Method for acquiring confrontation energy efficiency of unmanned aerial vehicle and electronic equipment

Abstract

The invention provides an unmanned aerial vehicle confrontation energy efficiency obtaining method and electronic equipment, and the method comprises the steps: obtaining the space coordinates of an unmanned aerial vehicle and a plurality of ground nodes, and determining the channel gain between an unmanned aerial vehicle base station and each ground node according to the space coordinates; the transmitting power of the unmanned aerial vehicle is obtained, the secrecy rate of the user and the communication behavior between the unmanned aerial vehicle and the user are determined according to the transmitting power and each channel gain, and the monitoring performance of the unmanned aerial vehicle is determined according to the communication behavior; acquiring the flight energy of the unmanned aerial vehicle, and determining the initial confrontation energy efficiency of the unmanned aerial vehicle according to the secrecy rate and the flight energy; determining a plurality of target optimization sub-problems according to the initial confrontation energy efficiency and the monitoring performance of the unmanned aerial vehicle; and obtaining a corresponding target solution according to each target optimization sub-problem, and determining the target confrontation energy efficiency of the unmanned aerial vehicle according to the target solutions, thereby achieving the technical effect of improving the confrontation energy efficiency obtaining efficiency of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a method for obtaining the energy efficiency of unmanned aerial vehicle combat equipment and an electronic device. Background Art

[0002] Due to the advantages of low manufacturing cost and flexible deployment, drones have been widely used in various fields of social production. In some specific scenarios, drones have shown great application potential. However, the open air communication environment can easily lead to attacks on confidential information transmission. How to effectively counter drones when they are operating has become the current research focus. Therefore, the method of obtaining drone countermeasures energy efficiency has become a promising direction.

[0003] In the prior art, the method for obtaining UAV countermeasures is mainly to use physical layer security technology to counter eavesdropping attacks and covert communication technology to counter monitoring attacks.

[0004] In the prior art, the use of physical layer security technology by drones will cause huge energy and time consumption, and there is a technical problem that the efficiency of drones in obtaining energy efficiency is low. Summary of the invention

[0005] The present application provides a method for acquiring UAV countermeasure energy efficiency and an electronic device, so as to achieve the technical effect of improving the efficiency of acquiring UAV countermeasure energy efficiency.

[0006] In a first aspect, the present application provides a method for obtaining UAV combat energy efficiency, comprising:

[0007] Obtaining the spatial coordinates of the drone and multiple ground nodes, and determining the channel gain between the drone base station and each ground node based on the spatial coordinates, wherein the ground nodes include users, eavesdroppers, and monitors;

[0008] Obtain the transmission power of the drone, determine the user's confidentiality rate and the communication behavior between the drone and the user based on the transmission power and each channel gain, and determine the monitoring performance of the drone based on the communication behavior, where the communication behavior includes the drone remaining silent or the drone distributing data;

[0009] Obtain the flight energy of the drone, and determine the initial confrontation energy efficiency of the drone based on the confidentiality rate and flight energy. The initial confrontation energy efficiency is used to indicate the initial service relationship between the drone and the user, the initial transmission power of the drone, and the initial trajectory;

[0010] According to the initial confrontation energy efficiency and the monitoring performance of the UAV, multiple target optimization sub-problems are determined, where the target optimization sub-problem includes the service relationship optimization sub-problem between the UAV and the user, the joint optimization sub-problem of the UAV's transmission power and trajectory, and the auxiliary variable optimization sub-problem;

[0011] According to each target optimization sub-problem, the corresponding target solution is obtained, and according to the target solution, the target confrontation energy efficiency of the UAV is determined, wherein each target optimization sub-problem corresponds to a target solution, and the target confrontation energy efficiency is used to indicate the optimal service relationship between the UAV and the user, the optimal transmission power and the optimal trajectory of the UAV.

[0012] Optionally, determining a confidentiality rate of a user according to the transmission power and each channel gain includes:

[0013] According to the transmission power of the UAV and the channel gain between each ground node, the signal-to-noise ratio of each ground node is determined, wherein the signal-to-noise ratio of the ground node includes the user signal-to-noise ratio, the eavesdropper signal-to-noise ratio and the monitor signal-to-noise ratio;

[0014] According to the eavesdropper's signal-to-noise ratio and the monitor's signal-to-noise ratio, the corresponding cooperative eavesdropping signal-to-noise ratio is determined, and according to the cooperative eavesdropping signal-to-noise ratio and the user's signal-to-noise ratio, the user's confidentiality rate is determined.

[0015] Optionally, based on the communication behavior, the monitoring performance of the UAV is determined, including:

[0016] When it is determined that the UAV remains silent, a corresponding first cross-correlation matrix is ​​obtained according to the transmission power and the channel gain;

[0017] When it is determined that the UAV is distributing data, a corresponding second cross-correlation matrix is ​​obtained according to the transmission power and the channel gain;

[0018] A likelihood ratio test is performed on the first mutual correlation matrix and the second mutual correlation matrix to determine the monitoring performance of the UAV.

[0019] Optionally, the communication behavior between the drone and the user can be obtained by:

[0020]

[0021] in, Indicates that the drone remains silent. indicates that the drone is distributing data, w indicates the listener, e indicates the eavesdropper, and y (l) [n] represents the message symbol received by the listener in the nth time slot, x (l) [n] represents the message symbol transmitted by the drone in the nth time slot, p u [n] represents the transmission power of the UAV in the nth time slot, g w[n] represents the channel gain between the drone base station and the listener in the nth time slot, g e [n] represents the channel gain between the drone base station and the eavesdropper in the nth time slot.

[0022] Optionally, the monitoring performance of the drone is obtained by:

[0023]

[0024]

[0025] Among them, ξ * [n] represents the monitoring performance of the UAV in the nth time slot, Indicates that the drone remains silent. Indicates that the drone is distributing data. Indicates from arrive The KL divergence of , σ is a constant;

[0026] Σ0 represents the first mutual correlation matrix, Σ1 represents the second mutual correlation matrix, which can be obtained as follows:

[0027] Σ0=diag(σ 2 ,σ 2 ),

[0028] Optionally, according to the initial adversarial energy efficiency and the monitoring performance of the UAV, multiple objective optimization sub-problems are determined, including:

[0029] According to the initial confrontation energy efficiency and the monitoring performance of the UAV, the corresponding first optimization problem is obtained;

[0030] Performing equivalent transformation on the first optimization problem to obtain a transformed second optimization problem;

[0031] According to the double-loop iterative optimization algorithm, the second optimization problem is transformed to determine the optimization sub-problem of the service relationship between the drone and the user, the joint optimization sub-problem of the drone's transmission power and trajectory, and the auxiliary variable optimization sub-problem;

[0032] The first optimization problem is obtained in the following way:

[0033]

[0034] C4:0≤p u [n]≤p max ,

[0035]

[0036] C8:q[1]=q[N],hu [1] = h u [N],

[0037]

[0038] in, represents the first optimization problem, C1~C9 are the constraints of the first optimization problem, EEk represents the first energy efficiency, ξ * [n] represents the monitoring performance of the UAV in the nth time slot, ε is the maximum allowed enemy detection error probability, is the maximum horizontal flight speed of the UAV, is the maximum vertical flight speed of the UAV, p max is the maximum transmission power of the UAV, h min and h max They represent the minimum and maximum altitudes allowed for UAV flight, ρ k [n] represents the service relationship between the UAV and user k in the nth time slot, and q[n] represents the trajectory of the UAV in the nth time slot.

[0039] The second optimization problem is obtained as follows:

[0040]

[0041] C1-C8,

[0042] in, represents the second optimization problem, C10, C11 and C1-C8 are the constraints of the second optimization problem, represents the introduced auxiliary vector variable, μ represents the penalty factor, and K represents the user's index sequence number set.

[0043] Optionally, the service relationship optimization sub-problem between the drone and the user is obtained by:

[0044]

[0045] C3,C10,

[0046] in, represents the sub-problem of optimizing the service relationship between drones and users, C1', C3, and C10 represent the constraints of the sub-problem of optimizing the service relationship between drones and users, represents a fixed constant when the UAV transmission power is given, represents a fixed constant when the drone trajectory is given, r represents the cooperative eavesdropping signal-to-noise ratio, μ (M) represents the Lagrange multiplier obtained in the fixed Mth outer loop.

[0047] Optionally, the UAV's transmission power and trajectory joint optimization subproblem is obtained by:

[0048]

[0049] C2,C4-C8

[0050]

[0051] in, represents the joint optimization subproblem of the UAV’s transmission power and trajectory, C is a constant, C2, C4-C8 represent the constraints of the joint optimization subproblem of the UAV’s transmission power and trajectory, and E UAV Indicates the flight energy of the drone.

[0052] Optionally, the auxiliary variable optimization subproblem is obtained by:

[0053]

[0054] stC11

[0055] in, Represents the auxiliary variable optimization subproblem.

[0056] In a second aspect, the present application provides a device for obtaining the energy efficiency of a drone, comprising:

[0057] A first acquisition module is used to acquire the spatial coordinates of the UAV and multiple ground nodes, and determine the channel gain between the UAV base station and each ground node according to the spatial coordinates, wherein the ground nodes include users, eavesdroppers, and monitors;

[0058] The second acquisition module is used to obtain the transmission power of the drone, determine the user's confidentiality rate and the communication behavior between the drone and the user according to the transmission power and each channel gain, and determine the monitoring performance of the drone according to the communication behavior, wherein the communication behavior includes that the drone remains silent or the drone is distributing data;

[0059] The third acquisition module is used to obtain the flight energy of the drone, and determine the initial confrontation energy efficiency of the drone according to the confidentiality rate and the flight energy. The initial confrontation energy efficiency is used to indicate the initial service relationship between the drone and the user, the initial transmission power and the initial trajectory of the drone;

[0060] The first processing module is used to determine multiple target optimization sub-problems according to the initial confrontation energy efficiency and the monitoring performance of the UAV, wherein the target optimization sub-problems include the service relationship optimization sub-problem between the UAV and the user, the transmission power and trajectory joint optimization sub-problem of the UAV, and the auxiliary variable optimization sub-problem;

[0061] The second processing module is used to obtain the corresponding target solution according to each target optimization sub-problem, and determine the target confrontation energy efficiency of the UAV according to the target solution, wherein each target optimization sub-problem corresponds to a target solution, and the target confrontation energy efficiency is used to indicate the optimal service relationship between the UAV and the user, the optimal transmission power and the optimal trajectory of the UAV.

[0062] Optionally, the second acquisition module is further used for:

[0063] According to the transmission power of the UAV and the channel gain between each ground node, the signal-to-noise ratio of each ground node is determined, wherein the signal-to-noise ratio of the ground node includes the user signal-to-noise ratio, the eavesdropper signal-to-noise ratio and the monitor signal-to-noise ratio;

[0064] According to the eavesdropper's signal-to-noise ratio and the monitor's signal-to-noise ratio, the corresponding cooperative eavesdropping signal-to-noise ratio is determined, and according to the cooperative eavesdropping signal-to-noise ratio and the user's signal-to-noise ratio, the user's confidentiality rate is determined.

[0065] Optionally, the second acquisition module is further used for:

[0066] When it is determined that the UAV remains silent, a corresponding first cross-correlation matrix is ​​obtained according to the transmission power and the channel gain;

[0067] When it is determined that the UAV is distributing data, a corresponding second cross-correlation matrix is ​​obtained according to the transmission power and the channel gain;

[0068] A likelihood ratio test is performed on the first mutual correlation matrix and the second mutual correlation matrix to determine the monitoring performance of the UAV.

[0069] Optionally, the second acquisition module is further used for:

[0070] The communication behavior between the drone and the user can be obtained in the following ways:

[0071]

[0072] in, Indicates that the drone remains silent. indicates that the drone is distributing data, w indicates the listener, e indicates the eavesdropper, and y (l) [n] represents the message symbol received by the listener in the nth time slot, x (l) [n] represents the message symbol transmitted by the drone in the nth time slot, p u [n] represents the transmission power of the UAV in the nth time slot, g w [n] represents the channel gain between the drone base station and the listener in the nth time slot, g e [n] represents the channel gain between the drone base station and the eavesdropper in the nth time slot.

[0073] Optionally, the second acquisition module is further used for:

[0074] The monitoring performance of the drone is obtained through:

[0075]

[0076] Among them, ξ * [n] represents the monitoring performance of the UAV in the nth time slot, Indicates that the drone remains silent. Indicates that the drone is distributing data. Indicates from arrive The KL divergence of , σ is a constant;

[0077] Σ0 represents the first mutual correlation matrix, Σ1 represents the second mutual correlation matrix, which can be obtained as follows:

[0078] Σ0=diag(σ 2 ,σ 2 ),

[0079] Optionally, the first processing module is further used for:

[0080] According to the initial confrontation energy efficiency and the monitoring performance of the UAV, the corresponding first optimization problem is obtained;

[0081] Performing equivalent transformation on the first optimization problem to obtain a transformed second optimization problem;

[0082] According to the double-loop iterative optimization algorithm, the second optimization problem is transformed to determine the optimization sub-problem of the service relationship between the drone and the user, the joint optimization sub-problem of the drone's transmission power and trajectory, and the auxiliary variable optimization sub-problem;

[0083] The first optimization problem is obtained in the following way:

[0084]

[0085] C4:0≤p u [n≤p max ,

[0086]

[0087] C8:q[1]=q[N],h u [1] = h u [N],

[0088]

[0089] in, represents the first optimization problem, C1~C9 are the constraints of the first optimization problem, EEk represents the first energy efficiency, ξ * [n] represents the monitoring performance of the UAV in the nth time slot, ε is the maximum allowed enemy detection error probability, is the maximum horizontal flight speed of the UAV, is the maximum vertical flight speed of the UAV, p max is the maximum transmission power of the UAV, h min and h max They represent the minimum and maximum altitudes allowed for UAV flight, ρ k [n] represents the service relationship between the UAV and user k in the nth time slot, and q[n] represents the trajectory of the UAV in the nth time slot.

[0090] The second optimization problem is obtained as follows:

[0091]

[0092] C1-C8,

[0093] in, represents the second optimization problem, C10, C11 and C1-C8 are the constraints of the second optimization problem, represents the introduced auxiliary vector variable, μ represents the penalty factor, and K represents the user's index sequence number set.

[0094] Optionally, the first processing module is further used for:

[0095] The service relationship optimization sub-problem between drones and users is obtained in the following way:

[0096]

[0097] C3,C10,

[0098] in, represents the sub-problem of optimizing the service relationship between drones and users, C1', C3, and C10 represent the constraints of the sub-problem of optimizing the service relationship between drones and users, represents a fixed constant when the UAV transmission power is given, represents a fixed constant when the drone trajectory is given, r represents the cooperative eavesdropping signal-to-noise ratio, μ (M) represents the Lagrange multiplier obtained in the fixed Mth outer loop.

[0099] Optionally, the first processing module is further used for:

[0100] The UAV launch power and trajectory joint optimization subproblem is obtained as follows:

[0101]

[0102] C2,C4-C8

[0103]

[0104] in, represents the joint optimization subproblem of the UAV’s transmission power and trajectory, C is a constant, C2, C4-C8 represent the constraints of the joint optimization subproblem of the UAV’s transmission power and trajectory, and E UAV Indicates the flight energy of the drone.

[0105] Optionally, the first processing module is further used for:

[0106] The auxiliary variable optimization subproblem is obtained as follows:

[0107]

[0108] stC11

[0109] in, Represents the auxiliary variable optimization subproblem.

[0110] In a third aspect, the present application provides a device for obtaining the energy efficiency of a drone, including:

[0111] Processor and memory;

[0112] Memory stores computer-executable instructions;

[0113] The processor executes the computer-executable instructions stored in the memory, so that the processor executes various possible implementations of the first aspect as described above.

[0114] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement various possible implementation methods as in the first aspect.

[0115] In a fifth aspect, the present application provides a computer program product, which, when executed by a processor, is used to implement various possible implementations as in the first aspect.

[0116] The present application provides a method and electronic device for obtaining the energy efficiency of a drone confrontation, which obtains the spatial coordinates of a drone and multiple ground nodes, and determines the channel gain between the drone base station and each ground node according to the spatial coordinates; obtains the transmission power of the drone, and determines the user's confidentiality rate and the communication behavior between the drone and the user according to the transmission power and each channel gain, and determines the monitoring performance of the drone according to the communication behavior; obtains the flight energy of the drone, and determines the initial confrontation energy efficiency of the drone according to the confidentiality rate and the flight energy; determines multiple target optimization sub-problems according to the initial confrontation energy efficiency and the monitoring performance of the drone; obtains the corresponding target optimization sub-problem according to each target optimization sub-problem The target solution is obtained, and the target confrontation energy efficiency of the UAV is determined according to the target solution. Thus, the detection performance of the UAV is determined by combining various communication behaviors of the UAV, thereby ensuring the accuracy and reliability of the UAV confrontation energy efficiency subsequently obtained. In addition, according to the initial energy efficiency and detection performance of the UAV, multiple target optimization sub-problems that need to be optimized are determined, and each target optimization sub-problem is solved to obtain the target confrontation energy efficiency corresponding to each target solution, thereby improving the speed and convenience of the overall acquisition process, reducing the waste of energy and time costs, solving the technical problem of low efficiency in acquiring UAV confrontation energy efficiency, and achieving the technical effect of improving the efficiency in acquiring UAV confrontation energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0117] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0118] Figure 1 A schematic diagram of a scenario for obtaining the energy efficiency of a drone according to an embodiment of the present application;

[0119] Figure 2 The process of the method for obtaining the energy efficiency of a drone provided in the embodiment of the present application Figure 1 ;

[0120] Figure 3 The process of the method for obtaining the energy efficiency of a drone provided in the embodiment of the present application Figure 2 ;

[0121] Figure 4 The process of the method for obtaining the energy efficiency of a drone provided in the embodiment of the present application Figure 3 ;

[0122] Figure 5 A schematic diagram of the structure of a device for obtaining the combat energy efficiency of a drone provided in an embodiment of the present application;

[0123] Figure 6 A hardware structure diagram of a device for acquiring the energy efficiency of a drone provided in an embodiment of the present application.

[0124] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope 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. DETAILED DESCRIPTION

[0125] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0126] In the prior art, the use of physical layer security technology by drones will cause huge energy and time consumption, and there is a technical problem that the efficiency of drones in obtaining energy efficiency is low.

[0127] The present application provides a method and electronic device for acquiring drone confrontation energy efficiency. By combining various communication behaviors of the drone, the detection performance of the drone is determined, thereby ensuring the accuracy and reliability of the drone confrontation energy efficiency subsequently acquired. In addition, based on the initial energy efficiency and detection performance of the drone, multiple target optimization sub-problems that need to be optimized are determined, and by obtaining the target solution corresponding to each target optimization sub-problem, the optimal target confrontation energy efficiency of the drone is obtained based on each target solution, thereby improving the speed and convenience of the overall acquisition process, reducing the waste of energy and time costs, solving the technical problem of low efficiency in acquiring drone confrontation energy efficiency, and achieving the technical effect of improving the efficiency in acquiring drone confrontation energy efficiency.

[0128] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments 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 in conjunction with the accompanying drawings.

[0129] Figure 1 A schematic diagram of a scenario for obtaining the energy efficiency of a drone according to an embodiment of the present application is shown in FIG. Figure 1As shown in the figure, the acquisition scenario of drone countermeasures energy efficiency involves four roles, namely, users, drones, eavesdroppers and monitors. During the flight (as shown by the dotted line path), the drone establishes a connection with the user through a communication link and transmits information. However, in this process, the eavesdropper uses the eavesdropping link to eavesdrop on the communication content between the drone and the user, while the monitor further monitors and interferes with the communication between the drone and the user through the monitoring link. At the same time, the eavesdropper and the monitor have a cooperative relationship, and they can share the acquired information with each other. Therefore, when performing tasks, the drone not only needs to prevent the information from being monitored and / or eavesdropped, but also needs to ensure the normal transmission of information between the drone and the user.

[0130] Figure 2 The process of the method for obtaining the energy efficiency of a drone provided in the embodiment of the present application Figure 1 .like Figure 2 As shown, the method for obtaining the combat energy efficiency of a drone provided in an embodiment of the present application includes:

[0131] S201, obtaining the spatial coordinates of the drone and multiple ground nodes, and determining the channel gain between the drone base station and each ground node according to the spatial coordinates;

[0132] In this embodiment, the ground nodes include users, eavesdroppers and monitors.

[0133] According to the scene where the drone and each ground node are located, the corresponding spatial coordinates are obtained, and according to the spatial coordinates of the drone and each ground node, the channel gain between the drone station and each ground node is determined. The channel gain between the drone station and each ground node is obtained by the following method:

[0134]

[0135] q[n]=(x u [n],y u [n])

[0136] Among them, g i [n] represents the channel gain between the drone base station and different ground nodes in the nth time slot, represents the unit distance channel gain parameter, q[n] represents the coordinates of the UAV in the nth time slot, and h u [n] represents the altitude of the drone in the nth time slot, u i [n] represents the coordinates of the user or eavesdropper or monitor in the nth time slot, K represents the index number set of the user, w represents the monitor, and e represents the eavesdropper.

[0137] S202, obtaining the transmission power of the drone, determining the user's confidentiality rate and the communication behavior between the drone and the user based on the transmission power and each channel gain, and determining the monitoring performance of the drone based on the communication behavior;

[0138] In this embodiment, the communication behavior includes the drone remaining silent or the drone distributing data.

[0139] After knowing the transmission power of the UAV and the channel gain between the UAV base station and each ground node, we can further determine the user's confidentiality rate and various communication behaviors between the UAV and the user, and determine the detection performance of the UAV based on all communication behaviors between the UAV and the user.

[0140] S203, obtaining the flight energy of the UAV, and determining the initial confrontation energy efficiency of the UAV according to the confidentiality rate and the flight energy;

[0141] In this embodiment, the initial confrontation energy efficiency is used to indicate the initial service relationship between the drone and the user, the initial transmission power and the initial trajectory of the drone.

[0142] Obtain the flight energy of the drone through the following methods:

[0143]

[0144] Among them, E UAV represents the flight energy of the drone, P h and P i They represent the blade profile power and induced power of the UAV in hovering state, U tip represents the rotor tip speed, v0 represents the average rotor induced speed of the drone in the hovering state, d0 represents the fuselage drag ratio, S0 represents the rotor stability, ρ represents the air density, A0 represents the rotor disk area, W represents the weight of the drone, and v xy [n] represents the horizontal speed of the UAV in the nth time slot, v z [n] represents the vertical speed of the UAV at the nth time slot;

[0145] After determining the flight energy of the drone and the confidentiality rate of its communication with each user, the initial countermeasure energy efficiency of the drone is determined by combining these two key factors in the following way:

[0146]

[0147] Among them, EE k Represents the first energy efficiency, R k represents the user's communication rate, ρ k [n] represents the service relationship between the drone and the user in the nth time slot, E UAVrepresents the flight energy of the drone, R k [n] represents the confidentiality rate of the user in time slot n.

[0148] S204, determining multiple target optimization sub-problems according to the initial confrontation energy efficiency and the monitoring performance of the UAV;

[0149] In this embodiment, the target optimization sub-problem includes the service relationship optimization sub-problem between the drone and the user, the joint optimization sub-problem of the drone's transmission power and trajectory, and the auxiliary variable optimization sub-problem.

[0150] According to the initial confrontation energy efficiency of the UAV and the previously determined detection performance, multiple optimization sub-problems of the service relationship between the UAV and the user, the joint optimization sub-problem of the UAV's transmission power and trajectory, and the auxiliary variable optimization sub-problem are established.

[0151] S205. According to each target optimization sub-problem, a corresponding target solution is obtained, and according to the target solution, the target confrontation energy efficiency of the UAV is determined.

[0152] In this embodiment, each target optimization sub-problem corresponds to a target solution, and the target confrontation energy efficiency is used to indicate the optimal service relationship between the drone and the user, the optimal transmission power and the optimal trajectory of the drone.

[0153] According to each target optimization sub-problem corresponding to the UAV, the corresponding target solution is obtained, and according to each target solution, the optimal service relationship between the UAV and the user, the optimal transmission power and the optimal trajectory of the UAV are further determined.

[0154] The present application provides a method for obtaining the energy efficiency of a drone confrontation, which obtains the spatial coordinates of a drone and multiple ground nodes, and determines the channel gain between the drone base station and each ground node according to the spatial coordinates; obtains the transmission power of the drone, and determines the user's confidentiality rate and the communication behavior between the drone and the user according to the transmission power and each channel gain, and determines the monitoring performance of the drone according to the communication behavior; obtains the flight energy of the drone, and determines the initial confrontation energy efficiency of the drone according to the confidentiality rate and the flight energy; determines multiple target optimization sub-problems according to the initial confrontation energy efficiency and the monitoring performance of the drone; obtains the corresponding target solution according to each target optimization sub-problem. , and according to the target solution, the target confrontation energy efficiency of the UAV is determined, so that the detection performance of the UAV is determined by combining various communication behaviors of the UAV, which ensures the accuracy and reliability of the UAV confrontation energy efficiency obtained subsequently. In addition, according to the initial energy efficiency and detection performance of the UAV, multiple target optimization sub-problems that need to be optimized are determined, and each target optimization sub-problem is solved to obtain the target confrontation energy efficiency corresponding to each target solution, which improves the speed and convenience of the overall acquisition process, reduces the waste of energy and time costs, solves the technical problem of low efficiency in obtaining UAV confrontation energy efficiency, and achieves the technical effect of improving the efficiency of obtaining UAV confrontation energy efficiency.

[0155] Figure 3 The process of the method for obtaining the energy efficiency of a drone provided in the embodiment of the present application Figure 2 .like Figure 3 As shown, this embodiment, based on the above embodiment, provides a supplementary description of the process of obtaining the monitoring performance of the drone, including:

[0156] S301, obtaining the transmission power of the UAV, and determining the communication behavior between the UAV and the user and the signal-to-noise ratio of each ground node according to the transmission power of the UAV and the channel gain between each ground node;

[0157] In this embodiment, the signal-to-noise ratio of the ground node includes the user signal-to-noise ratio, the eavesdropper signal-to-noise ratio and the monitor signal-to-noise ratio.

[0158] After determining the transmission power of the drone, this key parameter and the channel gain data between the drone and each ground node are used to accurately determine the various communication behaviors between the drone and the user. At the same time, based on these parameters, the signal-to-noise ratio of each ground node when receiving the signal is effectively calculated. The communication behavior between the drone and the user can be obtained in the following ways:

[0159]

[0160] in, Indicates that the drone remains silent. indicates that the drone is distributing data, w indicates the listener, e indicates the eavesdropper, and y (l) [n] represents the message symbol received by the listener in the nth time slot, x (l) [n] represents the message symbol transmitted by the drone in the nth time slot, p u [n] represents the transmission power of the UAV in the nth time slot, g w [n] represents the channel gain between the drone base station and the listener in the nth time slot, g e [n] represents the channel gain between the drone base station and the eavesdropper in the nth time slot;

[0161] The signal-to-noise ratio of each ground node can be obtained by:

[0162]

[0163] Among them, r i [n] represents the signal-to-noise ratio of the drone at the nth time slot, p u [n] represents the transmission power of the UAV in the nth time slot, g i [n] represents the channel gain between the UAV base station and different ground nodes in the nth time slot, the user's index number set, w represents the listener, and e represents the eavesdropper.

[0164] S302, determining a corresponding cooperative eavesdropping signal-to-noise ratio according to the eavesdropper signal-to-noise ratio and the monitor signal-to-noise ratio, and determining a user's confidentiality rate according to the cooperative eavesdropping signal-to-noise ratio and the user signal-to-noise ratio;

[0165] According to the eavesdropper's signal-to-noise ratio and the monitor's signal-to-noise ratio, the corresponding cooperative eavesdropping signal-to-noise ratio can be determined in the following way:

[0166]

[0167] Among them, r c [n] represents cooperative eavesdropping signal-to-noise ratio, r w [n] represents the listener signal-to-noise ratio, r e [n] represents the eavesdropper's signal-to-noise ratio;

[0168] After obtaining the cooperative eavesdropping signal-to-noise ratio, the user's signal-to-noise ratio can be calculated and the user's confidentiality rate can be further determined by the following methods:

[0169]

[0170] Among them, R k [n] represents the confidentiality rate of the user in time slot n, Q -1 (·) is the inverse function of the Q function, δ and δ cThey represent the maximum allowed demodulation error probability and the maximum allowed information leakage rate respectively.

[0171] S303, when it is determined that the UAV remains silent, obtaining a corresponding first cross-correlation matrix according to the transmission power and the channel gain;

[0172] When it is determined that the drone remains silent, according to the transmission power and channel gain, the corresponding first cross-correlation matrix can be obtained in the following way:

[0173] Σ0=diag(σ 2 ,σ 2 )

[0174] Wherein, Σ0 represents the first cross-correlation matrix, and σ is a constant.

[0175] S304, when it is determined that the UAV is distributing data, obtaining a corresponding second cross-correlation matrix according to the transmission power and the channel gain;

[0176] When it is determined that the drone is distributing data, the corresponding second cross-correlation matrix can be obtained in the following way according to the transmission power and channel gain:

[0177]

[0178] Wherein, Σ1 represents the second cross-correlation matrix.

[0179] S305: Perform likelihood ratio detection on the first mutual correlation matrix and the second mutual correlation matrix to determine the monitoring performance of the UAV.

[0180] The first mutual correlation matrix and the second mutual correlation matrix are subjected to likelihood ratio detection in the following manner to determine the monitoring performance of the UAV:

[0181]

[0182] Among them, ξ * [n] represents the monitoring performance of the UAV in the nth time slot, Indicates that the drone remains silent. Indicates that the drone is distributing data. Indicates from arrive The KL divergence of .

[0183] The present application provides a method for acquiring unmanned aerial vehicle (UAV) countermeasure energy efficiency. The method determines the communication behavior between the UAV and the user and the signal-to-noise ratio of each ground node according to the transmission power of the UAV and the channel gain between each ground node, and determines the user's confidentiality rate according to the signal-to-noise ratio of each ground node. The method acquires the corresponding first and second mutual correlation matrices according to the communication behavior, transmission power and channel gain between the UAV and the user, and determines the monitoring performance of the UAV according to the first and second mutual correlation matrices. The method ensures the accuracy and timeliness of the UAV countermeasure energy efficiency by considering various possible communication behaviors of the UAV. At the same time, the method determines the monitoring performance of the UAV by utilizing the first and second mutual correlation matrices, reduces the difficulty of calculating and acquiring the monitoring performance of the UAV and the time cost required for the calculation, solves the technical problem of low efficiency in acquiring the UAV countermeasure energy efficiency, and achieves the technical effect of improving the efficiency in acquiring the UAV countermeasure energy efficiency.

[0184] Figure 4 The process of the method for obtaining the energy efficiency of a drone provided in the embodiment of the present application Figure 3 .like Figure 4 As shown, based on the above embodiment, this embodiment provides a supplementary description of the acquisition process of each target optimization sub-problem, including:

[0185] S401, obtaining a first optimization problem according to the initial confrontation energy efficiency and the monitoring performance of the UAV;

[0186] According to the initial confrontation energy efficiency and the monitoring performance of the UAV, the corresponding first optimization problem is obtained in the following way:

[0187]

[0188] C4:0≤p u [n]≤p max ,

[0189]

[0190] C8:q[1]=q[N],h u [1] = h u [N],

[0191]

[0192] in, represents the first optimization problem, C1~C9 are the constraints of the first optimization problem, C1 represents the user energy efficiency constraint; C2 represents the concealment constraint of the drone against cooperative eavesdropping and monitoring attacks; C3 represents that the drone can only serve one user in a time slot; C4 represents the drone transmission power constraint; C5 represents the drone horizontal speed constraint; C6 represents the drone vertical speed constraint; C7 represents the drone flight altitude constraint; C8 represents the drone starting and ending point constraint; C9 represents the binary constraint EE of the variable k represents the first energy efficiency, ξ * [n] represents the monitoring performance of the UAV in the nth time slot, ε is the maximum allowed enemy detection error probability, is the maximum horizontal flight speed of the UAV, is the maximum vertical flight speed of the UAV, p max is the maximum transmission power of the UAV, h min and h max They represent the minimum and maximum altitudes allowed for UAV flight, ρ k [n] represents the service relationship between the UAV and user k in the nth time slot, and q[n] represents the trajectory of the UAV in the nth time slot.

[0193] S402, performing equivalent transformation on the first optimization problem to obtain a transformed second optimization problem;

[0194] The first optimization problem is equivalently transformed in the following way to obtain the transformed second optimization problem:

[0195]

[0196] C1-C8,

[0197] in, represents the second optimization problem, C10, C11 and C1-C8 are the constraints of the second optimization problem, represents the introduced auxiliary vector variable, μ represents the penalty factor, and K represents the user's index sequence number set.

[0198] S403. According to the double-loop iterative optimization algorithm, the second optimization problem is transformed to determine the service relationship optimization sub-problem between the drone and the user, the joint optimization sub-problem of the drone's transmission power and trajectory, and the auxiliary variable optimization sub-problem.

[0199] According to the double-loop iterative optimization algorithm, the second optimization problem is transformed to determine the service relationship optimization sub-problem between the drone and the user, the joint optimization sub-problem of the drone's transmission power and trajectory, and the auxiliary variable optimization sub-problem. Among them, the service relationship optimization sub-problem between the drone and the user is obtained by the following method:

[0200]

[0201] C3,C10,

[0202] in, represents the sub-problem of optimizing the service relationship between drones and users, C1', C3, and C10 represent the constraints of the sub-problem of optimizing the service relationship between drones and users, represents a fixed constant when the UAV transmission power is given, represents a fixed constant when the drone trajectory is given, r represents the cooperative eavesdropping signal-to-noise ratio, μ (M) Represents the Lagrange multiplier obtained in the fixed Mth outer loop:

[0203] The UAV launch power and trajectory joint optimization subproblem is obtained as follows:

[0204]

[0205] C2,C4-C8

[0206]

[0207] in, represents the joint optimization subproblem of the UAV’s transmission power and trajectory, C is a constant, C2, C4-C8 represent the constraints of the joint optimization subproblem of the UAV’s transmission power and trajectory, and E UAV Indicates the flight energy of the drone;

[0208] The auxiliary variable optimization subproblem is obtained as follows:

[0209]

[0210] stC11

[0211] in, Represents the auxiliary variable optimization subproblem.

[0212] It should be noted that the joint optimization subproblem of the UAV’s launch power and trajectory is a non-convex problem and is difficult to solve. To facilitate the solution, a slack variable ζ is introduced. k and Θ, the above problem is equivalently transformed into:

[0213]

[0214] C14:E UAV ≤Θ,

[0215] C2,C4-C8

[0216] For C12, and θ (m)Performing a first-order Taylor expansion, we can obtain its lower bound:

[0217]

[0218] In order to solve the problem of R k The non-concavity of [n] introduces slack variables

[0219] and The following conditions are met:

[0220] τ k [n]≤γ k [n],α i [n]≥γ i [n],

[0221]

[0222] You can get:

[0223]

[0224] Among them, log2(1+α c [n]) for α c [n] is a concave function, so Performing a first-order Taylor expansion at gives:

[0225]

[0226] For γ k [n], this application proposes to introduce slack variables

[0227] and The following conditions are met:

[0228]

[0229] In the above formula At a given point λ (m) [n] and Performing a first-order Taylor expansion at , we can obtain:

[0230]

[0231] For γ i [n], this application proposes to introduce slack variables and The following conditions are met:

[0232]

[0233]

[0234] Through z 2 [n] At a given z (m) A first-order Taylor expansion at [n] yields:

[0235]

[0236] Through At a given q (m) [n] and Performing a first-order Taylor expansion at gives:

[0237]

[0238] Notice and For β i [n] and α i [n] is a convex function, so given and Performing a first-order Taylor expansion on it yields:

[0239]

[0240] For the energy constraint C14, this application introduces a slack variable The following conditions are met:

[0241]

[0242] For the left side of the above equation, at the given point ω (m) [n] and q (m) A first-order Taylor expansion at [n] yields:

[0243]

[0244] C14 E UAV There is the following relationship:

[0245]

[0246] For the implicit constraint C2, the slack variable α is introduced c [n] can be converted to:

[0247]

[0248] Therefore, the joint optimization subproblem of UAV transmission power and trajectory can be relaxed into the following form:

[0249]

[0250] C2'C6-C8,

[0251] It can be seen that the above problem is a convex problem and can be solved using CVX.

[0252] The present application provides a method for obtaining the energy efficiency of unmanned aerial vehicle confrontation, which obtains the corresponding first optimization problem according to the initial confrontation energy efficiency and the monitoring performance of the unmanned aerial vehicle; performs an equivalent transformation on the first optimization problem to obtain the transformed second optimization problem; and transforms the second optimization problem according to a double-loop iterative optimization algorithm to determine the service relationship optimization subproblem between the unmanned aerial vehicle and the user, the joint optimization subproblem of the unmanned aerial vehicle's transmission power and trajectory, and the auxiliary variable optimization subproblem. Thus, by establishing the service relationship optimization subproblem between the unmanned aerial vehicle and the user, the joint optimization subproblem of the unmanned aerial vehicle's transmission power and trajectory, and the auxiliary variable optimization subproblem according to the initial confrontation energy efficiency and detection performance of the unmanned aerial vehicle, the speed and convenience of the overall acquisition process are improved, and the waste of energy and time costs is reduced. At the same time, by adopting a double-loop iterative optimization algorithm, the complexity of the overall calculation is reduced. By introducing the auxiliary variable optimization subproblem, the optimization process is further simplified, the flexibility and adaptability of the algorithm are enhanced, and the technical problem of low efficiency in obtaining the energy efficiency of the target unmanned aerial vehicle confrontation is solved, thereby achieving the technical effect of improving the efficiency in obtaining the energy efficiency of the target unmanned aerial vehicle confrontation.

[0253] Figure 5 This is a schematic diagram of the structure of the device for obtaining the energy efficiency of a drone provided in an embodiment of the present application. The device of this embodiment may be in the form of software and / or hardware. Figure 5 As shown, the device 500 for acquiring the energy efficiency of a drone provided in an embodiment of the present application includes: a first acquisition module 501, a second acquisition module 502, a third acquisition module 503, a first processing module 504, and a second processing module 505:

[0254] The first acquisition module 501 is used to acquire the spatial coordinates of the drone and multiple ground nodes, and determine the channel gain between the drone base station and each ground node according to the spatial coordinates, wherein the ground nodes include users, eavesdroppers, and monitors;

[0255] The second acquisition module 502 is used to obtain the transmission power of the drone, determine the confidentiality rate of the user and the communication behavior between the drone and the user according to the transmission power and each channel gain, and determine the monitoring performance of the drone according to the communication behavior, wherein the communication behavior includes that the drone remains silent or the drone is distributing data;

[0256] The third acquisition module 503 is used to obtain the flight energy of the drone, and determine the initial confrontation energy efficiency of the drone according to the confidentiality rate and the flight energy. The initial confrontation energy efficiency is used to indicate the initial service relationship between the drone and the user, the initial transmission power and the initial trajectory of the drone;

[0257] The first processing module 504 is used to determine multiple target optimization sub-problems according to the initial confrontation energy efficiency and the monitoring performance of the UAV, wherein the target optimization sub-problems include the service relationship optimization sub-problem between the UAV and the user, the transmission power and trajectory joint optimization sub-problem of the UAV, and the auxiliary variable optimization sub-problem;

[0258] The second processing module 505 is used to obtain the corresponding target solution according to each target optimization sub-problem, and determine the target confrontation energy efficiency of the UAV according to the target solution, wherein each target optimization sub-problem corresponds to a target solution, and the target confrontation energy efficiency is used to indicate the optimal service relationship between the UAV and the user, the optimal transmission power and the optimal trajectory of the UAV.

[0259] In a possible implementation, the second acquisition module 502 is further configured to:

[0260] According to the transmission power of the UAV and the channel gain between each ground node, the signal-to-noise ratio of each ground node is determined, wherein the signal-to-noise ratio of the ground node includes the user signal-to-noise ratio, the eavesdropper signal-to-noise ratio and the monitor signal-to-noise ratio;

[0261] According to the eavesdropper's signal-to-noise ratio and the monitor's signal-to-noise ratio, the corresponding cooperative eavesdropping signal-to-noise ratio is determined, and according to the cooperative eavesdropping signal-to-noise ratio and the user's signal-to-noise ratio, the user's confidentiality rate is determined.

[0262] In a possible implementation, the second acquisition module 502 is further configured to:

[0263] When it is determined that the UAV remains silent, a corresponding first cross-correlation matrix is ​​obtained according to the transmission power and the channel gain;

[0264] When it is determined that the UAV is distributing data, a corresponding second cross-correlation matrix is ​​obtained according to the transmission power and the channel gain;

[0265] A likelihood ratio test is performed on the first mutual correlation matrix and the second mutual correlation matrix to determine the monitoring performance of the UAV.

[0266] In a possible implementation, the second acquisition module 502 is further configured to:

[0267] The communication behavior between the drone and the user can be obtained in the following ways:

[0268]

[0269] in, Indicates that the drone remains silent. indicates that the drone is distributing data, w indicates the listener, e indicates the eavesdropper, and y (l) [n] represents the message symbol received by the listener in the nth time slot, x (l )[n] represents the message symbol transmitted by the drone in the nth time slot, p u [n] represents the transmission power of the UAV in the nth time slot, g w [n] represents the channel gain between the drone base station and the listener in the nth time slot, g e [n] represents the channel gain between the drone base station and the eavesdropper in the nth time slot.

[0270] In a possible implementation, the second acquisition module 502 is further configured to:

[0271] The monitoring performance of the drone is obtained through:

[0272]

[0273] Among them, ξ * [n] means no one time slot Test able, Indicates that the drone remains silent. It indicates that the drone is distributing data, represents the KL divergence from to , and σ is a constant;

[0274] Σ0 represents the first mutual correlation matrix, Σ1 represents the second mutual correlation matrix, which can be obtained as follows:

[0275] Σ0=diag(σ 2 ,σ 2 ),

[0276] In a possible implementation, the first processing module 504 is further configured to:

[0277] According to the initial confrontation energy efficiency and the monitoring performance of the UAV, the corresponding first optimization problem is obtained;

[0278] Performing equivalent transformation on the first optimization problem to obtain a transformed second optimization problem;

[0279] According to the double-loop iterative optimization algorithm, the second optimization problem is transformed to determine the optimization sub-problem of the service relationship between the drone and the user, the joint optimization sub-problem of the drone's transmission power and trajectory, and the auxiliary variable optimization sub-problem;

[0280] The first optimization problem is obtained in the following way:

[0281]

[0282] C4:0≤p u [n]≤p max ,

[0283]

[0284] C8:q[1]=q[N],h u [1] = h u [N],

[0285]

[0286] in, represents the first optimization problem, C1~C9 are the constraints of the first optimization problem, EEk represents the first energy efficiency, ξ * [n] represents the monitoring performance of the UAV in the nth time slot, ε is the maximum allowed enemy detection error probability, is the maximum horizontal flight speed of the UAV, is the maximum vertical flight speed of the UAV, p max is the maximum transmission power of the UAV, h min and h max They represent the minimum and maximum altitudes allowed for UAV flight, ρ k [n] represents the service relationship between the UAV and user k in the nth time slot, and q[n] represents the trajectory of the UAV in the nth time slot.

[0287] The second optimization problem is obtained as follows:

[0288]

[0289] C1-C8,

[0290] in, represents the second optimization problem, C10, C11 and C1-C8 are the constraints of the second optimization problem, represents the introduced auxiliary vector variable, μ represents the penalty factor, and K represents the user's index sequence number set.

[0291] In a possible implementation, the first processing module 504 is further configured to:

[0292] The service relationship optimization sub-problem between drones and users is obtained in the following way:

[0293]

[0294] C3,C10,

[0295] in, represents the sub-problem of optimizing the service relationship between drones and users, C1', C3, C10 represent the constraints of the sub-problem of optimizing the service relationship between drones and users, represents a fixed constant when the UAV transmission power is given, represents a fixed constant when the drone trajectory is given, r represents the cooperative eavesdropping signal-to-noise ratio, μ (M) represents the Lagrange multiplier obtained in the fixed Mth outer loop.

[0296] In a possible implementation, the first processing module 504 is further configured to:

[0297] The UAV launch power and trajectory joint optimization subproblem is obtained as follows:

[0298]

[0299] C2,C4-C8

[0300]

[0301] in, represents the joint optimization subproblem of the UAV’s transmission power and trajectory, C is a constant, C2, C4-C8 represent the constraints of the joint optimization subproblem of the UAV’s transmission power and trajectory, and E UAV Indicates the flight energy of the drone.

[0302] In a possible implementation, the first processing module 504 is further configured to:

[0303] The auxiliary variable optimization subproblem is obtained as follows:

[0304]

[0305] stC11

[0306] in, Represents the auxiliary variable optimization subproblem.

[0307] The present application provides a device for obtaining the combat energy efficiency of a drone, which can implement the above method embodiment. Its implementation principle and technical effects are similar, and this embodiment will not be repeated here.

[0308] Figure 6 This is a hardware structure diagram for obtaining the energy efficiency of a drone provided in an embodiment of the present application. Figure 6 As shown, the drone energy efficiency acquisition device 600 includes:

[0309] Processor 601 and memory 602;

[0310] Memory stores computer-executable instructions;

[0311] The processor executes the computer execution instructions stored in the memory 602, so that the drone confrontation energy efficiency acquisition device executes the drone confrontation energy efficiency acquisition method as described above.

[0312] It should be understood that the processor 601 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the invention can be directly implemented as a hardware processor or a combination of hardware and software modules in the processor.

[0313] The memory 602 may include a high-speed random access memory (RAM), and may also include a non-volatile memory (NVM), such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a disk or an optical disk, etc.

[0314] An embodiment of the present application correspondingly provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method for obtaining the drone countermeasure energy efficiency as described above.

[0315] The embodiment of the present application also provides a computer program product accordingly. When the computer program is executed by a processor, it is used to implement the method for obtaining the UAV countermeasure energy efficiency as described above.

[0316] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0317] It should be further noted that, although the various steps in the flowchart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0318] It should be understood that the above-mentioned device embodiments are only illustrative, and the device of the present application can also be implemented in other ways. For example, the division of units / modules in the above-mentioned embodiments is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0319] In addition, unless otherwise specified, each functional unit / module in each embodiment of the present application may be integrated into one unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The above-mentioned integrated unit / module may be implemented in the form of hardware or in the form of a software program module.

[0320] If the integrated unit / module is implemented in the form of hardware, the hardware may be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc. If not specifically stated, the processor may be any appropriate hardware processor, such as a CPU, a GPU, an FPGA, a DSP, an ASIC, etc. If not specifically stated, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc.

[0321] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0322] In the above embodiments, the description of each embodiment has its own emphasis. For the part not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0323] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0324] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for obtaining the energy efficiency of a drone, characterized in that: include: Acquire spatial coordinates of the drone and multiple ground nodes, and determine the channel gain between the drone base station and each of the ground nodes according to the spatial coordinates, wherein the ground nodes include users, eavesdroppers, and monitors; Acquire the transmission power of the drone, determine the confidentiality rate of the user and the communication behavior between the drone and the user according to the transmission power and each of the channel gains, and determine the monitoring performance of the drone according to the communication behavior, wherein the communication behavior includes that the drone remains silent or the drone is distributing data; Acquire the flight energy of the drone, and determine the initial confrontation energy efficiency of the drone according to the confidentiality rate and the flight energy, where the initial confrontation energy efficiency is used to indicate the initial service relationship between the drone and the user, the initial transmission power and the initial trajectory of the drone; Determine a plurality of target optimization sub-problems according to the initial confrontation energy efficiency and the monitoring performance of the UAV, wherein the target optimization sub-problems include a service relationship optimization sub-problem between the UAV and the user, a transmission power and trajectory joint optimization sub-problem of the UAV, and an auxiliary variable optimization sub-problem; According to each of the target optimization sub-problems, a corresponding target solution is obtained, and according to the target solution, the target confrontation energy efficiency of the UAV is determined, wherein each of the target optimization sub-problems corresponds to one target solution, and the target confrontation energy efficiency is used to indicate the optimal service relationship between the UAV and the user, the optimal transmission power and the optimal trajectory of the UAV.

2. The method according to claim 1, characterized in that The determining the confidentiality rate of the user according to the transmit power and each of the channel gains comprises: Determine the signal-to-noise ratio of each ground node according to the transmission power of the UAV and the channel gain between each ground node, wherein the signal-to-noise ratio of the ground node includes a user signal-to-noise ratio, an eavesdropper signal-to-noise ratio, and a monitor signal-to-noise ratio; The corresponding cooperative eavesdropping signal-to-noise ratio is determined according to the eavesdropper signal-to-noise ratio and the monitor signal-to-noise ratio, and the confidentiality rate of the user is determined according to the cooperative eavesdropping signal-to-noise ratio and the user signal-to-noise ratio.

3. The method according to claim 2, characterized in that Determining the monitoring performance of the drone according to the communication behavior includes: When it is determined that the UAV remains silent, acquiring a corresponding first cross-correlation matrix according to the transmit power and the channel gain; When it is determined that the UAV is distributing data, acquiring a corresponding second cross-correlation matrix according to the transmit power and the channel gain; A likelihood ratio test is performed on the first mutual correlation matrix and the second mutual correlation matrix to determine the monitoring performance of the UAV.

4. The method according to claim 3, characterized in that The communication behavior between the drone and the user can be obtained in the following ways: in, Indicates that the drone remains silent, indicates that the drone is distributing data, w indicates the listener, e indicates the eavesdropper, y (l) [n] represents the message symbol received by the listener in the nth time slot, x( l )[n] represents the message symbol transmitted by the UAV in the nth time slot, p u [n] represents the transmission power of the UAV in the nth time slot, g w [n] represents the channel gain between the drone base station and the listener in the nth time slot, g e [n] represents the channel gain between the drone base station and the eavesdropper in the nth time slot.

5. The method according to claim 4, characterized in that The monitoring performance of the drone is obtained by: Among them, ξ * [n] represents the monitoring performance of the UAV in the nth time slot, Indicates that the drone remains silent. Indicates that the drone is distributing data. Indicates from arrive The KL divergence of , σ is a constant; Σ0 represents the first mutual correlation matrix, and Σ1 represents the second mutual correlation matrix, which can be obtained by the following method: Σ0=diag(σ 2 ,s 2 ), 6. The method according to claim 5, characterized in that The method further comprises determining a plurality of target optimization sub-problems according to the initial confrontation energy efficiency and the monitoring performance of the UAV, including: Obtaining a corresponding first optimization problem according to the initial confrontation energy efficiency and the monitoring performance of the UAV; Performing an equivalent transformation on the first optimization problem to obtain a transformed second optimization problem; According to the double-loop iterative optimization algorithm, the second optimization problem is transformed to determine the service relationship optimization subproblem between the drone and the user, the transmission power and trajectory joint optimization subproblem of the drone, and the auxiliary variable optimization subproblem; Wherein, the first optimization problem is obtained by the following method: C4:0≤p u [n]≤P max ’ C8:q[1]=q[N],h u [1]=h u [N], in, represents the first optimization problem, C1-C9 are the constraints of the first optimization problem, EEk represents the first energy efficiency, ξ * [n] represents the monitoring performance of the UAV in the nth time slot, ε is the maximum allowed enemy detection error probability, is the maximum horizontal flight speed of the UAV, is the maximum vertical flight speed of the UAV, p max is the maximum transmission power of the UAV, h min and h max They represent the minimum and maximum altitudes allowed for UAV flight, ρ k [n] represents the service relationship between the UAV and user k in the nth time slot, and q[n] represents the trajectory of the UAV in the nth time slot; The second optimization problem is obtained by: C1-C8, in, represents the second optimization problem, C10, C11 and C1-C8 are the constraints of the second optimization problem, represents the introduced auxiliary vector variable, μ represents the penalty factor, and K represents the user's index sequence number set.

7. The method according to claim 6, characterized in that The service relationship optimization sub-problem between the drone and the user is obtained in the following way: C3,C10, in, represents the sub-problem of optimizing the service relationship between the drone and the user, C1', C3, C10 represent the constraints of the sub-problem of optimizing the service relationship between the drone and the user, represents a fixed constant when the transmitting power of the UAV is given, represents a fixed constant when the UAV trajectory is given, r represents the cooperative eavesdropping signal-to-noise ratio, μ (M) represents the Lagrange multiplier obtained in the fixed Mth outer loop.

8. The method according to claim 7, characterized in that The UAV transmission power and trajectory joint optimization sub-problem is obtained by the following method: C2,C4-C8 in, represents the joint optimization sub-problem of the transmission power and trajectory of the UAV, C is a constant, C2, C4-C8 represent the constraints of the joint optimization sub-problem of the transmission power and trajectory of the UAV, E UAV Represents the flight energy of the UAV.

9. The method according to claim 7, characterized in that: The auxiliary variable optimization subproblem is obtained in the following way: stC11 in, Represents the auxiliary variable optimization subproblem.

10. A device for obtaining the energy efficiency of a drone, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 9.

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