Multi-unmanned aerial vehicle sensing communication method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a multi-unmanned aerial vehicle sensing communication method and device, electronic equipment and a storage medium, and belongs to the technical field of unmanned aerial vehicle communication. The method comprises the following steps: performing position sensing on a sensing object based on a communication sensing technology to obtain sensing coordinate information; performing perception precision evaluation based on the perception coordinate information to obtain perception precision evaluation information; monitoring the state of each task execution terminal to obtain system monitoring information; performing control performance evaluation based on the system monitoring information to obtain control performance evaluation information; determining a target optimization problem based on the control performance evaluation information and the perception precision evaluation information, and performing problem disassembly optimization processing to obtain target optimization information; and executing at least one of the following operations based on the target optimization information: updating a task execution terminal served by the target unmanned aerial vehicle, updating the position of the target unmanned aerial vehicle, and updating the communication distribution power of the target unmanned aerial vehicle. According to the embodiment of the invention, the communication perception performance of the multi-unmanned-aerial-vehicle cooperative network can be improved.

Description

Technical Field

[0001] The present application relates to the field of drone communication technology, and in particular to a multi-drone perception communication method, device, electronic device, and storage medium. Background Art

[0002] Drones play a key role in the low-altitude economy, a sector based in low-altitude airspace that encompasses the entire supply chain, from general aviation aircraft R&D and manufacturing to market operations, comprehensive support, and extended services. Drones, with their flexibility, efficiency, and low cost, are widely used in logistics and transportation, emergency rescue, agricultural plant protection, infrastructure inspections, geographic mapping, urban security, and other fields, becoming a vital force driving the development of the low-altitude economy.

[0003] In a multi-drone collaborative network, drones not only need to act as aerial communication nodes, providing communication services to multiple ground devices, but also simultaneously perform perception tasks, collecting information on specific targets to provide decision support for ground devices. For example, in the agricultural plant protection field, plant protection drones use multispectral sensors to monitor crop growth in real time and generate precise pesticide application plans for spraying equipment, significantly improving pesticide coverage. For example, during emergency rescue operations, drones can quickly assess disaster situations and provide rescue support. Drones can assess the disaster situation and optimize route planning along the rescue team's route. They can also serve as communication relay stations to restore communications in the disaster area and guide rescue vehicles or mission execution terminals to carry out rescue work.

[0004] This multi-task collaborative model places higher demands on drones' communication capabilities, perception accuracy, and task scheduling, especially in terms of real-time performance and reliability in complex environments. Existing technologies, how to improve the communication and perception performance of multi-drone collaborative networks within limited resources, remains a pressing challenge. Summary of the Invention

[0005] The main purpose of the embodiments of the present application is to propose a multi-UAV perception communication method, device, electronic device and storage medium, aiming to improve the communication perception performance of a multi-UAV collaborative network.

[0006] To achieve the above objectives, a first aspect of an embodiment of the present application proposes a multi-UAV perception communication method, wherein a target UAV is one of multiple UAVs in a multi-UAV cooperative network, and the multi-UAV cooperative network further includes a perception object and multiple task execution terminals, each of the UAVs can only serve at most one task execution terminal, and each task execution terminal can only be served by one UAV. The method includes:

[0007] Performing position sensing on the sensing object based on communication sensing technology to obtain sensing coordinate information of the sensing object;

[0008] Performing a perception accuracy assessment based on the perception coordinate information to obtain perception accuracy assessment information;

[0009] Modeling each of the task execution terminals as a linear control system, and performing state monitoring on the linear control system to obtain system monitoring information;

[0010] Performing control performance evaluation based on the system monitoring information to obtain control performance evaluation information of the task execution terminal;

[0011] determining a target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information;

[0012] Perform problem decomposition and optimization processing based on the target optimization problem to obtain target optimization information;

[0013] Based on the target optimization information, at least one of the following operations is performed: updating the mission execution terminal served by the target drone, updating the position of the target drone, and updating the communication allocation power used by the target drone for communication.

[0014] In some embodiments, performing the perception accuracy assessment based on the perception coordinate information to obtain the perception accuracy assessment information includes:

[0015] Acquiring sensing distance information between each target UAV and the sensing object;

[0016] Obtaining signal-to-noise ratio information and current location information of each target drone;

[0017] Based on the signal-to-noise ratio information, the perception distance information, the current position information and the perception distance information, a Fisher information matrix of the perception coordinate information of the perception object is obtained and determined as the perception accuracy evaluation information.

[0018] In some embodiments, determining a target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information includes:

[0019] Based on the communication rate between the target UAV and the mission execution terminal, a control cost condition between the control performance evaluation information and the target UAV is obtained; wherein the control cost condition is related to the minimum permissible communication rate of the mission execution terminal;

[0020] A target optimization problem is determined based on the control cost condition, the control performance evaluation information, and the perception accuracy evaluation information.

[0021] In some embodiments, before obtaining the control performance evaluation information and the control cost condition of the target UAV based on the communication rate between the target UAV and the mission execution terminal, the process includes:

[0022] Acquire control distance information of each target UAV relative to each mission execution terminal and communication allocation power of the target UAV for communication;

[0023] Obtaining signal-to-interference-noise ratio information between each target UAV and the corresponding mission execution terminal based on the control distance information;

[0024] A communication rate evaluation based on finite code length transmission is performed according to the signal interference noise ratio information and the communication allocation power to obtain the communication rate between each target UAV and each mission execution terminal.

[0025] In some embodiments, determining the target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information further includes:

[0026] Normalizing the control performance evaluation information and the perception accuracy evaluation information to obtain a normalization factor and a weight coefficient;

[0027] Based on the weight coefficient and the normalization factor, a difference function between the control performance evaluation information and the perception accuracy evaluation information is constructed and determined as the target optimization problem.

[0028] In some embodiments, performing problem decomposition and optimization processing based on the target optimization problem to obtain target optimization information includes:

[0029] performing an influencing factor analysis based on the control performance evaluation information and the perception accuracy evaluation information to obtain basic influencing factors, wherein the basic influencing factors include a communication allocation power of the target UAV for communication, a pairing relationship between the target UAV and the mission execution terminal, and a flight position of the target UAV;

[0030] Decomposing the target optimization problem into a pairing subproblem, a power allocation subproblem, and a UAV position subproblem based on the basic influencing factors;

[0031] Alternating optimization processing is performed based on the pairing subproblem, the power allocation subproblem, and the UAV position subproblem until a preset convergence condition is met, thereby obtaining the target optimization information.

[0032] In some embodiments, performing alternating optimization processing based on the pairing subproblem, the power allocation subproblem, and the drone position subproblem until a preset convergence condition is satisfied to obtain the target optimization information specifically includes:

[0033] Solving the pairing subproblem based on the existing communication allocated power and the existing flight position to obtain the updated pairing relationship;

[0034] Solving the power allocation subproblem based on the updated pairing relationship and the existing flight position to obtain the updated communication allocation power;

[0035] Solve the UAV position subproblem based on the updated pairing relationship and the updated communication allocation power to obtain the updated flight position;

[0036] The target optimization information is obtained based on the updated pairing relationship, the updated communication allocation power, or the updated flight position.

[0037] To achieve the above-mentioned objectives, a second aspect of an embodiment of the present application provides a multi-UAV perception and communication device, the device comprising:

[0038] A position sensing module is used to sense the position of the sensing object based on communication sensing technology to obtain sensing coordinate information of the sensing object;

[0039] a perception accuracy assessment module, configured to perform perception accuracy assessment based on the perception coordinate information to obtain perception accuracy assessment information;

[0040] a state monitoring module, configured to model each of the task execution terminals as a linear control system, and perform state monitoring on the linear control system to obtain system monitoring information;

[0041] A control performance evaluation module, configured to perform control performance evaluation based on the system monitoring information to obtain control performance evaluation information of the task execution terminal;

[0042] an optimization problem determination module, configured to determine a target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information;

[0043] An optimization problem processing module is used to perform problem decomposition and optimization processing based on the target optimization problem to obtain target optimization information;

[0044] The drone status update module is used to perform at least one of the following operations based on the target optimization information: updating the task execution terminal served by the target drone, updating the position of the target drone, and updating the communication allocation power used by the target drone for communication.

[0045] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, and the memory stores a computer program. When the processor executes the computer program, it implements the multi-UAV perception communication method described in the first aspect above.

[0046] To achieve the above-mentioned objectives, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the multi-UAV perception communication method described in the first aspect above.

[0047] The multi-UAV perception communication method, device, electronic device, and storage medium proposed in this application perform position perception of a perception object based on communication perception technology to obtain perception coordinate information of the perception object; perform perception accuracy assessment based on the perception coordinate information to obtain perception accuracy assessment information; model each task execution terminal as a linear control system and perform state monitoring on the linear control system to obtain system monitoring information; perform control performance assessment based on the system monitoring information to obtain control performance assessment information of the task execution terminal; determine a target optimization problem based on the control performance assessment information and the perception accuracy assessment information; perform problem decomposition and optimization processing based on the target optimization problem to obtain target optimization information; and perform at least one of the following operations based on the target optimization information: updating the task execution terminal served by the target UAV, updating the position of the target UAV, or updating the communication allocation power used by the target UAV for communication. Therefore, the present application determines the target optimization problem by obtaining perception accuracy assessment information and control performance assessment information, and performs problem decomposition and optimization processing on the target optimization problem to obtain target optimization information, thereby updating relevant parameters of the UAVs, thereby improving the communication perception performance of the multi-UAV cooperative network under limited resources and achieving optimization of the multi-UAV cooperative network. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of the multi-UAV perception communication method provided by an embodiment of the present application;

[0049] Figure 2 yes Figure 1 Flowchart of step S102 in FIG.

[0050] Figure 3 yes Figure 1 Flowchart of step S105 in FIG.

[0051] Figure 4 yes Figure 3 The flowchart before step S301 in FIG.

[0052] Figure 5 yes Figure 1 Flowchart of step S105 in FIG.

[0053] Figure 6 yes Figure 1 Flowchart of step S106 in FIG.

[0054] Figure 7 yes Figure 6 Flowchart of step S603 in FIG.

[0055] Figure 8 This is a schematic diagram of the structure of a multi-UAV sensing and communication device provided in an embodiment of the present application;

[0056] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0058] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0060] First, let’s analyze some of the terms used in this application:

[0061] Integrated Sensing and Communication (ISAC) is a technology that integrates wireless communication and sensing functions into a unified framework. It enables the coexistence, mutual assistance, and shared benefits of communication and sensing by sharing spectrum, hardware, and signal processing resources. ISAC is considered one of the key candidate technologies for sixth-generation mobile communications (6G), significantly improving spectrum efficiency and supporting a variety of emerging applications. The concept of integrated communication and sensing has been proposed in the low-altitude economy, enabling drones to seamlessly perform communication and sensing tasks using shared wireless resources.

[0062] In wireless communications, Line of Sight (LoS) refers to a direct, unobstructed path between two communicating devices. This path is free of obstructions, ensuring the most direct signal transmission from the transmitter to the receiver. This type of link is commonly used in microwave communications, satellite communications, and, under certain conditions, radio communications.

[0063] Finite Block Length (FBL) refers to the encoding and decoding problems in information theory and communication systems when the coding block length (i.e., code length) is finite. The traditional Shannon theorem assumes that the code length tends to infinity, but in practical applications, the code length is finite, so it is necessary to study the theory and practice of information transmission under finite code length. Finite code length theory studies how to optimize encoding and decoding to achieve optimal transmission performance under finite block length. Short packet communication based on finite code length transmission shows significant potential in meeting the needs of delay-sensitive services in IoT networks. Finite code length transmission can meet the strict low-latency requirements of drone support systems, and its operating cycle is usually divided into multiple short time slices to optimize drone trajectory design.

[0064] The Linear Quadratic Regulator (LQR) is an optimal control strategy for linear systems. Its goal is to find the optimal control input by minimizing a quadratic cost function, thereby achieving a balance between the system state and the control input. LQR is widely used in control theory and engineering practice, particularly in scenarios requiring precise control and guaranteed stability.

[0065] The embodiments of the present application provide a multi-UAV perception communication method, device, electronic device, and storage medium, aiming to improve the communication perception performance of a multi-UAV collaborative network.

[0066] The multi-drone perception communication method, device, electronic device, and storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the multi-drone perception communication method in the embodiments of the present application is described.

[0067] The multi-drone perception communication method provided in the embodiment of the present application relates to the field of drone communication technology. The multi-drone perception communication method provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the multi-drone perception communication method, etc., but is not limited to the above forms.

[0068] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0069] The target drone among multiple drones in a multi-UAV cooperative network. The multi-UAV cooperative network also includes sensing objects and multiple mission execution terminals. Each drone can only serve at most one mission execution terminal, and each mission execution terminal can only be served by one drone. In addition, it is assumed that the channel from the drone to the mission execution terminal is dominated by the line of sight link. Figure 1 This is an optional flowchart of the multi-UAV perception communication method provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S107.

[0070] Step S101: performing position sensing on a sensing object based on communication sensing technology to obtain sensing coordinate information of the sensing object;

[0071] Step S102, performing perception accuracy evaluation based on the perception coordinate information to obtain perception accuracy evaluation information;

[0072] Step S103: Model each task execution terminal as a linear control system, and perform state monitoring on the linear control system to obtain system monitoring information;

[0073] Step S104: performing control performance evaluation based on the system monitoring information to obtain control performance evaluation information of the task execution terminal;

[0074] Step S105, determining a target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information;

[0075] Step S106: performing problem decomposition and optimization processing based on the target optimization problem to obtain target optimization information;

[0076] Step S107: Based on the target optimization information, perform at least one of the following operations: update the mission execution terminal served by the target UAV, update the position of the target UAV, and update the communication allocation power used by the target UAV for communication.

[0077] In the embodiment of the present application, steps S101 to S107 are as follows: performing position perception of the perception object based on communication perception technology to obtain the perception coordinate information of the perception object; performing perception accuracy assessment based on the perception coordinate information to obtain perception accuracy assessment information; modeling each task execution terminal as a linear control system and performing state monitoring on the linear control system to obtain system monitoring information; performing control performance assessment based on the system monitoring information to obtain control performance assessment information of the task execution terminal; determining a target optimization problem based on the control performance assessment information and the perception accuracy assessment information; performing problem decomposition and optimization processing based on the target optimization problem to obtain target optimization information; and performing at least one of the following operations based on the target optimization information: updating the task execution terminal served by the target UAV, updating the position of the target UAV, and updating the communication allocation power used by the target UAV for communication. It can be seen that the present application determines the target optimization problem by obtaining the perception accuracy assessment information and the control performance assessment information, and obtains the target optimization information by performing problem decomposition and optimization processing on the target optimization problem, and then updates the relevant parameters of the UAV, thereby improving the communication perception performance of the multi-UAV cooperative network under limited resources and achieving optimization of the multi-UAV cooperative network.

[0078] In step S101 of some embodiments, the position of a perceived object is sensed based on communication sensing technology, obtaining the perceived coordinate information of the perceived object. The target drone utilizes its onboard communication sensing equipment to transmit and receive specific wireless signals. These signals interact with the perceived object and return to the receiving end of the target drone. By analyzing the characteristics of the return signal, such as the signal's arrival time, arrival angle, and signal strength, a geometric positioning algorithm can be used to calculate the coordinate position of the perceived object, i.e., the perceived coordinate information.

[0079] The application of communication perception technology in multi-UAV collaborative networks enables drones to simultaneously perform communication tasks while simultaneously maintaining real-time environmental awareness. The key to this technology lies in effectively utilizing limited spectrum resources to achieve high-precision target positioning while ensuring communication quality. By acquiring the perceived coordinates of perceived objects, drones can provide critical environmental information for subsequent mission execution, such as optimizing route planning in logistics and disaster relief, and accurately monitoring crop status in agricultural plant protection. Furthermore, the accuracy of this perceived coordinate information directly impacts the efficiency and effectiveness of subsequent mission execution.

[0080] In step S102 of some embodiments, a perception accuracy assessment is performed based on the perception coordinate information to obtain perception accuracy assessment information. After the drone obtains the perception coordinate information of the perception object using communication perception technology, it is necessary to perform error analysis and confidence interval calculation on this information to evaluate the accuracy of the perception result and obtain corresponding perception accuracy assessment information. Perception accuracy assessment information is of great significance for subsequent optimization and decision-making. In a multi-drone collaborative network, perception accuracy assessment information can be used to adjust the drone's perception strategy and optimize the allocation of communication and perception resources to improve the performance and reliability of the overall system. Perception accuracy assessment information can also provide key environmental information to the task execution terminal to ensure the accuracy and efficiency of task execution. The task execution terminal is used to represent a terminal that communicates with the drone and responds to the drone's communication information to perform various tasks.

[0081] See also Figure 2 In some embodiments, step S102 may include but is not limited to steps S201 to S203:

[0082] Step S201, obtaining the sensing distance information between each target UAV and the sensing object;

[0083] Step S202, obtaining the signal-to-noise ratio information and current location information of each target UAV;

[0084] Step S203 : obtaining a Fisher information matrix of the perception coordinate information of the perception object based on the signal-to-noise ratio information, the perception distance information, the current position information, and the perception distance information, and determining the matrix as the perception accuracy evaluation information.

[0085] In step S201 of some embodiments, the perception distance information is obtained through the communication perception technology of the drone and can reflect the physical distance between the drone and the perception object.

[0086] In some embodiments, step S202 obtains signal-to-noise ratio (SNR) information and current location information for each target drone. The SNR information reflects the quality of the communication channel and is crucial for assessing the reliability of the perception data. The current location information of the target drone provides the precise location of the target drone in space.

[0087] In step S203 of some embodiments, based on the acquired signal-to-noise ratio information, perceived distance information, current location information, and perceived distance information, a Fisher Information Matrix (FIM) of the perceived coordinate information of the perceived object is calculated and used as the perception accuracy assessment information. The FIM serves as a key statistical tool for quantifying the amount of information contained in the perception data, thereby assessing perception accuracy.

[0088] In some embodiments, a multi-UAV collaborative network includes M single-antenna UAVs monitoring K mission execution terminals on the same frequency band channel, and the M single-antenna UAVs also sense and locate a sensing object. In emergency rescue, the sensing object can be a rescue path, and the mission execution terminal can be a rescue convoy or a rescue mission execution terminal. The UAV sends the communication and perception integration signal to the mission execution terminal, and the mission execution terminal uses the communication and perception integration signal to generate the optimal control strategy. At the same time, the transmitted communication and perception integration signal can also be radiated to the sensing object for sensing purposes. Define the position of the mth UAV as Define the location of the kth task execution terminal as The position of the positioning perception object is s = [x s ,y s , z s ] T ∈R 3 , that is, the perceived coordinate information. T is used to represent the transpose operation of the matrix. In addition, this application introduces a binary matrix Θ to represent the pairing relationship between the drone and the task execution terminal. Specifically, the (m, k)th element θ of the matrix Θ m,k = {0,1}, indicating the pairing status between the mth UAV and the kth task execution terminal, i.e., θ m,k =1 means that the mth UAV serves the kth mission execution terminal, otherwise it does not serve.

[0089] Define the perception distance information between each target UAV and the perception object. The specific calculation formula is shown in the following formula (1):

[0090]

[0091] Where c represents the speed of light, τ mrepresents the round trip delay of the communication perception integration signal between the target UAV and the perception object. To simplify the analysis, it is assumed that the perception distance information is affected by Gaussian noise, and d m The measured value of is given by the following analytical formula (2):

[0092]

[0093] in, is Additive White Gaussian Noise (AWGN), with zero mean and variance Generally speaking, the variance It is inversely proportional to the signal-to-noise ratio (SNR) of the drone. The SNR of the drone is obtained as shown in the following analytical formula (3):

[0094]

[0095] Among them, G p represents the signal processing gain of the UAV, represents the noise power of the UAV, p m It represents the communication allocation power allocated to each UAV, that is, the communication allocation power of the mth UAV. The two-way channel power gain between the UAV and the sensing object is given by express, Indicates the reference distance d m = Channel power at 1 meter, where G T Represents the transmitting antenna gain of the UAV, G r represents the receiving antenna gain of the sensing object, σ rcs represents the radar cross section (RCS), and λ represents the wavelength of the signal. Therefore, the variance It can be expressed as the following analytical formula (4):

[0096]

[0097] Here, ρ is a constant that depends on the system setup and is often used to represent measurement- or noise-related parameters.

[0098] In addition, after getting d m or The direction and angle of the perceived object can then be determined by analyzing the Doppler effect and angular spectrum of the echo signal from the integrated communication and perception signal. Further signal processing can be used to extract the object's three-dimensional geometric information, such as its coordinates, orientation, and velocity, from the scattered and reflected signals to obtain the perceived coordinate information. This can be achieved, for example, through orthogonal frequency division multiplexing, but is not limited to this method.

[0099] In the existing technology, the mean square error is usually used as the perception accuracy evaluation information of the perception object, which is defined as However, obtaining the mean square error and minimizing it is very challenging and the calculation process is also very difficult. Therefore, the Fisher information matrix of the perception coordinate information is used here to evaluate the positioning performance of the target UAV, which is expressed as detΦ s In order to calculate Φ s , the Fisher information matrix of the perception coordinate information can be converted into the Fisher information matrix of the perception distance information between the UAV and the perception object based on formula (1), which is denoted as Φ(d), where d = [d1, d2, ..., d m ] T . Applying the chain rule, Φ s It can be expressed as the following analytical formula (5):

[0100] Φ s =J(d)Φ(d)[J(d)] t (5)

[0101] Where J(d)∈R 3*M Indicates that the perceived coordinate information s = [x s ,y s , z s ] T The Jacobian matrix of d obtained under the above equation can be expressed as follows:

[0102]

[0103] According to the content of formula (2), To represent the measured distance between the perception object and all drones. According to the content of formula (2), we can get where Λ 2 Is a focus matrix, consisting of elements The composition can be expressed by the following analytical formula (7):

[0104]

[0105] Then, Φ(d) is derived as shown in the following analytical formula (8):

[0106]

[0107] By substituting J(d) and Φ(d) into equation (5), Φ s It can be expressed as the following analytical formula (9):

[0108]

[0109] Among them, Φ 11 , Φ 22 , Φ 33 , Φ 12 , Φ 13 , and Φ 23 They can be expressed as the following analytical equations (10) to (15):

[0110]

[0111] Through steps S201 to S203, a perception accuracy assessment is provided for the multi-UAV cooperative network. This not only ensures the accuracy and reliability of the perception data, but also provides the necessary data support for the determination and processing of subsequent target optimization problems.

[0112] In step S103 of some embodiments, each mission execution terminal is modeled as a linear control system, and the linear control system is monitored to obtain system monitoring information. Modeling the mission execution terminal as a linear control system facilitates describing the system state of the mission execution terminal based on time-dependent changes. The target drone monitors the system state of the linear control system to obtain system monitoring information.

[0113] In some embodiments, each task execution terminal is modeled as a linear control system, and the discrete time system equation of the k-th linear control system is expressed as the following formula (16):

[0114] x k,n+1 =A k x k,n +B k z k,n +v k (16)

[0115] Among them, x k,n ∈R t Indicates the system status of the task execution terminal, z k,n ∈R k Represents the control input information of the task execution terminal, v k ∈R t is a matrix with zero mean and covariance The control system noise, A k ∈R t*trepresents the fixed state evolution matrix, B k ∈R t*k represents the control input matrix, and t represents the dimension of the system state.

[0116] At time point n, the kth task execution terminal controller receives the system monitoring information of the target UAV and generates the optimal control action z based on the data received from the target UAV. k,n , to drive the task execution terminal to perform the corresponding task or make a reaction action. The UAV needs to monitor the system status of the task execution terminal to generate the corresponding control instructions. Therefore, the observation equation of the UAV is expressed as the following analytical formula (17):

[0117] y k,n =C k x k,n +w k (17)

[0118] Among them, y kn represents the system monitoring information observed by the target UAV, C k ∈R ζ*t is a certain observation matrix, ζ represents the dimension of the observation result, w k ∈R ζ represents a matrix with zero mean and covariance matrix The observation noise.

[0119] In step S104 of some embodiments, a control performance evaluation is performed based on the system monitoring information to obtain control performance evaluation information of the task execution terminal.

[0120] The LQR cost function is usually used to evaluate control performance. Therefore, the LQR cost function of the task execution terminal can be obtained based on the system monitoring information, thereby evaluating the control performance of the task execution terminal and obtaining control performance evaluation information, as shown in the following analytical formula (18):

[0121]

[0122] Among them, Q k It is used to balance the cost of the linear control system state deviating from the desired target state zero, and R k The cost of energy consumption for balancing linear control systems, Q k and R k It is a semi-positive definite matrix, ensuring the stability and energy efficiency of the system. k and R kThe choice of directly affects control performance. A lower LQR cost indicates a more stable linear control system and consumes less control energy. E represents the expected value, which is used to calculate the long-term average performance. N represents the upper limit of the time step, which is used to calculate the long-term average performance of the system over an infinite time domain.

[0123] In step S105 of some embodiments, a target optimization problem is determined based on the control performance evaluation information and the perception accuracy evaluation information. In a multi-UAV cooperative network, the control performance evaluation information reflects the stability and responsiveness of the task execution terminal, while the perception accuracy evaluation information provides key data regarding the accuracy of the perception task. Given limited resources, to improve the communication perception performance of the multi-UAV cooperative network, the resource loss (or control cost) of the target UAV's control performance should be minimized, while the target UAV's perception accuracy of the perception performance should be maximized, thereby determining the target optimization problem.

[0124] See also Figure 3 In some embodiments, step S105 may include but is not limited to steps S301 to S302:

[0125] Step S301: Based on the communication rate between the target UAV and the mission execution terminal, a control cost condition between the control performance evaluation information and the target UAV is obtained; wherein the control cost condition is related to the minimum permissible communication rate of the mission execution terminal;

[0126] Step S302 : determining a target optimization problem based on the control cost condition, the control performance evaluation information, and the perception accuracy evaluation information.

[0127] In step S301 of some embodiments, a control cost condition between the control performance evaluation information and the target UAV is obtained based on the communication rate between the target UAV and the mission execution terminal. The control cost condition is related to the minimum allowable communication rate of the mission execution terminal. This ensures that, under resource constraints, the communication rate between the UAV and the mission execution terminal can meet the minimum requirement while minimizing the UAV's control cost for the mission execution terminal. This ensures the stability of the UAV's communication with the mission execution terminal and ensures that the communication rate does not become a bottleneck for improving the performance of the multi-UAV collaborative network.

[0128] It should be noted that according to the analytical formula (18), the LQR cost function is mainly composed of the system state x k,n and control input information z k,n Therefore, another function of the control cost condition is to associate the LQR cost function, that is, the control performance evaluation information, with the communication performance of the UAV. To this end, it is necessary to first obtain the communication rate between the UAV and the mission execution terminal.

[0129] See also Figure 4 In some embodiments, before step S301, the following steps may also be included but not limited to steps S401 to S403:

[0130] Step S401, obtaining the control distance information of each target UAV relative to each mission execution terminal and the communication allocation power used by the target UAV for communication;

[0131] Step S402: obtaining signal-to-interference-and-noise ratio information between each target UAV and the corresponding mission execution terminal based on the control distance information;

[0132] Step S403 , performing communication rate evaluation based on finite code length transmission according to the signal-to-interference-and-noise ratio information and the communication allocation power, and obtaining the communication rate between each target UAV and each mission execution terminal.

[0133] In step S401 of some embodiments, control distance information of each target UAV relative to each mission execution terminal and communication power allocated for communication by the target UAV are obtained. The control distance information represents the physical distance between the target UAV and the mission execution terminal, while the communication power allocated represents the power resources allocated to the communication task.

[0134] In step S402 of some embodiments, the signal-to-interference-plus-noise ratio (SINR) between each target drone and the corresponding mission execution terminal is calculated based on the control distance information. This SINR information includes the signal-to-interference-plus-noise ratio (SINR), a key indicator of signal quality that comprehensively considers the effects of signal strength, interference, and noise. By calculating the SINR, the quality of the communication link can be assessed, providing a basis for subsequent communication rate evaluation.

[0135] In step S403 of some embodiments, a communication rate assessment based on finite code length transmission is performed based on the signal-to-interference-and-noise ratio information and the allocated communication power to determine the communication rate between each target UAV and each mission execution terminal. Finite code length transmission theory considers the impact of finite block length in actual communications, providing a more accurate rate assessment, thereby ensuring that communication rate calculation is accurate and reliable under limited resources and practical conditions.

[0136] In some embodiments, after the UAV observes the system monitoring information of the mission execution terminal, the UAV transmits the system monitoring information to the mission execution terminal through the communication perception integrated signal. The distance between the mth UAV and the kth mission execution terminal is defined as shown in the following analytical formula (19):

[0137]

[0138] When considering the line-of-sight link channel, the free space path loss model can be used to represent the communication model. The channel gain between the mth UAV and the kth mission execution terminal can be expressed as the following analytical formula (20):

[0139]

[0140] in, Indicates the reference distance d m,k = Channel power at 1 meter, where G T Represents the transmitting antenna gain of the UAV, G c represents the receiving antenna gain of the task execution terminal, and λ is the wavelength.

[0141] The kth mission execution terminal is served by the mth UAV, then the signal to interference plus noise ratio (SINR) at the kth mission execution terminal can be expressed as the following analytical formula (21):

[0142]

[0143] Among them, p m represents the communication power allocated to each UAV, ∑ i∈M,i≠m p i h i,k is co-channel interference, represents the noise power of the kth task execution terminal.

[0144] The communication rate between the mth UAV and the kth mission execution terminal can be expressed as follows:

[0145] R m,k =log2(1+Γ m,k ) (twenty two)

[0146] However, it should be noted that the transmission delay may significantly affect the control performance of the UAV for the task execution terminal. Therefore, in order to meet the strict delay requirements, this application conducts a communication rate evaluation based on finite code length transmission. Therefore, the communication rate between the mth UAV and the kth task execution terminal is rewritten as shown in the following analytical formula (23):

[0147]

[0148] Among them, l m,k represents the block length of the transmitted signal, ∈ represents the bit error rate (BLER), V m,k represents the channel dispersion, Q -1 () represents the inverse function of the Gaussian Q function, which is defined as shown in the following analytical formula (24):

[0149] V m,k =1-(1+Γ m,k ) -2 (twenty four)

[0150] Through steps S401 to S403, the communication rate between the target UAV and the mission execution terminal can be systematically evaluated, thereby ensuring the accuracy of the communication evaluation and providing reliable data support for subsequent control performance evaluation and optimization.

[0151] In step S302 of some embodiments, the target optimization problem is determined based on the control cost condition, control performance evaluation information, and perception accuracy evaluation information. This comprehensively considers the two key aspects of communication and perception, as well as the control cost condition regarding the communication rate, providing more reasonable constraints for the target optimization problem. This ensures that the solution to the target optimization problem not only focuses on the control performance of the multi-UAV cooperative network over the task execution terminal and the perception accuracy of the perceived object, but also considers the minimum communication rate requirement between the UAVs and the task execution terminal, thereby finding an optimal solution within reasonable constraints.

[0152] In order to improve the communication perception performance of the multi-UAV cooperative network under limited resources, the resource loss (or control cost) of the target UAV's control performance should be minimized, and the target UAV's perception accuracy of the perception performance should be maximized. When minimizing the resource loss of the target UAV's control performance, the data throughput received by the kth task execution terminal should be required to meet the minimum communication rate, that is, the control cost condition, which can be expressed by the following analytical formula (25):

[0153]

[0154] Where B represents the channel bandwidth, L k It can be expressed by the following analytical formula (26):

[0155]

[0156] in, L represents the intrinsic entropy rate, indicating the stability of the linear control system of the k-th task execution terminal. k The minimum communication rate used to limit the linear control system is the lower bound constraint between the communication rate and the control performance (LQR cost). This lower bound constraint is a function related to the LQR cost. It is a constraint parameter set to ensure that the linear control system can work stably at the minimum communication rate. (b k ) min represents the minimum LQR cost that can be achieved without communication constraints, which can be expressed by the following analytical formula (27):

[0157]

[0158] Equations (25) and (26) describe the constraint relationship between the communication rate and the LQR cost, ensuring that the communication rate meets the minimum requirement. This ensures that when the linear control system of the mission execution terminal operates stably, the communication rate will not become a bottleneck for the UAV to control the mission execution terminal. Furthermore, a connection is established between the UAV and the mission execution terminal at the communication and control levels, enabling variables related to the communication rate and variables related to control performance to be jointly optimized within a single objective optimization problem.

[0159] The above S k 、N k 、M k ,∑k is about the given control parameter (i.e. A k 、B k 、R k , Q k 、 and ) is the solution of the algebraic Riccati equation, where Q k The cost used to balance the linear control system state from the desired target state zero is the weight matrix of the state, and R k The cost of controlling energy consumption in a balanced linear control system is the control cost matrix, A k represents a fixed state evolution matrix (or state transfer matrix), B k represents the control input matrix, C k represents the observation matrix, S k is the covariance matrix of the state, M k is the control gain matrix, ∑k is the covariance matrix of the state estimate, which reflects the uncertainty of the state.

[0160] Specifically, S k and M k is the solution of the Riccati equation represented by the following analytical equations (28) and (29):

[0161] S k =Q k +A k T (S k -M k )A k (28)

[0162] M k =S k B k (R k +B kT S k B k ) -1 B k T S k (29)

[0163] ∑k is obtained through the Kalman filter and can be expressed by the following analytical formula (30):

[0164]

[0165] Among them, P k is the solution of the Riccati equation expressed by the following analytical formula (31):

[0166]

[0167] And K k represents the Kalman filter gain, which can be expressed by the following analytical formula (32):

[0168]

[0169] Partial observation steady-state covariance matrix N k , can be expressed by the following analytical formula (33):

[0170]

[0171] These algebraic Riccati equations are solved iteratively by numerical methods, continuously updating the equations until convergence within a predetermined tolerance.

[0172] See also Figure 5 In some embodiments, step S105 may also include but is not limited to steps S501 to S502:

[0173] Step S501, performing normalization based on the control performance evaluation information and the perception accuracy evaluation information to obtain a normalization factor and a weight coefficient;

[0174] Step S502 : constructing a difference function between the control performance evaluation information and the perception accuracy evaluation information based on the weight coefficient and the normalization factor, and determining it as a target optimization problem.

[0175] In step S501 of some embodiments, normalization processing is performed based on the control performance evaluation information and the perception accuracy evaluation information to obtain a normalization factor and a weight coefficient. The purpose of normalization processing is to eliminate the dimensional differences between the different evaluation information, so that the control performance evaluation information and the perception accuracy evaluation information can be compared on the same scale. The weight coefficient is used to balance the relative importance of control performance and perception accuracy in the multi-UAV cooperative network and is generally set based on the specific application scenario and processing task type.

[0176] In step S502 of some embodiments, a difference function between the control performance evaluation information and the perception accuracy evaluation information is constructed based on the weight coefficient and the normalization factor, and is determined as a target optimization problem. This difference function is designed to quantify the trade-off between control performance and perception accuracy. By minimizing the difference function, resource allocation and task execution efficiency can be optimized while meeting the system performance requirements of the multi-UAV cooperative network, ensuring that the system can achieve an optimal performance balance within limited resources, thereby significantly improving the efficiency and stability of the multi-UAV cooperative network.

[0177] In some embodiments, a target optimization problem may be determined based on the control performance evaluation information and the perception accuracy evaluation information, as expressed as (34a)-(34i):

[0178]

[0179] Where b=[b1,...,b k ] T Represents the LQR cost vector of the linear control system, η∈[0,1] is the weight coefficient, and a larger value of η indicates that control performance is given priority over positioning accuracy. c and Ψ s are the normalization factors of control performance evaluation information and perception accuracy evaluation information, respectively, representing ∑ k∈K b k and detΦ s The upper bound of P. max represents the maximum transmission power, (34b)-(34c) represent the power budget constraint, and (34d)-(34f) are used to ensure that the pairing between the UAV and the mission execution terminal meets the specified standard, that is, each UAV can only serve at most one mission execution terminal, and each mission execution terminal can only be served by one UAV. Specifically, the (m,k)th element θ of the matrix Θ is m,k = {0,1}, indicating the pairing status between the mth UAV and the kth task execution terminal, i.e., θ m,k =1 means that the mth UAV serves the kth task execution terminal, otherwise it does not serve. (34g)-(34h) correspond to the collision avoidance and flight boundary constraints of the UAV, where dmin represents the minimum allowed distance between any two UAVs, and D is the allowed flight area of ​​each UAV. (34i) is the control cost condition determined previously, which is used to ensure that the communication rate can meet the minimum requirement.

[0180] The target optimization problem constructed by (34a)-(34i) is a non-convex problem involving mixed binary and continuous variables, so it is difficult to obtain a global optimal solution. This application proposes an efficient iterative algorithm to solve the problem.

[0181] In step S106 of some embodiments, a problem decomposition optimization process is performed based on the target optimization problem to obtain target optimization information. In a multi-UAV collaborative network, the target optimization problem is typically a complex multivariable optimization problem. To effectively solve this problem, it is usually necessary to decompose the complex optimization problem into several sub-problems, each of which can be processed independently, thereby reducing the complexity of the problem. Appropriate optimization algorithms are then used to process the sub-problems, providing an efficient optimization solution for the multi-UAV collaborative network.

[0182] See also Figure 6 In some embodiments, step S106 may include but is not limited to steps S601 to S603:

[0183] Step S601: Analyze influencing factors based on the control performance evaluation information and the perception accuracy evaluation information to obtain basic influencing factors, where the basic influencing factors include the communication allocation power used by the target UAV for communication, the pairing relationship between the target UAV and the mission execution terminal, and the flight position of the target UAV;

[0184] Step S602: Decompose the target optimization problem into a pairing sub-problem, a power allocation sub-problem, and a UAV position sub-problem based on basic influencing factors;

[0185] In step S603, alternate optimization processing is performed based on the pairing subproblem, the power allocation subproblem, and the UAV position subproblem until the preset convergence conditions are met to obtain target optimization information.

[0186] In step S601 of some embodiments, an influencing factor analysis is performed based on the control performance evaluation information and the perception accuracy evaluation information to extract fundamental influencing factors. These fundamental influencing factors include the target drone's communication power allocation, the pairing relationship between the target drone and the mission execution terminal, and the target drone's flight position. These factors are key variables affecting the overall system performance, and their analysis can clarify the direction and focus of optimization.

[0187] In step S602 of some embodiments, based on these fundamental influencing factors, the complex target optimization problem is broken down into three subproblems: a pairing subproblem, a power allocation subproblem, and a drone positioning subproblem. This decomposition method helps reduce the complexity of the problem, allowing each subproblem to be addressed independently and optimized alternately, thereby obtaining a solution to the target optimization problem. The pairing subproblem focuses on the optimal pairing relationship between the drone and the mission execution terminal, the power allocation subproblem focuses on the reasonable allocation of communication power, and the drone positioning subproblem focuses on the optimal flight position of the drone.

[0188] In step S603 of some embodiments, an alternating optimization method is used to iteratively process the three subproblems. By alternately optimizing each subproblem, the optimal solution to the overall problem is gradually approached. This process continues until a preset convergence condition is met, that is, the change in the optimization result over several consecutive iterations is less than a certain threshold. The target optimization information obtained at this point is the final optimization result.

[0189] Through steps S601 to S603, the target optimization problem can be systematically processed to achieve efficient optimization of the multi-UAV cooperative network.

[0190] Specifically, this application preliminarily optimizes the LQR cost and associates its LQR with the pairing relationship between the target UAV and the mission execution terminal, the flight position of the target UAV, and the communication allocation power of the UAV. m The closed-form solution of the optimal LQR cost b of} is denoted as b * If any Θ, p and {q m}, the closed-form expression of the optimal LQR cost in equation (34a) is (35):

[0191]

[0192] in,

[0193]

[0194] It should be noted that in the target optimization problem, only (34i) imposes a restriction on b. Therefore, given any Θ, p, and {q m}, the problem of optimizing the LQR cost can be expressed as (37a)-(37b):

[0195]

[0196] It is understandable that the right side L of (37b) k It is b k is a monotonically decreasing function, so b kThe feasible solution of is achieved by taking the equal sign in (37b) to minimize ∑ k∈K b k .

[0197] In this paper, it is assumed that the multi-UAV cooperative network is in a stable scenario, where the data throughput is much larger than the inherent entropy rate, i.e. To ensure that the denominator in (35) is positive, this means that the linear control system at the task execution terminal can operate away from the unstable threshold, so that the focus can be placed on optimizing control performance rather than ensuring system stability.

[0198] Thus, by substituting (35) into (34a), the target optimization problem is reformulated as (38a-38h):

[0199]

[0200] Therefore, the solution can be obtained based on the basic influencing factors of the target optimization problem, namely the communication allocation power used by the target UAV for communication, the pairing relationship between the target UAV and the mission execution terminal, and the flight position of the target UAV.

[0201] The target optimization problem is decomposed into three sub-problems: pairing sub-problem, power allocation sub-problem and drone position sub-problem. Figure 7 In some embodiments, step S603 may include but is not limited to steps S701 to S704:

[0202] Step S701: Solve the pairing sub-problem based on the existing communication allocation power and the existing flight position to obtain an updated pairing relationship;

[0203] Step S702: Solve the power allocation sub-problem based on the updated pairing relationship and the existing flight position to obtain an updated communication allocation power;

[0204] Step S703: Solve the drone position sub-problem based on the updated pairing relationship and the updated communication allocation power to obtain an updated flight position;

[0205] Step S704: Obtain target optimization information based on the updated pairing relationship, the updated communication allocation power, or the updated flight position.

[0206] In step S701 of some embodiments, based on the existing communication allocation power and the existing flight position, the pairing sub-problem is solved, which can be expressed as (39a)-(39d):

[0207]

[0208] Among them, (39a) can be expressed as (40):

[0209]

[0210] In order to solve the pairing sub-problem of the pairing relationship between the UAV and the mission execution terminal, we first analyze the function b k The concave-convex nature of (Θ). To simplify the expression, its expression can be rewritten as (41):

[0211]

[0212] in,

[0213] b k The first-order derivative of (Θ) is (42):

[0214]

[0215] in, b k The second-order derivative of (Θ) is (43):

[0216]

[0217] Therefore, as long as Right now There is b k (Θ)″>0. This condition always holds true when the linear control system is stable. Therefore, the function b k (Θ) is convex with respect to Θ in this scenario.

[0218] However, due to the constraints of (39d), it is still very difficult to solve it directly. To this end, we can m,k Converted into an equivalent continuous form, the pairing subproblem can be equivalently converted to (44a)-(44d):

[0219]

[0220] (39b),(39c),(44d)

[0221] Among them, the two constraints (44b) and (44c) are to ensure that θ m,k The value of is restricted to 0 or 1, which is equivalent to the constraint of (39d). Although (44c) is still non-convex, it has been expressed as the difference of two convex functions, which makes it solvable by DC programming. In order to approximate the solution of (44c), ∑ m∈M ∑ k∈K θ m,k 2 The first-order Taylor series expansion of is (45):

[0222]

[0223] where a1 represents the a1th iteration of DC programming. Since the left-hand side (LHS) of (45) cannot be less than 0, solving it directly is challenging. However, the strong Lagrangian duality of the pairing subproblem (44a) holds. Therefore, (45) can be incorporated into the pairing subproblem through a penalty technique, further transforming the pairing subproblem into (46a)-(46b):

[0224]

[0225] st(39b),(39c),(44b),(46b)

[0226] in,

[0227]

[0228] Where μ is a non-negative penalty parameter. The pairing subproblems (46a)-(46b) can be solved using a penalty function-based DC optimization algorithm. First, the penalty parameter, optimization variable, maximum number of iterations, and error tolerance are initialized. Then, in each iteration, the updated variable solution is obtained by solving a non-convex optimization problem containing a penalty term, and the penalty coefficient is adjusted according to the set update rule. If the variable change is less than the set error threshold or the maximum number of iterations is reached, the iteration is stopped and the final matching result is output, resulting in the updated pairing information.

[0229] In step S702 of some embodiments, based on the updated pairing relationship and the existing flight positions, the power allocation sub-problem is solved, which can be expressed as (48a)-(48c):

[0230]

[0231] Among them, (48a) can be expressed as (49):

[0232]

[0233] It should be noted that the power allocation subproblem is a linear constrained optimization problem, but (49) is non-convex. Therefore, the optimal value can be solved by the projected gradient descent algorithm. First, an initial feasible power vector and related parameters such as step size and tolerance threshold are set. In each iteration, the power direction vector of the previous step is recorded, and the power allocation value is updated according to the current gradient direction. Then, the step size is adaptively adjusted according to the gradient norm, and the projection operation is performed to ensure that the updated power value meets the constraint conditions. When the change in the power vector is lower than the set threshold, the iteration is stopped and the final power allocation result is output as the updated communication allocation power.

[0234] In step S703 of some embodiments, based on the updated pairing relationship and the updated communication allocation power, the drone position subproblem is solved, which can be expressed as (50a)-(50c):

[0235]

[0236] Among them, (50a) can be expressed as (51):

[0237]

[0238] Due to the non-convexity of (50a) and the existence of constraint (50b), the UAV position subproblem is difficult to solve. Therefore, we use the continuous convex approximation method to approximate the non-convex terms through the first-order Taylor expansion. Constraint (50b) is about q m and q r The convex function of is lower bounded by (52):

[0239]

[0240] in, and denote the approximate positions of the mth and rth UAVs in the a2th iteration, respectively.

[0241] It should be noted that the function detΦ(s) involves 3D coordinates These coordinates are coupled to each other, which significantly increases the complexity of optimizing the drone position. To simplify the optimization, we reformulate Equations (10)-(15) as m functions, rather than individual coordinates The restated expression is (53):

[0242]

[0243] Then, (50a) is applied at a given feasible point The first-order Taylor expansion is used to approximate the equation (54):

[0244]

[0245] Therefore, the UAV position subproblems (50a)-(50c) are approximately solved as the following set of convex approximation problems (55a)-(55c):

[0246]

[0247] (50c),(55c)

[0248] In this way, standard convex optimization tools can be used to solve the problem and obtain a near-optimal solution, which can be used to obtain the updated flight position.

[0249] In step S704 of some embodiments, target optimization information is obtained based on the updated pairing relationship, the updated communication allocation power, or the updated flight position, and the parameters of the drones in the multi-drone cooperative network are adjusted.

[0250] Through steps S701 to S704, the optimal solution to the target optimization problem can be gradually approached. This process continuously optimizes system performance by iteratively updating pairing relationships, communication power allocation, and flight positions until the preset convergence conditions are met. Ultimately, under limited resources, the communication perception performance of the multi-UAV collaborative network is improved, achieving optimization of the multi-UAV collaborative network.

[0251] In step S107 of some embodiments, at least one of the following operations is performed based on the target optimization information: updating the mission execution terminal served by the target drone, updating the location of the target drone, and updating the communication allocation power used by the target drone for communication, thereby dynamically adjusting the drone configuration to improve the overall performance of the system. The mission execution terminal served by the target drone is updated, and the pairing relationship between the drone and the mission execution terminal is reallocated. By optimizing the pairing relationship, it is possible to ensure that each drone can maximize its function within its capabilities, thereby improving the efficiency and accuracy of mission execution. The location of the target drone is updated, and the deployment location of the drone is adjusted to optimize coverage and communication quality. The communication allocation power used by the target drone for communication is updated, and the drone's communication transmission power is adjusted according to the current communication environment and mission requirements to optimize communication efficiency, thereby affecting the control performance of the mission execution terminal. In summary, by dynamically adjusting the service objects, locations, and communication power of the drones, the multi-drone collaborative network can have better communication perception performance.

[0252] The embodiment of the present application performs position perception of a perception object based on communication perception technology to obtain perception coordinate information of the perception object; performs perception accuracy evaluation based on the perception coordinate information to obtain perception accuracy evaluation information; models each task execution terminal as a linear control system and performs state monitoring on the linear control system to obtain system monitoring information; performs control performance evaluation based on the system monitoring information to obtain control performance evaluation information of the task execution terminal; determines a target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information; performs problem decomposition and optimization processing based on the target optimization problem to obtain target optimization information; and performs at least one of the following operations based on the target optimization information: updating the task execution terminal served by the target drone, updating the position of the target drone, and updating the communication allocation power used by the target drone for communication. It can be seen that the present application determines the target optimization problem by obtaining perception accuracy evaluation information and control performance evaluation information, and obtains target optimization information by performing problem decomposition and optimization processing on the target optimization problem, and then updates the relevant parameters of the drone, thereby improving the communication perception performance of the multi-drone cooperative network under limited resources and achieving optimization of the multi-drone cooperative network.

[0253] See also Figure 8 The embodiment of the present application further provides a multi-UAV perception and communication device, which can implement the above-mentioned multi-UAV perception and communication method, and the device includes:

[0254] A position sensing module is used to sense the position of a sensing object based on communication sensing technology and obtain the sensing coordinate information of the sensing object;

[0255] A perception accuracy evaluation module, configured to perform perception accuracy evaluation based on the perception coordinate information to obtain perception accuracy evaluation information;

[0256] The state monitoring module is used to model each task execution terminal as a linear control system and perform state monitoring on the linear control system to obtain system monitoring information;

[0257] A control performance evaluation module is used to perform control performance evaluation based on system monitoring information and obtain control performance evaluation information of the task execution terminal;

[0258] An optimization problem determination module, configured to determine a target optimization problem based on control performance evaluation information and perception accuracy evaluation information;

[0259] The optimization problem processing module is used to perform problem decomposition and optimization processing based on the target optimization problem to obtain target optimization information;

[0260] The drone status update module is used to perform at least one of the following operations based on the target optimization information: updating the mission execution terminal served by the target drone, updating the position of the target drone, and updating the communication allocation power used by the target drone for communication.

[0261] The specific implementation of the multi-UAV perception communication device is basically the same as the specific embodiment of the above-mentioned multi-UAV perception communication method, and will not be repeated here.

[0262] The present application also provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the multi-UAV perception and communication method. The electronic device can be any smart terminal, such as a tablet computer or an in-vehicle computer.

[0263] See also Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0264] The processor 901 can be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0265] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program codes are stored in the memory 902 and are called by the processor 901 to execute the multi-UAV perception communication method of the embodiments of this application.

[0266] Input / output interface 903, used to implement information input and output;

[0267] Communication interface 904, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0268] Bus 905 , which transmits information between various components of the device (e.g., processor 901 , memory 902 , input / output interface 903 , and communication interface 904 );

[0269] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0270] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned multi-UAV perception communication method.

[0271] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0272] The multi-UAV perception communication method, device, electronic device, and storage medium provided in the embodiments of the present application perform position perception of a perception object based on communication perception technology to obtain perception coordinate information of the perception object; perform perception accuracy assessment based on the perception coordinate information to obtain perception accuracy assessment information; model each task execution terminal as a linear control system and perform state monitoring on the linear control system to obtain system monitoring information; perform control performance assessment based on the system monitoring information to obtain control performance assessment information of the task execution terminal; determine a target optimization problem based on the control performance assessment information and the perception accuracy assessment information; perform problem decomposition and optimization processing based on the target optimization problem to obtain target optimization information; and perform at least one of the following operations based on the target optimization information: updating the task execution terminal served by the target UAV, updating the position of the target UAV, and updating the communication allocation power used by the target UAV for communication. It can be seen that the present application determines the target optimization problem by obtaining perception accuracy assessment information and control performance assessment information, and obtains target optimization information by performing problem decomposition and optimization processing on the target optimization problem, and then updates the relevant parameters of the UAVs, thereby improving the communication perception performance of the multi-UAV cooperative network under limited resources and achieving optimization of the multi-UAV cooperative network.

[0273] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0274] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0275] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0276] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0277] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0278] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0279] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0280] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0281] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0282] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0283] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A multi-UAV perception communication method, characterized in that: A target drone among multiple drones in a multi-drone cooperative network, wherein the multi-drone cooperative network further includes a sensing object and multiple task execution terminals, each of the drones can only serve at most one task execution terminal, and each of the task execution terminals can only be served by one of the drones, and the method includes: Performing position sensing on the sensing object based on communication sensing technology to obtain sensing coordinate information of the sensing object; Performing a perception accuracy assessment based on the perception coordinate information to obtain perception accuracy assessment information; Modeling each of the task execution terminals as a linear control system, and performing state monitoring on the linear control system to obtain system monitoring information; Performing control performance evaluation based on the system monitoring information to obtain control performance evaluation information of the task execution terminal; determining a target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information; Perform problem decomposition and optimization processing based on the target optimization problem to obtain target optimization information; Based on the target optimization information, at least one of the following operations is performed: updating the mission execution terminal served by the target drone, updating the position of the target drone, and updating the communication allocation power used by the target drone for communication.

2. The method according to claim 1, characterized in that The performing perception accuracy evaluation based on the perception coordinate information to obtain perception accuracy evaluation information includes: Acquiring sensing distance information between each target UAV and the sensing object; Obtaining signal-to-noise ratio information and current location information of each target drone; Based on the signal-to-noise ratio information, the perception distance information, the current position information and the perception distance information, a Fisher information matrix of the perception coordinate information of the perception object is obtained and determined as the perception accuracy evaluation information.

3. The method according to claim 1, characterized in that The determining of a target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information includes: Based on the communication rate between the target UAV and the mission execution terminal, a control cost condition between the control performance evaluation information and the target UAV is obtained; wherein the control cost condition is related to the minimum permissible communication rate of the mission execution terminal; A target optimization problem is determined based on the control cost condition, the control performance evaluation information, and the perception accuracy evaluation information.

4. The method according to claim 3, characterized in that Before obtaining the control performance evaluation information and the control cost condition of the target UAV based on the communication rate between the target UAV and the task execution terminal, the method includes: Acquire control distance information of each target UAV relative to each mission execution terminal and communication allocation power of the target UAV for communication; Obtaining signal-to-interference-noise ratio information between each target UAV and the corresponding mission execution terminal based on the control distance information; A communication rate evaluation based on finite code length transmission is performed according to the signal interference noise ratio information and the communication allocation power to obtain the communication rate between each target UAV and each mission execution terminal.

5. The method according to claim 1, wherein The determining of a target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information includes: Normalizing the control performance evaluation information and the perception accuracy evaluation information to obtain a normalization factor and a weight coefficient; Based on the weight coefficient and the normalization factor, a difference function between the control performance evaluation information and the perception accuracy evaluation information is constructed and determined as the target optimization problem.

6. The method according to claim 1, characterized in that The problem decomposition and optimization process is performed based on the target optimization problem to obtain target optimization information, including: performing an influencing factor analysis based on the control performance evaluation information and the perception accuracy evaluation information to obtain basic influencing factors, wherein the basic influencing factors include a communication allocation power of the target UAV for communication, a pairing relationship between the target UAV and the mission execution terminal, and a flight position of the target UAV; Decomposing the target optimization problem into a pairing subproblem, a power allocation subproblem, and a UAV position subproblem based on the basic influencing factors; Alternating optimization processing is performed based on the pairing subproblem, the power allocation subproblem, and the UAV position subproblem until a preset convergence condition is met, thereby obtaining the target optimization information.

7. The method according to claim 6, characterized in that The alternate optimization process based on the pairing subproblem, the power allocation subproblem, and the UAV position subproblem is performed until a preset convergence condition is satisfied to obtain the target optimization information, specifically including: Solving the pairing subproblem based on the existing communication allocated power and the existing flight position to obtain the updated pairing relationship; Solving the power allocation subproblem based on the updated pairing relationship and the existing flight position to obtain the updated communication allocation power; Solve the UAV position subproblem based on the updated pairing relationship and the updated communication allocation power to obtain the updated flight position; The target optimization information is obtained based on the updated pairing relationship, the updated communication allocation power, or the updated flight position.

8. A multi-UAV sensing and communication device, characterized in that: A target drone among multiple drones in a multi-drone cooperative network, wherein the multi-drone cooperative network further includes a sensing object and multiple task execution terminals, each of the drones can only serve at most one task execution terminal, and each of the task execution terminals can only be served by one of the drones, and the device includes: A position sensing module is used to sense the position of the sensing object based on communication sensing technology to obtain sensing coordinate information of the sensing object; a perception accuracy assessment module, configured to perform perception accuracy assessment based on the perception coordinate information to obtain perception accuracy assessment information; a state monitoring module, configured to model each of the task execution terminals as a linear control system, and perform state monitoring on the linear control system to obtain system monitoring information; A control performance evaluation module, configured to perform control performance evaluation based on the system monitoring information to obtain control performance evaluation information of the task execution terminal; an optimization problem determination module, configured to determine a target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information; An optimization problem processing module is used to perform problem decomposition and optimization processing based on the target optimization problem to obtain target optimization information; The drone status update module is used to perform at least one of the following operations based on the target optimization information: updating the task execution terminal served by the target drone, updating the position of the target drone, and updating the communication allocation power used by the target drone for communication.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the multi-UAV perception communication method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the multi-UAV perception communication method according to any one of claims 1 to 7 is implemented.

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