Method, device and medium for resisting perception and communication dual eavesdropping

Abstract

The invention discloses a method and equipment for resisting perception and communication double eavesdropping, and a medium. The method for confronting double eavesdropping of perception and communication comprises the following steps: constructing a communication and sensing integrated system model; calculating channel gains between the unmanned aerial vehicle and the communication user and between the unmanned aerial vehicle and the eavesdropper; acquiring signal to interference and noise ratios of a communication user receiving end and an eavesdropper receiving end, which are respectively related to the waveform optimization variable and the unmanned aerial vehicle trajectory, and calculating a system physical layer safety rate sum; obtaining beam gains at the sensing target and the eavesdropper; the sum of the safety rates of the physical layers of the system is used as an objective function of an optimization problem, and constraint conditions such as maximum transmitting power, perception, perception safety and unmanned aerial vehicle tracks and a common inductance integrated system model are combined to construct an unmanned aerial vehicle track and resource scheduling joint optimization model and solve the unmanned aerial vehicle track and resource scheduling joint optimization model; and an unmanned aerial vehicle track and resource scheduling joint optimization scheme combining physical layer security and perception security based on communication and sensing integration is obtained.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method, device and medium for counteracting dual eavesdropping of perception and communication. Background Art

[0002] By achieving the dual functions of communication and perception, synaesthesia integration is considered to be one of the most promising key technologies for moving from 5G to post-5G and 6G networks. By sending common signals on shared spectrum and transmitting devices, synaesthesia integration can effectively utilize existing spectrum, hardware and processing resources. Uniform linear arrays have good directivity and beamforming characteristics, making them very suitable for communication and perception systems. However, due to the broadcast characteristics of wireless signals, both communication and perception systems are at risk of being eavesdropped, and synaesthesia integration systems face challenges in both communication and perception security. The issue of communication security has been widely studied, and a variety of solutions have been proposed for this purpose. The most commonly used one is physical layer security technology. The basic principle of physical layer security is to use the inherent randomness of noise and transmission media in data transmission to widen the performance gap between legitimate users and illegal users, thereby ensuring communication security. However, there are still very few studies on perception security.

[0003] To this end, it is necessary to design an optimization method that can balance communication needs and perception needs and maximize the sum of achievable safe rates while ensuring physical layer security and perception security. Summary of the invention

[0004] In order to overcome the shortcomings of the prior art, the present invention provides a method for counteracting dual eavesdropping of perception and communication; the method for counteracting dual eavesdropping of perception and communication can balance communication needs and perception needs, and maximize the sum of achievable security rates while ensuring physical layer security and perception security.

[0005] A second object of the present invention is to provide a corresponding electronic device.

[0006] A third object of the present invention is to provide a computer-readable storage medium.

[0007] The technical solution of the present invention to solve the above technical problems is:

[0008] A method for counteracting dual eavesdropping of perception and communication comprises the following steps:

[0009] S1. Initializing system parameters, wherein the system parameters include the position of the drone, the position of the communication user, the position of the perception target, the position of the double eavesdropper, the initial value of the communication signal and the initial value of the perception signal, and constructing a synaesthesia integrated system model based on these parameters;

[0010] S2. According to the positional relationship between the UAV and the communication user, the channel gain between the UAV and the communication user and between the UAV and the eavesdropper is calculated based on the line-of-sight wireless transmission model;

[0011] S3, according to the synaesthesia integrated system model constructed in step S1 and the channel gain calculated in step S2, obtain the signal-to-interference-noise ratios of the communication user receiving end and the eavesdropper receiving end respectively related to the waveform optimization variables and the UAV trajectory, and calculate the total physical layer security rate of the system;

[0012] S4, according to the synaesthesia integrated system model constructed in step S1, obtaining beam gains at the sensing target and the eavesdropper, and ensuring sensing performance and sensing security based on the beam gains;

[0013] S5. Taking the sum of the physical layer safety rates of the system as the objective function of the optimization problem, and combining the maximum transmission power constraint, perception constraint, perception safety constraint, UAV trajectory constraint and the synaesthesia integration system model determined in step S1, a joint optimization model of UAV trajectory and resource scheduling based on synaesthesia integration and combining physical layer safety and perception safety is constructed;

[0014] S6. An efficient algorithm combining alternating optimization, continuous convex approximation algorithm and semi-definite relaxation algorithm is used to solve the joint optimization model of UAV trajectory and resource scheduling, and a solution for joint optimization of UAV trajectory and resource scheduling based on synaesthesia integration that combines physical layer safety and perception safety is obtained.

[0015] As a preferred solution of the present invention, in step S1, the communication user, the sensing target, and the eavesdropper are all single or multiple.

[0016] As a preferred solution of the present invention, in step S1, the steps of constructing the synaesthesia integrated system model are:

[0017] Step S101: The drone is used as a dual-function base station. The drone is equipped with M antennas, which are arranged in a uniform linear array to serve a single-antenna communication user and a single-antenna perception target. At the same time, an eavesdropper is set on the horizontal plane. The eavesdropper can intercept communication information and perception information at the same time, serving as both a communication eavesdropper and a perception eavesdropper. In the three-dimensional Cartesian coordinate system, the coordinate position of the communication user is u=(x u ,y u ), the eavesdropper’s coordinate position is u e =(x e ,y e ), the position of the perceived target is known;

[0018] Step S102: Assume that the UAV maintains a fixed altitude H and the flight mission duration is T, and T is divided into N time slots, each time slot length is t s =T / N, where represents the set of time slots; in the nth time slot, the horizontal coordinate of the drone is q[n] = (x[n], y[n]); let q I =(x I ,y I ) and q F =(x F ,y F ) represent the horizontal coordinates of the start and end of the UAV, respectively, and the start and end point constraints of the UAV flight path are obtained:

[0019] q[1]=q I ,q[N]=q F ;

[0020] Step S103: Imposing the following flight restrictions on the drone:

[0021]

[0022] Where: V max represents the maximum displacement of the UAV in a single time slot; V max =v max t s , where v max Indicates the maximum speed of the drone;

[0023] Step S104: s[n] is represented as a communication signal required by a communication user in time slot n, w[n] is represented as a transmission beamforming vector associated with the communication signal; s0[n] is represented as a perception signal of a specific radar in time slot n; Among them, the communication signal s[n] is an independently generated circularly symmetric complex Gaussian random variable; the perception signal s0[n] is regarded as an independently generated random vector with a mean of zero, and the perception covariance matrix is a positive semidefinite matrix.

[0024] As a preferred solution of the present invention, in step S2, the channel gain between the UAV and the communication user is:

[0025]

[0026] Where: L0 is the reference channel power gain; d(q[n],u) is the distance between the communication user and the UAV in time slot n, where a(q[n],u) is the steering vector pointing to the communication user;

[0027] The channel gain between the drone and the eavesdropper is:

[0028]

[0029] Where: L0 is the reference channel power gain; d(q[n],u e ) is the distance between the eavesdropper and the drone in time slot n, where a(q[n],u e ) is the steering vector pointing to the eavesdropper.

[0030] As a preferred solution of the present invention, in step S3, the bit rates of the communication user receiving end and the eavesdropper receiving end are calculated as follows:

[0031] The bit rate at the receiving end of the communication user:

[0032]

[0033] The bit rate at the eavesdropper's receiving end:

[0034]

[0035] Where: 2 is additive white Gaussian noise.

[0036] As a preferred solution of the present invention, in step S4, the transmission beam pattern gain at the sensing target is ζ(q[n], m). In order to ensure the sensing performance, it is necessary to ensure that the transmission beam pattern gain ζ(q[n], m) meets the following requirements:

[0037]

[0038] Where: m is the position of the perceived target, is the distance between the perceived target and the UAV in time slot n; a(q,m) represents the corresponding guidance vector pointing to the perceived target; Γ m is a first preset threshold;

[0039] The transmission beam pattern gain at the eavesdropper is ζ(q[n],u e ), in order to ensure the perception security, the eavesdropper’s transmission beam pattern gain ζ(q[n],u e ) to meet the following requirements;

[0040]

[0041] Where: Γ e is the second preset threshold.

[0042] As a preferred solution of the present invention, in step S5, the joint optimization model of UAV trajectory and resource scheduling is:

[0043]

[0044] Where: P max Indicates the maximum power of the drone in a single time slot.

[0045] As a preferred solution of the present invention, in step S6, the steps of solving the joint optimization model of drone trajectory and resource scheduling are as follows:

[0046] Step S601: Decompose the optimization problem of the joint optimization model of drone trajectory and resource scheduling into two sub-problems, wherein the first sub-problem is to maximize the safety rate with the communication signal perception signal as a variable; the second sub-problem is to maximize the safety rate with the drone trajectory as a variable;

[0047] Step S602: For problem 1, use the first-order Taylor expansion formula and combine it with the semi-positive definite relaxation algorithm to convert subproblem 1 into a convex optimization problem and solve it through CVX;

[0048] Step S603: For problem 2, use the first-order Taylor expansion formula and the regional trust radius algorithm to convert subproblem 2 into a convex optimization problem and solve it through CVX;

[0049] Step S604: Use the alternating optimization algorithm and the regional trust radius-based algorithm to alternately optimize the communication signal, the perception signal, and the UAV trajectory until the iterative difference of the objective function is less than σ, stop the iteration, and calculate the maximum achievable safe rate sum.

[0050] As a preferred solution of the present invention, in step S603, the steps of alternately optimizing the communication signal, the perception signal and the drone trajectory are:

[0051] (1) Input the initial value w (o) [n], q (o) [n], and let O = 1;

[0052] (2) Using the solution obtained from the O-1th iteration as input, solve the convex optimization problem of the Oth subproblem 1 and obtain the optimization parameters and reconstruct it into a rank-one solution

[0053] (3) Use the solution obtained in the O-1th iteration as the initial value of the convex optimization problem of the Oth sub-problem 2, and set l = 1; (l-1) [n], Next, we optimize to get q (l)* [n]; among them,

[0054] If the objective function of subproblem 2 increases, let q(l) [n] = q (l)* [n], and l = l + 1;

[0055] If the objective function of subproblem 2 does not increase, then the trust radius is halved ψ (l) =ψ (l) / 2; when the trust radius is less than the threshold When , the loop is jumped out, and O=O+1;

[0056] (4) When the increment of the threshold of the objective function is less than the threshold δ, the loop is exited and the optimized w is output. opt [n], q opt [n], and calculate the maximum achievable safe rate sum.

[0057] An electronic device comprises a central processing unit and a memory, wherein the central processing unit is used to call and run a computer program stored in the memory to execute the steps of the method for counteracting dual eavesdropping of perception and communication.

[0058] A computer-readable storage medium stores a computer program implemented according to the method for counteracting dual eavesdropping of perception and communication in the form of computer-readable instructions. When the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

[0059] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0060] 1. The method for counteracting dual eavesdropping of perception and communication of the present invention utilizes drone trajectories, communication signals and perception signals, and performs multiple iterations. Under the premise of ensuring physical layer security and perception security, the achievable safe rate is maximized by alternately optimizing drone trajectories and resource scheduling, and at the same time, the communication performance and perception performance are balanced.

[0061] 2. The method for counteracting dual eavesdropping of perception and communication of the present invention models the problem of maximizing the achievable safe rate by deriving the functional relationship between the system parameters such as drone trajectory, communication signal and perception signal and the achievable safe rate. By jointly optimizing the drone trajectory, communication signal and perception signal, while satisfying the perception constraint, perception safety constraint, power constraint and drone trajectory constraint, the achievable safe rate is maximized, thereby improving the safety performance, while also balancing the communication and perception requirements.

[0062] 3. The method for counteracting dual eavesdropping of perception and communication of the present invention uses the drone as a base station and combines physical layer security and perception security, so that the drone as a base station needs to consider dual security at the same time to optimize the trajectory, thereby achieving a better trade-off between improving the achievable safe rate and ensuring dual security. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of the method for counteracting dual eavesdropping of perception and communication of the present invention;

[0064] Figure 2 It is a schematic diagram of the synaesthesia integrated system model in the method for counteracting dual eavesdropping of perception and communication of the present invention;

[0065] Figure 3 This is the first simulation result graph;

[0066] Figure 4 This is the second simulation result diagram. DETAILED DESCRIPTION

[0067] The present invention is further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0068] Example 1

[0069] See also Figure 1-Figure 4 The method for resisting dual eavesdropping of perception and communication of the present invention comprises the following steps:

[0070] S1. Initializing system parameters, wherein the system parameters include the position of the drone, the position of the communication user, the position of the perception target, the position of the double eavesdropper, the initial value of the communication signal and the initial value of the perception signal, and constructing a synaesthesia integrated system model based on these parameters;

[0071] S2. According to the positional relationship between the UAV and the communication user, the channel gain between the UAV and the communication user and between the UAV and the eavesdropper is calculated based on the line-of-sight wireless transmission model;

[0072] S3, according to the synaesthesia integrated system model constructed in step S1 and the channel gain calculated in step S2, obtain the signal-to-interference-noise ratios of the communication user receiving end and the eavesdropper receiving end respectively related to the waveform optimization variables and the UAV trajectory, and calculate the bit rates of the communication user receiving end and the eavesdropper receiving end;

[0073] S4, according to the synaesthesia integrated system model constructed in step S1, obtaining beam gains at the sensing target and the eavesdropper, and ensuring sensing performance and sensing security based on the beam gains;

[0074] S5. Taking the sum of the physical layer safety rates of the system as the objective function of the optimization problem, and combining the maximum transmission power constraint, perception constraint, perception safety constraint, UAV trajectory constraint and the synaesthesia integration system model determined in step S1, a joint optimization model of UAV trajectory and resource scheduling based on synaesthesia integration and combining physical layer safety and perception safety is constructed;

[0075] S6. An efficient algorithm combining alternating optimization, continuous convex approximation algorithm and semi-definite relaxation algorithm is used to solve the joint optimization model of UAV trajectory and resource scheduling in two steps, and the result of joint optimization of UAV trajectory and resource scheduling based on synaesthesia integration combining physical layer safety and perception safety is obtained.

[0076] In step S1, the steps of constructing the synaesthesia integrated system model are:

[0077] Step S101: The drone is used as a dual-function base station. The drone is equipped with M antennas, which are arranged in a uniform linear array to serve a single-antenna communication user and a single-antenna perception target. At the same time, an eavesdropper is set on the horizontal plane. The eavesdropper can intercept communication information and perception information at the same time, serving as both a communication eavesdropper and a perception eavesdropper. In the three-dimensional Cartesian coordinate system, the coordinate position of the communication user is u=(x u ,y u ), the eavesdropper’s coordinate position is u e =(x e ,y e ), the position of the perceived target is known;

[0078] Step S102: Assume that the UAV maintains a fixed altitude H and the flight mission duration is T, and T is divided into N time slots, each time slot length is t s =T / N, where represents the set of time slots; in the nth time slot, the horizontal coordinate of the drone is q[n] = (x[n], y[n]); let q I =(x I ,y I ) and q F =(x F ,y F ) represent the horizontal coordinates of the start and end of the UAV, respectively, and the start and end point constraints of the UAV flight path are obtained:

[0079] q[1]=q I ,q[N]=q F ;

[0080] Step S103: Imposing the following flight restrictions on the drone:

[0081]

[0082] Where: V max represents the maximum displacement of the UAV in a single time slot; V max =v max t s , where v maxIndicates the maximum speed of the drone;

[0083] Step S104: s[n] is represented as the communication signal required by the communication user in time slot n, w[n] is represented as the transmission beamforming vector associated with the communication signal; s0[n] is represented as the perception signal of the specific radar in time slot n; wherein Among them, the communication signal s[n] is an independently generated circularly symmetric complex Gaussian random variable; the perception signal s0[n] is regarded as an independently generated random vector with a mean of zero, and the perception covariance matrix is a positive semidefinite matrix.

[0084] In step S2, assuming that the channel between the drone and the communication user and the eavesdropper is a line-of-sight channel, the steering vector a(q[n],u) can be expressed as follows regarding the communication user receiving information:

[0085] a(q[n],u)=[φ1[n],...φ m [n],...φ M ] T ;

[0086] In the formula, Assume d = λ / 2, where λ and d represent the carrier wavelength and the distance between two adjacent antennas, respectively. This can effectively prevent mutual interference between adjacent antennas (i.e., reduce the coupling effect) and ensure the excellent performance of the array antenna in terms of directivity and gain. In addition, θ(q[n],u) represents the departure angle of the UAV relative to the communication user, and its value is equal to the arccosine value of the ratio of the height of the UAV to the horizontal distance between the UAV and the communication user.

[0087] The channel gain h(q[n],u) from the drone to the communication user in time slot n is:

[0088]

[0089] Where: L0 is the reference channel power gain; d(q[n],u) is the distance between the communication user and the UAV in time slot n, where a(q[n],u) is the steering vector pointing to the communication user;

[0090] The communication signal received by the communication user in the nth time slot can be written as:

[0091] z[n]=h H (q[n],u)(w[n]s[n]+s0[n])+v[n];

[0092] Where: v[n] is the additive white Gaussian noise at the receiving end of the communication user, with a mean of zero and a variance of σ 2is a circularly symmetric complex Gaussian random variable.

[0093] Similarly, the guidance vector a(q[n],u e ) is expressed as:

[0094]

[0095] The channel gain from the drone to the eavesdropper in the nth time slot is:

[0096]

[0097] Where: L0 is the reference channel power gain; d(q[n],u e ) is the distance between the eavesdropper and the drone in time slot n, where a(q[n],u e ) is the steering vector pointing to the eavesdropper.

[0098] In step S3, the bit rates of the communication user receiving end and the eavesdropper receiving end are calculated as follows:

[0099] The bit rate at the receiving end of the communication user:

[0100]

[0101] The bit rate at the eavesdropper's receiving end:

[0102]

[0103] The achievable rates of the communication user and the eavesdropper are expressed by using the Shannon formula, where for the communication user, the required power is only w[n], and the perceived signal R s [n] and additive white Gaussian noise σ 2 are considered interference.

[0104] In step S4, the transmission beam pattern gain ζ(q[n], m) at the sensing target, in order to ensure the sensing performance, needs to ensure that the transmission beam pattern gain ζ(q[n], m) meets the following requirements:

[0105]

[0106] Where: m is the position of the perceived target, d(q[n],m) is the distance between the perceived target and the UAV in time slot n; represents the corresponding guidance vector pointing to the perception target; Γ m is a first preset threshold;

[0107] The transmission beam pattern gain at the eavesdropper is ζ(q[n],u e), in order to ensure the perception security, the eavesdropper’s transmission beam pattern gain ζ(q[n],u e ) to meet the following requirements;

[0108]

[0109] Where: Γ e is the second preset threshold.

[0110] In step S5, the joint optimization model of UAV trajectory and resource scheduling is:

[0111]

[0112] Where: P max Indicates the maximum power of the drone in a single time slot.

[0113] In step S6, the steps for solving the joint optimization model of UAV trajectory and resource scheduling are as follows:

[0114] Step S601: Decompose the optimization problem of the joint optimization model of drone trajectory and resource scheduling into two sub-problems, wherein the first sub-problem is to maximize the safety rate with the communication signal and the perception signal as variables; the second sub-problem is to maximize the safety rate with the drone trajectory as a variable;

[0115] Step S602: For problem 1, use the first-order Taylor expansion formula and combine it with the semi-positive definite relaxation algorithm to convert subproblem 1 into a convex optimization problem and solve it through CVX;

[0116] Step S603: For problem 2, use the first-order Taylor expansion formula and the regional trust radius algorithm to convert subproblem 2 into a convex optimization problem and solve it through CVX;

[0117] Step S604: Use the alternating optimization algorithm and the regional trust radius-based algorithm to alternately optimize the communication signal, the perception signal, and the UAV trajectory until the iterative difference of the objective function is less than δ, stop the iteration, and calculate the sum of the maximum safe rates.

[0118] In step S602, the first-order Taylor expansion formula is used in combination with the semi-positive definite relaxation algorithm to convert subproblem 1 into a convex optimization problem and solve it through CVX; the specific steps are:

[0119] In subproblem 1, optimize the beamforming w and R of the communication signal and the sensing signal s , it is necessary to consider a fixed trajectory {q} of the drone at the same time; this leads to the objective function of sub-problem 1 being:

[0120]

[0121] By defining W = ww H , so that rank(W)≤1; in the case of fixed UAV trajectory, only the objective function and the rank-one constraint introduced by constructing the new variable W are non-convex; based on the solution W and R obtained from the semi-definite relaxation problem s , we can construct the following alternative solution:

[0122]

[0123] in, The constructed alternative solution is comparable to the solutions W and R s The same optimal objective function value, and it also satisfies the rank-one constraint. Therefore, it is the optimal solution to subproblem 1.

[0124] Next, we deal with the non-convex objective function in subproblem 1 and expand it into:

[0125]

[0126] Apply the first-order Taylor expansion formula to the last two logarithmic functions and use the SCA technique to achieve a convergent solution through iteration; in each iteration l≥1, assume that the current local point is given as W (l) and Then we have:

[0127]

[0128] in,

[0129]

[0130] At this point, subproblem 1 has become a convex problem and can be solved using CVX.

[0131] In step S603, the first-order Taylor expansion formula is used in combination with the regional trust radius algorithm to convert subproblem 2 into a convex optimization problem and solve it through CVX. The specific steps are:

[0132] Given the communication information and sensing signal beamforming w and R s , continue to optimize the trajectory of the drone {q}; sub-problem 2 can be expressed as follows:

[0133]

[0134] Since the UAV trajectory {q} is a variable in subproblem 2, the objective function of subproblem 2 as well as the perception constraint and perception safety constraint are non-convex at this point.

[0135] First, we need to deal with the non-convex objective function; by eliminating the same terms in the numerator and denominator of the signal-to-noise ratio (SI NR) in the objective function, simplify and expand the objective function, and then restructure the objective function of the problem as follows:

[0136]

[0137] Among them, η(W[n],d(q[n],u)) and μ(R s [n],d(q[n],u)) is represented as follows:

[0138]

[0139] Next, we deal with the non-convex objective function. Using the first-order Taylor expansion formula for the non-convex objective function, we can get the approximate value:

[0140]

[0141] in,

[0142]

[0143]

[0144] Then we deal with two non-convex constraints. To simplify the expression, we introduce G[n]=w[n]w H [n]+R s [n]; therefore, the element in the pth row and qth column is represented as [W[n]] p,q ,[R s [n]] p,q ,[G[n]] p,q The phases of the three are denoted as This rewrites the non-convex perception constraint as:

[0145]

[0146] Perform a first-order Taylor expansion to obtain the convex perception constraint:

[0147]

[0148] in,

[0149]

[0150] Similarly, a convex approximation is performed on the non-convex perceived safety constraint to obtain:

[0151]

[0152] in,

[0153]

[0154] In order to ensure the accuracy of the convex approximation, a trust region constraint is introduced; a successive convex approximation algorithm based on regional trust is performed iteratively; consider a specific iteration l and a local trajectory point q (l) [n]:

[0155]

[0156] Where: (l) represents the radius of the trust region; theoretically, if the trust region radius ψ (l) is chosen to be small enough, the convergence of the iteration can always be guaranteed.

[0157] In step S603, the steps of alternately optimizing the communication signal, the perception signal and the UAV trajectory are:

[0158] (1) Input the initial value w (o) [n], q (o) [n], and let O = 1;

[0159] (2) Using the solution obtained from the O-1th iteration as input, solve the convex optimization problem of the Oth subproblem 1 and obtain the optimization parameters and reconstruct it into a rank-one solution

[0160] (3) Use the solution obtained in the O-1th iteration as the initial value of the convex optimization problem of the Oth sub-problem 2, and set l = 1; (l-1) [n], Next, we optimize to get q (l)* [n]; among them,

[0161] If the objective function of subproblem 2 increases, let q (l) [n] = q (l)* [n], and l = l + 1;

[0162] If the objective function of subproblem 2 does not increase, then the trust radius is halved ψ (l) =ψ (l) / 2; when the trust radius is less than the threshold When , the loop is jumped out, and O=O+1;

[0163] (4) When the increment of the threshold of the objective function is less than the threshold δ, the loop is exited and the optimized w is output. opt [n], q opt [n], and calculate the maximum achievable safe rate sum.

[0164] like Figure 3 As shown, in the simulation, in addition to the optimization scheme of the present invention, four schemes including non-perceived safety, maximum ratio transmission, no trajectory optimization, and no transmission beam optimization are used as reference schemes to verify the effectiveness of the present invention.

[0165] The following parameters are used in the simulation:

[0166] V max =20,q I =[400,450] T ,q F =[500,450] T , m = [460, 250] T , u=[440,250] T ,

[0167] u e =[450,655] T , M = 12, P max =0.5W, β=-60dB, σ 2 =-110dBm.

[0168] from Figure 3 It can be seen from the optimized trajectory in that in the first half of the drone's flight path, the drone is closer to the communication user and farther away from the eavesdropper, which reduces the risk of information leakage to the eavesdropper and prioritizes communication security. In the second half of the drone's flight path, the drone is closer to the perception target and farther away from the eavesdropper, emphasizing the importance of ensuring perception security. At the same time, the trajectory of the optimization scheme proposed in the present invention is farther away from the eavesdropper than the trajectory without perception security constraints; in general, the trajectory shows a downward trend, which maximizes the achievable confidentiality rate while ensuring perception security.

[0169] from Figure 4 The relationship between the number of antennas M on a drone and the achievable confidentiality rate is shown in Figure 2. Four benchmark schemes are introduced for comparison. Numerical results demonstrate the superiority of the proposed scheme.

[0170] As the number of antennas M increases, the achievable confidentiality rate increases almost linearly. This is because increasing the number of antennas provides additional spatial degrees of freedom (DoFs), which in turn improves security performance. In addition, in the scheme without beamforming optimization, the transmission power constraint has little effect on the confidentiality performance because higher transmission power enhances the data signal while increasing the risk of information leakage to eavesdroppers. The maximum ratio transmission beamforming optimization scheme has a lower confidentiality rate because it ignores the perception and perception security constraints, which may cause the enhanced signal to be intercepted by eavesdroppers.

[0171] Example 2

[0172] The difference between this embodiment and embodiment 1 is that:

[0173] In this embodiment, there are K communication users J eavesdroppers Z perception targets In the three-dimensional Cartesian coordinate system, the coordinate position of the kth communication user is u k =(x uk ,y uk ), the coordinate position of the jth eavesdropper is u ej =(x ej ,y el ), the positions of multiple perception targets are known;

[0174] Therefore, the joint optimization model of UAV trajectory and resource scheduling is:

[0175]

[0176] q[1]=q I ,q[N]=q F ,

[0177]

[0178] The solution to the above-mentioned joint optimization model of UAV trajectory and resource scheduling can be implemented with reference to Example 1.

[0179] Example 3

[0180] The electronic device of the present invention comprises a central processing unit and a memory, wherein the central processing unit is used to call and run a computer program stored in the memory to execute the steps of the method for counteracting dual eavesdropping of perception and communication.

[0181] Example 4

[0182] The computer-readable storage medium of the present invention stores a computer program implemented according to the method for counteracting dual eavesdropping of perception and communication in the form of computer-readable instructions. When the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

[0183] The above is a preferred embodiment of the present invention, but the embodiment of the present invention is not limited to the above content. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A method for counteracting dual eavesdropping of perception and communication, characterized in that: The following steps are involved: S1. Initializing system parameters, wherein the system parameters include the position of the drone, the position of the communication user, the position of the perception target, the position of the double eavesdropper, the initial value of the communication signal and the initial value of the perception signal, and constructing a synaesthesia integrated system model based on these parameters; S2. According to the positional relationship between the UAV and the communication user, the channel gain between the UAV and the communication user and between the UAV and the eavesdropper is calculated based on the line-of-sight wireless transmission model; S3, according to the synaesthesia integrated system model constructed in step S1 and the channel gain calculated in step S2, obtain the signal-to-interference-noise ratios of the communication user receiving end and the eavesdropper receiving end respectively related to the waveform optimization variables and the UAV trajectory, and calculate the bit rates of the communication user receiving end and the eavesdropper receiving end; S4, according to the synaesthesia integrated system model constructed in step S1, obtaining beam gains at the sensing target and the eavesdropper, and ensuring sensing performance and sensing security based on the beam gains; S5. Taking the sum of the physical layer safety rates of the system as the objective function of the optimization problem, and combining the maximum transmission power constraint, perception constraint, perception safety constraint, UAV trajectory constraint and the synaesthesia integration system model determined in step S1, a joint optimization model of UAV trajectory and resource scheduling based on synaesthesia integration and combining physical layer safety and perception safety is constructed; S6. An efficient algorithm combining alternating optimization, continuous convex approximation algorithm and semi-definite relaxation algorithm is used to solve the joint optimization model of UAV trajectory and resource scheduling, and a solution for joint optimization of UAV trajectory and resource scheduling based on synaesthesia integration that combines physical layer safety and perception safety is obtained.

2. The method for counteracting dual eavesdropping of perception and communication according to claim 1, characterized in that: In step S1, the communication user, the sensing target, and the eavesdropper are single or multiple.

3. The method for counteracting dual eavesdropping of perception and communication according to claim 1, characterized in that: In step S1, the steps of constructing the synaesthesia integrated system model are: Step S101: The drone is used as a dual-function base station. The drone is equipped with M antennas, which are arranged in a uniform linear array to serve a single-antenna communication user and a single-antenna perception target. At the same time, an eavesdropper is set on the horizontal plane. The eavesdropper can intercept communication information and perception information at the same time, serving as both a communication eavesdropper and a perception eavesdropper. In the three-dimensional Cartesian coordinate system, the coordinate position of the communication user is u=(x u ,y u ), the coordinate position of the eavesdropper is u e =(x e ,y e ), the position of the perceived target is known; Step S102: Assume that the UAV maintains a fixed altitude H and the flight mission duration is T, and T is divided into N time slots, each time slot length is t s =T / N, where represents a set of time slots; in the nth time slot, the horizontal coordinate of the drone is q[n] = (x[n], y[n]); let q I =(x I ,y I ) and q F =(x F ,y F ) represent the horizontal coordinates of the start and end of the UAV, respectively, and the start and end point constraints of the UAV flight path are obtained: q[1]=q1,q[N]=qF; Step S103: Imposing the following flight restrictions on the drone: Where: V max represents the maximum displacement of the UAV in a single time slot; V max =v max t s , where v max Indicates the maximum speed of the drone; Step S104: s[n] is represented as a communication signal required by a communication user in time slot n, w[n] is represented as a transmission beamforming vector associated with the communication signal; s0[n] is represented as a perception signal of a specific radar in time slot n; Among them, the communication signal s[n] is an independently generated circularly symmetric complex Gaussian random variable; the perception signal s0[n] is regarded as an independently generated random vector with a mean of zero, and the perception covariance matrix is a positive semidefinite matrix.

4. The method for counteracting dual eavesdropping of perception and communication according to claim 3, characterized in that: In step S2, the channel gain between the UAV and the communication user is: Where: L0 is the reference channel power gain; d(q[n],u) is the distance between the communication user and the UAV in time slot n, where a(q[n],u) is the steering vector pointing to the communication user; The channel gain between the drone and the eavesdropper is: Where: L0 is the reference channel power gain; d(q[n], u e ) is the distance between the eavesdropper and the drone in time slot n, where a(q[n],u e ) is the steering vector pointing to the eavesdropper.

5. The method for counteracting dual eavesdropping of perception and communication according to claim 4, characterized in that: In step S3, the bit rates of the communication user receiving end and the eavesdropper receiving end are calculated as follows: The bit rate at the receiving end of the communication user: The bit rate at the eavesdropper's receiving end: Where: 2 is additive white Gaussian noise.

6. The method for counteracting dual eavesdropping of perception and communication according to claim 5, characterized in that: In step S4, the transmission beam pattern gain at the sensing target is ζ(q[n], m). In order to ensure the sensing performance, it is necessary to ensure that the transmission beam pattern gain ζ(q[n], m) meets the following requirements: Where: m is the position of the perceived target, is the distance between the sensing target and the UAV in time slot n; a(q, m) represents the corresponding guidance vector pointing to the sensing target; Γ m is a first preset threshold; The transmission beam pattern gain at the eavesdropper is ζ(q[n], u e ), in order to ensure the perception security, the transmission beam pattern gain ζ(q[n], u e ) is restricted to meet the following requirements; Where: Γ e is the second preset threshold.

7. The method for counteracting dual eavesdropping of perception and communication according to claim 6, characterized in that: In step S5, the joint optimization model of UAV trajectory and resource scheduling is: Where: P max Indicates the maximum power of the drone in a single time slot.

8. The method for counteracting dual eavesdropping of perception and communication according to claim 7, characterized in that: In step S6, the steps for solving the joint optimization model of UAV trajectory and resource scheduling are as follows: Step S601: Decompose the optimization problem of the joint optimization model of drone trajectory and resource scheduling into two sub-problems, wherein the first sub-problem is to maximize the safety rate with the communication signal perception signal as a variable; the second sub-problem is to maximize the safety rate with the drone trajectory as a variable; Step S602: For subproblem 1, use the first-order Taylor expansion formula and combine it with the semi-positive definite relaxation algorithm to convert subproblem 1 into a convex optimization problem and solve it through CVX; Step S603: For sub-problem 2, use the first-order Taylor expansion formula and combine it with the regional trust radius algorithm to convert sub-problem 2 into a convex optimization problem and solve it through CVX; Step S604: Use the alternating optimization algorithm and the regional trust radius-based algorithm to alternately optimize the communication signal, the perception signal, and the UAV trajectory until the iterative difference of the objective function is less than δ, stop the iteration, and calculate the maximum achievable safety rate sum.

9. An electronic device, comprising a central processing unit and a memory, characterized in that: The central processing unit is used to call and run the computer program stored in the memory to execute the steps of the method for counteracting dual eavesdropping of perception and communication as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: It stores a computer program implemented according to the method for counteracting dual eavesdropping of perception and communication as described in any one of claims 1 to 8 in the form of computer-readable instructions, and when the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

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