Radar communication integrated beam optimization method and device, equipment and storage medium

Abstract

The application discloses a radar communication integrated beam optimization method, aiming at the problem that the existing beam optimization method adopts an MM iteration algorithm or utilizes an SCA algorithm internal loop to solve the optimal value of an approximation target function, resulting in high overall operation complexity and difficulty in implementation, by combining the use of WMMSE and SDR algorithms, the scheme based on WMMSE and MM algorithms is improved and replaced, the MM algorithm which originally needs multiple iteration operations to obtain the optimal value is replaced by the SDR algorithm which can obtain the optimal value of this round once, the overall operation complexity is reduced, and the optimization efficiency is improved. Moreover, the relationship between the reflection matrix of the intelligent reflecting surface IRS and the circuit impedance matrix is used, and the SCA iteration algorithm solving process originally needed is replaced by the FP algorithm optimization, further reducing the operation complexity. The beam optimization efficiency is accelerated, and the calculation resources are saved.

Description

Technical Field

[0001] This invention belongs to the technical field of radar-communication integration, and particularly relates to a radar-communication integrated beam optimization method, apparatus, device and storage medium. Background Technology

[0002] In integrated radar-communication beam design, it is essential to ensure that the designed beam can effectively fulfill both radar and communication functions. For radar tracking, signal power needs to be concentrated at the target's azimuth, i.e., transmitting a directional beam signal pointing towards the target's azimuth. Therefore, beam control can be achieved through precoding and other design techniques. However, for communication, the beam direction and the precoding coefficients used may compromise the signal-to-noise ratio at the receiver, further affecting communication quality. Therefore, communication metrics must also be incorporated into the beam design process.

[0003] A classic integrated signal fusion application scenario model is as follows: Figure 1 As shown in the figure, the antenna model represents the integrated base station platform. The left side is the radar side, and the right side is the communication side. In this scenario, the integrated signal platform uses the same signal on both the radar and communication sides. That is, the integrated platform sends a fused signal, which enables the transmission of directional beams to multiple targets while serving multiple communication users. Note that the arrow on the communication side only indicates that the transmitted signal can reach the user, and does not mean that the transmitted signal is directed at that user.

[0004] The difference between the two sides of the radar communication in the figure shows that, since the service communication user does not need a directional beam like the radar side, the communication function is generally achieved by the sidelobe position of the signal. In this case, the deep fading channel on the communication side will affect the communication performance. Summary of the Invention

[0005] The purpose of this invention is to provide an integrated radar and communication beam optimization method, apparatus, device, and storage medium, which improves and simplifies existing optimization methods to achieve an optimization solution with lower overall complexity and easier understanding and implementation.

[0006] To solve the above problems, the technical solution of the present invention is as follows:

[0007] A radar-communication integrated beam optimization method includes:

[0008] Establish an integrated radar communication signal model suitable for multi-user communication scenarios, and add an IRS auxiliary communication link on the communication side to form an integrated radar communication signal model with IRS auxiliary communication.

[0009] A radar-communication integrated signal model based on IRS-assisted communication is used to determine radar and communication metrics.

[0010] Based on the radar metric index and the communication metric index, an optimization problem is constructed, and an alternating optimization method is used to solve the transmit beam vector W and the reflection matrix Θ of the IRS under the multicast communication;

[0011] Under the premise that the reflection matrix Θ is unchanged, the transmit beam vector W is updated in combination with the WMMSE algorithm and the SDR algorithm; based on the updated transmit beam vector W, the FP algorithm is used to optimize the reflection matrix Θ.

[0012] According to an embodiment of the present application, the radar communication integrated signal model based on IRS assisted communication, the determination of the radar metric index and the communication metric index further comprises:

[0013] The radar communication integrated signal model of the IRS assisted communication comprises: a uniform linear array composed of M antennas, N IRS IRS reflection units, N targets and K communication users exist in the space;

[0014] The communication metric index at the kth user at the lth moment, i.e., the weighted sum rate WSR, is represented as:

[0015]

[0016]

[0017] R k (W,Θ)=log2(1+γ k (W,Θ))

[0018] In the formula, represents the channel vector from the IRS to the kth user, represents the channel matrix from the radar communication integrated platform to the IRS, d k represents the line-of-sight direct channel from the radar communication integrated platform to the communication user k, represents the linear precoding vector of the communication symbol of the user i on the M antennas, represents a noise value subject to a complex Gaussian distribution, μ k represents the rate priority weight of the kth user, and different weights are obtained according to different application scenarios and users;

[0019] The radar metric index at the n th target azimuth, i.e., the radar detection power, is represented as:

[0020] P(θ n ,W)=a H (θ n )WW H a(θ n )

[0021] wherein, is the steering vector of the antenna array.

[0022] According to an embodiment of the present application, the optimization problem is represented as:

[0023]

[0024]

[0025] Θ = diag(Θ1, Θ2,..., Θ G )

[0026]

[0027] wherein, ρ is a regularization parameter for balancing the communication and radar detection functions; 1 M×1 is an M-long all-one vector, i.e. I represents an identity matrix.

[0028] According to an embodiment of the present application, the updating the transmit beam vector W under the premise that the reflection matrix Θ is invariant further comprises:

[0029] The optimization problem is converted into a first optimization problem:

[0030]

[0031]

[0032] The first optimization problem is equivalently converted into a minimum WMSE problem:

[0033]

[0034]

[0035] wherein, represents the estimation error of the optimal MMSE receiver equalizer;

[0036] The minimum WMSE problem is solved by using a low-complexity algorithm based on SDR to obtain the optimal solution of the transmit beam vector W

[0037] According to an embodiment of the present application, the low-complexity algorithm of SDR is realized by the CVX tool in MATLAB.

[0038] According to an embodiment of the present application, the optimization of the reflection matrix Θ based on the updated transmit beam vector W further comprises:

[0039] The optimization problem is converted into a second optimization problem:

[0040]

[0041] s.t.Θ=diag(Θ1,Θ2,...,Θ G )

[0042]

[0043] The Lagrange dual transformation is performed on the second optimization problem, and an auxiliary variable a is introduced k ,k=1,2,...,K, and an equivalent optimization problem is obtained as:

[0044]

[0045] s.t.Θ=diag(Θ1,Θ2,...,Θ G )

[0046]

[0047] The FP algorithm is used to solve the equivalent optimization problem, and the optimal solution of Θ is obtained.

[0048] A radar-communication integrated beam optimization device, comprising:

[0049] A model creation module is configured to establish a radar-communication integrated signal model applicable to a multi-user communication scenario, add an IRS auxiliary communication link on the communication side, and construct a radar-communication integrated signal model for IRS auxiliary communication.

[0050] An index configuration module is configured to determine radar metric indexes and communication metric indexes based on the radar-communication integrated signal model for IRS auxiliary communication.

[0051] An optimization module is configured to construct an optimization problem based on the radar metric indexes and the communication metric indexes, solve a transmit beam vector W and a reflection matrix Θ of the IRS under multicast communication by using an alternating optimization method, update the transmit beam vector W by combining a WMMSE algorithm and an SDR algorithm on the premise that the reflection matrix Θ is unchanged, and optimize the reflection matrix Θ by using an FP algorithm based on the updated transmit beam vector W.

[0052] A radar-communication integrated beam optimization device, comprising a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions are executed by the processor to enable the processor to perform steps in a radar-communication integrated beam optimization method according to an embodiment of the present application.

[0053] A storage medium storing computer readable instructions that, when executed by one or more processors, cause the one or more processors to perform steps in a radar communication integrated beam optimization method in an embodiment of the application.

[0054] The application has the following advantages and positive effects compared with the prior art due to the adoption of the above technical solutions:

[0055] The radar communication integrated beam optimization method in an embodiment of the application improves and replaces the scheme based on the WMMSE and MM algorithms by combining the use of the WMMSE and SDR algorithms, replacing the MM algorithm that originally needs to be iterated multiple times to obtain the optimal value with the SDR algorithm that can obtain the optimal value of this round with one operation, reducing the overall operation complexity and improving the optimization efficiency. Moreover, the relationship between the reflection matrix of the intelligent reflecting surface IRS and the circuit impedance matrix is used, and the SCA iterative algorithm solving process originally needed is replaced by the FP algorithm optimization, further reducing the operation complexity. The beam optimization efficiency of the radar communication integrated system is accelerated, and the computing resources are saved. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 The radar communication integrated model in an embodiment of the application is shown in the figure;

[0057] Figure 2 The principle diagram of the intelligent reflecting surface IRS in an embodiment of the application is shown in the figure;

[0058] Figure 3 The communication link diagram of the intelligent reflecting surface IRS in an embodiment of the application is shown in the figure;

[0059] Figure 4 The radar communication integrated signal model diagram of the IRS assisted communication in an embodiment of the application is shown in the figure;

[0060] Figure 5 The radar communication integrated beam optimization method flowchart in an embodiment of the application is shown in the figure;

[0061] Figure 6 The radar communication integrated beam optimization device block diagram in an embodiment of the application is shown in the figure;

[0062] Figure 7 The radar communication integrated beam optimization device diagram in an embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0063] The application will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the application will be more apparent from the following description and claims.

[0064] By Figure 1 It can be known that, since the service communication user does not need a directional beam like the radar side, generally the sidelobe position of the signal realizes the communication function, at this time the deep fading channel of the communication side will affect the performance of the communication, for this purpose, a new technology-intelligent reflecting surface (IRS) is introduced to assist communication to realize better communication side function.

[0065] As Figure 2 shown, the intelligent reflecting surface IRS is mainly composed of a series of passive reflecting units, each reflecting unit can independently change the amplitude and phase information of the incoming signal, so as to realize the expected electromagnetic wave transmission characteristics. The equivalent load impedance of each reflecting unit can be adjusted by changing the bias voltage.

[0066] In a multiple-input multiple-output communication system, assuming that the number of antennas of the transmitting array and the receiving array is Nt and Nr respectively, and the IRS has NI reflecting units, then the communication link model assisted by the IRS is as shown in Figure 3 .

[0067] Corresponding to Figure 3 , assuming that the transmitting signal is s, then the signal expression received by the receiving end is:

[0068]

[0069] In the formula, is the channel matrix between the transmitting array and the IRS, is the channel matrix between the IRS and the receiving array, is the reflection matrix of the IRS. At this time, the equivalent effective channel from the transmitting array to the receiving array is The actual response matrix of the channel can be changed by adjusting and optimizing Θ. If combined with the signal sending end precoding and other active beam forming means, the performance of the communication can be further improved.

[0070] By applying the IRS to the integrated signal application scene with fading on the communication side, the scene model as shown in Figure 4 can be obtained.

[0071] Figure 4In this system, an IRS auxiliary channel is added to the communication side. Compared with the direct channel, the auxiliary channel has fewer obstacles, so the impact of channel fading will be correspondingly smaller. The use of IRS further assists in ensuring good performance on the communication side.

[0072] based on Figure 4 The model, for subsequent quantitative analysis and optimization of the integrated signal beam design, can provide corresponding performance metrics for radar and communication. First, assume the integrated platform is a uniform linear array composed of M antennas, and the IRS is equipped with N... IRS If there are 1 reflector unit and K users are served for communication, then the received signal at the k-th user in time l is:

[0073]

[0074] In the formula, This represents the channel vector from the IRS to the k-th user. Represents the reflection matrix of the IRS. d represents the channel matrix from the integrated system platform to the IRS. k This represents the line-of-sight direct channel from the integrated platform to communication user k. This represents the linear precoding vector of the communication symbols used by user i on M antennas. This represents the noise value in the received signal that follows a complex Gaussian distribution.

[0075] make Then, the communication metric at user k, namely the weighted sum-rate (WSR), can be expressed as:

[0076]

[0077]

[0078] R k (W,Θ)=log2(1+γ k (W,Θ)) (4)

[0079] Where W is the transmit beamforming vector in multicast communication.

[0080] Similarly, suppose there are N targets in space, and the azimuth angle of the targets is θ. n For any n = 1, 2, ..., N, the corresponding radar metric, i.e., the detection power at the azimuth angle of the nth target, can be expressed as:

[0081] P(θ n ,W)=a H (θ n )WW H a(θn ) (5)

[0082] where, is the steering vector of the antenna array. For a uniform linear array consisting of M antennas, the steering vector is defined as:

[0083]

[0084] where d is the antenna spacing and λ is the wavelength of the signal. Without loss of generality, we set d = λ / 2.

[0085] Based on the purpose of optimizing the above two radar metric indicators and communication metric indicators, an optimization problem can be constructed as follows:

[0086]

[0087] where ρ is a regularization parameter, mainly used to trade off the communication and radar detection functions. The larger ρ is, the more important the improvement of communication performance will be in the optimization problem (P1), and vice versa.1 M×1 denotes an M-long all-one vector, i.e. The first constraint condition is mainly to ensure that each antenna has a uniform average transmit power.

[0088] For the optimization problem (P1), most existing schemes also use the idea of alternating optimization to solve the problem, i.e., first fix Θ to optimize W, then fix W to optimize Θ, and execute the optimization alternately until the convergence condition is met.

[0089] In the optimization problem of fixing Θ to optimize W, the Weighted Minimum Mean Square Error (WMMSE) method can be used, and by expanding P(θ n ,W), the optimization problem (P1) is converted into the form of (P2a) as follows:

[0090]

[0091] The definitions of the parameters in the formula will be expanded in the following part.

[0092] At this time, the optimization problem of maximizing WSR is equivalent to converting the minimum WMMSE problem. Since this problem is also non-convex, an iterative algorithm based on the MM algorithm framework is used for optimization. The main process of this method is to constantly find the best upper bound function of the objective function of the problem (P2a) through an inner iteration, find the minimum value of the upper bound function to approximate the minimum value of the original objective function, and obtain the optimal solution through multiple iterations.

[0093] Then in the optimization problem of fixing W to optimize Θ, a fractional programming (FP) method can be adopted to introduce intermediate variables through Lagrange dual transformation and quadratic transformation method in sequence, so as to finally convert the optimization problem into the following form:

[0094]

[0095] The definitions of the parameters in the formula will be described in the following part.

[0096] Similarly, the problem is also non-convex, and through the successive convex approximation (SCA) technique method, a convex substitute function of the target function is found through Taylor's second-order expansion, so as to convert the non-convex optimization problem into a series of convex problems, and finally an approximate solution of the original problem is obtained. Since the method is also a step-by-step approximation algorithm, an inner loop iteration needs to be opened to approximate the optimal solution of the original problem.

[0097] The optimization process scheme described above is relatively complex as a whole, and the MM algorithm framework in fixing Θ to optimize W is an iterative algorithm, which is nested in an outer alternating optimization loop, which will lead to relatively high complexity, and the solution process is not easy to understand. Similarly, the SCA algorithm in fixing W to optimize Θ also needs to use an inner loop to solve the optimal value of the approximation target function, which will also lead to relatively high complexity, and the overall iteration optimization efficiency cannot be guaranteed.

[0098] Therefore, the embodiment mainly improves and simplifies the optimization method described above, so as to realize an optimization solution with lower overall complexity and easier understanding and implementation.

[0099] The radar communication integrated beam optimization method in the embodiment will be described in detail below.

[0100] For the optimization problem (P1), the embodiment adopts an alternating optimization framework idea for solving.

[0101] (1) Fixing Θ to optimize W

[0102] At this time, the problem (P1) will be degenerated into the following form:

[0103]

[0104] In order to convert the maximum WSR method into the WMMSE method, based on formula (1), consider using the equalizer g k to decode the expected communication symbol s k , let The estimated symbol value is expressed as:

[0105]

[0106] The Mean Square Error (MSE) of the estimated signal and the expected signal is defined as:

[0107]

[0108] where, represents the expectation,

[0109] Then, by minimizing the MSE, i.e., solving equation, the value of the optimal MMSE receiving equalizer is:

[0110]

[0111] At this time, the optimal MMSE estimation error of the receiving end signal is:

[0112]

[0113] By comparing equation (2) and (10), the following relationship can be found: And by combining equation (4), there is If there is a relationship:

[0114]

[0115] The optimization problem (P3) of maximizing the WSR can be equivalently converted into the problem of minimizing the WMSE:

[0116]

[0117] It is noted that in the optimization problem (P4), according to equation (5), the latter term of the objective function is quadratic. In order to convert the function into a convex function, the following conversion is needed:

[0118]

[0119] where M is the number of antennas, P is the transmission power of a single antenna, and Z(θ n ) = M I - a(θ n )a H (θ n ). It can be derived that a(θ n )a H (θ n ) is a rank-one matrix, and the eigenvalue is ||a(θ n )|| 2 = M, so Z(θ n) is a semi-positive definite matrix. Re-stating the optimization problem (P4), we have:

[0120]

[0121] At this time, the objective function of the problem is a convex function, but the constraint is a quadratic equality constraint, which leads to the optimization problem (P5) is still non-convex. This embodiment improves and proposes to use a low-complexity algorithm based on semi-definite relaxation (SDR) to solve the above problem.

[0122] It is observed that (P5) is a non-homogeneous quadratic constraint quadratic programming (QCQP) problem, which first needs to be homogenized.

[0123] Based on equation (8), ε k (W) can be rewritten as:

[0124]

[0125] Neglecting the term that does not affect ε k (W) analysis constant An intermediate variable is introduced ε k (W) can be converted to the following form:

[0126]

[0127] In the formula, And |t i | = 1.

[0128] Similarly, in the optimization problem (P5), This term can be written as:

[0129]

[0130] The constraint term can also be rewritten as:

[0131]

[0132] Substitute (14), (15), (16) into problem (P4) to get the homogeneous form of optimization problem (P5):

[0133]

[0134] In the formula,

[0135]

[0136]

[0137]

[0138]

[0139] Let The final form of problem (P6) is:

[0140]

[0141] where Tr(·) denotes the trace of a matrix, denotes the element in the M+1th row and M+1th column of , and the value of this position element is exactly the variable |t k | 2 .

[0142] For homogeneous QCQP problem, SDR optimization method can be used to solve it. In practical use, CVX tool package has built-in SDR method, so the solution of problem (P7) can be obtained directly by calling MATLAB simulation software. After obtaining the optimal solution , the method of eigenvalue decomposition can be used to obtain the approximate optimal solution of The approximate optimal solution k of w in the original optimization problem can be obtained according to formula (17), that is

[0143]

[0144] (2) Fix Optimize Θ

[0145] At this time, the optimization problem (P1) is transformed into the following form:

[0146]

[0147] In order to solve this optimization problem, the fractional programming (FP) method is used to solve it in this embodiment.

[0148] Firstly, Lagrange dual transformation is carried out on (P8) to introduce auxiliary variable α k , k = 1, 2,..., K, and the equivalent optimization problem is obtained as follows:

[0149]

[0150] For problem (P9), fix Θ, (P9) will become an unconstrained convex optimization problem, and the basic method of taking derivative to get extreme value can get the optimal solution of this time For:

[0151]

[0152] Then fix (P9) can be simplified as the following optimization problem:

[0153]

[0154] Then, Θ needs to be vectorized, and since the Θ matrix is only block diagonal, it also needs to be block expanded, that is, make For It can be rewritten as:

[0155]

[0156]

[0157] At this time, the objective function of problem (P10) can be written as:

[0158]

[0159] Apply the quadratic transform method in the FP algorithm to equation (25) to get the equivalent form of problem (P10):

[0160]

[0161] For problem (P11), fix At this time, the above problem will be a convex optimization problem for β, and the basic method of taking derivative can be used to solve the optimal solution of β For:

[0162]

[0163] Finally, fix Expand Square the term and discard the constant term in problem (P11) that is irrelevant to The optimization problem can be represented as:

[0164]

[0165] Where,

[0166]

[0167]

[0168] and have

[0169] From equation (28), it can be seen that U is a positive semi-definite matrix, and the objective function of problem (P12) is a convex function. In order to make the optimization problem (P12) be solved by using the convex optimization toolbox, it is also necessary to convert the non-convex constraint condition. The present application discards the use of the SCA method, and adopts Θ g and the impedance matrix X I,g The relationship is:

[0170] Θ g = (jX I,g + Z0I) -1 (jX I,g -Z0I) (29)

[0171]

[0172] In the formula, the characteristic impedance Z0=50Ω generally.

[0173] Therefore, the optimization problem (P12) can be rewritten as:

[0174]

[0175] Since X I,g is a real symmetric matrix, and the matrix elements can be adjusted, the optimization problem (P13) is essentially an unconstrained optimization problem, and can be directly optimized by using the quasi-Newton method. At this point, The optimal solution

[0176] Finally, the original optimization problem (P1) can be optimized by alternately iterating W and Θ in the above two steps until the step convergence requirement is met, and the final optimization result is obtained.

[0177] The overall flow of the improved alternating optimization joint algorithm based on WMMSE and FP algorithm proposed by the present application is shown in Table 1, and the corresponding flow chart is shown in Figure 5 .

[0178] Table 1 Improved alternating optimization algorithm proposed by the present application

[0179]

[0180] The radar-communication integrated beam optimization method described above, in view of the problem that the existing beam optimization method uses the MM iterative algorithm or uses the internal loop of the SCA algorithm to solve the optimal value of the approximation target function, resulting in high overall operation complexity and difficulty in implementation, the WMMSE and SDR algorithms are combined and used to improve the scheme based on the WMMSE and MM algorithms. The MM algorithm, which originally needs to be iterated multiple times to obtain the optimal value, is replaced by the SDR algorithm, which can obtain the optimal value of this round with one operation, thereby reducing the overall operation complexity and improving the optimization efficiency. Moreover, the relationship between the reflection matrix of the intelligent reflecting surface IRS and the circuit impedance matrix is used, and the SCA iterative algorithm solving process originally needed is replaced by the FP algorithm optimization, further reducing the operation complexity and saving the calculation resources.

[0181] Embodiment Two

[0182] Based on the same concept, the present embodiment provides a radar-communication integrated beam optimization device, please refer to Figure 6 The device comprises:

[0183] A model creation module 1 is configured to establish a radar-communication integrated signal model applicable to a multi-user communication scenario, add an IRS auxiliary communication link on the communication side, and form a radar-communication integrated signal model for IRS auxiliary communication.

[0184] An index configuration module 2 is configured to determine radar metric indicators and communication metric indicators based on the radar-communication integrated signal model for IRS auxiliary communication.

[0185] An optimization module 3 is configured to construct an optimization problem based on the radar metric indicators and the communication metric indicators, use an alternating optimization method to solve the transmit beam vector W and the reflection matrix Θ of the IRS under multicast communication; under the premise that the reflection matrix Θ is unchanged, update the transmit beam vector W by combining the WMMSE algorithm and the SDR algorithm; and optimize the reflection matrix Θ based on the updated transmit beam vector W using the FP algorithm.

[0186] The embodiments of the model creation module 1, the index configuration module 2, and the optimization module 3 are as described in Embodiment One above, and will not be repeated here.

[0187] Embodiment Three

[0188] The present embodiment provides a radar-communication integrated beam optimization device. Please refer to Figure 7The radar-communication integrated beam optimization apparatus 500 can have a large difference in configuration or performance, and can include one or more processors (central processing units, CPUs) 510 (for example, x86, arm architecture processor or FPGA) and memories 520, one or more storage media 530 (for example, one or more mass storage devices) storing applications 533 or data 532. The memories 520 and the storage media 530 can be temporary storage or persistent storage. The programs stored in the storage media 530 can include one or more modules (not shown in the figure), and each module can include a series of instructions operating in the radar-communication integrated beam optimization apparatus 500.

[0189] Further, the processor 510 can be configured to communicate with the storage media 530 and execute a series of instructions in the storage media 530 on the radar-communication integrated beam optimization apparatus 500.

[0190] The radar-communication integrated beam optimization apparatus 500 can further include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input / output interfaces 560, and / or one or more operating systems 531, such as Windows Server, Vista, etc.

[0191] Those skilled in the art can understand that Figure 7 The structure of the radar-communication integrated beam optimization apparatus shown does not constitute a limitation on the radar-communication integrated beam optimization apparatus, and can include more or fewer components than shown, or combine certain components, or have a different arrangement of components.

[0192] Another embodiment of the present application also provides a computer readable storage medium.

[0193] The computer readable storage medium can be a non-volatile computer readable storage medium, and can also be a volatile computer readable storage medium. The computer readable storage medium stores instructions, and when the instructions are run on a computer, the computer executes the steps of the radar-communication integrated beam optimization apparatus method in embodiment one.

[0194] When the radar communication integrated beam optimization method is realized in the form of program instructions and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the embodiments can be embodied in the form of software, and the computer software is stored in a storage medium, including a plurality of instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present disclosure. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0195] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific execution of the system and device described above can refer to the corresponding process in the foregoing method embodiments.

[0196] The embodiments of the present application are described in detail above in combination with the drawings, but the present application is not limited to the above-described embodiments. Even if various changes are made to the present application, as long as the changes belong to the scope of the claims of the present application and equivalent technologies, they still fall within the protection scope of the present application.

Claims

1. A radar-communication integrated beam optimization method, characterized in that, The method comprises the steps of: establishing a radar-communication integrated signal model suitable for a multi-user communication scenario, adding an IRS assisted communication link on the communication side to form an IRS assisted communication radar-communication integrated signal model; determining radar metric indicators and communication metric indicators based on the IRS assisted communication radar-communication integrated signal model; based on the radar metric indicators and the communication metric indicators, constructing an optimization problem, and solving the multicast communication transmit beam vector W and the IRS reflection matrix Θ by using an alternating optimization method; under the premise that the reflection matrix Θ is unchanged, updating the transmit beam vector W by combining the WMMSE algorithm and the SDR algorithm; and optimizing the reflection matrix Θ by using the FP algorithm based on the updated transmit beam vector W. 2.The radar-communication integrated beam optimization method of claim 1, wherein, The radar metric indicators and the communication metric indicators determined based on the IRS assisted communication radar-communication integrated signal model further comprise: The radar communication integrated signal model of IRS auxiliary communication includes: a uniform linear array composed of M antennas, N IRS IRS reflecting units, N targets exist in space, and K communication users; the communication metric indicator at the kth user at the lth moment, i.e., the weighted rate and WSR, is represented as: R k (W,Θ) = log2(1 + γ k (W,Θ)) wherein, denotes the channel vector from the IRS to the kth user, denotes the channel matrix from the radar-communication integrated platform to the IRS, d k denotes the line-of-sight direct channel from the radar-communication integrated platform to the communication user k, denotes the linear precoding vector for the communication symbol of user i on M antennas, denotes the noise value subject to complex Gaussian distribution, μ k denotes the rate priority weight of the kth user, which has different weights according to different application scenarios and users; the radar metric indicator at the nth target azimuth, i.e., the radar detection power, is represented as: P(θ n ,W) = a H (θ n ) WW H a(θ n ) wherein is a steering vector of the antenna array, (·) H denotes the Hermitian matrix. 3.The radar-communication integrated beam optimization method of claim 2, wherein, The optimization problem constructed based on the radar metric indicators and the communication metric indicators is represented as: Θ = diag(Θ1, Θ2,..., Θ G ) where p is a regularization parameter that trades off the communication and radar detection functions; 1 M×1 denotes an M-long all-ones vector, i.e. I denotes the identity matrix. 4.The radar-communication integrated beam optimization method of claim 3, wherein, The step of updating the transmit beam vector W under the premise that the reflection matrix Θ is unchanged further comprises: the optimization problem is converted into a first optimization problem: the first optimization problem is equivalently converted into a minimum WMSE problem: wherein denotes the estimation error of the optimal MMSE receiver equalizer; solving the minimization WMSE problem by using a low complexity algorithm based on SDR to obtain an optimal solution of the transmit beam vector W 5.The radar-communication integrated beam optimization method of claim 4, wherein, a low-complexity algorithm of SDR is realized by using the CVX tool in MATLAB. 6.The radar-communication integrated beam optimization method of claim 3, wherein, The step of optimizing the reflection matrix Θ by using the FP algorithm based on the updated transmit beam vector W further comprises: the optimization problem is converted into a second optimization problem: s.t. Θ = diag(Θ1, Θ2,..., Θ G ) The Lagrange dual transformation is applied to the second optimization problem, introducing an auxiliary variable α k ,k = 1, 2,..., K, and the equivalent optimization problem is obtained as s.t. Θ = diag(Θ1, Θ2,..., Θ G ) the optimal solution of Θ is obtained by solving the equivalent optimization problem by using the FP algorithm.

7. A radar-communication integrated beam optimization device, characterized in that, The method comprises the steps of: a model creation module is configured to establish a radar-communication integrated signal model suitable for a multi-user communication scenario, add an IRS assisted communication link on the communication side to form an IRS assisted communication radar-communication integrated signal model; a metric configuration module is configured to determine radar metric indicators and communication metric indicators based on the IRS assisted communication radar-communication integrated signal model; an optimization module is configured to construct an optimization problem based on the radar metric indicators and the communication metric indicators, solve the multicast communication transmit beam vector W and the IRS reflection matrix Θ by using an alternating optimization method, update the transmit beam vector W by combining the WMMSE algorithm and the SDR algorithm under the premise that the reflection matrix Θ is unchanged, and optimize the reflection matrix Θ by using the FP algorithm based on the updated transmit beam vector W.

8. A radar-communication integrated beam optimization device, comprising: The method comprises the steps of: a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions are executed by the processor to make the processor execute the steps in the radar-communication integrated beam optimization method according to any one of claims 1 to 6.

9. A storage medium storing computer readable instructions, wherein, The computer readable instructions are executed by one or more processors to make the one or more processors execute the steps in the radar-communication integrated beam optimization method according to any one of claims 1 to 6.

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