtain an optimized step size configuration scheme.

4. The method according to claim 3, characterized in that, The step size-scan speed mapping model is expressed by the following formula: in, Indicates beam scanning speed. The scene adaptation coefficient is determined by the environmental interference level of the high dynamic scene, and its value range is [missing information]. , This indicates the initial step size adjustment parameter. This indicates the task priority weight, which is set according to the task requirements of the current scenario. This indicates the maximum permissible deviation of the beam pointing accuracy constraint. This indicates the target pointing accuracy requirement for the current scenario. This represents the precision sensitivity factor, and its value range. , This represents the real-time acceleration in the target's dynamic characteristics. This indicates the preset target maximum acceleration threshold. This indicates the baseline scan speed threshold.

5. The method according to claim 3, characterized in that, The matching degree index is calculated using the following formula: in, This represents the matching degree metric. , , These represent the accuracy adaptation weight, real-time adaptation weight, and dynamic tracking adaptation weight, which are dynamically allocated based on the task priority in the current scenario. This indicates the actual achievable pointing accuracy corresponding to the beam scanning speed configuration. This represents the required accuracy in the beam pointing accuracy constraint. Indicates the maximum permissible pointing deviation. This represents the precision-sensitive adjustment factor, with a value range of... , This indicates the actual response delay corresponding to the beam scanning speed configuration. This indicates the delay in response to real-time requirements. This represents the real-time acceleration in the target's dynamic characteristics. This indicates the preset target maximum acceleration threshold. Indicates the zero-bias coefficient. , Represents the dynamic sensitivity factor, with a value range of .

6. The method according to claim 1, characterized in that, The optimized beam parameters are obtained by optimizing and fine-tuning the beam shape based on the optimized step size configuration scheme. Then, based on the optimized beam parameters and sidelobe suppression requirements, updated control commands are obtained, including: Obtain the phase amplitude adjustment control parameters of the optimized step size configuration scheme; An initial beam pointing is generated based on the phase amplitude adjustment control parameters, and the deviation between the initial beam pointing and the target pointing is calculated. The deviation value is compared with the allowable deviation threshold in the preset beam pointing accuracy constraint. If the deviation value exceeds the allowable deviation threshold, the phase amplitude adjustment control parameter is fine-tuned according to the deviation compensation algorithm until the beam pointing deviation meets the allowable deviation threshold, and the optimized beam parameters are obtained. Based on the beam coverage range corresponding to the optimized beam parameters, and combined with the preset sidelobe suppression requirements, interference signal data within the beam coverage range is obtained through signal detection. Analyze the intensity distribution and frequency domain characteristics of the interference signal data, compare the intensity of the sidelobes of the interference signal data with the preset sidelobe suppression threshold, determine the sidelobe over-threshold region and the suppression requirement intensity, and obtain the beam sidelobe distribution adjustment scheme. Based on the aforementioned beam sidelobe distribution adjustment scheme, the target position dynamic update information is fused in real time using a timing synchronization algorithm to calculate the actual response delay and tracking error of beam pointing; the dynamic update information includes the target's real-time coordinates, position change rate, and update frequency. The actual response delay is compared with a preset response delay threshold and the actual tracking error is compared with a preset tracking error threshold to determine whether the real-time response capability of the beam pointing meets the preset requirements. If neither the actual response delay nor the actual tracking error exceeds the corresponding threshold, then an updated control command is generated. If the actual response delay or actual tracking error exceeds the corresponding threshold, the beam sidelobe distribution adjustment scheme is corrected based on the deviation compensation strategy, and the updated control command is generated.

7. The method according to claim 1, characterized in that, The updated control commands are corrected step by step through an iterative adjustment algorithm. Combined with the real-time operating status, the scanning speed and pointing accuracy are comprehensively balanced to obtain the dynamic control output results of the phased array antenna, including: The updated control command is executed to drive beam pointing adjustment, and the updated dynamic deviation data between the beam pointing and the target position is obtained. Based on the dynamic deviation data, a hierarchical iterative adjustment algorithm based on deviation threshold is used to calculate the pointing correction amount step by step to obtain preliminary correction parameters; the preliminary correction parameters include phase correction amount and scan speed correction coefficient; The scanning speed deviation is calculated using the preliminary correction parameters combined with the real-time operating status of the phased array antenna. The balance weight is dynamically allocated according to the ratio of the scanning speed deviation to the pointing accuracy deviation. The real-time operating status includes the current scanning speed, load power, and environmental interference intensity. The preliminary correction parameters are weighted and adjusted according to the aforementioned balance weights to generate optimized control commands. The optimized control command is executed to drive a secondary adjustment of the beam pointing, and the dynamic control output result is determined. The dynamic control output result includes the final phase and amplitude correction values, the real-time beam scanning speed parameters, and the sidelobe distribution optimization parameters.

8. A dynamic control system for a phased array antenna, characterized in that, The system includes: The data acquisition module is used to acquire real-time environmental data and target movement trajectory information of high dynamic scenes. It uses a pre-established beam scanning model to classify and process the real-time environmental data and target movement trajectory information to obtain the initial step size range and beam pointing accuracy constraints. The step size optimization module is used to determine the beam scanning speed and real-time requirements based on the initial step size range and beam pointing accuracy constraints using an adaptive step size adjustment algorithm, and obtain an optimized step size configuration scheme that adapts to the current scenario. The instruction update module is used to optimize and fine-tune the beam shape based on the optimized step size configuration scheme to obtain optimized beam parameters, and to obtain updated control instructions based on the optimized beam parameters and sidelobe suppression requirements. The deviation correction module is used to correct the updated control command step by step through an iterative adjustment algorithm, and combine the real-time operating status to comprehensively balance the scanning speed and pointing accuracy to obtain the dynamic control output result of the phased array antenna.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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