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

Abstract

The application provides a method for obtaining an unmanned aerial vehicle confrontation energy efficiency and an electronic device. The method comprises the following steps: obtaining spatial coordinates of an unmanned aerial vehicle and a plurality of ground nodes, and determining channel gains between a base station of the unmanned aerial vehicle and each ground node according to the spatial coordinates; obtaining a transmission power of the unmanned aerial vehicle, determining a user secret rate and a communication behavior between the unmanned aerial vehicle and the user according to the transmission power and each channel gain, and determining a monitoring performance of the unmanned aerial vehicle according to the communication behavior; obtaining a flight energy of the unmanned aerial vehicle, and determining an initial confrontation energy efficiency of the unmanned aerial vehicle according to the secret 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; obtaining a corresponding target solution according to each target optimization sub-problem, and determining a target confrontation energy efficiency of the unmanned aerial vehicle according to the target solution, so that the technical effect of improving the obtaining efficiency of the unmanned aerial vehicle confrontation energy efficiency is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, and particularly relates to a method for acquiring anti-energy efficiency of a UAV and an electronic device. BACKGROUND

[0002] Due to the advantages of low manufacturing cost and flexible deployment, the UAV has been widely applied in various fields of social production. In some specific scenarios, the UAV has shown great application potential. However, the open air propagation environment is easy to cause confidential information transmission to be attacked. How to effectively resist when the UAV is working has become the current research focus. Therefore, the method for acquiring anti-energy efficiency of the UAV has become a promising direction.

[0003] In the prior art, the method for acquiring anti-energy efficiency of the UAV mainly uses physical layer security technology to resist eavesdropping attacks and uses covert communication technology to resist monitoring attacks.

[0004] In the prior art, the use of physical layer security technology by the UAV causes huge energy and time consumption, and there is the technical problem of low efficiency of acquiring anti-energy efficiency of the UAV. SUMMARY

[0005] The present application provides a method for acquiring anti-energy efficiency of a UAV and an electronic device to improve the efficiency of acquiring anti-energy efficiency of the UAV.

[0006] In a first aspect, the present application provides a method for acquiring anti-energy efficiency of a UAV, comprising:

[0007] acquiring spatial coordinates of the UAV and a plurality of ground nodes, and determining channel gains between a UAV base station and each ground node according to the spatial coordinates, wherein the ground nodes include users, eavesdroppers and monitors;

[0008] acquiring transmission power of the UAV, determining a secrecy rate of the user and a communication behavior between the UAV and the user according to the transmission power and each channel gain, and determining monitoring performance of the UAV according to the communication behavior, wherein the communication behavior includes that the UAV remains silent or the UAV is performing data distribution;

[0009] acquiring flight energy of the UAV, and determining initial anti-energy efficiency of the UAV according to the secrecy rate and the flight energy, wherein the initial anti-energy efficiency is used to indicate an initial service relationship between the UAV and the user, initial transmission power of the UAV and an initial trajectory of the UAV;

[0010] According to the initial confrontation energy efficiency and the monitoring performance of the unmanned aerial vehicle, a plurality of target optimization sub-problems are determined, wherein the target optimization sub-problems include a service relationship optimization sub-problem between the unmanned aerial vehicle and the user, a launch power and trajectory joint optimization sub-problem of the unmanned aerial vehicle, and an auxiliary variable optimization sub-problem;

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

[0012] Optionally, according to the launch power and each channel gain, a secrecy rate of the user is determined, comprising:

[0013] According to the launch power of the unmanned aerial vehicle and the channel gain between each ground node, a signal-to-noise ratio of each ground node is determined, 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 listener signal-to-noise ratio;

[0014] According to the eavesdropper signal-to-noise ratio and the listener signal-to-noise ratio, a corresponding cooperative eavesdropping signal-to-noise ratio is determined, and according to the cooperative eavesdropping signal-to-noise ratio and the user signal-to-noise ratio, a secrecy rate of the user is determined.

[0015] Optionally, according to the communication behavior, the monitoring performance of the unmanned aerial vehicle is determined, comprising:

[0016] When it is determined that the unmanned aerial vehicle is silent, a corresponding first cross-correlation matrix is obtained according to the launch power and the channel gain;

[0017] When it is determined that the unmanned aerial vehicle is performing data distribution, a corresponding second cross-correlation matrix is obtained according to the launch power and the channel gain;

[0018] The first cross-correlation matrix and the second cross-correlation matrix are subjected to likelihood ratio detection to determine the monitoring performance of the unmanned aerial vehicle.

[0019] Optionally, the communication behavior between the unmanned aerial vehicle and the user can be obtained in the following manner:

[0020]

[0021] wherein, indicates that the unmanned aerial vehicle is silent, indicates that the unmanned aerial vehicle is performing data distribution, w indicates a listener, e indicates an eavesdropper, y (l) [n] indicates a message symbol received by the listener in the nth time slot, x (l) [n] indicates a message symbol transmitted by the unmanned aerial vehicle in the nth time slot, p u [n] indicates the launch power of the unmanned aerial vehicle in the nth time slot, g w[n] represents the channel gain between the UAV base station and the listener in the nth time slot, g e [n] represents the channel gain between the UAV base station and the eavesdropper in the nth time slot.

[0022] Optionally, the monitoring performance of the UAV is obtained by the following way:

[0023]

[0024]

[0025] wherein ξ * [n] represents the monitoring performance of the UAV in the nth time slot, represents that the UAV keeps silent, represents that the UAV is conducting data distribution, represents the KL divergence from to , and σ is a constant;

[0026] Σ0 represents the first cross-correlation matrix, and Σ1 represents the second cross-correlation matrix, which can be obtained by the following way:

[0027]

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

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

[0030] The first optimization problem is equivalently transformed to obtain a transformed second optimization problem;

[0031] According to the double-loop iterative optimization algorithm, the second optimization problem is transformed to determine 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;

[0032] wherein the first optimization problem is obtained by the following way:

[0033]

[0034] C4: 0≤p u [n]≤p max ,

[0035]

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

[0037]

[0038] wherein, represents the first optimization problem, C1-C9 are constraint conditions of the first optimization problem, EE k represents the initial adversarial energy efficiency, ξ * [n] represents the monitoring performance of the UAV in the nth time slot, and 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 respectively represent the minimum and maximum heights allowed for the UAV flight, ρ k [n] represents the service relationship between the UAV and the 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 in the following manner:

[0040]

[0041] C1-C8,

[0042] wherein, represents the second optimization problem, C10, C11 and C1-C8 are constraint conditions of the second optimization problem, represents an auxiliary vector variable introduced, μ represents a penalty factor, and K represents an index number set of users.

[0043] Optionally, the service relationship optimization sub-problem between the UAV and the user is obtained in the following manner:

[0044]

[0045] C3,C10,

[0046] wherein, represents the service relationship optimization sub-problem between the UAV and the user, C1’, C3, C10 represent constraint conditions of the service relationship optimization sub-problem between the UAV and the user, represents a fixed constant when the transmission power of the UAV is given, represents a fixed constant when the trajectory of the UAV is given, r represents the cooperation eavesdropping signal-to-noise ratio, μ (M) represents the Lagrange multiplier obtained in the fixed Mth outer loop.

[0047] Optionally, the transmission power and trajectory joint optimization sub-problem of the UAV is obtained in the following manner:

[0048]

[0049] C2,C4-C8

[0050]

[0051] wherein, represents a launch power and trajectory joint optimization sub-problem of the UAV, C is a constant, C2, C4-C8 represents a constraint condition of the launch power and trajectory joint optimization sub-problem of the UAV, E UAV represents the flight energy of the UAV.

[0052] Optionally, the auxiliary variable optimization sub-problem is obtained in the following manner:

[0053]

[0054] s.t.C11

[0055] wherein, represents an auxiliary variable optimization sub-problem.

[0056] In a second aspect, the application provides an acquisition device for a UAV confrontation energy efficiency, comprising:

[0057] A first acquisition module is configured to acquire spatial coordinates of a UAV and a plurality of ground nodes, and determine channel gains between a UAV base station and each ground node according to the spatial coordinates, wherein the ground nodes include users, eavesdroppers and listeners;

[0058] A second acquisition module is configured to acquire a launch power of the UAV, determine a secret rate of the users and a communication behavior between the UAV and the users according to the launch power and each channel gain, and determine a monitoring performance of the UAV according to the communication behavior, wherein the communication behavior includes that the UAV remains silent or the UAV is conducting data distribution;

[0059] A third acquisition module is configured to acquire a flight energy of the UAV, and determine an initial confrontation energy efficiency of the UAV according to the secret rate and the flight energy, wherein the initial confrontation energy efficiency is used to indicate an initial service relationship between the UAV and the users, an initial launch power of the UAV and an initial trajectory;

[0060] A first processing module is configured to 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 users, a launch power and trajectory joint optimization sub-problem of the UAV and an auxiliary variable optimization sub-problem;

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

[0062] Optionally, the second obtaining module is further configured to:

[0063] determine a signal-to-noise ratio of each ground node according to the transmission power of the UAV and a 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 listener signal-to-noise ratio;

[0064] determine a corresponding cooperative eavesdropping signal-to-noise ratio according to the eavesdropper signal-to-noise ratio and the listener signal-to-noise ratio, and determine a secrecy rate of the user according to the cooperative eavesdropping signal-to-noise ratio and the user signal-to-noise ratio.

[0065] Optionally, the second obtaining module is further configured to:

[0066] when it is determined that the UAV keeps silent, obtain a corresponding first cross-correlation matrix according to the transmission power and the channel gain;

[0067] when it is determined that the UAV is performing data distribution, obtain a corresponding second cross-correlation matrix according to the transmission power and the channel gain;

[0068] perform likelihood ratio detection on the first cross-correlation matrix and the second cross-correlation matrix to determine a monitoring performance of the UAV.

[0069] Optionally, the second obtaining module is further configured to:

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

[0071]

[0072] wherein, represents that the UAV keeps silent, represents that the UAV is performing data distribution, w represents a listener, e represents an eavesdropper, y (l) represents a message symbol received by the listener in the nth time slot, x (l) represents a message symbol transmitted by the UAV in the nth time slot, p u represents a transmission power of the UAV in the nth time slot, g w represents a channel gain between the UAV base station and the listener in the nth time slot, g e represents a channel gain between the UAV base station and the eavesdropper in the nth time slot.

[0073] Optionally, the second obtaining module is further configured to:

[0074] The monitoring performance of the UAV is obtained in the following manner:

[0075]

[0076] wherein ξ * [n] represents the monitoring performance of the UAV in the nth time slot, represents that the UAV is silent, represents that the UAV is performing data distribution, represents the KL divergence from to , and σ is a constant;

[0077] Σ0 represents the first cross-correlation matrix, and Σ1 represents the second cross-correlation matrix, which can be obtained in the following manner:

[0078]

[0079] Optionally, the first processing module is further configured to:

[0080] obtaining a first optimization problem corresponding to the initial adversarial energy efficiency and the monitoring performance of the UAV;

[0081] equivalently transforming the first optimization problem to obtain a second optimization problem after transformation;

[0082] transforming the second optimization problem according to a double-loop iterative optimization algorithm to determine a service relationship optimization sub-problem between the UAV and the users, a joint optimization sub-problem of the transmission power and the trajectory of the UAV, and an auxiliary variable optimization sub-problem;

[0083] wherein the first optimization problem is obtained in the following manner:

[0084]

[0085] C4: 0 ≤ p u [n] ≤ p max ,

[0086]

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

[0088]

[0089] wherein, represents the first optimization problem, C1-C9 are constraint conditions of the first optimization problem, and EE kdenotes the initial confrontation energy efficiency, ξ * denotes the monitoring performance of the UAV at the nth time slot, ε is the maximum allowed detection error probability of the enemy, denotes the maximum horizontal flight speed of the UAV, denotes the maximum vertical flight speed of the UAV, p max denotes the maximum transmission power of the UAV, h min and h max respectively denote the minimum and maximum allowed heights of the UAV flight, ρ k denotes the service relationship between the UAV and the user k at the nth time slot, q[n] denotes the trajectory of the UAV at the nth time slot.

[0090] The second optimization problem is obtained in the following manner:

[0091]

[0092] C1-C8,

[0093] wherein, denotes the second optimization problem, C10, C11 and C1-C8 are constraint conditions of the second optimization problem, denotes an introduced auxiliary vector variable, μ represents a penalty factor, and K represents an index number set of the users.

[0094] Optionally, the first processing module is further configured to:

[0095] The service relationship optimization sub-problem between the UAV and the user is obtained in the following manner:

[0096]

[0097] C3,C10,

[0098] wherein, denotes the service relationship optimization sub-problem between the UAV and the user, C1', C3, C10 represent constraint conditions of the service relationship optimization sub-problem between the UAV and the user, denotes a fixed constant when the transmission power of the UAV is given, denotes a fixed constant when the trajectory of the UAV is given, r represents a cooperative eavesdropping signal-to-noise ratio, μ (M) denotes a fixed Mth outer loop obtained Lagrange multiplier.

[0099] Optionally, the first processing module is further configured to:

[0100] The transmission power and trajectory joint optimization sub-problem of the UAV is obtained in the following manner:

[0101]

[0102] C2,C4-C8

[0103]

[0104] wherein, represents a launch power and trajectory joint optimization sub-problem of the UAV, C is a constant, C2, C4-C8 represent constraint conditions of the launch power and trajectory joint optimization sub-problem of the UAV, E UAV represents flight energy of the UAV.

[0105] Optionally, the first processing module is further configured to:

[0106] The auxiliary variable optimization sub-problem is obtained by:

[0107]

[0108] s.t.C11

[0109] wherein, represents an auxiliary variable optimization sub-problem.

[0110] In a third aspect, the present application provides a UAV energy efficiency confrontation obtaining device, comprising:

[0111] a processor and a memory;

[0112] the memory stores computer execution instructions;

[0113] the processor executes the computer execution instructions stored in the memory, so that the processor executes various possible implementation manners in the first aspect.

[0114] In a fourth aspect, the present application provides a computer readable storage medium, the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement various possible implementation manners in the first aspect.

[0115] In a fifth aspect, the present application provides a computer program product, the computer program is executed by the processor to implement various possible implementation manners in the first aspect.

[0116] The application provides an unmanned aerial vehicle (UAV) confrontation energy efficiency acquisition method and an electronic device. The spatial coordinates of the UAV and multiple ground nodes are acquired, and the channel gain between a UAV base station and each ground node is determined according to the spatial coordinates. The transmission power of the UAV is acquired, the user's secret rate and the communication behavior between the UAV and the user are determined according to the transmission power and each channel gain, and the monitoring performance of the UAV is determined according to the communication behavior. The flight energy of the UAV is acquired, and the initial confrontation energy efficiency of the UAV is determined according to the secret rate and the flight energy. According to the initial confrontation energy efficiency and the monitoring performance of the UAV, multiple target optimization sub-problems are determined. According to each target optimization sub-problem, a corresponding target solution is acquired, and the target confrontation energy efficiency of the UAV is determined according to the target solution. Thus, by combining various communication behaviors of the UAV, the detection performance of the UAV is determined, the accuracy and reliability of the subsequent acquired confrontation energy efficiency of the UAV are ensured. In addition, according to the initial energy efficiency of the UAV and the detection performance, multiple target optimization sub-problems that need to be optimized are determined, and each target optimization sub-problem is solved to acquire the target confrontation energy efficiency corresponding to each target solution, thereby improving the rapidity and convenience in the overall acquisition process, reducing the waste in energy and time costs, solving the technical problem of low acquisition efficiency of the confrontation energy efficiency of the UAV, and achieving the technical effect of improving the acquisition efficiency of the confrontation energy efficiency of the UAV. BRIEF DESCRIPTION OF DRAWINGS

[0117] The accompanying drawings, which are incorporated into and form a part of the specification, illustrate an embodiment consistent with the present application and, together with the description, serve to explain the principles of the application.

[0118] Figure 1 An acquisition scene diagram of the confrontation energy efficiency of the UAV provided by the embodiment of the application is shown in the figure.

[0119] Figure 2 A flowchart of the acquisition method of the confrontation energy efficiency of the UAV provided by the embodiment of the application is shown in the figure. Figure One ;

[0120] Figure 3 A flowchart of the acquisition method of the confrontation energy efficiency of the UAV provided by the embodiment of the application is shown in the figure. Figure Two ;

[0121] Figure 4 A flowchart of the acquisition method of the confrontation energy efficiency of the UAV provided by the embodiment of the application is shown in the figure. Figure Three ;

[0122] Figure 5 A structural diagram of the acquisition device of the confrontation energy efficiency of the UAV provided by the embodiment of the application is shown in the figure.

[0123] Figure 6 A hardware structure diagram of the acquisition device of the confrontation energy efficiency of the UAV provided by the embodiment of the application is shown in the figure.

[0124] The specific embodiments of the application have been shown by way of example in the above figures, and will be described in greater detail below. These figures and this written description are not intended to limit the scope of the inventive concept in any way, but rather to illustrate the inventive concept by reference to specific embodiments. DETAILED DESCRIPTION

[0125] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The same reference numbers in different drawings indicate the same or similar elements. The embodiments described in the following exemplary embodiments are not meant to represent all embodiments consistent with the application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the application as detailed in the appended claims.

[0126] In the prior art, using physical layer security technology for unmanned aerial vehicles causes huge energy and time consumption, and there is a technical problem of low efficiency in obtaining the energy efficiency of unmanned aerial vehicles.

[0127] The method and the electronic device provided by the application obtain the energy efficiency of unmanned aerial vehicles by combining various communication behaviors of the unmanned aerial vehicles, determine the detection performance of the unmanned aerial vehicles, and ensure the accuracy and reliability of the subsequently obtained energy efficiency of the unmanned aerial vehicles. In addition, the initial energy efficiency and the detection performance of the unmanned aerial vehicles are used to determine a plurality of target optimization sub-problems that need to be optimized, and the target solutions corresponding to each target optimization sub-problem are obtained, and then the optimal target energy efficiency of the unmanned aerial vehicles is obtained according to each target solution, which improves the speed and convenience of the overall obtaining process, reduces the waste of energy and time costs, solves the technical problem of low efficiency in obtaining the energy efficiency of unmanned aerial vehicles, and achieves the technical effect of improving the efficiency of obtaining the energy efficiency of unmanned aerial vehicles.

[0128] The technical solutions of the application and how the technical solutions solve the above technical problems will be 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 described again in some embodiments. The embodiments of the application will be described below with reference to the drawings.

[0129] Figure 1 The acquisition scenario of the energy efficiency of unmanned aerial vehicles provided by the embodiments of the application is shown in the following figure: Figure 1As shown, the acquisition scenario of the energy efficiency of the UAV confrontation involves four roles, i.e., a user, a UAV, an eavesdropper and a listener, the UAV establishes a connection with the user and transmits information through a communication link during flight (as shown by the dotted line path), but in this process, the eavesdropper eavesdrops on the communication content between the UAV and the user through an eavesdropping link, and the listener further monitors and interferes with the communication between the UAV and the user through a monitoring link, and the eavesdropper and the listener have a cooperative relationship and can share the information obtained by each other, therefore, while the UAV is performing a task, not only the information is prevented from being monitored and / or eavesdropped, but also the normal transmission of information between the UAV and the user is ensured.

[0130] Figure 2 The flow of the method for acquiring energy efficiency of UAV confrontation provided by the embodiments of the present application Figure One As shown in the figure, the method for acquiring energy efficiency of UAV confrontation provided by the embodiments of the present application comprises the following steps. Figure 2 As shown in the figure, the method for acquiring energy efficiency of UAV confrontation provided by the embodiments of the present application comprises the following steps.

[0131] S201, acquiring the spatial coordinates of the UAV and a plurality of ground nodes, and determining the channel gain between the UAV base station and each ground node according to the spatial coordinates;

[0132] In the embodiment, the ground nodes include a user, an eavesdropper and a listener.

[0133] According to the scene where the UAV and each ground node are located, the corresponding spatial coordinates are acquired, and the channel gain between the UAV base station and each ground node is determined according to the spatial coordinates of the UAV and each ground node, wherein the channel gain between the UAV base station and each ground node is acquired by the following method:

[0134]

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

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

[0137] S202, acquire the transmission power of the UAV, determine the secrecy rate of the user and the communication behavior between the UAV and the user according to the transmission power and the channel gain of each channel, and determine the monitoring performance of the UAV according to the communication behavior;

[0138] In the embodiment, the communication behavior includes that the UAV keeps silent or the UAV is performing data distribution.

[0139] After the transmission power of the UAV and the channel gain between the UAV base station and each ground node are mastered, the secrecy rate of the user and various communication behaviors between the UAV and the user can be further determined, and the detection performance of the UAV can be determined according to all the communication behaviors between the UAV and the user.

[0140] S203, acquire the flight energy of the UAV, and determine the initial counter-energy efficiency of the UAV according to the secrecy rate and the flight energy.

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

[0142] The flight energy of the UAV is acquired in the following manner:

[0143]

[0144] EE UAV represents the flight energy of the UAV, P h and P i respectively represent the blade section power and the induced power of the UAV in the hovering state, U tip represents the tip speed of the rotor, v0 represents the average rotor induced speed of the UAV in the hovering state, d0 represents the fuselage drag ratio, S0 represents the rotor solidity, p represents the air density, A0 represents the rotor disc area, and W represents the weight of the UAV, v xy [n] represents the horizontal speed of the UAV in the nth time slot, and v z [n] represents the vertical speed of the UAV in the nth time slot.

[0145] After the flight energy of the UAV and the secrecy rate of the UAV when communicating with each user are determined, the initial counter-energy efficiency of the UAV is determined by comprehensively considering the two key factors in the following manner:

[0146]

[0147] EE k represents the initial counter-energy efficiency, R k represents the communication rate of the user, p k [n] represents the service relationship between the UAV and the user in the nth time slot, and E UAVR represents the flight energy of the UAV k [n] represents the secret rate of the user in time slot n.

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

[0149] In this embodiment, the target optimization sub-problems include a service relationship optimization sub-problem between the UAV and the user, a joint optimization sub-problem of the transmission power and the trajectory of the UAV, and an auxiliary variable optimization sub-problem.

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

[0151] S205, according to each target optimization sub-problem, obtain the corresponding target solution, and according to the target solution, determine the target confrontation energy efficiency of the UAV.

[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 UAV and the user, the optimal transmission power and the optimal trajectory of the UAV.

[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 application provides a method for obtaining the energy efficiency of a UAV, which comprises the following steps: obtaining the spatial coordinates of the UAV and a plurality of ground nodes, and determining the channel gain between the UAV base station and each ground node according to the spatial coordinates; obtaining the transmission power of the UAV, determining the secret rate of the user and the communication behavior between the UAV and the user according to the transmission power and each channel gain, and determining the monitoring performance of the UAV according to the communication behavior; obtaining the flight energy of the UAV, and determining the initial energy efficiency of the UAV according to the secret rate and the flight energy; determining a plurality of target optimization sub-problems according to the initial energy efficiency and the monitoring performance of the UAV; obtaining the corresponding target solution according to each target optimization sub-problem, and determining the target energy efficiency of the UAV according to the target solution. Thus, the detection performance of the UAV is determined by combining various communication behaviors of the UAV, the accuracy and reliability of the subsequently obtained energy efficiency of the UAV are ensured, in addition, a plurality of target optimization sub-problems that need to be optimized are determined according to the initial energy efficiency and the detection performance of the UAV, each target solution is obtained by solving each target optimization sub-problem, and the target energy efficiency corresponding to each target solution is obtained, the rapidity and convenience in the overall obtaining process are improved, the waste of energy and time cost is reduced, the technical problem of low efficiency in obtaining the energy efficiency of the UAV is solved, and the technical effect of improving the efficiency in obtaining the energy efficiency of the UAV is achieved.

[0155] Figure 3 The method for obtaining the energy efficiency of the UAV provided by the embodiment of the application Figure Two . As Figure 3 shown, the embodiment is based on the above-mentioned embodiment, and the obtaining process of the monitoring performance of the UAV is supplemented, which comprises:

[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 the UAV and each ground node.

[0157] In the embodiment, the signal-to-noise ratio of the ground node comprises the signal-to-noise ratio of the user, the signal-to-noise ratio of the eavesdropper and the signal-to-noise ratio of the listener.

[0158] After the transmission power of the UAV is determined, the transmission power and the channel gain data between the UAV and each ground node are used to accurately determine the plurality of communication behaviors between the UAV and the user, and the signal-to-noise ratio of each ground node when receiving a signal is effectively calculated based on these parameters, wherein the communication behavior between the UAV and the user can be obtained in the following manner:

[0159]

[0160] Among them, indicates that the UAV keeps silent, u represents that the UAV is conducting data distribution, w represents the listener, e represents 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 UAV base station and the listener in the nth time slot, g e [n] represents the channel gain between the UAV base station and the eavesdropper in the nth time slot;

[0161] The signal-to-noise ratio of each ground node can be obtained in the following manner:

[0162]

[0163] wherein r i [n] represents the signal-to-noise ratio of the UAV in 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 index set of users, w represents the listener, and e represents the eavesdropper.

[0164] S302, according to the eavesdropper signal-to-noise ratio and the listener signal-to-noise ratio, determine the corresponding cooperative eavesdropping signal-to-noise ratio, and according to the cooperative eavesdropping signal-to-noise ratio and the user signal-to-noise ratio, determine the user's secret rate;

[0165] According to the eavesdropper signal-to-noise ratio and the listener signal-to-noise ratio, the corresponding cooperative eavesdropping signal-to-noise ratio can be determined in the following manner:

[0166]

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

[0168] After obtaining the cooperative eavesdropping signal-to-noise ratio, the user signal-to-noise ratio can be further determined in the following manner:

[0169]

[0170] wherein R k [n] represents the user's secret rate in the nth time slot, Q -1 (·) is the inverse function of the Q function, δ and δ crespectively represent the maximum allowed demodulation error probability and the maximum allowed information leakage rate.

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

[0172] When it is determined that the UAV keeps silent, the first cross-correlation matrix corresponding to the transmission power and the channel gain 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 conducting data distribution, a second cross-correlation matrix corresponding to the transmission power and the channel gain is obtained;

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

[0177]

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

[0179] S305, likelihood ratio detection is performed on the first cross-correlation matrix and the second cross-correlation matrix to determine the monitoring performance of the UAV.

[0180] The first cross-correlation matrix and the second cross-correlation matrix are detected by likelihood ratio in the following way, and then the monitoring performance of the UAV is determined:

[0181]

[0182] Wherein, ξ * [n] represents the monitoring performance of the UAV in the nth time slot, represents that the UAV keeps silent, represents that the UAV is conducting data distribution, represents the KL divergence from to .

[0183] The application provides a method for obtaining an unmanned aerial vehicle (UAV) confrontation energy efficiency, which comprises the following steps: determining the communication behavior between the UAV and a 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; determining the secret rate of the user according to the signal-to-noise ratio of each ground node; obtaining a corresponding first cross-correlation matrix and a second cross-correlation matrix according to the communication behavior between the UAV and the user, the transmission power and the channel gain; and determining the monitoring performance of the UAV according to the first cross-correlation matrix and the second cross-correlation matrix. Thus, the accuracy and timeliness of the UAV confrontation energy efficiency are ensured by considering various possible communication behaviors of the UAV, and the calculation difficulty and time cost of the monitoring performance of the UAV are reduced by using the first cross-correlation matrix and the second cross-correlation matrix to determine the monitoring performance of the UAV, thereby solving the technical problem of low efficiency of the UAV confrontation energy efficiency and achieving the technical effect of improving the efficiency of the UAV confrontation energy efficiency.

[0184] Figure 4 The method for obtaining the UAV confrontation energy efficiency provided by the embodiments of the application Figure Three . As Figure 4 shown, the embodiments are based on the above-mentioned embodiments, and the obtaining process of each target optimization sub-problem is supplemented, which comprises the following steps:

[0185] S401, obtaining a first optimization problem according to an 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 by the following method:

[0187]

[0188] C4: 0≤p u [n]≤p max ,

[0189]

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

[0191]

[0192] wherein, C1-C9 represent constraints of the first optimization problem, C1 represents a user energy efficiency constraint; C2 represents a concealment constraint of the UAV against cooperative eavesdropping and monitoring attacks; C3 represents a constraint that the UAV can serve at most one user in one time slot; C4 represents a UAV transmit power constraint; C5 represents a UAV horizontal speed constraint; C6 represents a UAV vertical speed constraint; C7 represents a UAV flight height constraint; C8 represents a UAV start and end point constraint; and C9 represents a binary constraint of a variable. k EE represents an initial confrontation energy efficiency, and ξ * [n] represents a monitoring performance of the UAV in the nth time slot, and ε is a maximum allowed enemy detection error probability, is a maximum horizontal flight speed of the UAV, is a maximum vertical flight speed of the UAV, p max is a maximum transmit power of the UAV, h min and h max respectively represent a minimum height and a maximum height allowed for UAV flight, ρ k [n] represents a service relationship between the UAV and the user k in the nth time slot, and q[n] represents a trajectory of the UAV in the nth time slot.

[0193] S402, equivalently transforming the first optimization problem to obtain a transformed second optimization problem;

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

[0195]

[0196] C1-C8,

[0197] wherein, represents the second optimization problem, C10, C11 and C1-C8 are constraint conditions of the second optimization problem, represents an introduced auxiliary vector variable, μ represents a penalty factor, and K represents an index number set of the users.

[0198] S403, transforming the second optimization problem according to a double-loop iterative optimization algorithm to determine a service relationship optimization sub-problem between the UAV and the users, a transmit power and trajectory joint optimization sub-problem of the UAV, and an auxiliary variable optimization sub-problem.

[0199] The second optimization problem is transformed according to a double-loop iterative optimization algorithm to determine a service relationship optimization sub-problem between the UAV and the users, a transmit power and trajectory joint optimization sub-problem of the UAV, and an auxiliary variable optimization sub-problem, wherein the service relationship optimization sub-problem between the UAV and the users is obtained in the following manner:

[0200]

[0201] C3,C10,

[0202] wherein, represents the service relationship optimization sub-problem between the UAV and the user, C1', C3, C10 represent the constraint conditions of the service relationship optimization sub-problem between the UAV and the user, represents a fixed constant when the UAV transmission power 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 fixed Mth outer loop obtained Lagrange multiplier:

[0203] The transmission power and trajectory joint optimization sub-problem of the UAV is obtained in the following manner:

[0204]

[0205] C2,C4-C8

[0206]

[0207] wherein, represents the transmission power and trajectory joint optimization sub-problem of the UAV, C is a constant, C2, C4-C8 represent the constraint conditions of the transmission power and trajectory joint optimization sub-problem of the UAV, E UAV represents the flight energy of the UAV;

[0208] The auxiliary variable optimization sub-problem is obtained in the following manner:

[0209]

[0210] s.t.C11

[0211] wherein, represents the auxiliary variable optimization sub-problem.

[0212] It should be noted that the transmission power and trajectory joint optimization sub-problem of the UAV is a non-convex problem, which is difficult to solve. In order to facilitate the solution, a slack variable ζ k and Θ are introduced, and the above problem is equivalent to:

[0213]

[0214] C14:E UAV ≤Θ,

[0215] C2,C4-C8

[0216] For C12, it is given and Θ (m)A first-order Taylor expansion can be performed to obtain its lower bound:

[0217]

[0218] To solve R k The relaxation variable and satisfy the following conditions:

[0219] τ k [n]≤γ k [n],α i [n]≥γ i [n],

[0220]

[0221] It can be obtained:

[0222]

[0223] Where, log2(1+α c [n]) is a concave function for α c [n], so a first-order Taylor expansion is performed at to obtain:

[0224]

[0225] For γ k [n], the relaxation variable and satisfy the following conditions:

[0226]

[0227] For in the above formula, a first-order Taylor expansion is performed at the given point λ (m) [n] and to obtain:

[0228]

[0229] For γ i [n], the relaxation variable and satisfy the following conditions:

[0230]

[0231]

[0232] By z 2[n] at given z (m) A first order Taylor expansion at [n] gives

[0233]

[0234] By taking the derivative of A first order Taylor expansion at [n] and [n] gives (m) [n] and

[0235] Note that

[0236] and are convex functions for β i [n] and α i [n] respectively, thus, a first order Taylor expansion at [n] and [n] gives

[0237]

[0238] For the energy constraint C14, we introduce a slack variable satisfying the following condition:

[0239]

[0240] A first order Taylor expansion at [n] and [n] of the left side of the above equation at given ω (m) [n] and q (m) [n] gives

[0241]

[0242] E UAV in C14 has the following relation:

[0243]

[0244] For the concealment constraint C2, we can transform it into c [n] by using the introduced slack variable α

[0245]

[0246] Thus, the subproblem of joint optimization of UAV transmit power and trajectory can be relaxed into the following form:

[0247]

[0248] C2'C6-C8,

[0249] ​​​Therefore, the above problem is a convex problem, which can be solved by using CVX.

[0250] The application provides an unmanned aerial vehicle (UAV) counter-energy efficiency obtaining method. The method comprises the following steps: obtaining a first optimization problem corresponding to an initial counter-energy efficiency and a monitoring performance of the UAV; performing equivalent conversion on the first optimization problem to obtain a second optimization problem after conversion; and performing conversion on the second optimization problem according to a double-loop iterative optimization algorithm to determine a service relationship optimization subproblem between the UAV and a user, a launch power and trajectory joint optimization subproblem of the UAV and an auxiliary variable optimization subproblem. Therefore, the service relationship optimization subproblem between the UAV and the user, the launch power and trajectory joint optimization subproblem of the UAV and the auxiliary variable optimization subproblem are established according to the initial counter-energy efficiency and the monitoring performance of the UAV, the rapidity and convenience in the overall obtaining process are improved, the waste of energy and time cost is reduced, the complexity of the overall calculation is reduced by using the double-loop iterative optimization algorithm, the optimization process is further simplified by introducing the auxiliary variable optimization subproblem, the flexibility and adaptability of the algorithm are enhanced, the technical problem of low efficiency of obtaining the counter-energy efficiency of the target UAV is solved, and the technical effect of improving the efficiency of obtaining the counter-energy efficiency of the target UAV is achieved.

[0251] Figure 5 The application provides an unmanned aerial vehicle (UAV) counter-energy efficiency obtaining device. The device can be in the form of software and / or hardware. As shown in the structural schematic diagram of the UAV counter-energy efficiency obtaining device provided in the application, Figure 5 The UAV counter-energy efficiency obtaining device 500 provided in the application comprises a first obtaining module 501, a second obtaining module 502, a third obtaining module 503, a first processing module 504 and a second processing module 505.

[0252] The first obtaining module 501 is configured to obtain spatial coordinates of the UAV and a plurality of ground nodes, and determine channel gains between a UAV base station and each ground node according to the spatial coordinates, wherein the ground nodes comprise users, eavesdroppers and listeners.

[0253] The second obtaining module 502 is configured to obtain a launch power of the UAV, determine a secrecy rate of the user and a communication behavior between the UAV and the user according to the launch power and each channel gain, and determine a monitoring performance of the UAV according to the communication behavior, wherein the communication behavior comprises that the UAV keeps silent or the UAV is performing data distribution.

[0254] The third obtaining module 503 is configured to obtain a flight energy of the UAV, and determine an initial counter-energy efficiency of the UAV according to the secrecy rate and the flight energy, wherein the initial counter-energy efficiency is used to indicate an initial service relationship between the UAV and the user, an initial launch power of the UAV and an initial trajectory.

[0255] The first processing module 504 is configured to 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 launch power and trajectory joint optimization sub-problem of the UAV, and an auxiliary variable optimization sub-problem.

[0256] The second processing module 505 is configured to obtain a corresponding target solution according to each target optimization sub-problem, and determine a target confrontation energy efficiency of the UAV according to the target solution, wherein each target optimization sub-problem corresponds to one target solution, and the target confrontation energy efficiency is used to indicate an optimal service relationship between the UAV and the user, an optimal launch power and an optimal trajectory of the UAV.

[0257] In a possible implementation, the second obtaining module 502 is further configured to:

[0258] determine a signal-to-noise ratio of each ground node according to the launch power of the UAV and a 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 listener signal-to-noise ratio;

[0259] determine a corresponding cooperative eavesdropping signal-to-noise ratio according to the eavesdropper signal-to-noise ratio and the listener signal-to-noise ratio, and determine a user secrecy rate according to the cooperative eavesdropping signal-to-noise ratio and the user signal-to-noise ratio.

[0260] In a possible implementation, the second obtaining module 502 is further configured to:

[0261] when it is determined that the UAV is silent, obtain a corresponding first cross-correlation matrix according to the launch power and the channel gain;

[0262] when it is determined that the UAV is performing data distribution, obtain a corresponding second cross-correlation matrix according to the launch power and the channel gain;

[0263] perform likelihood ratio detection on the first cross-correlation matrix and the second cross-correlation matrix to determine the monitoring performance of the UAV.

[0264] In a possible implementation, the second obtaining module 502 is further configured to:

[0265] The communication behavior between the UAV and the user can be obtained in the following manner:

[0266]

[0267] wherein, indicates that the UAV is silent, indicates that the UAV is performing data distribution, w indicates a listener, e indicates an eavesdropper, y (l) [n] indicates a 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 UAV base station and the listener in the nth time slot, g e n represents the channel gain between the UAV base station and the eavesdropper in the nth time slot.

[0268] In a possible implementation, the second acquisition module 502 is further configured to:

[0269] The monitoring performance of the UAV is obtained in the following manner:

[0270]

[0271] wherein ξ * n represents the monitoring performance of the UAV in the nth time slot, represents that the UAV is silent, represents that the UAV is performing data distribution, represents the KL divergence from to , and σ is a constant;

[0272] Σ0 represents the first cross-correlation matrix, and Σ1 represents the second cross-correlation matrix, which can be obtained in the following manner:

[0273]

[0274] In a possible implementation, the first processing module 504 is further configured to:

[0275] According to the initial adversarial energy efficiency and the monitoring performance of the UAV, a corresponding first optimization problem is acquired;

[0276] The first optimization problem is equivalently transformed to obtain a second optimization problem after transformation;

[0277] According to a double-loop iterative optimization algorithm, the second optimization problem is transformed to determine a service relationship optimization subproblem between the UAV and the user, a transmission power and trajectory joint optimization subproblem of the UAV, and an auxiliary variable optimization subproblem;

[0278] wherein the first optimization problem is obtained in the following manner:

[0279]

[0280] C4: 0 ≤ p u n ≤ p max ,

[0281]

[0282] C8: q[1] = q[N], h u [1] = h u [N],

[0283]

[0284] wherein, represents a first optimization problem, C1-C9 are constraint conditions of the first optimization problem, EE k represents an initial energy efficiency, ξ * [n] represents a monitoring performance of the UAV in the nth time slot, ε is a maximum allowed enemy detection error probability, is a maximum horizontal flight speed of the UAV, is a maximum vertical flight speed of the UAV, p max is a maximum transmission power of the UAV, h min and h max respectively represent a minimum allowed height and a maximum allowed height of the UAV flight, ρ k [n] represents a service relationship between the UAV and the user k in the nth time slot, q[n] represents a trajectory of the UAV in the nth time slot.

[0285] The second optimization problem is obtained in the following manner:

[0286]

[0287] C1-C8,

[0288] wherein, represents a second optimization problem, C10, C11 and C1-C8 are constraint conditions of the second optimization problem, represents an introduced auxiliary vector variable, μ represents a penalty factor, and K represents an index number set of the users.

[0289] In a possible implementation manner, the first processing module 504 is further configured to:

[0290] The service relationship optimization sub-problem between the UAV and the user is obtained in the following manner:

[0291]

[0292] C3,C10,

[0293] wherein, represents a service relationship optimization sub-problem between the UAV and the user, C1’, C3, C10 represent constraint conditions of the service relationship optimization sub-problem between the UAV and the user, represents a fixed constant when the transmission power of the UAV is given, represents a fixed constant when the UAV trajectory is given, r represents a cooperative eavesdropping signal-to-noise ratio, and μ (M) represents a fixed Lagrange multiplier obtained in the Mth outer loop.

[0294] In a possible implementation, the first processing module 504 is further configured to:

[0295] The launch power and trajectory joint optimization sub-problem of the UAV is obtained in the following manner:

[0296]

[0297] C2, C4-C8

[0298]

[0299] wherein, represents a launch power and trajectory joint optimization sub-problem of the UAV, C is a constant, C2, C4-C8 represent constraint conditions of the launch power and trajectory joint optimization sub-problem of the UAV, E UAV represents flight energy of the UAV.

[0300] In a possible implementation, the first processing module 504 is further configured to:

[0301] The auxiliary variable optimization sub-problem is obtained in the following manner:

[0302]

[0303] s.t.C11

[0304] wherein, represents an auxiliary variable optimization sub-problem.

[0305] The UAV energy efficiency acquisition device provided in the application can implement the method embodiments, and has similar implementation principles and technical effects, and thus details are not described herein.

[0306] Figure 6 A hardware structure diagram of the UAV energy efficiency acquisition device is provided in the application. As shown in the figure, Figure 6 The UAV energy efficiency acquisition device 600 includes:

[0307] a processor 601 and a memory 602;

[0308] The memory stores computer execution instructions.

[0309] The processor executes the computer execution instructions stored in the memory 602, so that the UAV energy efficiency acquisition device performs the UAV energy efficiency acquisition method as described above.

[0310] It should be appreciated that the processor 601 described above can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), or the like. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the application can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor.

[0311] The memory 602 can include a high-speed random access memory (RAM), and can also include a non-volatile memory (NVM), such as at least one disk memory, and can also be a U disk, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, etc.

[0312] The embodiments of the present application correspondingly provide a computer readable storage medium, and the computer readable storage medium stores computer execution instructions. When the computer execution instructions are executed by a processor, the computer execution instructions are used to implement the method for obtaining the energy efficiency of the UAV confrontation as described above.

[0313] The embodiments of the present application correspondingly also provide a computer program product, and the computer program is executed by a processor to implement the method for obtaining the energy efficiency of the UAV confrontation as described above.

[0314] It should be noted that, for the above-mentioned method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the action order described, because according to the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0315] It should be further noted that, although the various steps in the flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps may be performed 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 performed at the same time, but may be performed at different times. The execution order of these sub-steps or stages is not necessarily to be performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0316] It should be understood that the above-described device embodiments are merely illustrative, and the device of the present application may also be implemented in other ways. For example, the division of units / modules in the above-described embodiments is merely a logical functional division, and actual implementations may employ other division methods. For example, multiple units, modules, or components may be combined or integrated into another system, or some features may be omitted or not implemented.

[0317] In addition, unless otherwise specified, the functional units / modules in the various embodiments of the present application may be integrated into a single unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The aforementioned integrated units / modules may be implemented in the form of hardware or software program modules.

[0318] If the integrated unit / module is implemented in hardware, the hardware may be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor may be any appropriate hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC. Unless otherwise specified, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc.

[0319] If the integrated units / modules are implemented in the form of software program modules and sold or used as independent products, they can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a number of 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 embodiments of the method of the present application. The aforementioned memory includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0320] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments. The technical features of the above embodiments can be combined arbitrarily, and in order to make the description concise, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0321] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. The application is intended to cover any variations, uses or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice in the art to which the application pertains or can relate. The specification and examples are to be regarded as exemplary only, and the true scope and spirit of the application are indicated by the following claims.

[0322] It should be understood that the application is not limited to the precise construction that has been described above and shown in the accompanying drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the application is limited only by the claims that follow.

Claims

1. A method for acquiring energy efficiency of UAV confrontation, characterized in that, The method comprises the following steps: acquiring spatial coordinates of a UAV and a plurality of ground nodes, and determining channel gains between a UAV base station and each of the ground nodes according to the spatial coordinates, wherein the ground nodes include users, eavesdroppers and listeners; acquiring a transmission power of the UAV, determining a secrecy rate of the users and a communication behavior between the UAV and the users according to the transmission power and each of the channel gains, and determining a monitoring performance of the UAV according to the communication behavior, wherein the communication behavior includes that the UAV keeps silent or the UAV is conducting data distribution; acquiring a flight energy of the UAV, and determining an initial counter-energy efficiency of the UAV according to the secrecy rate and the flight energy, wherein the initial counter-energy efficiency is used to indicate an initial service relationship between the UAV and the users, an initial transmission power and an initial trajectory of the UAV; determining a plurality of target optimization sub-problems according to the initial counter-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 users, a transmission power and trajectory joint optimization sub-problem of the UAV, and an auxiliary variable optimization sub-problem; acquiring a corresponding target solution according to each of the target optimization sub-problems, and determining a target counter-energy efficiency of the UAV according to the target solution, wherein each of the target optimization sub-problems corresponds to one of the target solutions, and the target counter-energy efficiency is used to indicate an optimal service relationship between the UAV and the users, an optimal transmission power and an optimal trajectory of the UAV.

2. The method of claim 1, wherein, The determination of the secrecy rate of the users according to the transmission power and each of the channel gains comprises the following steps: determining a signal-to-noise ratio of each of the ground nodes according to the transmission power of the UAV and the channel gains between the UAV and each of the ground nodes, wherein the signal-to-noise ratio of each of the ground nodes includes a user signal-to-noise ratio, an eavesdropper signal-to-noise ratio and a listener signal-to-noise ratio; determining a corresponding cooperative eavesdropping signal-to-noise ratio according to the eavesdropper signal-to-noise ratio and the listener signal-to-noise ratio, and determining the secrecy rate of the users according to the cooperative eavesdropping signal-to-noise ratio and the user signal-to-noise ratio.

3. The method of claim 2, wherein, The determination of the monitoring performance of the UAV according to the communication behavior comprises the following steps: when it is determined that the UAV keeps silent, acquiring a corresponding first cross-correlation matrix according to the transmission power and the channel gains; when it is determined that the UAV is conducting data distribution, acquiring a corresponding second cross-correlation matrix according to the transmission power and the channel gains; performing likelihood ratio detection on the first cross-correlation matrix and the second cross-correlation matrix to determine the monitoring performance of the UAV.

4. The method of claim 3, wherein, The communication behavior between the UAV and the users can be obtained in the following manner: wherein, denotes that the drone is silent, denotes that the drone is performing data distribution, w denotes the listener, e denotes the eavesdropper, y (l) [n] denotes the message symbol received by the listener in the nth time slot, x (l) [n] denotes the message symbol transmitted by the drone in the nth time slot, p u [n] denotes the transmit power of the drone in the nth time slot, g w [n] denotes the channel gain between the drone base station and the listener in the nth time slot, g e [n] denotes the channel gain between the drone base station and the eavesdropper in the nth time slot.

5. The method of claim 4, wherein, The monitoring performance of the UAV can be obtained in the following manner: wherein, ξ * [n] represents the monitoring performance of the UAV at the nth time slot, represents that the UAV is silent, represents that the UAV is conducting data distribution, represents the KL divergence from to , and σ is a constant; Σ0 represents the first cross-correlation matrix and Σ1 represents the second cross-correlation matrix, which can be obtained in the following manner:

6. The method of claim 5, wherein, The determination of the plurality of target optimization sub-problems according to the initial counter-energy efficiency and the monitoring performance of the UAV comprises the following steps: acquiring a corresponding first optimization problem according to the initial counter-energy efficiency and the monitoring performance of the UAV; The first optimization problem is equivalently transformed to obtain a transformed second optimization problem; According to a double-loop iterative optimization algorithm, the second optimization problem is transformed to determine a service relationship optimization sub-problem between the UAV and the user, a launch power and trajectory joint optimization sub-problem of the UAV, and an auxiliary variable optimization sub-problem; The first optimization problem is obtained by the following manner: wherein, represents the first optimization problem, C1-C9 are constraint conditions of the first optimization problem, EE k represents the initial adversarial energy efficiency, ξ * [n] represents the monitoring performance of the UAV at the nth time slot, and 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 respectively represent the minimum and maximum heights allowed for UAV flight, ρ k [n] represents the service relationship between the UAV and the user k at the nth time slot, and q[n] represents the trajectory of the UAV at the nth time slot; The second optimization problem is obtained by the following manner: wherein, represents the second optimization problem, C10, C11, and C1-C8 are constraint conditions of the second optimization problem, represents an introduced auxiliary vector variable, μ represents a penalty factor, and K represents an index number set of users.

7. The method of claim 6, wherein, The service relationship optimization sub-problem between the UAV and the user is obtained by the following manner: wherein, represents the service relationship optimization sub-problem between the UAV and the user, C1', C3, C10 represent the constraint conditions of the service relationship optimization sub-problem between the UAV and the user, represents a fixed constant when the UAV transmission power is given, represents a fixed constant when the UAV trajectory is given, r represents the cooperative eavesdropping signal-to-noise ratio, μ (M) represents a fixed Mth outer loop obtained Lagrange multiplier.

8. The method of claim 7, wherein, The launch power and trajectory joint optimization sub-problem of the UAV is obtained by the following manner: wherein, represents the launch power and trajectory joint optimization sub-problem of the UAV, C is a constant, C2, C4-C8 represent the constraint conditions of the launch power and trajectory joint optimization sub-problem of the UAV, E UAV represents the flight energy of the UAV.

9. The method of claim 7, wherein, The auxiliary variable optimization sub-problem is obtained by the following manner: wherein, denotes the auxiliary variable optimization subproblem.

10. An acquisition device for energy efficiency of UAV countermeasure, characterized in that, comprising: a memory, a processor; The memory stores computer execution instructions; The processor executes the computer execution instructions stored in the memory, so that the processor executes the method in any one of claims 1-9.

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