Covert communication processing method and device of unmanned aerial vehicle, equipment and storage medium

Abstract

The invention provides a covert communication processing method and device of an unmanned aerial vehicle, equipment and a storage medium. The method comprises the steps of obtaining a communication object of covert communication of the unmanned aerial vehicle; establishing an unmanned aerial vehicle covert communication scene according to the communication object; determining an unmanned aerial vehicle covert communication channel model according to the unmanned aerial vehicle covert communication scene; establishing a concealment constraint model according to the unmanned aerial vehicle concealed communication channel model; according to the unmanned aerial vehicle covert communication channel model and the concealment constraint model, taking maximization of the minimum user communication demand satisfaction rate as a target to construct a joint optimization problem; wherein the joint optimization problem comprises beam forming, trajectory planning and antenna position; according to the joint optimization problem, determining a target unmanned aerial vehicle trajectory, a target beam assignment vector and a target antenna position for the communication object; the effect of improving the covert communication transmission capability is achieved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technologies, and in particular, to a method, apparatus, device, and storage medium for covert communication processing of unmanned aerial vehicles (UAVs). Background Art

[0002] UAVs can achieve on-demand and flexible communication coverage due to their three-dimensional position controllability and are widely used. However, the open air-ground transmission environment is prone to wireless communication security problems, and in an adversarial scenario, the transmission behavior may be exposed and even lead to physical attacks on the target. Currently, most UAV covert communication methods are based on single-antenna technology, and the trajectory is designed and the transmission power is adjusted to ensure covertness. However, reducing the transmission power will affect the signal quality of legitimate users. Therefore, developing a more high-performance UAV covert communication processing method has become a promising direction.

[0003] In the prior art, the UAV covert communication processing method mainly utilizes the multi-input multi-output (MIMO) technology with beamforming capabilities to further improve the spectral efficiency of the communication system while ensuring the covertness of the system.

[0004] However, the prior art methods mainly adopt a fixed and discretely deployed multi-antenna system, which seriously hinders the full utilization of the degrees of freedom of the antenna array elements in the continuous spatial domain, thus affecting the communication performance, and there is a technical problem of poor UAV covert communication transmission ability. Summary of the Invention

[0005] The present application provides a method, apparatus, device, and storage medium for UAV covert communication processing to achieve the effect of improving the UAV covert communication transmission ability.

[0006] In a first aspect, the present application provides a method for UAV covert communication processing, including:

[0007] Obtaining a communication object for UAV covert communication; wherein the communication object includes multiple users, a UAV configured with multiple movable antennas, and a listener;

[0008] Establishing a UAV covert communication scenario according to the communication object;

[0009] Determining a UAV covert communication channel model according to the UAV covert communication scenario;

[0010] Establishing a covertness constraint model according to the UAV covert communication channel model; wherein the covertness constraint model is used to constrain the probability of incorrect detection by the listener;

[0011] Based on the UAV covert communication channel model and the covertness constraint model, a joint optimization problem is constructed with the goal of maximizing the minimum user communication demand satisfaction rate; among them, the joint optimization problem includes beamforming, trajectory planning, and antenna position.

[0012] According to the joint optimization problem, determine the target UAV trajectory, the target beam assignment vector, and the target antenna position for the communication object.

[0013] In a possible implementation manner, according to the UAV covert communication scenario, determine the UAV covert communication channel model, including:

[0014] Define the UAV trajectory and the antenna position according to the UAV covert communication scenario.

[0015] Determine the channel vector from the UAV to the ground node according to the UAV trajectory and the antenna position.

[0016] Based on the channel vector, establish the UAV covert communication channel model, and based on the UAV covert communication channel model, determine the user data traffic during the entire flight period of the UAV.

[0017] In a possible implementation manner, according to the UAV covert communication channel model, establish the covertness constraint model, including:

[0018] Establish a binary hypothesis testing model according to the UAV covert communication channel model; among them, the binary hypothesis testing model is used to simulate the detection of whether the listener establishes a transmission for the base station.

[0019] Determine the probability that the listener misdetects the communication behavior between the UAV and the user according to the binary hypothesis testing model; among them, the probability includes the maximum correct detection probability and the minimum misdetection probability.

[0020] Establish the covertness constraint model according to the probability.

[0021] In a possible implementation manner, according to the UAV covert communication channel model and the covertness constraint model, with the goal of maximizing the minimum user communication demand satisfaction rate, construct a joint optimization problem, including:

[0022] According to the UAV covert communication channel model and the covertness constraint model, with the goal of maximizing the minimum user communication demand satisfaction rate, determine multiple joint optimization constraints.

[0023] Construct a joint optimization problem according to the multiple joint optimization constraints.

[0024] In a possible implementation, the joint optimization constraints include the user communication requirement satisfaction rate constraint, the total UAV transmission power constraint, the first constraint on the UAV's horizontal speed, the second constraint on the UAV's horizontal speed, the UAV's altitude constraint, the UAV's starting and ending point constraints, the moving area constraint of the movable antenna, the distance constraint between any two movable antennas, and the concealment constraint.

[0025] In a possible implementation, according to the joint optimization problem, determining the target UAV trajectory, the target beam assignment vector, and the target antenna position for the communication object includes:

[0026] Determining the first optimization sub-problem of UAV transmission beamforming according to the joint optimization problem;

[0027] Determining the second optimization sub-problem of the UAV trajectory according to the joint optimization problem;

[0028] Determining the third optimization sub-problem of the movable antenna position according to the joint optimization problem;

[0029] Iteratively optimizing the joint optimization problem, and calculating the first optimization sub-problem, the second optimization sub-problem, and the third optimization sub-problem during the iteration until the target UAV trajectory, the target beam assignment vector, and the target antenna position for the communication object are obtained.

[0030] In a possible implementation, after determining the target UAV trajectory, the target beam assignment vector, and the target antenna position for the communication object according to the joint optimization problem, it further includes:

[0031] Controlling the covert communication of the UAV according to the target UAV trajectory, the target beam assignment vector, and the target antenna position.

[0032] In a second aspect, the present application provides a covert communication processing device for a UAV, including:

[0033] An acquisition module, configured to acquire the communication object of the UAV's covert communication; wherein, the communication object includes multiple users, a UAV configured with multiple movable antennas, and a listener;

[0034] A first establishment module, configured to establish a UAV covert communication scenario according to the communication object;

[0035] A first determination module, configured to determine a UAV covert communication channel model according to the UAV covert communication scenario;

[0036] A second establishment module, configured to establish a concealment constraint model according to the UAV covert communication channel model; wherein, the concealment constraint model is used to constrain the listener's false detection probability;

[0037] A construction module, which is used to construct a joint optimization problem aiming at maximizing the minimum user communication demand satisfaction rate according to the UAV covert communication channel model and the concealment constraint model; wherein, the joint optimization problem includes beamforming, trajectory planning and antenna position.

[0038] A second determination module, which is used to determine the target UAV trajectory, the target beam assignment vector and the target antenna position for the communication object according to the joint optimization problem.

[0039] In a possible implementation manner, the first determination module is further used for:

[0040] Define the UAV trajectory and the antenna position according to the UAV covert communication scenario.

[0041] Determine the channel vector from the UAV to the ground node according to the UAV trajectory and the antenna position.

[0042] Establish a UAV covert communication channel model according to the channel vector, and determine the user data traffic during the entire flight period of the UAV based on the UAV covert communication channel model.

[0043] In a possible implementation manner, the second establishment module is further used for:

[0044] Establish a binary hypothesis testing model according to the UAV covert communication channel model; wherein, the binary hypothesis testing model is used to simulate the detection of whether the listener establishes a transmission for the base station.

[0045] Determine the probability that the listener misdetects the communication behavior between the UAV and the user according to the binary hypothesis testing model; wherein, the probability includes the maximum correct detection probability and the minimum misdetection probability.

[0046] Establish a concealment constraint model according to the probability.

[0047] In a possible implementation manner, the construction module is further used for:

[0048] Determine multiple joint optimization constraints aiming at maximizing the minimum user communication demand satisfaction rate according to the UAV covert communication channel model and the concealment constraint model.

[0049] Construct a joint optimization problem according to the multiple joint optimization constraints.

[0050] In a possible implementation manner, the joint optimization constraints used by the construction module include the user communication demand satisfaction rate constraint, the total UAV transmission power constraint, the first constraint on the UAV horizontal speed, the second constraint on the UAV horizontal speed, the UAV altitude constraint, the UAV start point and end point constraint, the moving area constraint of the movable antenna, the distance constraint between any two movable antennas, and the concealment constraint.

[0051] In a possible implementation manner, the second determination module is further configured to:

[0052] Determine a first optimization sub-problem of the UAV transmission beamforming according to the joint optimization problem;

[0053] Determine a second optimization sub-problem of the UAV trajectory according to the joint optimization problem;

[0054] Determine a third optimization sub-problem of the position of the movable antenna according to the joint optimization problem;

[0055] Iteratively optimize the joint optimization problem, and calculate the first optimization sub-problem, the second optimization sub-problem and the third optimization sub-problem during the iteration until the target UAV trajectory, the target beam assignment vector and the target antenna position for the communication object are obtained.

[0056] In a possible implementation manner, the second determination module is further configured to:

[0057] Control the covert communication of the UAV according to the target UAV trajectory, the target beam assignment vector and the target antenna position.

[0058] In a third aspect, the present application provides a covert communication processing device for a UAV, including: a memory, a processor;

[0059] The memory stores computer execution instructions;

[0060] The processor executes the computer execution instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementation manners of the first aspect.

[0061] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer execution instructions are stored, and when the computer execution instructions are executed by a processor, they are used to implement the above first aspect and / or various possible implementation manners of the first aspect.

[0062] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the above first aspect and / or various possible implementation manners of the first aspect.

[0063] A method, device, equipment, and storage medium for covert communication processing of an unmanned aerial vehicle provided by this application. By obtaining the communication objects of the unmanned aerial vehicle's covert communication, it clarifies the involved multiple users, the unmanned aerial vehicle with multiple movable antennas, and the listener, laying a foundation for subsequent processing and clearly defining the constituent objects of the system model. Establish a covert communication scenario of the unmanned aerial vehicle according to the communication objects. This scenario is an abstract simulation of the actual communication environment, providing a specific framework for subsequent analysis and facilitating various studies in this scenario. Determine the unmanned aerial vehicle's covert communication channel model based on the communication scenario. The channel model can accurately describe the transmission characteristics of signals among the unmanned aerial vehicle, users, and listeners, and is the key basis for establishing the subsequent constraint model and optimization problem. Establish a concealment constraint model based on the channel model. This model is used to constrain the probability of incorrect detection by the listener, ensuring that the communication behavior between the unmanned aerial vehicle and the user is not easily detected by the listener and enhancing the concealment of communication. Combine the channel model and the concealment constraint model to construct a joint optimization problem with the goal of maximizing the minimum user communication demand satisfaction rate. This problem comprehensively considers beamforming, trajectory planning, and antenna position, aiming to achieve a balanced optimization of communication performance and concealment. Finally, according to the joint optimization problem, determine the target unmanned aerial vehicle trajectory, the target beam assignment vector, and the target antenna position. By optimizing these parameters, the reliability and concealment of communication are enhanced, achieving the effect of improving the transmission ability of the unmanned aerial vehicle's covert communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with this application and, together with the specification, are used to explain the principles of this application.

[0065] Figure 1 Schematic diagram of an application data processing system architecture provided for an embodiment of this application;

[0066] Figure 2 Schematic flow of the method for processing covert communication of an unmanned aerial vehicle provided for an embodiment of this application Figure 1 ;

[0067] Figure 3 Schematic flow of the method for processing covert communication of an unmanned aerial vehicle provided for an embodiment of this application Figure 2 ;

[0068] Figure 4 Schematic diagram of a covert communication system model enhanced by movable antennas provided for an embodiment of this application;

[0069] Figure 5 Schematic flow of the method for processing covert communication of an unmanned aerial vehicle provided for an embodiment of this application Figure 3 ;

[0070] Figure 6 Schematic flow of the method for processing covert communication of an unmanned aerial vehicle provided for an embodiment of this applicationFigure 4 ;

[0071] Figure 7 Schematic structural diagram of the covert communication processing device of the unmanned aerial vehicle provided by the embodiment of the present application;

[0072] Figure 8 Schematic structural diagram of the covert communication processing equipment of the unmanned aerial vehicle provided by the embodiment of the present application.

[0073] Through the above drawings, specific embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and the written description are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Specific Embodiments

[0074] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0075] Since the existing technology methods mainly adopt a fixed and discretely deployed multi-antenna system, which seriously hinders the full utilization of the degrees of freedom of the antenna array elements in the continuous spatial domain, thereby affecting the communication performance, there is a technical problem of poor covert communication transmission ability of the unmanned aerial vehicle.

[0076] In view of the above problems, the embodiments of the present application provide a method, apparatus, device, and storage medium for covert communication processing of an unmanned aerial vehicle (UAV). By obtaining the communication objects of the UAV's covert communication, the UAV involving multiple users, configured with multiple movable antennas, and the eavesdropper are identified, laying a foundation for subsequent processing and clearly defining the constituent objects of the system model. According to the communication objects, a covert communication scenario of the UAV is established. This scenario is an abstract simulation of the actual communication environment, providing a specific framework for subsequent analysis and facilitating various studies in this scenario. Based on the communication scenario, a covert communication channel model of the UAV is determined. The channel model can accurately describe the transmission characteristics of signals among the UAV, users, and the eavesdropper, and is the key basis for establishing the constraint model and optimization problem subsequently. According to the channel model, a concealment constraint model is established. This model is used to constrain the probability of incorrect detection by the eavesdropper, ensuring that the communication behavior between the UAV and the user is not easily detected by the eavesdropper and enhancing the concealment of communication. Combining the channel model and the concealment constraint model, a joint optimization problem is constructed with the goal of maximizing the minimum user communication demand satisfaction rate. This problem comprehensively considers beamforming, trajectory planning, and antenna position, aiming to achieve a balanced optimization of communication performance and concealment. Finally, according to the joint optimization problem, the target UAV trajectory, the target beam assignment vector, and the target antenna position are determined. By optimizing these parameters, the reliability and concealment of communication are enhanced, achieving the effect of improving the transmission ability of the UAV's covert communication.

[0077] The following uses specific embodiments to elaborate in detail on the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems. These several specific embodiments below can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0078] Figure 1 FIG. is a schematic diagram of an application data processing system architecture provided by the embodiments of the present application, and this application data processing system is a computer device. As Figure 1 shown, the above architecture includes at least one of a data acquisition device 101, a processing device 102, and a display device 103.

[0079] It can be understood that the structure schematically shown in the embodiments of the present application does not constitute a specific limitation on the application data processing system architecture. In some other feasible embodiments of the present application, the above architecture may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or have different component arrangements, which can be specifically determined according to the actual application scenario and will not be limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0080] In the specific implementation process, the data acquisition device 101 may include an input / output interface or a communication interface. The data acquisition device 101 can be connected to the processing device through the input / output interface or the communication interface.

[0081] The processing device 102 can determine the communication objects of the UAV's covert communication, covering multiple users, a UAV configured with multiple movable antennas, and eavesdroppers. Then, based on the communication objects, construct a UAV covert communication scenario. Then, determine the communication channel model according to this scenario. Next, establish a concealment constraint model for constraining the eavesdropper's error detection probability based on the channel model. After that, combine the channel model and the concealment constraint model, and aim at maximizing the minimum user communication demand satisfaction rate to construct a joint optimization problem including beamforming, trajectory planning, and antenna position. Finally, based on the joint optimization problem, determine the target UAV trajectory, the target beam assignment vector, and the target antenna position for the communication objects, so as to improve the UAV's covert communication transmission ability.

[0082] The display device 103 can also be a touch display screen or the screen of the terminal device, which is used to receive user instructions while displaying the above content to realize interaction with the user.

[0083] It should be understood that the above processing device can be implemented by the processor reading and executing instructions in the memory, or can also be implemented by chip circuits.

[0084] In addition, the system architecture described in the embodiments of the present application is for more clearly explaining the technical solutions of the embodiments of the present application, and does not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those of ordinary skill in the art know that with the evolution of the system architecture, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0085] Figure 2 Flow diagram of the UAV's covert communication processing method provided by the embodiments of the present application Figure 1 , as Figure 2 shown, the UAV's covert communication processing method provided in this embodiment includes:

[0086] S201. Obtain the communication objects of the UAV's covert communication.

[0087] In this embodiment, the communication objects include multiple users, a UAV configured with multiple movable antennas, and eavesdroppers.

[0088] The communication objects are the basis for establishing the entire communication system. Therefore, it is necessary to first obtain the communication objects of the UAV's covert communication including multiple users, a UAV configured with multiple movable antennas, and eavesdroppers.

[0089] S202. Establish a UAV covert communication scenario according to the communication object.

[0090] According to the above communication object, construct a UAV covert communication scenario enhanced by a movable antenna. This scenario is a simulation of the actual communication environment, which places multiple users, UAVs, and eavesdroppers within a specific spatial and temporal framework, considering factors such as their relative positions and the spatial environment, providing a specific environmental background for subsequent channel modeling, constraint model establishment, etc.

[0091] S203. Determine the UAV covert communication channel model according to the UAV covert communication scenario.

[0092] In this embodiment, the UAV covert communication channel model is a mathematical model that describes the transmission characteristics of signals in the communication link.

[0093] According to the established UAV covert communication scenario above, further determine the UAV covert communication channel model. By establishing the channel model, the changing law of signals during transmission can be accurately grasped, providing a key physical basis for the subsequent establishment of the concealment constraint model and the construction of the joint optimization problem.

[0094] S204. Establish a concealment constraint model according to the UAV covert communication channel model.

[0095] In this embodiment, the concealment constraint model is used to constrain the probability of false detection by the eavesdropper.

[0096] In UAV covert communication, an important goal is to ensure that the communication is not detected by the eavesdropper. The eavesdropper will adopt various detection methods to try to discover the communication behavior of the UAV, and the goal of the concealment constraint model is to make the probability of false detection by the eavesdropper meet certain requirements. Therefore, it is necessary to establish the corresponding concealment constraint model according to the determined UAV covert communication channel model above.

[0097] S205. Construct a joint optimization problem with the goal of maximizing the minimum user communication demand satisfaction rate according to the UAV covert communication channel model and the concealment constraint model; where the joint optimization problem includes beamforming, trajectory planning, and antenna position.

[0098] After determining the channel model and the concealment constraint model, it is necessary to construct a joint optimization problem. The goal of the joint optimization problem is to maximize the minimum user communication demand satisfaction rate under the premise of meeting the concealment constraint. This is because in an actual UAV covert communication system, different users may have different communication demands, and communication resources are limited. By jointly optimizing parameters such as beamforming, trajectory planning, and antenna position, the communication demands of more users can be satisfied as much as possible while ensuring communication concealment.

[0099] S206. Determine the target UAV trajectory, target beam assignment vector, and target antenna position for the communication object according to the joint optimization problem.

[0100] According to the constructed joint optimization problem, use a suitable optimization algorithm to solve it to obtain the target UAV trajectory, target beam assignment vector, and target antenna position for the communication object. The target UAV trajectory refers to the sequence of positions of the UAV during flight, which determines the flight path and communication coverage of the UAV; the target beam assignment vector refers to the beam weights assigned to each antenna of the UAV, which determines the transmission direction and intensity of the signal; the target antenna position refers to the specific installation position of the antenna on the UAV, which affects the radiation characteristics and reception performance of the antenna. By determining these target parameters, effective control of UAV stealth communication can be achieved, enabling the UAV to efficiently meet the communication needs of users while meeting the stealth requirements.

[0101] The method for processing UAV stealth communication provided in this embodiment of the application, by obtaining the communication object of UAV stealth communication, clarifies the UAV involving multiple users, configured with multiple movable antennas, and the listener, laying a foundation for subsequent processing and clearly defining the components of the system model. Establish a UAV stealth communication scenario according to the communication object. This scenario is an abstract simulation of the actual communication environment, providing a specific framework for subsequent analysis and facilitating various studies in this scenario. Determine the UAV stealth communication channel model based on the communication scenario. The channel model can accurately describe the transmission characteristics of signals among the UAV, users, and listeners, and is the key basis for establishing the constraint model and optimization problem subsequently. Establish a stealth constraint model based on the channel model. This model is used to constrain the false detection probability of the listener, ensuring that the communication behavior between the UAV and the user is not easily detected by the listener and enhancing the stealth of communication. Combine the channel model and the stealth constraint model to construct a joint optimization problem with the goal of maximizing the minimum user communication demand satisfaction rate. This problem comprehensively considers beamforming, trajectory planning, and antenna position, aiming to achieve a balanced optimization of communication performance and stealth. Finally, according to the joint optimization problem, determine the target UAV trajectory, target beam assignment vector, and target antenna position. By optimizing these parameters, the reliability and stealth of communication are enhanced, achieving the effect of improving the UAV stealth communication transmission ability.

[0102] Figure 3 It is a flowchart of the method for processing UAV stealth communication provided in this embodiment of the application Figure 2 As Figure 3 shown, based on the above embodiment, this embodiment elaborates in detail the process of determining the UAV stealth communication channel model. The method includes:

[0103] S301. Define the UAV trajectory and antenna position according to the UAV stealth communication scenario.

[0104] Figure 4 Schematic diagram of a covert communication system model enhanced by a movable antenna provided by an embodiment of the present application. As Figure 4 shown, the covert communication scenario of the unmanned aerial vehicle enhanced by the movable antenna includes K users, 1 unmanned aerial vehicle equipped with M movable antennas, and 1 listener.

[0105] Then, the position of the m-th movable antenna can be expressed as , where C represents the size of the movable area of the antenna. Denote the set of positions of all movable antennas as .

[0106] In the embodiment of the present application, a pre-designed movable antenna array is used for covert communication during the entire flight cycle of the unmanned aerial vehicle. Assume that the entire flight cycle of the unmanned aerial vehicle is . To facilitate the design of the unmanned aerial vehicle trajectory, the entire flight cycle is discretized into time slots with a length of . Then, the position of the unmanned aerial vehicle in each time slot can approximately represent the trajectory of the unmanned aerial vehicle.

[0107] It can be understood that Figure 4 is only for reference of the display effect, not an improvement point, and does not affect the protection scope of the embodiment of the present application.

[0108] S302. Determine the channel vector from the unmanned aerial vehicle to the ground node according to the unmanned aerial vehicle trajectory and the antenna position.

[0109] Combined with the above Figure 4 , the horizontal position of the unmanned aerial vehicle in the n-th time slot can be expressed as , and the flight altitude is .

[0110] For the convenience of representation, denote the trajectory of the unmanned aerial vehicle as . Assume that the air-ground transmission channel is a Rice channel. Then, the channel vector from the unmanned aerial vehicle to the ground node can be expressed as:

[0111]

[0112]

[0113] where and are the Rice coefficient and the channel gain per unit distance, respectively. is the deterministic line-of-sight channel vector, which is expressed as:

[0114]

[0115] where is the carrier wavelength, is the normalized direction from the UAV to the ground node, and are the virtual pitch angle and azimuth angle, respectively defined as:

[0116]

[0117] S303. Establish a UAV covert communication channel model according to the channel vector, and determine the data traffic of users during the entire flight cycle of the UAV based on the UAV covert communication channel model.

[0118] Combined with the above Figure 4 , represents the non-line-of-sight component, which follows a complex Gaussian distribution, i.e., , is the spatial correlation matrix of antennas at different positions caused by random scatterers, and the element in its m1-th row and m2-th column can be expressed as:

[0119]

[0120] where .

[0121] In the n-th time slot, the signal received by user k from the UAV can be expressed as:

[0122]

[0123] where represents the Gaussian signal transmitted to user k, represents the Gaussian white noise signal, and its average power is . is the transmit beamforming vector of the UAV. For convenience, the transmit beamforming matrix is defined as , since the UAV only knows the statistical channel information between it and the users, the signal received by user k is reformulated as:

[0124]

[0125] Then, a lower bound of the achievable rate of user k in time slot n can be obtained as:

[0126]

[0127] where

[0128]

[0129] Therefore, during the entire flight cycle of the UAV, the data traffic of user k is:

[0130]

[0131] Among them, B is the system bandwidth.

[0132] For the method for processing covert communication of an unmanned aerial vehicle provided in the embodiments of the present application, first, the trajectory of the unmanned aerial vehicle and the antenna position are defined according to the covert communication scenario of the unmanned aerial vehicle. This lays the foundation for subsequent channel analysis, clarifies the key parameters of the signal propagation path, and makes the subsequent analysis more targeted. Secondly, the channel vector from the unmanned aerial vehicle to the ground node is determined according to the defined trajectory and position. This channel vector accurately describes the characteristics of the signal during the transmission process, is the key data support for establishing the channel model, and helps to accurately grasp the propagation law of the signal. Finally, a channel model is established based on the channel vector, and the data traffic of the user during the entire flight period of the unmanned aerial vehicle is determined. The channel model provides a theoretical basis for the design and optimization of the communication system, and the determination of the data traffic helps to reasonably allocate communication resources, guarantees the communication requirements of the user, and achieves the technical effect of improving the covert communication transmission ability of the unmanned aerial vehicle.

[0133] Figure 5 It is a flowchart of the method for processing covert communication of an unmanned aerial vehicle provided in the embodiments of the present application Figure 3 , as Figure 5 shown, based on the above embodiments, the process of establishing the concealment constraint model in this embodiment is described in detail, including:

[0134] S501. Establish a binary hypothesis testing model according to the covert communication channel model of the unmanned aerial vehicle.

[0135] In this embodiment, the binary hypothesis testing model is used to simulate the detection of whether the base station is transmitting established by the eavesdropper.

[0136] Specifically, assuming that the eavesdropper uses energy detection technology for binary hypothesis testing to detect whether the base station is transmitting, this binary hypothesis testing model can be modeled as:

[0137]

[0138] Among them, indicates that the base station is silent and not transmitting, indicates that the base station is transmitting. is the average power of the signal received by the eavesdropper, is the Gaussian white noise at the eavesdropper.

[0139] S502. Determine the probability that the eavesdropper misdetects the communication behavior between the unmanned aerial vehicle and the user according to the binary hypothesis testing model.

[0140] In this embodiment, the probability includes the maximum correct detection probability and the minimum false detection probability.

[0141] According to the above binary hypothesis testing model, assuming that the Gaussian white noise at the eavesdropper follows a log-uniform distribution, its probability density function is given by:

[0142]

[0143] In the above formula, μ≥1 is used to measure the uncertainty of the noise, is the rated noise power at the eavesdropper. Then, the eavesdropper can set the decision threshold for the binary hypothesis problem as:

[0144]

[0145] Accordingly, the maximum correct detection probability is given by:

[0146]

[0147] However, since the UAV only knows the statistical channel information between it and the eavesdropper, is used to measure the concealment of the UAV communication system. However, its closed-form expression is difficult to obtain. Then, Jensen's inequality is used to obtain an upper bound for it as follows:

[0148]

[0149] To ensure the high security of the system, it is necessary to ensure that the minimum false detection probability satisfies:

[0150]

[0151] where, is a very small positive number.

[0152] S503. Establish a concealment constraint model according to the probability.

[0153] According to the probability, by defining , the concealment constraint can be expressed as:

[0154]

[0155] The method for processing covert communication of an unmanned aerial vehicle (UAV) provided in the embodiments of the present application first establishes a binary hypothesis testing model based on the UAV covert communication channel model. This model can simulate the detection situation of the eavesdropper on the transmission state of the base station, providing a theoretical framework for subsequent analysis of the detection ability of the eavesdropper, making the assessment of the risks faced by covert communication more scientific and accurate. Secondly, based on the binary hypothesis testing model, the probability that the eavesdropper misdetects the communication behavior between the UAV and the user is determined. The clarification of the maximum correct detection probability and the minimum error detection probability helps to deeply understand the detection performance of the eavesdropper and provides an important basis for formulating covert communication strategies. Finally, a concealment constraint model is established according to these probabilities. This model can effectively constrain the communication behavior to ensure that the communication between the UAV and the user is carried out under the premise of meeting the concealment requirements, laying a solid foundation for subsequent joint optimization under the concealment constraint, helping to achieve more secure and reliable covert communication, and achieving the technical effect of improving the covert communication transmission ability of the UAV.

[0156] Figure 6 Schematic flow of the method for processing covert communication of an unmanned aerial vehicle provided in the embodiments of the present application Figure 4 , such as Figure 6 shown. On the basis of the above embodiments, this embodiment further elaborates on the determination process of the target UAV trajectory, the target beam assignment vector, and the target antenna position, including:

[0157] S601. According to the UAV covert communication channel model and the concealment constraint model, with the goal of maximizing the minimum user communication demand satisfaction rate, determine multiple joint optimization constraints;

[0158] In a possible implementation manner, the joint optimization constraints include the user communication demand satisfaction rate constraint, the total UAV transmission power constraint, the first constraint on the horizontal speed of the UAV, the second constraint on the horizontal speed of the UAV, the height constraint of the UAV, the start point and end point constraints of the UAV, the moving area constraint of the movable antenna, the distance constraint between any two movable antennas, and the concealment constraint.

[0159] With the goal of maximizing the minimum user communication demand satisfaction rate, construct a joint optimization problem of beamforming, trajectory planning, and antenna position for UAV covert communication enhanced by movable antennas.

[0160] S602. According to the multiple joint optimization constraints, construct a joint optimization problem.

[0161] Construct a joint optimization problem through the following formula:

[0162]

[0163] where D kis the data volume requirement of user k. C1 represents the constraint on the satisfaction rate of the user's communication requirement, and C2 represents that the total transmission power of the UAV cannot exceed p max , C3 represents that the horizontal speed of the UAV cannot exceed , C4 represents that the horizontal speed of the UAV cannot exceed , C5 represents that the altitude of the UAV cannot be lower than h min and cannot be higher than h max , C6 represents that the starting point of the UAV is q S , and the ending point is q F , C7 indicates that the movable antenna cannot exceed the moving area, and C8 indicates that the distance between any two movable antennas cannot be less than d min , C9 is the concealment constraint.

[0164] S603. Determine the first optimization sub-problem of the UAV transmission beamforming according to the joint optimization problem.

[0165] At the th iteration, given the obtained during the th iteration and , the first optimization sub-problem of the UAV transmission beamforming can be expressed as:

[0166]

[0167] It should be noted that since the above problem is a non-convex optimization problem, it is difficult to solve directly. Therefore, slack variables are introduced:

[0168]

[0169] and satisfy the following constraints:

[0170]

[0171] Then, the first optimization sub-problem can be transformed into:

[0172]

[0173] Furthermore, by expanding the third constraint in the above optimization problem, we will obtain:

[0174]

[0175] Since the left side of the above equation is a convex function, its first-order Taylor expansion is used to obtain a global lower bound of it:

[0176]

[0177] Therefore, the first optimization sub-problem can be further transformed into:

[0178]

[0179] The above problem is a convex optimization problem, and a convex optimization solver can be directly used for solving.

[0180] S604. Determine the second optimization sub-problem of the UAV trajectory according to the joint optimization problem.

[0181] At the th iteration, given the obtained during the th iteration and the obtained during the th iteration, the second optimization sub-problem of the UAV trajectory can be determined as:

[0182]

[0183] It should be noted that since the above problem is a non-convex optimization problem, the following constraints need to be introduced first for transformation:

[0184]

[0185] Among them, represents the maximum allowable change in the UAV trajectory between two consecutive iterations. Therefore, the optimization problem can be rewritten as:

[0186]

[0187] Among them,

[0188]

[0189] Since is a convex function with respect to , its first-order Taylor expansion is:

[0190]

[0191] Among them, is a constant.

[0192] To solve the second constraint in the optimization problem, adopt:

[0193]

[0194] Therefore, the second optimization sub-problem of the UAV trajectory can be relaxed to:

[0195]

[0196] The above problem is a convex optimization problem and can be directly solved using a convex optimization solver.

[0197] S605. Determine a third optimization sub - problem for the position of the movable antenna according to the joint optimization problem.

[0198] At the th iteration, given the obtained during the and th iteration, the third optimization sub - problem for the position of the movable antenna can be determined as:

[0199]

[0200] It should be noted that the above optimization problem is a highly non - convex optimization problem and is difficult to solve directly. New relaxation variables need to be introduced:

[0201]

[0202] Satisfying the following constraints:

[0203]

[0204] Furthermore, the third optimization sub - problem for the position of the movable antenna can be expressed as:

[0205]

[0206] Since this problem is still a non - convex optimization problem and is difficult to solve directly, first perform a first - order Taylor expansion on the in the first constraint:

[0207]

[0208] To handle the third constraint in the above optimization problem, the following method is adopted:

[0209]

[0210] Among them,

[0211]

[0212] Therefore, the third constraint in the optimization problem can be transformed into:

[0213]

[0214] To handle the second constraint in the above optimization problem, first make the following approximation for the in it:

[0215]

[0216] Among them,

[0217]

[0218] Then, construct an approximation function in the second constraint of the above optimization problem , which satisfies the following conditions:

[0219]

[0220] Then, according to the above two formulas, construct this approximation function as:

[0221]

[0222] Among them, The derivation is as follows:

[0223]

[0224]

[0225] Then, the second constraint of the optimization problem can be transformed into:

[0226]

[0227] Similarly, the fourth constraint of the optimization problem can be transformed into:

[0228]

[0229] To handle the C8 constraint, adopt the following method:

[0230]

[0231] Therefore, the third optimization sub-problem of the movable antenna position can be transformed into:

[0232]

[0233] The above optimization problem is a convex optimization problem and can be directly solved using a convex optimization solver.

[0234] S606. Iteratively optimize the joint optimization problem, and calculate the first optimization sub-problem, the second optimization sub-problem, and the third optimization sub-problem during the iteration until the target UAV trajectory, the target beam assignment vector, and the target antenna position for the communication object are obtained.

[0235] Iteratively solve the joint optimization problem, and during the iterative process, solve the first optimization sub-problem, the second optimization sub-problem, and the third optimization sub-problem. Denote the optimal solutions of the above-mentioned first optimization sub-problem, second optimization sub-problem, and third optimization sub-problem as:

[0236] ,

[0237] Then update the solution in the -th iteration according to the following formula:

[0238]

[0239] where is the update step size.

[0240] Repeat the above optimization steps (S603 - S605) until convergence, and finally obtain the target UAV trajectory, target beam assignment vector, and target antenna position for the communication object.

[0241] S607. Control the covert communication of the UAV according to the target UAV trajectory, target beam assignment vector, and target antenna position.

[0242] After obtaining the target UAV trajectory, target beam assignment vector, and target antenna position for the communication object, the UAV can be actually controlled to perform covert communication according to the target UAV trajectory, target beam assignment vector, and target antenna position.

[0243] Specifically, plan the UAV flight path according to the target trajectory, adjust the beam direction and intensity according to the target beam assignment vector, and deploy the antenna using the target antenna position, so as to ensure the covertness of communication while meeting the communication requirements, and achieve safe and efficient UAV covert communication.

[0244] The method for processing covert communication of an unmanned aerial vehicle (UAV) provided by an embodiment of the present application first determines multiple joint optimization constraints based on the UAV covert communication channel model and the covertness constraint model, aiming at maximizing the minimum user communication requirement satisfaction rate. These constraints comprehensively cover various factors such as communication requirements, power, speed, altitude, position, antenna movement, and covertness, providing clear and complete boundary conditions for subsequent optimization to ensure that the optimization process is carried out within a reasonable range. Secondly, a joint optimization problem is constructed according to these constraints, unifying multiple objectives into one problem framework for comprehensive analysis and solution. Further, the joint optimization problem is decomposed into three optimization sub-problems: UAV transmit beamforming, UAV trajectory, and position of movable antennas, reducing the complexity of the problem and facilitating the use of appropriate algorithms for solution respectively. Then, by iteratively optimizing the joint optimization problem and sequentially calculating the three sub-problems in the iteration, the optimal solution is gradually approached, and finally the target UAV trajectory, the target beam assignment vector, and the target antenna position are obtained, realizing the precise allocation of communication resources. Finally, the covert communication of the UAV is controlled based on these target parameters, enabling the UAV to efficiently meet the user communication requirements while satisfying the covertness requirements, effectively improving the reliability, security, and efficiency of communication, and achieving the technical effect of improving the covert communication transmission ability of the UAV.

[0245] Figure 7 It is a schematic structural diagram of a device for processing covert communication of an unmanned aerial vehicle provided by an embodiment of the present application. The device in this embodiment can be in the form of software and / or hardware. As Figure 7 shown, the device 700 for processing covert communication of an unmanned aerial vehicle provided by an embodiment of the present application includes: an acquisition module 701, a first establishment module 702, a first determination module 703, a second establishment module 704, a construction module 705, and a second determination module 706:

[0246] The acquisition module 701 is configured to acquire communication objects for the covert communication of the unmanned aerial vehicle; wherein, the communication objects include multiple users, an unmanned aerial vehicle configured with multiple movable antennas, and a listener;

[0247] The first establishment module 702 is configured to establish a UAV covert communication scenario according to the communication objects;

[0248] The first determination module 703 is configured to determine a UAV covert communication channel model according to the UAV covert communication scenario;

[0249] The second establishment module 704 is configured to establish a covertness constraint model according to the UAV covert communication channel model; wherein, the covertness constraint model is used to constrain the probability of false detection by the listener;

[0250] A construction module 705, configured to construct a joint optimization problem according to a UAV stealth communication channel model and a stealth constraint model, with the objective of maximizing the minimum user communication demand satisfaction rate; wherein, the joint optimization problem includes beamforming, trajectory planning, and antenna position.

[0251] A second determination module 706, configured to determine a target UAV trajectory, a target beam assignment vector, and a target antenna position for a communication object according to the joint optimization problem.

[0252] In a possible implementation manner, the first determination module 703 is further configured to:

[0253] Define a UAV trajectory and an antenna position according to the UAV stealth communication scenario.

[0254] Determine a channel vector from the UAV to a ground node according to the UAV trajectory and the antenna position.

[0255] Establish a UAV stealth communication channel model according to the channel vector, and determine the data traffic of a user during the entire flight period of the UAV based on the UAV stealth communication channel model.

[0256] In a possible implementation manner, the second establishment module 704 is further configured to:

[0257] Establish a binary hypothesis testing model according to the UAV stealth communication channel model; wherein, the binary hypothesis testing model is used to simulate the detection by a listener of whether a base station performs transmission.

[0258] Determine the probability that the listener misdetects the communication behavior between the UAV and the user according to the binary hypothesis testing model; wherein, the probability includes a maximum correct detection probability and a minimum false detection probability.

[0259] Establish a stealth constraint model according to the probability.

[0260] In a possible implementation manner, the construction module 705 is further configured to:

[0261] Determine multiple joint optimization constraints with the objective of maximizing the minimum user communication demand satisfaction rate according to the UAV stealth communication channel model and the stealth constraint model.

[0262] Construct a joint optimization problem according to the multiple joint optimization constraints.

[0263] In a possible implementation, the construction module 705 is further configured to jointly optimize constraints, including the user communication requirement satisfaction rate constraint, the total UAV transmission power constraint, the first constraint on the UAV's horizontal speed, the second constraint on the UAV's horizontal speed, the UAV's altitude constraint, the UAV's starting and ending point constraints, the moving area constraint of the movable antenna, the distance constraint between any two movable antennas, and the concealment constraint.

[0264] In a possible implementation, the second determination module 706 is further configured to:

[0265] Determine a first optimization sub-problem of the UAV transmission beamforming according to the joint optimization problem;

[0266] Determine a second optimization sub-problem of the UAV trajectory according to the joint optimization problem;

[0267] Determine a third optimization sub-problem of the movable antenna position according to the joint optimization problem;

[0268] Iteratively optimize the joint optimization problem, and calculate the first optimization sub-problem, the second optimization sub-problem, and the third optimization sub-problem during the iteration until the target UAV trajectory, the target beam assignment vector, and the target antenna position for the communication object are obtained.

[0269] In a possible implementation, the second determination module 706 is further configured to:

[0270] Control the covert communication of the UAV according to the target UAV trajectory, the target beam assignment vector, and the target antenna position.

[0271] The covert communication processing device of the UAV provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here in this embodiment.

[0272] Figure 8 It is a schematic structural diagram of the covert communication processing device of the UAV provided in the embodiment of the present application. As Figure 8 shown, the covert communication processing device 800 of the UAV provided in this embodiment includes: at least one processor 801 and a memory 802. Optionally, the device 800 further includes a communication component 803. Among them, the processor 801, the memory 802, and the communication component 803 are connected through a bus.

[0273] In a specific implementation process, at least one processor 801 executes the computer execution instructions stored in the memory 802, so that at least one processor 801 executes the above method.

[0274] The specific implementation process of the processor 801 can refer to the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here in this embodiment.

[0275] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU for short), or other general-purpose processors, digital signal processors (DSP for short), application specific integrated circuits (ASIC for short), etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in combination with the invention can be directly implemented by a hardware processor, or by a combination of hardware and software modules in the processor.

[0276] The memory may include a random access memory (RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory.

[0277] The bus may be an industry standard architecture (ISA) bus, a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the buses in the drawings of the present application are not limited to only one bus or one type of bus.

[0278] The embodiments of the present application further provide a computer program product, including a computer program, which implements the above method when executed by a processor.

[0279] The embodiments of the present application further provide a computer-readable storage medium, in which computer-executable instructions are stored, and when the processor executes the computer-executable instructions, the above method is implemented.

[0280] The above-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk. The readable storage medium can be any available medium accessible by a general-purpose or special-purpose computer.

[0281] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in a device.

[0282] The division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Additionally, the couplings or direct couplings or communication connections shown or discussed among each other can be indirect couplings or communication connections through some interfaces, and the couplings or connections of devices or units can be in electrical, mechanical, or other forms.

[0283] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0284] Furthermore, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0285] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0286] Those of ordinary skill in the art will understand that all or part of the steps to implement the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the aforementioned storage medium includes: various media such as ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0287] Finally, it should be noted that: After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily think of other embodiments of the present invention. The present invention is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include common general knowledge or conventional technical means in the technical field not disclosed in the present invention. It is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A method for processing concealed communication of an unmanned aerial vehicle, characterized in that, Applied to a computer device, the method includes: Obtain the communication objects of the stealth communication of the unmanned aerial vehicle (UAV); wherein, the communication objects include multiple users, a UAV configured with multiple movable antennas, and a listener; Establish a UAV stealth communication scenario according to the communication objects; Determine a UAV stealth communication channel model according to the UAV stealth communication scenario; Establish a concealment constraint model according to the UAV stealth communication channel model; wherein, the concealment constraint model is used to constrain the probability of the listener's false detection; Construct a joint optimization problem with the goal of maximizing the minimum user communication demand satisfaction rate according to the UAV stealth communication channel model and the concealment constraint model; wherein, the joint optimization problem includes beamforming, trajectory planning, and antenna position; Determine the target UAV trajectory, the target beam assignment vector, and the target antenna position for the communication objects according to the joint optimization problem.

2. The method according to claim 1, wherein The determining the UAV stealth communication channel model according to the UAV stealth communication scenario includes: Define the UAV trajectory and the antenna position according to the UAV stealth communication scenario; Determine the channel vector from the UAV to the ground node according to the UAV trajectory and the antenna position; Establish a UAV stealth communication channel model according to the channel vector, and determine the data traffic of the users during the entire flight period of the UAV based on the UAV stealth communication channel model.

3. The method according to claim 1, wherein The establishing the concealment constraint model according to the UAV stealth communication channel model includes: Establish a binary hypothesis testing model according to the UAV stealth communication channel model; wherein, the binary hypothesis testing model is used to simulate the detection of whether the listener establishes a transmission for the base station; Determine the probability that the listener falsely detects the communication behavior between the UAV and the users according to the binary hypothesis testing model; wherein, the probability includes the maximum correct detection probability and the minimum false detection probability; Establish a concealment constraint model according to the probability.

4. The method according to any one of claims 1 to 3, characterized in that The constructing the joint optimization problem with the goal of maximizing the minimum user communication demand satisfaction rate according to the UAV stealth communication channel model and the concealment constraint model includes: Determine multiple joint optimization constraints with the goal of maximizing the minimum user communication demand satisfaction rate according to the UAV stealth communication channel model and the concealment constraint model; Construct a joint optimization problem according to the multiple joint optimization constraints.

5. The method according to claim 4, characterized in that, The joint optimization constraints include user communication demand satisfaction rate constraint, total UAV transmission power constraint, first constraint on the horizontal speed of the UAV, second constraint on the horizontal speed of the UAV, height constraint of the UAV, start point and end point constraints of the UAV, moving area constraint of the movable antenna, distance constraint between any two movable antennas, and concealment constraint.

6. The method according to claim 5, wherein The determining the target UAV trajectory, the target beam assignment vector, and the target antenna position for the communication objects according to the joint optimization problem includes: Determine the first optimization sub-problem of the UAV transmission beamforming according to the joint optimization problem; Determine a second optimization sub - problem of the UAV trajectory according to the joint optimization problem; Determine a third optimization sub - problem of the position of the movable antenna according to the joint optimization problem; Iterate the joint optimization problem, and calculate the first optimization sub - problem, the second optimization sub - problem and the third optimization sub - problem during the iteration until the target UAV trajectory, the target beam assignment vector and the target antenna position for the communication object are obtained.

7. The method according to claim 5 or 6, characterized in that, After determining the target UAV trajectory, the target beam assignment vector and the target antenna position for the communication object according to the joint optimization problem, it further includes: Control the covert communication of the UAV according to the target UAV trajectory, the target beam assignment vector and the target antenna position.

8. A concealed communication processing device for a drone, characterized in that, Applied to a computer device, the device includes: An acquisition module, configured to acquire a communication object for the covert communication of the UAV; wherein, the communication object includes multiple users, a UAV configured with multiple movable antennas, and a listener; A first establishment module, configured to establish a UAV covert communication scenario according to the communication object; A first determination module, configured to determine a UAV covert communication channel model according to the UAV covert communication scenario; A second establishment module, configured to establish a concealment constraint model according to the UAV covert communication channel model; wherein, the concealment constraint model is used to constrain the probability of the listener's false detection; A construction module, configured to construct a joint optimization problem with the goal of maximizing the minimum user communication demand satisfaction rate according to the UAV covert communication channel model and the concealment constraint model; wherein, the joint optimization problem includes beamforming, trajectory planning, and antenna position; A second determination module, configured to determine a target UAV trajectory, a target beam assignment vector and a target antenna position for the communication object according to the joint optimization problem.

9. A concealed communication processing device for a drone, characterized in that, It includes: A memory, a processor; The memory stores computer - executable instructions; The processor executes the computer - executable instructions stored in the memory, so that the processor executes the method according to any one of claims 1 - 7.

10. A computer-readable storage medium, characterized in that Computer - executable instructions are stored in the computer - readable storage medium, and when the computer - executable instructions are executed by a processor, they are used to implement the method according to any one of claims 1 - 7.

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