RFID target imaging method based on plane subdivision and holographic positioning

Abstract

The invention discloses an RFID target imaging method based on plane subdivision and holographic positioning. The method specifically comprises the following steps: measuring carrier phases when a target exists and when no target exists so as to obtain phase difference data; estimating a propagation distance and acquiring a direct path propagation distance and an arrival angle of the signal; establishing a propagation distance starting point phase calculation model; positioning a starting point of the propagation distance; estimating a propagation termination point; optimizing a propagation distance starting point based on a particle swarm algorithm; acquiring optimal solutions of the starting point and the ending point of the propagation distance; and constructing a hologram set of the target edge points and generating a composite hologram of the imaging target. According to the method, the phase of the propagation distance starting point is calculated by using a dynamic target edge point direct detection strategy, and the propagation distance starting point is directly positioned by using a holographic positioning algorithm, so that the problem of poor imaging performance when the target contour is arc-shaped is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of mobile wireless communications and relates to an RFID target imaging method based on plane segmentation and holographic positioning. Background Art

[0002] Target imaging technology provides key information and data support for military, security, rescue, environment and other fields, improves the accuracy and efficiency of decision-making, and promotes the innovation and development of various industries and social progress. With the development of society, the demand for imaging technology in various fields is also constantly upgrading, especially in terms of improving imaging accuracy, reducing costs and enhancing system reliability. As a component of target imaging technology, target imaging technology based on radio frequency signals has become a research focus in the field of target imaging due to the advantages that the propagation of radio frequency signals is not affected by changes in ambient light, radio frequency signals can penetrate non-metallic obstacles, and target imaging systems based on radio frequency signals are lower in cost and safer. In the existing imaging technology based on radio frequency signals, most of the linear candidate target contours are used, which cannot meet the imaging requirements of arc-shaped target contours. For circular or elliptical arc-shaped targets, when there are only two tag arrays, the estimated image obtained has a large deviation from the actual image. In response to this problem, the present invention proposes an RFID target imaging method based on plane segmentation and holographic positioning. Summary of the invention

[0003] The purpose of the present invention is to provide an RFID target imaging method based on plane segmentation and holographic positioning, so as to solve the problem that the estimated image obtained by the prior art when imaging a circular or elliptical arc-shaped target has a large deviation from the actual image. The technical solution adopted by the present invention is to use ultra-high frequency RFID technology to obtain the phase value when there is a target and when there is no target, obtain the propagation distance of the signal inside the target and the phase of the starting point of the propagation distance, and then use the idea of ​​plane segmentation and holographic positioning to find the starting point of the propagation distance, and complete the imaging by optimizing the starting point of the propagation distance and reconstructing the target section image, which specifically includes the following steps:

[0004] Step 1: Use commercial RFID reader equipment and programmable mobile robots to build a tag phase acquisition system, collect phase and obtain the signal direct path propagation distance and arrival angle (AOA) data, establish a two-dimensional Cartesian coordinate system in the detection area with a length of L and a width of W, and divide the grid with a resolution of f, and the total number of grids is S grid, assume that the reader antenna starts from (0, W) at a uniform speed v and moves along a given trajectory, assume that the coordinates of the two tags are (L / 2, 0) and (0, W / 2), define the vertical direction where tag 1 is located as tag 1 direction, define the horizontal direction where tag 2 is located as tag 2 direction, the phase data read in the two tag directions are Nx and Ny respectively, read the tag phases in the case of no target between the antenna and the tag and in the case of target and compare, and two sets of phase differences can be obtained and The cumulative phase change method based on sliding window is used to detect the target contour boundary, and the phase data within the boundary is intercepted and recorded as and The data volumes are K x , K y , substitute the truncated phase into the formula D = (Δφ + 2ξπ) / (β tar -β air ) to obtain the propagation distance D of the signal inside the target x and D y , where β air and β tar Represent the phase constants of the signal in the air and in the target respectively. ξ is an integer. Since the phase change caused by the signal penetrating the target generally does not exceed 2π, ξ is usually 0. According to the robot's moving speed and the recorded timestamp, the position of the antenna when the signal is received is obtained, and the position A is defined x and A y is the signal capture point, and then the length Dt of the signal direct path segment in two directions is obtained x , Dt y And the AOA data of the signal θ x ,θ y The calculation formulas are in, and Respectively represent the coordinates of a signal capture point in the direction of tag 1 and tag 2;

[0005] Step 2: Construct a phase model for calculating the starting point of the propagation distance. Taking label 1 as an example, K x The distance between the starting point of the propagation distance and its corresponding signal capture point is set to Then the distance between the starting point of the nth propagation distance and its corresponding signal capture point is x n , the distance between the starting point of the nth propagation distance and the label is The length of the i-th direct path segment is expressed as i∈[1,K x] and i≠n, the angle between the i-th direct path segment and the direct path segment where the n-th propagation distance starting point is located can be expressed as i∈[1,K x ] and i≠n, using the formula The distance between the starting point of the nth propagation distance and other signal capture points can be obtained Then C n Substituting into the ideal phase expression φ n =(4πE n / λ)mod 2π, the theoretical phase value φ at the starting point of the nth propagation distance under ideal conditions can be obtained. n , and so on, we can get the theoretical phase of all the starting points of the propagation distance in the two tag directions and

[0006] Step 3: Use the idea of ​​holographic positioning to complete the positioning of the starting point of the propagation distance. When the reader antenna measures the tag phase, the distance between the antenna signal capture point coordinates and the grid center coordinates |GA| is obtained and substituted into the formula h grid (G, A) = (4π·|GA| / λ) mod 2π, the theoretical phase value h measured when the signal is reflected back from the grid G ​​is obtained. grid (G, A), and then the theoretical phase value φ of all grids can be obtained grid ,Will Respectively with φ grid By comparison, we can obtain the probability that the grid is the starting point of the propagation distance, and use the formula Calculate the pixel value for quantization, where i∈[1,S grid ], k∈[1, K], for all pixel values ​​of a single propagation distance starting point, the grid where the maximum pixel value is located is the location of the propagation distance starting point, and the center coordinates of the grid are the coordinates of the propagation distance starting point (x c ,y c );

[0007] Step 4: Construct an optimization model based on maximizing overlap to find the optimal starting point of the propagation distance. According to the coordinates of the starting point of the propagation distance, the propagation distance and the corresponding AOA data, the coordinates of the end point of the propagation distance (x′ c , y′ c ), the coordinate calculation formula of the end point of the propagation distance in the tag 1 direction is: c When <L / 2, x′ c =x c -PQ,y′ c =y c +P′Q, when x c >L / 2, x′c =x c +PQ,y′ c =y c +P′Q, when x c =L / 2, Where PQ = D x ·cosθ x , The calculation formula for the coordinates of the end point of the propagation distance in the tag 2 direction is: c When <W / 2, x′ c =x c +P′Q,y′ c =y c +P′Q, when y c >W / 2, x′ c =x c +P′Q,y′ c =y c +PQ, when y c =W / 2, y′ c =y c ,in, The obtained starting point and ending point of the propagation distance are connected in sequence in different directions to form two closed figures connected by points and lines. The closed figures are filled with colors to obtain two estimated images. After merging, the ratio of the overlapping area to the total area is compared to obtain the overlap of the two estimated images. The overlap of the estimated images in the two directions is maximized by adjusting the distance X between the starting point of the propagation distance and the signal capture point. The maximum number of iterations set is used as the termination condition to construct an optimization model for the starting point of the propagation distance. The particle swarm optimization (PSO) algorithm is introduced to solve the problem, so as to realize the fast and accurate search of the starting point and the ending point of the propagation distance.

[0008] Step 5: Aggregate all the optimal propagation distance starting points and ending points into a hologram to generate a composite hologram, and use the image reconstruction method based on the composite hologram to present the horizontal section image of the target, using the formula PX normmalization =PX / PX max Normalize all pixel values ​​at the starting point of a single propagation distance, where PX represents the unnormalized pixel value matrix at the starting point of a propagation distance, and PX maxRepresents the largest pixel value in the pixel value matrix, performs color mapping on pixel values ​​of different sizes and fills the corresponding grids to form a hologram of a single propagation distance starting point, assigns the same pixel value as the corresponding propagation distance starting point to the grid where the propagation distance ending point is located, constructs a hologram of the propagation distance starting point-ending point, and collectively refers to all propagation distance starting points and ending points as target edge points. The pixel values ​​of non-target edge point grids are suppressed using a weight coefficient ω in the range of 0 to 1, and a set of target edge point holograms in two label directions is established. The holograms contained in a single direction are superimposed to obtain a single-direction sub-hologram of the target horizontal section, and the two-direction sub-holograms are superimposed to generate a composite hologram of the target horizontal section.

[0009] It should be noted that the method for locating the starting point of the propagation distance in steps 2 and 3 is: analyze the geometric relationship between the label, the starting point of the propagation distance and the signal capture point, build a geometric model to calculate the distance between each propagation distance starting point and each signal capture point, and substitute it into the ideal phase expression to obtain the theoretical phase value of each propagation distance starting point. For a certain propagation distance starting point, compare its theoretical phase value with the theoretical phase value when the signal reaches each signal capture point after being reflected by each grid, and obtain the probability that the starting point of the propagation distance is located in each grid, and quantify it with pixel values. The grid with the largest pixel value is the starting position of the propagation distance.

[0010] The present invention realizes an RFID target imaging method based on plane subdivision and holographic positioning. The method uses the ideas of plane subdivision and holographic positioning to find the position of the starting point of the propagation distance, and obtains the end point of the propagation distance by calculation. The starting point and the end point of the propagation distance obtained from two tag directions are connected in sequence to form two estimated images, and the propagation distance starting point is optimized with the overlap maximization of the two estimated images as the optimization direction. The target horizontal section image is generated by reconstructing the target section image. The proposed method can well complete the target imaging, especially can solve the problem of low imaging accuracy of arc-shaped targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is the overall framework diagram of the system of the present invention;

[0012] Figure 2 It is a schematic diagram of the scene deployment of the present invention;

[0013] Figure 3 It is a geometrical diagram of phase calculation of the starting point of the propagation distance in the direction of tag 1 of the present invention;

[0014] Figure 4 It is a geometrical diagram of phase calculation of the starting point of the tag 2 propagation distance of the present invention;

[0015] Figure 5It is a geometrical schematic diagram of solving the coordinates of the end point of the propagation distance in the direction of tag 1 of the present invention;

[0016] Figure 6 It is a geometrical schematic diagram of solving the coordinates of the end point of the tag 2-direction propagation distance of the present invention;

[0017] Figure 7 This is an example of the hologram of the starting point of the propagation distance of the present invention;

[0018] Figure 8 It is an example of the propagation distance starting point-ending point hologram of the present invention;

[0019] Fig. 9 is an example of a composite hologram when the weight coefficient ω=0.1 of the present invention;

[0020] Fig.10 Schematic diagram of the deviation curve of two arc-shaped targets of the present invention;

[0021] Fig.11 Schematic diagram of deviation curves of two arc-shaped targets under different Gaussian white noise environments of the present invention;

[0022] Fig.12 1 is a schematic diagram for comparing deviation curves of imaging targets at different grid resolutions of the present invention; DETAILED DESCRIPTION

[0023] The purpose of the present invention is to provide an RFID target imaging method based on plane segmentation and holographic positioning, so as to solve the problem that the estimated image obtained by imaging circular or elliptical arc-shaped targets in the prior art has a large deviation from the actual image. The phase difference between the presence and absence of a target is obtained by ultra-high frequency RFID technology, and then the propagation distance is obtained, and a phase model of the starting point of the propagation distance is established. The starting point of the propagation distance is found by using the idea of ​​plane segmentation and holographic positioning, and the imaging is completed by optimizing the starting point of the propagation distance and reconstructing the target section image.

[0024] The following is combined with Figure 1 , Attachment Figure 2 , Attachment Figure 3 , Attachment Figure 4 , Attachment Figure 5 , Attachment Figure 6 , Attachment Figure 7 , Attachment Figure 8 , Attachment Fig. 9 , Attachment Fig.10 , Attachment Fig.11 , Attachment Fig.12 The embodiments of the present invention are described in further detail.

[0025] The system framework of the present invention is shown in the attached Figure 1As shown in the figure, we first build a planar holographic RFID imaging system. The system framework mainly includes: data collection and preparation, propagation distance starting point positioning, propagation distance starting point optimization, and target section image reconstruction. The system is mainly composed of a reader, antenna, tag, and background processor.

[0026] According to the attached Figure 2 After the deployment of the system scenario is completed, a tag phase acquisition system is built using commercial RFID reader equipment and a programmable mobile robot to collect phase and obtain the signal direct path propagation distance and arrival angle (AOA) data. A two-dimensional Cartesian coordinate system is established in the detection area with a length of L and a width of W, and the grid is divided with a resolution of f, and the total number of grids is S. grid , assume that the reader antenna starts from (0, W) at a uniform speed v and moves along a given trajectory, assume that the coordinates of the two tags are (L / 2, 0) and (0, W / 2), define the vertical direction where tag 1 is located as tag 1 direction, define the horizontal direction where tag 2 is located as tag 2 direction, and the amount of phase data read in the two tag directions is N x and N y , read the tag phase and compare it when there is no target between the antenna and the tag and when there is a target, and get two sets of phase differences and The cumulative phase change method based on sliding window is used to detect the target contour boundary, and the phase data within the boundary is intercepted and recorded as and The data volumes are K x , K y , substitute the truncated phase into the formula D = (Δφ + 2ξπ) / (β tar -β air ) to obtain the propagation distance D of the signal inside the target x and D y , where β air and β tar Represent the phase constants of the signal in the air and in the target respectively. ξ is an integer. Since the phase change caused by the signal penetrating the target generally does not exceed 2π, ξ is usually 0. According to the robot's moving speed and the recorded timestamp, the position of the antenna when the signal is received is obtained, and the position A is defined x and A y is the signal capture point, and then the length Dt of the signal direct path segment in two directions is obtained x , Dt y And the AOA data of the signal θ x ,θ y The calculation formulas are in, and Respectively represent the coordinates of a signal capture point in the direction of tag 1 and tag 2.

[0027] Construct a phase model for calculating the starting point of propagation distance. Taking label 1 as an example, K x The distance between the starting point of the propagation distance and its corresponding signal capture point is set to Then the distance between the starting point of the nth propagation distance and its corresponding signal capture point is x n , the distance between the starting point of the nth propagation distance and the label is The length of the i-th direct path segment is expressed as i∈[1,K x ] and i≠n, the angle between the i-th direct path segment and the direct path segment where the n-th propagation distance starting point is located can be expressed as i∈[1,K x ] and i≠n, using the formula The distance between the starting point of the nth propagation distance and other signal capture points can be obtained Then E n Substituting into the ideal phase expression φ n =(4πE n / λ)mod 2π, the theoretical phase value φ at the starting point of the nth propagation distance under ideal conditions can be obtained. n , and so on, we can get the theoretical phase of all the starting points of the propagation distance in the two tag directions and Attached Figure 3 is the geometric model for calculating the phase of the starting point of the propagation distance in the direction of tag 1, Figure 4 It is the geometric model for calculating the phase of the starting point of the propagation distance in the tag 2 direction.

[0028] The idea of ​​holographic positioning is used to locate the starting point of the propagation distance. When the reader antenna measures the tag phase, the distance between the antenna signal capture point coordinates and the grid center coordinates |GA| is obtained and substituted into the formula h grid (G, A) = (4π·|GA| / λ) mod 2π, the theoretical phase value h measured when the signal is reflected back from the grid G ​​is obtained. grid (G, A), and then the theoretical phase value φ of all grids can be obtained grid ,Will Respectively with φ grid By comparison, we can obtain the probability that the grid is the starting point of the propagation distance, and use the formula Calculate the pixel value for quantization, where i∈[1,S grid], k∈[1, K], for all pixel values ​​of a single propagation distance starting point, the grid where the maximum pixel value is located is the location of the propagation distance starting point, and the center coordinates of the grid are the coordinates of the propagation distance starting point (x c ,y c ).

[0029] A search model based on maximizing overlap is constructed to find the optimal starting point of the propagation distance. The coordinates of the end point of the propagation distance (x′) can be obtained according to the coordinates of the starting point of the propagation distance, the propagation distance and the corresponding AOA data. c , y′ c ), the coordinate calculation formula of the end point of the propagation distance in the tag 1 direction is: c When <L / 2, x′ c =x c -PQ,y′ c =y c +P′Q, when x c >L / 2, x′ c =x c +PQ,y′ c =y c +P′Q, when x c =L / 2, x′ c =x c , Where PQ = D x ·cosθ x , The calculation formula for the coordinates of the end point of the propagation distance in the tag 2 direction is: c When <W / 2, x′ c =x c +P′Q,y′ c =y c +P′Q, when y c >W / 2, x′ c =x c +P′g,y′ c =y c +PQ, when y c =W / 2, y′ c =y c ,in, Attached Figure 5 The geometric diagram for solving the coordinates of the end point of the propagation distance in the direction of tag 1 is attached. Figure 6The geometric diagram for solving the coordinates of the end point of the propagation distance of the tag in two directions is proposed. The starting point and the end point of the propagation distance are connected in sequence in different directions to form two closed figures connected by points and lines. The closed figures are filled with colors to obtain two estimated images. After merging, the ratio of the overlapping area to the total area is compared to obtain the overlap of the two estimated images. The overlap of the estimated images in the two directions is maximized by adjusting the distance X between the starting point of the propagation distance and the signal capture point. The maximum number of iterations set is used as the termination condition to construct an optimization model for the starting point of the propagation distance. The particle swarm optimization (PSO) algorithm is introduced to solve the problem, so as to realize the fast and accurate search of the starting point and the end point of the propagation distance.

[0030] All the optimal propagation distance starting points and ending points are aggregated into a hologram to generate a composite hologram. The horizontal section image of the target is presented using the image reconstruction method based on the composite hologram. The formula PX normalization =PX / PX max Normalize all pixel values ​​at the starting point of a single propagation distance, where PX represents the unnormalized pixel value matrix at the starting point of a propagation distance, and PX max Indicates the maximum pixel value in the pixel value matrix, performs color mapping on pixel values ​​of different sizes and fills the corresponding grids to form a hologram of a single propagation distance starting point, assigns the same pixel value as the corresponding propagation distance starting point to the grid where the propagation distance ending point is located, and constructs a hologram of the propagation distance starting point-ending point. Figure 7 This is an example of a hologram at the starting point of the propagation distance. Figure 8 For the propagation distance starting point-end point hologram example, all the propagation distance starting points and end points are collectively referred to as target edge points. The pixel values ​​of the non-target edge point grid are suppressed by using the weight coefficient ω with a value range of 0 to 1. A set of target edge point holograms in two label directions is established. The holograms contained in a single direction are superimposed to obtain a single-direction sub-hologram of the target horizontal section. The sub-holograms in two directions are superimposed to generate a composite hologram of the target horizontal section. Fig. 9 It is the composite hologram of the target horizontal section when the weight coefficient is 0.1.

[0031] The following is the specific implementation: Figure 2As shown in the figure, commercial RFID equipment and programmable robots are used to collect tag phase data. Specifically, a two-dimensional Cartesian coordinate system is established in a detection area with a length of L and a width of W. The robot starts from (0, W) at a speed v and moves along a given trajectory. The coordinates of the two tags are (L / 2, 0) and (0, W / 2), respectively. The vertical direction where tag 1 is located is defined as the tag 1 direction, and the horizontal direction where tag 2 is located is defined as the tag 2 direction. In the above scenario, the phase values ​​of the two tags are collected with and without a target, and two sets of phase differences are obtained. and As well as the signal direct path propagation distance and the AOA data of the signal, and establish a phase model of the propagation distance starting point through a geometric model, and then use the proposed propagation distance starting point holographic positioning method based on plane subdivision to determine the coordinates of the propagation distance starting point, and then obtain the coordinates of the propagation distance ending point according to the propagation distance starting point coordinates, propagation distance, and signal AOA data, and then connect the propagation distance starting point and ending point obtained in the direction of tag 1 and the direction of tag 2 in sequence to form two closed figures connected by points and lines, and establish a propagation distance starting point model based on maximizing the overlap of the two estimated images for optimization. All the optimal propagation distance starting points and ending points are aggregated into a hologram to generate a composite hologram. The horizontal section image of the target is presented using an image reconstruction method based on the composite hologram. Pixel values ​​of different sizes are color mapped and filled with the corresponding grids. The pixel values ​​of non-starting or ending point grids are suppressed using a weight coefficient ω ranging from 0 to 1. A set of target edge point holograms in two label directions is established. The holograms contained in a single direction are superimposed to obtain a single-direction sub-hologram of the target horizontal section. The two-directional sub-holograms are superimposed to generate a composite hologram of the target horizontal section.

[0032] In order to provide more objective data to verify the feasibility of the proposed imaging method, the imaging performance of the imaging system is evaluated by a target imaging accuracy evaluation method based on deviation. The image deviation refers to the degree to which each point on the edge of the estimated image target deviates from the corresponding point on the edge of the actual target. That is, the positioning error of the estimated point is used as the imaging error, which is numerically reflected as the average value of the distance between the estimated point and the actual point. The smaller the value, the higher the positioning accuracy of the target edge point, and thus the higher the accuracy of the estimated image. For the estimated image of the imaging target, the imaging error is quantified, and its deviation p is: Among them, (x′ n , y′ n ) represents the estimated coordinates of the nth point on the edge of the image; (x n ,y n) represents the coordinates of the nth point on the actual target edge; N represents the number of located target edge points. Two different types of arc-shaped targets were tested, where the horizontal section shape of target A is circular and the horizontal section shape of target B is elliptical. Fig.10 The deviation curves of two arc-shaped targets are shown in the figure below. Fig.11 The deviation curves of two arc-shaped targets under different Gaussian white noise environments are shown in the figure. Fig.12 Comparison of deviation curves of imaging targets at different grid resolutions.

[0033] By the attached Fig.10 It can be seen that as the optimization iteration proceeds and the overlap increases, the deviation curves of the two gradually decrease until they are flat. The deviation range of target A is 0.8246cm~2.5053cm. The deviation range of target B is 0.7651cm~1.8430cm. The above results show that the planar holographic RFID imaging system has good imaging performance for arc-shaped targets and can obtain estimated images with high-precision features; Fig.11 It can be seen that in different Gaussian white noise environments, the deviation range of target A is between 1.0877cm and 2.5749cm. For target B, the minimum deviation is 0.8184cm and the maximum deviation is 2.6342cm. The above results show that the planar holographic RFID imaging system can still show good imaging performance for arc-shaped targets in Gaussian white noise environments; Fig.12 It can be seen that when the grid resolution is f 1 When the grid resolution is f 2 When the grid resolution is f, the minimum deviation is 1.2161cm and the maximum deviation is 1.9889cm. 3 When , the deviation range is 1.3741cm~2.3229cm. With the increase of iteration times, the larger the grid resolution, the smaller the deviation of the imaging target, which proves that the increase of grid resolution helps to improve the imaging accuracy of the system.

Claims

1. An RFID target imaging method based on plane segmentation and holographic positioning, characterized in that: The specific steps are as follows: Step 1: Use commercial RFID reader equipment and programmable mobile robots to build a tag phase acquisition system, collect phase and obtain the signal direct path propagation distance and arrival angle (AOA) data, establish a two-dimensional Cartesian coordinate system in the detection area with a length of L and a width of W, and divide the grid with a resolution of f, and the total number of grids is S grid , assume that the reader antenna starts from (0, W) at a uniform speed v and moves along a given trajectory, assume that the coordinates of the two tags are (L / 2, 0) and (0, W / 2), define the vertical direction where tag 1 is located as tag 1 direction, define the horizontal direction where tag 2 is located as tag 2 direction, and the amount of phase data read in the two tag directions is N x and N y , read the tag phase and compare it when there is no target between the antenna and the tag and when there is a target, and get two sets of phase differences and The cumulative phase change method based on sliding window is used to detect the target contour boundary, and the phase data within the boundary is intercepted and recorded as and The data volumes are K x , K y , substitute the truncated phase into the formula D = (Δφ + 2ξπ) / (β tar -β air ) to obtain the propagation distance D of the signal inside the target x and D y , where β air and β tar Represent the phase constants of the signal in the air and in the target respectively. ξ is an integer. Since the phase change caused by the signal penetrating the target generally does not exceed 2π, ξ is usually 0. According to the robot's moving speed and the recorded timestamp, the position of the antenna when the signal is received is obtained, and the position A is defined x and A y is the signal capture point, and then the length Dt of the signal direct path segment in two directions is obtained x , Dt y And the AOA data of the signal θ x ,θ y The calculation formulas are in, and Respectively represent the coordinates of a signal capture point in the direction of tag 1 and tag 2; Step 2: Construct a phase model for calculating the starting point of the propagation distance. Taking label 1 as an example, K x The distance between the starting point of the propagation distance and its corresponding signal capture point is set to Then the distance between the starting point of the nth propagation distance and its corresponding signal capture point is x n , the distance between the starting point of the nth propagation distance and the label is The length of the i-th direct path segment is expressed as i∈[1,K x ] and i≠n, the angle between the i-th direct path segment and the direct path segment where the n-th propagation distance starting point is located can be expressed as i∈[1,K x ] and i≠n, using the formula The distance between the starting point of the nth propagation distance and other signal capture points can be obtained Then E n Substituting into the ideal phase expression φ n =(4πE n / λ)mod 2π, the theoretical phase value φ at the starting point of the nth propagation distance under ideal conditions can be obtained. n , and so on, we can get the theoretical phase of all the starting points of the propagation distance in the two tag directions and Step 3: Use the idea of ​​holographic positioning to locate the starting point of the propagation distance. When the reader antenna measures the tag phase, the distance between the antenna signal capture point coordinates and the grid center coordinates |GA| is obtained and substituted into the formula h grid (G, A) = (4π·|GA| / λ) mod 2π, the theoretical phase value h measured when the signal is reflected back from the grid G ​​is obtained. grid (G, A), and then the theoretical phase value φ of all grids can be obtained grid ,Will Respectively with φ grid By comparison, we can obtain the probability that the grid is the starting point of the propagation distance, and use the formula Calculate the pixel value for quantization, where i∈[1,S grid ], k∈[1, K], for all pixel values ​​of a single propagation distance starting point, the grid where the maximum pixel value is located is the location of the propagation distance starting point, and the center coordinates of the grid are the coordinates of the propagation distance starting point (x c ,y c ); Step 4: Construct an optimization model based on maximizing the overlap degree to find the optimal starting point of the propagation distance. According to the starting point coordinates of the propagation distance, the propagation distance, and the corresponding AOA data, the coordinates (x′ c , y′ c ) of the ending point of the propagation distance can be obtained. The calculation formula for the coordinates of the ending point of the propagation distance in the label 1 direction is: when x c < L / 2, x′ c = x c - PQ, y′ c = y c + P′Q. When x c > L / 2, x′ c = x c + PQ, y′ c = y c + P′Q. When x c = L / 2, x′ c = x c , where PQ = D x ·cosθ x , The calculation formula for the coordinates of the ending point of the propagation distance in the label 2 direction is: when y c < W / 2, x′ c = x c + P′Q, y′ c = y c + P′Q. When y c > W / 2, x′ c = x c + P′Q, y′ c = y c + PQ. When y c = W / 2, y′ c = y c , where, Connect the obtained starting point and ending point of the propagation distance in sequence by direction to form two closed figures connected by point lines. After filling the closed figures with colors, two estimated images are obtained. Compare the ratio of the overlapping area to the total area of the two estimated images to obtain the overlapping degree of the two estimated images. By adjusting the distance X between the starting point of the propagation distance and the signal capture point, maximize the overlapping degree of the estimated images in both directions. With the set maximum number of iterations as the termination condition, construct an optimization model for the starting point of the propagation distance, and introduce the Particle Swarm Optimization (PSO) algorithm for solution to achieve fast and accurate search for the starting point and ending point of the propagation distance; Step 5: Aggregate all the optimal propagation distance starting points and ending points into a hologram to generate a composite hologram, and use the image reconstruction method based on the composite hologram to present the horizontal section image of the target, using the formula PX normalization =PX / PX max Normalize all pixel values ​​at the starting point of a single propagation distance, where PX represents the unnormalized pixel value matrix at the starting point of a propagation distance, and PX max Represents the largest pixel value in the pixel value matrix, performs color mapping on pixel values ​​of different sizes and fills the corresponding grids to form a hologram of a single propagation distance starting point, assigns the same pixel value as the corresponding propagation distance starting point to the grid where the propagation distance ending point is located, constructs a hologram of the propagation distance starting point-ending point, and collectively refers to all propagation distance starting points and ending points as target edge points. The pixel values ​​of non-target edge point grids are suppressed using a weight coefficient ω in the range of 0 to 1, and a set of target edge point holograms in two label directions is established. The holograms contained in a single direction are superimposed to obtain a single-direction sub-hologram of the target horizontal section, and the two-direction sub-holograms are superimposed to generate a composite hologram of the target horizontal section.

2. The method according to claim 1, characterized in that The method for obtaining the phase of the starting point of the propagation distance in step 2 is: analyze the geometric relationship between the label, the starting point of the propagation distance and the signal capture point, build a geometric model to calculate the distance between each propagation distance starting point and each signal capture point, and substitute the ideal phase expression to obtain the theoretical phase value of each propagation distance starting point. The method for locating the starting point of the propagation distance in step 3 is: for any starting point of the propagation distance, compare its theoretical phase value with the theoretical phase value when the signal reaches each signal capture point after being reflected by each grid, obtain the probability that the starting point of the propagation distance is located in each grid, and quantify it with pixel values. The grid with the largest pixel value is the starting position of the propagation distance.

Images