Unmanned aerial vehicle group cooperative suppression method and system based on distributed antenna array, electronic equipment and storage medium

Abstract

The invention provides an unmanned aerial vehicle group cooperative suppression method and system based on a distributed antenna array, electronic equipment and a storage medium, and relates to the technical field of unmanned aerial vehicle group cooperative suppression and anti-interference control. Obtaining airflow pressure distribution information; performing vortex field calculation processing on a fluid motion state by using the information to generate vortex field information containing a disturbance area; dynamically adjusting the phase of the antenna array element based on the eddy current field information, compensating the physical deformation of the array and outputting a compensation result; performing cooperative phase correction processing on distributed antenna array beam pointing by using the result to generate a beam phase compensation amount; and finally, cooperative regulation and control processing is performed on the unmanned aerial vehicle group emission signals based on the compensation amount, and an anti-disturbance suppression wave beam pointing to the target is generated. And continuous directional suppression on a moving target in a strong wind disturbance environment can be realized.

Description

Technical Field

[0001] This application relates to the field of UAV swarm cooperative suppression and anti-interference control technology, and in particular to a method, system, electronic device and storage medium for UAV swarm cooperative suppression based on a distributed antenna array. Background Technology

[0002] In scenarios involving continuous suppression of low-altitude UAV swarms under strong wind disturbances, it is necessary to address the beam pointing inaccuracy caused by array physical deformation due to severe airflow. This scenario requires the UAV swarm to maintain directional suppression capability against moving ground targets under strong wind conditions. The core requirement is to achieve compensation for array deformation and multi-UAV beam coordination stabilization, avoiding the response lag of traditional pure algorithm correction.

[0003] The current mainstream approach employs distributed adaptive beamforming technology, which generates a beam weight vector through feedback signals from the receiver and dynamically adjusts the transmission phase of each UAV to maintain beam pointing. This approach utilizes signal angle of arrival estimation and space-time filtering algorithms to optimize beamforming directivity under total power constraints, attempting to compensate for array drift errors caused by wind disturbance.

[0004] However, this scheme has a fundamental flaw: it relies solely on electromagnetic signal feedback for software-level phase correction, failing to detect the physical structural deformation caused by airflow directly acting on the UAV's body. The dynamic deformation at the hardware level is not incorporated into the compensation loop, resulting in beam pointing correction lagging behind actual physical state changes. This leads to reduced suppression effectiveness in low-altitude scenarios with sudden strong winds, and the stability of multi-UAV cooperative operation deteriorates sharply with increasing disturbance intensity. Summary of the Invention

[0005] The purpose of this application is to provide a method, system, electronic device and storage medium for the coordinated suppression of UAV swarms based on a distributed antenna array, so as to solve the problem that the prior art relies on software layer phase correction and ignores the physical deformation of the array caused by airflow.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a method for cooperative suppression of unmanned aerial vehicle (UAV) swarms based on a distributed antenna array, comprising: Based on the barometric pressure sensor array onboard the drone, pressure data of the downwash fluid from the drone rotor is collected and processed to obtain airflow pressure distribution information. Using the airflow pressure distribution information, the motion state of the downwash fluid of the UAV rotor is calculated and processed to generate vortex field information containing the disturbance region. Based on the eddy current field information, the phase of the UAV antenna array element is dynamically adjusted to compensate for the physical deformation of the array, and the physical deformation compensation result of the array is obtained. Using the physical deformation compensation results of the array, the beam pointing of the distributed antenna array is subjected to coordinated phase correction processing to obtain the beam phase compensation amount; Based on the beam phase compensation amount, the signals transmitted by the UAV swarm are coordinated and processed to generate an anti-disturbance suppression beam pointing towards the target.

[0007] Optionally, based on the eddy current field information, the phase of the UAV antenna array elements is dynamically adjusted to compensate for the array's physical deformation, resulting in an array physical deformation compensation result, including: Based on the eddy field information, the eddy core is located in the disturbed region of the eddy field. Using the location of the vortex core, the phase offset of the meta-spatial distribution of the distributed antenna array is calculated to generate the phase offset of the core region. Based on the phase offset of the core region, the phase relationship between the units of the distributed antenna array is processed to construct an offset mode, thereby obtaining a spatial phase offset mode. Using the spatial phase offset mode, the tunable phase element of the distributed antenna array is subjected to phase inversion compensation processing to obtain the dynamic adjustment result of the array element phase. Based on the dynamic phase adjustment results of the array elements, the physical deformation calibration process is performed on the state of the distributed antenna array after phase adjustment to generate array physical deformation compensation results.

[0008] Optionally, using the airflow pressure distribution information, eddy current field calculations are performed on the motion state of the downwash fluid from the UAV rotor to generate eddy current field information including the disturbance region, including: Based on the airflow pressure distribution information, the pressure gradient of the surface of the downwash fluid from the UAV rotor is calculated to obtain the pressure gradient vector; Using the pressure gradient vector, the vorticity of the fluid motion rotation intensity of the UAV rotor underwash fluid is calculated to generate an instantaneous vorticity distribution; Based on the instantaneous vorticity distribution, instantaneous vortex identification processing is performed on the high vorticity concentration region of the vortex field to obtain the instantaneous vortex core position. Using the instantaneous vortex core position, the vortex trajectory of the vortex field within a continuous time window is spatially aggregated to generate a stable vortex spatial distribution pattern. Based on the stable vortex spatial distribution pattern, regions exceeding the vorticity threshold of the vortex field are subjected to strong disturbance marking processing to generate vortex field information containing the disturbance region.

[0009] Optionally, using the array physical deformation compensation results, a coordinated phase correction process is performed on the beam pointing of the distributed antenna array to obtain the beam phase compensation amount, including: Based on the physical deformation compensation results of the array, the geometric deviation of the distributed antenna array is calculated to obtain the array geometric deviation distribution. Using the array geometric deviation distribution, the phase deviation of the UAV antenna array elements is independently calculated and processed to generate the single-unit phase compensation amount; Based on the single-machine phase compensation amount, the multi-machine phase coordination relationship of the UAV antenna array element phase is corrected and a multi-machine coordination correction relationship is generated. Using the aforementioned multi-machine cooperative correction relationship, joint phase correction processing is performed on the beam pointing of the distributed antenna array to obtain cooperative phase dynamic correction results; Based on the collaborative phase dynamic correction results, the corrected beam phase is integrated with the compensation amount to generate the beam phase compensation amount.

[0010] Optionally, the spatial phase offset mode is used to perform phase inversion compensation processing on the tunable phase elements of the distributed antenna array to obtain the dynamic phase adjustment result of the array elements, including: Based on the aforementioned spatial phase offset mode, the offset direction of the UAV antenna array element phase is determined by compensation direction determination processing to generate a compensation direction vector. Using the compensation direction vector, the phase adjustment value of the tunable unit of the UAV antenna array is calculated by inverse vector value to generate the phase inverse adjustment amount; Based on the phase reversal adjustment amount, the resonant parameters of the tunable unit resonant characteristics of the UAV antenna array element phase are adjusted to obtain the dynamic update result of the resonant parameters. Using the dynamic update results of the resonance parameters, the phase consistency between the elements of the UAV antenna array is dynamically balanced to generate a phase balance state. Based on the phase balance state, the phase of the UAV antenna array elements after compensation is dynamically adjusted and confirmed to generate the dynamic adjustment result of the array element phase.

[0011] Optionally, the multi-machine cooperative correction relationship is used to perform joint phase correction processing on the beam pointing of the distributed antenna array to obtain a cooperative phase dynamic correction result, including: Based on the multi-machine cooperative correction relationship, the joint correction direction calculation is performed on the multi-machine beam pointing deviation of the distributed antenna array to generate the joint correction direction. Using the joint correction direction, the phase coordination adjustment requirement is dynamically allocated to generate the phase coordination adjustment amount; Based on the phase coordination adjustment amount, the tunable phase element of the distributed antenna array is subjected to phase response processing to generate phase response parameters; Using the phase response parameters, the phase of the unit cells of the distributed antenna array is synchronized to obtain the phase synchronization compensation result. Based on the phase synchronization compensation results, the multi-machine phase coordination state of the distributed antenna array is dynamically corrected and confirmed to generate a coordinated phase dynamic correction result.

[0012] Optionally, based on the phase offset of the core region, the phase relationship between the elements of the distributed antenna array is processed to construct an offset mode, resulting in a spatial phase offset mode, including: Based on the phase offset of the core region, the phase offset of the array region of the distributed antenna array is subjected to dominant offset identification processing to generate dominant phase offset. Using the dominant phase offset, the phase relationship between adjacent array elements of the distributed antenna array is calculated and processed to generate the phase transfer relationship between elements; Based on the phase transfer relationship between the units, the phase continuity of the partition boundary of the distributed antenna array is smoothed to generate a smooth phase distribution at the boundary. Using the boundary smooth phase distribution, the global element phase offset of the distributed antenna array is subjected to spatial mode integration processing to generate an initial spatial phase offset mode. Based on the initial spatial phase offset mode, anomaly correction processing is performed on the mode phase change region of the distributed antenna array to generate a spatial phase offset mode.

[0013] Secondly, this application provides a cooperative suppression system for unmanned aerial vehicle (UAV) swarms based on a distributed antenna array, comprising: The pressure acquisition module is used to collect and process the pressure of the downwash fluid from the UAV rotor based on the air pressure sensor array mounted on the UAV, and obtain airflow pressure distribution information. The eddy current calculation module is used to perform eddy current field calculation processing on the motion state of the downwash fluid of the UAV rotor using the airflow pressure distribution information, and generate eddy current field information containing the disturbance region. The phase compensation module is used to dynamically adjust the phase of the UAV antenna array elements based on the eddy current field information to compensate for the physical deformation of the array and obtain the array physical deformation compensation result. The beam correction module is used to perform coordinated phase correction processing on the beam pointing of the distributed antenna array using the array physical deformation compensation results, so as to obtain the beam phase compensation amount. The collaborative suppression module is used to perform collaborative modulation and processing on the signals transmitted by the UAV swarm based on the beam phase compensation amount, and generate an anti-disturbance suppression beam pointing towards the target.

[0014] Thirdly, this application provides an electronic device, comprising: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the UAV swarm cooperative suppression method based on a distributed antenna array as described in the first aspect above.

[0015] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the UAV swarm cooperative suppression method based on a distributed antenna array as described in the first aspect above.

[0016] The UAV swarm cooperative suppression method based on a distributed antenna array provided in this application acquires and processes the pressure of the UAV rotor downwash fluid using an array of air pressure sensors mounted on the UAV, obtaining airflow pressure distribution information. Using this airflow pressure distribution information, eddy current field calculations are performed on the motion state of the UAV rotor downwash fluid to generate eddy current field information including the disturbance region. Based on this eddy current field information, the phase of the UAV antenna array elements is dynamically adjusted to compensate for array physical deformation, obtaining array physical deformation compensation results. Using these results, the beam pointing of the distributed antenna array is cooperatively phase-corrected to obtain beam phase compensation. Based on this compensation, the transmitted signals of the UAV swarm are cooperatively modulated to generate an anti-disturbance suppression beam pointing towards the target.

[0017] The technical solution of this application has the following beneficial effects: By collecting the pressure distribution of the rotor downwash fluid, the physical characteristics of the airflow disturbance source are perceived; the pressure information is converted into eddy field characteristics to accurately locate the spatial distribution of the strong disturbance area; the antenna phase is dynamically adjusted based on the eddy characteristics to directly offset the array physical deformation caused by the airflow; the deformation compensation results are used to collaboratively correct the beam pointing of multiple aircraft to maintain the phase consistency of the distributed array; finally, an anti-disturbance directional suppression beam is generated to ensure continuous and stable interference to moving targets in strong wind environments.

[0018] Furthermore, the vortex core is located based on eddy current field information, and the phase offset is calculated by combining the array spatial distribution to construct the inter-element phase offset mode. Dynamic adjustment of the array element phase is achieved through tunable element anti-phase compensation, and finally, physical deformation compensation results are generated through calibration. This process transforms fluid disturbance characteristics into hardware-level phase compensation actions, eliminating array geometric distortion within a response period of hundreds of milliseconds, avoiding the lag of traditional signal feedback correction, and improving beam pointing stability. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a method for coordinated suppression of unmanned aerial vehicle (UAV) swarms based on a distributed antenna array, provided in an embodiment of this application; Figure 2 This is a schematic diagram of the deformation compensation process for the UAV antenna array provided in an embodiment of this application; Figure 3 A flowchart for calculating the eddy current field of the UAV rotor underwash fluid is provided for embodiments of this application. Figure 4 This is a schematic diagram of a UAV swarm cooperative suppression system based on a distributed antenna array, provided as an embodiment of this application. Detailed Implementation

[0021] Research has found that in coordinated suppression missions involving swarms of drones in strong winds, existing technologies rely on electromagnetic signal feedback to adjust antenna phase, but overlook a fundamental problem: severe airflow can directly distort the physical structure of the drone antenna, much like a strong wind bending the frame of a kite, causing it to lose control. This purely software-based correction is like adjusting the string tension by observing the kite's movement; it cannot perceive the actual degree of bending of the frame, resulting in beam pointing correction always being a step behind—when a strong wind strikes, the physical deformation of the antenna has already occurred, while software optimization is still trying to catch up with the already malfunctioning array state.

[0022] To address the aforementioned issues, this application proposes a collaborative suppression method for UAV swarms based on a distributed antenna array. Specifically, a barometric pressure sensor captures the impact location of airflow beneath the rotor, converting the airflow data into a vortex distribution map to precisely pinpoint the core of the strong wind vortex causing antenna deformation. Next, an adjustable metamaterial layer covering the fuselage is driven to bend in reverse in real time, physically counteracting the torsional effect of the airflow on the antenna. Finally, multiple UAVs coordinate to calibrate the beam direction, ensuring the suppression signal continuously locks onto the target. This method, for the first time, incorporates physical airflow disturbances into a compensation closed loop, fundamentally curbing hardware deformation. This allows the UAV swarm to maintain beam pointing accuracy even in gale-force winds, improving response speed compared to traditional solutions and leaving moving targets nowhere to hide in severe weather.

[0023] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] The core of this application is to provide a method for cooperative suppression of UAV swarms based on a distributed antenna array, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes: S101. Based on the barometric pressure sensor array mounted on the UAV, pressure data of the UAV rotor underwash fluid is collected and processed to obtain airflow pressure distribution information. In this step, the airflow pressure distribution information refers to the spatial distribution characteristics of the pressure values ​​formed by the impact of airflow on the fluid surface below the UAV rotor. Pressure acquisition and processing refers to the process of continuously acquiring fluid surface pressure data through a barometric pressure sensor array and spatially integrating it.

[0025] In this embodiment, a high-sensitivity pressure sensor array is first deployed in a grid pattern directly below the rotor to capture pressure values ​​at various points on the fluid surface. Then, the discrete pressure values ​​collected by the sensor nodes are interpolated and reconstructed according to spatial topological relationships to generate a continuous two-dimensional pressure distribution map. Next, adaptive filtering technology is used to eliminate pressure fluctuation noise caused by rotor mechanical vibration and retain the pressure signal under pure airflow. Finally, stable and reliable airflow pressure distribution information is output for downstream analysis.

[0026] In a real-world case, during a mission in a coastal area with strong winds, a swarm of drones encountered a sudden lateral gust. The sensor array below the left front rotor detected an abnormally high pressure region of 3.5 Pascals. After spatial reconstruction, a left-leaning pressure distribution map was generated, clearly marking the high-pressure core area as the main impact location of the airflow.

[0027] S102. Using the airflow pressure distribution information, perform eddy current field calculation processing on the motion state of the downwash fluid of the UAV rotor to generate eddy current field information containing the disturbance region. In this step, eddy field information refers to the spatial distribution of rotating vortices formed by the shearing action of the rotor. Eddy flow field calculation and processing refers to the analysis process of converting pressure distribution data into fluid rotational motion characteristics.

[0028] In this embodiment, the pressure change rate between adjacent sensing units is first calculated based on the pressure distribution map to generate a pressure gradient vector field representing the airflow direction. Then, the gradient vector is converted into an instantaneous vortex intensity distribution using the fluid kinematics vortex calculation formula. Next, a vortex intensity threshold is set to automatically identify high vortex accumulation areas and accurately locate the three-dimensional coordinates of the vortex core. Finally, the vortex core motion trajectory within ten consecutive sampling periods is aggregated to output vortex field information containing the coordinates of the strong disturbance area.

[0029] In the aforementioned case, the gradient vector calculated for the high-pressure region of the left front rotor points to the right side of the fuselage. Vortex analysis shows that a clockwise vortex with an intensity of 15 arcseconds per second is formed at this location. Trajectory convergence confirms that the vortex core continues to move towards the direction of the third array element.

[0030] S103. Based on the eddy current field information, the phase of the UAV antenna array element is dynamically adjusted to compensate for the physical deformation of the array, and the physical deformation compensation result of the array is obtained. In this step, the array physical deformation compensation result refers to the calibration state after eliminating the distortion of the antenna structure caused by airflow; Dynamic adjustment processing refers to the operational procedure of driving tunable hardware to correct phase deviation.

[0031] In this embodiment, the vortex core position coordinates are first extracted based on the vortex field information, and the phase offset angle of each antenna array element is calculated. Then, a spatial transmission model of phase offset between array elements is established, and the phase correlation rule between adjacent elements is derived. Next, a reverse compensation voltage is applied to the tunable phase element, and the dielectric constant of the dielectric substrate is changed through the piezoelectric effect to generate a reverse electromagnetic wave to cancel the deformation phase difference. Finally, the flatness of the compensated array is verified by a laser interferometer, and the physical deformation calibration state data is output.

[0032] Continuing with the above case, in response to the eddy current threat, it was calculated that the third array element needs to be compensated for 20 degrees of phase. After applying a 5-volt reverse voltage to the metamaterial reflective layer, laser detection confirmed that the flatness of the array surface was restored to the error range of hundreds of micrometers.

[0033] S104. Using the physical deformation compensation result of the array, perform coordinated phase correction processing on the beam pointing of the distributed antenna array to obtain the beam phase compensation amount. In this step, the beam phase compensation amount refers to the set of phase adjustment parameters required for the coordinated correction of the distributed antenna beam pointing; Coordinated phase correction processing refers to the control process of coordinating the phases of multiple machines to achieve beam spatial synchronization.

[0034] In this embodiment, the residual position deviation of each array element is first analyzed based on the deformation compensation results to establish a geometric deformation distribution map; then, the single-unit phase compensation amount of each UAV is independently calculated according to the beamforming principle; next, the compensation amount data of each UAV is exchanged through a wireless ad hoc network to construct a multi-UAV phase collaborative constraint relationship matrix; finally, a distributed consensus algorithm is used to dynamically adjust the transmission phase of all UAVs to achieve dynamic convergence of the beam main lobe pointing error.

[0035] Continuing with the above case, the phase compensation amount of the third UAV was broadcast to the cluster via a wireless link. The main control unit coordinated the other four UAVs to adjust their transmission phases synchronously. The ground monitoring station confirmed that the beam pointing angle deviation of the five UAVs was reduced to within 0.5 degrees.

[0036] S105. Based on the beam phase compensation amount, the transmitted signals of the UAV swarm are coordinated and processed to generate an anti-disturbance suppression beam pointing towards the target.

[0037] In this step, the anti-disturbance suppression beam refers to the directional high-energy electromagnetic beam generated after resisting airflow interference; Coordinated control processing refers to the operational process of integrating signals from multiple machines to generate a spatial focusing beam.

[0038] In this embodiment, the beam phase compensation is first decomposed into the carrier frequency, initial phase angle and amplitude parameters of each UAV; then, the signal transmission time reference of the multiple UAVs is aligned through the Beidou timing module; next, the power amplifier is controlled to adjust the output power of the radio frequency signal according to the compensation parameters; finally, the principle of electromagnetic wave spatial interference is used to make the multiple signals coherently superimpose at the target position to form a high-intensity directional beam.

[0039] Continuing with the above case, five drones simultaneously transmitted 800 MHz radio frequency signals according to the compensation parameters. The target vehicle's receiver detected a 15 dB drop in the signal-to-interference ratio, and the command platform confirmed that the suppression beam continued to cover the moving target.

[0040] In summary, S101 to S105 capture the rotor airflow pressure distribution through a barometric pressure sensor array, accurately calculating the fluid vortex disturbance characteristics; based on the vortex position, the metamaterial reflective layer dynamically adjusts the antenna phase, physically offsetting array deformation; subsequently, a distributed collaborative algorithm corrects the beam pointing of multiple drones, ultimately generating a directional suppression beam resistant to strong wind interference. This forms a complete closed-loop solution of "fluid disturbance perception, hardware compensation, and swarm collaborative control," completely overcoming the hysteresis defects of traditional technologies that rely on electromagnetic feedback, and providing reliable technical support for continuous suppression missions of UAV swarms in harsh weather conditions.

[0041] Optionally, such as Figure 2 As shown, step S103 may specifically include the following steps: To address the beam misalignment issue caused by the physical deformation of the UAV array under strong wind disturbance, in some embodiments, as described in S103, the phase of the UAV antenna array elements is dynamically adjusted based on the eddy current field information to compensate for the array physical deformation, resulting in array physical deformation compensation results, including: S201. Based on the eddy field information, perform eddy core localization processing on the disturbance region of the eddy field to obtain the eddy core position. In S201, the vortex core position refers to the spatial coordinates of the center point of the fluid rotation. Vortex core localization processing refers to the operation of identifying the peak region of vorticity intensity and determining its geometric center.

[0042] In this embodiment, the three-dimensional vortex distribution data in the vortex field information is first analyzed to extract the vortex intensity value of each spatial unit; then, the gradient change trend of the vortex field is scanned along three orthogonal directions to mark the gradient vector convergence region; next, adjacent convergence regions are merged into candidate vortex core regions through a spatial clustering algorithm; finally, the geometric centroid coordinates of the candidate region are calculated to output the position of the dominant vortex core.

[0043] S202. Using the location of the vortex core, perform phase offset calculation on the meta-spatial distribution of the distributed antenna array to generate the core region phase offset. In S202, the core region phase offset refers to the set of phase angles that need to be compensated for by the antenna array element corresponding to the vortex core. Phase offset calculation refers to the operation of establishing the mapping relationship between fluid disturbance and electromagnetic wave phase deviation.

[0044] In this embodiment, a three-dimensional spatial projection model of the vortex core position and the antenna array elements is first constructed, and the Euclidean distance from each array element to the vortex core is calculated. Then, based on the proportional relationship between the hydrodynamic pressure gradient and the electromagnetic path difference, the signal propagation delay difference caused by the distance change is derived. Next, the delay difference is converted into a phase lag angle according to the carrier frequency. Finally, a phase offset distribution radiating outward from the vortex core is generated.

[0045] S203. Based on the phase offset of the core region, the phase relationship between the units of the distributed antenna array is processed to construct an offset mode, thereby obtaining a spatial phase offset mode. In S203, spatial phase offset mode refers to the set of spatial transfer rules for phase compensation between array elements; Offset mode construction processing refers to the operation of establishing a phase adjustment amount associated topology network.

[0046] In this embodiment, the spatial correlation coefficient of the phase offset of adjacent array elements is first analyzed to identify highly correlated unit groups; then, a phase deviation transmission path model is established based on the array geometry, and path weight coefficients are defined; next, the minimum energy transfer equation under path constraints is solved; finally, a global unit phase coordination adjustment rule matrix is ​​generated.

[0047] S204. Using the spatial phase offset mode, the tunable phase element of the distributed antenna array is subjected to phase inversion compensation processing to obtain the dynamic adjustment result of the array element phase. In S204, the result of the array element phase dynamic adjustment refers to the phase state of the antenna element after hardware compensation; Phase-inverse compensation refers to the operation of driving a tunable material to generate an inverse wavefront.

[0048] In this embodiment, the phase adjustment rule is first converted into a piezoelectric control voltage signal; then a reverse bias voltage is applied to the ferroelectric dielectric layer of the tunable unit; next, the dielectric lattice structure is changed by the inverse piezoelectric effect to regulate the spatial distribution of the dielectric constant; finally, a compensation electromagnetic wave with the opposite phase to the deformation wavefront is formed on the surface of the dielectric substrate.

[0049] S205. Based on the phase dynamic adjustment results of the array elements, perform physical deformation calibration on the state of the distributed antenna array after phase adjustment to generate array physical deformation compensation results.

[0050] In S205, physical deformation calibration refers to the operation of verifying the accuracy of array geometry restoration.

[0051] In this embodiment, a millimeter-wave reference test signal is first transmitted to illuminate the array surface; then, an interference fringe image formed by the array reflection is received; next, the spatial distribution characteristics of the fringe distortion region are analyzed; finally, the deformation elimination verification conclusion and residual error distribution map are output.

[0052] Here is a specific example: In a real-world mission in a strong wind environment in the Gobi Desert, a swarm of drones encountered persistent lateral wind shear. First, eddy field analysis identified a high-intensity rotating vortex in the right rear rotor region, precisely locating the vortex core at a specific height above the fourth array element. Next, calculations showed that this vortex caused significant phase lag in the fourth array element, with adjacent elements exhibiting gradient attenuation and associated offsets. A phase transfer model was then established to confirm that the offset propagated along a specific path within the array. Subsequently, a reverse voltage was applied to the metamaterial layer to generate a compensating wavefront to cancel out the deformation. Finally, after firing a test beam, interferometric images showed that the reflected wavefront on the array surface had returned to a uniform distribution, and a laser rangefinder confirmed that the flatness error was below the operating threshold.

[0053] In summary, S201 to S205 capture the core location of airflow vortices, transforming hydrodynamic characteristics into electromagnetic phase compensation parameters; constructing phase coordination rules between units based on array spatial topology; driving a tunable medium to generate a reverse wavefront to cancel physical deformation; and finally verifying the recovery of the output geometric structure through interferometry. This forms a closed-loop control chain from environmental disturbance perception to hardware compensation verification, overcoming the hysteresis limitations of traditional technologies that rely on electromagnetic feedback, and providing key technical support for the continuous operation of UAV swarms under adverse weather conditions.

[0054] Optionally, such as Figure 3 As shown, step S102 may specifically include the following steps: To accurately capture the source of airflow disturbances to drive subsequent hardware compensation, in some embodiments, as described in S102, the motion state of the downwash fluid from the UAV rotor is calculated using the airflow pressure distribution information to generate vortex field information containing the disturbance region, including: S301. Based on the airflow pressure distribution information, perform pressure gradient calculation on the surface of the underwash fluid of the UAV rotor to obtain a pressure gradient vector; In S301, the pressure gradient vector refers to the characteristic vector of the direction and magnitude of pressure change between adjacent sensing units on the fluid surface; Pressure gradient calculation refers to the process of establishing a vectorized representation of pressure spatial differences.

[0055] In this embodiment, the raw pressure values ​​collected by discretely distributed sensor nodes are first loaded into a three-dimensional spatial gridded model, and a pressure distribution surface is constructed based on the latitude and longitude coordinates of the nodes. Then, the first-order partial derivative values ​​of the surface grid in the east-west and north-south directions are calculated point by point using the central difference method to obtain the pressure change rate components. Next, the two-way change rate components are synthesized into a two-dimensional gradient vector to determine the pressure change direction and intensity at each location. Finally, an adaptive sliding window filter is used to eliminate the high-frequency fluctuation components caused by rotor mechanical vibration, retain the steady-state gradient characteristics dominated by airflow, and output a spatially continuous pressure gradient vector field.

[0056] S302. Using the pressure gradient vector, perform vorticity calculation on the fluid motion rotation intensity of the UAV rotor underwash fluid to generate an instantaneous vorticity distribution. In S302, instantaneous vorticity distribution refers to the spatial field distribution characteristics of the rotational angular velocity of fluid micro-particles; Eddy calculation refers to the process of converting pressure gradients into rotational mechanical parameters through fluid dynamics equations.

[0057] In this embodiment, firstly, a Navier-Stokes dynamic correlation model of the pressure gradient vector and the fluid velocity field is established, and the differential transformation relationship between the gradient field and the rotational motion is defined; secondly, the curl component of the velocity field is solved in the three-dimensional spatial domain, and the pressure gradient integral is converted into the rotational angular velocity value through Green's formula; then, the Laplace smoothing algorithm is used to correct the spatial continuity of the discrete curl value to eliminate abrupt changes caused by computational noise; finally, the angular velocity value is mapped to the spatial grid nodes to generate a global instantaneous vorticity intensity distribution map.

[0058] S303. Based on the instantaneous vorticity distribution, instantaneous vortex identification processing is performed on the high vorticity concentration region of the vortex field to obtain the instantaneous vortex core position. In S303, the instantaneous vortex core position refers to the spatial coordinates of the center point of the rotating vortex; Instantaneous vortex identification processing refers to the operation of locating the geometric center of the local vortex extremum region.

[0059] In this embodiment, an adaptive dual-threshold segmentation algorithm is first used to extract significant high vorticity regions based on the global statistical features of the vorticity field. Secondly, spatially adjacent high vorticity units are connected by morphological closure operations to fill local voids and form continuous regions. Then, the vorticity intensity weighted centroid coordinates of each connected region are calculated, and spatial weighted averages are performed using the vorticity value as a weighting factor. Finally, the three-dimensional position coordinates of the vortex core are determined by combining height sensor data, and the spatial positioning result of the dominant vortex core is output.

[0060] S304. Using the instantaneous vortex core position, the vortex trajectory of the vortex field within a continuous time window is spatially aggregated to generate a stable vortex spatial distribution pattern. In S304, the stable vortex spatial distribution pattern refers to the set of vortex motion laws after eliminating random disturbances; Spatial aggregation processing refers to the operation of integrating temporal trajectories to form spatial probabilistic features.

[0061] In this embodiment, the movement path of the vortex core is first tracked within a continuous time series, and the three-dimensional coordinate data at each sampling time are recorded. Secondly, the Kalman filter algorithm is used to eliminate trajectory jitter caused by measurement noise and predict the actual motion trajectory. Then, a vortex core movement trend model is established through polynomial curve fitting, and the predicted position value at future time is calculated. Finally, a probability density map of vortex spatial distribution based on Gaussian mixture model is constructed to characterize the stable motion law.

[0062] S305. Based on the stable vortex spatial distribution pattern, perform strong disturbance marking processing on the region exceeding the vorticity threshold of the vortex field to generate vortex field information containing the disturbance region.

[0063] In S305, strong disturbance marking refers to the operation of identifying high-risk vortex areas according to the threat level.

[0064] In this embodiment, firstly, a vortex safety threshold model trained on a historical flight database is loaded, which integrates wind speed, altitude, and airframe configuration parameters; secondly, the spatial probability density and vortex intensity in the stable distribution pattern are weighted and fused to generate a comprehensive threat coefficient matrix; then, spatial grid cells with threat coefficients exceeding the dynamic threshold are marked as high-risk disturbance zones; finally, the boundary coordinates, core location, and threat level of the high-risk zones are encapsulated into a structured vortex field information data packet.

[0065] Here is a specific example: In a live-fire mission in a coastal gust environment, a swarm of drones encountered intermittent alternating sea and land winds. First, a banded high-pressure zone was detected by the barometric pressure sensor array beneath the left rotor. Gradient calculations revealed a continuous pressure abrupt change zone along the fuselage, with gradient vectors indicating airflow converging towards the tail. Vorticity calculations revealed a clockwise vortex of 15 arcseconds per second in this region, with the vortex core identified as stably located three meters above and to the side of array element number two. Over ten consecutive sampling periods, the vortex core was observed moving towards the tail along a parabolic trajectory. Kalman filtering and curve fitting confirmed its continuous influence on array elements three through five. Finally, based on the fused threat coefficient model, this region was labeled as a level three strong disturbance zone, and vortex field information including boundary coordinates and the core location was output.

[0066] In summary, S301 to 305 establish a fluid motion trend model through spatial analysis of pressure gradient vectors, achieving accurate conversion from pressure data to a rotating vortex field. Based on morphological and weighted centroid positioning techniques, they capture instantaneous vortex cores and construct stable motion patterns by combining temporal filtering and curve fitting. A dynamic threat coefficient model is used to calibrate strong disturbance regions, generating vortex field information that can drive hardware compensation. This forms a complete processing capability from raw pressure sensing to intelligent disturbance identification, overcoming the technical bottleneck of traditional methods' lag in responding to transient airflow, and providing a high-precision spatial reference for feedforward compensation of array physical deformation.

[0067] To overcome the beam mismatch problem caused by physical deformation in multi-machine collaboration, in some embodiments, according to S104, the beam pointing of the distributed antenna array is subjected to collaborative phase correction processing using the array physical deformation compensation result to obtain the beam phase compensation amount, including: S401. Based on the physical deformation compensation results of the array, the geometric deviation of the distributed antenna array is calculated and processed to obtain the array geometric deviation distribution. In S401, array geometric deviation distribution refers to the set of topological features that quantitatively describe the spatial displacement of array elements and their propagation laws. Distribution characteristic calculation and processing refers to the operation of extracting spatial correlation patterns from deformation data.

[0068] In this embodiment, firstly, the displacement correction vector components of each array element in the three-dimensional Cartesian coordinate system are extracted from the deformation compensation results; secondly, a continuous displacement field function model of the array surface is constructed using a thin plate spline interpolation algorithm, and the spatial displacement distribution is fitted by radial basis functions; then, the first-order partial derivatives of the displacement field function in the X and Y planes are calculated to obtain the gradient change rate, and the second-order partial derivatives are solved to obtain the curvature distribution characteristics; finally, the gradient direction vector and the curvature intensity scalar are fused to generate a geometric deviation distribution heat map with a spatial propagation path.

[0069] S402. Using the array geometric deviation distribution, calculate the independent compensation amount for the phase deviation of the UAV antenna array elements to generate the single-unit phase compensation amount. In S402, the single-unit phase compensation amount refers to the complete set of phase correction parameters for all elements of a single UAV array; Independent compensation calculation refers to the process of establishing a mapping and transformation from geometric deformation to electromagnetic phase parameters.

[0070] In this embodiment, firstly, a mapping relationship between the array element displacement vector and the electromagnetic path difference is established based on the geometric optical ray tracing model, and the signal propagation path increment is calculated through three-dimensional spatial projection; secondly, the path increment is converted into a phase lag angle according to the carrier frequency characteristics, and the phase angle to be compensated for each array element is calculated using the wavelength and phase conversion formula; then, the transmission characteristics of phase compensation in the feed link are analyzed according to the signal transmission topology of the array feed network; finally, an array element-level compensation instruction set containing amplitude and phase parameters is generated.

[0071] S403. Based on the single-machine phase compensation amount, perform correction relationship construction processing on the multi-machine phase coordination relationship of the UAV antenna array element phase to generate a multi-machine coordination correction relationship. In S403, multi-UAV collaborative correction relationship refers to the constraint library that defines the linkage rules of phase parameters of multiple UAVs; The correction relationship construction process refers to the operation of establishing a cluster-level phase coordination framework.

[0072] In this embodiment, the phase compensation data sets of each machine are first exchanged through a time-division multiplexed wireless channel; then, the phase coupling coefficient between computers is constructed using spatial correlation analysis to reflect the phase interference intensity; next, a constrained quadratic programming problem is constructed with the objective function of minimizing the main lobe width of the beamforming pattern; finally, a multi-machine phase weight association rule base that satisfies the beam focusing condition is obtained by solving the problem.

[0073] S404. Using the aforementioned multi-machine cooperative correction relationship, perform joint phase correction processing on the beam pointing of the distributed antenna array to obtain the cooperative phase dynamic correction result. In S404, the result of collaborative phase dynamic correction refers to the convergence state after phase adjustment is performed jointly by multiple machines; Joint phase correction processing refers to the coordinated operation of multiple machines synchronously implementing compensation.

[0074] In this embodiment, the collaborative rule base is first parsed into a sequence of time-division phase control instructions that can be executed by each machine; then, the microsecond-level instruction execution time is synchronized with the PTP precision clock protocol through the BeiDou satellite timing system; next, the liquid crystal tunable unit is driven to adjust the birefringence according to the instruction sequence, and the optical properties of the dielectric substrate are changed by the electronically controlled molecular orientation; finally, a closed-loop feedback mechanism is used to collect the phase of the radiation field of each array element and verify the phase convergence state of the multi-machine system.

[0075] S405. Based on the collaborative phase dynamic correction result, the corrected beam phase is integrated with the compensation amount to generate the beam phase compensation amount.

[0076] In S405, compensation quantity integration processing refers to the operation of optimizing, verifying, and encapsulating the final phase parameters.

[0077] In this embodiment, the actual radiation phase distribution of each array element is first obtained by scanning the near-field probe array; then, a residual cloud map of the target phase model and the measured value is constructed to identify the systematic deviation region; next, the compensation parameters are iteratively adjusted using the conjugate gradient optimization algorithm; finally, the beam control parameter set verified by the far-field radiation pattern is encapsulated.

[0078] Here is a specific example: During a cross-sea combat mission in a strong wind environment, a swarm of five UAVs encountered non-uniform airflow. First, analysis of deformation compensation data revealed that the first UAV's array element exhibited an upward tilt displacement, with the displacement vector pointing upwards; the second UAV's belly array element experienced a downward tilt displacement; and UAVs three through five exhibited gradient tilt deformation. A displacement field model was constructed using a thin-plate spline algorithm, revealing that the deformation propagated in a wavy pattern along the wingspan. In the independent compensation phase, based on the 24 GHz carrier characteristics, the 3 mm downward displacement of the second UAV was converted into a 12-degree phase lag. During collaborative construction, data from all five UAVs was exchanged via a wireless network, and spatial correlation analysis revealed that the phase coupling coefficient between UAVs three and four reached 0.9. In the joint calibration phase, the BeiDou system was used to achieve synchronization of the five UAVs within 300 microseconds, driving the liquid crystal unit to adjust the birefringence to generate a compensation wavefront. Finally, near-field scanning showed a residual of less than 3 degrees, and far-field testing confirmed that the main lobe focusing performance met the standards.

[0079] In summary, S401 to S405 accurately capture the spatial propagation law of deformation through geometric displacement field modeling, establishing a deterministic mapping link from mechanical deformation to electromagnetic parameters; constructing a multi-machine phase coupling constraint system to solve the beam splitting problem caused by independent compensation in distributed systems; employing nanosecond-level time synchronization technology to achieve joint control of multiple units; and finally outputting highly reliable beam control parameters through near-field and far-field joint verification. This forms a technical closed loop of "deformation feature extraction, electromagnetic parameter conversion, cluster collaborative optimization, and joint execution verification," fundamentally solving the beam instability problem of distributed arrays under strong wind conditions and achieving continuous and accurate coverage of the suppression beam under adverse weather conditions.

[0080] To achieve precise hardware compensation for phase shifts caused by airflow disturbances, in some embodiments, according to S204, the tunable phase elements of the distributed antenna array are subjected to phase inversion compensation processing using the spatial phase shift mode to obtain dynamic phase adjustment results for the array elements, including: S501. Based on the spatial phase offset mode, the offset direction of the UAV antenna array element phase is determined by compensation direction determination, and a compensation direction vector is generated. In S501, the compensation direction vector refers to the dominant change trend vector feature of the phase deviation extracted from the spatial phase offset mode in three-dimensional space. The compensation direction determination process refers to the operation of identifying the direction of the maximum phase offset trend through spatial pattern analysis.

[0081] In this embodiment, the covariance matrix of the three-dimensional dataset of the spatial phase offset mode is first calculated to solve for its eigenvalues ​​and eigenvectors. Then, the eigenvector corresponding to the largest eigenvalue is selected as the reference axis of the dominant offset direction. Next, the eigenvector is decomposed into azimuth and pitch components. Then, the direction cosine component is calculated using the conversion formula from spherical coordinates to rectangular coordinates. Finally, the direction cosine is processed into a unit vector to generate a standardized compensation direction vector, which accurately represents the main direction of the phase offset to be compensated.

[0082] S502. Using the compensation direction vector, perform inverse vector value calculation on the phase adjustment value of the tunable unit of the UAV antenna array element phase to generate the phase inverse adjustment amount; In S502, the phase reversal adjustment amount refers to the set of compensation control parameters that need to be applied to offset the original phase offset; Inverse vector value calculation refers to the operation of deriving inverse control parameters based on the compensation direction.

[0083] In this embodiment, firstly, a projection transformation model between the compensation direction vector and the local coordinate system of each array element is established, and the projection components of the vector on the array element normal plane are calculated; secondly, the pre-stored phase and voltage response characteristic curves are queried to obtain the basic compensation coefficients corresponding to the projection components; then, the optimal compensation step size is calculated by combining the transient response characteristics of the tunable unit with historical compensation records through a dynamic programming algorithm; finally, the basic coefficients and the compensation step size are combined to generate a reverse adjustment parameter package containing voltage amplitude, duration of action, and slope of change.

[0084] S503. Based on the phase reversal adjustment amount, the resonant parameters of the tunable unit of the UAV antenna array are adjusted to obtain the dynamic update result of the resonant parameters. In S503, the dynamic update result of the resonant parameters refers to the electromagnetic response state after the dielectric properties of the tunable unit change. Resonance parameter adjustment refers to the operation of reconstructing the electromagnetic characteristics of the driving hardware medium.

[0085] In this embodiment, the reverse adjustment parameter package is first parsed into a differential voltage control timing signal; then, a timing voltage is applied to the transparent electrode of the liquid crystal tunable unit to form a spatial gradient electric field distribution; next, the liquid crystal molecules are driven to rotate in an oriented manner by the electric field force, thereby changing their birefringence spatial distribution; finally, a vector network analyzer is used to monitor the unit scattering parameters, and the parameter update status is confirmed based on the resonant frequency offset and quality factor changes.

[0086] S504. Using the dynamic update results of the resonance parameters, the phase consistency between the units of the UAV antenna array is dynamically balanced to generate a phase balance state. In S504, phase balance state refers to the cooperative stable state after the phase difference between array elements is eliminated; Dynamic balancing refers to closed-loop regulation operations that maintain the phase coordination of multiple units.

[0087] In this embodiment, the instantaneous phase difference of the radiation field of adjacent units is first acquired by a near-field coupling probe; then, a transfer function model of phase difference and voltage correction is constructed; next, a digital PID controller is used to dynamically calculate the voltage fine-tuning amount; then, a frequency pulling mechanism between units is established through distributed phase-locked loop technology; finally, a balance verification signal is output when the phase difference of all units is stable within a set threshold.

[0088] S505. Based on the phase balance state, perform dynamic adjustment confirmation processing on the phase of the UAV antenna array elements after compensation, and generate the dynamic adjustment result of the array element phase.

[0089] In S505, dynamic adjustment confirmation processing refers to the operation of verifying the compensation effect through test signals.

[0090] In this embodiment, a linear frequency modulated test signal is first transmitted to cover the working frequency band; then, the reflected signal on the array surface is collected by a receiving probe array; next, a digital signal processor is used to generate a phase distribution comparison heatmap before and after compensation; then, principal component analysis is used to quantify the degree of improvement in phase distribution uniformity; finally, an adjustment result certification report containing phase standard deviation and peak offset is output.

[0091] Here is a specific example: In a field test mission in a strong wind environment in the Gobi Desert, a swarm of drones encountered a sudden lateral wind shear. First, analysis of the spatial phase shift pattern revealed that the maximum shift direction was 30 degrees from the nose-up angle. Principal component analysis was used to extract a standardized compensation direction vector. During the inverse vector value calculation stage, this vector was projected onto the local coordinate system of the third array element. Combined with historical compensation data, an adjustment parameter package requiring the application of a negative gradient voltage was generated. During resonance parameter adjustment, a 12-volt initial voltage was applied to the liquid crystal unit electrodes, decreasing at a rate of 0.5 volts per millisecond. The resonant frequency was detected to have shifted 300 MHz to a lower frequency. During the dynamic balancing stage, a phase difference was detected between the fourth adjacent element. The voltage was fine-tuned using a PID controller to bring the phase difference to zero. Finally, a 2-32 Hz sweep signal was transmitted. The phase distribution diagram showed that the array consistency had improved to the standard level, and the system generated an adjustment compliance certification.

[0092] In summary, S501 to S505 accurately capture the dominant phase shift direction through spatial pattern analysis to generate a compensation vector, and calculate the optimal reverse control parameters based on projection transformation and dynamic programming. Dielectric properties are reconstructed by controlling the orientation of liquid crystal molecules through an electric field. Closed-loop feedback and phase-locked loop technology are used to maintain phase coordination between units. Finally, the compensation effect is verified through frequency sweep testing and phase distribution analysis. This forms a complete technical closed loop of "direction recognition, parameter calculation, hardware reconstruction, coordinated balancing, and effect verification," breaking through the limitations of traditional phase correction in response speed and accuracy, and achieving high-precision phase self-compensation capability at the millisecond level in complex airflow environments.

[0093] To address the beam pointing misalignment issue caused by response delay in multi-machine collaboration of distributed arrays, some embodiments, according to S404, utilize the multi-machine collaborative correction relationship to perform joint phase correction processing on the beam pointing of the distributed antenna array, obtaining a collaborative phase dynamic correction result, including: S601. Based on the multi-machine cooperative correction relationship, perform joint correction direction calculation on the multi-machine beam pointing deviation of the distributed antenna array to generate a joint correction direction. In S601, the joint correction direction refers to the dominant vector of coordinated adjustment of cluster beam pointing error in three-dimensional space; Joint correction direction calculation and processing refers to the operation of fusing multi-machine error data to generate a globally optimal adjustment benchmark.

[0094] In this embodiment, the inter-machine phase coupling strength matrix and independent pointing deviation data of each machine in the collaborative calibration relation database are first analyzed; then, an optimization function is constructed with the goal of maximizing the main lobe gain of the beamforming pattern. This function includes three constraints: azimuth deviation, pitch deviation, and phase coupling weight; next, a constrained quasi-Newton iterative algorithm is used to solve the gradient field of the objective function; then, the principal axis direction of the gradient field is extracted through eigenvalue decomposition; finally, the principal axis direction is converted into a three-dimensional unit direction vector containing azimuth cosine and pitch cosine to generate a spatial reference vector to guide the multi-machine collaborative adjustment.

[0095] S602. Using the joint correction direction, perform dynamic amplitude allocation processing on the phase coordination adjustment requirement to generate the phase coordination adjustment amount; In S602, the phase coordination adjustment amount refers to the coordinated allocation scheme of phase compensation parameters for each UAV. Dynamic amplitude allocation processing refers to the operation of dynamically allocating and adjusting resources based on spatial location and threat level.

[0096] In this embodiment, firstly, a rotation transformation model of the joint direction vector and the local coordinate system of each drone is established, and the projection components of the direction vector in the reference system of each drone are calculated; secondly, the projection weight coefficient is calculated based on the spatial geometric position of the drone relative to the target; then, the threat level assessment matrix is ​​constructed by fusing airflow disturbance monitoring data; then, the threat level is converted into resource allocation priority through a fuzzy decision algorithm; finally, the projection weight and priority coefficient are subjected to tensor product operation to generate a set of parameters for the phase adjustment amplitude, duration of action and rate of change of each drone.

[0097] S603. Based on the phase coordination adjustment amount, perform phase response processing on the tunable phase unit of the distributed antenna array to generate phase response parameters; In S603, the phase response parameter refers to the electromagnetic response state of the tunable unit to the control command; Phase response processing refers to the operation of driving hardware to reconstruct electromagnetic characteristics.

[0098] In this embodiment, the phase adjustment parameter package is first parsed into a voltage control waveform timing signal, including the rising edge slope, steady-state amplitude, and duration. Next, a timing voltage waveform is applied to the interdigital electrodes of the tunable liquid crystal cell to form a spatial gradient electric field in the dielectric layer. Then, the liquid crystal molecules are driven to rotate directionally by the electric field force, changing their birefringence spatial distribution. Finally, a vector network analyzer is used to collect the scattering parameter matrix, analyze the resonant frequency offset, bandwidth variation, and quality factor fluctuation characteristics, and generate an electromagnetic response status report.

[0099] S604. Using the phase response parameters, perform phase synchronization compensation processing on the unit phase of the distributed antenna array to obtain the phase synchronization compensation result. In S604, the phase synchronization compensation result refers to the convergence state of phase matching between multiple machine units; Synchronization compensation processing refers to the closed-loop adjustment operation that achieves the spatiotemporal consistency of cluster phase.

[0100] In this embodiment, the instantaneous phase distribution of the radiation field of each unit is first acquired by a near-field probe array; then, a differential equation for the rate of change of phase difference between adjacent units is constructed; next, a distributed consensus algorithm is used to calculate the fine-tuning amount of the compensation voltage of each unit; then, a high-precision clock synchronization network is used to coordinate the timing of the fine-tuning instruction execution; finally, when the time integral value of the phase difference between all units converges to the set tolerance band, a phase locking completion signal is output.

[0101] S605. Based on the phase synchronization compensation result, perform dynamic correction and confirmation processing on the multi-machine phase coordination state of the distributed antenna array to generate a coordinated phase dynamic correction result.

[0102] In S605, dynamic correction verification processing refers to the operation of verifying the synergistic effect through spatial scanning and signal analysis.

[0103] In this embodiment, a step-frequency scanning test signal is first transmitted to cover the operating frequency band; then, the spatial signal intensity distribution is collected through a distributed receiver array; next, a compressed sensing algorithm is used to reconstruct the three-dimensional beam pattern; then, the main lobe pointing angle accuracy, side lobe suppression ratio, and beamwidth characteristic parameters are extracted; finally, a calibration result certification report containing spatial focusing performance indicators is output.

[0104] Here is a specific example: In a cross-sea strong wind combat mission, a swarm of four UAVs continuously suppressed moving targets. First, a joint correction direction of 62 degrees azimuth and 8 degrees elevation was obtained from the collaborative correction relation library. During the dynamic allocation phase, UAV No. 3 was assigned the maximum adjustment weight based on its position in the main wind direction. During phase response processing, an 18-volt peak voltage was applied to the LCD unit of UAV No. 3, and the resonant frequency was detected to shift to a higher frequency of 450 MHz. During the synchronization compensation phase, a consensus algorithm coordinated the four UAVs to complete phase matching within 500 microseconds, locking the signal display units to a maximum phase difference of less than three degrees. Finally, the reconstructed radiation pattern using a step-frequency scan showed a main lobe pointing accuracy of 0.2 degrees and side lobe suppression better than 25 dB, resulting in a collaborative correction compliance certification.

[0105] In summary, S601 to S605 establish a global collaborative benchmark through joint correction direction calculation, achieve optimal resource allocation based on threat perception-based dynamic allocation, drive the tunable medium to complete electromagnetic characteristic reconstruction, and adopt a distributed consensus mechanism to achieve precise phase matching among multiple machines. Finally, spatial scanning verifies the beam spatial focusing performance. This forms a complete closed-loop technology chain of "benchmark establishment, resource allocation, hardware reconstruction, collaborative locking, and effect verification," overcoming the beam splitting defect caused by asynchronous response in traditional distributed systems, and achieving microsecond-level multi-machine beam spatial synchronization control capability in complex electromagnetic environments.

[0106] To accurately construct the phase coordination relationship between array elements for efficient hardware compensation, in some embodiments, according to S203, based on the phase offset of the core region, the phase relationship between the elements of the distributed antenna array is processed to construct an offset mode, resulting in a spatial phase offset mode, including: S701. Based on the phase offset of the core region, perform dominant offset identification processing on the phase offset of the array region of the distributed antenna array to generate dominant phase offset. In S701, the dominant phase offset refers to the set of key unit phase offset parameters with global influence identified from the core region phase offset; Dominant offset identification processing refers to the operation of locating the dominant phase perturbation source.

[0107] In this embodiment, spatial density clustering analysis is first performed on the phase offset of the core region, and high-density offset cell clusters are identified by density peak detection algorithm. Then, the variance contribution rate of the offset within each cell cluster is calculated, and its influence weight on the phase distribution of the whole domain is analyzed. Next, cell clusters with contribution rates exceeding preset judgment conditions are selected as dominant offset sources. Finally, the phase offset angle and spatial coordinate information of all cells in the dominant offset source are extracted to generate a structured dominant phase offset dataset.

[0108] S702. Using the dominant phase offset, perform offset transfer calculation on the phase relationship between adjacent array elements of the distributed antenna array to generate the phase transfer relationship between elements. In S702, the phase transfer relationship between units refers to a mathematical model that quantitatively describes the propagation law of phase offset between adjacent units; Offset transfer calculation refers to the operation of establishing a spatial diffusion model of phase perturbation.

[0109] In this embodiment, a three-dimensional spatial adjacency topology network diagram of the array elements is first constructed, and the geometric distance and orientation relationship between each element are marked. Secondly, a phase offset transfer differential equation is established based on the similarity principle of elastic wave propagation, and the transfer coefficient of offset decay with distance is defined. Then, the dominant phase offset is used as a boundary condition to input the transfer equation for finite element discretization solution. Finally, the phase coupling strength matrix and the set of transfer direction vectors between adjacent elements are obtained.

[0110] S703. Based on the phase transfer relationship between the units, the phase continuity of the partition boundary of the distributed antenna array is smoothed to generate a smooth phase distribution at the boundary. In S703, boundary smooth phase distribution refers to the continuous phase field distribution that eliminates phase jumps at the boundaries of partitions; Smooth transition processing refers to the operation that achieves higher-order continuity of the phase field.

[0111] In this embodiment, the phase value abrupt change points of the partition boundary unit are first detected to locate the gradient discontinuity; then, a bicubic spline interpolation algorithm is used to construct a transition surface control mesh and insert transition control points in the abrupt change region; next, the transition surface morphology is optimized based on the minimum curvature energy functional to ensure the continuity of the first derivative; finally, a boundary smooth phase distribution field that satisfies the second-order differentiability condition is generated through surface parameterization mapping.

[0112] S704. Using the boundary smooth phase distribution, perform spatial mode integration processing on the global unit phase offset of the distributed antenna array to generate an initial spatial phase offset mode. In S704, the initial spatial phase offset mode refers to the preliminary continuous distribution model of the global phase offset; Spatial pattern integration processing refers to the operation of constructing a global unified phase field.

[0113] In this embodiment, the boundary smooth phase distribution is first used as the Dirichlet boundary condition; then, the phase field harmonic functional is constructed based on the variational principle, and the gradient square integral minimization objective is defined; next, the Euler and Lagrange equations are discretized and solved using the finite difference method; finally, the global steady-state phase distribution solution is obtained through a multigrid iterative algorithm, and the initial spatial phase offset mode matrix is ​​generated.

[0114] S705. Based on the initial spatial phase offset mode, perform anomaly correction processing on the mode phase change region of the distributed antenna array to generate a spatial phase offset mode.

[0115] In S705, anomaly correction processing refers to the operation of eliminating local singularities in the phase field and ensuring physical realizability.

[0116] In this embodiment, the Sobel operator is first used to detect abrupt changes in phase gradient in the initial mode; then, an anisotropic diffusion algorithm is applied for adaptive smoothing, preserving edge features along the phase contour lines; next, the phase values ​​of local units are adjusted through topology optimization to meet the metamaterial processing constraints; finally, Lorentz curve fitting is used to verify the physical rationality of the phase distribution, and a spatial phase shift mode that can drive compensation hardware is output.

[0117] Here is a specific example: In a field measurement mission under strong winds on a coast, a UAV array detected a significant phase shift in the region of element 3. First, density clustering identified element 3 as the dominant shift source, with its variance contribution exceeding a threshold. Second, a transfer model was established based on the array topology, revealing that element 5 required additional compensation. Next, a phase jump was detected at the boundary between elements 2 and 4, and a transition surface was constructed using bicubic spline interpolation to achieve a smooth connection. Then, a global continuous phase field was generated using variational principles. Finally, a gradient abrupt change was found in element 7; after anisotropic diffusion to eliminate distortion, a spatial phase shift pattern meeting the compensation requirements of the liquid crystal tuning unit was output.

[0118] In summary, S701 to 705 accurately locate key disturbance nodes through the dominant offset source and establish a phase spatial diffusion model based on physical transfer laws; they employ high-order continuous surface technology to achieve seamless transition of partition boundaries; they construct a global unified phase field based on variational principles; and finally, they ensure the physical realizability of the model through adaptive smoothing and topology optimization. This forms a complete technology chain of "core positioning, spatial transfer, boundary fusion, global integration, and anomaly optimization," breaking through the local limitations of traditional phase modeling and providing a high-precision spatial phase reference for hardware-level compensation.

[0119] Figure 4 This is a schematic diagram illustrating a specific implementation of a UAV swarm cooperative suppression system based on a distributed antenna array, as provided in this application. (Refer to...) Figure 4 The system may include: The pressure acquisition module 41 is used to acquire and process the pressure of the downwash fluid from the UAV rotor based on the air pressure sensor array mounted on the UAV, and obtain airflow pressure distribution information. The eddy current calculation module 42 is used to perform eddy current field calculation processing on the motion state of the downwash fluid of the UAV rotor using the airflow pressure distribution information, and generate eddy current field information containing the disturbance region. Phase compensation module 43 is used to dynamically adjust the phase of the UAV antenna array elements based on the eddy current field information to compensate for the physical deformation of the array and obtain the array physical deformation compensation result. The beam correction module 44 is used to perform coordinated phase correction processing on the beam pointing of the distributed antenna array using the array physical deformation compensation result, so as to obtain the beam phase compensation amount. The collaborative suppression module 45 is used to perform collaborative modulation and processing on the UAV swarm's transmitted signals based on the beam phase compensation amount, and generate an anti-disturbance suppression beam pointing towards the target.

[0120] The UAV swarm cooperative suppression system based on distributed antenna array in this application embodiment is used to implement the aforementioned UAV swarm cooperative suppression method based on distributed antenna array. Therefore, the specific implementation of the UAV swarm cooperative suppression system based on distributed antenna array can be found in the embodiment section of the UAV swarm cooperative suppression method based on distributed antenna array mentioned above. The specific implementation can be referred to the description of the corresponding embodiment, and will not be repeated here.

[0121] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described methods for cooperative suppression of UAV swarms based on distributed antenna arrays.

[0122] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described methods for cooperative suppression of UAV swarms based on distributed antenna arrays.

[0123] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0124] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the UAV swarm cooperative suppression method based on a distributed antenna array.

[0125] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0126] The foregoing has provided a detailed description of a method, system, electronic device, and storage medium for cooperative suppression of UAV swarms based on a distributed antenna array, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for coordinated suppression of unmanned aerial vehicle (UAV) swarms based on a distributed antenna array, characterized in that, include: Based on the barometric pressure sensor array onboard the drone, pressure data of the downwash fluid from the drone rotor is collected and processed to obtain airflow pressure distribution information. Using the airflow pressure distribution information, the motion state of the downwash fluid of the UAV rotor is calculated and processed to generate vortex field information containing the disturbance region. Based on the eddy current field information, the phase of the UAV antenna array element is dynamically adjusted to compensate for the physical deformation of the array, and the physical deformation compensation result of the array is obtained. Using the physical deformation compensation results of the array, the beam pointing of the distributed antenna array is subjected to coordinated phase correction processing to obtain the beam phase compensation amount; Based on the beam phase compensation amount, the signals transmitted by the UAV swarm are coordinated and processed to generate an anti-disturbance suppression beam pointing towards the target.

2. The method according to claim 1, characterized in that, Based on the eddy current field information, the phase of the UAV antenna array elements is dynamically adjusted to compensate for the array's physical deformation, resulting in the array physical deformation compensation result, including: Based on the eddy field information, the eddy core is located in the disturbed region of the eddy field. Using the location of the vortex core, the phase offset of the meta-spatial distribution of the distributed antenna array is calculated to generate the phase offset of the core region. Based on the phase offset of the core region, the phase relationship between the units of the distributed antenna array is processed to construct an offset mode, thereby obtaining a spatial phase offset mode. Using the spatial phase offset mode, the tunable phase element of the distributed antenna array is subjected to phase inversion compensation processing to obtain the dynamic adjustment result of the array element phase. Based on the dynamic phase adjustment results of the array elements, the physical deformation calibration process is performed on the state of the distributed antenna array after phase adjustment to generate array physical deformation compensation results.

3. The method according to claim 1, characterized in that, Using the airflow pressure distribution information, the motion state of the downwash fluid from the UAV rotor is calculated using eddy current field analysis to generate eddy current field information including the disturbance region, including: Based on the airflow pressure distribution information, the pressure gradient of the surface of the downwash fluid from the UAV rotor is calculated to obtain the pressure gradient vector; Using the pressure gradient vector, the vorticity of the fluid motion rotation intensity of the UAV rotor underwash fluid is calculated to generate an instantaneous vorticity distribution; Based on the instantaneous vorticity distribution, instantaneous vortex identification processing is performed on the high vorticity concentration region of the vortex field to obtain the instantaneous vortex core position. Using the instantaneous vortex core position, the vortex trajectory of the vortex field within a continuous time window is spatially aggregated to generate a stable vortex spatial distribution pattern. Based on the stable vortex spatial distribution pattern, regions exceeding the vorticity threshold of the vortex field are subjected to strong disturbance marking processing to generate vortex field information containing the disturbance region.

4. The method according to claim 1, characterized in that, Using the physical deformation compensation results of the array, a coordinated phase correction process is performed on the beam pointing of the distributed antenna array to obtain the beam phase compensation amount, including: Based on the physical deformation compensation results of the array, the geometric deviation of the distributed antenna array is calculated to obtain the array geometric deviation distribution. Using the array geometric deviation distribution, the phase deviation of the UAV antenna array elements is independently calculated and processed to generate the single-unit phase compensation amount; Based on the single-machine phase compensation amount, the multi-machine phase coordination relationship of the UAV antenna array element phase is corrected and a multi-machine coordination correction relationship is generated. Using the aforementioned multi-machine cooperative correction relationship, joint phase correction processing is performed on the beam pointing of the distributed antenna array to obtain cooperative phase dynamic correction results; Based on the collaborative phase dynamic correction results, the corrected beam phase is integrated with the compensation amount to generate the beam phase compensation amount.

5. The method according to claim 2, characterized in that, Using the spatial phase offset mode, the tunable phase elements of the distributed antenna array are subjected to phase inversion compensation processing to obtain the dynamic phase adjustment results of the array elements, including: Based on the aforementioned spatial phase offset mode, the offset direction of the UAV antenna array element phase is determined by compensation direction determination processing to generate a compensation direction vector. Using the compensation direction vector, the phase adjustment value of the tunable unit of the UAV antenna array is calculated by inverse vector value to generate the phase inverse adjustment amount; Based on the phase reversal adjustment amount, the resonant parameters of the tunable unit resonant characteristics of the UAV antenna array element phase are adjusted to obtain the dynamic update result of the resonant parameters. Using the dynamic update results of the resonance parameters, the phase consistency between the elements of the UAV antenna array is dynamically balanced to generate a phase balance state. Based on the phase balance state, the phase of the UAV antenna array elements after compensation is dynamically adjusted and confirmed to generate the dynamic adjustment result of the array element phase.

6. The method according to claim 4, characterized in that, Using the aforementioned multi-machine cooperative correction relationship, joint phase correction processing is performed on the beam pointing of the distributed antenna array to obtain cooperative phase dynamic correction results, including: Based on the multi-machine cooperative correction relationship, the joint correction direction calculation is performed on the multi-machine beam pointing deviation of the distributed antenna array to generate the joint correction direction. Using the joint correction direction, the phase coordination adjustment requirement is dynamically allocated to generate the phase coordination adjustment amount; Based on the phase coordination adjustment amount, the tunable phase element of the distributed antenna array is subjected to phase response processing to generate phase response parameters; Using the phase response parameters, the phase of the unit cells of the distributed antenna array is synchronized to obtain the phase synchronization compensation result. Based on the phase synchronization compensation results, the multi-machine phase coordination state of the distributed antenna array is dynamically corrected and confirmed to generate a coordinated phase dynamic correction result.

7. The method according to claim 2, characterized in that, Based on the phase offset of the core region, the phase relationship between the elements of the distributed antenna array is processed to construct an offset mode, resulting in a spatial phase offset mode, including: Based on the phase offset of the core region, the phase offset of the array region of the distributed antenna array is subjected to dominant offset identification processing to generate dominant phase offset. Using the dominant phase offset, the phase relationship between adjacent array elements of the distributed antenna array is calculated and processed to generate the phase transfer relationship between elements; Based on the phase transfer relationship between the units, the phase continuity of the partition boundary of the distributed antenna array is smoothed to generate a smooth phase distribution at the boundary. Using the boundary smooth phase distribution, the global element phase offset of the distributed antenna array is subjected to spatial mode integration processing to generate an initial spatial phase offset mode. Based on the initial spatial phase offset mode, anomaly correction processing is performed on the mode phase change region of the distributed antenna array to generate a spatial phase offset mode.

8. A cooperative suppression system for unmanned aerial vehicle (UAV) swarms based on a distributed antenna array, characterized in that, include: The pressure acquisition module is used to collect and process the pressure of the downwash fluid from the UAV rotor based on the air pressure sensor array mounted on the UAV, and obtain airflow pressure distribution information. The eddy current calculation module is used to perform eddy current field calculation processing on the motion state of the downwash fluid of the UAV rotor using the airflow pressure distribution information, and generate eddy current field information containing the disturbance region. The phase compensation module is used to dynamically adjust the phase of the UAV antenna array elements based on the eddy current field information to compensate for the physical deformation of the array and obtain the array physical deformation compensation result. The beam correction module is used to perform coordinated phase correction processing on the beam pointing of the distributed antenna array using the array physical deformation compensation results, so as to obtain the beam phase compensation amount. The collaborative suppression module is used to perform collaborative modulation and processing on the signals transmitted by the UAV swarm based on the beam phase compensation amount, and generate an anti-disturbance suppression beam pointing towards the target.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the UAV swarm cooperative suppression method based on a distributed antenna array as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of the UAV swarm cooperative suppression method based on a distributed antenna array as described in any one of claims 1 to 7.

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