Target detection and channel estimation method for near-field sensing integrated system

Abstract

The invention discloses a target detection and channel estimation method for a near-field sensing integrated system. The method 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 to estimate a user channel; according to the characteristics of a near-field channel, a sensing and communication signal model based on a spherical wavefront hypothesis is established; according to the characteristics of a near-field channel, firstly, a near-field channel model is approximated by adopting Fresnel approximation, then a low-overhead dictionary based on near-field spherical wave approximation is provided, and on the basis, an improved orthogonal matching pursuit algorithm is designed for near-field communication perception to estimate parameters of a perception target and a communication channel. According to the invention, parameters of a near-field sensing target and a channel can be estimated with low overhead, and the estimation precision of the parameters in a near-field scene is improved.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technology, and in particular to a target detection and channel estimation method for a near-field synaesthesia system. Background Art

[0002] With the rapid development of wireless communication technology, the current 5G wireless network is evolving towards the 6G network at the technical and business levels. One of the core technologies of 5G is the massive multiple-input multiple-output (MIMO) technology, which significantly improves the spectrum efficiency compared to the small-scale MIMO technology. However, with the explosive growth of the number of mobile users and business traffic in the future 6G network, new technologies are needed to support higher business traffic and larger machine connections. In order to meet the above-mentioned multi-dimensional extreme performance requirements, the future 6G technology needs to deeply integrate traditional wireless perception functions such as positioning and imaging into wireless transmission, giving rise to communication perception integration (abbreviated as: interawareness integration) technology. The International Telecommunication Union (ITU) also listed interawareness integration as one of the six typical application scenarios of 6G networks in June 2023. In the future interawareness integration scenario, 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 the 6G telepathy integration scenario not only means an increase in the number of antennas, but also leads to fundamental changes in electromagnetic characteristics. In the traditional 5G massive MIMO system, since the number of antennas is not large, the near-field area at this time is only a few meters and can be ignored. However, in the ultra-large-scale MIMO telepathy integration system, due to the substantial increase in the number of antennas and carrier frequency, the near-field area can reach hundreds of meters, so the near-field area cannot be ignored. The traditional plane wavefront assumption is no longer used in the ultra-large-scale MIMO telepathy integration system, and a new spherical wavefront assumption is needed to model the channel model of the system.

[0004] Joint target detection and channel estimation have always been a research topic for many scholars in the synaesthesia integrated system. In the ultra-large-scale MIMO near-field synaesthesia integrated system, since it is necessary to consider the spherical wavefront assumption in the near-field scenario, how to perform accurate target detection and channel estimation and design a low-overhead target detection and channel estimation algorithm for the near-field synaesthesia integrated system are issues that need to be urgently addressed.

[0005] Based on the above problems, in order to realize joint target detection and channel estimation in ultra-large-scale MIMO scenarios and improve the accuracy of detection and estimation, a low-overhead joint target detection and channel estimation algorithm is proposed to improve the comprehensive performance of the near-field synaesthesia integrated system. Summary of the invention

[0006] The object of the present invention is to achieve the following technical solution: a target detection and channel estimation method for a near-field synaesthesia system, the method comprising:

[0007] S1. Modeling the communication channel by using a spherical wave model in a near-field scenario, including: modeling the signals sent and received by the base station to the user, or modeling the channel by using the signals sent by the user received by the base station, wherein the channel includes the direction vector obtained by the angle of arrival and the distance obtained by the base station;

[0008] S2, using Fresnel approximation to approximate the near-field channel, using the correlation between the vectors in different directions of the approximate post-polarity channel to analyze, and obtaining the sampling condition with the maximum correlation to construct a near-field dictionary;

[0009] S3. Use the orthogonal matching pursuit algorithm to jointly estimate the parameters and channel information of the detection target: use the near-field dictionary to represent the original perception channel in the polar domain, use the orthogonal matching pursuit algorithm to estimate the parameters of the perception target, and obtain the RCS, angle and distance of the detection target; perform channel estimation during the communication process: use two antenna subsets for channel estimation, and independently update each column of the dictionary each time the dictionary is updated. After updating the dictionary, construct the support set according to the estimated angles of the two antenna subsets and calculate the parameter vector of the channel to reconstruct the channel.

[0010] Further, the modeling of the reflected signals sent and received by the user according to the base station includes:

[0011] The sending downlink pilot signal is recorded as Where N is the number of base station antennas. After the base station sends the pilot signal, it reaches the detection target and then reflects after being reflected by the target. The signal received by the base station is expressed as

[0012] y r =H r x r +n r ;

[0013] in is the sensing channel matrix, It has zero mean and variance σ r The perception channel matrix is ​​determined by the arrival angle and RCS of the perceived target and is expressed as H r =α r a(θ r ,r)a H (θ r ,r);

[0014] where α r ,θ rand r represent the RCS of the perceived target, the arrival angle of the ultra-large-scale antenna array during the perception process, and the distance to the reference antenna, respectively. a(θ r ,r) represents the direction vector corresponding to the perceived target;

[0015] Consider an array of N linearly arranged antennas, establish a Cartesian plane coordinate system, and take the first antenna as the origin of the coordinate system. Then the distance r between the nth antenna and the user is (n) Calculated as

[0016]

[0017] where r (1) represents the distance from the user to the first antenna, d represents the spacing between antennas, and θ is the corresponding arrival angle, so the direction vector is expressed as

[0018]

[0019] where f c and c represent the carrier frequency and the speed of light respectively.

[0020] Further, performing channel modeling according to the user transmission signal received by the base station includes:

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

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

[0023] y c =h c x c +n c

[0024] in is the communication channel vector, is additive white Gaussian noise, and the communication channel vector is expressed as:

[0025] Where L is the number of paths in the channel, and are the channel gain and arrival angle corresponding to the lth path, is the corresponding direction vector, where the channel gain It obeys a complex Gaussian distribution with a mean of 0;

[0026] The direction vector of this process is

[0027]

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

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

[0030]

[0031] d represents the spacing between antennas in the lth path, and l is the corresponding arrival angle in the lth path.

[0032] Furthermore, the analysis using the correlation between different directional vectors of the approximate back-domain channel specifically includes: the correlation between the directional vectors a1, r1 and a2, r2 is

[0033]

[0034] f c and c represent the carrier frequency and the speed of light respectively; using the Fresnel approximation, it is derived that when When the angle sampling interval is set to cosθ1-cosθ2=2 / N, the column correlation of D(θ,r) reaches the maximum. When the distance of the user is much larger than the length of the antenna array, the columns of D(θ,r) 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 constructing of the near-field dictionary includes:

[0036] The dictionary for constructing a near-field sparse channel using a direction vector utilizes the relationship between angle and distance, represents the distance with an angle, and determines the size of the dictionary by the angle resolution, specifically including:

[0037]

[0038] in is the direction vector, where They respectively represent the arrival angle and distance corresponding to the i-th antenna as a reference.

[0039] Furthermore, the estimation process of the parameters of the detection target in S3 includes:

[0040] The designed near-field dictionary is used to represent the original perception channel in the polar domain, and the corresponding signal is shown below

[0041]

[0042] in is the representation of the channel in the polar domain of the perception channel. The superscript r refers to the perception process, including the RCS, distance and azimuth of the perception target. D is the dictionary matrix. It has zero mean and variance σ r Additive Gaussian white noise; The downlink pilot signal sent by the base station is used to estimate the parameters of the perceived target using the orthogonal matching pursuit algorithm. First, the distance range of the target is set to [r min ,r max ] and the angle space is divided into N blocks, the initial dictionary is constructed from each column vector as Right now

[0043]

[0044] in It is determined according to the above near-field channel model; the residual of the initialization support set and the perception process w=y r , and then iterate, first calculate the inner product of the received signal and the dictionary, and get

[0045] ζ=D H w, and then detect the new support set as follows

[0046]

[0047] Where p is the pth element of vector ζ;

[0048] Then update the support set Γ = Γ ∪ p *, and then get the orthogonal mapping

[0049] Where D :,Γ is the column in the Γ set corresponding to the dictionary matrix D, D :,Γ of pseudo-rebellion;

[0050] Then update the residual

[0051]

[0052] After iterating to the preset maximum number of iterations, Then the RCS, angle and distance of the detected target are obtained.

[0053] Furthermore, the channel information estimation process in S3 includes:

[0054] Two antenna subsets are selected from the very large antenna array, and the length of each antenna subset is defined as Where Q is less than the number of antennas N. In order to eliminate the impact of multipath interference on angle estimation, each column of the dictionary is updated independently at each update, that is,

[0055]

[0056] in

[0057] First, define the received signals of the two antenna subsets as and The initial dictionaries are and The maximum and minimum distances between the user and the antenna are set as r max and r min , the distance between the two antenna subsets is δ, initialize the support set and the residual Then estimate the angle Obtained according to the following formula:

[0058]

[0059] in D1 is the first dictionary subset, p is the pth element of vector κ1;

[0060] Then estimate the angle Based on known get A rough estimate of the range of

[0061]

[0062] in

[0063] Select a subset D of D2 within the above range 2,sub , and calculate This gives the angle

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

[0065]

[0066] Then update each column of dictionaries D1 and D2 based on the above dictionary update steps; after the number of updates is completed, update Based on Γ θ and Γ r Construct the support set vector B. The lth column of the support set is given by Calculated; then the parameter vector By solving the following optimization problem

[0067]

[0068] Where B1 is a subset of B, solving the above problem yields

[0069] Calculate the residual signal Where B1 and B2 are subsets of B selected according to 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 synesthesia system is also provided, comprising a memory and one or more processors, wherein the memory stores executable code, and when the processor executes the executable code, the target detection and channel estimation method for the near-field synesthesia system is implemented.

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

[0073] Beneficial effects of the present invention:

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

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

[0076] (3) Aiming at the near-field synaesthesia integration scenario, the present invention designs an algorithm based on joint target detection and channel estimation, which has low overhead, can accurately perform target detection and channel estimation, and improves the overall performance of the synaesthesia integration system. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 A step diagram of a target detection and channel estimation method for a near-field synaesthesia system provided by an embodiment of the present invention;

[0078] Figure 2 A schematic diagram of the convergence of the algorithm proposed under different signal-to-noise ratios provided by an embodiment of the present invention;

[0079] Figure 3 A schematic diagram of normalized mean square error of different estimation methods under different signal-to-noise ratios provided by an embodiment of the present invention;

[0080] Figure 4 A schematic diagram of normalized mean square error of different estimation methods under different distance conditions provided by an embodiment of the present invention;

[0081] Figure 5 A schematic diagram of normalized mean square error of different estimation methods under different numbers of base station antennas provided by an embodiment of the present invention;

[0082] Figure 6 A schematic diagram of a target detection and channel estimation device for a near-field synaesthesia system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0083] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings.

[0084] Consider a time-division massive MIMO interaceptive integrated system, where the base station provides services to single-antenna mobile users and detects and senses targets at the same time. In this system, the base station is equipped with N very large-scale antenna arrays. In the channel estimation stage, for simplicity, 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 the case of users with multiple antennas. The proposed scheme consists of two stages, target detection and channel estimation. In the target detection stage, the base station sends a downlink pilot signal and receives the echo signal reflected from the detected target for estimation; in the channel estimation stage, the user sends an uplink pilot signal to the base station, and the base station performs channel estimation based on the received channel.

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

[0086] S1. The base station sends a downlink pilot signal to the sensing target, then receives the echo signal reflected from the sensing target and estimates the arrival angle and RCS (radar cross section) of the sensing target;

[0087] The sending downlink pilot signal is recorded as Where N is the number of base station antennas. After the base station sends the pilot signal, it reaches the detection target and then reflects after being reflected by the target. The signal received by the base station is expressed as

[0088] y r =H r x r +n r

[0089] in is the sensing channel matrix, It has zero mean and variance σ rThe perception channel matrix is ​​determined by the arrival angle and RCS of the perceived target and can be expressed as H r =α r a(θ r ,r)a H (θ r ,r)

[0090] where α r ,θ r and r represent the RCS of the perceived target and the arrival angle of the ultra-large-scale antenna array and the distance to the reference antenna during the perception process, respectively. a(θ r ,r) 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 pilot signal and performs channel estimation.

[0092] The uplink pilot signal is defined as Then the uplink pilot signal y received by the base station c As shown below

[0093] y c =h c x c +n c

[0094] in is the communication channel vector, is additive Gaussian white noise. Here the communication channel vector can be expressed as

[0095]

[0096] Where L is the number of channel paths, and are the channel gain and arrival angle corresponding to the lth path, is the corresponding direction vector, assuming here that the channel gain It obeys a complex Gaussian distribution with zero mean.

[0097] S3, the base station processes the received user signal and the echo signal of the perceived target and establishes a near-field channel model;

[0098] The near-field channel model is as follows. In the scenario of ultra-large-scale antenna arrays, for example, the Rayleigh distance of an array of 3200 antennas can reach 200 meters at 2.4 GHz. In this scenario, the near-field effect cannot be ignored in the ultra-large antenna array scenario. The traditional plane wave channel assumption is not applicable in this scenario. Therefore, it is necessary to consider the spherical wave model in the near-field scenario to model the channel. First, the channel model of the perception process in step S1 is modeled. Consider an array of N linearly arranged antennas, establish a Cartesian plane coordinate system, and use the first antenna as the origin of the coordinate system. The distance r between the nth antenna and the user is (n) Calculated as

[0099]

[0100] where r (1) represents the distance from the user to the first antenna, which is the origin of the coordinate system. d represents the spacing between antennas. θ is the corresponding arrival angle. So the direction vector is expressed as

[0101]

[0102] where f 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 the communication process, so the direction vector of the process is

[0104]

[0105] in 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 channel estimation method based on plane waves is not applicable. Therefore, a low-overhead channel estimation and target detection parameter estimation method based on polar domain channels is proposed to reduce the storage space.

[0107] The target detection and channel estimation method is as follows: First, the distance from the communication user or the sensing target to the nth antenna of the base station in the near-field scenario is approximated by Fresnel approximation:

[0108]

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

[0110]

[0111] Similarly, the distance from the sensing target to the nth antenna of the base station in the sensing scene 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 the two dimensions of angle and distance, the angle and distance sampling intervals need to be as small as possible to obtain a finer decomposition rate, which will result in a larger storage space for the dictionary. In practice, this will result in higher storage overhead, so the dictionary needs to be redesigned.

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

[0115]

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

[0117] Different from the traditional two-dimensional dictionary based on polar channels, this patent uses the relationship between angle and distance to express distance as angle, so the size of the dictionary can be determined by the angle resolution. The following is an analysis of the correlation of the direction vector of the polar channel. Definition For vector and The correlation is derived as follows

[0118]

[0119] According to the above definition of distance, we can further deduce

[0120]

[0121] when And assuming cosθ1-cosθ2=2 / N, we have Using the Fresnel approximation we can get

[0122]

[0123] Similarly, let θ1 = θ2 and And using the Fresnel approximation we can get

[0124]

[0125] From the above two formulas, we can see that when When the angle sampling interval is set to cosθ1-cosθ2=2 / N, the column correlation of D(θ,r) reaches the maximum. When the distance of the user is much larger than the length of the antenna array, the column of D(θ,r) can be approximately orthogonal. According to the above analysis, it can be concluded that the coherence of the dictionary proposed above is smaller than the existing dictionary characterization based on the polar domain. At the same time, the dictionary has a reduced storage space and lower overhead compared to the existing dictionary. Therefore, in the design of the dictionary, the angle sampling interval is set to 1 / 2N, and the distance sampling interval is set to 1m.

[0126] Next, we will design a joint target detection and channel estimation algorithm in the form of time division multiple access (TDD). First, we will estimate the parameters of the target detection process. First, for the previous target detection model, we will use the designed near-field dictionary to represent the original perception channel in the polar domain. The corresponding signal can be obtained as shown below

[0127]

[0128] in The superscript r refers to the perception process, which includes the RCS, distance and azimuth of the perceived target. The above problem is a compressed sensing problem. Here, the orthogonal matching pursuit algorithm is used to estimate the parameters of the perceived target. First, it is assumed that the distance range of the target is [r min ,r max ] and the angle space is divided into N blocks, the initial dictionary is constructed from each column vector as Right now

[0129]

[0130] in It is determined according to the above near-field channel model. Next, initialize the set w=y r , and then iterate, first calculate the inner product of the received signal and the dictionary, and get

[0131] ζ=D H w, and then detect the new support set as follows

[0132]

[0133] Where p is the pth element of the vector ζ

[0134] Then update the support set Γ = Γ ∪ p *, and then get the orthogonal mapping

[0135]

[0136] Where D :,Γ is the column in the Γ set corresponding to the dictionary matrix D, D :,Γ The pseudo-inverse.

[0137] Then update the residual

[0138]

[0139] After a given number of iterations, by calculating Then the RCS, angle and distance of the detected target are obtained.

[0140] Next, we consider channel estimation during the communication process. Since the influence of multipath needs to be considered during uplink communication, in order to reduce the pilot overhead, two antenna subsets are selected from the super large antenna array. The length of each antenna subset is defined as Here 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 at each update, that is,

[0141]

[0142] in

[0143] First, define the received signals of the two antenna subsets as and The initial dictionaries are and The maximum and minimum distances are set to r max and r min , the distance between the two antenna subsets is δ, initialize Then estimate the angle Similarly, we can obtain

[0144]

[0145] in D1 is the first dictionary subset, and p is the pth element of vector κ1.

[0146] Then estimate the angle In order to reduce the computational complexity, based on the known get A rough estimate of the range of

[0147]

[0148] in

[0149] Then select a subset D of D2 within the above range 2,sub , and calculate This gives the angle

[0150] Then calculate the distance between the two antenna subsets based on the angle

[0151]

[0152] Then update each column of dictionaries D1 and D2 based on the above dictionary update steps. After the number of updates is completed, update Then based on Γ θ and Γ r Construct the support set vector B. The lth column of the support set can be obtained by Then the parameter vector We can solve the following optimization problem

[0153]

[0154] Where B1 is a subset of B. Solving the above problem, we can get

[0155] Then calculate the residual signal Where B1 and B2 are subsets of B selected according to the received antenna index. After traversing the number of channel paths, the channel is reconstructed according to the following formula

[0156]

[0157] The functions and effects of the present invention are further illustrated by the following simulation experiments:

[0158] Simulation conditions: Orthogonal frequency division multiplexing technology is used here, the carrier frequency is set to 25 GHz, the bandwidth is 100 MHz, the number of subcarriers is set to 64, the number of antennas of the base station super-large antenna array is set to 256, and the number of channel multipaths is set to 10. In the simulation comparison, the normalized mean square error (NMSE) is used to show the estimation performance. The NMSE of target detection and channel estimation are defined as and respectively. in and are the estimated target parameter set and the estimated channel information, respectively. and They are respectively the real target parameter set and the real channel information. Here, NMSE=NMSE1+NMSE2 of the comprehensive target parameter and channel information is used as the measurement index. During simulation, the present invention will be compared with the existing synchronized weighted orthogonal matching pursuit algorithm (SW-OMP) and the polar domain orthogonal matching pursuit algorithm (P-OMP) to highlight the performance of the algorithm proposed by the present invention.

[0159] Simulation results: Figure 2In this paper, we show the convergence analysis of the proposed joint target detection and channel estimation algorithm. It can be seen that the proposed estimation algorithm can converge quickly, and basically converges after about 10 iterations. At the same time, different SNRs converge to different NMSE results. A high SNR can converge to a relatively low NMSE and obtain better detection results.

[0160] Figure 3 The NMSE performance of the three algorithms under different SNR conditions is shown. Here we can see that as the SNR increases, the NMSE performance will be better, and this trend result is also obvious. In addition, it can be seen that the joint target detection and channel estimation algorithm of the present invention is significantly better than the traditional P-OMP and SW-OMP algorithms in NMSE performance, highlighting that the proposed algorithm has good estimation performance in joint target detection and channel estimation of ultra-large antenna array near-field synaesthesia system.

[0161] Figure 4 The present invention and the other two benchmark algorithms are shown at different target distances. Here, the Rayleigh distance is 100 meters. It can be seen that the present invention and the P-OMP algorithm have almost no significant changes in estimation performance at different distances, while the SW-OMP algorithm has a relatively poor estimation effect when the distance is small, and a better estimation effect when the distance is large. This is because when the distance is small, the target and the user are in the near field area, and the traditional far-field-based estimation algorithm is not applicable, so the SW-OMP algorithm has a relatively poor effect. When the distance gradually increases, the near field area becomes the far field area, and the SW-OMP algorithm is applicable again, so the estimation effect is better. At the same time, the performance of the joint target detection and channel estimation of the present invention at different distances is better than that of the other two benchmark algorithms.

[0162] at last, Figure 5 The comparison between the proposed method and the other two benchmark algorithms under different numbers of base station antennas is shown. It can be seen that the estimation performance of the three algorithms becomes more accurate as the number of antennas increases, so the number of antennas plays an important role in improving perception and communication performance. Similarly, the NMSE performance of the algorithm proposed in the present invention is better than the other two benchmark algorithms under different numbers of antennas, highlighting the superior performance of the proposed method in joint target detection and channel estimation in near-field scenarios.

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

[0164] See also Figure 6An embodiment of the present invention provides a target detection and channel estimation device for a near-field synaesthesia system, comprising a memory and one or more processors, wherein 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 synaesthesia system in the above embodiment.

[0165] The embodiment of the target detection and channel estimation device of a near-field synaesthesia system provided by the present invention can be applied to any device with data processing capabilities, and the device with data processing capabilities can be a device or apparatus such as a computer. The device embodiment can be implemented through software, or through hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capabilities in which it is located reading the corresponding computer program instructions in the non-volatile memory into the internal memory for execution. From a hardware perspective, if Figure 6 As shown, it is a hardware structure diagram of a target detection and channel estimation device of a near-field synaesthesia system provided by the present invention, in which any device with data processing capability is located, except Figure 6 In addition to the processor, memory, network interface, and non-volatile memory shown, any device with data processing capabilities in which the apparatus in the embodiments is located may also include other hardware, generally based on the actual functions of the device with data processing capabilities, which will not be described in detail.

[0166] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0167] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0168] An embodiment of the present invention further provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, a target detection and channel estimation method of a near-field synaesthesia system in the above embodiment is implemented.

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

[0170] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, the target detection and channel estimation method of the near-field synaesthesia system is implemented.

[0171] Those skilled in the art will readily appreciate other embodiments of the present application after considering the description and practicing the contents disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The description and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0172] It should be understood that the above general description and the detailed description below are only exemplary and explanatory and cannot limit the present application. The present application is not limited to the precise structure described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is limited only by the attached claims.

Claims

1. A method for target detection and channel estimation in a near-field synaesthesia system, characterized in that: The method includes: S1. Modeling the communication channel by using a spherical wave model in a near-field scenario, including: modeling the signals sent and received by the base station to the user, or modeling the channel by using the signals sent by the user received by the base station, wherein the channel includes the direction vector obtained by the angle of arrival and the distance obtained by the base station; S2, using Fresnel approximation to approximate the near-field channel, using the correlation between the vectors in different directions of the approximate post-polarity channel to analyze, and obtaining the sampling condition with the maximum correlation to construct a near-field dictionary; S3. Use the orthogonal matching pursuit algorithm to jointly estimate the parameters and channel information of the detection target: use the near-field dictionary to represent the original perception channel in the polar domain, use the orthogonal matching pursuit algorithm to estimate the parameters of the perception target, and obtain the RCS, angle and distance of the detection target; perform channel estimation during the communication process: use two antenna subsets for channel estimation, and independently update each column of the dictionary each time the dictionary is updated. After updating the dictionary, construct the support set according to the estimated angles of the two antenna subsets and calculate the parameter vector of the channel to reconstruct the channel.

2. The target detection and channel estimation method of a near-field synaesthesia system according to claim 1, characterized in that: The modeling of the signals sent and received by the user according to the base station includes: The sending downlink pilot signal is recorded as Where N is the number of base station antennas. After the base station sends the pilot signal, it reaches the detection target and then reflects after being reflected by the target. The signal received by the base station is expressed as y r =H r x r +n r ; in is the sensing channel matrix, It has zero mean and variance σ r The perception channel matrix is ​​determined by the arrival angle and RCS of the perceived target and is expressed as H r =α r a(θ r ,r)a H (θ r ,r); where α r ,θ r and r represent the RCS of the perceived target, the arrival angle of the ultra-large-scale antenna array during the perception process, and the distance to the reference antenna, respectively. a(θ r ,r) represents the direction vector corresponding to the perceived target; Consider an array of N linearly arranged antennas, establish a Cartesian plane coordinate system, and take the first antenna as the origin of the coordinate system. Then the distance r between the nth antenna and the user is (n) Calculated as where r (1) represents the distance from the user to the first antenna, d represents the spacing between antennas, and θ is the corresponding arrival angle, so the direction vector is expressed as where f c and c represent the carrier frequency and the speed of light respectively.

3. The target detection and channel estimation method of a near-field synaesthesia system according to claim 1, characterized in that: The performing channel modeling according to the user transmission signal received by the base station comprises: The user sends an uplink pilot signal to the base station, and the base station receives the pilot signal and performs channel estimation; The uplink pilot signal sent by the user to the base station is The uplink pilot signal y received by the base station c As shown below: y c =h c x c +n c in is the communication channel vector, is additive white Gaussian noise, and the communication channel vector is expressed as: Where L is the number of paths in the channel, and are the channel gain and arrival angle corresponding to the lth path, is the corresponding direction vector, where the channel gain It obeys a complex Gaussian distribution with zero mean; The direction vector of this process is in represents the distance from the communication user to the nth antenna, f c and c represent the carrier frequency and the speed of light respectively.

4. The target detection and channel estimation method of a near-field synaesthesia system according to claim 1, characterized in that: The Fresnel approximation for the near-field channel approximation includes: calculating the distance from the communication user or the sensing target to the nth antenna. Perform the Fresnel approximation: d represents the spacing between antennas in the lth path, and l is the corresponding arrival angle in the lth path.

5. The method for target detection and channel estimation of a near-field synaesthesia system according to claim 4, characterized in that: The analysis using the correlation between different direction vectors of the approximate back-domain channel specifically includes: the correlation between the direction vectors a1, r1 and a2, r2 is f c and c represent the carrier frequency and the speed of light respectively; using the Fresnel approximation, it is derived that when When the angle sampling interval is set to cosθ1-cosθ2=2 / N, the column correlation of D(θ,r) reaches the maximum. When the distance of the user is much larger than the length of the antenna array, the columns of D(θ,r) 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.

6. The method for target detection and channel estimation of a near-field synaesthesia system according to claim 1, characterized in that: The constructing of the near-field dictionary comprises: The dictionary for constructing a near-field sparse channel using a direction vector utilizes the relationship between angle and distance, represents the distance with an angle, and determines the size of the dictionary by the angle resolution, specifically including: in is the direction vector, where r i They respectively represent the arrival angle and distance corresponding to the i-th antenna as a reference.

7. The method for target detection and channel estimation of a near-field synaesthesia system according to claim 1, characterized in that: The estimation process of the parameters of the detection target in S3 includes: The designed near-field dictionary is used to represent the original perception channel in the polar domain, and the corresponding signal is shown below in is the representation of the channel in the polar domain of the perception channel. The superscript r refers to the perception process, including the RCS, distance and azimuth of the perception target. D is the dictionary matrix. It has zero mean and variance σ r Additive Gaussian white noise; The downlink pilot signal sent by the base station is used to estimate the parameters of the perceived target using the orthogonal matching pursuit algorithm. First, the distance range of the target is set to [r min ,r max ] and the angle space is divided into N blocks, the initial dictionary is constructed from each column vector as Right now in It is determined according to the above near-field channel model; the residual of the initialization support set and the perception process w=y r , and then iterate, first calculate the inner product of the received signal and the dictionary, and get ζ=D H w, and then detect the new support set as follows Where p is the pth element of the vector ζ; Then update the support set Γ = Γ ∪ p *, and then get the orthogonal mapping Where D :,Γ is the column in the Γ set corresponding to the dictionary matrix D, D :,Γ of pseudo-rebellion; Then update the residual After iterating to the preset maximum number of iterations, Then the RCS, angle and distance of the detected target are obtained.

8. The method for target detection and channel estimation of a near-field synaesthesia system according to claim 1, characterized in that: The channel information estimation process in S3 includes: Two antenna subsets are selected from the very large antenna array, and the length of each antenna subset is defined as Where Q is less than the number of antennas N. In order to eliminate the impact of multipath interference on angle estimation, each column of the dictionary is updated independently at each update, that is, in First, define the received signals of the two antenna subsets as and The initial dictionaries are and The maximum and minimum distances between the user and the antenna are set as r max and r min , the distance between the two antenna subsets is δ, initialize the support set and the residual Then estimate the angle Obtained according to the following formula: in D1 is the first dictionary subset, p is the pth element of vector κ1; Then estimate the angle Based on known get A rough estimate of the range of Select a subset D of D2 within the above range 2,sub , and calculate This gives the angle Calculate the distance between two antenna subsets based on the angle: Then update each column of dictionaries D1 and D2 based on the above dictionary update steps; after the number of updates is completed, update Based on Γ θ and Γ r Construct the support set vector B. The lth column of the support set is given by Calculated; then the parameter vector By solving the following optimization problem Where B1 is a subset of B, solving the above problem yields Calculate the residual signal Where B1 and B2 are subsets of B selected according to the receiving antenna index. After traversing the number of channel paths, the channel is reconstructed according to the following formula 9. A target detection and channel estimation device for a near-field synaesthesia 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, the target detection and channel estimation method of the near-field synaesthesia system according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, a target detection and channel estimation method for a near-field synaesthesia system according to any one of claims 1 to 8 is implemented.

Images