Beamforming method for mu-mimo radar communication integration

Abstract

This invention relates to a beamforming method for integrated MU-MIMO radar and communication, belonging to the field of integrated radar and communication beamforming. This invention addresses the problem of poor radar performance in traditional algorithms under high communication performance requirements. The technical solution of this invention is as follows: Step 1: The base station is equipped with a uniform linear antenna array. The base station simultaneously transmits radar and communication signals. The transmitted radar and communication signals are processed by a beamformer to obtain the signal to be transmitted, which is then transmitted via the antenna. The signal to be transmitted is transmitted through the channel to obtain the received signal y of the i-th communication user. i Step 2: Calculate the received signal y of the i-th communication user. i signal-to-noise ratio η i Step 3: Based on η i The objective function is solved to obtain the covariance matrix of the radar signal and the actual beamforming matrix set; step four: the results of step three are input into the antenna array to obtain the final beam pattern. This invention can be applied to beamforming in integrated communication and radar systems.

Description

Technical Field

[0001] This invention belongs to the field of beamforming for integrated communication and radar, specifically relating to a beamforming method for integrated MU-MIMO (Multi-User Multiple-Input Multiple-Output) radar and communication. Background Technology

[0002] A dual-function radar and communication system (DFRC) is an integrated signal system that simultaneously performs radar and communication functions. Radar and communication share the same hardware platform, achieving a high degree of integration. However, in scenarios with high communication requirements, traditional algorithms exhibit poor radar performance. Therefore, it is necessary to optimize traditional joint convex optimization and step-by-step convex optimization algorithms to achieve a beamforming algorithm that simultaneously meets the requirements of high communication and radar performance. Summary of the Invention

[0003] The purpose of this invention is to solve the problem of poor radar performance of traditional algorithms under high communication performance requirements, and to propose a beamforming method for integrated MU-MIMO radar and communication.

[0004] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0005] According to one aspect of the present invention, a beamforming method for integrated MU-MIMO radar and communication is provided, the method specifically including the following steps:

[0006] Step 1: Equip the base station with a uniform linear antenna array consisting of N antennas. The base station transmits radar and communication signals simultaneously. The transmitted radar and communication signals are processed by a beamformer to obtain the signal to be transmitted x, which is then transmitted through the antenna.

[0007] After the transmitted signal x is transmitted through the channel, the received signal y of the i-th communication user is obtained. i ;

[0008] Step 2: Calculate the received signal y of the i-th communication user. i signal-to-noise ratio η i ;

[0009] Step 3: Based on η i A joint convex optimization objective function is established, and then the established objective function is solved to obtain the covariance matrix R of the radar signal. d and the actual beamforming matrix set {T′ k};

[0010] Step 4: Take the R obtained in Step 3 d and {T′ k The signal is input into the antenna array to obtain the final beam pattern.

[0011] According to another aspect of the present invention, a beamforming method for integrated MU-MIMO radar and communication is provided, the method specifically including the following steps:

[0012] Step 1: Equip the base station with a uniform linear antenna array consisting of N antennas. The base station transmits radar and communication signals simultaneously. The transmitted radar and communication signals are processed by a beamformer to obtain the signal to be transmitted x, which is then transmitted through the antenna.

[0013] After the transmitted signal x is transmitted through the channel, the received signal y of the i-th communication user is obtained. i ;

[0014] Step 2: Calculate the received signal y of the i-th communication user. i signal-to-noise ratio η i ;

[0015] Step 3: Based on η i A distributed convex optimization objective function is established, and then the established objective function is solved to obtain the covariance matrix R of the radar signal. d and the actual beamforming matrix set {T′ k};

[0016] Step 4: Take the R obtained in Step 3 d and {T′ k The signal is input into the antenna array to obtain the final beam pattern.

[0017] The beneficial effects of this invention are:

[0018] This invention optimizes both joint and stepwise convex optimization methods, and incorporates the cross-correlation of radar targets as an index into the objective function. This achieves improved radar performance under high communication performance requirements, making the obtained radar performance close to that of a single radar scheme without spectrum sharing, while minimizing complexity. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of a MIMO radar-communication integrated system based on DFRC;

[0020] Figure 2 This is a ULA structure diagram;

[0021] Figure 3 It is a classic algorithm for joint convex optimization;

[0022] Figure 4It is a radiation pattern obtained by a weighted optimization algorithm for radar and communication indices using joint convex optimization.

[0023] Figure 5 It is the classic algorithm pattern of stepwise convex optimization;

[0024] Figure 6 It is a step-by-step convex optimization algorithm for radar and communication performance weighted optimization patterns;

[0025] Figure 7 This is a schematic diagram illustrating the radar performance of the joint convex optimization algorithm;

[0026] Figure 8 This is a schematic diagram illustrating the radar performance of the stepwise convex optimization algorithm;

[0027] Figure 9 This is a schematic diagram illustrating the communication performance of the joint convex optimization algorithm;

[0028] Figure 10 This is a schematic diagram illustrating the communication performance of the stepwise convex optimization algorithm. Detailed Implementation

[0029] Specific implementation method one: Combining Figure 1 and Figure 2 This embodiment describes a beamforming method for integrated MU-MIMO radar and communication, which specifically includes the following steps:

[0030] Step 1: Equip the base station with a uniform linear array (ULA) consisting of N antennas. The base station transmits radar and communication signals simultaneously. The transmitted radar and communication signals are processed by a beamformer to obtain the signal to be transmitted x, which is then transmitted via the antenna.

[0031] After the transmitted signal x is transmitted through the channel, the received signal y of the i-th communication user is obtained. i ;

[0032] Step 2: Calculate the received signal y of the i-th communication user. i signal-to-noise ratio η i ;

[0033] Step 3: Based on η i A joint convex optimization objective function is established, and then the established objective function is solved to obtain the covariance matrix R of the radar signal. d and the actual beamforming matrix set {T′ k};

[0034] Step 4: Take the R obtained in Step 3 d and {T′ kThe signal is input into the antenna array to obtain the final beam pattern.

[0035] This implementation uses a DFRC-based downlink system, with each Communication User (CU) or Radar Target (RT) equipped with one antenna. The base station simultaneously transmits radar and communication signals, which, after beamforming, enable communication transmission to K CUs and simultaneous detection of L RTs.

[0036] Specific Implementation Method Two: This implementation method differs from Specific Implementation Method One in that the signal to be transmitted x is:

[0037]

[0038] in, Represents a complex number. This represents the beamforming vector of the k-th communication user, k = 1, 2, ..., K, where K represents the number of communication users, d k This represents the communication data of the k-th communication user, where s represents the radar pulse signal vector.

[0039] The received signal y of the i-th communication user i for:

[0040]

[0041] Among them, g i This represents the channel vector from the base station to the i-th communication user. It is g i The transpose of n i Represents the received noise of the i-th communication user. Representing a complex Gaussian distribution, N0 is the received noise n i variance This represents a set of communication users.

[0042] N0=K×B×T0

[0043] Where K is Boltzmann's constant, B is the bandwidth, and T0 is the open temperature.

[0044] This invention makes the following assumptions:

[0045] (1) The communication signal has zero mean and unit variance. The data d between K communication users... k They are independent of each other.

[0046] (2) The radar signal has zero mean, and the covariance matrix of the radar signal is: It represents expectations.

[0047] (3) The pilot signal can perfectly estimate the channel information, i.e., g i Given that Rayleigh fading is flat.

[0048] The other steps and parameters are the same as in Specific Implementation Method 1.

[0049] Specific Implementation Method Three: This implementation method differs from Specific Implementation Method One or Two in that the calculation of the received signal y of the i-th communication user is... i signal-to-noise ratio η i The specific process is as follows:

[0050]

[0051] Where the superscript * denotes conjugate, the superscript T represents transpose, the superscript H represents Hermitian transpose, tr(·) represents the trace of the matrix, and T i Let R represent the beamforming matrix of the i-th communication user. d It is the covariance matrix of the radar signal.

[0052] Other steps and parameters are the same as in specific implementation method one or two.

[0053] Specific Implementation Method Four: This implementation method differs from Specific Implementation Methods One to Three in that the objective function of the joint convex optimization is:

[0054]

[0055] Where w1 is the combined weighting factor of the radar and communication main lobes, w2 is the radar cross-correlation weighting factor, and w l w represents the radar main lobe gain weighting factor. c The value represents the main lobe gain weighting factor, L is the number of detected targets, and θ is the number of detected targets. l It is the angle of the l-th detected target, a(θ) l ) is the angle θ l The array steering vector, θ p It is the angle of the p-th detected target, a(θ) p ) is the angle θ p The array steering vector, Ω1 is the angle set of L detection targets, θ n It is the angle of the nth detected target, a(θ) n ) is the angle θ n The array steering vector, a(θ) i′ ) is the angle θ i′ The array steering vector, θ i′∈Ω, where Ω is a set of discrete angles between 0° and 180°, spaced at 1° intervals, Ω=(θ1,θ2,…,θ M ), that is, θ1=0°, θ2=1°,…,θ M =180°, θ 1n =θ n -beamwith, θ 2n =θ n +beamwith, where beamwith is the set beamwidth, a(θ) 1n ) is the angle θ 1n The array steering vector, a(θ) 2n ) is the angle θ 2n The array steering vector, θ q It is the angle of the q-th communication user, a(θ) q ) is the angle θ q The array steering vector, θ 1q =θ q -beamwith, θ 2q =θ q +beamwith,a(θ 1q ) is the angle θ 1q The array steering vector, a(θ) 2q ) is the angle θ 2q The array steering vector, Ω2 is the angle set of K communication users, Γ i It is the signal-to-noise ratio threshold for the i-th communication user. P is the received noise variance of the i-th communication user (default is N0). c P represents the power budget for communication. r The power budget of the radar is represented by rank(·), ≥ represents positive definiteness, t1 represents the amount by which the radar main lobe beam gain is higher than the side lobe, and t2 represents the amount by which the communication main lobe beam gain is higher than the side lobe.

[0056] The established objective function is solved to obtain the covariance matrix R of the radar signal. d and the actual beamforming matrix set {T′ k The specific process is as follows:

[0057] Step 1: Input the channel vector g from the base station to each communication user. i Signal-to-noise ratio threshold Γ i Number of radar-detected targets L, number of communication users K, power P c and P r ;

[0058] Step 2: Based on the input from Step 1, obtain the radar covariance matrix R by relaxing the rank-1 constraint of the objective function of the joint convex optimization.d and the ideal beamforming matrix set {T k};

[0059] Step 3, for {T k Gaussian randomization is performed to obtain the beamforming vector set {t}. k};

[0060] Step 4: Calculate {t} k The covariance of} yields the actual beamforming matrix set {T′}. k}

[0061] This invention incorporates the cross-correlation of radar targets as one of the weighted indicators into the objective function, while simultaneously improving the beam pointing in the direction of the communication user. Based on the signal-to-noise ratio η... i Establish an objective function, which requires that η be satisfied. i Above the signal-to-noise ratio threshold Γ i The constraints are specifically reflected in the constraint conditions of equation (4). middle.

[0062] For joint convex optimization algorithms, it is clear that they are all non-convex. This is achieved by omitting rank(T). k With the constraint 1, the joint convex optimization algorithm becomes a semidefinite program (SDP) problem, which can then be approximated using EVD or Gaussian randomization. The algorithms are shown in Table 1.

[0063] Table 1 SDR algorithm for joint convex optimization problem

[0064]

[0065] The other steps and parameters are the same as those in one of the specific implementation methods one to three.

[0066] Specific Implementation Method Five: This implementation method differs from Specific Implementation Methods One to Four in that the specific process of step four is as follows:

[0067]

[0068] in, The beam pattern is shown. λ is the wavelength of the carrier wave, d is the distance between two adjacent antennas in the linear antenna array, j is the imaginary unit, and e is the base of the natural logarithm.

[0069] The other steps and parameters are the same as those in one of the specific implementation methods one to four.

[0070] Specific Implementation Method Six: Combination Figure 1 and Figure 2This embodiment describes a beamforming method for integrated MU-MIMO radar and communication, which specifically includes the following steps:

[0071] Step 1: Equip the base station with a uniform linear array (ULA) consisting of N antennas. The base station transmits radar and communication signals simultaneously. The transmitted radar and communication signals are processed by a beamformer to obtain the signal to be transmitted x, which is then transmitted via the antenna.

[0072] After the transmitted signal x is transmitted through the channel, the received signal y of the i-th communication user is obtained. i ;

[0073] Step 2: Calculate the received signal y of the i-th communication user. i signal-to-noise ratio η i ;

[0074] Step 3: Based on η i A distributed convex optimization objective function is established, and then the established objective function is solved to obtain the covariance matrix R of the radar signal. d and the actual beamforming matrix set {T′ k};

[0075] Step 4: Take the R obtained in Step 3 d and {T′ k The signal is input into the antenna array to obtain the final beam pattern.

[0076] This implementation uses a DFRC-based downlink system, with each Communication User (CU) or Radar Target (RT) equipped with one antenna. The base station simultaneously transmits radar and communication signals, which, after beamforming, enable communication transmission to K CUs and simultaneous detection of L RTs.

[0077] Specific Implementation Method Seven: This implementation method differs from Specific Implementation Method Six in that the signal to be transmitted x is:

[0078]

[0079] in, Represents a complex number. This represents the beamforming vector of the k-th communication user, k = 1, 2, ..., K, where K represents the number of communication users, d k This represents the communication data of the k-th communication user, where s represents the radar pulse signal vector.

[0080] The received signal y of the i-th communication user i for:

[0081]

[0082] Among them, g i This represents the channel vector from the base station to the i-th communication user. It is g i The transpose of n i Represents the received noise of the i-th communication user. Representing a complex Gaussian distribution, N0 is the received noise n i variance This represents a set of communication users.

[0083] N0=K×B×T0

[0084] Where K is Boltzmann's constant, B is the bandwidth, and T0 is the open temperature.

[0085] This invention makes the following assumptions:

[0086] (1) The communication signal has zero mean and unit variance. The data d between K communication users... k They are independent of each other.

[0087] (2) The radar signal has zero mean, and the covariance matrix of the radar signal is: It represents expectations.

[0088] (3) The pilot signal can perfectly estimate the channel information, i.e., g i Given that Rayleigh fading is flat.

[0089] The other steps and parameters are the same as in Specific Implementation Method Six.

[0090] Specific Implementation Method Eight: This implementation method differs from Specific Implementation Methods Six or Seven in that the calculation of the received signal y of the i-th communication user is... i signal-to-noise ratio η i The specific process is as follows:

[0091]

[0092] Where the superscript * denotes conjugate, the superscript T represents transpose, the superscript H represents Hermitian transpose, tr(·) represents the trace of the matrix, and T i Let R represent the beamforming matrix of the i-th communication user. d It is the covariance matrix of the radar signal.

[0093] The other steps and parameters are the same as in specific implementation methods six or seven.

[0094] Specific Implementation Method Nine: This implementation method differs from Specific Implementation Methods Six to Eight in that the objective function for the distributed convex optimization is:

[0095]

[0096] Among them, w l The main lobe gain weighting factor represents the radar beam gain; t is the amount by which the radar main lobe beam gain is higher than that of the side lobes; w e θ is the weighting factor for radar cross-correlation, L is the number of detected targets, and θ is the weighting factor for radar cross-correlation. l It is the angle of the l-th detected target, a(θ) l ) is the angle θ l The array steering vector, θ p It is the angle of the p-th detected target, a(θ) p ) is the angle θ p The array steering vector, Ω1 is the angle set of L detection targets, θ n It is the angle of the nth detected target, a(θ) n ) is the angle θ n The array steering vector, a(θ) i′ ) is the angle θ i′ The array steering vector, θ i′ ∈Ω, where Ω is a set of discrete angles between 0° and 180°, spaced at 1° intervals, Ω=(θ1,θ2,…,θ M ), that is, θ1=0°, θ2=1°,…,θ M =180°, θ 1n =θ n -beamwith, θ 2n =θ n +beamwith, where beamwith is the set beamwidth, a(θ) 1n ) is the angle θ 1n The array steering vector, a(θ) 2n ) is the angle θ 2n The array guide vector, P r This represents the power budget of the radar, where I represents the identity matrix, ≥ represents positive definiteness, and diag(·) represents a diagonal matrix.

[0097]

[0098] Where ||·|| represents the 2-norm, w m It is the main lobe weighting factor for communication, w n It is the communication cross-correlation weighting factor, α is the scaling factor, and θ is the cross-correlation weighting factor. q It is the angle of the q-th communication user, a(θ) q ) is the angle θ qThe array steering vector, θ m It is the angle of the m-th communication user, a(θ) m ) is the angle θ m The array guide vector, P c The power budget for communication is represented by the array steering vector A = [a(θ1), a(θ2), ..., a(θ...]. M )],a(θ1),a(θ2),...,a(θ M ) are θ1, θ2, ..., θ M The array steering vector, Ω, is a set of discrete angles between 0° and 180°, spaced at 1° intervals, Ω = (θ1, θ2, ..., θ...). M ), Γ i It is the signal-to-noise ratio threshold for the i-th communication user. It is the received noise variance of the i-th communication user (default is N0);

[0099] The established objective function is solved to obtain the covariance matrix R of the radar signal. d and the actual beamforming matrix {T′ k The specific process is as follows:

[0100] Step 1: Input the channel vector g from the base station to each communication user. i Signal-to-noise ratio threshold Γ i Number of radar-detected targets L, number of communication users K, power P c and P r ;

[0101] Step 2: Based on the input from Step 1, obtain the radar covariance matrix R by relaxing the rank-1 constraint of the objective function of the joint convex optimization. d and the ideal beamforming matrix set {T k};

[0102] Step 3, for {T k Gaussian randomization is performed to obtain the beamforming vector set {t}. k};

[0103] Step 4: Calculate {t} k The covariance of} yields the actual beamforming matrix set {T′}. k}

[0104] This invention incorporates the cross-correlation of radar targets as one of the weighted indicators into the objective function, while simultaneously employing methods to reduce communication cross-correlation to improve the user's SINR. Based on the signal-to-noise ratio η... i Establish an objective function, which requires that η be satisfied. i Above the signal-to-noise ratio threshold Γ iThe constraints are specifically reflected in the constraints of equation (10). middle.

[0105] For stepwise convex optimization problems, equation (9) is a convex problem and can be solved directly. However, equation (10) is non-convex and requires the SDR method to solve. Algorithms for stepwise convex optimization problems are shown in Table 2:

[0106] Table 2 SDR Algorithm for Stepwise Convex Optimization Problem

[0107]

[0108] The other steps and parameters are the same as those in specific implementation methods six to eight.

[0109] Specific Implementation Method Ten: This implementation method differs from Specific Implementation Methods Six to Forty-Nine in that the specific process of step four is as follows:

[0110]

[0111] in, The beam pattern is shown. λ is the wavelength of the carrier wave, d is the distance between two adjacent antennas in the linear antenna array, j is the imaginary unit, and e is the base of the natural logarithm.

[0112] The other steps and parameters are the same as those in one of the specific implementation methods six to nine.

[0113] The simulation results of this invention are given below. The parameter settings are shown in Table 3:

[0114] Table 3 Simulation parameter settings

[0115]

[0116]

[0117] Figure 3 and Figure 4 These are the radiation patterns of the classical algorithm using joint convex optimization and the weighted optimization algorithm for radar and communication metrics, respectively, both with a communication threshold of 6dB. In the figures, the thin solid line indicates the angle of the radar target, and the dashed line indicates the angle of the communication user. A radiation peak can be seen at the angle of the radar target. (Comparison) Figure 3 ,from Figure 4 It can be visually observed that the radiation pattern sidelobes are lower after weighting by radar indicators. The presence of peaks at the communication user's angle indicates that the algorithm of this invention improves the gain at the communication user's angle.

[0118] Figure 5 and Figure 6These are the radiation patterns of the classical algorithm using step-by-step convex optimization and the radar and communication index weighted optimization algorithm using step-by-step convex optimization, respectively, with a communication threshold of 6dB. Similarly, a comparison is made... Figure 5 ,from Figure 6 It can be intuitively seen that the sidelobes of the radiation pattern after being weighted by radar indicators are lower, while the main lobe in the radar direction decreases very little, and at the same time the main lobe in the communication direction is increased.

[0119] Figure 7 This section describes the radar performance using a joint convex optimization algorithm. Radar performance is primarily evaluated using the Peak-Side Lobe Ratio (PSLR). It's evident that the weighted optimization algorithm, which combines radar and communication metrics, significantly improves PSLR compared to the classical algorithm. Furthermore, it's noteworthy that with a relatively large number of communication users, the classical algorithm's radar performance deteriorates considerably to prioritize these users, rendering it almost unusable in practical applications. Moreover, the PSLR decreases further as the communication threshold increases. In contrast, the optimized algorithm yields usable results, exhibiting minimal change with increasing SINR threshold, demonstrating more stable radar performance.

[0120] Figure 8 This shows the radar performance of the step-by-step convex optimization algorithm. It is evident that the radar performance of the step-by-step radar and communication index weighted optimization algorithm is also improved compared to the classic algorithm. Similarly, the optimized algorithm exhibits more stable radar performance.

[0121] Figure 9 and Figure 10 The figures show the communication performance of the joint convex optimization algorithm and the step-by-step convex optimization algorithm, respectively. It can be seen that the SINR metric improves with increasing threshold. It is worth noting that the radar and communication metric weighted optimization algorithm suffers some signal-to-noise ratio loss compared to the classical algorithm, but the loss is very small.

[0122] Moreover, while achieving the objectives of this invention, the step-by-step radar and communication index weighted optimization algorithm of this invention adds very little execution time, proving the effectiveness of the method of this invention.

[0123] The above examples of the present invention are merely illustrative of the computational model and process of the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is impossible to exhaustively list all possible implementations here. Any obvious variations or modifications derived from the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A beamforming method for integrated MU-MIMO radar and communication, characterized in that, The method specifically includes the following steps: Step 1: Equip the base station with a uniform linear antenna array consisting of N antennas. The base station simultaneously transmits radar and communication signals. The transmitted radar and communication signals are processed by a beamformer to obtain the signal to be transmitted. Signal to be transmitted Then transmitted via antenna; Signal to be transmitted After transmission through the channel, the first Received signals from a communication user ; Step 2: Calculate the first... Received signals from a communication user signal-to-noise ratio ; Step 3, according to A joint convex optimization objective function is established, and then the established objective function is solved to obtain the covariance matrix of the radar signal. and the actual beamforming matrix set ; The objective function of the joint convex optimization is: （4） in, It is a combined weighting factor for the radar and communication main lobes. It is the radar cross-correlation weighting factor. Represents the radar main lobe gain weighting factor. Represents the main lobe gain weighting factor in communication. It refers to the number of targets being detected. It is the first The angle of each detected target It's an angle. The array guide vector, It is the first The angle of each detected target It's an angle. The array guide vector, yes A set of angles for each detection target. It is the first The angle of each detected target It's an angle. The array guide vector, It's an angle. The array guide vector, , It is a set of discrete angles between 0° and 180°, with intervals of 1°. , , , It is the beamwidth. It's an angle. The array guide vector, It's an angle. The array guide vector, It is the first From the perspective of a communication user It's an angle. The array guide vector, , , It's an angle. The array guide vector, It's an angle. The array guide vector, It is the set of perspectives of K communication users. It is the first Signal-to-noise ratio threshold for each communication user It is the first The received noise variance of a communication user Indicates the power budget for communication. This represents the radar's power budget. Represents the rank of the matrix. Representing Zhengding, This indicates that the radar main lobe beam gain is higher than that of the side lobes. This indicates the amount by which the main lobe beam gain is higher than that of the side lobes in communication. The established objective function is solved to obtain the covariance matrix of the radar signal. and the actual beamforming matrix set The specific process is as follows: Step 1: Input the channel vector from the base station to each communication user. Signal-to-noise ratio threshold Number of targets detected by radar Number of communication users ,power and ; Step 2: Based on the input from Step 1, obtain the radar covariance matrix by relaxing the rank-1 constraint of the objective function of the joint convex optimization. and the ideal beamforming matrix set ; Step 3, for Gaussian randomization is performed to obtain the beamforming vector set. ; Step 4, Calculation The covariance yields the actual beamforming matrix set. ; Step 4: Obtain the results from Step 3 and The signal is input into the antenna array to obtain the final beam pattern.

2. The beamforming method for integrated MU-MIMO radar and communication according to claim 1, characterized in that, The signal to be transmitted for: （1） in, , Represents a complex number. Indicates the first Beamforming vectors for each communication user , Represents the number of communication users. Indicates the first Communication data of individual users Represents the radar pulse signal vector. ; No. Received signals from a communication user for: （2） in, The base station has reached the first Channel vectors of each communication user yes transpose, Representing the Received noise for individual communication users , Represents a complex Gaussian distribution. It is received noise variance This represents a set of communication users.

3. The beamforming method for integrated MU-MIMO radar and communication according to claim 2, characterized in that, The calculation of the first Received signals from a communication user signal-to-noise ratio The specific process is as follows: （3） In this context, the superscript * indicates conjugation, the superscript T represents transpose, and the superscript H represents Hermitian transpose. Represents the trace of the matrix. Indicates the first Beamforming matrix for each communication user It is the covariance matrix of the radar signal.

4. The beamforming method for integrated MU-MIMO radar and communication according to claim 3, characterized in that, The specific process of step four is as follows: （5） in, The beam pattern is shown. , , It is the wavelength of the carrier wave. It is the distance between two adjacent antennas in a linear antenna array. It is the imaginary unit. It is the base of the natural logarithm.

5. A beamforming method for integrated MU-MIMO radar and communication, characterized in that, The method specifically includes the following steps: Step 1: Equip the base station with a uniform linear antenna array consisting of N antennas. The base station simultaneously transmits radar and communication signals. The transmitted radar and communication signals are processed by a beamformer to obtain the signal to be transmitted. Signal to be transmitted Then transmitted via antenna; Signal to be transmitted After transmission through the channel, the first Received signals from a communication user ; Step 2: Calculate the first... Received signals from a communication user signal-to-noise ratio ; Step 3, according to A distributed convex optimization objective function is established, and then the established objective function is solved to obtain the covariance matrix of the radar signal. and the actual beamforming matrix set ; The objective function for the distributed convex optimization is: （9） in, Represents the radar main lobe gain weighting factor. It is the amount by which the radar main lobe beam gain is higher than that of the side lobes. It is the weighting factor of radar cross-correlation. It refers to the number of targets being detected. It is the first The angle of each detected target It's an angle. The array guide vector, It is the first The angle of each detected target It's an angle. The array guide vector, yes A set of angles for each detection target. It is the first The angle of each detected target It's an angle. The array guide vector, It's an angle. The array guide vector, , It is a set of discrete angles between 0° and 180°, with intervals of 1°. , , , It is the beamwidth. It's an angle. The array guide vector, It's an angle. The array guide vector, This represents the radar's power budget. Represents the identity matrix. Representing Zhengding, Represents a diagonal matrix; （10） in, Represents the 2-norm. It is the main lobe weighting factor for communication. It is a communication cross-correlation weighting factor. It is a scaling factor. It is the first From the perspective of a communication user It's an angle. The array guide vector, It is the first From the perspective of a communication user It's an angle. The array guide vector, Represents the power budget for communication, array steering vector , They are The array guide vector, It is the first Signal-to-noise ratio threshold for each communication user It is the first The variance of received noise for each communication user; The established objective function is solved to obtain the covariance matrix of the radar signal. and the actual beamforming matrix The specific process is as follows: Step 1: Input the channel vector from the base station to each communication user. Signal-to-noise ratio threshold Number of targets detected by radar Number of communication users ,power and ; Step 2: Based on the input from Step 1, obtain the radar covariance matrix by relaxing the rank-1 constraint of the objective function of the joint convex optimization. and the ideal beamforming matrix set ; Step 3, for Gaussian randomization is performed to obtain the beamforming vector set. ; Step 4, Calculation The covariance yields the actual beamforming matrix set. ; Step 4: Obtain the results from Step 3 and The signal is input into the antenna array to obtain the final beam pattern.

6. The beamforming method for integrated MU-MIMO radar and communication according to claim 5, characterized in that, The signal to be transmitted for: （6） in, , Represents a complex number. Indicates the first Beamforming vectors for each communication user , Represents the number of communication users. Indicates the first Communication data of individual users Represents the radar pulse signal vector. ; No. Received signals from a communication user for: （7） in, The base station has reached the first Channel vectors of each communication user yes transpose, Representing the Received noise for individual communication users , Represents a complex Gaussian distribution. It is received noise variance This represents a set of communication users.

7. The beamforming method for integrated MU-MIMO radar and communication according to claim 6, characterized in that, The calculation of the first Received signals from a communication user signal-to-noise ratio The specific process is as follows: （8） In this context, the superscript * indicates conjugation, the superscript T represents transpose, and the superscript H represents Hermitian transpose. Represents the trace of the matrix. Indicates the first Beamforming matrix for each communication user It is the covariance matrix of the radar signal.

8. The beamforming method for integrated MU-MIMO radar and communication according to claim 7, characterized in that, The specific process of step four is as follows: （11） in, The beam pattern is shown. , , It is the wavelength of the carrier wave. It is the distance between two adjacent antennas in a linear antenna array. It is the imaginary unit. It is the base of the natural logarithm.

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