A method for configuring parameters of an integrated inductive network for a space-fed transmission array

Abstract

A method for configuring parameters in a sensing-integrated network using a transmission array fed by an air feeder is characterized by the following steps: constructing a near-field spherical wave channel model from the transmission array to the communication user and the sensing target; establishing a joint communication and sensing transmission model, a communication reception model, a user reachable rate model, and a sensing echo reception model; constructing an unknown parameter vector and a Fisher information matrix based on the sensing echo; and establishing Cramer-Rao lower bound matrices for the target distance, elevation angle, and azimuth angle; and jointly optimizing the communication beam, sensing covariance matrix, and transmission array beamforming matrix under the conditions of satisfying the minimum rate constraints for each communication user, the total system transmission power constraints, and the positive semidefinite covariance constraints, thereby improving the communication performance, sensing accuracy, and integrated service capabilities of the sensing-integrated network.

Description

Technical Field

[0001] This invention relates to the field of wireless communication and sensing fusion technology, and particularly to a parameter configuration method for jointly modeling and optimizing communication beams, sensing signal covariance matrices, and transmission array beamforming parameters under near-field propagation conditions in the context of integrated communication and sensing networks. Background Technology

[0002] The integrated sensing and communication network can simultaneously realize wireless communication, target perception and parameter estimation, and has broad application prospects in intelligent transportation, unmanned systems, smart cities, industrial monitoring and environmental perception, thus becoming an important development direction for future 6G wireless communication networks.

[0003] However, in three-dimensional spatial perception scenarios, the integration of communication and sensing is susceptible to propagation loss, beam mismatch, and complex electromagnetic propagation environments, leading to decreased communication transmission performance and insufficient accuracy in target parameter estimation. Traditional signal modeling and resource allocation methods based on far-field conditions struggle to accurately characterize the propagation characteristics of near-field spherical waves, thus limiting further improvements in system communication and sensing performance. Transmissive-Reconfigurable Intelligent Surface (T-RIS) can flexibly control the electromagnetic propagation environment through programmable beamforming, providing directional energy transmission and enhancement links for communication users and sensing targets.

[0004] Therefore, how to rationally configure key parameters such as communication beams, sensing signal covariance matrix, and transmission array beamforming matrix to reduce target distance, elevation angle, and azimuth angle estimation errors and improve the overall performance of the integrated sensing network while meeting the minimum rate requirements of communication users and system transmit power constraints has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] The purpose of this invention is to provide a method for configuring the integrated communication and sensing network parameters of an air-fed transmission array, in order to improve the difficulties in designing communication and sensing coupling in complex propagation environments and the insufficient accuracy of target parameter estimation, thereby enhancing the system's communication and sensing performance. The specific technical solution is as follows:

[0006] Step 200: Construct a near-field spherical wave channel model from T-RIS to the communication user and the sensing target.

[0007] Step 210: Transmit joint communication and sensing signals within the coherent time block and establish a transmission covariance model.

[0008] Step 220: Based on T-RIS beamforming, establish communication reception, user rate, and sensing echo models.

[0009] Step 230: Construct the Fisher information matrix based on the sensed echo and establish the theoretical lower bound for the estimation errors of the target range, elevation angle and azimuth angle parameters.

[0010] Step 240: Under the constraints of communication rate and transmit power, solve for the optimal configuration of system parameters.

[0011] Step 200: Construct the near-field spherical wave channel model from T-RIS to the communication user and the sensing target, specifically including:

[0012] A near-field spherical wave channel model is constructed from the transmission array to the communication user and the sensing target. The T-RIS includes... T-RIS of radiating units, wherein This indicates the number of elements in the array along the horizontal direction. This represents the number of elements in the array along the vertical direction, and satisfies... , Single-antenna communication user Individual perception target and The T-RIS employs a single-station sensing system, using each feed source as both a transmitter and receiver to achieve coordinated processing of communication user information transmission and target detection. The T-RIS is modeled as a uniform planar array with a lateral spacing of [missing information]. The vertical spacing is Therefore, the transverse aperture of the array is Longitudinal aperture The signal wavelength is The T-RIS center is set as the origin of the three-dimensional spatial coordinate system to facilitate the establishment of a unified spatial geometric relationship. The position of any communication user or sensing target in space can be determined by spherical coordinate parameters. It means that, among them, The propagation distance and pitch angle between the target point and the center of T-RIS With azimuth The function is then represented by its corresponding Cartesian coordinates as follows: , wherein and Equivalent adjustments can be made according to specific coordinate definitions, all of which should fall within the spatial angle range defined in this invention. The first... The coordinates of each element are represented as follows: ,in , Based on the near-field Fresnel zone conditions ,in, This represents the characteristic size of the T-RIS, indicating that the communication user or sensing target is in the near-field propagation region with the T-RIS. Under this condition, the... The distance from each element to the array element is Pitch angle is azimuth angle is The propagation distance of the communication user or the perceived target is expressed as

[0013] (1)

[0014] The above modeling method can accurately characterize the spatial distance differences between each element of the T-RIS and the communication user or sensing target. Based on the established near-field propagation geometric model, a near-field single-element channel model between the T-RIS and the communication user or sensing target is further constructed. For those located in spatial positions... The communication user or sensing target at the location, the first in T-RIS Each array element and its near-field element channel can be represented as

[0015] (2)

[0016] in, Indicates the first Individual elements reach the target location The actual propagation distance Indicates the carrier wavelength. It represents the imaginary unit. Indicates the amplitude attenuation coefficient related to propagation distance. , This represents the path loss constant at the reference distance. , This represents the complex channel gain. Therefore, the common propagation phase term in the single-element channel can be extracted, thus uniformly representing the near-field single-element channel as a distance relative to the array center reference distance. The phase difference form of T-RIS with the user or target Near-field array response vectors Represented as

[0017] (3) Represents the array response vector. This represents the transpose operation, and the near-field array response vector. The Each component is

[0018] (4)

[0019] Under near-field propagation conditions, the phase corresponding to different array elements is not only related to the incident direction, but also to the distance from the target. Significantly correlated, the array response no longer satisfies the traditional plane wave model determined solely by angle under far-field conditions, but instead exhibits spherical wave characteristics coupled with both distance and angle. For the first... A communication user, whose spatial location parameter is represented as Its near-field communication channel vector is represented as

[0020] (5)

[0021] in, Indicates the first The complex channel gain corresponding to each communication user This indicates that T-RIS points to the first The near-field array response vector of the location of the communication user. For the ... For each sensing target, its near-field round-trip sensing channel matrix is ​​represented as follows:

[0022] (6)

[0023] in, Indicates T-RIS and the first Near-field round-trip sensing channel matrix between sensing targets This represents the complex channel gain corresponding to the sensing target. Indicates the location at the target position The near-field array response vector at the location. The round-trip sensing channel matrix is ​​used to characterize the propagation characteristics of electromagnetic waves transmitted via T-RIS to the sensing target and reflected back to T-RIS. Unlike one-way communication channels, the round-trip sensing channel is affected by both the outbound and return propagation paths; therefore, its phase change and amplitude attenuation are closely related to the spatial position parameters of the target. Since the propagation distances from each array element to the target differ in the near-field propagation environment, the sensing channel matrix is ​​related not only to the target's direction parameters but also to the target's distance parameters, thus exhibiting range- and angle-coupled spherical wave characteristics.

[0024] Step 210: Transmit joint communication and sensing signals within the coherent time block and establish a transmission covariance model, specifically including:

[0025] Set length as The coherent time blocks are defined, and it is assumed that the communication channel parameters and sensing target parameters remain approximately constant within each coherent time block. Based on this assumption, conventional channel estimation methods can be used to obtain communication channel state information at the beginning of each coherent time block. During the remaining time periods of the coherent time block, the information is then obtained by... Each feed source transmits joint communication and sensing signals to perform wireless communication and target sensing functions in parallel. At discrete times... At this point, the joint communication and sensing baseband transmitted signal is represented as

[0026] (7)

[0027] in, Represents the feed source index set, Indicates the first The transmit beam vector corresponding to the road information symbol is used to carry the communication information symbol. , This represents a sensing signal vector specifically used for environmental perception or target detection. Communication information symbols satisfy the independent unity power constraint.

[0028] (8)

[0029] in, This indicates the conjugate operation. This represents the statistical expectation. The above constraints indicate that the communication symbols carried by different feed sources are independent of each other, and each communication symbol is normalized. Dedicated sensing signal. The covariance matrix is

[0030] (9)

[0031] Then transmit signal The covariance matrix is ​​represented as

[0032] (10)

[0033] in, This represents the conjugate transpose operation. Therefore, the transmitted signal covariance matrix is ​​composed of the communication beam power term and the sensing signal covariance term, and can simultaneously characterize the system's communication resource allocation state and sensing energy transmission state.

[0034] Step 220, based on T-RIS beamforming, establish communication reception, user rate, and perceived echo models, specifically including:

[0035] The joint communication and sensing transmission signal at the T-RIS end is represented as follows:

[0036] (11)

[0037] in, This is the T-RIS beamforming matrix, used to map the joint signal generated on the feed side to each radiating element of the T-RIS, and satisfies the unit mode constraint. ,in, Representation matrix The Middle Line 1 In one embodiment, the column elements are the simulated beamforming matrix of T-RIS. The Listed as , can be represented as

[0038] (12)

[0039] in, This represents the near-field array response vector. Indicates the first Spatial location parameters corresponding to each communication user The simulated beamforming matrix represents the spatial location parameters corresponding to the perceived target. The beams are configured to be aligned. The near-field array response direction of one communication user is configured, and the remaining columns are configured as near-field array response directions aligned with the sensing target. Through the above beam configuration, the system can simultaneously meet the data transmission needs of communication users and the sensing and detection needs of environmental targets under the same hardware architecture, thereby realizing integrated beam control for joint communication and sensing.

[0040] Based on the above definition, the emission covariance matrix at the T-RIS end can be further expressed as:

[0041] (13)

[0042] It should be noted that the covariance matrix expressed in the above equation reflects the equivalent emission statistics after T-RIS simulated beamforming. For the... The received signal model for a single communication user is represented as follows:

[0043] (14)

[0044] in, Indicates T-RIS to the 1st The equivalent near-field communication channel vector for each communication user Corresponding to the first The expected communication signal item for each communication user This indicates multi-user interference items from other users' data streams. This indicates the additional interference caused by the dedicated sensing signal to the communication user. Indicates receiver noise. , Indicates the first The noise power of the receiver at each communication user. Assuming other user signals and sensed signals are considered as interference, the noise power of the first... The achievable rate for a communication user is represented as:

[0045] (15)

[0046] Wherein, the numerator term represents the term assigned to the first term. The effective signal power of each communication user is represented by the denominator, which indicates the interference and noise power after deducting the useful signal from the total received power. This includes multi-user interference, sensed signal leakage, and thermal noise. The sensed echo received signal model is expressed as follows:

[0047] (16)

[0048] in, Indicates at discrete time The received sensing echo signal vector at the location, This represents the near-field round-trip sensing channel matrix between the T-RIS and the target. This indicates the joint communication and sensing signals transmitted by the T-RIS terminal. This represents additive white Gaussian noise, where... Indicates noise power. express 3D identity matrix This represents the analog combining matrix at the receiving end, used for radio frequency domain combining of the echo signals received by the T-RIS. For T-RIS, it can be... Each element is randomly selected from the unit circle, and the simulated merging matrix satisfies the unit modulus constraint. When the number of receiving antennas is sufficiently large, the simulated merging matrix satisfies the approximate orthogonality property. .

[0049] Step 230: Construct the Fisher information matrix based on the sensed echo, and establish theoretical lower bounds for the estimation errors of the target range, elevation angle, and azimuth angle parameters, specifically including:

[0050] The goal of sensing is to estimate target parameters based on the received echo signal samples within the entire coherent time block, representing the received echo signal within a coherent time block as... , This represents the received data matrix composed of echo observation samples from multiple time points. Due to the near-field sensing channel... Containing both distance and angle information, the echo signal carries not only the target's azimuth parameters but also its distance parameters, reflecting the target's radial position. Based on this near-field spherical wave characteristic, joint estimation of the target's distance and angle parameters can be achieved.

[0051] In this embodiment, the Multiple Signal Classification (MUSIC) algorithm is used to estimate the target parameters. The MUSIC algorithm utilizes the orthogonality between the signal subspace and the noise subspace to perform a spectral search on the target's range, elevation angle, and azimuth angle to determine the estimated values ​​of the target parameters. First, a sample covariance matrix is ​​constructed based on the received echo signal, and this sample covariance matrix is ​​represented as...

[0052] (17)

[0053] in, This represents the estimated value of the covariance matrix of the received echo signal. Based on this covariance matrix... Through eigenvalue decomposition, we can obtain

[0054] (18)

[0055] in, and Each by the largest The signal subspace is composed of eigenvalues ​​and their corresponding eigenvectors. and Each eigenvalue is composed of the remaining eigenvalues ​​and their corresponding eigenvectors, and is used to characterize the noise subspace. When When , it indicates that only a single sensing target is considered in the current scene. In this case, the signal subspace is spanned by the steering vector corresponding to the single target. and All are real-valued diagonal matrices. To characterize the orthogonality between the guiding vector corresponding to any candidate spatial parameter and the noise subspace, the noise subspace projection matrix is ​​defined as follows:

[0056] (19)

[0057] For any steering vector Its projection onto the noise subspace can be expressed as

[0058] (20)

[0059] in, Represents candidate parameter points The projection value onto the noise subspace is used to measure the correlation between the candidate steering vector and the noise subspace. Since the signal subspace and noise subspace are orthogonal, the estimated parameter satisfies this condition if and only if... , as well as At that time, the guiding vector The projection function is approximately orthogonal to the noise subspace. It reaches a minimum value, ideally approaching zero.

[0060] Based on the aforementioned orthogonality, a joint search can be performed on the distance, elevation angle, and azimuth angle in the candidate parameter space, and the target parameter estimation results can be obtained by finding the minimum value of the projection function. Specifically, the target parameter estimates can be expressed as follows:

[0061] (twenty one)

[0062] in, , and These represent the estimated values ​​for the target distance, pitch angle, and azimuth angle, respectively.

[0063] Due to the mean square error of the target distance Mean square error of pitch angle and azimuth mean square error The closed-form expression of the target parameter estimation algorithm is difficult to obtain directly. Therefore, this invention introduces the Crammér-Rao Bound (CRB) as a performance evaluation index. The CRB represents the theoretical lower bound that the mean square error of any unbiased parameter estimate can reach, and can be used to measure the optimal performance upper limit of the target parameter estimation algorithm. Furthermore, a tractable closed-form expression of the CRB can be represented as...

[0064] (twenty two)

[0065] in, , as well as The submatrices after dividing the Fisher information matrix into blocks are used to characterize the information coupling relationship between the target space parameters and the complex reflection coefficient parameters, respectively. Specifically, the CRB matrix can be derived from the inverse matrix of the Fisher information matrix corresponding to the unknown parameters to be estimated, for the sensing channel. Define the unknown parameter vector as ,in , representing the complex reflection coefficient The real part, Represents the complex reflection coefficient The imaginary part. The matrix of sensed echo signals received within a coherent time block. Vectorize the data column-wise, defining the observation vector as follows: ,in, Represents the docking recovery wave signal matrix Perform column-wise vectorization based on the observation vector. For unknown parameter vectors When making the estimation, its Fisher information matrix can be expressed as:

[0066] (twenty three)

[0067] in,

[0068] (twenty four)

[0069] (25)

[0070] (26)

[0071] in, Used to characterize the statistical relationships between target location parameters. Used to characterize the coupling relationship between target position parameters and complex reflection coefficient parameters. Statistical information used to characterize the real and imaginary parts of the complex reflection coefficients. The general form of each matrix element is as follows:

[0072] (27)

[0073] in, .

[0074] Define the equivalent near-field round-trip channel matrix after processing by the receiver's analog combining matrix as follows:

[0075] (28)

[0076] Therefore, the partial derivatives of the equivalent channel matrix with respect to the target range, elevation angle, and azimuth angle can be defined as follows:

[0077] , , (29)

[0078] The corresponding Fisher information matrix elements can be further represented as

[0079] (30)

[0080] in, This represents the matrix trace operation. The coupling terms between the position parameters and the real and imaginary parts of the complex reflection coefficients can be expressed as follows:

[0081] (31)

[0082] (32)

[0083] Based on the Fisher information matrix mentioned above, the target distance... and angle , The CRB matrix can be obtained, and the target parameter estimation error satisfies , , The diagonal elements of the CRB matrix provide theoretical lower bounds for the estimation errors of target range, elevation angle, and azimuth angle parameters.

[0084] Step 240, under the constraints of communication rate and transmit power, solve for the optimal configuration of system parameters, specifically including:

[0085] Under the condition of meeting the minimum communication rate requirements of each user, the CRB used for joint estimation of target distance and angle is minimized. The changes in target parameters between adjacent coherent time blocks are usually small. Therefore, the target distance and angle estimation results obtained from the previous coherent time block can be used for the current system design. Accordingly, in the optimization problem, the target distance is considered... and angle , These are considered as known fixed parameters; since the diagonal elements of the CRB matrix are related to the estimation errors of the corresponding parameters, the optimization problem of the sonic system can be constructed as follows:

[0086] (33)

[0087] in, Indicates the first Minimum rate requirement for each user The objective function of the above optimization problem, which represents the maximum transmit power constraint, involves the inverse operation of the CRB matrix. Furthermore, there is a non-linear coupling relationship between the rate constraint and the covariance matrix variables. Therefore, the original problem is a non-convex optimization problem, making it difficult to directly obtain the global optimum. To improve the feasibility of the solution, this embodiment further introduces auxiliary matrix variables, transforming the original non-convex objective function related to CRB into a more manageable equivalent form. To solve this problem, the optimization problem can be equivalently transformed into the following more manageable form.

[0088] (34)

[0089] in, The auxiliary matrix introduced here transforms the original non-convex objective function related to CRB into a convex constraint form. A semidefinite relaxation algorithm is then employed, defining auxiliary variables. It can be deduced that Therefore, the original constraint can be written in convex constraint form.

[0090] (35)

[0091] in , In removing After applying the rank-one constraint, the original optimization problem can be rewritten as follows: After the above transformation, the resulting problem is a convex optimization problem, which can be solved using the standard interior-point method. However, for any globally optimal solution to this problem... and Each of these can be further constructed to obtain a rank-1 solution with the same objective function value. , .

[0092] Based on the joint configuration of the covariance matrix parameters of the sensing signals and the parameters of the communication sensing resources, the combination of network parameters that optimizes the overall performance of the system is determined under the conditions of satisfying the minimum rate constraints of each communication user, the maximum transmit power constraints of the system, and the estimation accuracy constraints of target distance, elevation angle and azimuth angle.

[0093] Beneficial effects

[0094] As can be seen from the above, the present invention provides a method for configuring parameters of an integrated communication and sensing network for a space-fed transmission array. It constructs a near-field spherical wave channel model for communication users and sensing targets, and establishes a joint communication-sensing transmission, communication rate, and sensing echo analysis model. Furthermore, it quantifies the accuracy of target distance and angle estimation by using the Fisher information matrix and the Cramer-Rao lower bound. Under the condition of satisfying the constraints of communication rate and transmission power, it achieves joint optimization of the communication beam, sensing covariance matrix, and T-RIS beamforming matrix, thereby effectively improving the system's communication performance, spatial resolution, and target sensing accuracy. Attached Figure Description

[0095] Figure 1 This is a system model diagram of a method for configuring parameters of an integrated inductive network for a space-fed transmission array according to the present invention.

[0096] Figure 2 This is a flowchart illustrating the implementation of the present invention;

[0097] Figure 3 This is a schematic diagram of the target range intensity of the transmission array;

[0098] Figure 4 This is a schematic diagram of the azimuth and elevation angles of the target in the transmission array; Detailed Implementation

[0099] To clarify the objectives, implementation schemes, and technical advantages of this invention, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0100] Figure 1 This invention provides a practical application scenario for the integrated inductive and conductive network parameter configuration method of an air-fed transmission array:

[0101] As a key auxiliary node in a sensing-communication integrated network, the transmission array transmits joint communication and sensing signals via a feed source. After beamforming by the transmission array, it simultaneously serves both communication users and sensing targets, thereby enhancing the system's ability to control the spatial direction and distance dimensions of the target. This invention constructs a near-field spherical wave channel model from the transmission array to both communication users and sensing targets, comprehensively analyzes the system's communication rate and target parameter sensing accuracy, and combines this with the beamforming capability of the transmission array to achieve optimized configuration of sensing-communication integrated network parameters under different conditions.

[0102] Figure 2 This invention provides a flowchart illustrating a method for configuring parameters of an integrated sensing network for a space-fed transmission array, including...

[0103] Step 300: Construct a near-field spherical wave channel model from T-RIS to the communication user and the sensing target.

[0104] Step 310: Transmit joint communication and sensing signals within the coherent time block and establish a transmission covariance model.

[0105] Step 320: Based on T-RIS beamforming, establish communication reception, user rate, and sensing echo models.

[0106] Step 330: Construct the Fisher information matrix based on the sensed echo and establish the theoretical lower bound for the estimation errors of the target range, elevation angle and azimuth angle parameters.

[0107] Step 340: Under the constraints of communication rate and transmit power, solve for the optimal configuration of system parameters.

[0108] Step 300: Construct the near-field spherical wave channel model from T-RIS to the communication user and the sensing target, specifically including:

[0109] A near-field spherical wave channel model is constructed from the transmission array to the communication user and the sensing target. The T-RIS includes... T-RIS of radiating units, wherein This indicates the number of elements in the array along the horizontal direction. This represents the number of elements in the array along the vertical direction, and satisfies... , Single-antenna communication user Individual perception target and The T-RIS employs a single-station sensing system, using each feed source as both a transmitter and receiver to achieve coordinated processing of communication user information transmission and target detection. The T-RIS is modeled as a uniform planar array with a lateral spacing of [missing information]. The vertical spacing is Therefore, the transverse aperture of the array is Longitudinal aperture The signal wavelength is The T-RIS center is set as the origin of the three-dimensional spatial coordinate system to facilitate the establishment of a unified spatial geometric relationship. The position of any communication user or sensing target in space can be determined by spherical coordinate parameters. It means that, among them, The propagation distance and pitch angle between the target point and the center of T-RIS With azimuth The function is then represented by its corresponding Cartesian coordinates as follows: , wherein and Equivalent adjustments can be made according to specific coordinate definitions, all of which should fall within the spatial angle range defined in this invention. The first... The coordinates of each element are represented as follows: ,in , Based on the near-field Fresnel zone conditions ,in, This represents the characteristic size of the T-RIS, indicating that the communication user or sensing target is in the near-field propagation region with the T-RIS. Under this condition, the... The distance from each element to the array element is Pitch angle is azimuth angle is The propagation distance of the communication user or sensing target is expressed by equation (1). For a spatial location The communication user or sensing target at the location, the first in T-RIS The near-field single-element channel between each array element and the user or target can be expressed as equation (2). Near-field array response vectors This is expressed as equation (3). For the first... The near-field round-trip sensing channel matrix of a sensing target is expressed as Equation (6).

[0110] Step 310: Transmit joint communication and sensing signals within the coherent time block and establish a transmission covariance model, specifically including:

[0111] Set length as The coherent time blocks are assumed, and it is assumed that within each coherent time block, the communication channel parameters and the sensing target parameters remain approximately constant. At discrete times... At this point, the joint communication and sensing baseband transmission signal is expressed as equation (7), and the dedicated sensing signal... The covariance matrix is ​​given by equation (9), then the transmitted signal The covariance matrix is ​​expressed as equation (10). It can be seen that the transmitted signal covariance matrix is ​​composed of the communication beam power term and the sensing signal covariance term, and can simultaneously characterize the system's communication resource allocation state and sensing energy transmission state.

[0112] Step 320, based on T-RIS beamforming, establish communication reception, user rate, and perceived echo models, specifically including:

[0113] The joint communication and sensing transmission signal at the T-RIS end is expressed as Equation (11), and the transmission covariance matrix can be further expressed as Equation (13). For the first... The received signal model of a communication user is expressed as Equation (14). Under the condition that other user signals and sensed signals are regarded as interference, the first... The achievable rate for a single communication user is expressed as Equation (15), and the sensing echo received signal model is expressed as Equation (16). For T-RIS, it can be... Each element is randomly selected from the unit circle, and the simulated merging matrix satisfies the unit modulus constraint. When the number of receiving antennas is sufficiently large, the simulated merging matrix satisfies the approximate orthogonality property. .

[0114] Step 330: Construct the Fisher information matrix based on the sensed echo, and establish theoretical lower bounds for the estimation errors of the target range, elevation angle, and azimuth angle parameters, specifically including:

[0115] The goal of sensing is to estimate target parameters based on the received echo signal samples within the entire coherent time block, representing the received echo signal within a coherent time block as... , This represents the received data matrix composed of echo observation samples from multiple time points. Due to the near-field sensing channel... Containing both distance and angle information, the echo signal carries not only the target's azimuth parameters but also its distance parameters, reflecting the target's radial position. Based on this near-field spherical wave characteristic, joint estimation of the target's distance and angle parameters can be achieved.

[0116] In this embodiment, the Multiple Signal Classification (MUSIC) algorithm is used to estimate the target parameters. The MUSIC algorithm utilizes the orthogonality between the signal subspace and the noise subspace to perform a spectral search on the target's range, elevation angle, and azimuth angle to determine the estimated values ​​of the target parameters. First, a sample covariance matrix is ​​constructed based on the received echo signal. This sample covariance matrix is ​​expressed as equation (17), and equation (18) can be obtained through eigenvalue decomposition. To characterize the orthogonality between the steering vector corresponding to any candidate spatial parameter and the noise subspace, the noise subspace projection matrix is ​​defined as equation (19). For any steering vector... Its projection onto the noise subspace can be expressed as equation (20). Since the signal subspace and the noise subspace are orthogonal, the parameter to be estimated satisfies the following condition: , as well as At that time, the guiding vector The projection function is approximately orthogonal to the noise subspace. It reaches a minimum value, ideally approaching zero.

[0117] Due to the mean square error of the target distance Mean square error of pitch angle and azimuth mean square error The closed-form expression of the parameter estimation is difficult to obtain directly. Therefore, in this invention, the Cramer-Rao Bound (CRB) is introduced as a performance evaluation index for sensing. The CRB represents the theoretical lower bound that the mean square error of any unbiased parameter estimate can reach, and can be used to measure the optimal performance upper limit of the target parameter estimation algorithm. Furthermore, the tractable closed-form expression of the CRB can be expressed as equation (22). Specifically, the CRB matrix can be derived from the inverse matrix of the Fisher information matrix corresponding to the unknown parameter to be estimated. For the sensing channel... Define the unknown parameter vector as ,in , representing the complex reflection coefficient The real part, Represents the complex reflection coefficient The imaginary part. The matrix of sensed echo signals received within a coherent time block. Vectorize the data column-wise, defining the observation vector as follows: ,in, Represents the docking recovery wave signal matrix Perform column-wise vectorization based on the observation vector. For unknown parameter vectors When making the estimation, its Fisher information matrix can be expressed as equation (23). Based on the above Fisher information matrix, the target distance is... and angle , The CRB matrix can be obtained, and the target parameter estimation error satisfies , , The diagonal elements of the CRB matrix provide theoretical lower bounds for the estimation errors of target range, elevation angle, and azimuth angle parameters.

[0118] Step 340, under the constraints of communication rate and transmit power, solve for the optimal configuration of system parameters, specifically including:

[0119] Under the condition of meeting the minimum communication rate requirements of each user, the CRB used for joint estimation of target distance and angle is minimized. The changes in target parameters between adjacent coherent time blocks are usually small. Therefore, the target distance and angle estimation results obtained from the previous coherent time block can be used for the current system design. Accordingly, in the optimization problem, the target distance is considered... and angle , Considered as known fixed parameters; since the diagonal elements of the CRB matrix are related to the estimation error of the corresponding parameters, the optimization problem of the syn-sensory system can be constructed as equation (33). The objective function of the above optimization problem includes the inverse operation of the CRB matrix. At the same time, there is a nonlinear coupling relationship between the rate constraint and the covariance matrix variable. Therefore, the original problem is a non-convex optimization problem, and it is difficult to directly obtain the global optimal solution. In order to improve the feasibility of the solution, this embodiment further introduces an auxiliary matrix variable to transform the original non-convex objective function related to CRB into an equivalent form that is easier to handle. In order to solve this problem, the optimization problem can be equivalently transformed into the following easier-to-handle equation (34). By introducing the auxiliary matrix, the original non-convex objective function related to CRB can be transformed into a convex constraint form. Using the semidefinite relaxation algorithm, the new problem obtained is a convex optimization problem. Therefore, it can be solved using the standard interior point method. However, for any global optimal solution of this problem, and Each of these can be further constructed to obtain a rank-one solution with the same objective function value.

[0120] Based on the joint configuration of the covariance matrix parameters of the sensing signals and the parameters of the communication sensing resources, the combination of network parameters that optimizes the overall performance of the system is determined under the conditions of satisfying the minimum rate constraints of each communication user, the maximum transmit power constraints of the system, and the estimation accuracy constraints of target distance, elevation angle and azimuth angle.

[0121] Figure 3 and Figure 4 This reflects the three-dimensional synesthetic characteristics of the present invention based on the T-RIS near-field spherical wave channel model, wherein, Figure 3 The three-dimensional distribution of beam intensity as a function of azimuth and elevation angles is shown. It can be seen that there is a significant peak at a specific angular position, indicating that the present invention can achieve effective energy focusing on the spatial direction of the target through T-RIS beamforming and has strong angular resolution capability. Figure 4 The distribution of beam intensity as a function of distance is shown. It can be seen that a significant peak is formed near the agreed target distance, and the beam intensity weakens significantly after deviating from this distance. This indicates that the present invention can not only distinguish targets in the angular dimension, but also achieve focusing and resolution in the distance dimension by relying on the near-field propagation characteristics.

Claims

1. A method for configuring parameters of an integrated inductive-conductive network for a space-fed transmission array, characterized in that, include: By setting fixed parameters for the integrated sensing network of the air-fed transmission array, including: the total number of radiating elements of the transmission array, element spacing, array aperture, number of feed sources, number of communication users, number of sensing targets, minimum rate requirement for communication users, maximum system transmit power, communication noise power, and sensing noise power; constructing a near-field spherical wave channel model from the transmission array to the communication users and sensing targets; establishing a joint communication and sensing transmit signal model, a communication receive model, a user reachable rate model, and a sensing echo receive model; constructing the Fisher information matrix and the Crammér-Rao lower bound (CRB) matrix based on the sensing echo; and, under the conditions of satisfying the minimum rate constraints of each communication user, the system transmit power constraints, and the covariance matrix constraints, determining the communication beam, the sensing signal covariance matrix, and the transmission array beamforming matrix with the goal of minimizing the CRB matrix trace, thereby completing the parameter configuration of the integrated sensing network of the air-fed transmission array.

2. The method according to claim 1, characterized in that, The transmission array is modeled as a uniform planar array; based on the positions of the transmission array elements, the communication user positions, and the sensing target positions, a propagation distance model from the array elements to the communication user or sensing target is established, and a near-field spherical wave propagation scenario is constructed (Formula (1) in the specification).

3. The method according to claim 1, characterized in that, Based on the near-field spherical wave propagation scenario, a near-field single-element channel model is established; a near-field array response vector (Equation (3) in the specification), a communication channel vector (Equation (5) in the specification), and a round-trip sensing channel matrix (Equation (6) in the specification) are constructed between the transmission array and the communication user or sensing target.

4. The method according to claim 1, characterized in that, Set a coherent time block and assume that the communication channel parameters and sensing target parameters remain approximately constant within the coherent time block; acquire the communication channel state information at the beginning of each coherent time block (Equation (7) in the specification); transmit joint communication and sensing signals from the feed source within the coherent time block to construct the transmission signal covariance matrix (Equation (10) in the specification).

5. The method according to claim 1, characterized in that, Establish a joint communication and sensing transmission signal model for the transmission array end (Equation (14) in the specification), construct the beamforming matrix of the transmission array and make it satisfy the unit mode constraint; configure some beams to be aligned with the direction of the communication user and the remaining beams to be aligned with the direction of the sensing target (Equation (11) in the specification).

6. The method according to claim 1, characterized in that, Based on the beamforming matrix of the transmission array, a communication user signal receiving model and a user reachable rate model are established (Equation (15) in the specification); at the same time, a sensing echo receiving model is established to characterize the receiving process of the target echo signal (Equation (16) in the specification).

7. The method according to claim 1, characterized in that, Based on the received sensing echo signal samples within the entire coherent time block, an echo covariance matrix is ​​constructed (Equation (17) in the specification); the signal subspace and noise subspace are obtained through eigenvalue decomposition, and a multi-signal classification algorithm is used to jointly estimate the target distance, elevation angle and azimuth angle (Equation (20) in the specification).

8. The method according to claim 1, characterized in that, For the target range, pitch angle and azimuth angle parameters, construct the unknown parameter vector and Fisher information matrix (Equation (23) in the specification); construct the CRB matrix based on the Fisher information matrix, and use the diagonal elements of the CRB matrix to represent the lower bound of the estimation error of the target range, pitch angle and azimuth angle.

9. The method according to claim 1, characterized in that, Under the conditions of satisfying the minimum rate constraints of each communication user and the maximum transmit power constraints of the system, a parameter configuration optimization problem is established with the goal of minimizing the trace of the CRB matrix (Equation (33) in the specification); by introducing auxiliary variables and combining them with the semidefinite relaxation method (Equation (34) in the specification), the original non-convex optimization problem is transformed into a convex optimization problem.

10. The method according to claim 1, characterized in that, Based on the joint configuration of the covariance matrix parameters of the sensing signals and the parameters of the communication sensing resources, the combination of network parameters that optimizes the overall performance of the system is determined under the conditions of satisfying the minimum rate constraints of each communication user, the maximum transmit power constraints of the system, and the estimation accuracy constraints of target distance, elevation angle and azimuth angle.

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