A method, device, and medium for counteracting dual eavesdropping on perception and communication

Abstract

The present invention discloses a method, device and medium for combating dual eavesdropping of sensing and communication; wherein, the method for combating dual eavesdropping of sensing and communication is: constructing a communication-sensing integrated system model; calculating the channel gains between the unmanned aerial vehicle (UAV) and the communication user and the eavesdropper respectively; obtaining the signal-to-interference-plus-noise ratios (SINRs) of the communication user's receiver and the eavesdropper's receiver respectively related to the waveform optimization variable and the UAV trajectory, and calculating the total physical layer security rate of the system; obtaining the beam gains at the sensing target and the eavesdropper; taking the total physical layer security rate of the system as the objective function of the optimization problem, and combining constraints such as the maximum transmit power, sensing, sensing security, UAV trajectory, etc. and the communication-sensing integrated system model, constructing a joint optimization model of UAV trajectory and resource scheduling and solving it to obtain a joint optimization scheme of UAV trajectory and resource scheduling that combines physical layer security and sensing security based on communication-sensing integration.

Description

Technical Field

[0001] The present invention relates to the field of communication technologies, and particularly to a method, device, and medium for countering dual eavesdropping on sensing and communication. Background Art

[0002] By realizing the dual functions of communication and sensing, integrated communication and sensing is considered to be one of the most promising key technologies for moving from 5G to post-5G and 6G networks. By transmitting common signals on shared spectrum and transmitting devices, integrated communication and sensing can effectively utilize existing spectrum, hardware, and processing resources. And the uniform linear array has good directivity and beamforming characteristics, making it very suitable for communication and sensing systems. However, due to the broadcast nature of wireless signals, both communication and sensing systems face the risk of being eavesdropped, and there are challenges in the security of both communication and sensing in integrated communication and sensing systems. The problem of communication security has been widely studied, and various solutions have been proposed for this, and the commonly used one is physical layer security technology. The basic principle of physical layer security is to utilize the noise and inherent randomness of the transmission medium in data transmission to widen the performance gap between legitimate users and illegal users, thereby ensuring communication security. However, the research on sensing security is still extremely rare at present.

[0003] Therefore, it is necessary to design an optimization method that can balance communication requirements and sensing requirements and maximize the sum of achievable security rates on the premise of ensuring physical layer security and sensing security. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the present invention provides a method for countering dual eavesdropping on sensing and communication; the method for countering dual eavesdropping on sensing and communication can balance communication requirements and sensing requirements and maximize the sum of achievable security rates on the premise of ensuring physical layer security and sensing security.

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

[0006] The 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 as follows:

[0008] A method for countering dual eavesdropping on sensing and communication includes the following steps:

[0009] S1. Initialize system parameters, where the system parameters include the positions of the unmanned aerial vehicle, communication users, sensing targets, dual eavesdroppers, the initial value of the communication signal, and the initial value of the sensing signal, and construct an integrated communication and sensing system model based on this;

[0010] S2. Calculate the channel gains between the UAV and the communication user, and between the UAV and the eavesdropper based on the line-of-sight wireless transmission model according to the positional relationship between the UAV and the communication user.

[0011] S3. According to the integrated communication and sensing system model constructed in step S1 and the channel gains calculated in step S2, obtain the signal-to-interference-plus-noise ratios (SINRs) related to the waveform optimization variables and the UAV trajectory at the receiving ends of the communication user and the eavesdropper respectively, and calculate the total physical layer security rate of the system.

[0012] S4. According to the integrated communication and sensing system model constructed in step S1, obtain the beam gains at the sensing target and the eavesdropper, and based on this beam gain, ensure the sensing performance and sensing security.

[0013] S5. Take the total physical layer security rate of the system as the objective function of the optimization problem, and combine the maximum transmit power constraint, sensing constraint, sensing security constraint, UAV trajectory constraint, and the integrated communication and sensing system model determined in step S1 to construct a joint optimization model of UAV trajectory and resource scheduling that combines physical layer security and sensing security based on integrated communication and sensing.

[0014] S6. Use an efficient algorithm that combines the alternating optimization, successive convex approximation algorithm, and semidefinite relaxation algorithm to solve the joint optimization model of UAV trajectory and resource scheduling, and obtain a joint optimization solution of UAV trajectory and resource scheduling that combines physical layer security and sensing security based on integrated communication and sensing.

[0015] As a preferred embodiment 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 embodiment of the present invention, in step S1, the steps for constructing the integrated communication and sensing system model are as follows:

[0017] Step S101: Regard the UAV as a dual-functional base station, which is equipped with M antennas. The M antennas are arranged in a uniform linear array and are used to serve a single-antenna communication user and a single-antenna sensing target. At the same time, there is an eavesdropper on the horizontal plane, which can intercept both communication information and sensing information, acting as both a communication eavesdropper and a sensing eavesdropper. In a 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 ), and the position of the sensing target is known.

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

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

[0020] Step S103: Apply the following flight restrictions to the UAV:

[0021]

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

[0023] Step S104: Let s[n] represent the communication signal required by the communication user in time slot n, and let w[n] represent the transmission beamforming vector associated with this communication signal; let s 0 [n] represent the sensing signal of a specific radar in time slot n; where, the communication signal s[n] is an independently generated circularly symmetric complex Gaussian random variable; the sensing signal s 0 [n] is regarded as an independently generated random vector with a mean of zero, and the sensing covariance matrix is a positive semi - definite 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: L 0 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 value between the UAV and the eavesdropper is:

[0028]

[0029] where: L 0 is the reference channel power gain; d(q[n], u e ) is the distance between the eavesdropper and the UAV in time slot n, where a(q[n], u e ) is the steering vector pointing to the eavesdropper.

[0030] As a preferred embodiment of the present invention, in step S3, the calculation methods of the bit rates of the communication user receiver and the eavesdropper receiver are:

[0031] Bit rate of the communication user receiver:

[0032]

[0033] Bit rate of the eavesdropper receiver:

[0034]

[0035] where: σ 2 is the additive white Gaussian noise.

[0036] As a preferred embodiment of the present invention, in step S4, the transmission beam pattern gain at the sensing target is ζ(q[n], m). 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 sensing target, is the distance between the sensing target and the UAV in time slot n; a(q, m) represents the corresponding steering vector pointing to the sensing target; Γ m is the first preset threshold;

[0039] The transmission beam pattern gain at the eavesdropper is ζ(q[n], u e ). To ensure the sensing security, it is necessary to limit the transmission beam pattern gain ζ(q[n], u e ) of the eavesdropper so that it meets the following requirements;

[0040]

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

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

[0043]

[0044] In the formula: P max represents the maximum power of the UAV in a single time slot.

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

[0046] Step S601: Decompose the optimization problem of the joint optimization model of UAV trajectory and resource scheduling into two sub-problems. Among them, sub-problem one is to maximize the security rate with the communication signal and sensing signal as variables; sub-problem two is to maximize the security rate with the UAV trajectory as the variable;

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

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

[0049] Step S604: Adopt the alternating optimization algorithm and the region trust radius algorithm to alternately optimize the communication signal, sensing signal and UAV trajectory until the iterative difference of the objective function is less than σ, stop the iteration, and calculate the sum of the maximum achievable security rates.

[0050] As a preferred embodiment of the present invention, in step S603, the steps of alternately optimizing the communication signal, sensing signal and UAV trajectory are as follows:

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

[0052] (2). Use the solution obtained from the (O - 1)-th iteration as the input to solve the convex optimization problem of sub-problem one in the O-th time, and obtain the optimization parameter and reconstruct it into a rank-one solution

[0053] (3). Use the solution obtained from the (O - 1)-th iteration as the initial value of the convex optimization problem of sub-problem two in the O-th time, and let l = 1; under q (l-1) [n], optimize to obtain q (l)*[n]; where,

[0054] If the objective function of sub-problem two increases, then let q (l) [n] = q (l)* [n], and l = l + 1;

[0055] If the objective function of sub-problem two does not increase, then let the trust radius be halved ψ (l) = ψ (l) / 2; When the trust radius is less than the threshold , then break out of the loop, and O = O + 1;

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

[0057] An electronic device includes a central processing unit and a memory. The central processing unit is configured to call and run a computer program stored in the memory to execute the steps of the method for countering dual eavesdropping of sensing and communication as described above.

[0058] A computer-readable storage medium stores in the form of computer-readable instructions a computer program implemented according to the method for countering dual eavesdropping of sensing and communication as described above. When the computer program is called and run by a computer, it executes the steps included in the corresponding method.

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

[0060] 1. The method for countering dual eavesdropping of sensing and communication of the present invention utilizes the UAV trajectory, communication signals, and sensing signals, and performs multiple iterations. On the premise of ensuring physical layer security and sensing security, by alternately optimizing the UAV trajectory and resource scheduling, the achievable security rate is maximized, and at the same time, the communication performance and sensing performance are balanced.

[0061] 2. The method for countering dual eavesdropping of sensing and communication of the present invention models the problem of maximizing the achievable security rate by deriving the functional relationship between system parameters such as the UAV trajectory, communication signals, and sensing signals and the achievable security rate. By jointly optimizing the UAV trajectory, communication signals, and sensing signals, while satisfying sensing constraints, sensing security constraints, power constraints, and UAV trajectory constraints, the achievable security rate is maximized, thereby improving the security performance, and at the same time, the communication and sensing requirements are balanced.

[0062] 3. The method for combating dual eavesdropping of sensing and communication in the present invention uses a drone as a base station and combines physical layer security and sensing security, such that the drone serving as the base station needs to consider dual security simultaneously for trajectory optimization, thereby achieving a better trade-off between improving the achievable security rate and ensuring dual security. Description of the Drawings

[0063] Figure 1 is a schematic flowchart of the method for combating dual eavesdropping of sensing and communication in the present invention;

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

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

[0066] Figure 4 is the second simulation result graph. Detailed Embodiments

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

[0068] Embodiment 1

[0069] See Figures 1 - 4 , the method for combating dual eavesdropping of sensing and communication in the present invention includes the following steps:

[0070] S1. Initialize the system parameters, where the system parameters include the position of the drone, the position of the communication user, the position of the sensing target, the position of the dual eavesdropper, the initial value of the communication signal, and the initial value of the sensing signal, and construct an integrated communication and sensing system model based on this;

[0071] S2. According to the positional relationship between the drone and the communication user, calculate the channel gains between the drone and the communication user and between the drone and the eavesdropper based on the line-of-sight wireless transmission model;

[0072] S3. According to the integrated communication and sensing system model constructed in step S1 and the channel gains calculated in step S2, obtain the signal-to-interference-plus-noise ratios (SINRs) related to the waveform optimization variables and the drone trajectory at the receiving ends of the communication user and the eavesdropper respectively, and calculate the bit rates at the receiving ends of the communication user and the eavesdropper;

[0073] S4. According to the integrated communication and sensing system model constructed in step S1, obtain the beam gains at the sensing target and the eavesdropper, and based on this beam gain, ensure the sensing performance and sensing security;

[0074] S5. Take the sum of the system's physical layer security rates as the objective function of the optimization problem, and combine the maximum transmit power constraint, sensing constraint, sensing security constraint, UAV trajectory constraint, and the integrated communication and sensing system model determined in step S1 to construct a joint optimization model for UAV trajectory and resource scheduling that combines physical layer security and sensing security based on integrated communication and sensing.

[0075] S6. Use an efficient algorithm that combines the alternating optimization, successive convex approximation algorithm, and semidefinite relaxation algorithm to solve the joint optimization model for UAV trajectory and resource scheduling in two steps, and obtain the result of the joint optimization of UAV trajectory and resource scheduling that combines physical layer security and sensing security based on integrated communication and sensing.

[0076] In step S1, the steps for constructing the integrated communication and sensing system model are as follows:

[0077] Step S101: Consider the UAV as a dual-functional base station equipped with M antennas arranged in a uniform linear array to serve a single-antenna communication user and a single-antenna sensing target. At the same time, there is an eavesdropper on the horizontal plane that can intercept both communication information and sensing information, acting as both a communication eavesdropper and a sensing eavesdropper. In a 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 ), and the position of the sensing target is known.

[0078] Step S102: Assume that the UAV flies at a fixed height H for a flight mission duration of T, and divide T into N time slots, each with a length of t s = T / N. Here, use to represent the set of time slots. At the nth time slot, the horizontal coordinates of the UAV are q[n] = (x[n], y[n]). Let q I = (x I , y I ) and q F = (x F , y F ) represent the starting and ending horizontal coordinates of the UAV's flight path respectively, then the starting and ending point constraints of the UAV's flight path are obtained:

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

[0080] Step S103: Impose the following flight restrictions on the UAV:

[0081]

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

[0083] Step S104: Denote s[n] as the communication signal required by the communication user within time slot n, and denote w[n] as the transmission beamforming vector associated with this communication signal; Denote s 0 [n] as the sensing signal of a specific radar within time slot n; where where the communication signal s[n] is an independently generated circularly symmetric complex Gaussian random variable; the sensing signal s 0 [n] is regarded as an independently generated random vector with a mean of zero, and the sensing covariance matrix is a positive semi - definite matrix.

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

[0085] a(q[n],u) = [φ 1 [n],...φ m [n],...φ M T ;

[0086] Wherein, let d = λ / 2, where λ and d represent the carrier wavelength and the distance between adjacent two antennas respectively, which can effectively prevent the mutual interference between adjacent antennas (i.e., reduce the coupling effect), and ensure excellent performance in the directivity and gain of the array antenna; in addition, θ(q[n],u) represents the departure angle related to the UAV and the communication user, and its value is equal to the arccosine 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 UAV to the communication user within time slot n is:

[0088]

[0089] Where: L 0 is the reference channel power gain; d(q[n],u) is the distance between the communication user and the UAV within 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 n-th time slot can be written as:

[0091] z[n] = h H (q[n], u)(w[n]s[n] + s 0 [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 2 is a circularly symmetric complex Gaussian random variable.

[0093] Similarly, the steering vector a(q[n], u e ) from the UAV to the eavesdropper in the n-th time slot is expressed as:

[0094]

[0095] The channel gain from the UAV to the eavesdropper in the n-th time slot is:

[0096]

[0097] where: L 0 is the reference channel power gain; d(q[n], u e ) is the distance between the eavesdropper and the UAV 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 receiving ends of the communication user and the eavesdropper are calculated as follows:

[0099] Bit rate of the receiving end of the communication user:

[0100]

[0101] Bit rate of the receiving end of the eavesdropper:

[0102]

[0103] By using the Shannon formula to represent the achievable rates of the communication user and the eavesdropper, where, for the communication user, the required power is only w[n], while the sensing signal R s [n] and the additive white Gaussian noise σ 2 are both regarded as interference.

[0104] In step S4, for the transmission beam pattern gain ζ(q[n], m) at the sensing target, 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:

[0105]

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

[0107] The transmission beam pattern gain at the eavesdropper is ζ(q[n], u e ), to ensure sensing security, it is necessary to limit the 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 represents the maximum power of the UAV in a single time slot.

[0113] In step S6, the steps to solve 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 UAV trajectory and resource scheduling into two sub-problems. Among them, sub-problem one is to maximize the security rate with communication signals and sensing signals as variables; sub-problem two is to maximize the security rate with the UAV trajectory as a variable;

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

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

[0117] Step S604: Adopt the alternating optimization algorithm and the region trust radius algorithm to alternately optimize the communication signals, sensing signals and UAV trajectory until the iterative difference of the objective function is less than δ, stop the iteration, and calculate the sum of the maximum security rates.

[0118] In step S602, the first-order Taylor expansion formula is used, combined with the semidefinite relaxation algorithm, to transform sub-problem one into a convex optimization problem and solve it using CVX. The specific steps are as follows:

[0119] Optimize the beamforming w and R of the communication signal and the sensing signal in sub-problem one s , and a fixed trajectory {q} of a UAV needs to be considered simultaneously; this results in the objective function of sub-problem one being:

[0120]

[0121] By defining W = ww H , such that rank(W) ≤ 1; with the UAV trajectory fixed, only the objective function and the rank-one constraint introduced due to the construction of the new variable W are non-convex; based on the solutions W and R obtained from the semidefinite relaxation problem s , the following alternative solution can be constructed:

[0122]

[0123] where the constructed alternative solution achieves the same optimal objective function value as the solutions W and R s while satisfying the rank-one constraint. Therefore, it is the optimal solution to sub-problem one.

[0124] Next, deal with the non-convex objective function in sub-problem one and expand it as:

[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 the current local point is given as W (l) and then there is:

[0127]

[0128] where

[0129]

[0130] At this point, sub-problem one has become a convex problem and can be solved using CVX.

[0131] In step S603, the first-order Taylor expansion formula is used, combined with the trust-region radius algorithm, to transform sub-problem two into a convex optimization problem and solve it using CVX. The specific steps are as follows:

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

[0133]

[0134] Since the drone trajectory {q} is a variable in sub-problem two, the objective function of sub-problem two and the sensing constraints and sensing security constraints are non-convex at this point.

[0135] First, the non-convex objective function needs to be processed; by eliminating the same terms in the numerator and denominator of the signal-to-interference-plus-noise ratio (SINR) in the objective function, simplifying and expanding the objective function, and then reconstructing the objective function of the problem as follows:

[0136]

[0137] where, η(W[n], d(q[n], u)) and μ(R s [n], d(q[n], u)) are expressed as follows:

[0138]

[0139] Next, deal with the non-convex objective function. Using the first-order Taylor expansion formula for this non-convex objective function, it can be approximated as:

[0140]

[0141] where,

[0142]

[0143]

[0144] Subsequently, deal with the two non-convex constraints. To simplify the expression, introduce G[n] = w[n]w H [n] + R s [n]; therefore, represent the element in the p-th row and q-th column as [W[n]] p,q , [R s [n]] p,q , [G[n]] p,q , and record the phases of the three as Rewrite the non-convex sensing constraint condition in this way:

[0145]

[0146] Perform a first-order Taylor expansion on it to obtain the convex sensing constraint condition:

[0147]

[0148] where,

[0149]

[0150] Similarly, a convex approximation is made to the non-convex perception security constraint, resulting in:

[0151]

[0152] where

[0153]

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

[0155]

[0156] where: ψ (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 for alternately optimizing the communication signal, the perception signal, and the UAV trajectory are as follows:

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

[0159] (2) Use the solution obtained from the (O - 1)-th iteration as the input to solve the convex optimization problem of the O-th sub-problem one, and obtain the optimization parameter and reconstruct it into a rank-one solution

[0160] (3) Use the solution obtained from the (O - 1)-th iteration as the initial value of the convex optimization problem of the O-th sub-problem two, and let l = 1; at q (l-1) [n], optimize to obtain q (l)* [n]; where

[0161] if the objective function of the sub-problem two increases, then let q (l) [n] = q (l)* [n], and l = l + 1;

[0162] if the objective function of the sub-problem two does not increase, then halve the trust radius ψ (l) = ψ (l) / 2; when the trust radius is less than the threshold If so, break out of the loop and O = O + 1;

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

[0164] As Figure 3 shown, in the simulation, in addition to the optimization scheme of the present invention, four reference schemes of oblivious security, maximum ratio transmission, no trajectory optimization, and no transmit beam optimization are used 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 the optimized trajectory, it can be seen that in the first half of the UAV flight path, the UAV is closer to the communication user and farther from the eavesdropper, which reduces the risk of information leakage to the eavesdropper and gives priority to communication security. In the second half of the UAV flight path, the UAV approaches the sensing target and is far from the eavesdropper, emphasizing the importance of ensuring sensing security. At the same time, the trajectory of the optimization scheme proposed by the present invention is farther from the eavesdropper than the trajectory without oblivious security constraints; generally speaking, the trajectory shows a downward trend, which maximizes the achievable secrecy rate while ensuring sensing security.

[0169] From Figure 4 it shows the relationship between the number of antennas M on the UAV and the achievable secrecy rate. Four benchmark schemes are introduced for comparison. The numerical results prove the superiority of the scheme proposed by the present invention.

[0170] As the number of antennas M increases, the achievable secrecy rate almost grows linearly. This is because increasing the number of antennas provides additional spatial degrees of freedom (DoFs), thereby improving the security performance. In addition, in the scheme without beamforming optimization, the transmission power constraint has little impact on the secrecy performance because higher transmission power increases the risk of information leakage to eavesdroppers while enhancing the data signal. The secrecy rate of the maximum ratio transmission beamforming optimization scheme is low because it ignores the sensing and sensing security constraints, which may lead to the intercepted enhanced signal by eavesdroppers.

[0171] Embodiment 2

[0172] The difference between this embodiment and Embodiment 1 is as follows:

[0173] There are K communication users in this embodiment J eavesdroppers Z sensing targets In a three-dimensional Cartesian coordinate system, the coordinate position of the k-th communication user is u k =(x uk , y uk ), and the coordinate position of the j-th eavesdropper is u ej =(x ej , y el ), and the positions of multiple sensing targets are all known;

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

[0175]

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

[0177]

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

[0179] Embodiment 3

[0180] The electronic device of the present invention includes a central processor and a memory. The central processor is used to call and run a computer program stored in the memory to execute the steps of the method for combating dual eavesdropping of sensing and communication as described above.

[0181] Embodiment 4

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

[0183] The above is a preferred embodiment of the present invention. However, the embodiments of the present invention are not limited to the above content. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all 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 eavesdropper’s coordinate position 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]=q I ,q[N]=q F ; 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 perceived target and the UAV in time slot n; a(q[n], m) represents the corresponding guidance vector pointing to the perceived 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: 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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