Low-altitude unmanned aerial vehicle communication and perception collaborative control method and system based on rotatable antenna array

Abstract

This invention relates to the field of integrated communication and sensing technology for unmanned aerial vehicles (UAVs). To address the limitations of existing multi-base station collaborative communication and sensing systems, such as the inability to accurately characterize the geometric relationships of real airspace, this invention proposes a method and system for coordinated communication and sensing control of low-altitude UAVs based on rotatable antenna arrays. The method includes: setting up rotatable antenna arrays on various ground base stations to form multiple array base stations; adjusting the spatial orientation of each array base station by adjusting the array attitude of the rotatable antenna arrays; coordinating and controlling the joint beamforming parameters and array attitude parameters of each array base station through a central controller; determining a three-dimensional sensing space to characterize key low-altitude monitoring and energy coverage areas based on the requirements of low-altitude monitoring tasks, and performing voxel sampling on it; solving the joint optimization model using an alternating optimization method, and outputting beam pointing results and array attitude adjustment results that meet the requirements, thereby completing the coordinated communication and sensing control of low-altitude UAVs.

Description

Technical Field

[0001] This invention relates to the field of wireless communication and sensing integration technology for unmanned aerial vehicles (UAVs), and particularly to a method and system for coordinated control of communication and sensing for low-altitude UAVs based on a rotatable antenna array. Background Technology

[0002] With the rapid development of the low-altitude economy, the application of drones in scenarios such as inspection and monitoring, emergency rescue, logistics and distribution, and airspace security continues to expand. Low-altitude wireless networks have become an important part of the future intelligent information infrastructure. In low-altitude application scenarios, airborne users, targets to be sensed, and potential intrusion objects are usually distributed in a dynamically changing three-dimensional space. The system needs to provide reliable and stable wireless communication services at the same time, and have the ability to sense, locate, and monitor low-altitude airspace in real time.

[0003] The integrated communication and sensing technology provides a unified framework for the deep integration of communication and sensing functions by sharing spectrum resources, hardware platforms and signal processing modules, and is widely regarded as the core technology path to support the intelligent evolution of low-altitude scenarios.

[0004] Currently, multi-base station collaborative sensing integrated systems are commonly used to meet the communication and sensing coordination needs in low-altitude UAV scenarios. On the one hand, multiple ground base stations can significantly improve the coverage efficiency of low-altitude areas through joint transmission and collaborative processing, effectively overcoming the limitations of single base stations in terms of service range, observation angle and link reliability. On the other hand, distributed base stations can observe the same target from multiple spatial directions, which helps to improve the accuracy and robustness of low-altitude airspace monitoring.

[0005] However, most existing research on multi-base station collaborative sensing integration is based on fixed arrays or two-dimensional sensing area modeling, which has obvious limitations in characterizing the three-dimensional activity space of low-altitude UAVs. In particular, when communication users and sensing targets are distributed across multiple altitude layers, using only two-dimensional planar sensing areas to describe key monitoring areas or only considering array turning within a single plane cannot accurately characterize the geometric relationship of the real airspace, and also restricts the system's ability to finely shape the energy distribution in three-dimensional space.

[0006] How to organically combine multi-base station collaborative sensing integration with a three-dimensional rotatable array mechanism in low-altitude UAV scenarios to achieve precise energy shaping and communication assurance in the three-dimensional sensing airspace is a key technical problem that urgently needs to be solved.

[0007] Therefore, it is necessary to improve one or more of the problems existing in the above-mentioned related technical solutions.

[0008] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0009] The purpose of this invention is to solve the obvious limitations of existing multi-base station collaborative sensing integration, such as the inability to accurately depict the geometric relationships of the real airspace, and to provide a low-altitude UAV communication and sensing collaborative control method and system based on a rotatable antenna array.

[0010] The design concept of this embodiment is as follows: by deploying rotatable antenna arrays at multiple ground base stations to form multiple array base stations, and by performing joint beam design and array attitude control under the coordination of the central controller, communication coverage and sensing energy focusing for low-altitude three-dimensional target areas can be achieved.

[0011] To achieve the above objectives, the technical solution proposed by this invention is as follows: The low-altitude UAV communication and sensing coordinated control method based on rotatable antenna array is unique in that it includes: Multiple ground base stations are established, and rotatable antenna arrays are set on each of the ground base stations to form multiple array base stations; By adjusting the array attitude of each of the rotatable antenna arrays, the spatial orientation of each of the array base stations is adjusted, thereby determining the beam direction of each of the array base stations to adapt to multiple low-altitude UAVs with different spatial orientations. The central controller coordinates and controls the joint beamforming parameters and array attitude parameters of each array base station based on communication and sensing performance constraints, and constructs a joint optimization model to enable multiple array base stations to work together; the array base stations jointly transmit signals to provide communication services for multiple low-altitude UAVs, while simultaneously sensing and monitoring low-altitude target areas; Based on the requirements of low-altitude monitoring tasks, a three-dimensional sensing space is determined to characterize the key low-altitude monitoring and energy coverage areas. Voxel sampling is performed on the three-dimensional sensing space and its surrounding observation space. An energy distribution optimization target is constructed based on the sensing task weights and the communication status of low-altitude UAVs. The joint optimization model is solved using an alternating optimization method, and then the beam pointing result and array attitude adjustment result that meet the communication and sensing requirements of low-altitude UAVs are output, thereby completing the coordinated control of low-altitude UAV communication and sensing.

[0012] Furthermore, the rotatable antenna array includes multiple array surfaces, and each array surface is provided with multiple array elements. By adjusting the azimuth and elevation angles of each array surface of the rotatable antenna array, the spatial orientation of each array base station is adjusted, thereby determining the beam direction of each array base station, which is used to provide precise beam coverage for multiple low-altitude UAVs at different locations and altitudes. The array attitude includes the azimuth and elevation angles of the upper surface of the rotatable antenna array.

[0013] Furthermore, the construction of the joint optimization model specifically involves: Establish single-array steering vectors corresponding to each of the array base stations. Based on the single-array steering vectors and the array attitude parameters, construct a joint steering vector and a joint transmission pattern model to form a joint optimization model to characterize the spatial energy distribution under the coordinated action of multiple array base stations.

[0014] Furthermore, the establishment of the single-array steering vector corresponding to each of the array base stations is specifically as follows: The number of array base stations is set to M, and the number of array elements on each rotatable antenna array is N; Azimuth vector of each of the array base stations It can be expressed as the following formula: Where: m∈(1,2,...,M), azimuth vector It is an M×1 dimensional column vector; The elevation angle vector of each of the array base stations It can be expressed as the following formula: Where: m∈(1,2,...,M), is the pitch angle vector. It is an M×1 dimensional column vector; The array element coordinate matrix vectors of each of the array base stations after rotation It can be expressed as the following formula: Where: m∈(1,2,...,M), the rotated matrix element coordinate vector is an M×N×3 dimensional matrix vector; Let p be any point in the three-dimensional sensing space. Based on the spherical wave propagation model, construct the single-array steering vector corresponding to each array base station, which is expressed as follows: in: Let be the carrier wavelength, and p be any point in the three-dimensional sensing space. Let n be the Euclidean distance from the nth element of the mth array base station to the point, where n ∈ (1, 2, ..., N). Let be the azimuth vector of the m-th array base station. Let be the elevation angle vector of the m-th array base station.

[0015] Furthermore, the construction of the joint steering vector and joint launch pattern model specifically involves: Based on the spherical wave propagation model, the joint steering vector corresponding to any point p in the three-dimensional sensing space is calculated by the following formula: ; in: For the m-th array base station The Euclidean distance from each array element to point p in the three-dimensional perception space, where n∈(1,2,...,N); The joint emission pattern model corresponding to any point p in the three-dimensional sensing space is calculated by the following formula: in: This is the joint beamforming matrix.

[0016] Furthermore, based on communication and sensing performance constraints, the central controller coordinates the joint transmission of signals by each array base station. The joint transmission signals include joint transmission of communication signals and sensing signals. The joint transmission signal vector of each array base station is calculated using the following formula: Set the communication signal vector as The sensing signal vector is The two satisfy the principle of mutual independence; The joint transmit signal vector X of the m-th array base station is expressed as follows: ,m∈(1,2,...,M); in: Let m be the beamforming matrix of the m-th array base station oriented towards the communication task. The beamforming matrix of the m-th array base station for sensing tasks; Joint beamforming matrix of the m-th array base station It can be expressed as the following formula: .

[0017] Furthermore, voxel sampling is performed on the three-dimensional sensing space and its surrounding observation space. Based on the sensing task weights and the communication status of the low-altitude UAV, an energy distribution optimization objective is constructed as follows: Voxel sampling is performed on the three-dimensional perception space and its surrounding observation space to construct the radiation pattern matching error; The low-altitude key monitoring area with actual spatial range is defined as a three-dimensional entity perception space. , ; 3D entity perception space The observation space and its surrounding area are discretized into Individual sampling points ,in ; Introducing weighting coefficients, the pattern matching error is calculated using the following formula: in: For the first The relative importance of individual pixel sampling points in perceptual tasks. For the first The ideal radiation pattern value corresponding to the voxel sampling point is used to characterize the target emission energy level expected to be obtained at that voxel sampling point; Let the number of low-altitude UAVs be K, then the joint channel vector of the k-th low-altitude UAV is... Represented as: in, Let be the three-dimensional coordinates of the k-th low-altitude UAV; The received signal of the kth low-altitude UAV Represented as: in: For multiple array base stations to the first A joint channel vector composed of channels between low-altitude UAVs It is additive white Gaussian noise; The signal-to-interference-plus-noise ratio of the k-th low-altitude UAV Represented as: in: For the first Communication beamforming vector for a low-altitude UAV For the first The communication beamforming vectors of a low-altitude UAV user, where ; Voxel sampling is performed on the three-dimensional sensing space and its surrounding observation space. An energy distribution optimization target is constructed based on the sensing task weight and the communication status of the low-altitude UAV, thereby achieving precise energy focusing on the three-dimensional sensing space.

[0018] Furthermore, the method of using alternating optimization to solve the joint optimization model specifically involves: The alternating optimization method is used to solve the joint beamforming parameters and array attitude parameters in blocks; The iteration period is set to t. In each iteration, the array attitude of each array base station is fixed, the joint beamforming subproblem is solved, and the joint beamforming matrix is ​​optimized. With a fixed joint beamforming matrix, solve the array attitude sub-problem and optimize and update the azimuth and elevation angles of each array base station; By iteratively updating the joint beamforming matrix, the azimuth and elevation angles of each array base station, the pattern matching error in the three-dimensional sensing space is reduced, thereby outputting beam pointing results and array attitude adjustment results that meet the communication and sensing requirements of low-altitude UAVs, and completing the coordinated control of low-altitude UAV communication and sensing.

[0019] Furthermore, the specific solution steps for the joint optimization model include: Step a: Initialize the array attitude and joint beamforming matrix of each array base station, wherein the array attitude includes azimuth and elevation angles; The initialized array attitude vector is: ; The initialized joint beamforming matrix is: ; Step b: Set the iteration period to t, fix the azimuth and elevation angles of each array base station, transform the joint beamforming subproblem into a positive semidefinite programming problem about the joint beamforming matrix, and solve it using the positive semidefinite relaxation method to obtain the t-th iteration. Joint beamforming matrix under the next iteration; Step c: Fix the joint beamforming matrix Let the set of attitude variables of all array base stations be denoted as: Introducing a penalty function to construct an unconstrained approximation function: in: As a penalty factor, For the set of array attitude variables, , For the current array attitude variables Next The signal-to-interference-plus-noise ratio corresponding to a low-altitude drone; Step d: Define the attitude variable block of the m-th array base station as follows: For the attitude variable block of the m-th array base station, its corresponding local gradient is calculated according to the following formula: in: The objective function after introducing the communication constraint penalty function, Indicates the first The set of attitude variables composed of all array base station attitude variable blocks at the next iteration; Step e: Introduce the L-BFGS-B method, which combines dynamic penalty factor, block update strategy, and adaptive feasible region projection, to construct the first... The local approximate inverse Hessian matrix corresponding to each array base station The array attitude variables are updated according to the following formula: in: The m-th base station is at the... Adaptive step size in the next iteration Indicates projection to the first Each base station corresponds to an adaptive feasible angle range. Operations on; Step f: If the change in the objective function between two adjacent iterations is less than a preset threshold, stop the iteration and output the optimal joint beamforming matrix and the optimal array attitude vector; otherwise, return to step b until the optimal joint beamforming matrix and the optimal array attitude vector are output, thereby completing the low-altitude UAV communication and perception coordinated control.

[0020] This invention also proposes a low-altitude UAV communication and sensing collaborative control system based on a rotatable antenna array, used to implement the aforementioned low-altitude UAV communication and sensing collaborative control method based on a rotatable antenna array. Its unique feature is that: This includes multiple ground base stations, multiple low-altitude drones, and a central controller; Each of the ground base stations is equipped with a rotatable antenna array to form multiple array base stations, which are used to jointly transmit communication signals and sensing signals to provide communication services for multiple low-altitude UAVs, while simultaneously sensing and monitoring low-altitude target areas. The rotatable antenna array includes multiple array surfaces, and each array surface is provided with multiple array elements. By adjusting the azimuth and elevation angles of the array surfaces of each rotatable antenna array, the spatial orientation of each array base station can be adjusted. The multiple low-altitude UAVs are used to receive communication signals and sensing signals transmitted by the array base station, and to communicate and interact with the array base station. The central controller is used to coordinate the beamforming parameters and array attitude parameters of each array base station, enabling multiple array base stations to work together.

[0021] The technical solution proposed in this invention may include the following beneficial effects: (1) The low-altitude UAV communication and sensing collaborative control method based on rotatable antenna array relies on multiple ground base stations equipped with three-dimensional rotatable antenna arrays. By adjusting the array attitude of the rotatable antenna array, the spatial orientation of the array base station can be adjusted, which can accurately depict the three-dimensional distribution characteristics of low-altitude UAVs and airspace targets across multiple altitude layers, accurately reflect the geometric relationship in the real airspace, and significantly expand the coverage of low-altitude communication and sensing. Under the coordination of the central controller, joint beam design and array attitude control are carried out to achieve communication coverage and sensing energy focusing in the low-altitude three-dimensional sensing space area. By constructing a joint optimization model, the dynamic allocation of communication and sensing resources is realized, improving the concurrent service capability of multiple UAVs, and effectively adapting to the dynamic changes in the number and location of users in low-altitude scenarios. Combined with the voxel modeling of the three-dimensional sensing space, the efficient utilization of airspace resources and interference avoidance are realized, and the robustness of the system in complex electromagnetic environments is improved. At the same time, the hierarchical alternating optimization mechanism can significantly reduce the computational complexity, meet the real-time collaborative control requirements, provide low-latency and high-reliability integrated communication and sensing services for low-altitude UAV clusters, and effectively improve the accurate energy shaping capability and communication guarantee of the three-dimensional sensing airspace.

[0022] (2) This invention enhances the tracking accuracy of fast-moving targets and reduces beam pointing error by finely adjusting the azimuth and elevation angles of the rotatable antenna array, thereby achieving simultaneous and accurate coverage of UAV targets at multiple altitudes and in multiple azimuths and improving the ability to provide concurrent services to multiple targets. Through the coordinated optimization of azimuth and elevation angles, the beamwidth and pointing angle can be flexibly adjusted, which improves the utilization rate of airspace energy while ensuring coverage and reduces signal interference to non-target areas, effectively improving the universality and compatibility of the method.

[0023] (3) The low-altitude UAV communication and sensing collaborative control system based on the rotatable antenna array of the present invention has a simple structure, is easy to deploy, maintain and expand, can be quickly adapted to low-altitude communication networks of different scales, effectively ensures the continuous and stable operation of low-altitude communication and sensing services, has a high degree of integration, greatly improves the accuracy and robustness of low-altitude airspace detection, and has low system deployment cost, which is conducive to subsequent maintenance and operation, effectively reduces production cost and operation and maintenance cost, and ensures the stability and reliability of the system in long-term operation. Attached Figure Description

[0024] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0025] Figure 1This is a flowchart of an embodiment of the low-altitude UAV communication and sensing cooperative control method based on a rotatable antenna array according to the present invention; Figure 2 This is a schematic diagram of the system structure of an embodiment of the low-altitude UAV communication and sensing collaborative control system based on a rotatable antenna array according to the present invention; Figure 3 This is a schematic diagram of the array attitude of a single array base station in an embodiment of the present invention; Figure 4 This is a schematic diagram of a voxel sampling model for a three-dimensional perception space and its surrounding observation space in an embodiment of the present invention; Figure 5 This is a flowchart illustrating the optimization of the joint optimization model according to an embodiment of the present invention; Figure label: 1-Ground base station, 2-Low-altitude UAV, 3-Central controller, 4-Rotating antenna array. Detailed Implementation

[0026] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0027] The design concept of this embodiment is as follows: Based on the low-altitude scenario, a collaborative sensing system model of multiple ground base stations 1 is constructed to determine the spatial distribution relationship between ground base stations 1, the UAV, and the target sensing space; subsequently, a three-dimensional attitude model of the rotatable antenna array 4 is established, and the spatial orientation of the array base stations is adjusted using azimuth and elevation angles; further, a joint steering vector and transmission pattern model is established based on the spherical wave propagation model, and voxel sampling is performed on the three-dimensional sensing space to construct a pattern matching error index; under the conditions of satisfying communication quality constraints, transmission power constraints, and array mechanical rotation range constraints, a joint beamforming and array attitude joint optimization model is constructed; finally, the alternating optimization method is used to solve the joint optimization model, outputting beam design results and array attitude results that meet the communication and sensing requirements of the low-altitude UAV 2.

[0028] Among them, the three-dimensional attitude model changes the azimuth and elevation angles of each array base station facet, enabling the rotatable antenna array 4 to form an energy distribution more adaptable to low-altitude scenarios in different spatial directions; the voxel sampling process of the three-dimensional sensing space is to divide the target area and its surrounding observation area into multiple voxel sampling points to achieve a fine characterization of the energy distribution inside and outside the sensing area; the process of establishing a joint optimization model comprehensively considers the quality requirements of communication users and the sensing coverage requirements of the target area, so as to improve the energy focusing capability of the three-dimensional sensing area while ensuring communication performance.

[0029] like Figure 1 As shown, the communication and sensing coordinated control method for a low-altitude UAV 2 based on a rotatable antenna array 4 includes: Step S1: Establish multiple ground base stations 1, and set up rotatable antenna arrays 4 on each ground base station 1 to form multiple array base stations; Step S2: The rotatable antenna array 4 includes multiple array surfaces, and each array surface is equipped with multiple array elements. By adjusting the azimuth and elevation angles of the array surfaces of each rotatable antenna array 4, the spatial orientation of each array base station is adjusted, thereby determining the beam direction of each array base station and performing precise beam coverage on multiple low-altitude UAVs 2 at different locations and altitudes. The array attitude of the rotatable antenna array 4 is the azimuth and elevation angles of its array surfaces. Step S3: The central controller 3 coordinates and controls the joint beamforming parameters and array attitude parameters of each array base station based on communication and sensing performance constraints, and constructs a joint optimization model to realize the collaborative work of multiple array base stations. The joint optimization model first establishes the single array steering vector corresponding to each array base station, and then constructs a joint steering vector and joint transmission pattern model based on the single array steering vector and array attitude parameters. This joint optimization model is used to characterize the spatial energy distribution under the coordinated action of multiple array base stations.

[0030] like Figure 3 As shown, step S3.1, establishing the single-array steering vector corresponding to each array base station, specifically involves: Step S3.1.1: Set the number of array base stations to M, and the number of array elements on each rotatable antenna array 4 to N; Then the azimuth angle of the m-th array base station and pitch angle The three-dimensional rotation matrix is ​​expressed as follows: in: Let be the rotation matrix of the m-th array base station about the z-axis of the three-dimensional coordinate system. Let be the rotation matrix of the m-th array base station about the y-axis of the three-dimensional coordinate system; The array element coordinate matrix after the m-th array base station is rotated It can be expressed as the following formula: in: For the first The center coordinate matrix of the base station array For the first The initial element coordinate matrix of a base station array; Step S3.1.2, Azimuth vector of each array base station It can be expressed as the following formula: Where: m∈(1,2,...,M), azimuth vector It is an M×1 dimensional column vector; Elevation angle vectors of each array base station It can be expressed as the following formula: Where: m∈(1,2,...,M), is the pitch angle vector. It is an M×1 dimensional column vector; After rotation, the coordinate matrix vectors of each array base station element It can be expressed as the following formula: Where: m∈(1,2,...,M), the rotated matrix element coordinate vector is an M×N×3 dimensional matrix vector; Step S3.1.3: Set any point p in the three-dimensional sensing space. Based on the spherical wave propagation model, construct the single-array steering vector corresponding to each array base station. For the first... Each array base station and any spatial point in the three-dimensional sensing space The single array steering vector is represented as: in: Let be the carrier wavelength, and p be any point in the three-dimensional sensing space. Let n be the Euclidean distance from the nth element of the mth array base station to the point, where n ∈ (1, 2, ..., N). Let be the azimuth vector of the m-th array base station. Let m be the elevation angle vector of the m-th array base station; due to the array attitude... Will change the first The spatial coordinates of each array element of the array base station, therefore the distance It changes with the array's orientation; Step S3.2: Based on the single array steering vector and array attitude parameters, construct a joint steering vector and joint transmission pattern model to form a joint optimization model to characterize the spatial energy distribution under the coordinated action of multiple array base stations; Step S3.2.1: Based on the spherical wave propagation model, the joint steering vector corresponding to any point p in the three-dimensional sensing space is calculated using the following formula: ; in: For the m-th array base station The Euclidean distance from each array element to point p in the three-dimensional perception space, n∈(1,2,...,N); The spherical wave propagation model can simultaneously reflect the effects of three-dimensional array rotation, path loss, and near-field propagation phase changes on spatial energy distribution. Step S3.2.2: The joint emission pattern corresponding to any point p in the three-dimensional sensing space is expressed as follows: in: For joint beamforming matrix; This joint optimization model is used to uniformly characterize the influence of array attitude and joint beamforming on the energy distribution in three-dimensional space.

[0031] Step S4: Based on communication and sensing performance constraints, the central controller 3 coordinates the joint transmission of signals by each array base station. The joint transmission signals include joint transmission of communication signals and sensing signals, which are used to provide communication services for multiple low-altitude UAVs 2, and at the same time to perform sensing and monitoring of low-altitude target areas. Step S4.1: Set the communication signal vector as... The sensing signal vector is The two satisfy the principle of mutual independence; The joint transmit signal vector X of the m-th array base station is expressed as follows: ,m∈(1,2,...,M); in: Let m be the beamforming matrix of the m-th array base station oriented towards the communication task. The beamforming matrix of the m-th array base station for sensing tasks; Step S4.2, Joint beamforming matrix of the m-th array base station It can be expressed as the following formula: ; Step S5: Based on the requirements of low-altitude monitoring tasks, determine the three-dimensional sensing space used to characterize the key low-altitude monitoring and energy coverage areas, and perform voxel sampling on the three-dimensional sensing space and its surrounding observation space. Construct an energy distribution optimization target based on the sensing task weights and the communication status of the low-altitude UAV 2. Among them, the process of spatial sampling discretization of the three-dimensional sensing space is to divide the three-dimensional sensing space and its surrounding observation space into multiple voxel sampling points to achieve a fine characterization of the energy distribution inside and outside the sensing area. Step S5.1, as follows Figure 4 As shown, voxel sampling is performed on the three-dimensional sensing space, and the low-altitude key monitoring area with actual spatial range is defined as the three-dimensional entity sensing space. , The three-dimensional entity perception space specifically includes the airspace above no-fly zones, the airspace near low-altitude air routes, or the low-altitude three-dimensional area around specific facilities. 3D entity perception space The observation space and its surrounding area are discretized into Individual sampling points ,in ; For any voxel sampling point The joint transmit pattern of the system under the array attitude and joint beamforming matrix still satisfies: ; The ideal emission pattern in the three-dimensional entity perception space is represented as: ; Step S5.2: The equal-weighted orientation pattern matching error is used to measure the perception performance of the three-dimensional perception space. The expression is as follows: in: For the first The ideal radiation pattern value corresponding to the voxel sampling point is used to characterize the target emission energy level expected to be obtained at that voxel sampling point; Step S5.3: Introduce weighting coefficients and calculate the pattern matching error using the following formula: in: For the first The relative importance of individual pixel sampling points in the perception task; Among the larger ones This means that the pattern matching error at the corresponding location will be more severely penalized during the optimization process, thereby guiding the system to focus more transmission energy on key altitude layers, key monitoring areas, or near specific functional channels; smaller This indicates that a relatively larger pattern mismatch is allowed at this position; to avoid simply changing the weight scale from affecting the magnitude of the objective function, the weights can be normalized so that the average value of the normalized weights is 1.

[0032] Step S5.4: Set the number of low-altitude drones 2 to K, then the position of the kth low-altitude drone 2 is represented as: k∈(1,2,...,K); The joint channel vector of the k-th low-altitude UAV 2 Represented as: ; The received signal of the kth low-altitude drone 2 Represented as: ; in: For multiple array base stations to the first A joint channel vector composed of channels between low-altitude UAVs It is additive white Gaussian noise; The signal-to-interference-plus-noise ratio of the k-th low-altitude UAV Represented as: in: For the first Communication beamforming vector for a low-altitude UAV For the first The communication beamforming vectors of a low-altitude UAV user, where ; It can be seen that this embodiment can achieve precise energy focusing in the three-dimensional sensing airspace while ensuring the communication quality of the low-altitude UAV 2 users.

[0033] Step S6: Solve the joint optimization model using the alternating optimization method, and then output the beam pointing result and array attitude adjustment result that meet the communication and sensing requirements of the low-altitude UAV 2, thereby completing the communication and sensing coordinated control of the low-altitude UAV 2. The process of optimizing the joint optimization model by using the alternating optimization method is to comprehensively consider the quality requirements of communication drone users and the perception coverage requirements of the target area, so as to improve the energy focusing capability of the three-dimensional perception area while ensuring communication performance. like Figure 5 As shown, the alternating optimization method is used to solve the joint optimization model, which involves solving the joint beamforming parameters and array attitude parameters in blocks. Specifically, this includes the following steps: Step a: Initialize the array attitude and joint beamforming matrix of each array base station, wherein the array attitude includes azimuth and elevation angles; The initialized array attitude vector is: ; The initialized joint beamforming matrix is: ; Step b: Set the iteration period to t, and fix the azimuth and elevation angles of each array base station. The joint beamforming matrix is ​​then optimized based on the joint transmission pattern obtained by solving the joint beamforming problem using the following formula. ; Where: K represents the number of low-altitude drones (2). , For the first The pattern response matrix corresponding to the voxel sampling point, and ,when hour, For the first The communication transmission covariance matrix corresponding to two low-altitude UAVs; when hour, The emission covariance matrix corresponding to the sensed signal; Step c: Fix the joint beamforming matrix We introduce a penalty function to construct an unconstrained approximate function: in: As a penalty factor, For the set of array attitude variables, , For the current array attitude variables Next The signal-to-interference-plus-noise ratio corresponding to 2 low-altitude drones; Step d: Define the attitude variable block of the m-th array base station as follows: For the attitude variable block of the m-th array base station, its corresponding local gradient is calculated according to the following formula: in: The objective function after introducing the communication constraint penalty function, Indicates the first The set of attitude variables composed of all array base station attitude variable blocks at the next iteration; Step e: Introduce the block-based adaptive L-BFGS-B method to construct a locally approximate inverse Hessian matrix. The array attitude variables are updated according to the following formula: in: The m-th base station is at the... Adaptive step size in the next iteration Indicates projection to the first Each base station corresponds to an adaptive feasible angle range. Operations on; Step f: If the change in the objective function between two adjacent iterations is less than a preset threshold, stop the iteration and output the optimal joint beamforming matrix and the optimal array attitude vector; otherwise, return to step b until the optimal joint beamforming matrix and the optimal array attitude vector are output, thereby completing the communication and sensing coordinated control of the low-altitude UAV 2.

[0034] This embodiment also proposes a communication and sensing collaborative control system for low-altitude UAV 2 based on a rotatable antenna array 4, used to implement the aforementioned communication and sensing collaborative control method for low-altitude UAV 2 based on a rotatable antenna array 4: like Figure 2 As shown, it includes multiple ground base stations 1, multiple low-altitude drones 2, and a central controller 3; Each ground base station 1 is equipped with a rotatable antenna array 4 to form multiple array base stations. These multiple array base stations serve as cooperative transmission nodes to jointly transmit communication signals and sensing signals. The rotatable antenna array 4 includes multiple array surfaces, each with multiple array elements. By adjusting the azimuth and elevation angles of each array surface on the rotatable antenna array 4, the spatial orientation of each array base station can be adjusted, thereby improving the system's beam control capability in the target airspace and enabling the array to form an energy distribution more suitable for low-altitude scenarios in different spatial directions. Multiple low-altitude UAVs 2 serve as communication service targets, receiving communication and sensing signals transmitted by the array base station and interacting with the array base station. The central controller 3 is used to coordinate the beamforming parameters and array attitude parameters of each array base station, so as to enable multiple array base stations to work together.

Claims

1. A method for coordinated communication and sensing control of low-altitude unmanned aerial vehicles based on a rotatable antenna array, characterized in that, include: Multiple ground base stations are established, and rotatable antenna arrays are set on each of the ground base stations to form multiple array base stations; By adjusting the array attitude of each of the rotatable antenna arrays, the spatial orientation of each of the array base stations is adjusted, thereby determining the beam direction of each of the array base stations to adapt to multiple low-altitude UAVs with different spatial orientations. The central controller coordinates and controls the joint beamforming parameters and array attitude parameters of each array base station based on communication and sensing performance constraints, and constructs a joint optimization model to enable multiple array base stations to work together; the array base stations jointly transmit signals to provide communication services for multiple low-altitude UAVs, while simultaneously sensing and monitoring low-altitude target areas; Based on the requirements of low-altitude monitoring tasks, a three-dimensional sensing space is determined to characterize the key low-altitude monitoring and energy coverage areas. Voxel sampling is performed on the three-dimensional sensing space and its surrounding observation space. An energy distribution optimization target is constructed based on the sensing task weights and the communication status of low-altitude UAVs. The joint optimization model is solved using an alternating optimization method, and then the beam pointing result and array attitude adjustment result that meet the communication and sensing requirements of low-altitude UAVs are output, thereby completing the coordinated control of low-altitude UAV communication and sensing.

2. The low-altitude UAV communication and sensing coordinated control method based on a rotatable antenna array according to claim 1, characterized in that: The rotatable antenna array includes multiple array surfaces, and each array surface is provided with multiple array elements. By adjusting the azimuth and elevation angles of each array surface, the spatial orientation of each array base station is adjusted, thereby determining the beam direction of each array base station, which is used to provide precise beam coverage for multiple low-altitude UAVs at different locations and altitudes. The array attitude includes the azimuth and elevation angles of the upper surface of the rotatable antenna array.

3. The low-altitude UAV communication and sensing coordinated control method based on a rotatable antenna array according to claim 2, characterized in that, The construction of the joint optimization model specifically involves: Establish single-array steering vectors corresponding to each of the array base stations. Based on the single-array steering vectors and the array attitude parameters, construct a joint steering vector and a joint transmission pattern model to form a joint optimization model to characterize the spatial energy distribution under the coordinated action of multiple array base stations.

4. The low-altitude UAV communication and sensing coordinated control method based on a rotatable antenna array according to claim 3, characterized in that, The specific steps for establishing the single-array steering vector corresponding to each of the aforementioned array base stations are as follows: The number of array base stations is set to M, and the number of array elements on each rotatable antenna array is N; Azimuth vector of each of the array base stations It can be expressed as the following formula: Where: m∈(1,2,...,M), azimuth vector It is an M×1 dimensional column vector; The elevation angle vector of each of the array base stations It can be expressed as the following formula: Where: m∈(1,2,...,M), is the pitch angle vector. It is an M×1 dimensional column vector; The array element coordinate matrix vectors of each of the array base stations after rotation It can be expressed as the following formula: Where: m∈(1,2,...,M), the rotated matrix element coordinate vector is an M×N×3 dimensional matrix vector; Let p be any point in the three-dimensional sensing space. Based on the spherical wave propagation model, construct the single-array steering vector corresponding to each array base station, which is expressed as follows: in: Let be the carrier wavelength, and p be any point in the three-dimensional sensing space. Let n be the Euclidean distance from the nth element of the mth array base station to the point, where n ∈ (1, 2, ..., N). Let be the azimuth vector of the m-th array base station. Let be the elevation angle vector of the m-th array base station.

5. The low-altitude UAV communication and sensing coordinated control method based on a rotatable antenna array according to claim 4, characterized in that, The specific steps for constructing the joint steering vector and joint launch pattern model are as follows: Based on the spherical wave propagation model, the joint steering vector corresponding to any point p in the three-dimensional sensing space is calculated by the following formula: ； in: For the m-th array base station The Euclidean distance from each array element to point p in the three-dimensional perception space, where n∈(1,2,...,N); The joint emission pattern model corresponding to any point p in the three-dimensional sensing space is calculated by the following formula: in: This is the joint beamforming matrix.

6. The low-altitude UAV communication and sensing coordinated control method based on a rotatable antenna array according to claim 5, characterized in that: Based on communication and sensing performance constraints, the central controller coordinates the joint transmission of signals by each array base station. The joint transmission signals include joint communication signals and sensing signals. The joint transmission signal vector of each array base station is calculated using the following formula: Set the communication signal vector as The sensing signal vector is The two satisfy the principle of mutual independence; The joint transmit signal vector X of the m-th array base station is expressed as follows: ,m∈(1,2,...,M); in: Let m be the beamforming matrix of the m-th array base station oriented towards the communication task. The beamforming matrix of the m-th array base station for sensing tasks; Joint beamforming matrix of the m-th array base station It can be expressed as the following formula: 。 7. The low-altitude UAV communication and sensing coordinated control method based on a rotatable antenna array according to claim 6, characterized in that: Voxel sampling is performed on the three-dimensional sensing space and its surrounding observation space. The energy distribution optimization objective is constructed based on the sensing task weights and the communication status of the low-altitude UAV. Specifically: Voxel sampling is performed on the three-dimensional perception space and its surrounding observation space to construct the radiation pattern matching error; The low-altitude key monitoring area with actual spatial range is defined as a three-dimensional entity perception space. , ; 3D entity perception space The observation space and its surrounding area are discretized into Individual sampling points ,in ; Introducing weighting coefficients, the pattern matching error is calculated using the following formula: in: For the first The relative importance of individual pixel sampling points in perceptual tasks. For the first The ideal radiation pattern value corresponding to the voxel sampling point is used to characterize the target emission energy level expected to be obtained at that voxel sampling point; Let the number of low-altitude UAVs be K, then the joint channel vector of the k-th low-altitude UAV is... Represented as: in, Let be the three-dimensional coordinates of the k-th low-altitude UAV; The received signal of the kth low-altitude UAV Represented as: in: For multiple array base stations to the first A joint channel vector composed of channels between low-altitude UAVs It is additive white Gaussian noise; The signal-to-interference-plus-noise ratio of the k-th low-altitude UAV Represented as: in: For the first Communication beamforming vector of a low-altitude UAV For the first The communication beamforming vectors of a low-altitude UAV user, where ; Voxel sampling is performed on the three-dimensional sensing space and its surrounding observation space. An energy distribution optimization target is constructed based on the sensing task weight and the communication status of the low-altitude UAV, thereby achieving precise energy focusing on the three-dimensional sensing space.

8. The low-altitude UAV communication and sensing coordinated control method based on a rotatable antenna array according to claim 7, characterized in that: The method of using alternating optimization to solve the joint optimization model specifically involves: The alternating optimization method is used to solve the joint beamforming parameters and array attitude parameters in blocks; The iteration period is set to t. In each iteration, the array attitude of each array base station is fixed, the joint beamforming subproblem is solved, and the joint beamforming matrix is ​​optimized. With a fixed joint beamforming matrix, solve the array attitude sub-problem and optimize and update the azimuth and elevation angles of each array base station; By iteratively updating the joint beamforming matrix, the azimuth and elevation angles of each array base station, the pattern matching error in the three-dimensional sensing space is reduced, thereby outputting beam pointing results and array attitude adjustment results that meet the communication and sensing requirements of low-altitude UAVs, and completing the coordinated control of low-altitude UAV communication and sensing.

9. The low-altitude UAV communication and sensing coordinated control method based on a rotatable antenna array according to claim 8, characterized in that: The specific solution steps for the joint optimization model include: Step a: Initialize the array attitude and joint beamforming matrix of each array base station, wherein the array attitude includes azimuth and elevation angles; The initialized array attitude vector is: ; The initialized joint beamforming matrix is: ; Step b: Set the iteration period to t, fix the azimuth and elevation angles of each array base station, transform the joint beamforming subproblem into a positive semidefinite programming problem about the joint beamforming matrix, and solve it using the positive semidefinite relaxation method to obtain the t-th iteration. Joint beamforming matrix under the next iteration; Step c: Fix the joint beamforming matrix Let the set of attitude variables of all array base stations be denoted as: Introducing a penalty function to construct an unconstrained approximation function: in: As a penalty factor, For the set of array attitude variables, , For the current array attitude variables Next The signal-to-interference-plus-noise ratio corresponding to a low-altitude drone; Step d: Define the attitude variable block of the m-th array base station as follows: For the attitude variable block of the m-th array base station, its corresponding local gradient is calculated according to the following formula: in: The objective function after introducing the communication constraint penalty function, Indicates the first The set of attitude variables composed of all array base station attitude variable blocks at the next iteration; Step e: Introduce the L-BFGS-B method, which combines dynamic penalty factor, block update strategy, and adaptive feasible region projection, to construct the first... The local approximate inverse Hessian matrix corresponding to each array base station The array attitude variables are updated according to the following formula: in: The m-th base station is at the... Adaptive step size in the next iteration Indicates projection to the first Each base station corresponds to an adaptive feasible angle range. Operations on; Step f: If the change in the objective function between two adjacent iterations is less than a preset threshold, stop the iteration and output the optimal joint beamforming matrix and the optimal array attitude vector; otherwise, return to step b until the optimal joint beamforming matrix and the optimal array attitude vector are output, thereby completing the low-altitude UAV communication and perception coordinated control.

10. A low-altitude unmanned aerial vehicle (UAV) communication and sensing collaborative control system based on a rotatable antenna array, used to implement the low-altitude UAV communication and sensing collaborative control method based on a rotatable antenna array as described in any one of claims 1-9, characterized in that: This includes multiple ground base stations, multiple low-altitude drones, and a central controller; Each of the ground base stations is equipped with a rotatable antenna array to form multiple array base stations, which are used to jointly transmit communication signals and sensing signals to provide communication services for multiple low-altitude UAVs, while simultaneously sensing and monitoring low-altitude target areas. The rotatable antenna array includes multiple array surfaces, and each array surface is provided with multiple array elements. By adjusting the azimuth and elevation angles of the array surfaces of each rotatable antenna array, the spatial orientation of each array base station can be adjusted. The multiple low-altitude UAVs are used to receive communication signals and sensing signals transmitted by the array base station, and to communicate and interact with the array base station. The central controller is used to coordinate the beamforming parameters and array attitude parameters of each array base station, enabling multiple array base stations to work together.

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