A target detection and channel estimation method for a near field communication and sensing integrated system

Abstract

The application discloses a target detection and channel estimation method of a near-field sensing and communication integrated system, and comprises the following steps: a base station sends a downlink pilot signal to a sensing target and receives a corresponding echo signal; then a user sends an uplink pilot signal and a user channel is estimated; according to the characteristics of a near-field channel, a sensing and communication signal model is established based on a spherical wave front assumption; according to the characteristics of the near-field channel, firstly, a Fresnel approximation is adopted to approximate the near-field channel model, then a low-overhead dictionary based on a near-field spherical wave approximation is proposed, and on this basis, an improved orthogonal matching pursuit algorithm is designed for the near-field communication sensing to estimate the parameter estimation method of the sensing target and the communication channel. The application can estimate the parameters of the near-field sensing target and the channel with low overhead, and improves the estimation precision of the parameters in the near-field scene.

Description

Technical Field

[0001] This invention relates to the field of mobile communication technology, and particularly to a target detection and channel estimation method for a near-field sensing integrated system. Background Technology

[0002] With the rapid development of wireless communication technology, current 5G wireless networks are evolving towards 6G networks at both the technical and service levels. One of the core technologies of 5G is massive MIMO (Multiple-Input Multiple-Output) technology, which significantly improves spectral efficiency compared to small-scale MIMO. However, with the explosive growth in the number of mobile users and traffic in future 6G networks, there is a need for new technologies to support higher traffic volumes and larger machine connections. To meet these multi-dimensional performance requirements, future 6G technology needs to deeply integrate traditional wireless sensing functions such as positioning and imaging into wireless transmission, giving rise to integrated communication and sensing (referred to as: integrated communication and sensing) technology. In June 2023, the International Telecommunication Union (ITU) also listed integrated communication and sensing as one of the six typical application scenarios for 6G networks. In future integrated communication and sensing scenarios, each base station needs to support more traffic, which requires the use of more antennas, thus leading to ultra-large-scale MIMO technology.

[0003] The emergence of ultra-large-scale MIMO in 6G sensing-integrated scenarios not only means an increase in the number of antennas but also leads to fundamental changes in electromagnetic characteristics. In traditional 5G massive MIMO systems, the near-field region is only a few meters wide and can be ignored due to the relatively small number of antennas. However, in ultra-large-scale MIMO sensing-integrated systems, the near-field region can reach hundreds of meters due to the significant increase in the number of antennas and carrier frequency, making it non-negligible. The traditional planar wavefront assumption is no longer used in ultra-large-scale MIMO sensing-integrated systems, and a new spherical wavefront assumption is required to model the system's channel model.

[0004] Joint target detection and channel estimation have been research topics for many scholars in integrated sensing systems. In ultra-large-scale MIMO near-field integrated sensing systems, due to the need to consider the spherical wavefront assumption in near-field scenarios, how to perform accurate target detection and channel estimation, and design low-overhead target detection and channel estimation algorithms for near-field integrated sensing systems are urgent problems to be solved.

[0005] To address the aforementioned issues and improve the accuracy of both target detection and channel estimation in ultra-large-scale MIMO scenarios, a low-overhead joint target detection and channel estimation algorithm is proposed to enhance the overall performance of near-field sensing integrated systems. Summary of the Invention

[0006] The objective of this invention is achieved through the following technical solution: a target detection and channel estimation method for a near-field sensing integrated system, the method comprising:

[0007] S1. Consider the spherical wave model in the near field scenario to model the communication channel, including: modeling the signal sent and received by the base station to the user, or modeling the channel based on the signal sent by the user received by the base station. The channel includes the direction vector obtained by the angle of arrival and distance obtained by the base station.

[0008] S2. The Fresnel approximation is used to approximate the near-field channel. The correlation between different directional vectors of the approximate polar domain channel is analyzed to obtain the sampling conditions with the highest correlation and construct the near-field dictionary.

[0009] S3. Joint estimation of target parameters and channel information using orthogonal matching pursuit algorithm: The original sensing channel is represented in polar domain using a near-field dictionary. The parameters of the sensing target are estimated using orthogonal matching pursuit algorithm to obtain the RCS, angle and range of the target. Channel estimation during communication: Channel estimation is performed using two antenna subsets. Each column of the dictionary is updated independently each time the dictionary is updated. After updating the dictionary, a support set is constructed based on the estimated angles of the two antenna subsets and the parameter vector of the channel is calculated to reconstruct the channel.

[0010] Furthermore, the modeling of the signals transmitted and received by the user based on the base station includes:

[0011] The transmitted downlink pilot signal is denoted as Where N is the number of base station antennas, after the base station sends the pilot signal, it reaches the target and is then reflected by the target. The signal received by the base station from the target is represented as follows:

[0012] ;

[0013] in For the sensing channel matrix, With zero mean and variance Additive white Gaussian noise, where the sensing channel matrix is ​​determined by the angle of arrival and RCS of the sensing target, denoted as... ;

[0014] in , and These represent the RCS of the target being sensed, the angle of arrival of the VMI array during the sensing process, and the distance to the reference antenna, respectively. This represents the direction vector corresponding to the perceived target;

[0015] Consider an array of N antennas arranged linearly. Establish a Cartesian coordinate system, with the first antenna as the origin. Then, the distance between the nth antenna and the user is... Calculated as

[0016]

[0017] in d represents the distance from the user to the first antenna, and d represents the spacing between the antennas. Given the corresponding arrival angle, the direction vector is represented as follows:

[0018]

[0019] in c and c represent the carrier frequency and the speed of light, respectively.

[0020] Furthermore, the channel modeling based on the user-transmitted signals received by the base station includes:

[0021] The user sends an uplink pilot signal to the base station, and the base station receives the received pilot signal and performs channel estimation.

[0022] The uplink pilot signal sent by the user to the base station is The uplink pilot signal received by the base station As shown below:

[0023]

[0024] in For communication channel vectors, Given additive white Gaussian noise, the communication channel vector is represented as: ;

[0025] Where L is the number of paths in the channel. and These represent the channel gain and angle of arrival for the l-th path, respectively. For the corresponding direction vector, here is the channel gain. It follows a complex Gaussian distribution with a mean of 0.

[0026] The direction vector of this process is

[0027]

[0028] in This represents the distance from the communication user to the nth antenna. c and c represent the carrier frequency and the speed of light, respectively.

[0029] Furthermore, the near-field channel approximation process using the Fresnel approximation includes: calculating the distance from the communication user or sensing target to the nth antenna. Perform the Fresnel approximation:

[0030]

[0031] d represents the spacing between antennas in the l-th path. This represents the arrival angle corresponding to the l-th path.

[0032] Furthermore, the analysis of the correlation between different direction vectors of the approximate post-polar domain channel specifically includes: direction vectors and The correlation is

[0033]

[0034] Let c and represent the carrier frequency and the speed of light, respectively; derived using the Fresnel approximation, when When the angle sampling interval is set to , The column correlation reaches its maximum when the user's distance is much greater than the antenna array length. The columns are approximately orthogonal; therefore, in the construction of the dictionary, the angle sampling interval is set to 1 / 2N, and the distance sampling interval is set to 1m.

[0035] Furthermore, the construction of the near-field dictionary includes:

[0036] The dictionary for constructing a near-field sparse channel using direction vectors utilizes the relationship between angle and distance, representing distance in terms of angle, and determining the size of the dictionary through angular resolution. Specifically, it includes:

[0037] in Let be the direction vector, where Let represent the angle of arrival and distance when the i-th antenna is used as a reference, respectively.

[0038] Furthermore, the estimation process of the parameters of the detected target in S3 includes:

[0039] The designed near-field dictionary is used to represent the original sensing channel in its polar domain, and the corresponding signal is shown below.

[0040]

[0041] in This is the representation of the sensing channel in the polar domain. The superscript r refers to the sensing process, which includes the RCS, range, and azimuth of the sensed target. D is the dictionary matrix. With zero mean and variance Additive white Gaussian noise; The downlink pilot signal transmitted by the base station is used to estimate the parameters of the sensed target using an orthogonal matching pursuit algorithm; first, the distance range of the target is assumed to be... Furthermore, the angle space is divided into N blocks, and the initial dictionary is constructed from each column vector. ,Right now

[0042]

[0043] in This is determined based on the aforementioned near-field channel model; initialization of the support set and the residuals of the sensing process. , Then, iterate, first calculating the inner product of the received signal and the dictionary, to obtain...

[0044] Then, by detecting the new support set, specifically as follows:

[0045]

[0046] Where p is a vector The p-th element;

[0047] Next, update the support set. Then we get an orthogonal mapping.

[0048] ,in For the corresponding dictionary matrix of Columns in a set for The false reversal;

[0049] Next, update the residuals.

[0050]

[0051] After iterating to the preset maximum number of iterations, by calculating... This allows us to obtain the RCS, angle, and distance of the detected target.

[0052] Furthermore, the channel information estimation process in S3 includes:

[0053] Select two antenna subsets from the very large antenna array, where the length of each antenna subset is defined as... Where Q is less than the number of antennas N, in order to eliminate the influence of multipath interference on angle estimation, each column of the dictionary is updated independently during each update, i.e.

[0054]

[0055] in ;

[0056] First, define the received signals of the two antenna subsets as follows: and The initial dictionaries are respectively and The maximum and minimum distances between the user and the antenna are set to and The distance between the two antenna subsets is Initialize the support set and residuals , , Then estimate the angle. It is obtained according to the following formula:

[0057]

[0058] in ; Let p be the first subset of the dictionary, and p be a vector. The p-th element;

[0059] Then estimate the angle. Based on known get A rough estimate of the range,

[0060] ,

[0061] in ,

[0062] Select within the above range subset of And calculate From this, the angle is derived. ;

[0063] ,

[0064] Calculate the distance between the two antenna subsets at the angle:

[0065] ,

[0066] Then update the dictionary based on the above dictionary update steps. and Each column; after updating the number of times, update... , ;based on and Construct support set vectors The l-th column of the support set is composed of Calculated; then the parameter vector By solving the following optimization problem

[0067]

[0068] in yes A subset of, which, when solved for the above problem, yields ,

[0069] Calculate the residual signal , ,in and These are subsets of B selected based on the receiving antenna index. After traversing the number of channel paths, the channel is reconstructed according to the following formula.

[0070] .

[0071] According to another aspect of the specification, a target detection and channel estimation device for a near-field sensing integrated system is also provided, including a memory and one or more processors. The memory stores executable code, and when the processor executes the executable code, it implements the target detection and channel estimation method of the near-field sensing integrated system.

[0072] According to another aspect of the specification, a computer-readable storage medium is also provided, on which a program is stored, which, when executed by a processor, implements the target detection and channel estimation method of the near-field sensing integrated system.

[0073] The beneficial effects of this invention are:

[0074] (1) The target detection and channel estimation algorithm of the present invention can realize target detection and channel estimation in the scenario of ultra-large-scale antenna array, and realize the performance requirements of the integrated sensing system;

[0075] (2) In view of the spherical wave characteristics of the near-field channel, the present invention designs a dictionary codebook based on the polar domain, which has a low storage space and can effectively characterize the sparse characteristics of the near-field channel.

[0076] (3) This invention designs a joint target detection and channel estimation algorithm for near-field sensing integration scenarios. It has low overhead, can accurately perform target detection and channel estimation, and improves the overall performance of the sensing integration system. Attached Figure Description

[0077] Figure 1This is a flowchart illustrating the target detection and channel estimation method of a near-field sensing integrated system provided in an embodiment of the present invention.

[0078] Figure 2 This is a schematic diagram illustrating the convergence of the proposed algorithm under different signal-to-noise ratio conditions provided in the embodiments of the present invention;

[0079] Figure 3 This is a schematic diagram of the normalized mean square error of different estimation methods under different signal-to-noise ratios provided in the embodiments of the present invention.

[0080] Figure 4 This is a schematic diagram illustrating the normalized mean square error of different estimation methods under different distance conditions provided in the embodiments of the present invention;

[0081] Figure 5 This is a schematic diagram illustrating the normalized mean square error of different estimation methods under different numbers of base station antennas provided in embodiments of the present invention.

[0082] Figure 6 This is a schematic diagram of a target detection and channel estimation device for a near-field sensing integrated system provided in an embodiment of the present invention. Detailed Implementation

[0083] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0084] Consider a time-division multiplexing (TDD) massive MIMO sensing system where a base station provides services to a single-antenna mobile user while simultaneously detecting and sensing targets. In this system, the base station is equipped with N very large-scale antenna arrays. For simplicity, during the channel estimation phase, we focus on a single-antenna user. Since orthogonal uplink pilot signals can be assigned to different antennas, the proposed scheme can be easily extended to users with multiple antennas. The proposed scheme comprises two phases: target detection and channel estimation. In the target detection phase, the base station transmits downlink pilot signals and receives the echo signals reflected from the detected target for estimation. In the channel estimation phase, the user transmits uplink pilot signals to the base station, and the base station performs channel estimation based on the received channel information.

[0085] like Figure 1 As shown, the present invention provides a target detection and channel estimation method for a near-field sensing integrated system, the specific implementation steps of which are as follows:

[0086] S1. The base station sends downlink pilot signals to the target and then receives the echo signals reflected from the target and estimates the target's angle of arrival and RCS (radar cross section).

[0087] The transmitted downlink pilot signal is denoted as Where N is the number of base station antennas. After the base station sends the pilot signal, it reaches the target and is then reflected by the target. The signal received by the base station from the target is represented as follows:

[0088]

[0089] in For the sensing channel matrix, With zero mean and variance The additive white Gaussian noise, where the sensing channel matrix is ​​determined by the angle of arrival and RCS of the sensing target, can be expressed as:

[0090] in , and These represent the RCS of the target being sensed, the angle of arrival of the VMI array during the sensing process, and the distance to the reference antenna, respectively. This represents the direction vector corresponding to the perceived target.

[0091] S2. The user sends an uplink pilot signal to the base station, and the base station receives the received pilot signal and performs channel estimation.

[0092] The uplink pilot signal is defined as follows: The uplink pilot signal received by the base station As shown below

[0093]

[0094] in For communication channel vectors, It is additive white Gaussian noise. Here, the communication channel vector can be represented as...

[0095]

[0096] Where L is the number of paths in the channel. and These represent the channel gain and angle of arrival for the l-th path, respectively. Let's assume the channel gain is the corresponding direction vector. It follows a complex Gaussian distribution with a mean of 0.

[0097] S3. The base station processes the received user signals and the echo signals from the sensed target, and establishes a near-field channel model.

[0098] The near-field channel model is as follows. In scenarios involving very large-scale antenna arrays, such as a 3200-antenna array at 2.4 GHz, the Rayleigh distance can reach 200 meters. In this scenario, the near-field effect is not negligible, and the traditional plane-wave channel assumption is not applicable. Therefore, a spherical wave model is needed to model the channel in the near-field scenario. First, the channel model for the sensing process in step S1 is modeled. Considering an N-line linearly arranged antenna array, a Cartesian plane coordinate system is established, with the first antenna as the origin. The distance between the nth antenna and the user is then... Calculated as

[0099]

[0100] in d represents the distance from the user to the first antenna, which is the origin of the coordinate system, and d represents the spacing between the antennas. Given the corresponding arrival angle, the direction vector is represented as follows:

[0101]

[0102] in c and c represent the carrier frequency and the speed of light, respectively.

[0103] Similarly, a near-field channel model needs to be constructed during communication, and the direction vector for this process is:

[0104]

[0105] in This represents the distance from the communication user to the nth antenna.

[0106] S4. Since the near-field channel has spherical wave characteristics due to the ultra-large antenna array, the traditional plane wave-based channel estimation method is not applicable. Therefore, a low-overhead channel estimation and target detection parameter estimation method based on polar domain channel is proposed, which reduces storage space.

[0107] The target detection and channel estimation methods are as follows: First, the Fresnel approximation is used to approximate the distance from the communication user or sensing target to the nth antenna of the base station in near-field scenarios as follows:

[0108]

[0109] Based on the distance formula obtained in S3, taking the communication channel as an example, it can be expressed as:

[0110]

[0111] Similarly, in a sensing scenario, the distance from the sensing target to the nth antenna of the base station can be expressed as:

[0112]

[0113] It can be seen that the phase of the near-field channel is a quadratic function of n. If the dictionary is constructed using two dimensions, angle and distance, the dictionary storage space will be large because the angle and distance sampling intervals need to be as small as possible to obtain a finer decomposition rate. In practice, this will lead to high storage overhead. Therefore, the dictionary needs to be redesigned.

[0114] The main idea of ​​the target estimation and channel estimation method based on polar-domain channels is to first construct a dictionary of polar-domain channels, and then use an orthogonal matching pursuit algorithm to jointly estimate the parameters of the detected target and the channel information. Taking a sensing channel model as an example, the dictionary of near-field sparse channels is first defined as follows:

[0115]

[0116] in The definition is shown in step S3, where Let represent the angle of arrival and distance when the i-th antenna is used as a reference, respectively.

[0117] Unlike traditional two-dimensional dictionaries based on polar-domain channels, this patent utilizes the relationship between angle and distance, representing distance in terms of angle. Therefore, the size of the dictionary can be determined by the angular resolution. The correlation of the direction vectors of the polar-domain channel is analyzed below. Definition For vectors and The correlation can be derived as follows:

[0118]

[0119] Based on the above definition of distance, we can further deduce...

[0120]

[0121] when And set At that time, we had Using the Fresnel approximation, we can obtain .

[0122] Similarly and And by using the Fresnel approximation, we can obtain

[0123] .

[0124] From the two formulas above, it can be seen that when When the angle sampling interval is set to , The column correlation reaches its maximum when the user's distance is much greater than the antenna array length. The columns can be approximated as orthogonal. Based on the above analysis, it can be concluded that the proposed dictionary has lower coherence than existing dictionaries based on polar regions, and at the same time, it has lower storage space and lower overhead compared to existing dictionaries. Therefore, in the dictionary design, the angle sampling interval is set to 1 / 2N, and the distance sampling interval is set to 1m.

[0125] The following section will design a joint target detection and channel estimation algorithm using Time Division Multiple Access (TDD). First, parameter estimation will be performed for the target detection process. Based on the previous target detection model, the designed near-field dictionary will be used to represent the original sensing channel in the polar domain, yielding the corresponding signal as shown below.

[0126]

[0127] in The channel representation in the polar domain is given by the superscript r, which refers to the sensing process, including the RCS, range, and azimuth of the sensing target. The problem described above is a compressed sensing problem, and an orthogonal matching pursuit algorithm is used to estimate the parameters of the sensing target. First, it is assumed that the target's range is within the range of... Furthermore, the angle space is divided into N blocks, and the initial dictionary is constructed from each column vector. ,Right now

[0128]

[0129] in This is determined based on the aforementioned near-field channel model. Next, the set is initialized. , Then, iterate, first calculating the inner product of the received signal and the dictionary, to obtain...

[0130] Then, by detecting the new support set, specifically as follows:

[0131]

[0132] Where p is a vector The p-th element

[0133] Next, update the support set. Then we get an orthogonal mapping.

[0134] ,

[0135] in For the corresponding dictionary matrix of Columns in a set for The false rebellion.

[0136] Next, update the residuals.

[0137]

[0138] After a given number of iterations, by calculating This allows us to obtain the RCS, angle, and distance of the detected target.

[0139] Next, we consider channel estimation during communication. Since the effects of multipath propagation need to be considered during uplink communication, to reduce pilot overhead, we select two antenna subsets from the large antenna array. The length of each antenna subset is defined as... Here, Q is less than the number of antennas N. To eliminate the impact of multipath interference on angle estimation, each column of the dictionary is updated independently with each update, i.e.

[0140]

[0141] in .

[0142] First, define the received signals of the two antenna subsets as follows: and The initial dictionaries are respectively and The maximum and minimum distances are set to and The distance between the two antenna subsets is ,initialization , , Then immediately estimate the angle. Similarly, it can be obtained from the following formula.

[0143]

[0144] in , Let p be the first subset of the dictionary, and p be a vector. The p-th element.

[0145] Then estimate the angle. To reduce computational complexity, this is based on known... get A rough estimate of the range,

[0146] ,

[0147] in ,

[0148] Then select from the above range subset of And calculate From this, the angle is derived.

[0149] ,

[0150] Then, the distance between the two antenna subsets is calculated based on the angle.

[0151] ,

[0152] Then update the dictionary based on the above dictionary update steps. and Each column. After updating the count, update... , Following on and Construct support set vectors The l-th column of the support set can be composed of The calculation is obtained. Then the parameter vector... This can be achieved by solving the following optimization problem.

[0153]

[0154] in yes A subset of, which can be used to solve the above problem to obtain .

[0155] Then calculate the residual signal. , ,in and These are subsets of B selected based on the received antenna index. After traversing the channel path count, the channel is reconstructed using the following formula.

[0156]

[0157] The functions and effects of this invention are further illustrated and demonstrated through the following simulation experiments:

[0158] Simulation conditions: Orthogonal frequency division multiplexing (OFDM) technology is used here, with a carrier frequency of 25 GHz, a bandwidth of 100 MHz, 64 subcarriers, 256 antennas in the base station's large antenna array, and 10 multipath channels. In the simulation comparison, Normalized Mean Square Error (NMSE) is used to demonstrate the estimation performance. The NMSE for target detection and channel estimation are defined as [definition values ​​for NMSE and NMSE for channel estimation]. ,in and Let them be the set of target parameters to be estimated and the channel information to be estimated, respectively. and These are the actual target parameter set and the actual channel information, respectively. Here, a combination of target parameters and channel information is used. As a benchmark, this invention will be compared with existing synchronous weighted orthogonal matching pursuit algorithms (SW-OMP) and polar domain orthogonal matching pursuit algorithms (P-OMP) during simulation to highlight the performance of the algorithm proposed in this invention.

[0159] Simulation results: Figure 2 In the paper, we present a convergence analysis of the proposed joint target detection and channel estimation algorithm. It can be seen that the proposed estimation algorithm converges relatively quickly, reaching convergence in approximately 10 iterations. Furthermore, different SNRs result in different NMSE values; higher SNRs tend to converge to lower NMSEs, leading to better detection results.

[0160] Figure 3 The NMSE performance of the three algorithms under different SNR conditions is shown. It can be seen that the NMSE performance improves with increasing SNR, a trend that is quite evident. Furthermore, it can be seen that the joint target detection and channel estimation algorithm of this invention significantly outperforms the traditional P-OMP and SW-OMP algorithms in terms of NMSE performance, highlighting the superior estimation performance of the proposed algorithm in joint target detection and channel estimation for ultra-large antenna array near-field sensing integrated systems.

[0161] Figure 4 The invention and two other benchmark algorithms are demonstrated under different target distances, with a Rayleigh distance of 100 meters. It can be seen that the estimation performance of the present invention and the P-OMP algorithm shows almost no significant change at different distances. However, the SW-OMP algorithm performs poorly at shorter distances but better at longer distances. This is because at shorter distances, the target and user are in the near-field region, where traditional far-field-based estimation algorithms are not applicable, hence the poor performance of the SW-OMP algorithm. As the distance gradually increases, the near-field region becomes the far-field region, at which point the SW-OMP algorithm becomes applicable and thus performs better. Meanwhile, the performance of the present invention in jointly detecting the target and estimating the channel at different distances is superior to the other two benchmark algorithms.

[0162] at last, Figure 5The comparison between the proposed algorithm and two benchmark algorithms is shown, with varying numbers of base station antennas. It can be seen that the estimation performance of all three algorithms becomes more accurate with increasing antenna numbers, demonstrating the crucial role of antenna number in improving sensing and communication performance. Similarly, the proposed algorithm outperforms the other two benchmark algorithms in NMSE performance across different antenna numbers, highlighting the superior performance of the proposed algorithm in jointly performing target detection and channel estimation in near-field scenarios.

[0163] Corresponding to the aforementioned embodiment of a target detection and channel estimation method for a near-field sensing integrated system, the present invention also provides an embodiment of a target detection and channel estimation device for a near-field sensing integrated system.

[0164] See Figure 6 The present invention provides a target detection and channel estimation device for a near-field sensing integrated system, comprising a memory and one or more processors. The memory stores executable code, and when the processor executes the executable code, it is used to implement a target detection and channel estimation method for a near-field sensing integrated system as described in the above embodiment.

[0165] The target detection and channel estimation device of the near-field sensing integrated system provided by this invention can be applied to any device with data processing capabilities, such as a computer. The device embodiment can be implemented in software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by the processor of any data processing device loading the corresponding computer program instructions from non-volatile memory into memory for execution. From a hardware perspective, such as... Figure 6 The diagram shown is a hardware structure diagram of any device with data processing capabilities, which includes the target detection and channel estimation device of the near-field sensing integrated system provided by the present invention. (Except for...) Figure 6 In addition to the processor, memory, network interface, and non-volatile memory shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0166] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0167] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0168] This invention also provides a computer-readable storage medium storing a program thereon, which, when executed by a processor, implements a target detection and channel estimation method for a near-field sensing integrated system as described in the above embodiments.

[0169] The computer-readable storage medium can be an internal storage unit of any data processing device described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device of any data processing device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units and external storage devices of any data processing device. The computer-readable storage medium is used to store the computer program and other programs and data required by the data processing device, and can also be used to temporarily store data that has been output or will be output.

[0170] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the target detection and channel estimation method of the near-field sensing integrated system.

[0171] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0172] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this application. This application is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A target detection and channel estimation method for a near-field sensing integrated system, characterized in that, The method includes: S1. Consider the spherical wave model in the near field scenario to model the communication channel, including: modeling the signal sent and received by the base station to the user, or modeling the channel based on the signal sent by the user received by the base station. The channel includes the direction vector obtained by the angle of arrival and distance obtained by the base station. The modeling of signals transmitted and received by the user from the base station includes: The transmitted downlink pilot signal is denoted as Where N is the number of base station antennas, after the base station sends the pilot signal, it reaches the target and is then reflected by the target. The signal received by the base station from the target is represented as follows: ； in For the sensing channel matrix, With zero mean and variance Additive white Gaussian noise, where the sensing channel matrix is ​​determined by the angle of arrival and RCS of the sensing target, denoted as... ; in , and These represent the RCS of the target being sensed, the angle of arrival of the VMI array during the sensing process, and the distance to the reference antenna, respectively. This represents the direction vector corresponding to the perceived target; Consider an array of N antennas arranged linearly. Establish a Cartesian coordinate system, with the first antenna as the origin. Then, the distance between the nth antenna and the user is... Calculated as in d represents the distance from the user to the first antenna, and d represents the spacing between the antennas. Given the corresponding arrival angle, the direction vector is represented as follows: in c represents the carrier frequency and the speed of light, respectively; The channel modeling based on user-transmitted signals received by the base station includes: The user sends an uplink pilot signal to the base station, and the base station receives the received pilot signal and performs channel estimation. The uplink pilot signal sent by the user to the base station is The uplink pilot signal received by the base station As shown below: in For communication channel vectors, Given additive white Gaussian noise, the communication channel vector is represented as: ; Where L is the number of paths in the channel. and These represent the channel gain and angle of arrival for the l-th path, respectively. For the corresponding direction vector, here is the channel gain. It follows a complex Gaussian distribution with a mean of 0. The direction vector of this process is in This represents the distance from the communication user to the nth antenna. c represents the carrier frequency and the speed of light, respectively; S2. The Fresnel approximation is used to approximate the near-field channel. The correlation between different directional vectors of the approximate polar domain channel is analyzed to obtain the sampling conditions with the highest correlation and construct the near-field dictionary. The Fresnel approximation for near-field channel approximation includes: calculating the distance from the communication user or sensing target to the nth antenna. Perform the Fresnel approximation: d represents the spacing between antennas in the l-th path. The angle of arrival for the l-th path; The analysis of the correlation between different direction vectors of the approximate polar-domain channel specifically includes: direction vectors and The correlation is Let c and represent the carrier frequency and the speed of light, respectively; derived using the Fresnel approximation, when When the angle sampling interval is set to , The column correlation reaches its maximum when the user's distance is much greater than the antenna array length. The columns are approximately orthogonal; therefore, in the construction of the dictionary, the angle sampling interval is set to 1 / 2N, and the distance sampling interval is set to 1 meter. The construction of the near-field dictionary includes: A dictionary for constructing a near-field sparse channel using direction vectors utilizes the relationship between angle and distance, representing distance in terms of angle. The size of this dictionary is determined by the angular resolution, specifically including: in Let be the direction vector, where Let represent the angle of arrival and distance when the i-th antenna is used as a reference, respectively; S3. Joint estimation of target parameters and channel information using orthogonal matching pursuit algorithm: The original sensing channel is represented in polar domain using a near-field dictionary. The parameters of the sensing target are estimated using orthogonal matching pursuit algorithm to obtain the RCS, angle and range of the target. Channel estimation during communication: Channel estimation is performed using two antenna subsets. Each column of the dictionary is updated independently each time the dictionary is updated. After updating the dictionary, a support set is constructed based on the estimated angles of the two antenna subsets and the parameter vector of the channel is calculated to reconstruct the channel.

2. The target detection and channel estimation method for a near-field sensing integrated system according to claim 1, characterized in that, The estimation process of the parameters of the detected target in S3 includes: The designed near-field dictionary is used to represent the original sensing channel in its polar domain, and the corresponding signal is shown below. in This is the representation of the sensing channel in the polar domain. The superscript r refers to the sensing process, which includes the RCS, range, and azimuth of the sensed target. D is the dictionary matrix. With zero mean and variance Additive white Gaussian noise; The downlink pilot signal transmitted by the base station is used to estimate the parameters of the sensed target using an orthogonal matching pursuit algorithm; first, the distance range of the target is assumed to be... Furthermore, the angle space is divided into N blocks, and the initial dictionary is constructed from each column vector. ,Right now in This is determined based on the aforementioned near-field channel model; initialization of the support set and the residuals of the sensing process. , Then, iterate, first calculating the inner product of the received signal and the dictionary, to obtain... Then, by detecting the new support set, specifically as follows: Where p is a vector The p-th element; Next, update the support set. Then we get an orthogonal mapping. ,in For the corresponding dictionary matrix of Columns in a set for The false reversal; Next, update the residuals. After iterating to the preset maximum number of iterations, by calculating... This allows us to obtain the RCS, angle, and distance of the detected target.

3. The target detection and channel estimation method for a near-field sensing integrated system according to claim 1, characterized in that, The estimation process of channel information in S3 includes: Select two antenna subsets from the very large antenna array, where the length of each antenna subset is defined as... Where Q is less than the number of antennas N, in order to eliminate the influence of multipath interference on angle estimation, each column of the dictionary is updated independently during each update, i.e. in ; First, define the received signals of the two antenna subsets as follows: and The initial dictionaries are respectively and The maximum and minimum distances between the user and the antenna are set to and The distance between the two antenna subsets is Initialize the support set and residuals , , Then estimate the angle. It is obtained according to the following formula: in ; Let p be the first subset of the dictionary, and p be a vector. The p-th element; Then estimate the angle. Based on known get A rough estimate of the range, ， in , Select within the above range subset of And calculate From this, the angle is derived. ; ， Calculate the distance between the two antenna subsets based on their angles: ， Then update the dictionary based on the above dictionary update steps. and Each column; after updating the number of times, update... , ;based on and Construct support set vectors The l-th column of the support set is composed of Calculated; then the parameter vector By solving the following optimization problem in yes A subset of, which, when solved for the above problem, yields , Calculate the residual signal , ,in and These are subsets of B selected based on the receiving antenna index. After traversing the number of channel paths, the channel is reconstructed according to the following formula. 。 4. A target detection and channel estimation device for a near-field sensing integrated system, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that, When the processor executes the executable code, it implements the target detection and channel estimation method of the near-field sensing integrated system as described in any one of claims 1-3.

5. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements a target detection and channel estimation method for a near-field sensing integrated system as described in any one of claims 1-3.

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