A UAV trajectory optimization method for information security

Abstract

The present invention relates to a method for optimizing drone trajectories for information security, and belongs to the field of communication technology. The method comprises: establishing a system framework including multiple users, drones, no-fly zones, eavesdroppers, and targets; then, establishing a drone mobility model, a multi-user channel model, and defining performance indicators. Then, based on the eavesdropper position and channel characteristics, an interference model is established to reduce the eavesdropping effect while ensuring a safe distance between drones. On this basis, an optimization problem is constructed with the goal of maximizing communication rate and coverage and minimizing eavesdropping effects. The problem is decomposed using the block coordinate descent method, and it is solved iteratively in combination with the continuous convex approximation technology. By alternately optimizing the drone trajectory, updating variables and constraints, it is ensured that the algorithm converges to the global optimum. The method of the present invention improves the efficiency and security of task execution, and has important theoretical and application value.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology and relates to an information security-oriented unmanned aerial vehicle trajectory optimization method. Background Art

[0002] With the rapid development of unmanned aerial vehicle (UAV) technology, its application in communications, security, logistics, perception, and other fields has become increasingly profound. In the field of wireless communications, in particular, UAVs, with their flexible deployment, efficient coverage, and three-dimensional mobility, have become a valuable choice for aerial base stations, communication relays, and dynamic hotspots. However, with the widespread application of UAV communication networks, security issues in complex environments have become increasingly prominent, particularly in terms of physical layer security (PLS).

[0003] Physical layer security is a technical approach that enhances the confidentiality of communication systems by leveraging the characteristics of wireless channels. While traditional cryptography-based security methods require efficient computing and key management, physical layer security enhances the security of communication networks by optimizing wireless channel characteristics and suppressing eavesdroppers' received signals at the hardware level. This approach is particularly well-suited to addressing the multiple challenges posed by the dynamic location and complex environments of drone communication networks.

[0004] In practical applications, drone communication networks face numerous security threats. For example, malicious eavesdroppers deploy eavesdropping drones or ground-based eavesdropping devices to intercept sensitive information between communication base station drones and ground users. To combat such threats, jamming drones are often introduced to transmit jamming signals to reduce the eavesdropper's signal-to-interference-and-noise ratio (SINR), thereby enhancing the system's anti-eavesdropping capabilities. At the same time, base station drones need to optimize their flight trajectories to improve communication rates and coverage, avoid no-fly zones in dynamic environments, and maintain a minimum safe distance between drones. However, current research has largely focused on optimizing a single task, neglecting the synergistic effects between communication and jamming tasks. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a UAV trajectory optimization method for information security. A communication base station UAV (UAV-M) and a jamming UAV (UAV-J) are deployed in the system respectively, and the multi-user communication quality and eavesdropping interference capability during the communication process are modeled and optimized. By jointly optimizing the multi-objective model, the communication rate, perception coverage performance and the effect of minimizing the eavesdropper's eavesdropping are maximized. By optimizing the flight trajectories of the two UAVs, the collaborative performance of the communication and jamming tasks is guaranteed, and the possibility of eavesdropping is reduced, thereby effectively enhancing the physical layer security of the system.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] An information security-oriented UAV trajectory optimization method, the method comprising the following steps:

[0008] S1: Establish a system framework based on a network scenario that includes multiple communication users, a base station drone, a jamming drone, a no-fly zone, multiple eavesdroppers, and detection targets;

[0009] S2: Within the system framework, establish the mobility models and no-fly zones for base station drones and jammer drones. Based on the mission requirements of base station drones, establish a multi-user channel model and define the communication rate and coverage ratio, which measures the perception capability.

[0010] S3: Based on the eavesdropper's location and channel characteristics, a jamming model for the jamming drone is established to reduce the eavesdropper's eavesdropping effect while ensuring a safe distance between the jamming drone and the base station drone.

[0011] S4: Based on the communication model and the perception model, an optimization problem is established with the objective function of maximizing the achievable communication rate and the coverage of the perception target and minimizing the eavesdropper's eavesdropping effect;

[0012] S5: The optimization problem is decomposed into two sub-problems by using the block coordinate descent technique, and solved iteratively by the continuous convex approximation technique in the subsequent steps;

[0013] S6: Under the condition of fixed interference drone trajectory, optimize the base station drone trajectory Q with the goal of maximizing communication rate and perception coverage M ;

[0014] S7: Under the condition of fixed base station drone trajectory, the trajectory Q of the jamming drone is optimized with the goal of minimizing the eavesdropper's eavesdropping effect. J , introducing auxiliary variables, using continuous convex approximation technology to transform the non-convex objective function and constraints into a convex optimization problem, and combining the solver for iterative solution;

[0015] S8: By alternately optimizing the UAV trajectory between subproblems and iteratively updating the auxiliary variables and constraints, the optimization algorithm is ensured to converge to the global optimal solution within a limited number of iterations.

[0016] Furthermore, in step S1, in the established dual-UAV assisted interawareness integrated system framework, the base station UAV provides services for K single-antenna users, while sensing targets in the environment, and the jamming UAV transmits jamming signals to confuse eavesdroppers, thereby protecting the communication security of the base station UAV within the flight time T.

[0017] Further, in step S2, the flight time T is divided into N equal time periods δt , that is, T = Nδ t , where it is determined that the drone is in each time period δ t The position of remains unchanged, then the trajectories of the base station UAV M and the interference UAV J in the time period T are expressed as:

[0018] q M [n]=[x M [n],y M [n],z M [n]] T

[0019] q J [n]=[x J [n],y J [n],z J [n]] T

[0020] Among them, x M [n],y M [n],z M [n] represents the three-dimensional coordinates of the base station drone in the nth time period, x J [n],y J [n],z J [n] represents the three-dimensional coordinates of the base station drone in the nth time period, The following mobility constraints are met:

[0021] ||q M [n+1]-q M [n]|| 2 ≤D 2 ,n=1,…,N-1,

[0022] ||q M [1]-q M0 || 2 ≤D 2 ,q M [N] = q MF ,

[0023] ||q J [n+1]-q J [n]|| 2 ≤D 2 ,n=1,…,N-1,

[0024] ||q J [1]-q J0 || 2 ≤D 2 ,q J [N] = q JF ,

[0025] H min ≤z M [n]≤H max ,

[0026] H min ≤z J [n]≤H max ,

[0027] Where D = Vδ t Indicates the maximum distance each UAV flies at the maximum flight speed V in each time period, q M0 represents the starting point of the base station drone, q MF represents the endpoint of the base station drone, q J0 Indicates the starting point of the jamming drone, q JF Indicates the end point of the jamming drone, H min is the minimum altitude of the drone, H max It is the maximum altitude at which the drone can fly;

[0028] Assume that the no-fly zone is represented by a sphere in the air with center C and radius r, which should satisfy the following constraints:

[0029] q J [n]-C||≥r,n=1,…,N,

[0030] q M [n]-C||≥r,n=1,…,N,

[0031] The channel from the base station drone M to the ground node is affected by the line-of-sight path. In time period n, the channel power gain from the base station drone M to the ground user k satisfies the free space path loss model, expressed as:

[0032]

[0033] Among them, d Mk represents the distance between the base station drone M and the ground user k in time period n, ρ0 represents the channel power gain at the reference distance d0 meters, l k represents the horizontal position of the kth user;

[0034] The channel power gain from the base station drone M to the ground target to be detected is:

[0035]

[0036] Among them, d Mp represents the distance between the UAV M and the ground target to be detected in the time period n, and p1 represents the horizontal position of the target to be detected;

[0037] The communication rate of the base station drone M in each time period is:

[0038]

[0039] Among them, P M [n] represents the transmission power of the base station drone at time period n, P J [n] represents the transmission power of the interfering UAV at time period n, and σ represents the noise power at the user or eavesdropper;

[0040] The coverage rate is used to measure the size of the perception capability, that is, whether the base station drone M covers the perception target in each time period:

[0041]

[0042] Among them, q M [n] represents the position of the base station drone in time period n, p1 represents the horizontal position of the target to be sensed, and γ represents the radius of the sensing range. is the indicator function. When the base station drone is within the coverage radius of the sensing target in time period n, otherwise,

[0043] Furthermore, in step S3, the channel power gain from the jamming UAV J to the eavesdropper j on the ground is:

[0044]

[0045] e j represents the horizontal position of the jth eavesdropper;

[0046] The interference rate of the jamming drone J on the eavesdropper j in each time period is:

[0047]

[0048] The collision avoidance constraints between the jammer UAV J and the base station UAV M are:

[0049] ||q M [n]-q J [n]|| 2 ≥d 2 min ,n=1,…,N,

[0050] d min Indicates the minimum distance between the base station drone and the jammer drone.

[0051] Furthermore, in step S4, the objective function is established as:

[0052]

[0053] The constraints are:

[0054] ||q M [n + 1] - q M [n]|| 2 ≤D 2 , n = 1, …, N - 1,

[0055] ||q M [1] - q M0 || 2 ≤D 2 , q M [N] = q MF ,

[0056] ||q J [n + 1] - q J [n]|| 2 ≤D 2 , n = 1, …, N - 1,

[0057] ||q J [1] - q J0 || 2 ≤D 2 , q J [N] = q JF ,

[0058] ||q M [n] - q J [n]|| 2 ≥d 2 min , n = 1, …, N,

[0059] H min ≤z M [n] ≤ H max ,

[0060] H min ≤z J [n] ≤ H max ,

[0061] ||q J [n] - C|| ≥ r, n = 1, …, N,

[0062] ||q M [n] - C|| ≥ r, n = 1, …, N,

[0063] ||v[n]|| ≤ v max , n = 1, …, N,

[0064] ||v[n]|| ≥ v min , n = 1, …, N,

[0065] ||a[n]||≤a max ,n=1,…,N,

[0066]

[0067] v[n]=v[n-1]+a[n]δ t ,n=2,…,N,

[0068] C p ≥C min

[0069] Among them, C min is the threshold of minimum coverage, and a[n] is the acceleration of the UAV.

[0070] Furthermore, in step S5, the optimization problem is decomposed into two sub-problems by using block coordinate descent and continuous convex approximation technology, wherein the decomposed sub-problem P1 is:

[0071]

[0072] The decomposed sub-problem P2 is:

[0073]

[0074] In steps S6 to S8, the two sub-problems P1 and P2 are iteratively solved.

[0075] Furthermore, in step S6, for sub-problem P1, let:

[0076]

[0077] in,

[0078] A continuous function is used to approximate the trajectory Q of the jamming drone J :

[0079]

[0080] Then, by introducing the slack variable T = {t[n] = || (x M [n],y M [n])-l k || 2 +z M [n] 2} and U={u[n]=||(x M [n],y M [n])-e j || 2 +z M [n]2}Optimize subproblem P1:

[0081]

[0082] By applying the continuous convex approximation technique, the term Replace it with its convex lower bound and replace -||(x M [n],y M [n])-e j || 2 Replace it with a concave upper bound at a given initial point;

[0083] definition is the trajectory of the base station drone in the k0th iteration, then the following inequality can be obtained:

[0084]

[0085] Therefore, subproblem P1 can be approximated as follows:

[0086]

[0087] The non-convex constraint ||v[n]||≥v in the above constraints min ,n=1,…,N,||q M [n]-C||≥r,n=1,…,N, Use SCA to convert into convex constraints:

[0088]

[0089] ||q M0 [n]-C|| 2 +2(q M0 [n]-C) T (q M [n]-q M0 [n])≥r 2

[0090]

[0091] Among them, v0[n] and q M0 [n] indicates a reference point;

[0092] Use the solving tool to find the optimal solution to the transformed subproblem.

[0093] Furthermore, in step S7, for subproblem P2, slack variables are introduced:

[0094]

[0095] Transform subproblem P2 into:

[0096]

[0097] in, make represents the flight trajectory of the jamming UAV in the k0th iteration, then the term and -||(x J [n],y J [n])-l k || 2 The upper bounds of are expressed as:

[0098]

[0099] Among them, F k0 [n]=||(x k0 J [n],y k0 J [n])|| 2 -2[((x k0 J [n],y k0 J [n])-l k )] T (x J [n],y J [n])-||l k || 2 , m k0 [n]=||(x k0 J [n],y ko J [n])-e j || 2 .

[0100] Therefore, the trajectory optimization problem P2 of the jamming UAV J is approximately as follows:

[0101]

[0102] Among them, the non-convex constraint ||v[n]||≥v min ,n=1,…,N,||q M [n]-q J [n]|| 2 ≥d 2 min ,n=1,…,N,||q J [n]-C||≥r,n=1,…,N is transformed into a convex constraint using SCA:

[0103]

[0104] ||q J0 [n]-C|| 2 +2(q J0 [n]-C) T (q J [n]-q J0 [n])≥r 2

[0105]

[0106] Among them, v0[n] and q J0 [n] indicates a reference point;

[0107] Subproblem P2 after optimization by the solver.

[0108] Furthermore, in step S8, the trajectories of the two UAVs are continuously optimized alternately, and the number of iterations is updated by k0=k0+1 until the relative increment of the objective function is less than a small threshold Γ0.

[0109] The beneficial effects of the present invention are:

[0110] This invention builds a dual-UAV collaborative communication and jamming system model and employs an optimization algorithm to jointly optimize the trajectories of the base station UAV and the jamming UAV. While ensuring communication speed and sensor coverage, it effectively suppresses eavesdropping while strictly adhering to multiple constraints, such as no-fly zone avoidance and safe distance. This method significantly enhances the communication and jamming coordination capabilities of a multi-UAV system in complex environments, possessing significant theoretical value and practical application prospects.

[0111] The present invention adopts the multi-user communication rate in the communication process as the communication performance indicator, and the perception coverage as the perception performance indicator, to design an optimization problem in order to maximize the communication rate, perception coverage performance and minimize the effect of eavesdroppers eavesdropping. The optimization problem is solved by block coordinate descent and continuous convex approximation, which ensures the performance of communication and perception and reduces the possibility of eavesdropping, thereby effectively enhancing the physical layer security of the system.

[0112] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0113] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0114] Figure 1 This is a flow chart of a method for optimizing the trajectory of a UAV for information security according to the present invention;

[0115] Figure 2 This is a system architecture diagram of an information security-oriented drone trajectory optimization method of the present invention. DETAILED DESCRIPTION

[0116] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0117] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0118] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0119] See also Figures 1 and 2 , which is a UAV trajectory optimization method for information security.

[0120] Example

[0121] This embodiment provides an overall implementation process of a UAV trajectory optimization method for information security. Figure 1 As shown, the method includes the following steps:

[0122] S1: Establish a system framework based on a network scenario that includes multiple communication users, a base station drone, a jamming drone, a no-fly zone, multiple eavesdroppers, and detection targets;

[0123] In the embodiment of the present invention, Figure 2 As shown in the figure, in the dual-UAV assisted interawareness integrated system, the base station UAV provides services for K single-antenna users, while sensing targets in the environment. The jamming UAV transmits jamming signals to confuse eavesdroppers, thereby protecting the communication security of the base station UAV within the flight time T.

[0124] S2: Under the system framework of step S1, establish the mobility model and no-fly zone of the two drones. According to the mission requirements of the base station drone, establish a multi-user channel model and define performance indicators such as communication rate and coverage rate to measure the size of perception capability.

[0125] In the embodiment of the present invention, for the convenience of analysis, the flight time T is divided into N time periods of equal length, that is, T=Nδ t , where δ t Choose a value that is small enough so that the position of the UAV can be considered unchanged in each time period. The trajectories of the base station UAV and the jamming UAV in the T time period are expressed as: M [n]=[x M [n],y M [n],z M [n]] T and q J [n]=[x J [n],y J [n],z J [n]] T ,in The following mobility constraints are met:

[0126] ||q M [n+1]-q M [n]|| 2 ≤D 2 ,n=1,…,N-1,

[0127] ||q M [1]-q M0 || 2 ≤D 2 ,q M [N] = q MF ,

[0128] ||q J [n+1]-q J [n]|| 2 ≤D 2 ,n=1,…,N-1,

[0129] ||q J [1]-q J0 || 2 ≤D 2 ,q J [N] = q JF ,

[0130] H min ≤z M [n]≤H max ,

[0131] H min ≤z J [n]≤H max ,

[0132] Where D = Vδ t Indicates the maximum distance each UAV can fly at the maximum flight speed V in each time period, q M0 represents the starting point of the base station drone, q MF represents the endpoint of the base station drone, q J0 Indicates the starting point of the jamming drone, q JF Indicates the end point of the jamming drone, H min It is the minimum altitude that the drone is required to fly. max It is the maximum altitude at which the drone is required to fly.

[0133] Sometimes, due to specific environments or regulations, drones cannot enter certain areas. These areas are usually called "no-fly zones." Assume that the no-fly zone is represented by a sphere in the air with a center of C and a radius of r. It should meet the following constraints:

[0134] ||q J [n]-C||≥r,n=1,…,N,

[0135] ||q M [n]-C||≥r,n=1,…,N,

[0136] The channel from the base station drone (M) to the ground node is mainly affected by the line-of-sight (LOS) path. In time period n, the channel power gain from the base station drone (M) to the ground user k satisfies the free space path loss model, expressed as:

[0137]

[0138] Among them, d Mk represents the distance between UAV M and ground user k in time period n, ρ0 represents the channel power gain at the reference distance d0 = 1 meter, l k represents the horizontal position of the k-th user.

[0139] By the same token, the channel power gain from the base station drone (M) to the ground target to be detected is:

[0140]

[0141] Among them, d Mp represents the distance between the UAV M and the ground target to be detected in the time period n, and p1 represents the horizontal position of the target to be detected;

[0142] The communication rate of the base station drone in each time period is:

[0143]

[0144] Among them, P M [n] represents the transmission power of the base station drone at time period n, P J [n] represents the transmission power of the interfering UAV at time period n, and σ represents the noise power at the user or eavesdropper;

[0145] The coverage rate is used to measure the size of the perception capability, and whether the base station drone covers the perception target in each time period:

[0146]

[0147] Among them, q M [n] represents the position of the base station drone in time period n, p1 represents the horizontal position of the perceived target, γ represents the radius of the perception range, is the indicator function. When the base station drone is within the coverage radius of the sensing target in time period n, otherwise,

[0148] S3: Based on the eavesdropper's location and channel characteristics, a jamming model for the jamming drone is established to reduce the eavesdropper's eavesdropping effect while ensuring a safe distance between the jamming drone and the base station drone.

[0149] In this embodiment of the present invention, the channel power gain from the jamming drone (J) to the eavesdropper j on the ground is:

[0150]

[0151] e j represents the horizontal position of the jth eavesdropper;

[0152] The interference rate of the jamming drone on the eavesdropper in each time period is:

[0153]

[0154] The interference drone and the base station drone must be prevented from colliding with each other. The constraints are:

[0155] ||q M [n]-q J [n]|| 2 ≥d 2 min ,n=1,…,N,

[0156] S4: Based on the communication model and the perception model, an optimization problem is established with the objective function of maximizing the achievable communication rate and the coverage of the perception target and minimizing the eavesdropper's eavesdropping effect;

[0157] In the embodiment of the present invention, the objective function established is as follows:

[0158]

[0159] The constraints are:

[0160] ||q M [n+1]-q M [n]|| 2 ≤D 2 ,n=1,…,N-1,

[0161] ||q M [1]-q M0 || 2 ≤D 2 ,q M [N]=q MF ,

[0162] ||q J [n+1]-q J [n]|| 2 ≤D 2 ,n=1,…,N-1,

[0163] ||q J [1]-q J0 || 2 ≤D 2 ,q J [N] = q JF ,

[0164] ||q M [n]-q J [n]|| 2 ≥d 2 min ,n=1,…,N,

[0165] H min ≤z M [n]≤H max ,

[0166] H min ≤z J [n]≤H max ,

[0167] ||q J [n]-C||≥r,n=1,…,N,

[0168] ||q M [n]-C||≥r,n=1,…,N,

[0169] ||v[n]||≤v max ,n=1,…,N,

[0170] ||v[n]||≥v min ,n=1,…,N,

[0171] ||a[n]||≤a max ,n=1,…,N,

[0172]

[0173] v[n]=v[n-1]+a[n]δ t ,n=2,…,N,

[0174] C p ≥C min

[0175] Among them, C min is the threshold of minimum coverage, and a[n] is the acceleration of the drone. By observation, the optimization problem is non-convex and difficult to solve.

[0176] S5: The optimization problem is implemented by block coordinate descent (BCD) and continuous convex approximation (SCA) technology, and it is solved iteratively in steps S6 to S8;

[0177] In an embodiment of the present invention, the optimization problem is decomposed into two sub-problems through block coordinate descent.

[0178] Sub-problem 1 is:

[0179]

[0180] Sub-problem 2 is:

[0181]

[0182] S6: Under the condition of fixed interference drone trajectory, optimize the base station drone trajectory Q with the goal of maximizing communication rate and perception coverage M ;

[0183] In the embodiment of the present invention, for sub-problem 1, by observing the above objective function, let in,

[0184] For a given jamming drone trajectory Q J In order to solve the discontinuity of the indicator function, a continuous function is used to approximate it, namely: Then, by introducing the slack variable T = {t[n] = || (x M [n],y M [n])-l k || 2 +z M [n] 2} and U={u[n]=||(x M [n],y M [n])-e j || 2 +z M [n] 2 The above optimization problem can be reformulated as:

[0185]

[0186] By observing that the optimization problem is still non-convex, it is difficult to directly find the optimal solution. By applying the continuous convex approximation technique, the term Replace it with its convex lower bound and replace -||(x M [n],y M [n])-e j || 2 is replaced by a concave upper bound at the given initial point. Definition is the trajectory of the base station drone in the k0th iteration, then the following inequality can be obtained:

[0187]

[0188] Therefore, subproblem 1 can be approximated as the following problem:

[0189]

[0190] The non-convex constraint ||v[n]||≥v in the above constraints min ,n=1,…,N,||q M [n]-C||≥r,n=1,…,N, SCA can also be used to convert it into a convex constraint:

[0191]

[0192] ||qM0 [n]-C|| 2 +2(q M0 [n]-C) T (q M [n]-q M0 [n])≥r 2

[0193]

[0194] Among them, v0[n] and q M0 [n] indicates the reference point.

[0195] After the above processing, the objective function of the optimization problem is concave and its feasible domain is convex, so the solving tool can be used to obtain the optimal solution.

[0196] S7: Under the condition of fixed base station drone trajectory, the trajectory Q of the jamming drone is optimized with the goal of minimizing the eavesdropper's eavesdropping effect. J , introducing auxiliary variables, using continuous convex approximation technology to transform the non-convex objective function and constraints into a convex optimization problem, and combining the solver for iterative solution;

[0197] In the embodiment of the present invention, for sub-problem 2, by introducing the slack variable and The optimization problem can be reformulated as:

[0198]

[0199] in, make represents the flight trajectory of the interfering UAV in the k0th iteration. and -||(x J [n],y J [n])-l k || 2 The upper bounds of can be expressed as:

[0200]

[0201] Among them, F k0 [n]=||(x k0 J [n],y k0 J [n])|| 2 -2[((x k0 J [n],y k0 J [n])-l k )] T (x J[n],y J [n])-||l k || 2 , m k0 [n]=||(x k0 J [n],y ko J [n])-e j || 2 .

[0202] Therefore, the trajectory optimization problem of jamming UAV can be approximated as:

[0203]

[0204] Among them, the non-convex constraint ||v[n]||≥v min ,n=1,…,N,||q M [n]-q J [n]|| 2 ≥d 2 min ,n=1,…,N,||q J [n]-C||≥r,n=1,…,N, can be transformed into a convex constraint using SCA:

[0205]

[0206] ||q J0 [n]-C|| 2 +2(q J0 [n]-C) T (q J [n]-q J0 [n])≥r 2

[0207]

[0208] Among them, v0[n] and q J0 [n] indicates the reference point.

[0209] Since the above has been converted into a convex optimization problem, it can be solved efficiently by the solver.

[0210] S8: By alternately optimizing the UAV trajectory between subproblems and iteratively updating the auxiliary variables and constraints, the optimization algorithm is ensured to converge to the global optimal solution within a limited number of iterations.

[0211] In the embodiment of the present invention, the trajectories of the two UAVs are continuously optimized alternately, and the number of iterations is updated by k0=k0+1 until the relative increment of the objective function is less than a small threshold Γ0.

[0212] Table 1 is the pseudo code of the algorithm of the information security-oriented UAV trajectory optimization method of the present invention:

[0213] Table 1

[0214]

[0215] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A UAV trajectory optimization method for information security, characterized by: The method comprises the following steps: S1: Establish a system framework based on a network scenario that includes multiple communication users, a base station drone, a jamming drone, a no-fly zone, multiple eavesdroppers, and detection targets; S2: Within the system framework, establish the mobility models and no-fly zones for base station drones and jammer drones. Based on the mission requirements of base station drones, establish a multi-user channel model and define the communication rate and coverage ratio, which measures the sensing capability. S3: Based on the eavesdropper's location and channel characteristics, a jamming model for the jamming drone is established to reduce the eavesdropper's eavesdropping effect while ensuring a safe distance between the jamming drone and the base station drone. S4: Based on the communication model and the perception model, an optimization problem is established with the objective function of maximizing the achievable communication rate and the coverage of the perception target and minimizing the eavesdropper's eavesdropping effect; S5: The optimization problem is decomposed into two sub-problems by using the block coordinate descent technique, and solved iteratively by the continuous convex approximation technique in the subsequent steps; S6: Under the condition of fixed interference drone trajectory, optimize the base station drone trajectory Q with the goal of maximizing communication rate and perception coverage M ; S7: Under the condition of fixed base station drone trajectory, the trajectory Q of the jamming drone is optimized with the goal of minimizing the eavesdropper's eavesdropping effect. J , introducing auxiliary variables, using continuous convex approximation technology to transform the non-convex objective function and constraints into a convex optimization problem, and combining the solver for iterative solution; S8: By alternately optimizing the UAV trajectory between subproblems and iteratively updating the auxiliary variables and constraints, the optimization algorithm is ensured to converge to the global optimal solution within a limited number of iterations.

2. The information security-oriented UAV trajectory optimization method according to claim 1, characterized in that: In step S1, in the established dual-UAV assisted interawareness integrated system framework, the base station UAV provides services for K single-antenna users, while sensing targets in the environment, and the jamming UAV transmits jamming signals to confuse eavesdroppers, protecting the communication security of the base station UAV within the flight time T.

3. The information security-oriented UAV trajectory optimization method according to claim 2, characterized in that: In step S2, the flight time T is divided into N equal time periods δ t , that is, T = Nδ t , where it is determined that the drone is in each time period δ t The position of remains unchanged, then the trajectories of the base station UAV M and the interference UAV J in the time period T are expressed as: q M [n]=[x M [n],y M [n],z M [n]] T q J [n]=[x J [n],y J [n],z J [n]] T Among them, x M [n],y M [n],z M [n] represents the three-dimensional coordinates of the base station drone in the nth time period, x J [n],y J [n],z J [n] represents the three-dimensional coordinates of the base station drone in the nth time period, The following mobility constraints are met: ||q M [n+1]-q M [n]|| 2 ≤D 2 ,n=1,…,N-1, ||q M [1]-q M0 || 2 ≤D 2 ,q M [N]=q MF , ||q J [n+1]-q J [n]|| 2 ≤D 2 ,n=1,…,N-1, ||q J [1]-q J0 || 2 ≤D 2 ,q J [N]=q JF , H min ≤z M [n]≤H max , H min ≤z J [n]≤H max , Where D = Vδ t Indicates that each UAV is at its maximum flight speed v in each time period max The maximum distance of flight, q M0 represents the starting point of the base station drone, q MF represents the endpoint of the base station drone, q J0 Indicates the starting point of the jamming drone, q JF Indicates the end point of the jamming drone, H min is the minimum altitude of the drone, H max It is the maximum altitude at which the drone can fly; Assume that the no-fly zone is represented by a sphere in the air with center C and radius r, which should satisfy the following constraints: ||q J [n]-C||≥r,n=1,…,N, ||q M [n]-C||≥r,n=1,…,N, The channel from the base station drone M to the ground node is affected by the line-of-sight path. In time period n, the channel power gain from the base station drone M to the ground user k satisfies the free space path loss model, expressed as: Among them, d Mk represents the distance between the base station drone M and the ground user k in time period n, ρ0 represents the channel power gain at the reference distance d0 meters, l k represents the horizontal position of the kth user; The channel power gain from the base station drone M to the ground target to be detected is: Among them, d Mp represents the distance between the UAV M and the ground target to be detected in time period n; p1 represents the horizontal position of the target to be detected; The communication rate of the base station drone M in each time period is: Among them, P M [n] represents the transmission power of the base station drone at time period n, P J [n] represents the transmission power of the interfering UAV at time period n, and σ represents the noise power at the user or eavesdropper; The coverage rate is used to measure the size of the perception capability, that is, whether the base station drone M covers the perception target in each time period: Among them, q M [n] represents the position of the base station drone in time period n, p1 represents the horizontal position of the target to be sensed, and γ represents the radius of the sensing range. is the indicator function. When the base station drone is within the coverage radius of the sensing target in time period n, otherwise, 4. The information security-oriented UAV trajectory optimization method according to claim 3, characterized in that: In step S3, the channel power gain from the jamming UAV J to the eavesdropper j on the ground is: e j represents the horizontal position of the jth eavesdropper; The interference rate of the jamming drone J on the eavesdropper j in each time period is: The collision avoidance constraints between the jammer UAV J and the base station UAV M are: ||q M [n]-q J [n]|| 2 ≥d 2 min ,n=1,…,N, d min Indicates the minimum distance between the base station drone and the jammer drone.

5. The information security-oriented UAV trajectory optimization method according to claim 4, characterized in that: In step S4, the objective function is established as: The constraints are: ||q M [n+1]-q M [n]|| 2 ≤D 2 ,n=1,…,N-1, ||q M [1]-q M0 || 2 ≤D 2 ,q M [N]=q MF , ||q J [n+1]-q J [n]|| 2 ≤D 2 ,n=1,…,N-1, ||q J [1]-q J0 || 2 ≤D 2 ,q J [N]=q JF , ||q M [n]-q J [n]|| 2 ≥d 2 min ,n=1,…,N, H min ≤z M [n]≤H max , H min ≤z J [n]≤H max , ||q J [n]-C||≥r,n=1,…,N, ||q M [n]-C||≥r,n=1,…,N, ||v[n]||≤v max ,n=1,…,N, ||v[n]||≥v min ,n=1,…,N, ||a[n]||≤a max ,n=1,…,N, v[n]=v[n-1]+a[n]δ t ,n=2,…,N, C p ≥C min Among them, C min is the threshold of minimum coverage, and a[n] is the acceleration of the UAV.

6. The information security-oriented UAV trajectory optimization method according to claim 5, characterized in that: In step S5, the optimization problem is decomposed into two sub-problems by using block coordinate descent and continuous convex approximation technology, wherein the decomposed sub-problem P1 is: s.t.||q M [n+1]-q M [n]|| 2 ≤D 2 ,n=1,…,N-1, ||q M [1]-q M0 || 2 ≤D 2 ,q M [N]=q MF , ||q M [n]-q J [n]|| 2 ≥d 2 min ,n=1,…,N, H min ≤z M [n]≤H max , ||q M [n]-C||≥r,n=1,…,N, ||v[n]||≤v max ,n=1,…,N, ||v[n]||≥v min ,n=1,…,N, ||a[n]||≤a max ,n=1,…,N, v[n]=v[n-1]+a[n]δ t ,n=2,…,N, C p ≥C min The decomposed sub-problem P2 is: s.t.||q J [n+1]-q J [n]|| 2 ≤D 2 ,n=1,…,N-1, ||q J [1]-q J0 || 2 ≤D 2 ,q J [N]=q JF , ||q M [n]-q J [n]|| 2 ≥d 2 min ,n=1,…,N, H min ≤z J [n]≤H max , ||q J [n]-C||≥r,n=1,…,N, ||v[n]||≤v max ,n=1,…,N, ||v[n]||≥v min ,n=1,…,N, ||a[n]||≤a max ,n=1,…,N, v[n]=v[n-1]+a[n]δ t ,n=2,…,N, C p ≥C min In steps S6 to S8, the two sub-problems P1 and P2 are iteratively solved.

7. The information security-oriented UAV trajectory optimization method according to claim 6, characterized in that: In step S6, for sub-problem P1, let: in, A continuous function is used to approximate the trajectory Q of the jamming drone J : Then, by introducing the slack variable T = {t[n] = || (x M [n],y M [n])-l k || 2 +z M [n] 2 } and U={u[n]=||(x M [n],y M [n])-e j || 2 +z M [n] 2 }Optimize subproblem P1: s.t.||q M [n+1]-q M [n]|| 2 ≤D 2 ,n=1,…,N-1, ||q M [1]-q M0 || 2 ≤D 2 ,q M [N]=q MF , ||q M [n]-q J [n]|| 2 ≥d 2 min ,n=1,…,N, H min ≤z M [n]≤H max , ||q M [n]-C||≥r,n=1,…,N, ||v[n]||≤v max ,n=1,…,N, ||v[n]||≥v min ,n=1,…,N, ||a[n]||≤a max ,n=1,…,N, v[n]=v[n-1]+a[n]δ t ,n=2,…,N, C p ≥C min ||(x M [n],y M [n])-l k || 2 +z M [n] 2 -t[n]≤0, u[n]-||(x M [n],y M [n])-e j || 2 -z M [n] 2 ≤0, u[n]≥0, By applying the continuous convex approximation technique, the term Replace it with its convex lower bound and replace -||(x M [n],y M [n])-e j || 2 Replace it with a concave upper bound at a given initial point; definition is the trajectory of the base station drone in the k0th iteration, then the following inequality can be obtained: Therefore, subproblem P1 can be approximated as follows: s.t.||q M [n+1]-q M [n]|| 2 ≤D 2 ,n=1,…,N-1, ||q M [1]-q M0 || 2 ≤D 2 ,q M [N]=q MF , ||q M [n]-q J [n]|| 2 ≥d 2 min ,n=1,…,N, H min ≤z M [n]≤H max , ||q M [n]-C||≥r,n=1,…,N, ||v[n]||≤v max ,n=1,…,N, ||v[n]||≥v min ,n=1,…,N, ||a[n]||≤a max ,n=1,…,N, v[n]=v[n-1]+a[n]δ t ,n=2,…,N, C p ≥C min ||(x M [n],y M [n])-l k || 2 +z M [n] 2 -t[n]≤0, u[n]+||(x k0 M [n],y k0 M [n])|| 2 -||e j || 2 -z M [n] 2 −2[(x k0 M [n],y k0 M [n])-e j ] T ((x M [n],y M [n]))≤0, u[n]≥0, The non-convex constraint ||v[n]||≥v in the above constraints min ,n=1,…,N,||q M [n]-C||≥r,n=1,…,N, Use SCA to convert into convex constraints: ||q M0 [n]-C|| 2 +2(q M0 [n]-C) T (q M [n]-q M0 [n])≥r 2 Among them, v0[n] and q M0 [n] indicates a reference point; Use the solving tool to find the optimal solution to the transformed subproblem.

8. The information security-oriented UAV trajectory optimization method according to claim 7, characterized in that: In step S7, for subproblem P2, by introducing slack variables and Transform subproblem P2 into: s.t.||q J [n+1]-q J [n]|| 2 ≤D 2 ,n=1,…,N-1, ||q J [1]-q J0 || 2 ≤D 2 ,q J [N]=q JF , ||q M [n]-q J [n]|| 2 ≥d 2 min ,n=1,…,N, H min ≤z J [n]≤H max , ||q J [n]-C||≥r,n=1,…,N, ||v[n]||≤v max ,n=1,…,N, ||v[n]||≥v min ,n=1,…,N, ||a[n]||≤a max ,n=1,…,N, v[n]=v[n-1]+a[n]δ t ,n=2,…,N, C p ≥C min , l[n]−||(x J [n],y J [n])-l k || 2 -z J [n] 2 ≤0, l[n]≥0, ||(x J [n],y J [n])-e j || 2 +z J [n] 2 -m[n]≤0, in, make represents the flight trajectory of the jamming UAV in the k0th iteration, then the term and -||(x J [n],y J [n])-l k || 2 The upper bounds of are expressed as: -||(x J [n],y J [n])-l k || 2 ≤F k0 [n] where, F k0 [n] = ||(x k0 J [n], y k0 J [n])|| 2 - 2[((x k0 J [n], y k0 J [n]) - l k )] T (x J [n], y J [n]) - ||l k || 2 , m k0 [n] = ||(x k0 J [n], y ko J [n]) - e j || 2 ; Therefore, the trajectory optimization problem P2 of the jamming UAV J is approximately as follows: s.t.||q J [n+1]-q J [n]|| 2 ≤D 2 ,n=1,…,N-1, ||q J [1]-q J0 || 2 ≤D 2 ,q J [N]=q JF , ||q M [n]-q J [n]|| 2 ≥d 2 min ,n=1,…,N, H min ≤z J [n]≤H max , ||q J [n]-C||≥r,n=1,…,N, ||v[n]||≤v max ,n=1,…,N, ||v[n]||≥v min ,n=1,…,N, ||a[n]||≤a max ,n=1,…,N, v[n]=v[n-1]+a[n]δ t ,n=2,…,N, C p ≥C min , l[n]+F k0 [n]-z J [n] 2 ≤0, l[n]≥0, ||(x J [n],y J [n])-e j || 2 +z J [n] 2 -m[n]≤0, Among them, the non-convex constraint ||v[n]||≥v min ,n=1,…,N,||q M [n]-q J [n]|| 2 ≥d 2 min ,n=1,…,N,||q J [n]-C||≥r,n=1,…,N is transformed into a convex constraint using SCA: ||q J0 [n]-C|| 2 +2(q J0 [n]-C) T (q J [n]-q J0 [n])≥r 2 Among them, v0[n] and q J0 [n] indicates a reference point; Subproblem P2 after optimization by the solver.

9. The information security-oriented UAV trajectory optimization method according to claim 8, characterized in that: In step S8, the trajectories of the two UAVs are continuously optimized alternately, and the number of iterations is updated by k0=k0+1 until the relative increment of the objective function is less than a small threshold Γ0.

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