A robust beamforming method for integrated radar and communication

Abstract

The present invention discloses a robust beamforming method for radar-communication integration. The method comprises the following steps: S1: establishing an integrated radar-communication system to obtain transmit and receive beamforming vectors; constructing an objective function that maximizes the SINR of the radar output signal to solve a maximization beamforming optimization problem; S2: assigning a communication QoS constraint to the beamforming optimization problem; S3: adding an error term to the beamforming optimization problem to obtain a beamforming optimization problem with a certain robustness; S4: simplifying the beamforming optimization problem with a certain robustness to obtain a new beamforming optimization problem; and S5: solving the transmit and receive beamforming vectors for the new beamforming optimization problem using an alternating optimization method to obtain an optimal solution. The present invention incorporates new robust constraints into the beamforming optimization problem of a DFRC system, thereby improving the robustness of the system and making it more practical.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular to a robust beamforming method integrated with radar and communication. Background Art

[0002] Due to the tremendous growth in wireless connectivity and mobile device applications, the wireless spectrum is becoming increasingly congested. To meet the growing demand for additional spectrum, sharing spectrum, and even hardware platforms, between radar and communications functions has become a promising solution for improving efficiency and reducing costs. In recent years, dual-functional radar-communications (DFRC) systems have been developed. In DFRC systems, the transmitted waveform is specifically designed to simultaneously detect MIMO radar targets and communicate wirelessly with downlink users, which presents many challenges. While most existing DFRC system studies achieve good performance trade-offs, most models fail to consider more realistic scenarios (such as when the detection target location is known and free from clutter, and when the communication channel state information is accurate). Furthermore, most use beam patterns as performance indicators, despite the often more complex real-world scenarios.

[0003] The prior art discloses an optimization method and system for robust adaptive beamforming. A function for maximizing the signal-to-interference-and-noise ratio (SINR) of the beamforming output is constructed, a sampling matrix is ​​introduced, and the optimization problem is equivalent to optimizing the weight vector. The optimization problem is solved, the optimal weight vector is obtained, and the maximum signal-to-interference-and-noise ratio (SINR) is achieved. This invention simplifies the calculation process and reduces the amount of calculation by constructing a function for maximizing the signal-to-interference-and-noise ratio (SINR) and introducing a sampling matrix to replace the covariance matrix Ri+n of the interference plus noise. It can obtain the optimal steering vector solution in a short time. By mathematically equating the maximizing signal-to-interference-and-noise ratio function to an optimization problem with optimizing the weight vector w as the goal, the beamformer is made more robust and has stronger anti-interference capabilities. However, the angular error of the steering vector and the received steering vector is not considered. Summary of the Invention

[0004] In order to overcome at least one of the above technical problems, the present invention provides a radar communication integrated robust beamforming method for obtaining an optimal solution for the radar communication integrated robust beamforming.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] A robust beamforming method for integrated radar and communication includes the following steps:

[0007] S1: Establish a radar communication integrated system to obtain the transmit beamforming vector and the receive beamforming vector; construct an objective function to maximize the radar output signal SINR to solve the maximization beamforming optimization problem;

[0008] S2: Given communication QoS constraints for the beamforming optimization problem;

[0009] S3: Add error terms to the beamforming optimization problem to obtain a beamforming optimization problem with a certain robustness;

[0010] S4: Simplify the beamforming optimization problem with a certain robustness to obtain a new beamforming optimization problem;

[0011] S5: For the new beamforming optimization problem, an alternating optimization method is used to solve the transmit beamforming vector and the receive beamforming vector to obtain the optimal solution.

[0012] Furthermore, the radar communication integrated system in step S1 includes a DFRC base station, a radar, a primary user, and a secondary user. The DFRC base station is equipped with N t transmitting antennas and N r receive antennas, where N t The transmitting antennas act as secondary transmitters to perform multicast communication to K secondary users and generate communication interference to M primary users. While the system communicates with the users, it also detects point targets at the radar location. r A receiving antenna receives the radar echo signal of the point target.

[0013] Furthermore, the method for constructing the objective function to maximize the radar output signal SINR is as follows:

[0014] 1) Build a communication signal model

[0015] The communication signal model includes primary users, secondary users, interference channels g m and multicast communication channel h k , will interfere with channel g m and multicast communication channel h k As a communication channel, the interference channel g is between the DFRC base station and the mth primary user. m The multicast communication channel h is between the DFRC base station and the kth secondary user. k DFRC base station transmit signal vector x: x = ws,

[0016] Where w is the transmit beamforming vector in multicast communication, s is a data vector whose covariance function satisfies E[|s| 2 ]=1, the covariance function of the transmitted signal is E[xx H ]=wwH , (·) H represents the conjugate transpose, and the signal vector received by the secondary user is:

[0017]

[0018] Assume that the communication channel is a slowly time-varying packet Rician fading channel, n k is the Gaussian white noise vector of the kth communication channel, and ) is the noise variance; assuming that the maximum signal-to-noise ratio is combined to receive the communication beams of all secondary users, the communication signal-to-noise ratio of the kth secondary user is:

[0019]

[0020] The interference power caused by the secondary transmitter to the primary user is:

[0021]

[0022] 2) Build a radar detection model

[0023] The radar detection model includes a radar channel and a MIMO radar target, and the DFRC base station and the MIMO radar target communicate with each other through the radar channel;

[0024] The received signal model of the MIMO radar target is expressed as:

[0025]

[0026] Among them, α0A(θ0)x is the transmission signal of the DFRC base station, is the interference term of the DFRC base station from I clutter sources, n is Gaussian white noise, α0 and α i denote the complex amplitude of the MIMO radar target and the complex amplitude of the i-th interference source, θ0 and θ i denote the angular error of the MIMO radar target with respect to the transmission and the angular error of the i-th interference source with respect to the transmission respectively; A(θ) is the directional matrix of the uniform linear array antenna with half-wavelength spacing units, defined as:

[0027]

[0028] in, is the launch guidance vector,

[0029] To receive a steering vector;

[0030] According to the general filtering processing of the radar output, the received signal model of the MIMO radar target is expressed as:

[0031]

[0032] in, is the receive beamforming vector;

[0033] Then the SINR of the MIMO radar target output signal is expressed as:

[0034]

[0035] make The above formula can be expressed as:

[0036]

[0037] The objective function for maximizing the radar output signal SINR is obtained:

[0038]

[0039] Among them, the objective function of maximizing the radar output signal SINR is the SINR of the MIMO radar target output signal; w and u are used as optimization variables, C n Refers to the n-dimensional vector in the complex domain space, and the objective function is the SINR of the radar output signal. A(θ) is the directional matrix of a uniform linear array antenna with half-wavelength spacing elements.

[0040] Furthermore, in step S2, the communication QoS constraint is:

[0041] First constraint:

[0042] Second constraint:

[0043] The first constraint represents the SNR constraint of the kth secondary user communication, and the second constraint represents the interference power constraint of the primary user; γ k represents the minimum SNR value allowed for the kth secondary user, η m Indicates the maximum interference power value of the secondary transmitter to the primary user, h k is the multicast communication channel between the DFRC base station and the kth secondary user, g m is the interference channel from the secondary transmitter to the mth primary user.

[0044] Furthermore, step S3 is specifically as follows:

[0045] Communication channel h k Contains estimated channel and error amount Interference channel g m Contains estimated channel and error amount Considering the position uncertainty of the MIMO radar target, the guidance vector a of the MIMO radar target r (θ0) and a t (θ0) plus the error term δ r and δ t , the interference source’s steering vector is also added with error term δ r and δ t , the MIMO radar target guidance vector and the interference source guidance vector are respectively added with ellipsoid constraints, and a certain robust beamforming optimization problem is obtained:

[0046]

[0047]

[0048] Furthermore, the simplification in step S4 is specifically as follows:

[0049] According to the existing criteria for solving the residual modulus maximum and minimum problems, we have:

[0050]

[0051]

[0052] in, represents the upper bound of the norm constraint of the guidance vector error term;

[0053] Similarly, Can become:

[0054] The new beamforming optimization problem is obtained:

[0055]

[0056]

[0057]

[0058] Here, ||·|| represents the 2-norm of the vector.

[0059] Furthermore, the alternate optimization method described in step S4 is used to solve the transmit beamforming vector, specifically:

[0060] If w is fixed, the optimization variable is u; introduce auxiliary variables make Then we have:

[0061]

[0062] The absolute value term is processed as its real value: Re(·), and the above formula is transformed into:

[0063]

[0064] make At this time, the beamforming optimization problem becomes:

[0065]

[0066]

[0067]

[0068] The objective function of the above beamforming optimization problem is a convex function with respect to v, and the first constraint is Using the second-order cone optimization approximation as a convex constraint, the optimal solution is obtained Bring the optimal solution back to the objective function to obtain the optimal value when w is fixed:

[0069]

[0070] Furthermore, the alternate optimization method described in step S4 is used to solve the receive beamforming vector, specifically:

[0071] Fix u, the optimization variable is w, and the terms related to u are regarded as constant terms. Let:

[0072]

[0073]

[0074] At this time, the beamforming optimization problem becomes:

[0075]

[0076]

[0077]

[0078] Using the existing linear fractional programming principle, the numerator and denominator of the objective function are multiplied by an adjustment parameter z 2 , and multiply both sides of the constraint inequality by z, where z ≥ 0. At the same time, define w: = wz, that is, use w to represent wz, and finally obtain the following beamforming optimization problem:

[0079]

[0080]

[0081]

[0082] z≥0

[0083] Adjust the parameters so that the denominator of the objective function Convert this equation into an equivalent convex inequality, and we have Then the above beamforming optimization problem becomes:

[0084]

[0085]

[0086]

[0087]

[0088] z≥0

[0089] Objective function to maximize the SINR of radar output signal is a concave function with respect to w, which is equivalent to minimizing a convex function with respect to w, except for the third constraint The remaining terms are convex function constraints, and the third constraint The second-order cone optimization approximation is used to solve the convex constraint, so the optimization problem is a convex problem, and the optimal solution w is obtained by solving * And z value, according to w * :=w * z, “*” refers to the optimal solution, w * Refers to the optimal value of w, that is, the optimal solution.

[0090] Furthermore, the convex problem is solved using the CVX toolkit in MATLAB to obtain the optimal solution w * and z-value.

[0091] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0092] 1. Traditional methods only consider perfect channel state information, while the present invention considers imperfect channel state information and also considers angle error estimation. Compared with only considering the error term for the angle matrix as a whole, the angle error is added to the transmit steering vector and the receive steering vector separately, making the system of the present invention more reasonable.

[0093] 2. The present invention adds new robust constraints to the beamforming optimization problem of the DFRC system, thereby improving the robustness of the system and making the system more practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0095] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0096] It is understood by those skilled in the art that some well-known structures and descriptions thereof may be omitted in the drawings.

[0097] Figure 1 A schematic flow chart of a radar-communication integrated robust beamforming method provided in an embodiment of the present invention;

[0098] Figure 2 Schematic diagram of a radar communication integrated system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0099] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of 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 making creative efforts are within the scope of protection of the present invention.

[0100] Example 1

[0101] For easier understanding, see Figure 1 and Figure 2 An embodiment of a radar communication integrated robust beamforming method provided by the present invention includes the following steps:

[0102] S1: Establish a radar communication integrated system to obtain the transmit beamforming vector and the receive beamforming vector; construct an objective function to maximize the radar output signal SINR to solve the maximization beamforming optimization problem;

[0103] S2: Given communication QoS constraints for the beamforming optimization problem;

[0104] S3: Add an error term to the beamforming optimization problem to obtain a beamforming optimization problem with a certain degree of robustness;

[0105] S4: Simplify the beamforming optimization problem with a certain robustness to obtain a new beamforming optimization problem;

[0106] S5: For the new beamforming optimization problem, an alternating optimization method is used to solve the transmit beamforming vector and the receive beamforming vector to obtain the optimal solution.

[0107] Channel uncertainty refers to the fact that the channel consists of two parts: the theoretical estimated channel and the channel error. For example, the communication channel h k Contains estimated channel and error amount Interference channel g m Contains estimated channel and error amount

[0108] The robust beamforming method proposed in this paper not only considers target position uncertainty but also imperfect channel state information. For example, the target being detected is a Multiple Input Multiple Output (MIMO) radar target (e.g., a drone located far from a DFRC base station). However, detection of this target will be affected by some clutter. Therefore, the SINR (Signal to Interference plus Noise Ratio) of the target's received output must be maximized to ensure detection performance. Regarding communications, if the DFRC system acts as a secondary transmitter and performs multicast communications with a single-antenna secondary user, it is necessary to minimize interference to the primary user and ensure the signal-to-noise ratio performance of communications with the secondary user. Therefore, a robust user Quality of Service (QoS) constraint is established.

[0109] Example 2

[0110] Specifically, based on Example 1, the solution is described in combination with specific implementation examples to further demonstrate the technical effects of this solution. Specifically:

[0111] A robust beamforming method for integrated radar and communication includes the following steps:

[0112] S1: Establish a radar communication integrated system to obtain the transmit beamforming vector and the receive beamforming vector; construct an objective function to maximize the radar output signal SINR to solve the maximization beamforming optimization problem;

[0113] S2: Given communication QoS constraints for the beamforming optimization problem;

[0114] S3: Add an error term to the beamforming optimization problem to obtain a beamforming optimization problem with a certain degree of robustness;

[0115] S4: Simplify the beamforming optimization problem with a certain robustness to obtain a new beamforming optimization problem;

[0116] S5: For the new beamforming optimization problem, an alternating optimization method is used to solve the transmit beamforming vector and the receive beamforming vector to obtain the optimal solution.

[0117] Specifically, the radar communication integrated system in step S1 includes a DFRC base station, a radar, a primary user, and a secondary user. The DFRC base station is equipped with N t transmitting antennas and N r receive antennas, where N t The transmitting antennas act as secondary transmitters to perform multicast communication to K secondary users and generate communication interference to M primary users. While the system communicates with the users, it also detects point targets at the radar location. r A receiving antenna receives the radar echo signal of the point target.

[0118] Specifically, the method for constructing the objective function of maximizing the radar output signal SINR is as follows:

[0119] 1) Build a communication signal model

[0120] The communication signal model includes primary users, secondary users, interference channels g m and multicast communication channel h k , will interfere with channel g m and multicast communication channel h k As a communication channel, the interference channel g is between the DFRC base station and the mth primary user. m The multicast communication channel h is between the DFRC base station and the kth secondary user. k DFRC base station transmit signal vector x: x = ws,

[0121] Where w is the transmit beamforming vector in multicast communication, s is a data vector whose covariance function satisfies E[|s| 2 ]=1, the covariance function of the transmitted signal is E[xx H ]=ww H , (·) H represents the conjugate transpose, and the signal vector received by the secondary user is:

[0122]

[0123] Assume that the communication channel is a slowly time-varying packet Rician fading channel, n k is the Gaussian white noise vector of the kth communication channel, and Assuming that the maximum signal-to-noise ratio is combined to receive the communication beams of all secondary users, the communication signal-to-noise ratio of the kth secondary user is:

[0124]

[0125] The interference power caused by the secondary transmitter to the primary user is:

[0126]

[0127] 2) Build a radar detection model

[0128] The radar detection model includes a radar channel and a MIMO radar target, and the DFRC base station and the MIMO radar target communicate with each other through the radar channel;

[0129] The received signal model of the MIMO radar target is expressed as:

[0130]

[0131] Among them, α0A(θ0)x is the transmission signal of the DFRC base station, is the interference term of the DFRC base station from I clutter sources, n is Gaussian white noise, α0 and α i denote the complex amplitude of the MIMO radar target and the complex amplitude of the i-th interference source, θ0 and θ i denote the angular error of the MIMO radar target with respect to the transmission and the angular error of the i-th interference source with respect to the transmission respectively; A(θ) is the directional matrix of the uniform linear array antenna with half-wavelength spacing units, defined as:

[0132]

[0133] in, is the launch guidance vector, To receive a steering vector;

[0134] According to the general filtering processing of the radar output, the received signal model of the MIMO radar target is expressed as:

[0135]

[0136] in, is the receive beamforming vector;

[0137] Then the SINR of the MIMO radar target output signal is expressed as:

[0138]

[0139] make The above formula can be expressed as:

[0140]

[0141] The objective function for maximizing the radar output signal SINR is obtained:

[0142]

[0143] Among them, the objective function of maximizing the radar output signal SINR is the SINR of the MIMO radar target output signal; w and u are used as optimization variables, C n Refers to the n-dimensional vector in the complex domain space, and the objective function is the SINR of the radar output signal. A(θ) is the directional matrix of a uniform linear array antenna with half-wavelength spacing elements.

[0144] Specifically, in step S2, the communication QoS constraint is:

[0145] First constraint:

[0146] Second constraint:

[0147] The first constraint represents the SNR constraint of the kth secondary user communication, and the second constraint represents the interference power constraint of the primary user; γ k represents the minimum SNR value allowed for the kth secondary user, η m Indicates the maximum interference power value of the secondary transmitter to the primary user, h k is the multicast communication channel between the DFRC base station and the kth secondary user, g m is the interference channel from the secondary transmitter to the mth primary user.

[0148] Specifically, step S3 is as follows:

[0149] Communication channel h k Contains estimated channel and error amount Interference channel g m Contains estimated channel and error amount Considering the position uncertainty of the MIMO radar target, the guidance vector a of the MIMO radar target r (θ0) plus the error term δ r and δ t , the interference source's steering vector a t (θ0) plus the error term δ r and δ t , the MIMO radar target guidance vector and the interference source guidance vector are respectively added with ellipsoid constraints, and a certain robust beamforming optimization problem is obtained:

[0150]

[0151]

[0152]

[0153] Specifically, the alternate optimization method is used in step S4 to solve the transmit beamforming vector, specifically:

[0154] If w is fixed, the optimization variable is u; introduce auxiliary variables make Then we have:

[0155]

[0156] The absolute value term is processed as its real value: Re(·), and the above formula is transformed into:

[0157]

[0158] make At this time, the beamforming optimization problem becomes:

[0159]

[0160]

[0161]

[0162] The objective function of the above beamforming optimization problem is a convex function with respect to v, and the first constraint is Using the second-order cone optimization approximation as a convex constraint, the optimal solution is obtained Bring the optimal solution back to the objective function to obtain the optimal value when w is fixed:

[0163]

[0164] Specifically, the alternate optimization method is used in step S4 to solve the receive beamforming vector, specifically:

[0165] Fix u, the optimization variable is w, and the terms related to u are regarded as constant terms. Let:

[0166]

[0167]

[0168] At this time, the beamforming optimization problem becomes:

[0169]

[0170]

[0171]

[0172] The numerator and denominator of the objective function are multiplied by an adjustment parameter z 2, and multiply both sides of the constraint inequality by z, where z ≥ 0. At the same time, define w: = wz, that is, use w to represent wz, and finally obtain the following beamforming optimization problem:

[0173]

[0174]

[0175]

[0176] z≥0

[0177] Adjust the parameters so that the denominator of the objective function Convert this equation into an equivalent convex inequality, and we have Then the above beamforming optimization problem becomes:

[0178]

[0179]

[0180]

[0181]

[0182] z≥0

[0183] Objective function to maximize the SINR of radar output signal is a concave function with respect to w, which is equivalent to minimizing a convex function with respect to w, except for the third constraint The remaining terms are convex function constraints, and the third constraint The second-order cone optimization approximation is used to solve the convex constraint, so the optimization problem is a convex problem, and the optimal solution w is obtained by solving * And z value, according to w * :=w * z, “*” refers to the optimal solution, w * Refers to the optimal value of w, that is, the optimal solution.

[0184] In this invention, a DFRC base station acts as a secondary transmitter, performing multicast communications to K secondary users and generating communication interference to M primary users. While communicating with these users, the DFRC base station simultaneously detects a MIMO radar target and receives its echo signal. Simultaneously, detecting the MIMO radar target is affected by I signal-related clutter. To better address practical applications, the uncertainty of the DFRC base station's communication channel with the secondary user, the uncertainty of the DFRC base station's communication channel with the primary user, and the uncertainty of the MIMO radar target's position are taken into account. Under the constraints of the minimum signal-to-noise ratio (SNR) between the DFRC base station and the secondary user, and the maximum interference power that the DFRC base station can cause to the primary user, the SINR of the output radar signal is used as the objective function to optimize the system's transmit and receive beams.

[0185] Taking into account the imperfect channel state information that cannot be accurately obtained and the uncertainty of the MIMO radar target position, the SINR of the radar target output signal is maximized by solving the transmit beamforming vector and the receive beamforming vector.

[0186] The robust beamforming method proposed in this invention combines the multicast communication channel h k and interference channel g m At the same time, angle error estimation is also considered, that is, new robust constraints are added to the beamforming optimization problem to improve the robustness of the system and make the system closer to practical applications. At the same time, the alternating optimization method is used to solve the transmit beamforming vector and the receive beamforming vector in this problem, and good optimization results are achieved.

[0187] Example 3

[0188] Specifically, based on Example 1, the solution is described in conjunction with specific implementation examples to further demonstrate the technical effects of this solution. Specifically:

[0189] Solving convex problems involves applying the CVX toolkit in MATLAB to solve convex problems and obtain the optimal solution.

[0190] In the presence of estimation errors, it is difficult to obtain perfect channel state information. The present invention proposes a robust beamforming method for radar communication integration, which takes into account imperfect channel state information. The channel consists of two parts: the theoretical estimated channel and the channel error. The angle error estimation is also considered. An objective function that maximizes the SINR of the radar output signal is constructed to solve the maximized beamforming optimization problem. Robust user service quality constraints (i.e., the SNR constraint for secondary user communications and the interference power constraint for primary users) are given. The robustness constraints in the beamforming optimization problem are simplified by applying the existing technical criteria for solving the residual modulus maximum and minimum problems and the existing linear fractional programming principles. An alternating optimization method is used to solve the transmit beamforming vector and the receive beamforming vector, transforming the beamforming optimization problem into a convex constraint. The convex constraint is solved using the CVX toolkit, achieving a rapid solution to the optimal solution of the maximized beamforming optimization problem, improving the robustness of the system and making the system more practical.

[0191] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention and are not intended to limit the embodiments of the present invention. A person skilled in the art would be able to make other variations or modifications based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A robust beamforming method for integrated radar and communication, characterized in that: The following steps are involved: S1: Establish a radar communication integrated system to obtain the transmit beamforming vector and the receive beamforming vector; construct an objective function to maximize the radar output signal SINR to solve the maximization beamforming optimization problem; The method for constructing the objective function of maximizing the radar output signal SINR is as follows: 1) Build a communication signal model The communication signal model includes primary users, secondary users, interference channels g m and multicast communication channel h k , will interfere with channel g m and multicast communication channel h k As a communication channel, the interference channel g is between the DFRC base station and the mth primary user. m The multicast communication channel h is between the DFRC base station and the kth secondary user. k DFRC base station transmit signal vector x: x = ws, Where w is the transmit beamforming vector in multicast communication, N t represents the number of transmitting antennas, s is the data vector, and its covariance function satisfies E[|s| 2 ]=1, the covariance function of the transmitted signal is E[xx H ]=ww H , (·) H represents the conjugate transpose, and the signal vector received by the secondary user is: Assume that the communication channel is a slowly time-varying packet Rician fading channel, n k is the Gaussian white noise vector of the kth communication channel, and is the noise variance; assuming that the communication beams of all secondary users are received by combining the maximum signal-to-noise ratio, the communication signal-to-noise ratio of the kth secondary user is: The interference power caused by the secondary transmitter to the primary user is: 2) Build a radar detection model The radar detection model includes a radar channel and a MIMO radar target, and the DFRC base station and the MIMO radar target communicate with each other through the radar channel; The received signal model of the MIMO radar target is expressed as: Where r represents the signal received by the MIMO radar target, α0A(θ0)x is the transmitted signal of the DFRC base station, is the interference term of the DFRC base station from I clutter sources, n is Gaussian white noise, α0 and α i denote the complex amplitude of the MIMO radar target and the complex amplitude of the i-th interference source, θ0 and θ i denote the angular error of the MIMO radar target with respect to the transmission and the angular error of the i-th interference source with respect to the transmission respectively; A(θ) is the directional matrix of the uniform linear array antenna with half-wavelength spacing units, defined as: in, is the launch guidance vector, N t Indicates the number of transmitting antennas, To receive the steering vector, N r Indicates the number of receiving antennas; According to the general filtering processing of the radar output, the received signal model of the MIMO radar target is expressed as: in, is the receive beamforming vector; Then the SINR of the MIMO radar target output signal is expressed as: make The above formula can be expressed as: The objective function for maximizing the radar output signal SINR is obtained: Among them, the objective function of maximizing the radar output signal SINR is the SINR of the MIMO radar target output signal; w and u are used as optimization variables, C n Refers to the n-dimensional vector in the complex domain space, and the objective function is the SINR of the radar output signal. A(θ) is the directional matrix of a uniform linear array antenna with half-wavelength spacing elements; S2: Given communication QoS constraints for the beamforming optimization problem; S3: Add an error term to the beamforming optimization problem to obtain a beamforming optimization problem with a certain degree of robustness; S4: Simplify the beamforming optimization problem with a certain robustness to obtain a new beamforming optimization problem; S5: For the new beamforming optimization problem, an alternating optimization method is used to solve the transmit beamforming vector and the receive beamforming vector to obtain the optimal solution.

2. The radar-communication integrated robust beamforming method according to claim 1, characterized in that: The radar communication integrated system in step S1 includes a DFRC base station, a radar, a primary user, and a secondary user. The DFRC base station is equipped with N t transmitting antennas and N r receiving antennas, where N t The transmitting antennas act as secondary transmitters to perform multicast communication to K secondary users and generate communication interference to M primary users. While the system communicates with the users, it also detects point targets at the radar location. r A receiving antenna receives the radar echo signal of the point target.

3. The radar-communication integrated robust beamforming method according to claim 1, characterized in that: In step S2, the communication QoS constraints are: First constraint: Second constraint: The first constraint represents the SNR constraint of the kth secondary user communication, and the second constraint represents the interference power constraint of the primary user; γ k represents the minimum SNR value allowed for the kth secondary user, η m Indicates the maximum interference power value of the secondary transmitter to the primary user, h k is the multicast communication channel between the DFRC base station and the kth secondary user, g m is the interference channel from the secondary transmitter to the mth primary user.

4. The radar-communication integrated robust beamforming method according to claim 3, characterized in that: Step S3 is specifically as follows: Communication channel h k Contains estimated channel and error amount Interference channel g m Contains estimated channel and error amount Considering the position uncertainty of the MIMO radar target, the guidance vector a of the MIMO radar target r (θ0) plus the error term δ r and δ t , the interference source's steering vector a t (θ0) plus the error term δ r and δ t , the MIMO radar target guidance vector and the interference source guidance vector are respectively added with ellipsoid constraints, and a certain robust beamforming optimization problem is obtained:

5. The radar-communication integrated robust beamforming method according to claim 4, characterized in that: The simplification in step S4 is specifically as follows: According to the existing criteria for solving the residual modulus maximum and minimum problems, we have: Similarly, Can become: The new beamforming optimization problem is obtained: Here, ||·|| represents the 2-norm of the vector.

6. The radar-communication integrated robust beamforming method according to claim 5, characterized in that: The alternate optimization method described in step S4 is used to solve the transmit beamforming vector, specifically: If w is fixed, the optimization variable is u; introduce auxiliary variables make Then we have: The absolute value term is processed as its real value: Re(·), and the above formula is transformed into: make At this time, the beamforming optimization problem becomes: The objective function of the above beamforming optimization problem is a convex function with respect to v, and the first constraint is Using the second-order cone optimization approximation as a convex constraint, the optimal solution is obtained Bring the optimal solution back to the objective function to obtain the optimal value when w is fixed:

7. The radar-communication integrated robust beamforming method according to claim 6, characterized in that: The alternate optimization method described in step S4 is used to solve the receive beamforming vector, specifically: Fix u, the optimization variable is w, and the terms related to u are regarded as constant terms. Let: At this time, the beamforming optimization problem becomes: The numerator and denominator of the objective function are multiplied by an adjustment parameter z 2 , and multiply both sides of the constraint inequality by z, where z ≥ 0. At the same time, define w: = wz, that is, use w to represent wz, and finally obtain the following beamforming optimization problem: z≥0 Adjust the parameters so that the denominator of the objective function This optimization problem is a maximization problem. Convert this equation into an equivalent convex inequality, and we have Then the above beamforming optimization problem becomes: z≥0。 8. The radar-communication integrated robust beamforming method according to claim 7, characterized in that: Objective function to maximize the SINR of radar output signal is a concave function with respect to w, which is equivalent to minimizing a convex function with respect to w, except for the third constraint The remaining terms are convex function constraints, and the third constraint The second-order cone optimization approximation is used to solve the convex constraint, so the optimization problem is a convex problem, and the optimal solution w is obtained by solving * And z value, according to w * :=w * z, "*" refers to the optimal solution, w * Refers to the optimal value of w, that is, the optimal solution.

9. The radar-communication integrated robust beamforming method according to claim 8, characterized in that: The convex problem is solved using the CVX toolkit in MATLAB and the optimal solution w is obtained. * and z-value.

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