A method and device for joint design of virtual antenna array and beamforming of unmanned aerial vehicle

Abstract

This invention relates to the field of communication technology, specifically to a method and apparatus for the joint design of a virtual antenna array and beamforming for unmanned aerial vehicles (UAVs). The method includes: constructing a near-field signal transmission model based on the positions of each UAV, the distance between the communication user and the sensing target; determining an integrated sensing beamforming model based on the virtual antenna array; determining the downlink signal transmission model for the communication user and the sensing signal echo model based on the near-field signal transmission model; determining the Fisher information matrix and Cramer-Rhodes bound of the sensing target; constructing an objective function with the goal of minimizing the Cramer-Rhodes bound; and solving the objective function using an inner loop algorithm and an outer loop algorithm to determine the positions of each UAV and the beamforming matrix. This invention combines the coordinated position adjustment of multiple UAVs with cooperative beamforming to improve the communication quality for the communication user and the sensing performance for the sensing target, thereby fully utilizing the flexible deployment characteristics of UAVs.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and specifically to a method and apparatus for the joint design of a virtual antenna array and beamforming for unmanned aerial vehicles (UAVs). Background Technology

[0002] Existing UAV networks typically employ communication systems to transmit downlink data to ground communication users and radar systems to sense and locate ground targets. However, existing UAV networks have the following drawbacks in performing communication and sensing tasks:

[0003] Disadvantage 1: The separate design of communication and sensing systems in drone networks leads to hardware redundancy and low efficiency in software resource utilization. Specifically, if an independent hardware design is adopted, the drone's payload capacity is limited, and its flight time is strictly constrained by battery capacity. Simultaneously equipping both communication and sensing systems will significantly increase the drone's weight, thereby shortening its effective operating time and reducing overall energy efficiency.

[0004] Disadvantage 2: Although existing beamforming methods can improve downlink data rates for ground communication users and positioning accuracy for ground sensing targets, UAVs are limited by their size, making it difficult to install large antenna arrays on a single UAV. Therefore, using beamforming methods on a single UAV cannot meet the high-precision communication and sensing requirements of current 5G and future 6G.

[0005] Disadvantage 3: The current beamforming approach in UAV networks is limited to using the beamforming methods of ground base stations, and fails to fully explore the uniqueness of UAVs, namely the flexibility and dynamic change of UAV network topology. Summary of the Invention

[0006] In view of this, the present invention provides a method and apparatus for the joint design of a virtual antenna array and beamforming for unmanned aerial vehicles (UAVs), in order to improve the problems existing in the prior art where communication and sensing systems are designed separately when using UAV networks for communication and sensing.

[0007] In a first aspect, the present invention provides a method for the joint design of a virtual antenna array and beamforming for unmanned aerial vehicles (UAVs). The method includes: constructing a near-field transmission model of a signal based on the positions of each UAV in the virtual antenna array, the distance between the communication user and the sensing target; determining a communication signal transmission model and a sensing signal echo model based on the integrated sensing beam transmitted by the virtual antenna array and the near-field transmission model of the signal; determining the Fisher information matrix of the sensing target based on the sensing signal echo model, and determining the Cramer-Rao bound of the sensing target; determining constraints based on the signal-to-interference-plus-noise ratio (SIR) of the communication user, the total energy consumption of the UAV, and the total transmit power of the UAV, and constructing an objective function with the goal of minimizing the Cramer-Rao bound, wherein the SIR is determined by the communication signal transmission model; and solving the objective function using an inner loop algorithm and an outer loop algorithm to determine the positions of each UAV in the virtual antenna array and the beamforming matrix of the integrated sensing beam.

[0008] In this invention, considering the flexibility and dynamic changes of the UAV network topology, the collaborative position adjustment of multiple UAVs and cooperative beamforming are considered together in the UAV network to improve the communication quality for communication users and the perception performance for perceived targets, thereby making full use of the flexible deployment characteristics of UAVs.

[0009] In one optional implementation, the virtual antenna array consists of multiple UAVs equipped with transmitting and receiving antennas. A near-field transmission model for signals is constructed based on the positions of each UAV in the virtual antenna array, the distances between the communication user and the sensing target, and the distances between the communication user and the sensing target. This includes: constructing a three-dimensional Cartesian coordinate system with the central reference point of the virtual antenna array as the origin; determining the near-field transmission model for the communication user's signals based on the Euclidean distances between the positions of each UAV and the communication user in the three-dimensional Cartesian coordinate system; and determining the near-field transmission model for the sensing target's signals based on the Euclidean distances between the positions of each UAV and the sensing target in the three-dimensional Cartesian coordinate system.

[0010] In this invention, a near-field channel model for transmitting and receiving signals is constructed using the specific distances between the user or target and each antenna, such as Euclidean distances, so that the specific location of the target can be obtained through the echo.

[0011] In one optional implementation, the communication signal transmission model and the sensing signal echo model are determined based on the integrated sensing beam transmitted by the virtual antenna array and the signal near-field transmission model, including: determining the integrated sensing beam based on the data to be transmitted by the communication user and the digital beamforming matrix of the data to be transmitted; determining the response matrix of the virtual antenna array to the downlink channel of the communication user and the sensing target based on the signal near-field transmission model; constructing the communication signal transmission model based on an ideal signal, interference between multiple communication users, and noise, wherein the ideal signal is determined based on the downlink channel and the user beam containing the downlink data of the corresponding communication user in the integrated sensing beam, and the interference between multiple communication users is determined based on the downlink channel and the difference between the integrated sensing beam and the user beam; and constructing the sensing signal echo model based on the echo and noise, wherein the echo is determined based on the response matrix of the sensing target and the integrated sensing beam.

[0012] In this invention, by using a near-field transmission model and a sensing-integrated beamforming system, a precise communication and sensing signal model is constructed. This model can clearly define ideal communication signals, multi-user interference, and noise, and can also clearly describe sensing echoes and noise. This effectively supports subsequent performance optimization and joint design of the sensing-integrated system, thereby improving communication quality and sensing accuracy.

[0013] In one optional implementation, determining the Fisher information matrix of the sensing target based on the sensing signal echo model and determining the Cramer-Rhodes bound of the sensing target includes: determining the Fisher information matrix of the sensing target based on the conditional likelihood function of the echo signal with respect to the position of the sensing target, wherein the echo signal is determined based on the sensing signal echo model; and determining the Cramer-Rhodes bound of the sensing target in three-dimensional coordinates based on the inverse matrix of the Fisher information matrix, wherein the inverse matrix of the Fisher information matrix is ​​the theoretical limit of the error covariance matrix between the estimated and true values ​​of the position of the sensing target.

[0014] In this invention, the Fisher information matrix is ​​first determined by the conditional likelihood function of the echo signal through the sensing signal echo model, and then the Cramer-Rhodes bound of the three-dimensional coordinates of the sensing target is obtained through its inverse matrix. This establishes a theoretical limit for the estimation error of the sensing target position, which can accurately quantify the sensing positioning accuracy and provide a reliable basis for subsequent sensing performance optimization.

[0015] In one optional implementation, the objective function is solved using an inner loop algorithm and an outer loop algorithm to determine the positions of each UAV in the virtual antenna array and the beamforming matrix of the integrated inductive beam. This includes: constructing a first subproblem of solving the positions of each UAV in the virtual antenna array when minimizing the Cramer-Rao bound and a second subproblem of solving the beamforming matrix of the integrated inductive beam when minimizing the Cramer-Rao bound based on the objective function; when solving the first and second subproblems in the outer loop, the inner loop algorithm is used to determine the positions of each UAV and the beamforming matrix of the current loop, and this is used as the input for the next loop in the outer loop to obtain the positions of each UAV and the beamforming matrix when the loop is stable.

[0016] In this invention, a solution is obtained by combining outer and inner loop algorithms, thus avoiding the problem that the outer loop algorithm cannot determine the closed-form solution.

[0017] In one optional implementation, an inner loop algorithm is used to determine the position and beamforming matrix of each UAV in the current loop, including: determining the Cramer-Rhodes bound based on the position and beamforming matrix of each UAV obtained in the previous loop, and converting the objective functions in the first and second subproblems into linear expressions based on the first-order Taylor expansion of the position and beamforming matrix of each UAV; solving the objective functions converted into linear expressions to obtain the position and beamforming matrix of each UAV in the current loop.

[0018] In this invention, an inner loop algorithm is used to transform the objective function into a linear expression based on the results of the previous loop and solve it through a first-order Taylor expansion. This can efficiently iteratively determine the position of the UAV and the beamforming matrix, reduce the complexity of the optimization problem and improve the solution efficiency while ensuring the performance of the integrated communication and sensing system, thereby quickly realizing the joint optimization of communication and sensing.

[0019] In one optional implementation, the constraints include: the signal-to-interference-plus-noise ratio of the communication user is greater than or equal to a preset threshold, the total energy consumption of the UAV is less than or equal to the power supplied by the UAV battery, and the total transmission power is less than or equal to the upper limit of the UAV antenna power.

[0020] Secondly, the present invention provides a joint design device for a UAV virtual antenna array and beamforming, the device comprising: a first model building module for constructing a signal near-field transmission model based on the positions of each UAV in the virtual antenna array, the distance between the communication user and the sensing target; a second model building module for determining a communication signal transmission model and a sensing signal echo model based on the integrated sensing beam transmitted by the virtual antenna array and the signal near-field transmission model; a Cramer-Rao boundary determination module for determining the Fisher information matrix of the sensing target based on the sensing signal echo model and determining the Cramer-Rao boundary of the sensing target; an objective function construction module for determining constraints based on the signal-to-interference-plus-noise ratio (SIR) of the communication user, the total energy consumption of the UAV, and the total transmit power, and constructing an objective function with the goal of minimizing the Cramer-Rao boundary, wherein the SIR is determined by the communication signal transmission model; and a joint design module for solving the objective function using an inner loop algorithm and an outer loop algorithm to determine the positions of each UAV in the virtual antenna array and the beamforming matrix of the integrated sensing beam.

[0021] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the UAV virtual antenna array and beamforming joint design method of the first aspect or any corresponding embodiment described above.

[0022] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the UAV virtual antenna array and beamforming co-design method of the first aspect or any corresponding embodiment described above.

[0023] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the UAV virtual antenna array and beamforming joint design method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the hardware components of an unmanned aerial vehicle (UAV) system based on relevant technologies;

[0026] Figure 2 This is a diagram illustrating the allocation of software resources for a drone system based on relevant technologies.

[0027] Figure 3 This is a flowchart illustrating the joint design method of UAV virtual antenna array and beamforming according to an embodiment of the present invention;

[0028] Figure 4 This is a flowchart illustrating another method for the joint design of a UAV virtual antenna array and beamforming according to an embodiment of the present invention.

[0029] Figure 5 This is a schematic diagram of the azimuth angle according to an embodiment of the present invention;

[0030] Figure 6 These are schematic diagrams illustrating the CRB performance under different design methods according to embodiments of the present invention;

[0031] Figure 7 This is a schematic diagram comparing CRB values ​​at various azimuth angles according to an embodiment of the present invention;

[0032] Figure 8 This is a schematic diagram showing the comparison between CRB and SINR according to an embodiment of the present invention;

[0033] Figure 9 This is a structural block diagram of a drone virtual antenna array and beamforming co-design device according to an embodiment of the present invention;

[0034] Figure 10 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0035] As described in the background section, existing technologies employ a method of separately designing the communication system and the sensing system within an unmanned aerial vehicle (UAV) network to accomplish communication and sensing tasks. Specifically, existing technologies generally use independent communication steps and sensing methods within UAV networks, which can be categorized into the following two types:

[0036] In the first communication and sensing method, an independent communication and sensing system is installed on each drone, such as... Figure 1 As shown. This system achieves communication and sensing through the following steps:

[0037] Step 1: Install communication and sensing hardware on a single drone.

[0038] Step two: The communication system and the sensing system can each be considered as a multi-input multi-output (MIMO) system. In the communication system, the UAV antenna array transmits the data needed by the user to the ground communication user; in the sensing system, the UAV antenna array transmits sensing beams to the ground target and receives the echoes for target localization.

[0039] Step 3: Beamforming is performed on the communication data and sensing pulses in their respective MIMO systems to form independent communication beams and sensing beams.

[0040] In the second communication and sensing method, a hardware system is installed on each drone, which includes... Figure 1 The hardware required by both the communication and sensing systems in China includes the additional hardware equipment needed by each system. For example... Figure 2 As shown, this design employs the following steps to achieve communication and sensing:

[0041] Step 1: Install two antenna arrays with multiple antennas on a single drone, one for transmitting and the other for receiving signals. At this point, the single drone can be regarded as a MIMO system.

[0042] Step 2: Divide the MIMO system into communication and sensing periods using a time allocation method. During the communication period, the UAV antenna array transmits the data required by the ground communication user; during the sensing period, the UAV antenna array transmits sensing beams to ground targets and receives their echoes for target localization. Alternatively, a frequency allocation method can be used to perform communication and sensing within their respective frequency ranges.

[0043] Step 3: In the MIMO system, beamforming is performed on the communication data and sensing pulses respectively to form independent communication beams and sensing beams.

[0044] However, in the first approach, which uses independent hardware design, the drone's payload capacity is limited and its flight time is strictly constrained by battery capacity. If it is equipped with both communication and sensing systems, the drone's weight will be significantly increased, thereby shortening its effective operating time and reducing overall energy efficiency. In the second approach, which uses communication and sensing based on software resource allocation, the utilization rate of time or spectrum resources will be low.

[0045] Based on this, this embodiment introduces an integrated sensing beam to replace the existing separate communication and sensing system design. This beam carries communication data to ground users and also has the shape characteristics of a sensing beam. The integrated sensing beam of the UAV network enables resource sharing between communication and sensing functions in hardware and wireless software, thereby reducing the deployment cost of communication and sensing functions. At the same time, it adds the step of designing the position of each UAV in the UAV swarm, thereby constructing a topology composed of multiple UAVs. This topology forms a virtual antenna array (VAA), and the beamforming of this virtual antenna array is designed to generate the integrated sensing beam.

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] According to an embodiment of the present invention, a method for jointly designing a virtual antenna array and beamforming for a UAV is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0048] This embodiment provides a method for jointly designing a virtual antenna array and beamforming for unmanned aerial vehicles (UAVs), which can be used in electronic devices such as computers, mobile phones, and tablets. Figure 3 This is a flowchart of a joint design method for a UAV virtual antenna array and beamforming according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0049] Step S101: Construct a near-field signal transmission model based on the positions of each UAV in the virtual antenna array of the UAV, the distances between communication users and sensing targets, and the distances between them. The virtual antenna array includes transmitting and receiving antennas mounted on multiple UAVs. Communication users refer to nodes with signal transceiver devices, such as pedestrians holding mobile phones or cars equipped with antennas. Sensing targets refer to nodes without signal transceiver capabilities, such as ground obstacles, pedestrians, buildings, or cars without signal transceiver antennas.

[0050] Specifically, when constructing a near-field transmission model for signals in related technologies, the directional parameters of the communication user or sensing target are generally considered, i.e., T = [θ, φ].T Where θ and φ are the azimuth and elevation angles of the communication user or sensing target relative to the antenna array, respectively. In this case, when constructing the near-field communication channel model of the antenna array, the model used is the relationship between the communication user or sensing target and each antenna in the antenna array [x]. n ,y n ,z n ] T The relative position, i.e., Δd n =d n -r=-(x n sinφcosθ+y n sinφsinθ+z n cosφ), where r is the distance between the antenna array and the communication user or sensing target; d n It is the distance between the nth antenna in the antenna array and the communication user or sensing target. It can be seen that the above Δd... n The detailed expression for 'r' is not shown. Therefore, when designing beams for sensing targets, beams are not suitable for obtaining the target's specific location.

[0051] Based on this, when constructing the near-field communication channel model in this embodiment, the specific distances between the communication user or target and each antenna are used. For example, the specific distances between the communication user and each antenna, as well as the specific distances between the sensing target and each antenna, can be determined using Euclidean distance. Thus, the specific location of the sensing target can be obtained through the echo.

[0052] Step S102: Based on the integrated sensing beam transmitted by the virtual antenna array and the near-field transmission model of the signal, determine the communication signal transmission model and the sensing signal echo model. Specifically, the integrated sensing beam here means that the transmitting antenna uses the same waveform when transmitting signals to the sensing target and the communication user respectively. This integrated sensing beam can be determined based on the data transmitted to the communication user, so that the beam carries both communication data (i.e., data transmitted to the communication user) to the ground user and has the shape characteristics of a sensing beam. At the same time, since the communication data is known to the UAV, it is feasible to perform target detection by filtering the echo reflected from the sensing target.

[0053] The communication signal transmission model represents the model of the communication signal received by the communication user, which can be specifically determined by its corresponding near-field transmission model (the specific distance between the communication user and each antenna) and the integrated inductive beamforming. The sensing signal echo model represents the model of the reflected echo of the sensing target received by the virtual antenna array, which can be specifically determined by its corresponding near-field transmission model (the specific distance between the sensing target and each antenna) and the integrated inductive beamforming.

[0054] Step S103: Determine the Fisher information matrix of the sensing target based on the sensing signal echo model, and determine the Cramer-Rhodes bound of the sensing target. Specifically, since the sensing signal echo model includes reflected echoes, calculating its Fisher information matrix can quantify the amount of positional information of the sensing target contained in the echo signal. The Cramer-Rhodes bound calculated using the Fisher information matrix can describe the achievable positioning accuracy (positioning of the sensing target).

[0055] Step S104: Based on the signal-to-interference-plus-noise ratio (SIR) of the communication user, the total energy consumption of the UAV, and the total transmission power, determine the constraints, and construct the objective function with the minimization of the Cramer-Rao bound as the objective. The SIR is determined by the communication signal transmission model.

[0056] Step S105: Solve the objective function using an inner loop algorithm and an outer loop algorithm to determine the position of each UAV in the virtual antenna array and the beamforming matrix of the integrated sensing beam.

[0057] Specifically, the smaller the Cramer-Rao boundary, the higher the positioning accuracy. Therefore, this embodiment aims to minimize the Cramer-Rao boundary and constructs an objective function. Simultaneously, the objective function aims to achieve joint design optimization of the positions of each UAV and the integrated sensing beam within the virtual antenna array. Therefore, when solving the objective function, the UAV positions and the integrated sensing beam can be adjusted to find the optimal solution that satisfies the positioning accuracy requirements. Furthermore, because the Cramer-Rao boundary itself is non-convex, it is difficult to obtain a closed-form solution. This embodiment employs a nested outer loop algorithm and an inner loop algorithm; that is, in each loop of the outer loop algorithm, the inner loop algorithm is used to determine the solution for each loop.

[0058] The UAV virtual antenna array and beamforming joint design method provided in this embodiment of the invention takes into account the flexibility and dynamic changes of the UAV network topology. It combines the cooperative position adjustment of multiple UAVs with cooperative beamforming in the UAV network to improve the communication quality for communication users and the perception performance for perceived targets, thereby making full use of the flexible deployment characteristics of UAVs.

[0059] This embodiment provides a method for the joint design of a UAV virtual antenna array and beamforming, which includes the following steps:

[0060] Step S201: Construct a near-field transmission model of signals based on the positions of each UAV in the virtual antenna array of the UAV, the distance between the communication user and the sensing target.

[0061] Specifically, step S201 includes:

[0062] Step S2011: Construct a three-dimensional Cartesian coordinate system with the center reference point of the virtual antenna array as the origin. In this embodiment, N drones are deployed in the drone network, each equipped with a single transmitting antenna and a single receiving antenna. To intuitively describe the topology of the drone network and the corresponding virtual antenna array, this embodiment uses a three-dimensional Cartesian coordinate system to represent the position of each drone. The origin of the coordinate system serves as the center reference point (ReferencePoint, RP) of the virtual antenna array, and the coordinates of the nth drone are... Using matrix Q u This represents the location of all drones, which will be determined in subsequent steps.

[0063] Step S2012: Determine the near-field transmission model of the communication user's signal based on the Euclidean distance between the positions of each UAV and the communication user in the three-dimensional Cartesian coordinate system.

[0064] Step S2013: Determine the near-field transmission model of the sensing target signal based on the Euclidean distance between the positions of each UAV and the sensing target in the three-dimensional Cartesian coordinate system.

[0065] Specifically, assume there are M communication users and K sensing targets on the ground. The positions of the m-th communication user and the k-th sensing target can be described using a Cartesian coordinate system. and According to the near-field transmission model of the signal, it is necessary to calculate the distance between each antenna in the virtual antenna array and the user or target, which is the distance between each UAV and the communication user or sensing target. Specifically, the distance between the nth UAV and the communication user or sensing target is expressed by the following formulas (1) and (2):

[0066]

[0067] Step S202: Determine the communication signal transmission model and the sensing signal echo model based on the integrated beam transmitted by the virtual antenna array and the near-field transmission model of the signal.

[0068] Specifically, step S202 includes:

[0069] Step S2021: Determine the integrated sensing beam based on the communication user's data to be transmitted and the digital beamforming matrix of the data. Specifically, the integrated sensing beam is represented by the following formula:

[0070] X wave =WS DL (3)

[0071] In the formula, S DL W represents the data to be transmitted to the communication user; W represents the data to be transmitted to S.DL The digital beamforming matrix W can be solved by the following method to design an integrated inductive transmission waveform using communication data.

[0072] Step S2022: Determine the downlink channel response matrix of the virtual antenna array to the communication user and the sensing target based on the near-field transmission model of the signal; specifically, the downlink channel response of the virtual antenna array to the communication user is expressed by the following formula:

[0073]

[0074] In the formula, This represents the signal fading from the virtual antenna array to the m-th communication user; Let λ represent the distance from the nth UAV to the mth communication user, and let λ represent the wavelength of the transmitted modulated signal.

[0075] The response matrix of the perceived target is represented by the following formula:

[0076]

[0077] In the formula, β k Represents the reflection coefficient. This represents the distance from the nth drone to the kth sensing target.

[0078] Step S2023: Construct a communication signal transmission model based on an ideal signal, interference between multiple communication users, and noise. The ideal signal is determined based on the downlink channel and the user beam containing the downlink data of the corresponding communication user in the integrated inductive beam. The interference between multiple communication users is determined based on the downlink channel and the difference between the integrated inductive beam and the user beam. Specifically, the communication signal transmission model is expressed by the following formula:

[0079]

[0080] In the formula, X ideal X represents wave The beam contains downlink data from the m-th communication user. It should be noted that in the integrated sensing beam, S... DL The downlink communication data to be transmitted contains the data to be transmitted from all M communication users. X is generated through the beamforming matrix W. wave At that time, the matrix can be adjusted according to each user's location. Channel conditions By assigning directional weights to the data streams of different users, the data of the corresponding user is focused in the direction of that user, forming a directional sub-beam X that is only for that user. ideal Furthermore, X waveIncluding the sub-beams and sensed signal components of all users, for the m-th user, excluding its own X ideal Other users' sub-beams will leak into user m's receiving range due to incomplete beam direction isolation. These leaked signals are known as multi-user interference.

[0081] Step S2024: Construct a sensing signal echo model based on the echo and noise. The echo is determined based on the response matrix of the sensing target and the integrated sensing beam. Specifically, the sensing signal echo model is determined using the following formula:

[0082]

[0083] It should be noted that for noise in communication signal transmission models and sensing signal echo models, we can use experimental methods, such as collecting background noise and calculating power, depending on the noise source and type; or we can use theoretical estimation methods, based on relevant parameters or formulas.

[0084] Step S203: Determine the Fisher information matrix of the sensing target based on the sensing signal echo model, and determine the Cramer-Rhodes boundary of the sensing target.

[0085] Specifically, step S203 includes:

[0086] Step S2031: Determine the Fisher information matrix of the sensing target based on the conditional likelihood function of the echo signal with respect to the position of the sensing target. The echo signal is determined based on the sensing signal echo model. Specifically, the Fisher information matrix is ​​expressed by the following formula:

[0087]

[0088] In the formula, This indicates a demand for expectation; Indicates echo signal Y VAA about The conditional likelihood function. This conditional likelihood function represents the true position of target k. At that time, the current echo Y is received. VAA The probability density.

[0089] Step S2032: Determine the Cramer-Rhodes bound of the perceived target in three-dimensional coordinates based on the inverse of the Fisher information matrix, where the inverse of the Fisher information matrix is ​​the theoretical limit of the error covariance matrix between the estimated and true values ​​of the perceived target's position. Specifically, The Cramé-Rhodes of elements are represented as follows:

[0090]

[0091] Specifically, due to the target location For a three-dimensional vector (containing x, y, z coordinates), the Fisher information matrix It is a 3×3 symmetric matrix. If there are K sensing targets on the ground, the total parameter vector is 3K-dimensional (K three-dimensional vectors). In this embodiment, the K three-dimensional vectors are represented by categorizing them according to their coordinate dimensions. That is, Equation 9 represents... The element in the k-th row and k-th column corresponds to the x-coordinate of the K sensed targets, as shown in Formula 10. The element in the (K+k)th row and (K+k)th column corresponds to the y-coordinate of the K sensed targets, as shown in Formula 11. The element in the 2K+kth row and 2K+kth column corresponds to the z-coordinate of the K sensing targets.

[0092] In addition, the estimated value and The relationship is:

[0093]

[0094] In Equation 12, the left side represents the estimated value. Compared with the true value The error covariance matrix, the estimated value This can be understood as a subjective calculation result based on the echo signal, the true value. This represents the objective, true location of the perceived target k. This formula represents any... The unbiased estimate (denoted as) The covariance matrix of the error will never be less than the inverse of the Fisher information matrix. That is, the covariance matrix of the error has a theoretical lower bound and cannot be reduced indefinitely.

[0095] Step S204: Based on the signal-to-interference-plus-noise ratio (SIR) of the communication user, the total energy consumption of the UAV, and the total transmission power, determine the constraints, and construct an objective function with the minimization of the Cramer-Rao bound as the objective. The SIR is determined by the communication signal transmission model. The constraints include: the SIR of the communication user is greater than or equal to a preset threshold, the total energy consumption of the UAV is less than or equal to the power supplied by the UAV battery, and the total transmission power is less than or equal to the upper limit of the UAV antenna power.

[0096] Specifically, the objective function is expressed by the following formula:

[0097]

[0098] stC1:SINR m,DL ≥SINR min (14)

[0099] C2:E n ≤E tot (15)

[0100]

[0101] In the formula, Indicates that F is determined s Q when minimized u And W. E n E represents the total energy consumed by the nth drone, which is determined by factors such as the drone's flight trajectory; tot This indicates that the drone is powered by its battery, meaning the amount of energy that the battery in the drone can provide. W represents the total transmit power of the nth UAV. :,n P represents the nth column of W, i.e., the total transmit power is determined by the integrated inductive beam. t This indicates the upper limit of the drone's antenna power.

[0102] SINR m,DL The signal-to-interference-plus-noise ratio (SIR) of the signal received by the m-th communication user from the transmitted signal of the virtual antenna array is calculated using the following formula:

[0103]

[0104] Among them, SINR min This indicates a preset threshold, which can be determined based on the actual situation.

[0105] Step S205: Solve the objective function using an inner loop algorithm and an outer loop algorithm to determine the position of each UAV in the virtual antenna array and the beamforming matrix of the integrated sensing beam.

[0106] Specifically, step S205 includes:

[0107] Step S2051: Based on the objective function, construct the first sub-problem of solving the position of each UAV in the virtual antenna array when minimizing the Cramer-Rao bound and the second sub-problem of solving the beamforming matrix of the inductive beam when minimizing the Cramer-Rao bound.

[0108] Specifically, because Q u There is coupling between Q and W, so Q needs to be solved separately. u And W. Thus, the first and second subproblems are constructed. The first subproblem is expressed by the following formula:

[0109]

[0110] The second subproblem is expressed by the following formula:

[0111]

[0112] The constraints for both subproblems are the aforementioned constraints C1, C2, and C3. When solving using the outer loop algorithm, the two subproblems are solved separately to obtain Q. u W refers to the joint design of virtual antenna arrays and beamforming.

[0113] Step S2052: When solving the first and second subproblems in the outer loop, the inner loop algorithm is used to determine the positions and beamforming matrices of each UAV in the current loop, and this is used as the input for the next loop in the outer loop to obtain the positions and beamforming matrices of each UAV when stable. Specifically, since F s Because it is non-convex, Q cannot be obtained directly. u And the closed-form solution of W. Therefore, for each loop, this embodiment further employs an inner loop algorithm to determine the position of each UAV and the beamforming matrix of the current loop, and uses it as the input for the next loop in the outer loop, until Q. u The values ​​of W tend to stabilize.

[0114] Specifically, the inner loop algorithm for determining the position of each UAV and the beamforming matrix in the current loop includes:

[0115] Step a1: Based on the positions and beamforming matrices of each UAV obtained in the previous iteration, determine the Cramer-Rao bounds respectively. Then, using a first-order Taylor expansion of the positions and beamforming matrices of each UAV, convert the objective functions in the first and second subproblems into linear expressions. Specifically, based on the Cramer-Rao bounds after the first-order Taylor expansion, the objective function of the first subproblem, converted into a linear expression, is represented by the following formula:

[0116]

[0117] In the formula, {Q u} l-1 The value represents the drone's position obtained in the previous loop, and l represents the current loop.

[0118] The objective function of the second subproblem, transformed into a linear expression, is expressed by the following formula:

[0119]

[0120] In the formula, {W} l-1 This represents the beamforming matrix obtained in the previous iteration.

[0121] Step a2 involves solving the objective function, which has been converted into a linear expression, to obtain the positions of each UAV and the beamforming matrix for the current loop. Specifically, gradient descent can be used in the solution process.

[0122] As a specific application embodiment of the present invention, such as Figure 4 As shown, the joint design method of UAV virtual antenna array and beamforming is implemented through the following process:

[0123] Step A, UAV Network Modeling and Initialization. Construct scenario models of the UAV network, ground communication users, and sensing targets to generate corresponding mathematical expressions for the signals.

[0124] Specifically, in the UAV network of this embodiment, a total of N UAVs are deployed, each equipped with a single transmitting antenna and a single receiving antenna, thereby constructing a virtual antenna array. In this virtual antenna array, a three-dimensional rectangular coordinate system is used to represent the position of each UAV. At the same time, the distance between each antenna in the virtual antenna array and the user or target is calculated using the above formulas (1) and (2).

[0125] Step B: Communication and Sensing Signal Modeling. Based on the scenario constructed in Step A, a mathematical model is built for the UAV's communication and sensing signals in order to propose a joint design problem of virtual antenna array and beamforming.

[0126] Specifically, beamforming is first applied to the communication user data to achieve the design of an integrated sensing and sensing transmission waveform. Based on the designed integrated sensing and sensing transmission waveform, integrated sensing and sensing signals can be transmitted to both the communication user and the sensing target. The communication user receives the integrated sensing and sensing signal to obtain the communication user data, and the sensing target reflects the signal back to the UAV after receiving the integrated sensing and sensing signal. In order to characterize the communication signal received by the communication user and the signal reflected by the sensing target, a communication signal transmission model and a sensing signal echo model are constructed, which are represented by the above formulas (6) and (7), respectively.

[0127] Step C: Constructing the joint design problem of UAV virtual antenna array and beamforming. This involves constructing the performance parameters needed for the joint design problem and the constraints related to the UAV network, and then modeling the joint design problem.

[0128] Step C1: Determine the communication performance index (i.e., Equation 17) based on the signal-to-interference-plus-noise ratio (SINR) of the signal received by the m-th communication user from the virtual antenna array.

[0129] Step C2: The Cramer-Rao Bound (CRB) of the target's three-dimensional coordinates is used to describe the positioning accuracy during perception, thereby determining the perception performance index. Specifically, the Fisher information matrix is ​​first determined using Formula 8 above, and then the Cramer-Rao Bound is determined using the inverse of the Fisher information matrix.

[0130] Step C3: Constructing the joint design problem. By solving for the positions of each UAV and the digital beamforming matrix, the three-dimensional coordinates of the perceived target are minimized while ensuring the downlink performance of the communication user. The Cramer-Rhodes boundary. Simultaneously, ensure that the signal-to-interference-plus-noise ratio (SINR) of all communication users meets a preset threshold, i.e., SINR. m,DL ≥SINR min .

[0131] Step D, the double-loop design method. Specifically, because Q u There is coupling between Q and W, so Q needs to be solved separately. u And W, that is, construct two subproblems and solve them using an outer loop algorithm. Since F s Because it is non-convex, it is impossible to directly solve the two-word problem to obtain Q. u And the closed-form solution of W. Therefore, the gradient descent method is used to solve the two subproblems iteratively. First, based on the {Q} obtained in the previous iteration... u} l-1 and {W} l-1 (l is the current loop), calculate F respectively. s Based on Q u The first-order Taylor expansion of W is then performed, followed by the objective function F of the two subproblems. s Transforming it into a linear expression yields a new subproblem, which is then solved to obtain {Q}. u} l and {W} l , {Q u} l and {W} l It can continue to be used as input for the next loop, until Q. u The values ​​of W and W tend to stabilize, yielding the final positions Q of each UAV. u And the beamforming matrix W.

[0132] This embodiment further illustrates the advantages of using a near-field signal transmission model in the integration of sensing and communication in unmanned aerial vehicles (UAVs). For example... Figure 5 As shown, a target is first created. Assume the target is located at an azimuth angle of 0.5π, and its Z-coordinate is... The value is 0, and its horizontal distance from the center of the drone network is 0. They are 150m or 250m respectively. In Figure 6 In the diagram, the four sub-graphs correspond to four different virtual antenna array design methods for UAVs, where the black dots represent the specific horizontal positions of the UAV:

[0133] (a) "Circular planar virtual antenna array": As a benchmark method in contrast to the design method in this embodiment, each UAV is configured in a horizontal plane to form a uniform circular array;

[0134] (b) "The 3D virtual antenna array designed in this embodiment, D" s=150m”: For a target to be perceived 150m away from the center of the UAV network, a three-dimensional virtual antenna array of the UAV is obtained through the design method proposed in this embodiment;

[0135] (c) "The 3D virtual antenna array designed in this embodiment, D" s =250m”: For a target 250m away from the center of the UAV network, a three-dimensional virtual antenna array for the UAV is obtained through the design method proposed in this embodiment;

[0136] (d) "The 2D virtual antenna array designed in this embodiment, D" s =250m”: For a sensing target 250m away from the center of the UAV network, a virtual antenna array for UAVs is obtained using the design method proposed in this embodiment. However, the altitude of the UAVs is not specifically designed; therefore, the virtual antenna array composed of multiple UAVs is a two-dimensional horizontal array.

[0137] pass Figure 6 It can be seen that:

[0138] First, compared with the baseline method (a), the virtual antenna array design method (b)-(d) proposed in this embodiment can significantly reduce the three-dimensional coordinate CRB value of the perceived target at the 0.5π azimuth, indicating that when designing the virtual antenna array using the method of this embodiment, the perception function of the UAV network can be better focused on the direction of the perceived target.

[0139] Second, the comparison target is in different D s The results of the virtual antenna array design (i.e., (b) and (c)) show that when the perceived target is farther away from the UAV network (D) s When the distance is 250m, the CRB value of the virtual antenna array is lower in the 0.5π azimuth angle region. This also confirms the core innovation of this embodiment, namely, the near-field improvement for the sensing model. Under the near-field signal transmission model, changes in the distance between the UAV network and the sensing target will significantly affect the design results of the virtual antenna array, thereby changing the sensing characteristics of the UAV network. Therefore, it is necessary to deploy UAV sensing integration under the near-field sensing model.

[0140] Figure 7 Showing Figure 6 The specific CRB values ​​of four different virtual antenna arrays at all omnidirectional angles (from -π to π) were further analyzed. Figure 6 The experimental results will be analyzed in detail. Figure 7 The two images, one above the other, represent... Figure 6 The CRB values ​​of the omnidirectional angle at distances of 150m and 250m from the center point of the UAV network. Figure 7 Simulation results show that:

[0141] First, when using the design method of this application, the UAV virtual antenna array can obtain the lowest CRB value in the direction of the perceived target (i.e., the azimuth angle is 0.5π). At -0.5π, it exhibits suboptimal performance, which is due to the mathematical symmetry of CRB itself.

[0142] Second, a 3D virtual antenna array designed for a target distance of 250m ((c) the 3D virtual antenna array designed in this application, D) s =250m) The CRB value obtained is compared with the target of 150m (b) the 3D virtual antenna array designed in this application, D s Compared to when =150m), there is a decrease. Even when using the 2D planar deployment method ((d) the 2D virtual antenna array designed in this application), D s Even at a distance of 250m, its performance still outperforms that of distance-mismatched virtual antenna arrays. These findings strongly validate the core technical advantages of the proposed method: under a near-field signal transmission model, adaptive virtual antenna array design based on the perceived target distance enables more precise beam control for different target distances, a feature that traditional far-field models cannot achieve. These experimental results highlight the adaptability and performance advantages of the proposed method in practical applications.

[0143] like Figure 8 As shown, the technical advantages of this application are as follows:

[0144] First, comparing the integrated sensing method (integrated sensing) proposed in this application with the separate design method for communication and sensing (communication only), simulation data shows that: the UAV network based on integrated sensing can guarantee the lowest signal-to-interference-plus-noise ratio (SINR) for communication users. min Under the premise of meeting the requirements, the positioning accuracy of the perceived target can be improved simultaneously, achieving dual gain of synesthesia;

[0145] Second, under the integrated sensing method proposed in this application, comparing the joint design method of 3D virtual antenna array and beamforming of this application (the 3D virtual antenna array designed in this application) with the traditional circular planar array (circular planar virtual antenna array), simulation data shows that the joint design method in this application can significantly reduce the CRB value to improve the positioning performance of the sensed target.

[0146] This embodiment also provides a device for the joint design of a UAV virtual antenna array and beamforming, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0147] This embodiment provides a device for the joint design of a virtual antenna array and beamforming for unmanned aerial vehicles (UAVs), such as... Figure 9 As shown, it includes:

[0148] The first model construction module 91 is used to construct a near-field transmission model of signals based on the positions of each UAV in the virtual antenna array of the UAV, the communication users, and the distance between them and the sensing target.

[0149] The second model construction module 92 is used to determine the communication signal transmission model and the sensing signal echo model based on the integrated beam of the virtual antenna array and the signal near-field transmission model.

[0150] Cramer-Rao boundary determination module 93 is used to determine the Fisher information matrix of the sensing target based on the sensing signal echo model, and to determine the Cramer-Rao boundary of the sensing target;

[0151] The objective function construction module 94 is used to determine the constraints based on the signal-to-interference-plus-noise ratio of the communication user, the total energy consumption of the UAV and the total transmission power, and to construct the objective function with the minimization of the Cramer-Rao bound as the objective. The signal-to-interference-plus-noise ratio is determined by the communication signal transmission model.

[0152] The joint design module 95 is used to solve the objective function using an inner loop algorithm and an outer loop algorithm to determine the position of each UAV in the virtual antenna array and the beamforming matrix of the integrated sensing beam.

[0153] Further functional descriptions of the above modules are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0154] This invention also provides a computer device having the above-described features. Figure 9 The device shown is a joint design device for a drone virtual antenna array and beamforming.

[0155] Please see Figure 10 , Figure 10 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 10As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 10 Take a processor 10 as an example.

[0156] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0157] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0158] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device as shown by a landing page for an app. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0159] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0160] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0161] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0162] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0163] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for jointly designing a virtual antenna array and beamforming for unmanned aerial vehicles (UAVs), characterized in that, The method includes: A near-field transmission model of signals is constructed based on the positions of each UAV, the communication users, and the distances between them and the sensing targets in the virtual antenna array of UAVs. Based on the integrated sensing beam transmitted by the virtual antenna array and the near-field transmission model of the signal, the communication signal transmission model and the sensing signal echo model are determined. Based on the aforementioned sensing signal echo model, the Fisher information matrix of the sensing target is determined, and the Cramer-Rhodes bound of the sensing target is determined, including: The Fisher information matrix of the sensing target is determined based on the conditional likelihood function of the echo signal with respect to the position of the sensing target, wherein the echo signal is determined based on the sensing signal echo model. The inverse of the Fisher information matrix is ​​used to determine the Cramer-Rao bound of the perceived target in three-dimensional coordinates. The inverse of the Fisher information matrix is ​​the theoretical limit of the error covariance matrix between the estimated and true values ​​of the perceived target's position. Constraints are determined based on the signal-to-interference-plus-noise ratio (SINR) of the communication user, the total energy consumption of the UAV, and the total transmit power. An objective function is constructed with the minimization of the Cramer-Rao bound as the objective. The SINR is determined by the communication signal transmission model. The objective function is solved using an inner loop algorithm and an outer loop algorithm to determine the position of each UAV in the virtual antenna array and the beamforming matrix of the integrated sensing beam.

2. The method according to claim 1, characterized in that, The virtual antenna array consists of multiple UAVs equipped with transmitting and receiving antennas. A near-field signal transmission model is constructed based on the positions of each UAV in the virtual antenna array, the distances between the communication users and the perceived targets, including: A three-dimensional Cartesian coordinate system is constructed with the center reference point of the virtual antenna array as the origin. The near-field transmission model of the communication user's signal is determined based on the Euclidean distance between the positions of each UAV and the communication user in the three-dimensional Cartesian coordinate system. The near-field transmission model of the sensing target signal is determined based on the Euclidean distance between the positions of each UAV and the sensing target in the three-dimensional Cartesian coordinate system.

3. The method according to claim 1, characterized in that, Based on the integrated sensing beam transmitted by the virtual antenna array and the near-field transmission model of the signal, the communication signal transmission model and the sensing signal echo model are determined, including: The integrated sensing beam is determined based on the data to be transmitted by the communication user and the digital beamforming matrix of the data to be transmitted. The response matrix of the virtual antenna array to the downlink channel of the communication user and the sensing target is determined based on the near-field transmission model of the signal. A communication signal transmission model is constructed based on an ideal signal, interference between multiple communication users, and noise. The ideal signal is determined based on the downlink channel and the user beam containing the downlink data of the corresponding communication user in the integrated inductive beam. The interference between multiple communication users is determined based on the downlink channel and the difference between the integrated inductive beam and the user beam. A sensing signal echo model is constructed based on the echo and noise, and the echo is determined based on the response matrix of the sensing target and the integrated sensing beam.

4. The method according to claim 1, characterized in that, The objective function is solved using an inner loop algorithm and an outer loop algorithm to determine the positions of each UAV in the virtual antenna array and the beamforming matrix of the integrated sensing beam, including: Based on the objective function, we construct the first subproblem of solving the position of each UAV in the virtual antenna array when minimizing the Cramer-Rao bound and the second subproblem of solving the beamforming matrix of the inductive beam when minimizing the Cramer-Rao bound; When solving the first and second subproblems in the outer loop, the inner loop algorithm is used to determine the position and beamforming matrix of each UAV in the current loop, and uses it as the input for the next loop in the outer loop to obtain the position and beamforming matrix of each UAV when it is stable.

5. The method according to claim 4, characterized in that, The inner loop algorithm is used to determine the position and beamforming matrix of each UAV in the current loop, including: Based on the positions and beamforming matrices of each UAV obtained in the previous iteration, the Cramer-Rhodes bounds are determined respectively. Based on the first-order Taylor expansion of the positions and beamforming matrices of each UAV, the objective functions in the first and second subproblems are transformed into linear expressions. Solve the objective function, which is converted into a linear expression, to obtain the positions of each UAV and the beamforming matrix in the current loop.

6. The method according to claim 1, characterized in that, The constraints include: the signal-to-interference-plus-noise ratio of the communication user is greater than or equal to a preset threshold, the total energy consumption of the UAV is less than or equal to the power supplied by the UAV battery, and the total transmission power is less than or equal to the upper limit of the UAV antenna power.

7. A device for the joint design of a virtual antenna array and beamforming for unmanned aerial vehicles (UAVs), characterized in that, The device includes: The first model building module is used to build a near-field transmission model of signals based on the positions of each UAV in the virtual antenna array of the UAV, the communication users, and the distance between them and the sensing target. The second model construction module is used to determine the communication signal transmission model and the sensing signal echo model based on the integrated beam of communication and sensing transmitted by the virtual antenna array and the signal near-field transmission model. The Cramer-Rao bound determination module is used to determine the Fisher information matrix of the sensing target based on the sensing signal echo model, and to determine the Cramer-Rao bound of the sensing target, including: determining the Fisher information matrix of the sensing target based on the conditional likelihood function of the echo signal with respect to the position of the sensing target, wherein the echo signal is determined based on the sensing signal echo model; and determining the Cramer-Rao bound of the sensing target in three-dimensional coordinates based on the inverse matrix of the Fisher information matrix, wherein the inverse matrix of the Fisher information matrix is ​​the theoretical limit of the error covariance matrix between the estimated and true values ​​of the position of the sensing target. The objective function construction module is used to determine the constraints based on the signal-to-interference-plus-noise ratio (SINR) of the communication user, the total energy consumption of the UAV, and the total transmit power, and to construct the objective function with the minimization of the Cramer-Rao bound as the objective. The SINR is determined by the communication signal transmission model. The joint design module is used to solve the objective function using an inner loop algorithm and an outer loop algorithm to determine the position of each UAV in the virtual antenna array and the beamforming matrix of the integrated sensing beam.

8. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the UAV virtual antenna array and beamforming co-design method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the UAV virtual antenna array and beamforming joint design method as described in any one of claims 1 to 6.

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