Multi-uav perception communication method and device, electronic equipment and storage medium

Abstract

The embodiment of the application provides a kind of multi-unmanned plane perception communication method, device, electronic equipment and storage medium, belong to unmanned plane communication technical field.The method comprises: the position of the sensing object is sensed based on communication sensing technology, and sensing coordinate information is obtained;Sensing accuracy evaluation information is obtained based on sensing coordinate information by sensing accuracy evaluation;System monitoring information is obtained by monitoring the state of each task execution terminal;Control performance evaluation information is obtained by control performance evaluation based on system monitoring information;Determine target optimization problem and problem decomposition optimization processing based on control performance evaluation information and sensing accuracy evaluation information, and obtain target optimization information;At least one of the following operations is performed based on target optimization information: updating the task execution terminal served by the target unmanned plane, updating the position of the target unmanned plane, updating the communication distribution power of the target unmanned plane.The embodiment of the application can improve the communication sensing performance of multi-unmanned plane cooperative network.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a multi-UAV sensing and communication method, device, electronic device, and storage medium. Background Technology

[0002] Drones play a crucial role in the low-altitude economy, an economic model that relies on low-altitude airspace and encompasses the entire industrial chain, including general aviation aircraft research and development, manufacturing, market operation, comprehensive support, and extended services. With their advantages of flexibility, efficiency, and low cost, drones are widely used in logistics, emergency rescue, agricultural plant protection, infrastructure inspection, geographic surveying, and urban security, becoming an important force driving the development of the low-altitude economy.

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

[0004] This multi-task collaborative mode places higher demands on the communication capabilities, perception accuracy, and task scheduling of UAVs, especially in terms of real-time performance and reliability in complex environments. Currently, improving the communication and perception performance of multi-UAV collaborative networks with limited resources remains a pressing challenge. Summary of the Invention

[0005] The main objective of this application is to propose a multi-UAV sensing and communication method, device, electronic device, and storage medium, which aims to improve the communication and sensing performance of multi-UAV cooperative networks.

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

[0007] The location of the sensing object is perceived based on communication sensing technology, and the sensing coordinate information of the sensing object is obtained.

[0008] Based on the perceived coordinate information, the perception accuracy is evaluated to obtain perception accuracy evaluation information.

[0009] Each task execution terminal is modeled as a linear control system, and the state of the linear control system is monitored to obtain system monitoring information;

[0010] Based on the system monitoring information, a control performance evaluation is performed to obtain the control performance evaluation information of the task execution terminal.

[0011] The target optimization problem is determined based on the control performance evaluation information and the perception accuracy evaluation information.

[0012] Based on the target optimization problem, the problem is decomposed and optimized to obtain target optimization information;

[0013] Based on the target optimization information, perform at least one of the following operations: update the task execution terminal served by the target UAV, update the location of the target UAV, and update the communication allocation power used by the target UAV for communication.

[0014] In some embodiments, the step of evaluating perception accuracy based on the perceived coordinate information to obtain perception accuracy evaluation information includes:

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

[0016] Obtain the signal-to-noise ratio information and current location information of each target UAV;

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

[0018] In some embodiments, determining the 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, the control performance evaluation information and the control cost condition between the target UAV are obtained; wherein, the control cost condition is related to the minimum allowable communication rate of the mission execution terminal;

[0020] The 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 conditions of the target UAV based on the communication rate between the target UAV and the task execution terminal, the following steps are included:

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

[0023] Based on the control distance information, the signal-to-interference-to-noise ratio information between each target UAV and the corresponding task execution terminal is obtained;

[0024] Based on the signal-to-interference-to-noise ratio information and the communication allocation power, a communication rate assessment based on finite code length transmission is performed 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] Based on the control performance evaluation information and the perception accuracy evaluation information, normalization is performed to obtain the normalization factor and weight coefficient.

[0027] Based on the weighting coefficients and the normalization factor, a difference function is constructed between the control performance evaluation information and the perception accuracy evaluation information, which is then used to determine the target optimization problem.

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

[0029] Based on the control performance evaluation information and the perception accuracy evaluation information, an influencing factor analysis is performed to obtain basic influencing factors, including the communication allocation power of the target UAV for communication, the pairing relationship between the target UAV and the task execution terminal, and the flight position of the target UAV.

[0030] Based on the aforementioned fundamental influencing factors, the target optimization problem is decomposed into a pairing sub-problem, a power allocation sub-problem, and a UAV position sub-problem.

[0031] The pairing subproblem, the power allocation subproblem, and the UAV position subproblem are alternately optimized until a preset convergence condition is met, thereby obtaining the target optimization information.

[0032] In some embodiments, the process of alternately optimizing the pairing sub-problem, the power allocation sub-problem, and the UAV position sub-problem until a preset convergence condition is met to obtain the target optimization information specifically includes:

[0033] Based on the existing communication allocation power and the existing flight position, the pairing sub-problem is solved to obtain the updated pairing relationship;

[0034] Based on the updated pairing relationship and the existing flight positions, the power allocation subproblem is solved to obtain the updated communication allocation power;

[0035] Based on the updated pairing relationship and the updated communication allocation power, the UAV position subproblem is solved 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 objectives, a second aspect of this application provides a multi-UAV sensing and communication device, the device comprising:

[0038] The location awareness module is used to perform location awareness on the sensing object based on communication sensing technology, and obtain the sensing coordinate information of the sensing object;

[0039] The perception accuracy assessment module is used to assess the perception accuracy based on the perception coordinate information and obtain perception accuracy assessment information.

[0040] The status monitoring module is used to model each of the task execution terminals as a linear control system and to monitor the status of the linear control system to obtain system monitoring information.

[0041] The control performance evaluation module is used to perform control performance evaluation based on the system monitoring information to obtain the control performance evaluation information of the task execution terminal.

[0042] The optimization problem determination module is used to determine the target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information.

[0043] 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;

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

[0045] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes 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 described in the first aspect.

[0046] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the multi-UAV perception and communication method described in the first aspect.

[0047] The multi-UAV sensing and communication method, device, electronic device, and storage medium proposed in this application obtain sensing coordinate information of the sensing object by performing position sensing based on communication sensing technology; performing sensing accuracy assessment based on the sensing coordinate information to obtain sensing 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 the target optimization problem based on the control performance assessment information and the sensing 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. Therefore, this application determines the target optimization problem by obtaining sensing accuracy assessment information and control performance assessment information, and obtains target optimization information by decomposing and optimizing the target optimization problem, thereby updating the relevant parameters of the UAVs. This improves the communication sensing performance of the multi-UAV cooperative network under limited resources, achieving optimization of the multi-UAV cooperative network. Attached Figure Description

[0048] Figure 1 This is a flowchart of the multi-UAV perception and communication method provided in the embodiments of this application;

[0049] Figure 2 yes Figure 1 The flowchart of step S102 in the document;

[0050] Figure 3 yes Figure 1 The flowchart of step S105 in the process;

[0051] Figure 4 yes Figure 3 The flowchart preceding step S301;

[0052] Figure 5 yes Figure 1 The flowchart of step S105 in the process;

[0053] Figure 6 yes Figure 1 The flowchart of step S106 in the process;

[0054] Figure 7 yes Figure 6 The flowchart of step S603 in the process;

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

[0056] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is 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 achieves coexistence, mutual assistance, and shared benefits between communication and sensing by sharing spectrum, hardware, and signal processing resources. ISAC is considered one of the key candidate technologies for sixth-generation mobile communication (6G), capable of significantly improving spectrum efficiency and supporting a variety of emerging applications. The concept of ISAC originated in the low-altitude economy, enabling unmanned aerial vehicles (UAVs) to seamlessly perform communication and sensing tasks using shared wireless resources.

[0062] In wireless communication, a line-of-sight (LoS) link refers to a direct, unobstructed path between two communication devices. This path ensures that the signal travels directly from the transmitter to the receiver. This type of link is commonly used in microwave communication, satellite communication, and radio communication under certain conditions.

[0063] Finite Block Length (FBL) refers to the encoding and decoding problem in information theory and communication systems when the code block length (i.e., code length) is finite. Traditional Shannon's theorem assumes the code length tends to infinity, but in practical applications, the code length is finite. Therefore, 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 the best transmission performance under finite block length. Short packet communication based on finite code length transmission shows significant potential in meeting the needs of latency-sensitive services in IoT networks. Finite code length transmission can meet the stringent low-latency requirements of UAV support systems, whose operating cycles are typically divided into multiple short time slices to optimize UAV trajectory design.

[0064] A 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, especially in scenarios requiring precise control and stability guarantees.

[0065] This application provides a multi-UAV sensing and communication method, device, electronic device, and storage medium, aiming to improve the communication and sensing performance of multi-UAV cooperative networks.

[0066] The multi-UAV sensing and communication method, device, electronic device, and storage medium provided in this application are specifically described through the following embodiments. First, the multi-UAV sensing and communication method in this application is described.

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

[0068] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: 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, and distributed computing environments including any of the above systems or devices. This 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, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

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

[0070] Step S101: Based on communication sensing technology, the position of the sensing object is sensed to obtain the sensing coordinate information of the sensing object;

[0071] Step S102: Based on the sensing coordinate information, perform a sensing accuracy assessment to obtain sensing accuracy assessment 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: Based on system monitoring information, perform control performance evaluation to obtain control performance evaluation information of the task execution terminal;

[0074] Step S105: Determine the target optimization problem based on control performance evaluation information and perception accuracy evaluation information;

[0075] Step S106: Based on the target optimization problem, perform problem decomposition and optimization processing to obtain target optimization information;

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

[0077] Steps S101 to S107 of this application embodiment involve: obtaining the perceived coordinate information of the perceived object by performing position perception based on communication sensing technology; evaluating the perception accuracy based on the perceived coordinate information to obtain perception accuracy evaluation information; modeling each task execution terminal as a linear control system and monitoring the state of the linear control system to obtain system monitoring information; evaluating the control performance based on the system monitoring information to obtain control performance evaluation information of the task execution terminal; determining the target optimization problem based on the control performance evaluation information and the perception accuracy evaluation 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. Therefore, this application determines the target optimization problem by obtaining perception accuracy evaluation information and control performance evaluation information, and obtains target optimization information by decomposing and optimizing the target optimization problem, thereby updating the relevant parameters of the UAV, thus improving the communication sensing 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 location of the sensing object is perceived based on communication sensing technology to obtain the sensing coordinate information of the sensing object. The target UAV uses its onboard communication sensing equipment to transmit and receive specific wireless signals, which return to the receiver of the target UAV after interacting with the sensing object. By analyzing the characteristics of the returned signals, such as the arrival time, angle of arrival, and signal strength, the coordinate position of the sensing object, i.e., the sensing coordinate information, can be calculated using a geometric positioning algorithm.

[0079] The application of communication sensing technology in multi-UAV collaborative networks enables UAVs to simultaneously perform communication tasks and achieve real-time environmental perception. 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 the target, UAVs can provide crucial environmental information for subsequent mission execution, such as optimizing path planning in logistics transportation or disaster relief, and accurately monitoring crop status in agricultural plant protection. Furthermore, the accuracy of the perceived coordinates 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 perceived coordinate information to obtain perception accuracy assessment information. After the UAV acquires the perceived coordinate information of the perceived object using communication sensing technology, it needs to perform error analysis and confidence interval calculation on this information to assess the accuracy of the perception results and obtain the corresponding perception accuracy assessment information. The perception accuracy assessment information is of great significance for subsequent optimization and decision-making. In a multi-UAV cooperative network, the perception accuracy assessment information can be used to adjust the UAV's perception strategy and optimize the allocation of communication and perception resources to improve the overall system performance and reliability. The perception accuracy assessment information can also provide key environmental information for the task execution terminal, ensuring the accuracy and efficiency of task execution. The task execution terminal is used to represent the terminal that communicates with the UAV and reacts based on the UAV's communication information to execute various tasks.

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

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

[0083] Step S202: Obtain the signal-to-noise ratio information and current location information for each target UAV;

[0084] Step S203: Based on the signal-to-noise ratio information, sensing distance information, current location information, and sensing distance information, obtain the Fisher information matrix of the sensing coordinate information of the sensing object, and determine it as the sensing accuracy evaluation information.

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

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

[0087] In step S203 of some embodiments, based on the signal-to-noise ratio information, sensing distance information, current location information, and sensing distance information obtained above, a Fisher information matrix of the sensing coordinates of the sensing object is calculated and determined as the sensing accuracy evaluation information. The Fisher information matrix serves as a key statistical tool here, used to quantify the amount of information contained in the sensing data, thereby evaluating the sensing accuracy.

[0088] In some embodiments, a multi-UAV collaborative network includes M single-antenna UAVs monitoring K task execution terminals on the same frequency band channel. Simultaneously, the M single-antenna UAVs also perform sensing and localization of a target object. In emergency rescue, the target object can be a rescue route, and the task execution terminal can be a rescue convoy or a rescue task execution terminal. The UAVs transmit integrated communication and sensing signals to the task execution terminals, which use these signals to generate optimal control strategies. Simultaneously, the transmitted integrated communication and sensing signals can also be radiated to the target object for sensing purposes. The position of the m-th UAV is defined as... Define the position of the terminal executing the k-th task as The position of the perceived object is s = [x s y s , z s ] T ∈R 3 This refers to sensing coordinate information. T is used to represent the matrix transpose operation. Furthermore, this application introduces a binary matrix Θ to represent the pairing relationship between the UAV and the mission execution terminal. Specifically, the (m,k)th element θ of matrix Θ... m,k ={0,1}, representing the pairing state between the m-th drone and the k-th task execution terminal, i.e., θ m,k =1 indicates that the m-th drone serves the k-th task execution terminal, otherwise it does not serve.

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

[0090]

[0091] Where c represents the speed of light, τ mThis represents the round-trip delay of the integrated communication and sensing signal between the target UAV and the sensing object. To simplify the analysis, it is assumed that the measurement of sensing distance information is affected by Gaussian noise, and d can be obtained. m The measured value is shown in the following analytical expression (2):

[0092]

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

[0094]

[0095] Among them, G p Indicates the signal processing gain of the drone. p represents the noise power of the drone. m This represents the communication power allocated to each UAV, specifically the communication power allocated to the m-th UAV. The bidirectional channel power gain between the UAV and the sensing object is determined by... express, Indicates the reference distance d m =Channel power at 1 meter, where G T G represents the transmit antenna gain of the drone. r σ represents the receiving antenna gain of the sensing object. rcs Let represent the radar cross section (RCS), and λ represent the wavelength of the signal. Therefore, the variance... It can be expressed by the following analytical expression (4):

[0096]

[0097] Wherein, ρ is a constant related to system settings, typically used to represent parameters related to measurement or noise.

[0098] Additionally, after obtaining d m or Subsequently, the direction and angle of the sensed object can be determined by analyzing the Doppler effect and angular spectrum information of the echo signal from the integrated communication and sensing signal. Further signal processing can then extract the three-dimensional geometric information of the sensed object, such as coordinates, orientation, and velocity, from the scattered and reflected signals, thus obtaining the sensed coordinate information. For example, orthogonal frequency division multiplexing (OFDM) can be used to obtain the sensed coordinate information of the sensed object, but this is not an exception.

[0099] In existing technologies, mean square error is typically used as the information for evaluating the sensing accuracy of the object being sensed, defined as... However, obtaining and minimizing the mean square error is extremely challenging and computationally difficult. Therefore, the Fisher information matrix of the perceived coordinate information is used here to evaluate the positioning performance of the target UAV, denoted as detΦ. s To calculate Φ s The Fisher information matrix of the perceived coordinate information can be converted into the Fisher information matrix of the perceived distance information between the UAV and the perceived object based on formula (1), denoted as Φ(d), where d=[d1,d2,...,d m ] T Applying the chain rule, Φ s It can be expressed by the following analytical expression (5):

[0100] Φ s =J(d)Φ(d)[J(d)] t (5)

[0101] Where J(d)∈R 3*M This indicates that in perceiving coordinate information s = [x s y s , z s ] T The Jacobian matrix of d obtained below can be expressed by the following analytical expression (6):

[0102]

[0103] According to the content of formula (2), Let represent the measured distance between the perceived object and all UAVs. And according to the content of formula (2), we can obtain... Among them Λ 2 It is a focus matrix, consisting of elements The composition can be represented by the following analytical expression (7):

[0104]

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

[0106]

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

[0108]

[0109] Where, Φ 11 Φ 22 Φ 33 Φ 12 Φ 13 and Φ 23 They can be represented by the following analytical expressions (10) to (15):

[0110]

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

[0112] In step S103 of some embodiments, each task execution terminal is modeled as a linear control system, and the state of this linear control system is monitored to obtain system monitoring information. Modeling the task execution terminal as a linear control system facilitates the description of the system state of the task execution terminal based on time-varying relationships. System monitoring information is obtained by monitoring the system state of the linear control system through the target UAV.

[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] Where, x k,n ∈R t Indicates the system status of the task execution terminal, z k,n ∈R k This indicates the control input information of the task execution terminal, v k ∈R t It is a matrix with zero mean and covariance. Control system noise, A k ∈R t*tB represents a fixed state evolution matrix. k ∈R t*k Let represent the control input matrix, and t represent the dimension of the system state.

[0116] At time point n, the k-th task execution terminal controller receives system monitoring information from the target UAV and generates the optimal control action z based on the received data from the target UAV. k,n This drives 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 corresponding control commands. Therefore, the observation equation of the UAV is expressed by the following analytical expression (17):

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

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

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

[0120] The LQR cost function is typically used to evaluate control performance. Therefore, the LQR cost function of the task execution terminal can be obtained based on 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 expression (18):

[0121]

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

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

[0124] Please see 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, obtain the control performance evaluation information and the control cost condition between the target UAV; wherein, the control cost condition is related to the minimum allowable communication rate of the mission execution terminal.

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

[0127] In step S301 of some embodiments, control performance evaluation information and control cost conditions between the target UAV and the task execution terminal are obtained based on the communication rate between the target UAV and the task execution terminal. The control cost conditions are related to the minimum allowable communication rate of the task execution terminal. This ensures that, under resource constraints, the communication rate between the UAV and the task execution terminal can meet the minimum requirements while minimizing the control cost of the UAV to the task execution terminal, thereby ensuring the stability of communication between the UAV and the task execution terminal and ensuring that the communication rate does not become a bottleneck for improving the performance of multi-UAV cooperative networks.

[0128] It should be noted that, according to analytical equation (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 correlate the LQR cost function, i.e., the control performance evaluation information, with the communication performance of the UAV. To do this, it is necessary to first obtain the communication rate between the UAV and the mission execution terminal.

[0129] Please see Figure 4 In some embodiments, steps S401 to S403 may be included, but are not limited to, before step S301:

[0130] Step S401: Obtain the control distance information of each target UAV relative to each task execution terminal and the communication allocation power of the target UAV for communication;

[0131] Step S402: Based on the control distance information, obtain the signal-to-interference-to-noise ratio information between each target UAV and its corresponding task execution terminal;

[0132] Step S403: Based on the signal-to-interference-to-noise ratio information and communication allocation power, perform a communication rate evaluation based on finite code length transmission to obtain 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 task execution terminal and communication allocation power of the target UAV for communication are obtained. The control distance information represents the physical distance between the target UAV and the task execution terminal, while the communication allocation power represents the power resources allocated to the communication task.

[0134] In step S402 of some embodiments, the signal-to-interference-plus-noise ratio (SINR) information between each target UAV and its corresponding mission execution terminal is calculated based on the control distance information. The SINR information includes the signal-to-interference-plus-noise ratio (SINR), a key indicator for measuring 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 evaluated, providing a basis for subsequent communication rate assessment.

[0135] In step S403 of some embodiments, a communication rate assessment based on finite code length transmission is performed according to the signal-to-interference-to-noise ratio information and communication allocation power to obtain the communication rate between each target UAV and each mission execution terminal. The finite code length transmission theory takes into account the influence of finite block lengths in actual communication, providing a more accurate rate assessment, thereby ensuring that the calculation of the communication rate is accurate and reliable under limited resources and practical conditions.

[0136] In some embodiments, after the UAV observes the system monitoring information of the task execution terminal, the UAV transmits the system monitoring information to the task execution terminal through the integrated communication and sensing signal. The distance between the m-th UAV and the k-th task execution terminal is defined as shown in the following analytical expression (19):

[0137]

[0138] Considering the line-of-sight link channel, the communication model can be represented by the free space path loss model. Thus, the channel gain between the m-th UAV and the k-th task execution terminal can be expressed as the following analytical equation (20):

[0139]

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

[0141] If the k-th task execution terminal is served by the m-th UAV, then the signal-to-interference-plus-noise ratio (SINR) at the k-th task execution terminal can be expressed by the following analytical formula (21):

[0142]

[0143] Where, p m This represents the communication power allocated to each drone, ∑ i∈M,i≠m p i h i,k It is co-channel interference. This represents the noise power of the terminal executing the k-th task.

[0144] The communication rate between the m-th UAV and the k-th task execution terminal can be expressed by the following analytical expression (22):

[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 on 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 m-th UAV and the k-th task execution terminal is rewritten as shown in the following analytical expression (23):

[0147]

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

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

[0150] Steps S401 to S403 systematically evaluate the communication rate between the target UAV and the mission execution terminal. This ensures the accuracy of the communication evaluation and provides 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 control cost conditions, control performance evaluation information, and perception accuracy evaluation information. By comprehensively considering both communication and perception—two key aspects—and also taking into account control cost conditions related to communication rate, more reasonable constraints are provided for the target optimization problem. This ensures that the solution process for the target optimization problem not only focuses on the control performance of the multi-UAV cooperative network on the task execution terminal and the perception accuracy of the perceived object, but also considers the minimum communication rate requirements between the UAV and the task execution terminal, thereby finding the optimal solution under reasonable constraints.

[0152] To improve the communication and perception performance of a multi-UAV collaborative network under limited resources, the resource consumption (or control cost) of the target UAV's control performance should be minimized, while maximizing the target UAV's perception accuracy. To minimize the resource consumption of the target UAV's control performance, the data throughput received by the k-th task execution terminal should meet the minimum communication rate, i.e., the control cost condition, which can be expressed by the following analytical expression (25):

[0153]

[0154] Where B represents the channel bandwidth, L k It can be represented by the following analytical expression (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 a 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 and is a constraint parameter set to ensure that the linear control system can operate stably at the minimum communication rate. k ) min The minimum LQR cost achievable without communication constraints can be expressed by the following analytical expression (27):

[0157]

[0158] Analytical equations (25) and (26) describe the constraint relationship between communication rate and LQR cost, thereby ensuring that the communication rate meets the minimum requirements. This ensures that the communication rate will not become a bottleneck for the UAV to control the mission execution terminal when the linear control system of the mission execution terminal is operating stably. Furthermore, the correlation between the UAV and the mission execution terminal at the communication and control levels is established, allowing variables related to communication rate and 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 The solution to the algebraic Riccati equation, where Q k The cost used to balance the deviation of the linear control system's state from the desired target state of zero is a weighted matrix of states, and R... k The cost used to balance the energy consumption of a linear control system is the control cost matrix, A. k B represents a fixed state evolution matrix (or state transition matrix). k C represents the control input matrix. k S represents the observation matrix. k M is the covariance matrix of the states. k ∑k is the control gain matrix, and ∑k is the covariance matrix of the state estimate, which reflects the uncertainty of the state.

[0160] Specifically, S k and M k The solutions to the Riccati equation are expressed by the following analytical expressions (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 expression (30):

[0164]

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

[0166]

[0167] And K k The Kalman filter gain can be expressed by the following analytical expression (32):

[0168]

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

[0170]

[0171] These algebraic Riccati equations are solved iteratively using numerical methods, continuously updating the equations until they converge within a predetermined tolerance range.

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

[0173] Step S501: Normalize the control performance evaluation information and the perception accuracy evaluation information to obtain the normalization factor and weight coefficient.

[0174] Step S502: Based on the weighting coefficients and normalization factors, construct the difference function between the control performance evaluation information and the perception accuracy evaluation information, and determine it as the objective 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 normalization factors and weighting coefficients. The purpose of normalization processing is to eliminate the dimensional differences between different evaluation information, so that the control performance evaluation information and the perception accuracy evaluation information can be compared on the same scale. The weighting coefficients are used to balance the relative importance of control performance and perception accuracy in multi-UAV cooperative networks, and are usually set according to the specific application scenario and processing task type.

[0176] In step S502 of some embodiments, a difference function between control performance evaluation information and perception accuracy evaluation information is constructed based on weighting coefficients and normalization factors, and this difference function is identified as the objective optimization problem. The 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 multi-UAV cooperative networks, ensuring that the system can achieve optimal performance balance with limited resources, thereby significantly improving the efficiency and stability of multi-UAV cooperative networks.

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

[0178]

[0179] Where b = [b1,...,b k ] T Ψ represents the LQR cost vector of a linear control system, where η∈[0,1] are weighting coefficients. A larger η value indicates that control performance is prioritized over positioning accuracy. c and Ψ s These are the normalization factors for control performance evaluation information and perception accuracy evaluation information, respectively, representing ∑ k∈K b k and detΦ s The upper bound of P. max The maximum transmission power is represented by (34b)-(34c), which represent the power budget constraint. (34d)-(34f) are used to ensure that the pairing between the UAV and the mission execution terminal meets the specified criteria, that is, each UAV can 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 matrix Θ is... m,k ={0,1}, representing the pairing state between the m-th drone and the k-th task execution terminal, i.e., θ m,k =1 indicates that the m-th UAV serves the k-th task execution terminal, otherwise it does not serve. (34g)-(34h) corresponds to the collision avoidance and flight boundary constraints of the UAV, where dmin Let represent the minimum permissible distance between any two UAVs, and D be the permissible flight area for each UAV. (34i) is a previously determined control cost condition used to ensure that the communication rate meets the minimum requirements.

[0180] The objective optimization problem constructed by (34a)-(34i) is a non-convex problem involving mixed binary and continuous variables, making it difficult to obtain the 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 multi-UAV cooperative networks, the target optimization problem is usually a complex multivariate optimization problem. In order to effectively solve this problem, it is usually necessary to decompose the complex optimization problem into several sub-problems, each of which can be handled independently, thereby reducing the complexity of the problem and using appropriate optimization algorithms to process them, thus providing an efficient optimization solution for multi-UAV cooperative networks.

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

[0183] Step S601: Based on the control performance evaluation information and the perception accuracy evaluation information, an influencing factor analysis is performed to obtain the basic influencing factors. 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: Based on the fundamental influencing factors, the target optimization problem is decomposed into a pairing sub-problem, a power allocation sub-problem, and a UAV position sub-problem;

[0185] Step S603: Alternate optimization processes are performed based on the pairing sub-problem, power allocation sub-problem, and UAV position sub-problem until the preset convergence condition is met, and the target optimization information is obtained.

[0186] In step S601 of some embodiments, an influencing factor analysis is performed based on control performance evaluation information and perception accuracy evaluation information to extract fundamental influencing factors. Through analysis, these fundamental 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. These factors are key variables affecting the overall system performance; by analyzing them, the direction and focus of optimization can be clarified.

[0187] In step S602 of some embodiments, based on these fundamental influencing factors, the complex objective optimization problem is decomposed into three sub-problems: a pairing sub-problem, a power allocation sub-problem, and a UAV position sub-problem. This decomposition method helps reduce the complexity of the problem, allowing each sub-problem to be handled independently and optimized alternately to obtain a solution to the objective optimization problem. The pairing sub-problem focuses on the optimal pairing relationship between the UAV and the mission execution terminal, the power allocation sub-problem focuses on the rational allocation of communication power, and the UAV position sub-problem focuses on the optimal flight position of the UAV.

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

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

[0190] Specifically, this application preliminarily optimizes the LQR cost and correlates it 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. From this, we can obtain the LQR relative to Θ, p, and {q}. m The closed-form solution of the optimal LQR cost b of} is denoted as b. * If given any Θ, p, and {q} m The closed-form expression for the optimal LQR cost in analytical equation (34a) is (35):

[0191]

[0192] in,

[0193]

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

[0195]

[0196] Understandably, the right side L of (37b) k It is b k b is a monotonically decreasing function, therefore b kA feasible solution is achieved by taking the equality sign in (37b) to minimize ∑ k∈K b k .

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

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

[0199]

[0200] Therefore, the solution can be based on the fundamental 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 objective optimization problem is broken down into three sub-problems: the pairing sub-problem, the power allocation sub-problem, and the UAV positioning sub-problem. (See also...) Figure 7 In some embodiments, step S603 may include, but is not limited to, steps S701 to S704:

[0202] Step S701: Based on the existing communication allocation power and the existing flight position, solve the pairing problem to obtain the updated pairing relationship;

[0203] Step S702: Based on the updated pairing relationship and the existing flight positions, solve the power allocation subproblem to obtain the updated communication allocation power;

[0204] Step S703: Based on the updated pairing relationship and the updated communication allocation power, solve the UAV position subproblem to obtain the updated flight position;

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

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

[0207]

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

[0209]

[0210] To solve the pairing sub-problem of the pairing relationship between UAVs and mission execution terminals, we first analyze the function b. k The concavity and convexity properties of (Θ). For simplification, its expression can be rewritten as (41):

[0211]

[0212] in,

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

[0214]

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

[0216]

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

[0218] However, due to the constraints of (39d), a direct solution remains very difficult. Therefore, it is possible to solve for θ... m,k Transformed into an equivalent continuous form, the pairing subproblem can be equivalently transformed into (44a)-(44d):

[0219]

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

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

[0222]

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

[0224]

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

[0226] in,

[0227]

[0228] Here, μ is a non-negative penalty parameter, which can be used to solve the pairing subproblem (46a)-(46b) using a penalty-based DC optimization algorithm. First, the penalty parameter, optimization variables, 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 the 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 stops and the final matching result is output, obtaining the updated pairing information.

[0229] In step S702 of some embodiments, the power allocation subproblem is solved based on the updated pairing relationship and the existing flight position, 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 linearly constrained optimization problem, but (49) is non-convex. Therefore, the optimal value can be solved by the projective 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 updated power value is ensured to meet the constraint conditions through projection operation. 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, the UAV position subproblem is solved based on the updated pairing relationship and the updated communication allocation power, which can be expressed as (50a)-(50c):

[0235]

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

[0237]

[0238] Due to the nonconvexity of (50a) and the existence of constraint (50b), the UAV position subproblem is difficult to solve. Therefore, we adopt a continuous convex approximation method, using a first-order Taylor expansion to approximate the solution of the nonconvex terms. Constraint (50b) is about q. m and q r The convex function has a lower bound of (52):

[0239]

[0240] in, and Let represent the approximate positions of the m-th and r-th UAVs in the a2-th iteration, respectively.

[0241] It is important to note that the function detΦ(s) involves 3D coordinates. These coordinates are coupled to each other, which significantly increases the complexity of optimizing the UAV position. To simplify the optimization, we reformulate equations (10)-(15) as follows: m A function, not just coordinates. The restated expression is (53):

[0242]

[0243] Then, (50a) at the given feasible point Using a first-order Taylor expansion for approximation, we obtain formula (54):

[0244]

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

[0246]

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

[0248] Thus, standard convex optimization tools can be used to solve the problem and obtain an approximate optimal solution, resulting in 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 UAVs in the multi-UAV cooperative network are adjusted.

[0250] Through steps S701 to S704, the optimal solution to the target optimization problem can be gradually approximated. 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 and perception performance of the multi-UAV cooperative network is improved, thus achieving optimization of the multi-UAV cooperative network.

[0251] In step S107 of some embodiments, based on the target optimization information, at least one of the following operations is performed: updating the task execution terminal served by the target UAV, updating the location of the target UAV, and updating the communication allocation power used by the target UAV for communication, thereby dynamically adjusting the configuration of the UAV to improve the overall performance of the system. Updating the task execution terminal served by the target UAV reassigns the pairing relationship between the UAV and the task execution terminal. By optimizing the pairing relationship, it can be ensured that each UAV can maximize its role within its capabilities, improving the efficiency and accuracy of task execution. Updating the location of the target UAV adjusts the deployment location of the UAV to optimize coverage and communication quality. Updating the communication allocation power used by the target UAV adjusts the transmission power of the UAV for communication based on the current communication environment and task requirements to optimize communication efficiency, thereby affecting the control performance of the task execution terminal. In summary, by dynamically adjusting the service targets, locations, and communication power of the UAVs, multi-UAV collaborative networks can achieve better communication perception performance.

[0252] This application embodiment uses communication sensing technology to perform position sensing on a sensing object, obtaining the sensing coordinate information of the sensing object; based on the sensing coordinate information, it performs sensing accuracy assessment, obtaining sensing accuracy assessment information; it models each task execution terminal as a linear control system and performs state monitoring on the linear control system, obtaining system monitoring information; based on the system monitoring information, it performs control performance assessment, obtaining control performance assessment information of the task execution terminal; based on the control performance assessment information and the sensing accuracy assessment information, it determines a target optimization problem; based on the target optimization problem, it performs problem decomposition and optimization processing, obtaining target optimization information; based on the target optimization information, it performs at least one of the following operations: 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. Therefore, this application determines the target optimization problem by obtaining sensing accuracy assessment information and control performance assessment information, and obtains target optimization information by decomposing and optimizing the target optimization problem, thereby updating the relevant parameters of the UAV, thus improving the communication sensing performance of a multi-UAV cooperative network under limited resources, and achieving optimization of the multi-UAV cooperative network.

[0253] Please see Figure 8 This application also provides a multi-UAV sensing and communication device that can implement the above-described multi-UAV sensing and communication method. The device includes:

[0254] The position awareness module is used to perceive the position of the object based on communication sensing technology and obtain the sensing coordinate information of the object.

[0255] The perception accuracy assessment module is used to assess perception accuracy based on perception coordinate information and obtain perception accuracy assessment information.

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

[0257] The control performance evaluation module is used to evaluate the control performance based on system monitoring information and obtain the control performance evaluation information of the task execution terminal.

[0258] The optimization problem determination module is used to determine the target optimization problem based on control performance evaluation information and perception accuracy evaluation information.

[0259] The optimization problem processing module is used to decompose and optimize the target optimization problem to obtain target optimization information.

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

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

[0262] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned multi-UAV sensing and communication method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0263] Please see Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0264] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, 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 this application.

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

[0266] The input / output interface 903 is used to implement information input and output;

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

[0268] Bus 905 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, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0270] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described multi-UAV perception and communication method.

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

[0272] The multi-UAV sensing and communication method, apparatus, electronic device, and storage medium provided in this application embodiment perform position sensing of the sensing object based on communication sensing technology to obtain the sensing coordinate information of the sensing object; perform sensing accuracy evaluation based on the sensing coordinate information to obtain sensing accuracy evaluation 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 evaluation based on the system monitoring information to obtain control performance evaluation information of the task execution terminal; determine the target optimization problem based on the control performance evaluation information and the sensing accuracy evaluation 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: update the task 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. Therefore, this application determines the target optimization problem by obtaining sensing accuracy evaluation information and control performance evaluation information, and obtains target optimization information by decomposing and optimizing the target optimization problem, thereby updating the relevant parameters of the UAV, thus improving the communication sensing performance of the multi-UAV cooperative network under limited resources, and achieving optimization of the multi-UAV cooperative network.

[0273] The embodiments described in this application are for the purpose of more clearly illustrating 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. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by 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 this application, and may include more or fewer steps than shown, or combine 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; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

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

[0277] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0278] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

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

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

[0281] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0282] If the integrated unit is implemented as 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 this application, in essence, 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. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0283] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A multi-UAV sensing and communication method, characterized in that, A target drone in a multi-drone collaborative network, the multi-drone collaborative network also includes a sensing object and multiple task execution terminals, each drone can serve at most one task execution terminal, and each task execution terminal can only be served by one drone, the method includes: The location of the sensing object is perceived based on communication sensing technology, and the sensing coordinate information of the sensing object is obtained. Based on the perceived coordinate information, the perception accuracy is evaluated to obtain perception accuracy evaluation information. Each task execution terminal is modeled as a linear control system, and the state of the linear control system is monitored to obtain system monitoring information; Based on the system monitoring information, a control performance evaluation is performed to obtain the control performance evaluation information of the task execution terminal. The target optimization problem is determined based on the control performance evaluation information and the perception accuracy evaluation information. The step of determining the target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information includes: obtaining the control cost condition between the control performance evaluation information and the target UAV based on the communication rate between the target UAV and the task execution terminal; wherein the control cost condition is related to the minimum allowable communication rate of the task execution terminal; and determining the target optimization problem based on the control cost condition, the control performance evaluation information, and the perception accuracy evaluation information. Before obtaining the control performance evaluation information and the control cost condition between the target UAV and the task execution terminal based on the communication rate between the target UAV and the task execution terminal, the process includes: obtaining control distance information of each target UAV relative to each task execution terminal and the communication allocation power of the target UAV for communication; obtaining signal-to-interference-plus-noise ratio (SNR) information between each target UAV and the corresponding task execution terminal based on the control distance information; and performing a communication rate evaluation based on finite code length transmission according to the SNR information and the communication allocation power to obtain the communication rate between each target UAV and each task execution terminal. Based on the target optimization problem, the problem is decomposed and optimized to obtain target optimization information; Based on the target optimization information, perform at least one of the following operations: update the task execution terminal served by the target UAV, update the location of the target UAV, and update the communication allocation power used by the target UAV for communication.

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

3. The method according to claim 1, characterized in that, The process of determining the target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information includes: Based on the control performance evaluation information and the perception accuracy evaluation information, normalization is performed to obtain the normalization factor and weight coefficient. Based on the weighting coefficients and the normalization factor, a difference function is constructed between the control performance evaluation information and the perception accuracy evaluation information, which is then used to determine the target optimization problem.

4. The method according to claim 1, characterized in that, The process of decomposing and optimizing the target optimization problem to obtain target optimization information includes: Based on the control performance evaluation information and the perception accuracy evaluation information, an influencing factor analysis is performed to obtain basic influencing factors, including the communication allocation power of the target UAV for communication, the pairing relationship between the target UAV and the task execution terminal, and the flight position of the target UAV. Based on the aforementioned fundamental influencing factors, the target optimization problem is decomposed into a pairing sub-problem, a power allocation sub-problem, and a UAV position sub-problem. The pairing subproblem, the power allocation subproblem, and the UAV position subproblem are alternately optimized until a preset convergence condition is met, thereby obtaining the target optimization information.

5. The method according to claim 4, characterized in that, The process of alternately optimizing the pairing sub-problem, the power allocation sub-problem, and the UAV position sub-problem until a preset convergence condition is met, thereby obtaining the target optimization information, specifically includes: Based on the existing communication allocation power and the existing flight position, the pairing sub-problem is solved to obtain the updated pairing relationship; Based on the updated pairing relationship and the existing flight positions, the power allocation subproblem is solved to obtain the updated communication allocation power; Based on the updated pairing relationship and the updated communication allocation power, the UAV position subproblem is solved 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.

6. A multi-UAV sensing and communication device, characterized in that, A target drone in a multi-drone collaborative network, the multi-drone collaborative network also includes a sensing object and multiple task execution terminals, each drone can serve at most one task execution terminal, and each task execution terminal can only be served by one drone, the device comprising: The location awareness module is used to perform location awareness on the sensing object based on communication sensing technology, and obtain the sensing coordinate information of the sensing object; The perception accuracy assessment module is used to assess the perception accuracy based on the perception coordinate information and obtain perception accuracy assessment information. The status monitoring module is used to model each of the task execution terminals as a linear control system and to monitor the status of the linear control system to obtain system monitoring information. The control performance evaluation module is used to perform control performance evaluation based on the system monitoring information to obtain the control performance evaluation information of the task execution terminal. An optimization problem determination module is used to determine a target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information. The determination of the target optimization problem based on the control performance evaluation information and the perception accuracy evaluation information includes: obtaining a control cost condition between the control performance evaluation information and the target UAV based on the communication rate between the target UAV and the task execution terminal; wherein the control cost condition is related to the minimum allowable communication rate of the task execution terminal; determining the target optimization problem based on the control cost condition, the control performance evaluation information, and the perception accuracy evaluation information; before obtaining the control cost condition between the control performance evaluation information and the target UAV based on the communication rate between the target UAV and the task execution terminal, the module includes: acquiring control distance information of each target UAV relative to each task execution terminal and the communication allocation power of the target UAV for communication; obtaining signal-to-interference-plus-noise ratio (SNR) information between each target UAV and the corresponding task execution terminal based on the control distance information; and performing a communication rate evaluation based on finite code length transmission according to the SNR information and the communication allocation power to obtain the communication rate between each target UAV and each task execution terminal. 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; The UAV status update module is used to perform at least one of the following operations based on the target optimization information: update the task execution terminal served by the target UAV, update the location of the target UAV, and update the communication allocation power used by the target UAV for communication.

7. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the multi-UAV sensing and communication method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-UAV perception and communication method according to any one of claims 1 to 5.

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