Robust transceiving design method based on difunctional radar communication system

Abstract

The invention discloses a robust transmit-receive design method based on a bifunctional radar communication system. The method comprises the following steps: establishing a signal model when an MIMO DFRC system has an error; the signal model comprises a radar SINR in the presence of an array amplitude-phase error and a communication signal model in the presence of a CSI estimation error; building a joint design problem of transmitted waveform and received beam forming based on a signal model; the joint design problem takes maximization of radar SINR in a worst error scene as a target function, and takes communication constraint, radar waveform constraint and energy constraint as constraint conditions; an AM-SCA algorithm is used for solving the joint design problem in an alternating iteration mode until the target function converges or the number of iterations reaches the preset maximum number of iterations, and a transmitted waveform and a received beam forming vector are obtained, so that the channel state information of the dual-function radar communication system is estimated under the condition that array amplitude phase errors and CSI estimation errors exist in the dual-function radar communication system. And target detection and multi-user communication can be realized at the same time.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of radar and communication systems, and particularly relates to a robust transceiver design method based on a dual-function radar-communication system. BACKGROUND

[0002] In recent years, integrated radar and communication (IRAC) systems have attracted increasing attention, which enable spectrum sharing and thus mitigate spectrum conflicts among wireless devices. Existing research on IRAC systems can be divided into two directions: radar-communication coexistence (RCC) and dual-function radar-communication (DFRC). RCC systems require radar and communication subsystems to operate independently, and achieve spectrum sharing through interference suppression, resource allocation, and other strategies. However, DFRC systems aim to perform radar and communication functions within a unified platform, which improves resource utilization efficiency and reduces hardware costs.

[0003] Among various DFRC system design schemes, multiple-input multiple-output (MIMO) DFRC systems have attracted much attention due to their high spatial degrees of freedom. Some design methods focus on the system transmitting end, in which the transmitting waveform is directly optimized, or indirectly synthesized through the design of transmitting beamforming weights. In addition to the transmitting end design, many studies further explore the joint optimization design of transmitting waveform / beamforming and receiving filter to improve the signal-to-interference-plus-noise ratio (SINR) at the radar receiving end and the communication user. However, the above work usually assumes ideal channel state information (CSI) and ideal array. In actual systems, CSI estimation error and amplitude-phase error between antenna arrays are often inevitable, which will reduce the performance of radar and communication. Although there are currently some robust design methods for radar or communication functions, robust transmit-receive design considering CSI estimation error and amplitude-phase error for multi-user MIMO DFRC systems has not been explored. SUMMARY

[0004] In order to solve the above problems existing in the prior art, the application provides a robust transceiver design method based on a dual-function radar-communication system.

[0005] The technical problem to be solved by this invention is achieved through the following technical solution: In a first aspect, the present invention provides a robust transceiver design method based on a dual-function radar communication system, the robust transceiver design method comprising: A signal model for a MIMO DFRC system with errors is established; the signal model includes a radar SINR model with array amplitude and phase errors and a communication signal model with CSI estimation errors. Based on the signal model, a joint design problem for transmitted waveform and received beamforming is constructed. The joint design problem takes maximizing the radar SINR under the worst error scenario as the objective function and communication constraints, radar waveform constraints, and energy constraints as constraints. The AM-SCA algorithm is used to iteratively solve the joint design problem until the objective function converges or the number of iterations reaches the preset maximum number of iterations, thus obtaining the transmitted waveform and the received beamforming vector.

[0006] Optionally, the radar SINR includes: ; in, This indicates the radar's SINR; Indicates the attenuation coefficient of the target; Indicates the received beamforming vector; superscript This represents the conjugate transpose operation of a matrix; The transmit-receive steering vector matrix representing the radar direction; Indicates the radar direction; This represents the transmitted waveform; , Indicates the total number of interference sources; Indicates the first Attenuation coefficient of each interference source; Indicates the direction of interference; The transmit-receive steering vector matrix representing the direction of interference; This represents the additive white Gaussian noise matrix; This represents the Euclidean norm.

[0007] Optionally, the communication signal model includes: ; in, This represents the communication signal model; Represents the actual channel matrix; This represents the transmitted waveform; Represents the white Gaussian noise matrix; This represents the desired communication signal matrix.

[0008] Optionally, the joint design problem comprises: ; ; ; ; ; wherein, denotes the transmit waveform; denotes the receive beamforming vector; denotes the error matrix in radar direction ; denotes the error matrix in interference direction ; denotes the radar SINR; denotes the actual channel matrix; denotes the desired communication signal matrix; denotes the Frobenius norm; and denote the threshold of MUI power and radar waveform similarity mismatch, respectively; denotes the error channel matrix; denotes the uncertainty set corresponding to the error channel matrix; denotes the real transmit steering vector in radar direction; superscript denotes the conjugate transpose operation of a matrix; denotes the desired radar waveform; denotes the Euclidean norm; denotes the error vector corresponding to the real receive steering vector in the radar direction; denotes the uncertainty set corresponding to ; denotes the transmit maximum energy; denotes the uncertainty set corresponding to ;

[0009] Optionally, the joint design problem is solved by using an AM-SCA algorithm to iteratively solve the joint design problem until the objective function converges or the number of iterations reaches a preset maximum number of iterations, to obtain the transmit waveform and the receive beamforming vector, comprising: transforming the objective function into a convex function with respect to the transmit waveform and the receive beamforming vector; transforming the communication constraint and the radar waveform constraint into a convex constraint form of the communication constraint and a convex constraint form of the radar waveform constraint, respectively; integrating the convex function, the convex constraint form of the communication constraint, the convex constraint form of the radar waveform constraint, and the energy constraint to obtain a convex optimization problem; solving the convex optimization problem by using an alternating minimization algorithm until the convex function converges or the number of iterations reaches a preset maximum number of iterations, to obtain a transmit waveform and a receive beamforming vector.

[0010] Optionally, the convex optimization problem comprises: ; wherein, denotes a numerator over variable; denotes a denominator over variable; denotes the transmit waveform; denotes the receive beamforming vector; denotes a Frobenius norm; denotes a maximum transmit energy; denotes an estimated channel matrix; denotes an expected communication signal matrix; denotes an error bound corresponding to an error channel matrix; and denote a MUI power and a threshold of radar waveform similarity mismatch, respectively; denotes a desired steering vector corresponding to the transmit end in a radar direction; a superscript denotes a conjugate transpose operation of a matrix; denotes a desired radar waveform; denotes and a corresponding error bound; denotes an error vector corresponding to a real transmit steering vector; denotes an error vector corresponding to a real receive steering vector.

[0011] In a second aspect, the present application provides a robust transceiver design device based on a dual-function radar communication system, the robust transceiver design device comprising: a building module configured to build a signal model under array amplitude and phase errors in a MIMO DFRC system; the signal model comprising a radar SINR under array amplitude and phase errors and a communication signal model under CSI estimation errors; a problem forming module configured to build a joint design problem of a transmit waveform and a receive beamforming based on the signal model; the joint design problem taking a radar SINR under a worst error scenario as an objective function, and taking a communication constraint, a radar waveform constraint and an energy constraint as constraint conditions; a solving module configured to solve the joint design problem by using an AM-SCA algorithm until the objective function converges or the number of iterations reaches a preset maximum number of iterations, to obtain a transmit waveform and a receive beamforming vector.

[0012] In a third aspect, the present application provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete the communication among each other through the communication bus. The memory is used for storing a computer program. The processor is used for executing the computer program stored on the memory to realize the method steps of any one of the above-mentioned robust transceiver design methods based on a dual-functional radar communication system.

[0013] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the method steps of any one of the above-mentioned robust transceiver design methods based on a dual-functional radar communication system.

[0014] The present application provides a robust transceiver design method based on a dual-functional radar communication system, which firstly establishes a signal model under the existence of MIMO DFRC system errors; the signal model comprises a radar SINR under the existence of array amplitude and phase errors and a communication signal model under the existence of CSI estimation errors; and then a joint design problem of a transmitting waveform and a receiving beam forming is constructed based on the signal model; wherein the joint design problem takes the maximization of the radar SINR under the worst error scenario as an objective function, and takes a communication constraint, a radar waveform constraint and an energy constraint as constraint conditions, so that the SINR can be maximized and MUI can be suppressed under the CSI estimation errors and the amplitude and phase errors, and meanwhile the similarity between a synthesized waveform and an expected waveform can be ensured.

[0015] The joint design problem is solved by using an AM-SCA algorithm to iteratively obtain a transmitting waveform and a receiving beam forming vector, so that target detection and multi-user communication can be simultaneously realized under the existence of array amplitude and phase errors and CSI estimation errors of the dual-functional radar communication system.

[0016] The present application will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a flowchart of a robust transceiver design method based on a dual-functional radar communication system provided by an embodiment of the present application; Figure 2 is a MIMO DFRC system model diagram provided by an embodiment of the present application; Figure 3 is a contrast diagram of normalized transmitting and receiving beam pattern gains; Figure 4 is a contrast diagram of changes in pulse compression gains between antenna arrays; Figure 5 is a diagram of changes in achievable sum rates under different CSI estimation errors; Figure 6 is a structural schematic diagram of a robust transceiver design device based on a dual-function radar communication system provided by an embodiment of the present application. Figure 7 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0018] The present application will be further described in detail below in conjunction with specific embodiments, but the embodiments of the present application are not limited thereto.

[0019] In order to solve the problem that the robust transmission-reception design of the existing multi-user MIMO DFRC system does not simultaneously consider the CSI estimation error and the amplitude-phase error, an embodiment of the present application provides a robust transceiver design method based on a dual-function radar communication system, as shown in Figure 1 , Figure 1 is a flowchart of the robust transceiver design method based on a dual-function radar communication system provided by an embodiment of the present application, and specifically includes the following steps: Step S101, a signal model under the existence of errors of a MIMO DFRC system is established; the signal model includes a radar SINR under the existence of array amplitude-phase errors and a communication signal model under the existence of CSI estimation errors.

[0020] Referring to Figure 2 , Figure 2 is a MIMO DFRC system model diagram provided by an embodiment of the present application, which considers a MIMO DFRC system equipped with a uniform linear array (ULA) composed of antennas with a half-wavelength interval. The system simultaneously serves single-antenna users (Users) through downlink communication and detects a far-field point target. Let represent a discrete-time transmit waveform of the mth antenna, where and , where, represents the total number of antennas, represents the length of the waveform. The transmit waveform is formulated as , where , and the superscript represents the transpose operation of a matrix.

[0021] In the embodiment of the present application, the signal model includes a radar SINR under the existence of array amplitude-phase errors and a communication signal model under the existence of CSI (Channel State Information, communication channel state information) estimation errors.

[0022] The radar SINR under the existence of array amplitude-phase errors is specifically as follows: Assuming the point target is located in the radar direction And in Existing in direction J One source of interference related to the signal. This indicates the direction of interference; the signal-dependent interference source refers to signal-dependent clutter. Under amplitude and phase errors, the true transmit and receive steering vectors can be modeled as: ; in, This represents the actual launch guidance vector; Indicates the receiving guide vector. and These are the error vectors corresponding to the actual transmit steering vector and receive steering vector, respectively. express and The corresponding error limits, and , It is the ideal steering vector corresponding to the transmitter. It is the ideal steering vector corresponding to the receiving end. It's the wavelength. It refers to the antenna spacing; superscript Represents the imaginary unit; when Subscript r The time indicates the radar direction, when Subscript j The time indicates the direction of the interference.

[0023] Therefore, the transmit-receive steering vector matrix can be represented as: ; in, It is the error matrix. This is the corresponding error limit. This represents the Frobenius norm.

[0024] Then, the received signal at the MIMO DFRC system Represented as: ; in, It is the beamforming vector. It is an additive white Gaussian noise matrix. and These are the target and the first The attenuation coefficient of each interference source, It's the radar direction. It's the direction of the interference. Radar SINR It can be represented as: ; Transmit waveform and receive beamforming vectors to maximize radar SINR are optimized.

[0025] In embodiments of the invention, there is a communication signal model under CSI estimation error, which is specified as follows: Under CSI estimation error, the actual channel vector between the MIMO DFRC system and the kth user can be modeled as: k ; wherein, is the estimated channel vector that can be obtained from the uplink pilot of the kth user, k denotes an identity matrix, denotes an error bound, denotes the channel error vector of the kth user, and the actual channel matrix can be represented as: k ; wherein, is the estimated channel matrix, is the error channel matrix, is the corresponding error bound.

[0026] Then, the received signal at the downlink communication user, i.e., the communication signal model is given by: ; wherein, denotes the desired communication signal matrix, denotes a complex set, and the desired communication signal consists of three parts: a reference signal for synchronization, channel information provided to the user, and data signals. is an AWGN (Additive White Gaussian Noise) matrix, denotes the noise signal at the mth sampling point. The term represents the multi-user interference (MUI). Minimizing the MUI is shown to enhance the SINR of the users and thus improve the achievable sum rate, optimizing the communication performance by suppressing the MUI power.

[0027] ​​​​​​​In step S102, a joint design problem of transmit waveform and receive beamforming is constructed based on the signal model; the joint design problem takes maximizing radar SINR in the worst error scenario as an objective function, and takes communication constraints, radar waveform constraints and energy constraints as constraint conditions.

[0028] In the embodiments of the present application, the radar SINR in the worst case, i.e., the worst error scenario, is maximized while the MUI power is suppressed to ensure the communication performance. Specifically, the worst case can be understood as the case that the array amplitude and phase errors and the communication channel estimation errors occur in the most unfavorable way to the system performance. In addition, a similarity constraint is imposed between the synthesized radar waveform and the expected radar waveform to further guarantee the detection performance. Based on these considerations, the robust joint design problem of transmit waveform and receive beamforming is formulated as: ; ; ; ; ; wherein, denotes the transmit waveform; denotes the receive beamforming vector; denotes the error matrix in the radar direction ; denotes the error matrix in the interference direction ; denotes the radar SINR; denotes the actual channel matrix; denotes the expected communication signal matrix; denotes the Frobenius norm; and denote the threshold values of the MUI power and the radar waveform similarity mismatch, respectively; denotes the error channel matrix; denotes the uncertainty set corresponding to the error channel matrix; denotes the real transmit steering vector in the radar direction; the superscript denotes the conjugate transpose operation of a matrix; denotes the expected radar waveform; denotes the Euclidean norm; denotes the error vector corresponding to the real receive steering vector in the radar direction; denotes the uncertainty set corresponding to ; denotes the maximum transmit energy; denotes the uncertainty set corresponding to ; wherein, denotes the error vector corresponding to the real received steering vector; denotes the corresponding error bound; denotes the error matrix, denotes the corresponding error bound.

[0029] The communication constraint is used to suppress MUI power, and specifically includes: The radar waveform constraint is used to ensure radar waveform similarity, and specifically includes: The energy constraint is used to limit the transmission energy, and specifically includes: Thus, the joint design problem of the transmission waveform and the reception beam forming is obtained.

[0030] In step S103, the AM-SCA algorithm is used to iteratively solve the joint design problem until the objective function converges or the number of iterations reaches a preset maximum number of iterations, and the transmission waveform and the reception beam forming vector are obtained.

[0031] Due to the non-convex fractional objective function and the constraint condition, the problem in the objective function is difficult to be directly solved, and therefore, the AM-SCA algorithm is proposed in the embodiments of the present application.

[0032] In the embodiments of the present application, the AM-SCA algorithm is used to iteratively solve the joint design problem until the objective function converges or the number of iterations reaches a preset maximum number of iterations, and the transmission waveform and the reception beam forming vector are obtained, including: converting the objective function into a convex function about the transmission waveform and the reception beam forming vector; converting the communication constraint and the radar waveform constraint into a convex constraint form of the communication constraint and a convex constraint form of the radar waveform constraint, respectively; integrating the convex function, the convex constraint form of the communication constraint, the convex constraint form of the radar waveform constraint, and the energy constraint to obtain a convex optimization problem; using an alternating minimization algorithm to solve the convex optimization problem until the convex function converges or the number of iterations reaches a preset maximum number of iterations, and obtaining the transmission waveform and the reception beam forming vector.

[0033] Specifically, first, based on the triangle inequality and the Cauchy-Schwarz inequality, the objective function can be upper bounded as: wherein,​​​​​​​ , where the numerator and denominator are denoted as numerator over variable and denominator over variable .

[0034] To derive the inner approximation of the constraints in the communication constraints and radar waveform constraints, the triangle inequality and Cauchy-Schwarz inequality are also applied, which results in the following form: ; Therefore, the communication constraints and radar waveform constraints can be rewritten as the communication constraints in the form of convex constraints and the radar waveform constraints in the form of convex constraints: ; ; Then, the transmit waveform and receive beamforming joint design problem can be reformulated as a convex optimization problem: ; An alternating minimization algorithm is used to solve the reformulated convex optimization problem, where the transmit waveform and the receive beamforming vector are alternately optimized.

[0035] 1) Optimize : At the th iteration, when is fixed, the can be optimized by solving the following problem: ; Note that the fractional objective function in the above equation is still non-convex. To solve this problem, a convex inner approximation is constructed by applying the first-order Taylor expansion of the objective function at the current point , which can be reformulated as: ; where ; ; ; where denotes the real part; denotes the trace; denotes the gradient operator; In the embodiments of the present application, the convex inner approximation of the fractional objective function is constructed by applying the first-order Taylor expansion of the objective function at the current point The reformulated problem is a Second Order Cone Programming (SOCP) problem, which can be solved using CVX toolbox (a MATLAB-based convex optimization modeling system) to obtain .

[0036] 2) Optimization : When is fixed, can be optimized by solving the following problem .

[0037] ; This problem can be further written as ; where the constraints in the above equation are non-convex. To solve this problem, a convex interior approximation is constructed by applying the first-order Taylor expansion of at the current point , which can be reformulated as ; where ; Note that the reformulated problem by applying the first-order Taylor expansion of at the current point is a convex Quadratically Constrained Quadratic Programming (QCQP) problem, which can also be solved using CVX toolbox to obtain .

[0038] The above steps for optimizing and are repeated in the process of alternating iterative solution. When the preset maximum number of iterations is reached or the objective function converges, the iteration is terminated, i.e.: ; where is the convergence threshold.

[0039] The proposed AM-SCA algorithm is summarized as follows: a) input ; b) repeat; c) update by solving the following problem ; d) update by the following question : ; e) ; f) until convergence or the number of iterations reaches a preset maximum number of iterations; g) output transmit waveform and receive beamforming vector .

[0040] Next, the convergence and complexity of the AM-SCA algorithm proposed in the embodiments of the application are analyzed.

[0041] 1) Convergence analysis: In order to analyze the convergence of the proposed AM-SCA algorithm, consider the convex optimization problem obtained from the original problem in equation (8) by transforming the objective function and using the SCA (Sequential Convex Approximation) method to apply an inner approximation to the non-convex constraint.

[0042] In each iteration of the proposed AM-SCA algorithm, the non-convex problem after the joint design of the transmit waveform and receive beamforming problem is solved by an alternating minimization framework. Specifically, for the optimization of , the non-convex fractional objective is approximated by its first-order Taylor expansion around the current point , resulting in a convex SOCP problem. For the optimization of , the non-convex constraint is similarly linearized by a first-order approximation at , resulting in a convex QCQP problem. These two convex sub-problems are solved alternately in each iteration.

[0043] Since each sub-problem is convex and solved to global optimality in its respective iteration, it is guaranteed that the objective value in the reformulated joint design of the transmit waveform and receive beamforming problem is non-increasing. In addition, under conditions such as continuity and boundedness of the feasible set, the iterative solution generated by the AM-SCA algorithm converges to a stationary point that satisfies the KKT conditions (a set of necessary conditions in optimization theory, mainly used to solve nonlinear programming problems with equality and inequality constraints) of the original non-convex problem. Therefore, the proposed AM-SCA algorithm is convergent.

[0044] 2) Complexity analysis: The complexity of the proposed AM-SCA algorithm mainly depends on solving the SOCP problem and the convex QCQP problem. Since these problems are solved using the CVX toolbox, which usually uses the proximal point method, the complexity of each iteration of solving the SOCP problem is , and the complexity of each iteration of solving the convex QCQP problem is The total computational complexity of the AM-SCA algorithm is... ,in, It represents the number of iterations.

[0045] In this embodiment of the invention, a signal model of the MIMO DFRC system under the presence of errors is first established. This signal model includes the radar SINR under the presence of array amplitude and phase errors and the communication signal model under the presence of CSI estimation errors. Then, a joint design problem of the transmitted waveform and the received beamforming is constructed based on the signal model. The joint design problem takes maximizing the radar SINR under the worst error scenario as the objective function and communication constraints, radar waveform constraints, and energy constraints as constraints. Therefore, it can maximize the SINR and suppress the MUI under CSI estimation errors and amplitude and phase errors, while ensuring the similarity between the synthesized waveform and the desired waveform.

[0046] The AM-SCA algorithm is used to iteratively solve the joint design problem, and the transmitted waveform and the received beamforming vector are obtained. Thus, target detection and multi-user communication can be achieved simultaneously in the case of array amplitude and phase error and CSI estimation error in the dual-function radar communication system.

[0047] The simulation experiment of a robust transceiver design method for a dual-function radar communication system provided by the embodiments of the present invention is as follows: Simulation conditions: This simulation experiment was conducted in the software environment of MATLAB R2020a and VScode.

[0048] The performance metrics are as follows: 1) Transmit and Receive Beam Patterns: The transmit beam pattern gain is given by the following formula: ; Furthermore, the received beam pattern gain is given by the following formula: ; in, and higher values ​​and and The lower values ​​indicate better beam pattern performance. Indicates in Direction , Indicates in Direction , Indicates in Direction , Indicates in Direction .

[0049] 2) Pulse compression gain: To evaluate the radar performance, the pulse compression gain can be expressed as: ; where, represents the actual received signal. The higher the value of

[0050] 3) Communication and rate: To evaluate the communication performance, the achievable sum-rate can be expressed as: ; where, , is the actual channel vector of the th user, is the desired signal, is the noise vector. The higher the value of

[0051] The simulation results are as follows: Consider a 16-antenna MIMO DFRC system, the desired radar waveform is a Linear Frequency Modulated (LFM) waveform, the target is located at , there are interferers located at and downlink communication users, the desired communication signal adopts Quadrature Phase Shift Keying (QPSK) modulation. The maximum transmit power of the MIMO DFRC system is normalized to . The noise power at the radar receiver and the communication users is set to , while the interference power is set to . To compare, the Minimum-Norm Optimization (MNO) method, the Transmission Energy (TE) method, and the Region-Of-Interest (ROI) are also shown. The results are averaged over 500 Monte Carlo trials, and the threshold is set to .

[0052] Referring to Figure 3 , Figure 3 is a normalized transmit and receive beam pattern gain comparison diagram, Figure 3 (a) and (b) in show the amplitude error of The normalized transmit and receive beam pattern gain at that time. It can be observed that the robust transmit / receive design method for a dual-function radar communication system provided in this embodiment of the invention has the deepest nulls in the interference direction. Furthermore, in Figure 3 In (a) of this invention, the robust transmit / receive design method for a dual-function radar communication system provided by this embodiment achieves a transmit beam pattern gain in the target direction that is higher than that of the ROI, TE, and MNO methods, respectively. , and Similarly, in Figure 3 In (b) of the study, compared with the ROI, TE, and MNO methods, the received beam pattern gain in the target direction is improved. , and .exist Figure 3 As can be seen from the embodiments of the present invention, the robust transceiver design method based on a dual-function radar communication system can achieve optimal beam pattern performance under amplitude and phase errors.

[0053] See Figure 4 , Figure 4 This is a schematic diagram comparing the changes in pulse compression gain between antenna arrays. Figure 4 (a) and Figure 4 Figure (b) shows the variation of pulse compression gain at different amplitude and phase error levels between antenna arrays. Figure 4 In (a) of the above, the amplitude error is fixed at... , and Figure 4 In (b) of the equation, the phase error is fixed at... .exist Figure 4 In the study, the pulse compression gain decreases with increasing phase or amplitude error, demonstrating that the proposed method outperforms the ROI, TE, and MNO methods. The pulse compression gain without amplitude or phase error is... This is the upper limit, which can be used as a baseline. Compared with other methods, the robust transceiver design method based on a dual-function radar communication system provided in this embodiment of the invention exhibits the least performance degradation and the strongest robustness to amplitude and phase errors.

[0054] See Figure 5 , Figure 5 This is a schematic diagram illustrating the achievable sum rate variation under different CSI estimation errors. Figure 5 In (a) of the data, the magnitude of the CSI estimation error is fixed at... , and Figure 5 In (b) of the equation, the phase error is fixed at... .exist Figure 5The achievable sum rate decreases as the phase or amplitude error increases. Compared with the ROI, TE and MNO methods, the robust transceiver design method based on the dual-functional radar communication system provided by the embodiment of the application can obtain the highest sum rate. In addition, the achievable sum rate under perfect CSI is , which is the upper limit and serves as the baseline. Compared with other methods, the performance degradation of the robust transceiver design method based on the dual-functional radar communication system provided by the embodiment of the application is minimal, which means that the method has the strongest robustness to CSI estimation errors.

[0055] Based on the same inventive concept, the embodiment of the application also provides a robust transceiver design device based on a dual-functional radar communication system, which is described below with reference to Figure 6 , Figure 6 FIG. 1 is a structural schematic diagram of a robust transceiver design device based on a dual-functional radar communication system provided by the embodiment of the application. The robust transceiver design device comprises: The establishment module 601 is configured to establish a signal model under the presence of errors of the MIMO DFRC system. The signal model comprises a radar SINR under the presence of array amplitude and phase errors and a communication signal model under the presence of CSI estimation errors. The problem forming module 602 is configured to construct a joint design problem of transmit waveforms and receive beamforming based on the signal model. The joint design problem takes the radar SINR under the worst error scenario as an objective function and takes communication constraints, radar waveform constraints and energy constraints as constraint conditions. The solving module 603 is configured to solve the joint design problem by using an AM-SCA algorithm for iteration until the objective function converges or the number of iterations reaches a preset maximum number of iterations, so as to obtain transmit waveforms and receive beamforming vectors.

[0056] In the embodiment of the application, a signal model under the presence of errors of the MIMO DFRC system is first established. The signal model comprises a radar SINR under the presence of array amplitude and phase errors and a communication signal model under the presence of CSI estimation errors. Then, a joint design problem of transmit waveforms and receive beamforming is constructed based on the signal model. The joint design problem takes the radar SINR under the worst error scenario as an objective function and takes communication constraints, radar waveform constraints and energy constraints as constraint conditions, so as to maximize the SINR under the CSI estimation errors and the amplitude and phase errors, suppress MUI, and ensure the similarity between the synthesized waveforms and the expected waveforms.

[0057] The joint design problem is solved by using an AM-SCA algorithm for iteration, so as to obtain transmit waveforms and receive beamforming vectors, thereby simultaneously realizing target detection and multi-user communication under the presence of array amplitude and phase errors and CSI estimation errors of the dual-functional radar communication system.

[0058] Optionally, the radar SINR comprises: ; wherein, denotes the radar SINR; denotes the attenuation coefficient of the target; denotes the receive beamforming vector; superscript denotes the conjugate transpose operation of a matrix; denotes the transmit-receive steering vector matrix of a radar direction; denotes a radar direction; denotes the transmit waveform; , denotes the total number of interference sources; denotes the attenuation coefficient of the th interference source; denotes an interference direction; denotes the transmit-receive steering vector matrix of an interference direction; denotes the additive white Gaussian noise matrix; denotes the Euclidean norm.

[0059] Optionally, the communication signal model comprises: ; wherein, denotes the communication signal model; denotes the actual channel matrix; denotes the transmit waveform; denotes the white Gaussian noise matrix; denotes the desired communication signal matrix.

[0060] Optionally, the joint design problem comprises: ; ; ; ; ; wherein, denotes the transmit waveform; denotes the receive beamforming vector; denotes the error matrix at a radar direction ; denotes the error matrix at an interference direction ; denotes the radar SINR; denotes the actual channel matrix; denotes the desired communication signal matrix; denotes the Frobenius norm; and denote the threshold of MUI power and radar waveform similarity mismatch, respectively; denotes the error channel matrix; denotes the uncertainty set corresponding to the error channel matrix; denotes the real transmit steering vector in the radar direction; superscript denotes the conjugate transpose operation of a matrix; denotes the desired radar waveform; denotes the Euclidean norm; denotes the error vector corresponding to the real receive steering vector in the radar direction; denotes the uncertainty set corresponding to denotes the transmit maximum energy; denotes the uncertainty set corresponding to

[0061] Optionally, the solving module is specifically configured to: convert the target function into a convex function about the transmit waveform and the receive beamforming vector; convert the communication constraint and the radar waveform constraint into a convex constraint form communication constraint and a convex constraint form radar waveform constraint, respectively; integrate the convex function, the convex constraint form communication constraint, the convex constraint form radar waveform constraint, and the energy constraint to obtain a convex optimization problem; and solve the convex optimization problem by using an alternating minimization algorithm until the convex function converges or the number of iterations reaches a preset maximum number of iterations, to obtain the transmit waveform and the receive beamforming vector.

[0062] Optionally, the convex optimization problem comprises: ; wherein, denotes a numerator over variable; denotes a denominator over variable; denotes the transmit waveform; denotes the receive beamforming vector; denotes the Frobenius norm; denotes the transmit maximum energy; denotes an estimated channel matrix; denotes an expected communication signal matrix; denotes an error bound corresponding to the error channel matrix; and denote the threshold of MUI power and radar waveform similarity mismatch, respectively; denotes an ideal steering vector corresponding to the transmit end in the radar direction; superscript denotes a conjugate transpose operation of a matrix; denotes a desired radar waveform; denotes and corresponding error bounds; denotes an error vector corresponding to a real transmit steering vector; denotes an error vector corresponding to a real receive steering vector.

[0063] The embodiment of the present application further provides an electronic device, such as Figure 7 As shown in the figure, the electronic device comprises a processor 701, a communication interface 702, a memory 703 and a communication bus 704, wherein the processor 701, the communication interface 702 and the memory 703 complete mutual communication through the communication bus 704, The memory 703 is used for storing a computer program. The processor 701 is used for executing the program stored in the memory 703, and realizes the method steps of the above-mentioned any one kind of robust transceiver design method based on a dual-function radar communication system.

[0064] The communication bus mentioned in the above-mentioned electronic device can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus and the like. The communication bus can be divided into an address bus, a data bus, a control bus and the like. In order to facilitate representation, only one thick line is used to represent in the figure, but it does not represent that there is only one bus or only one type of bus.

[0065] The communication interface is used for communication between the above-mentioned electronic device and other devices.

[0066] The memory can comprise a random access memory (RAM) and can also comprise a non-volatile memory (NVM), for example at least one disk memory. Optionally, the memory can also be at least one storage device located away from the aforementioned processor.

[0067] The processor described above can be a general processor, including a central processing unit (CPU), a network processor (NP), etc.; can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0068] The application further provides a computer readable storage medium. The computer readable storage medium stores a computer program. When the computer program is executed by a processor, the method steps of any one of the robust transceiver design methods based on a dual-function radar communication system are implemented.

[0069] Optionally, the computer readable storage medium can be a non-volatile memory (NVM), for example, at least one disk memory.

[0070] Optionally, the computer readable storage medium can also be at least one storage device located away from the processor.

[0071] In another embodiment of the application, a computer program product containing instructions, which, when run on a computer, causes the computer to perform the method steps of any one of the robust transceiver design methods based on a dual-function radar communication system.

[0072] It should be noted that the terms "first", "second", etc. are used to distinguish similar objects, and do not necessarily have to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the application. Rather, they are merely examples of devices and methods consistent with some aspects of the application.

[0073] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the description of the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in the specification.

[0074] Although the present application is described herein in conjunction with various embodiments, those skilled in the art, with reference to the drawings and the disclosure, can understand and implement other variations of the disclosed embodiments in the implementation of the claimed application. In the description of the present application, the word "comprising" does not exclude other components or steps, "one" or "an" does not exclude a plurality, and "plurality" means two or more, unless otherwise explicitly specified. In addition, some measures are described in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0075] The method provided by the embodiments of the present application can be applied to electronic devices. Specifically, the electronic device can be: desktop computer, portable computer, smart mobile terminal, server, etc. Herein, any electronic device that can implement the present application belongs to the protection scope of the present application.

[0076] For device / electronic device / storage medium embodiments, because they are basically similar to method embodiments, the description is relatively simple, and the relevant part can be referred to the part of the method embodiment.

[0077] It should be noted that the device, electronic device and storage medium of the embodiments of the present application are respectively the device, electronic device and storage medium of the above-mentioned one kind of robust transceiver design method based on dual-function radar communication system, then all embodiments of the above-mentioned one kind of robust transceiver design method based on dual-function radar communication system are suitable for the device, electronic device and storage medium, and can achieve the same or similar beneficial effects.

[0078] The above is a further detailed description of the present application in conjunction with specific preferred embodiments, and the specific implementation of the present application cannot be limited to these descriptions. For those skilled in the art, without departing from the concept of the present application, a number of simple deductions or substitutions can be made, which should be regarded as falling within the protection scope of the present application.

Claims

1. A robust transceiver design method based on a dual-function radar communication system, characterized in that, The robust transceiver design method comprises: establishing a signal model under the presence of errors of the MIMO DFRC system; the signal model comprises a radar SINR under the presence of array amplitude and phase errors and a communication signal model under the presence of CSI estimation errors; constructing a joint design problem of a transmit waveform and a receive beamforming based on the signal model; the joint design problem takes maximizing the radar SINR under the worst error scenario as an objective function, and takes a communication constraint, a radar waveform constraint and an energy constraint as constraint conditions; solving the joint design problem by using an AM-SCA algorithm iteratively until the objective function converges or the number of iterations reaches a preset maximum number of iterations, to obtain a transmit waveform and a receive beamforming vector.

2. The method of claim 1, wherein, The radar SINR comprises: ； wherein denotes the radar SINR; denotes the attenuation coefficient of the target; denotes the receive beamforming vector; superscript denotes the conjugate transpose operation of a matrix; denotes the transmit-receive steering vector matrix of a radar direction; denotes a radar direction; denotes the transmit waveform; , denotes the total number of interferers; denotes the attenuation coefficient of the interferer; denotes an interference direction; denotes the transmit-receive steering vector matrix of an interference direction; denotes the additive white Gaussian noise matrix; denotes the Euclidean norm.

3. The method of claim 1, wherein, The communication signal model comprises: ； wherein, represents the communication signal model; represents the actual channel matrix; represents the transmit waveform; represents the white Gaussian noise matrix; represents the desired communication signal matrix.

4. The method of claim 1, wherein, The joint design problem comprises: ； ； ； ； ； wherein, denotes the transmit waveform; denotes the receive beamforming vector; denotes the error matrix in radar direction ; denotes the error matrix in interference direction ; denotes the radar SINR; denotes the actual channel matrix; denotes the desired communication signal matrix; denotes the Frobenius norm; and denote the threshold of MUI power and radar waveform similarity mismatch, respectively; denotes the error channel matrix; denotes the uncertainty set corresponding to the error channel matrix; denotes the true transmit steering vector in radar direction; superscript denotes the conjugate transpose operation of a matrix; denotes the desired radar waveform; denotes the Euclidean norm; denotes the error vector corresponding to the true receive steering vector in the radar direction; denotes the uncertainty set corresponding to ; denotes the transmit maximum energy; denotes the uncertainty set corresponding to 5. The method of claim 1, wherein, solving the joint design problem by using an AM-SCA algorithm iteratively until the objective function converges or the number of iterations reaches a preset maximum number of iterations, to obtain a transmit waveform and a receive beamforming vector, which comprises: converting the objective function into a convex function about the transmit waveform and the receive beamforming vector; converting the communication constraint and the radar waveform constraint into a communication constraint in a convex constraint form and a radar waveform constraint in a convex constraint form respectively; integrating the convex function, the communication constraint in the convex constraint form, the radar waveform constraint in the convex constraint form and the energy constraint to obtain a convex optimization problem; solving the convex optimization problem by using an alternating minimization algorithm until the convex function converges or the number of iterations reaches a preset maximum number of iterations, to obtain a transmit waveform and a receive beamforming vector.

6. The method of claim 5, wherein, The convex optimization problem comprises: ； wherein, denotes a numerator over variable; denotes a denominator over variable; denotes the transmit waveform; denotes the receive beamforming vector; denotes the Frobenius norm; denotes the transmit maximum energy; denotes the estimated channel matrix; denotes the desired communication signal matrix; denotes the error bound corresponding to the error channel matrix; and denote the MUI power and radar waveform similarity mismatch threshold, respectively; denotes the ideal steering vector corresponding to the transmitter in the radar direction; superscript denotes the conjugate transpose operation of a matrix; denotes the desired radar waveform; denotes and the error bound corresponding to; denotes the error vector corresponding to the real transmit steering vector; denotes the error vector corresponding to the real receive steering vector.

7. A robust transceiver design apparatus based on a dual-function radar communication system, characterized by, The robust transceiver design device comprises: a establishing module configured to establish a signal model under the presence of errors of the MIMO DFRC system; the signal model comprises a radar SINR under the presence of array amplitude and phase errors and a communication signal model under the presence of CSI estimation errors; a problem forming module configured to construct a joint design problem of a transmit waveform and a receive beamforming based on the signal model; the joint design problem takes maximizing the radar SINR under the worst error scenario as an objective function, and takes a communication constraint, a radar waveform constraint and an energy constraint as constraint conditions; a solving module configured to solve the joint design problem by using an AM-SCA algorithm iteratively until the objective function converges or the number of iterations reaches a preset maximum number of iterations, to obtain a transmit waveform and a receive beamforming vector.

8. The robust transceiving design apparatus of claim 7, wherein, The radar SINR comprises: ； wherein denotes the radar SINR; denotes the attenuation coefficient of the target; denotes the receive beamforming vector; superscript denotes the conjugate transpose operation of a matrix; denotes the transmit-receive steering vector matrix of a radar direction; denotes a radar direction; denotes the transmit waveform; , denotes the total number of interferers; denotes the attenuation coefficient of the interferer; denotes an interference direction; denotes the transmit-receive steering vector matrix of an interference direction; denotes the additive white Gaussian noise matrix; denotes the Euclidean norm.

9. An electronic device, comprising: The device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; the memory is used to store a computer program; the processor is used to execute the computer program stored on the memory, to implement the robust transceiver design method based on the dual-function radar communication system according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the robust transceiver design method based on the dual-function radar communication system according to any one of claims 1-6.

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