Physical layer security-based sensing integrated beam forming method

Abstract

The invention discloses a communication and sensing integrated beam forming method based on physical layer security, solves the problem of how to realize optimal sensing performance on the premise that a satellite-ground three-dimensional network does not leak sensitive information, and belongs to the field of fusion of communication and sensing technologies in the satellite-ground three-dimensional network. The method provided by the invention comprises the following steps of: establishing a communication and sensing integrated network which comprises a base station BS and K pieces of user equipment within the coverage range of the base station BS, and arranging a reconfigurable intelligent surface RIS between the base station and the user equipment for communication and sensing; a target in the communication and sensing integrated network is provided with an antenna, and a sensor is also arranged in the communication and sensing integrated network and is used for monitoring a signal of the target; the reconfigurable intelligent surface RIS comprises N units and is divided into two parts, one part is a transmission refraction unit, and the other part is a transmission reflection unit. And carrying out joint waveform design to complete optimal distribution of communication and sensing signals of the RIS.

Description

Technical Field

[0001] The invention relates to a synaesthesia integrated beamforming method based on physical layer security, and belongs to the field of integration of communication and perception technology in satellite-ground stereoscopic network. Background Art

[0002] With the rapid development of information technology, satellite-ground stereoscopic network, as a new frontier of the integration of communication and perception technology, has gradually become a research hotspot. By integrating satellite communications and ground networks to build a three-dimensional communication perception system, the satellite-ground stereoscopic network can effectively improve the communication coverage and enhance the perception capabilities on the ground and in the air.

[0003] As the core of this field, synaesthesia integration technology can simultaneously achieve efficient information transmission and accurate perception, and perform real-time signal control in complex spatial environments, thereby significantly improving communication reliability and perception accuracy. The development of microelectronics and nanotechnology has promoted synaesthesia integration equipment to move towards miniaturization, integration, and intelligence.

[0004] The satellite-ground three-dimensional network integrates satellite communications and ground communications. Unauthorized satellites in the high altitude of the system may cover communication users on the ground and conduct unauthorized eavesdropping, which urgently needs to be solved through physical layer security technology. Summary of the invention

[0005] In order to solve the problem of how to achieve optimal perception performance without leaking sensitive information in a satellite-ground stereoscopic network, the present invention provides a synaesthesia integrated beamforming method based on physical layer security.

[0006] The present invention provides a physical layer security-based synaesthesia integrated beamforming method, comprising:

[0007] S1. Establish a synaesthesia integrated network, including a base station BS and K user devices within its coverage, and a reconfigurable intelligent surface RIS is set between the base station and the user devices for communication and perception; the target in the synaesthesia integrated network is provided with an antenna, and the synaesthesia integrated network is also provided with a sensor for monitoring the signal of the target; the reconfigurable intelligent surface RIS includes N units and is divided into two parts, one part is a transmission refraction unit, and the other part is a transmission reflection unit;

[0008] S2, conduct joint waveform design to achieve optimal allocation of RIS communication and perception signals;

[0009] Preferably, in S2, the communication and perception signals of the RIS are optimally allocated by establishing an optimization problem, wherein the optimization problem is:

[0010]

[0011] tr(R x )≤P

[0012] B≥0

[0013]

[0014] Where P represents the transmit beamforming matrix vector, B is the intermediate variable introduced for solving the optimization problem, tr() represents the trace of the matrix, and the phase deflection factors of the transmission refraction unit and the transmission reflection unit of the reconfigurable smart surface RIS are Θ respectively. r and θ f ; i∈{f,r},β i,n ∈[0,1] represents the amplitude variation coefficient of the unit, φ i,n ∈[0,2π] represents the phase shift coefficient of the unit, n=1,2,…,N; R x Represents the autocorrelation matrix of the transmitted signal, SINR seq,k represents the safe rate of user equipment k, α represents the radar cross-sectional area coefficient of the target, and the estimated vector ξ=[[φ h ,φ v ],[R(α),I(α)]],φ h and φ v Respectively represent the azimuth and elevation of the monitored target, R(α) and I(α) represent the real and imaginary parts of α, respectively; the Fisher information matrix of ξ Among them, J αα , J φα and J φφ are the elements of the Fisher information matrix FIM;

[0015] Solve the optimization problem and get P and Θ i , according to P and Θ i For the configured reconfigurable intelligent surface RIS, the communication and perception signals of RIS are optimally allocated.

[0016] As a preferred method, the method for solving the optimization problem is:

[0017] Using the block coordinate descent algorithm, with Θ fixed, the optimization problem is transformed into a convex problem, and the convex problem is solved to obtain B, P, and R x ;

[0018] Using the augmented Lagrangian method, construct the quadratic penalty term of Θ, based on the calculated B, P and R x , find the optimal Θ.

[0019] Preferably, the convex problem is:

[0020]

[0021] a,b∈h,v

[0022]

[0023] SINR s <β

[0024]

[0025] Among them, η is the expectation of the received signal, α * is the conjugate of α, vec() represents the vectorization operator, and h s represents the channel matrix between RIS and the target, X is the transmitted signal vector, G is the channel matrix between RIS and the base station, j represents the imaginary unit, σ s 2 is the variance of the received signal, represents the real part operation, β is the auxiliary signal to interference and noise ratio variable, SINR k is the signal-to-interference-and-noise ratio (SINR) of the user equipment s is the signal-to-interference-to-noise ratio of the eavesdropping channel, the intermediate variable

[0026] As a preference, Among them, R s represents the autocorrelation matrix of the transmitted radar signal, σ 2 represents the thermal noise power.

[0027] Preferably, the channels between the RIS and the user equipment are all Rice channels, and the channel matrix G between the RIS and the base station is:

[0028]

[0029] Among them, d BS,RIS represents the distance between the base station and RIS, α BS,RIS represents the path loss coefficient, ε represents the Rice factor between 0 and 1, ρ0 represents the path loss factor per unit distance, G LOS represents the fixed line-of-sight component of the channel between RIS and the base station, G NLOS Represents the random non-line-of-sight component of the channel between RIS and the base station;

[0030] The channel matrix h between user equipment k and RIS k for:

[0031]

[0032] in, represents the distance between user device k and RIS, represents the path loss coefficient, represents the fixed line-of-sight component of the channel between user equipment k and RIS, represents the random non-line-of-sight component of the channel between user equipment k and RIS;

[0033] The channel matrix h between RIS and the target s for:

[0034]

[0035] h R (φ h ,φ v ) and h t (φ h ,φ v ) represent the steering vectors of the refraction part and the sensing receiving sensor respectively;

[0036]

[0037] Where λ represents the signal wavelength, d x ,d y and d z They respectively represent the unit coordinates of the three axes of the target to be detected in the Cartesian coordinate system.

[0038] As a preference, the quadratic penalty term of Θ is:

[0039]

[0040] Beneficial effects of the present invention: The physical layer security mechanism of the present invention optimizes beamforming and signal phase modulation by introducing a reconfigurable intelligent surface (RIS) to ensure the security and accuracy of communication and perception. The present invention adopts a spectrum resource optimization configuration and a low bit error rate coding scheme for a satellite-to-ground stereoscopic network to ensure the reliability of long-distance communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a schematic diagram of synaesthesia integration of a DFRC system according to an embodiment of the present invention;

[0042] Figure 2 1 is a performance comparison diagram of the method of the present invention and the comparative algorithm CRB, (a) is the method of the present invention, (b) is the comparative algorithm;

[0043] Figure 3 2 is a diagram of the perceptual performance of the method of the present invention and a comparative algorithm, wherein (a) is the present invention and (b) is the comparative algorithm. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0046] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0047] The synaesthesia integrated beamforming method based on physical layer security in this embodiment integrates satellite communication and ground communication. Unauthorized satellites in the high altitude of the system may cover the user equipment on the ground and conduct unauthorized monitoring, which urgently needs to be solved through physical layer security technical means. In response to the security issues of the physical layer, the synaesthesia integrated beamforming method based on physical layer security in this embodiment is a technical solution that combines enhanced sensing performance and safety rate constraints. This solution realizes multi-user communication and perception through the base station of the dual-function radar communication system (DFRC), and uses the reconfigurable intelligent surface RIS for optimization to ensure data security during signal transmission.

[0048] Step 1: Establish an integrated synaesthesia network, which is responsible for information transmission and environmental perception at the same time, and can achieve precise control and dynamic management of electromagnetic waves through signal processing and coding strategies. Figure 1 As shown in FIG. 1 , the synaesthesia integrated network includes a satellite, a base station BS, K user devices within its coverage area, and a reconfigurable intelligent surface RIS between the base station and the user devices. Due to the obstruction of urban structures, the direct link between the BS and the UE is blocked, so RIS is needed for signal transmission. Sensors are also set up in the synaesthesia integrated network.

[0049] Base station BS: equipped with M antennas for sending and receiving signals.

[0050] User Equipments (UEs): Each UE is equipped with a single antenna for communicating with the base station.

[0051] Targets: Also equipped with a single antenna, although in conventional ISAC systems targets may not directly participate in communications, it is assumed here that they have detectable characteristics to facilitate perception).

[0052] The reconfigurable smart surface RIS with simultaneous and same-frequency full-duplex operation has N units, which are divided into two parts: N c Units are used for communication, N rThe STAR RIS is capable of performing both reflection and refraction operations for communication and perception functions respectively.

[0053] Reflection link: used to enhance the communication signal between the BS and the UE, and optimize the beamforming by adjusting the phase of the RIS, thereby improving the signal strength and coverage.

[0054] Refraction link: focuses on perception tasks, such as detecting targets or potential threats in the environment. Through the refraction capability of RIS, the system can capture the reflected signal of the target and perform corresponding processing and analysis.

[0055] Sensors: The system also includes one or more sensors to monitor potential information leaks. These sensors may be deployed near the RIS or other key locations in the network to ensure the security of the communication and perception process.

[0056] In such a system, RIS not only plays the role of signal relay, but also realizes the deep integration of communication and perception functions through its flexible configuration and optimization capabilities. By intelligently adjusting the phase and unit allocation of RIS, the system can improve the accuracy and real-time performance of perception while ensuring efficient communication. In addition, the introduction of sensor monitoring provides an additional layer of security for the system, which helps to detect and respond to potential security threats in a timely manner.

[0057] This implementation targets a scenario where a dual-function radar communication (DFRC) base station serves as a common source of both communication and perception information. The main challenge in this scenario is the inherent conflict between the high entropy required for effective communication and the low entropy required for accurate perception. In order to reconcile these two conflicting requirements, a joint waveform design strategy must be adopted to achieve the optimal allocation of communication and perception signals for the RIS; this approach is critical to achieving a harmonious balance between communication system performance and perception information clarity.

[0058] Step 2: Use RIS to dynamically phase modulate and optimize the signal beam, so as to achieve optimal allocation of RIS communication and perception signals;

[0059] Assume that the autonomous coherence duration between the communication domain and the perception domain is L. During this period, the stability of the communication channel and the target position remain unchanged, and the symbol duration of the transmitter is less than the coherence duration of the channel. Therefore, the total energy P of the transmitter is divided into two parts, one for communication and the other for the perception task. The signal expression of the transmitter is as follows:

[0060]

[0061] Among them, p k represents the transmit beamforming matrix, c k(t) represents the normalized information communication matrix of user device k, and s(t) represents the perception information matrix at time t. X, P, C and S are their vectorized representations respectively.

[0062] Due to the total power consumption limit of communication and perception, the optimization of both performance cannot be achieved at the same time, which leads to a scenario where a deliberate balance between the two capabilities is required. In practical applications, optimization algorithms are needed to find the best balance between communication and perception to achieve efficient resource utilization while meeting system requirements.

[0063] We choose to use RIS to change the amplitude and phase of the signal because we need to provide services to the communication user equipment UE and the sensing target distributed on opposite sides. r ∈C and Θ f ∈C is used to represent the phase deflection factor of the transmission refraction unit and the reflection unit, as shown below:

[0064]

[0065] In the above equation, β i,N 、e jφi,N represents the transmission coefficient of unit N, where β i,N ∈[0,1] represents the amplitude variation coefficient, and φ i , N∈[0,2π] represents the phase shift coefficient. β f,n and β r,n There is a correlation between them, as follows:

[0066]

[0067] In this embodiment, the amplitude variation coefficients of the RIS are independent.

[0068] Based on the provided equations, the channel matrix between the base station and the user equipment, and the channel matrix between the base station and the RIS are derived. Assume that the channels between the RIS and the user equipment are Ricean channels. Therefore, the channel matrix G between the RIS and the base station is as follows:

[0069]

[0070] Among them, d BS,RIS represents the distance between the base station and RIS, α BS,RIS represents the path loss coefficient, ε represents the Rice factor between 0 and 1, and ρ0 represents the path loss factor per unit distance. LOS Represents the fixed line-of-sight (LOS) component, G NLOS Represents the random non-line-of-sight component.

[0071] The channel matrix h between user equipment k and RISk :

[0072]

[0073] in, represents the distance between user device k and RIS, represents the path loss coefficient. In addition, represents the fixed sight distance component, and represents the random non-line-of-sight component. All non-line-of-sight channels are designed as Rayleigh channel models.

[0074] Therefore, the signal received by user equipment k is as follows:

[0075]

[0076] n k (t) represents the interference caused by the system thermal noise, N k n k Vector representation of (t);

[0077] The above formula consists of four parts: the first part represents the expected signal of user equipment k, the second part represents the interference generated by other user equipment, the third part represents the interference caused by the perceived signal, and the last part represents the thermal noise encountered by user equipment k.

[0078] Therefore, the signal-to-interference-plus-noise ratio (SINR) of user equipment k and the SINR of the eavesdropped channel can be calculated as follows:

[0079]

[0080] Among them, R s represents the autocorrelation matrix of the transmitted radar signal, σ 2 represents the thermal noise power;

[0081] h s Indicates the channel between RIS and the target, as shown below:

[0082]

[0083] Among them, φ h and φ v Respectively represent the azimuth and elevation of the target, which are the most important factors in the perception process. s (t) represents thermal additive noise. h R (φ h ,φ v ) and h t (φ h ,φ v ) represent the steering vectors of the refracted part and the sensing receiving sensor, respectively. They can be calculated as follows:

[0084]

[0085] Where λ represents the signal wavelength, d x ,d y and d z They represent the unit coordinates in the Cartesian coordinate system respectively. The safe rate of user equipment k can be calculated as follows:

[0086] SINR seq,k =SINR k -SINR e (10)

[0087] Among them, SINR e Indicates the signal-to-interference-to-noise ratio of the monitoring channel;

[0088] The channel between RIS, BS and the target is a line-of-sight channel, so the echo sensing signal received by the sensor in RIS is as follows:

[0089] y s (t) = αh s Θ r Gs(t)+n s (t) (11)

[0090] in, α Represents the radar cross-section coefficient of the target. If the channel and the transmitted signal are fixed, the received radar signal will follow a Gaussian distribution.

[0091] After the synaesthesia integrated network model is determined, the communication and perception signals of RIS are optimally allocated by establishing an optimization problem. The process of establishing the optimization problem includes:

[0092] In this embodiment, the Cramer-Rao lower bound CRB is used to evaluate the accuracy of the perception results. CRB is the lower limit of the variance bound that can be achieved by parameter estimation, emphasizing the importance of achieving the minimum bound to obtain the best performance. CRB can be calculated by calculating the inverse of the FIM matrix (Fisher Information Matrix). Use ξ to represent the estimation vector ξ=[[φ h ,φ v ],[R(α),I(α)]],φ h represents the pitch angle of the target, φ v represents the azimuth of the target, and R(α) represents α The real part of α The imaginary part of , and the FIM matrix of ξ is as follows:

[0093]

[0094] In the formula, J αα , J φα and J φφ is the second-order partial derivative of the echo signal with respect to the perceived value. For a Gaussian distribution matrix, the (i, j)th element of the FIM matrix of the received signal can be calculated as follows:

[0095]

[0096] in, represents the real part operation, represents the variance of the received signal, and η represents the expectation of the received signal.

[0097] We focus on enhancing the beamforming matrix and phase offset factor of RIS by reducing the trace of the CRB matrix. The CRB matrix serves as a lower bound for the worst-case estimated mean square error (MSE) while complying with constraints related to transmit power and physical layer security. Therefore, the optimization problem is as follows:

[0098]

[0099] Among them, B is the intermediate variable introduced for solving the optimization problem; R x represents the autocorrelation matrix of the transmitted signal.

[0100] The first two constraints in the optimization problem mean optimizing the factors B, Θ, P, and R x To obtain the minimum trace of the CRB matrix. The third ensures that each user has a high security rate. The fourth is the limit of the transmission power, while the fifth means the limit of the phase offset factor. The optimization problem is not a convex problem. Therefore, finding a global optimal solution is a huge challenge. Therefore, this embodiment proposes a method using an augmented Lagrangian method (ALM) and a block coordinate descent (BCD) algorithm to solve the optimization problem. The BCD method is needed to mainly transform the problem into two different convex problems. One problem requires a fixed factor Θ, and a convex optimization algorithm is used to calculate other parameters to be optimized. Using the conceptual framework of the algorithm, the decomposed problem incorporates the Lagrangian penalty term into its formula. Through a series of iterations, the initial non-convex problem is gradually shaped into a convex problem, allowing a more feasible and systematic approach to find a solution. Specifically:

[0101] First, for the optimization of B, P, and R with a fixed factor Θ x , the formula is transformed into an easily solvable convex problem through tools such as the CVX toolbox. The optimization problem is converted to:

[0102]

[0103] Where M is σs 2 is the variance of the received signal.

[0104] Therefore, the constraint is converted into its approximate form for optimization. Physical layer security needs to be converted into a convex function. Regarding the optimization problem of CRB and signal-to-noise ratio, the SNR constraint can be converted into a convex constraint. In order to simplify the non-convex constraints introduced by physical layer security, this embodiment proposes a conversion method, which converts the security rate constraint into the SINR constraint of a single communication link by introducing an auxiliary signal-to-interference-noise ratio variable β, thereby simplifying the problem solving process. The physical layer security constraint can be changed into the following form:

[0105]

[0106] Then, the optimization problem can be solved as a traditional CRB and SINR optimization problem using the CVX toolbox for the optimization problem.

[0107] For the optimization factor Θ, it is necessary to add the Lagrangian penalty term to the problem formulation and transform the non-convex problem into an easily solved closed-form problem. According to the ALM algorithm, the BCD algorithm is then used to solve the closed-form problem. Therefore, the most important step is to find a suitable penalty term. According to the ALM algorithm, a quadratic penalty term is used to approximate the original problem. Extracting the information related to R from the sensory signal x and θ r Then, the secondary penalty term is constructed according to the ALM algorithm as follows:

[0108]

[0109] Therefore, a penalty term is added, and since the added constraint is an equality, the additional first-order constraint function can be omitted, leaving only the penalty term. This reduces the computational complexity and enhances the stability of the optimization.

[0110] The BCD algorithm is used to iterate and refine the variables B, P, Rx, and Θ in turn, which can efficiently converge to the optimal solution. Then, fix B, P, R x , we need to find the optimal Θ factor. After that, we can use the SDR algorithm to solve the optimization problem.

[0111] Numerical simulation results using the Monte Carlo algorithm are presented to verify the advantages of the proposed algorithm under physical layer security constraints. Assume that 16 radar sensors are deployed on the RIS surface and 6 communication users are randomly distributed between 20 meters and 30 meters around the base station, on the RIS transmission side. The sensing target is deployed 1500 meters above the ground, farther than the users. The base station is equipped with 10 antennas and the RIS surface consists of 16 reflection and transmission units, half of which are used for communication and the other half for sensing. The transmission power of the DFRC base station is 30dBm and the noise power is -110dBm. In addition, the path loss exponent is defined as 1.5, ρ0 is set to 30dB, and the Rician factor is 3dB. The duration of a coherent time block is specified as 100 units. The azimuth and angle of arrival (DOA) of the target are set to 150° and 20°, respectively. The optimization iteration convergence threshold is set to 10 4 A random set of physical layer security SINR requirements is generated by Gaussian randomization. Figure 2 In the paper, the proposed method is compared with the traditional communication and sensing waveforms optimized for SINR. As the number of iterations increases, Figure 2 The CRB index in (a) continues to decrease, and the perception performance gradually improves. The comparison curve shows that the algorithm can effectively improve the accuracy of remote target perception under the physical layer security requirements of communication users with lower complexity.

[0112] exist Figure 3 In the example, the target is located at 20 degrees in azimuth and 30 degrees in elevation. The mean square error (MSE) estimation is then used to evaluate the performance of the two algorithms in estimating the target position. It can be observed that Figure 3 In the two figures, the estimated target is mainly composed of several relatively concentrated bright spots and patches, and the performance of the present invention is better.

[0113] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that features described in conjunction with individual embodiments may be used in other described embodiments.

Claims

1. A synaesthesia integrated beamforming method based on physical layer security, characterized in that: The method comprises: S1. Establish a synaesthesia integrated network, including a base station BS and K user devices within its coverage, and a reconfigurable intelligent surface RIS is set between the base station and the user devices for communication and perception; the target in the synaesthesia integrated network is provided with an antenna, and the synaesthesia integrated network is also provided with a sensor for monitoring the signal of the target; the reconfigurable intelligent surface RIS includes N units and is divided into two parts, one part is a transmission refraction unit, and the other part is a transmission reflection unit; S2. Perform joint waveform design to achieve optimal allocation of RIS communication and perception signals.

2. The synaesthesia integrated beamforming method based on physical layer security according to claim 1 is characterized in that: In S2, the communication and perception signals of RIS are optimally allocated by establishing an optimization problem, and the optimization problem is: tr(R x )≤P B≥0 Where P represents the transmit beamforming matrix vector, B is the intermediate variable introduced for solving the optimization problem, tr() represents the trace of the matrix, and the phase deflection factors of the transmission refraction unit and the transmission reflection unit of the reconfigurable smart surface RIS are Θ respectively. r and θ f ; represents the amplitude variation coefficient of the unit, φ i,n ∈[0,2π] represents the phase shift coefficient of the unit, n=1,2,…,N; R x Represents the autocorrelation matrix of the transmitted signal, SINR seq,k represents the safe rate of user equipment k, α represents the radar cross-sectional area coefficient of the target, and the estimated vector ξ=[[φ h ,φ v ],[R(α),I(α)]],φ h and φ v Respectively represent the azimuth and elevation of the monitored target, R(α) and I(α) represent the real and imaginary parts of α, respectively; the Fisher information matrix of ξ Among them, J αα , J φα and J φφ are the elements of the Fisher information matrix FIM; Solve the optimization problem and get P and Θ i , according to P and Θ i For the configured reconfigurable intelligent surface RIS, the communication and perception signals of RIS are optimally allocated.

3. The synaesthesia integrated beamforming method based on physical layer security according to claim 1 is characterized in that: The method to solve the optimization problem is: Using the block coordinate descent algorithm, with Θ fixed, the optimization problem is transformed into a convex problem, and the convex problem is solved to obtain B, P, and R x ; Using the augmented Lagrangian method, construct the quadratic penalty term of Θ, based on the calculated B, P and R x , find the optimal Θ.

4. The synaesthesia integrated beamforming method based on physical layer security according to claim 3 is characterized in that: The convex problem is: a,b∈h,v SINR s <b Among them, η is the expectation of the received signal, α * is the conjugate of α, vec() represents the vectorization operator, and h s represents the channel matrix between RIS and the target, X is the transmitted signal vector, G is the channel matrix between RIS and the base station, j represents the imaginary unit, σ s 2 is the variance of the received signal, represents the real part operation, β is the auxiliary signal to interference and noise ratio variable, SINR k is the signal-to-interference-and-noise ratio (SINR) of the user equipment s is the signal-to-interference-to-noise ratio of the eavesdropping channel, the intermediate variable 5. The synaesthesia integrated beamforming method based on physical layer security according to claim 4 is characterized in that: t∈{k,s} Among them, R s represents the autocorrelation matrix of the transmitted radar signal, σ 2 represents the thermal noise power.

6. The synaesthesia integrated beamforming method based on physical layer security according to claim 5 is characterized in that: The channels between RIS and user equipment are all Rice channels, and the channel matrix G between RIS and base station is: Among them, d BS,RIS represents the distance between the base station and RIS, α BS,RIS represents the path loss coefficient, ε represents the Rice factor between 0 and 1, ρ0 represents the path loss factor per unit distance, G LOS represents the fixed line-of-sight component of the channel between RIS and the base station, G NLOS Represents the random non-line-of-sight component of the channel between RIS and the base station; The channel matrix h between user equipment k and RIS k for: in, represents the distance between user device k and RIS, represents the path loss coefficient, represents the fixed line-of-sight component of the channel between user equipment k and RIS, represents the random non-line-of-sight component of the channel between user equipment k and RIS; The channel matrix h between RIS and the target s for: h R (φ h ,φ v ) and h t (φ h ,φ v ) represent the steering vectors of the refraction part and the sensing receiving sensor respectively; Where λ represents the signal wavelength, d x d y and d z They respectively represent the unit coordinates of the three axes of the target to be detected in the Cartesian coordinate system.

7. The synaesthesia integrated beamforming method based on physical layer security according to claim 3 is characterized in that: The quadratic penalty term of Θ is:

8. A computer-readable storage device storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the synaesthesia integrated beamforming method based on physical layer security according to any one of claims 1 to 7 are implemented.

9. A synaesthesia integrated beamforming device based on physical layer security, comprising a storage device, a processor, and a computer program stored in the storage device and executable on the processor, characterized in that: The processor executes the computer program to implement the steps of the synaesthesia integrated beamforming method based on physical layer security according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the synaesthesia integrated beamforming method based on physical layer security as claimed in any one of claims 1 to 7 are implemented.

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