Method, device and equipment for realizing near-field sensing based on mobile antenna array

Abstract

The present invention belongs to the field of positioning technology, and specifically relates to a method, device and equipment for realizing near-field sensing based on a mobile antenna array. The method comprises: linearly arranging a plurality of co-located antenna units to form an antenna array, and the antenna array moves at a uniform speed along the extension direction of the antenna unit to detect a planar detection area. S2: Sampling the received signals of each antenna unit to obtain a plurality of received signals, and combining the obtained plurality of received signals into an observation signal. S3: Using the maximum likelihood estimation algorithm to establish a log-likelihood function according to the observation signal and its mean vector and covariance matrix; S4: Constructing an optimization function characterizing the probability of a target appearing at any position in the detection area according to the log-likelihood function; solving the coordinates in the detection area that satisfy the maximum value of the optimization function is the spatial position of the target. The present invention solves the problem that the existing target positioning technology cannot strike a balance between positioning accuracy, cost and power consumption.

Description

Technical Field

[0001] The present invention belongs to the field of positioning technology, and specifically relates to a method for realizing near-field sensing based on a mobile antenna array, and a corresponding near-field sensing device and near-field sensing equipment. Background Art

[0002] The sixth generation (6G) of wireless networks will provide advanced perception and high-resolution positioning capabilities to support a variety of new applications including smart cities, smart logistics, autonomous driving, and robotic navigation. However, traditional multiple-input multiple-output (MIMO) radar arrays are limited by the element spacing (antenna spacing), resulting in poor resolution.

[0003] To address this problem, massive antenna array technology is used to synthesize sensing beams with high angular resolution and high beamforming gain. Despite its potential performance advantages, massive antenna arrays have become economically unaffordable due to the exponential increase in the number of antennas, which leads to a sharp increase in manufacturing costs and operating power consumption. In addition, when the target is far away from the massive antenna array and the array element spacing in the massive antenna array is limited, the fixed array perception performance is very poor and it is almost impossible to complete the target positioning task.

[0004] In summary, the existing solution for target positioning based on fixed antenna arrays has a contradiction: when the antenna array is small, the positioning performance of the target is poor. When the antenna array is enlarged, although the positioning performance can be improved, it will cause the hardware manufacturing cost to be high and the power consumption of the equipment to be high. Summary of the invention

[0005] In order to solve the problem that the existing target positioning technology based on fixed antenna arrays cannot take into account both strong target positioning performance and low cost and power consumption, the present invention provides a method for realizing near-field sensing based on a mobile antenna array, and its corresponding near-field sensing device and near-field sensing equipment.

[0006] The technical solution provided by the present invention is:

[0007] A method for realizing near-field sensing based on a mobile antenna array, which is used to spatially locate a target in a near-field, comprises the following steps:

[0008] S1: N The antenna array is composed of a plurality of co-located antenna units arranged linearly, and the antenna array moves at a uniform speed along the extension direction of the antenna units to detect a planar detection area.

[0009] The transmission signal of each antenna unit in the detection process is recorded as :

[0010]

[0011] In the above formula, the superscript T represents the transpose operation.

[0012] S2: The process of the antenna array moving from one side to the other side is recorded as a sensing cycle; the received signal of each antenna unit is sampled according to the preset sampling period Ts to obtain multiple received signals, and L received signals are obtained. Combined into an observation signal r:

[0013]

[0014] S3: Use the maximum likelihood estimation algorithm based on the observed signal r and its mean vector and the covariance matrix Build a log-likelihood function :

[0015]

[0016] In the above formula, the superscript H represents the transpose conjugate operation;

[0017] S4: Based on the log-likelihood function, an optimization function is constructed to characterize the probability of the target appearing at any position in the detection area. ; Search for the coordinates that satisfy the maximum optimization function value in the detection area ( x , y ) is the spatial position of the target:

[0018]

[0019] In the above formula, the movement direction of the antenna array is along the Y-axis; represents the estimated value of the complex target reflection coefficient; Represents the steering vectors for transmission and reception of the antenna array.

[0020] As a further improvement of the present invention, in step S1, each antenna unit in the antenna array includes a transmitting antenna and a receiving antenna; the spacing between two adjacent antenna units in the antenna array is not less than the carrier wavelength. half.

[0021] As a further improvement of the present invention, in step S2, the antenna array obtains the received signal in each sampling period The expression is as follows:

[0022]

[0023] In the above formula, represents the noise at the array; b represents the complex target reflection coefficient; Represents the steering vectors for transmission and reception of the antenna array.

[0024] As a further improvement of the present invention, the steering vector The vector expression of is as follows:

[0025]

[0026] Each element in According to the near-field spherical wavefront model, it is expressed as follows:

[0027]

[0028] In the above formula, Indicates n The antenna element is The distance to the target during a sampling period.

[0029] As a further improvement of the present invention, The calculation method is as follows:

[0030] Assuming the moving speed of the antenna array is v, then n The antenna element is The vertical axis of the sampling period is :

[0031]

[0032] In the above formula, Indicates the distance between adjacent antenna elements in the antenna array.

[0033] Assume that the position coordinates of the target within the detection range are ( x , y ), then n The antenna element is The distance to the target during each sampling period Satisfy the following formula:

[0034]

[0035] As a further improvement of the present invention, in step S3, the mean vector The expression is as follows:

[0036]

[0037] Covariance matrix The expression is as follows:

[0038]

[0039] In the above formula, Represents the power of noise; I NL Represents the identity matrix of dimension NL.

[0040] As a further improvement of the present invention, in step S4, Satisfy the following formula:

[0041]

[0042] In the above formula, Represents the steering vector The conjugate matrix of .

[0043] As a further improvement of the present invention, in step S4, the search process of the spatial position of the target in the detection area is as follows:

[0044] (1) According to the preset resolution, the detection area is divided into M independent grids.

[0045] (2) The coordinates of the same relative position in each grid ( x , y ) as input, calculate the optimization function corresponding to each grid The value of .

[0046] (3) Traverse all the grids in the detection area and The center coordinates of the grid with the largest value ( x , y ) as the perceived spatial location of the target.

[0047] The present invention also includes a near-field sensing device, which includes a memory, a processor, and a computer program stored in the memory and running in the processor. When the processor executes the computer program, the method for realizing near-field sensing based on a mobile antenna array as described above is adopted to solve the position coordinates of the target within the detection range according to the transmission signal, the observation signal, the structural parameters and the movement speed of the antenna array within a sensing cycle.

[0048] The present invention also includes a near-field sensing device, which is used to achieve near-field sensing of a target and determine the spatial position of the target, and includes: an antenna array, a motion platform and a data processing module.

[0049] The antenna array consists of N The antenna units at the same location are arranged linearly at equal intervals. Each antenna unit includes a transmitting antenna and a receiving antenna. The distance between adjacent antenna units is not less than the carrier wavelength. half.

[0050] The motion platform is used to drive the antenna array to move at a uniform speed along the extension direction of the antenna unit.

[0051] The data processing module is used to obtain the transmitting and receiving signals of the antenna array, and adopts the method of realizing near-field perception based on the mobile antenna array as mentioned above, and solves the position coordinates of the target within the detection range according to the transmitting signal, observation signal and structural parameters and movement speed of the antenna array within a perception cycle.

[0052] The technical solution provided by the present invention has the following beneficial effects:

[0053] The present invention proposes a method for realizing near-field sensing by using a mobile antenna array. The scheme uses the near-field channel characteristics generated by the movement of the antenna array to realize the positioning of the target. The scheme can achieve target positioning performance close to that of a large-scale fixed antenna by using a relatively small antenna array, and overcome the defects of high cost and high power consumption of large-scale fixed antennas. Therefore, it has higher practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flowchart of the steps of the method for implementing near-field sensing based on a mobile antenna array provided in Example 1 of the present invention.

[0055] Figure 2 This is a schematic diagram of a near-field target positioning method based on a mobile antenna array in Example 1 of the present invention.

[0056] Figure 3 This is a comparison chart of the Cramer-Rao lower bound performance of three different schemes at different transmit powers in the verification experiment.

[0057] Figure 4 Visualization of the log-likelihood function of the observed signal using a moving antenna array in the validation experiment.

[0058] Figure 5 Visualization of the log-likelihood function of the observed signal using a conventional fixed array of the same size used in the validation experiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0060] Example 1

[0061] For target positioning technology based on traditional antenna arrays, the improvement of positioning resolution and detection accuracy depends on expanding the size of the antenna array and increasing the number of antenna elements. This will inevitably lead to a significant increase in the hardware cost and operating power consumption of the solution. In order to strike a balance between positioning performance and cost and power consumption, this embodiment proposes a method for implementing near-field perception based on a mobile antenna array, which can reduce the manufacturing cost of the antenna array while improving the resolution and accuracy of the radar.

[0062] In the solution provided in this embodiment, the antenna array is deployed on a movable platform, and a large virtual aperture is formed by moving the antenna as a whole to further improve the degree of freedom and spatial gain of the MIMO radar. Different from the far-field target perception design based on arrival angle (DoA) estimation, this embodiment installs a co-located MIMO radar array on a movable platform, and locates the target coordinates in space by utilizing the near-field channel characteristics generated by the movement of the antenna array. Then, a smaller antenna array is used to achieve target spatial position estimation performance similar to that of a large-scale fixed antenna array.

[0063] In detail, Figure 1 As shown, the method for implementing near-field sensing based on a mobile antenna array provided in this embodiment includes the following steps:

[0064] S1: N The antenna array is composed of a plurality of co-located antenna units arranged linearly, and the antenna array moves at a uniform speed along the extension direction of the antenna units to detect a planar detection area.

[0065] In this embodiment, the antenna array is a uniform linear array (ULA), and the antenna array is composed of N The antenna array is composed of a linear arrangement of co-located antenna units. Each antenna unit includes a transmitting antenna and a receiving antenna; the distance between two adjacent antenna units in the antenna array is not less than the carrier wavelength. half of Assuming that the initial position of the antenna array is the origin O, the extension direction of each antenna unit in the antenna array is along the Y-axis, and the signal transmission path of each antenna unit for signal transmission and reception is located in a two-dimensional plane XOY, when each antenna unit in the antenna array is driven to move along the Y-axis, the spatial position of the target in the XOY plane can be detected.

[0066] In practical applications, such as Figure 2As shown in the figure, the process of the antenna array moving from the origin O to the extreme position on the Y axis represents a complete perception task. On this basis, by changing the direction of the antenna array's movement along the Y axis and adjusting the direction of the antenna array's signal generation along the X axis, it is possible to perceive targets in all quadrants of the two-dimensional plane XOY. In addition, when the antenna array moves back and forth between the origin and the extreme position of the Y axis, multiple rounds of perception tasks can be completed in sequence, thereby achieving dynamic tracking of targets in the detection area.

[0067] In this embodiment, the transmission signal of each antenna unit during the detection process is recorded as :

[0068]

[0069] In the above formula, the superscript T represents the transpose operation.

[0070] S2: The process of the antenna array moving from one side to the other is recorded as a sensing cycle; according to the preset sampling cycle T s By sampling the received signals of each antenna unit, multiple received signals can be obtained. L Received signal Can be combined into an observation signal r:

[0071]

[0072] In this embodiment, it is assumed that the duration of a complete sensing cycle is equal to the duration of the sampling cycle. L times, the entire perception transmission time can be divided into L symbols, each symbol lasts for T s At this time, each transmission symbol The corresponding sampling time can be recorded as The emission signal corresponding to the sampling time is In this embodiment, the sample covariance matrix R of the transmitted signal should satisfy the transmit power constraint ,in P 0 represents the maximum transmission power of the base station, and the expression of R is as follows:

[0073]

[0074] In each symbol At the corresponding sampling time, the signal received by the antenna array is recorded as In order to estimate the coordinates of the target, the sampled L The received signals are combined into an observed signal r:

[0075]

[0076] In this embodiment, the received signal The signal transmission process is as follows:

[0077] In each sensing task, the antenna unit continuously transmits a pre-designed signal for sensing the target into the detection area. The transmitted signal is reflected by the target in the detection area and then received by the mobile antenna array. In this process, it is assumed that the antenna array starts from the origin and moves at a speed of v Start moving. Then transmit symbol Corresponding sampling time, antenna unit n The vertical coordinate for:

[0078]

[0079] In the above formula, Indicates the distance between adjacent antenna elements in the antenna array.

[0080] At this time, n The antenna units are located in the detection area ( x , y ) at the target Satisfy the following formula:

[0081]

[0082] in, .

[0083] Since the signal return delay is much shorter than the duration of each symbol during the antenna array perception process, the movement of the antenna array during signal propagation can be ignored. Based on this situation, At the corresponding sampling moment, the signal received by the antenna array is The expression is:

[0084]

[0085] In the above formula, shows the noise at the array, ; b Represents the complex target reflection coefficient, which is proportional to the radar cross-section (RCS) of the target and is isotropic for point targets. represents the transmit and receive steering vectors of the array, and its expression is: .

[0086] In the guide vector In each element According to the near-field spherical wavefront model, it is expressed as follows:

[0087]

[0088] After obtaining the above observation signal r, the receiver of this embodiment deploys a practical near-field estimation algorithm such as approximate loop optimization based on the maximum likelihood (ML) criterion to estimate the coordinates of the target in space. The process consists of two main steps. The first step is to obtain a centralized negative log-likelihood function based on the coordinates of all targets, and the second step is to apply loop optimization technology to solve the coordinates of each target. The two steps are described in detail below:

[0089] S3: Use the maximum likelihood estimation algorithm based on the observed signal r and its mean vector and the covariance matrix Build a log-likelihood function .

[0090] In this embodiment, the essence of the perception task is to estimate the following unknown parameters based on the observed signal r :

[0091] .

[0092] in, and are complex target reflection coefficients b The real and imaginary parts of .

[0093] During the signal transmission process, the mean vector of the observed signal r and the covariance matrix They are as follows:

[0094]

[0095] in, Represents the power of noise; I NL Represents the identity matrix of dimension NL.

[0096] This embodiment proposes a practical near-field target positioning algorithm based on the maximum likelihood estimation algorithm. The log-likelihood function of is:

[0097]

[0098] In the above formula, the superscript H represents the transpose conjugate operation.

[0099] S4: In order to maximize the log-likelihood function constructed in the previous step, this embodiment establishes an optimization function that characterizes the probability of the target appearing at any position in the detection area: :

[0100]

[0101] In the above formula, the movement direction of the antenna array is along the Y-axis; represents the estimated value of the complex target reflection coefficient; Represents the steering vectors for transmission and reception of the antenna array. represents the estimated value of the complex target reflection coefficient, which satisfies the following formula:

[0102]

[0103] In the above formula, Represents the steering vector The conjugate matrix of .

[0104] Based on the optimization function established , search for the coordinates that can optimize the maximum function value in the detection area ( x , y ), which is the spatial position of the target.

[0105] In the solution of this embodiment, the search process of the spatial position of the target in the detection area is as follows:

[0106] (1) According to the preset resolution, the detection area is divided into M independent grids.

[0107] For example, assuming that the detection range of an antenna array is 1000m, the resolution is 0.1m, and the detection area is 1000m×1000m, the detection area can be divided into a 10000×10000 grid.

[0108] (2) The coordinates of the same relative position in each grid ( x , y ) as input, calculate the optimization function corresponding to each grid The value of .

[0109] In this embodiment, "the same relative position" refers to the designated point at the same position in each grid, such as the center point of the grid or a designated corner point. Taking the aforementioned 10000×10000 grid as an example, when the same corner point is selected, the coordinates of each grid can be recorded as: (0, 0), (0, 0.1), (0.1, 0), (0.1, 0.1) ..., and so on.

[0110] (3) Traverse all the grids in the detection area and The center coordinates of the grid with the largest value ( x , y ) as the perceived spatial location of the target.

[0111] Example 2

[0112] The method for realizing near-field sensing based on a mobile antenna array provided in Example 1 includes two stages. The first stage uses a movable small-scale linear antenna array to "scan" the detection area, and the second stage generates the location information of the target in the detection area according to the transmission signal and the observation signal during the scanning process. Among them, the former is implemented based on a specific radar system, and the latter requires the use of computer equipment to process signal data according to a specific data processing method to achieve.

[0113] Therefore, based on the scheme of Example 1, this embodiment further provides a near-field sensing device, which is used to complete specific data processing tasks in the second stage, including: using the near-field characteristics generated by antenna movement to perform channel modeling, processing received signals, and analyzing target positioning performance.

[0114] Specifically, the near-field sensing device provided in this embodiment includes a memory, a processor, and a computer program stored in the memory and running in the processor. When the processor executes the computer program, the method for implementing near-field sensing based on a mobile antenna array as in Example 1 is adopted to solve the position coordinates of the target within the detection range according to the transmitted signal, the observed signal, the structural parameters and the movement speed of the antenna array within a sensing cycle.

[0115] The near-field sensing device provided in this embodiment is essentially a computer device for implementing the solution in Example 1. The computer device can be an embedded device installed in the receiver of the radar system and used to process the signal. It can also be a back-end device independent of the radar system, such as a base station with a sensing function. Then, after the front-end radar system obtains the data of a complete sensing cycle, the location information of the target is generated at the back end according to the detection signal uploaded by the radar system. In this embodiment, the computer device can also use an intelligent terminal, a tablet computer, a laptop computer, a desktop computer, a rack server, a blade server, a tower server or a cabinet server (including an independent server, or a server cluster composed of multiple servers) that can execute programs.

[0116] The computer device indicated in this embodiment includes at least but is not limited to: a memory and a processor that can be connected to each other through a system bus. Among them, the memory (i.e., a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory can be an internal storage unit of a computer device, such as a hard disk or a memory of the computer device. In other embodiments, the memory can also be an external storage device of a computer device, such as a plug-in hard disk equipped on the computer device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory can also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the memory is generally used to store an operating system and various application software installed on the computer device. In addition, the memory can also be used to temporarily store various types of data that have been output or are to be output.

[0117] The processor may be a central processing unit (CPU), a graphics processing unit (GPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor is generally used to control the overall operation of a computer device. In this embodiment, the processor is used to run program codes stored in a memory or process data.

[0118] Example 3

[0119] Based on embodiments 1 and 2, this embodiment further provides a near-field sensing device, which is used to realize near-field sensing of a target and determine the spatial position of the target. The near-field sensing device in this embodiment includes a front-end radar system for scanning a detection area, and also includes a computer device for generating the spatial position of the target according to the detection signal of the radar system. Specifically, the near-field sensing device includes: an antenna array, a motion platform, and a data processing module.

[0120] The antenna array consists of N The antenna units at the same location are arranged linearly at equal intervals. Each antenna unit includes a transmitting antenna and a receiving antenna. The distance between adjacent antenna units is not less than the carrier wavelength. The motion platform is used to drive the antenna array to move at a constant speed along the extension direction of the antenna unit. A conveyor belt is installed on the motion platform of this embodiment, so it can be moved at a constant speed by a motor.

[0121] The data processing module is used to obtain the transmission signal and the receiving signal of the antenna array, and adopt the method of realizing near-field sensing based on the mobile antenna array as in Example 1, and solve the position coordinates of the target within the detection range according to the transmission signal, the observation signal, the structural parameters and the movement speed of the antenna array within a sensing cycle. The data processing module in this embodiment is the near-field sensing device in Example 2.

[0122] Verification experiment

[0123] In order to verify the effectiveness of the method for realizing near-field sensing based on a mobile antenna array provided by the present invention and highlight the advantages of the solution, technicians conducted theoretical analysis and numerical simulation on the solution of the present invention and compared the performance of the solution of the present invention with that of the existing solution based on a fixed antenna array.

[0124] 1. Theoretical Analysis

[0125] This experiment intends to measure whether the accuracy of the target's spatial position estimated from the radar signal is optimal by deriving the Cramér-Rao Lower Bound (CRLB) of the scheme. The Cramer-Rao lower bound matrix is ​​defined as , where F is the Fisher information matrix (FIM). Based on this, the target coordinates are estimated ( x , y ) is .

[0126] For the target positioning implemented by the mobile antenna array proposed in the present invention, when the sample covariance matrix is Under isotropic transmission, the Cramer-Rao lower bound is:

[0127] ,

[0128] in,

[0129] ,

[0130] ,

[0131] In the above formula, , The two respectively represent the first antenna unit in the i and j The distance to the target during a sampling period.

[0132] In addition, for any antenna n , because the antenna spacing is much smaller than the distance from the target to the array, there is , .

[0133] To demonstrate the performance of the mobile antenna array target positioning method proposed in the present invention, the above-mentioned Cramer-Rao lower bound is compared with the Cramer-Rao lower bound of target positioning using a traditional fixed array and a large-scale fixed array of the same size.

[0134] Consider a traditional fixed array equipped with N antennas located at This is equivalent to fixing the mobile array at the position of the transmission symbol At the corresponding sampling time point. Based on isotropic transmission, the Cramer-Rao lower bound for target positioning using the traditional fixed array of the same size is:

[0135] ,

[0136] in,

[0137] ,

[0138] ,

[0139] Consider a large, extended fixed array equipped with L antennas located at This is equivalent to L At the sampling time corresponding to the transmission symbol, an antenna is placed at the position of the first antenna of the mobile array. Based on isotropic transmission, the Cramer-Rao lower bound for target positioning using this large-scale fixed array is:

[0140] ,

[0141] in,

[0142] ,

[0143] .

[0144] Compared with the traditional fixed array of the same size, the mobile antenna array proposed in the present invention has a similar form to the Cramer-Rao lower bound of the large-scale fixed array. This shows that the movement of the array effectively expands its effective size and increases the number of equivalent antennas, thereby greatly improving the perception performance. Specifically, by comparing the Cramer-Rao lower bounds of target positioning using the mobile antenna array proposed in the present invention with the traditional fixed array and the large-scale fixed array of the same size, the following estimates are made:

[0145] ,

[0146] ,

[0147] ,

[0148] in, d is the approximate distance from the target to the antenna array. From the above estimation, it can be obtained that the mobile antenna array proposed in the present invention has approximately The performance is significantly improved by orders of magnitude; compared with the huge cost of large-scale antenna arrays, the performance is only about This indicates that the present invention can provide an economical and efficient solution for the high-precision and high-resolution target perception and positioning requirements of the sixth-generation wireless network.

[0149] 2. Experimental Verification

[0150] This utility model verifies the target positioning performance of the mobile antenna array proposed in the present invention through numerical simulation results, and compares its performance with that of a traditional fixed array and a large-scale fixed array of the same size.

[0151] The system parameters in the experiment are: the target is located at coordinates ( x , y ) = (10; 0) meters. The mobile antenna array is equipped with 16 (N=16) antennas, and the antenna spacing is 2.5 cm (half wavelength of the carrier) =2.5). The array moves from the origin along the positive direction of the Y axis at a speed of 5 m / s ( v =5). A complete sensing cycle consists of 1000 symbols, and the duration of each transmitted symbol is 1 millisecond. Therefore, the size of the mobile antenna array and the traditional fixed array of the same size is 0.4 meters ( = 0.4), while the corresponding large-scale fixed array size is 5 meters ( =5). In addition, the array’s transmit power P 0 Set between 20 dBm and 40 dBm, noise power is -70 dBm ( =-70). Mobile antenna arrays, conventional fixed arrays of the same size, and large-scale fixed arrays all use isotropic transmission.

[0152] On this basis, the Cramer-Rao lower bounds achieved by the mobile antenna array of the present invention, the traditional fixed array of the same size in the control group, and the large-scale fixed array for target positioning at different transmission powers are as follows: Figure 3 shown.

[0153] analyze Figure 3 From the data, we can see that:

[0154] The Cramer-Rao lower bounds of all designs are inversely proportional to the transmit power, which is consistent with the definition of the Cramer-Rao lower bound. Compared with the traditional fixed array of the same size, the Cramer-Rao lower bound of the mobile antenna array has a performance improvement of more than four orders of magnitude; and compared with the large-scale fixed array, the Cramer-Rao lower bound performance of the mobile antenna array is only less than two orders of magnitude behind. This shows that the mobile antenna array proposed in the present invention significantly increases the effective size of the array and the number of equivalent antennas, thereby significantly improving the performance of target estimation. In practical applications, in order to obtain high-precision perception and positioning performance, traditional fixed arrays usually require a very large number of antennas or extremely sparse antenna arrangements. In contrast, the mobile antenna array proposed in the present invention can achieve the required estimation performance by using a smaller-sized array, thereby significantly reducing the manufacturing cost of the antenna and the operating cost of the radar. Therefore, the present invention further endows the sixth-generation wireless network with the ability of high-precision perception and high-resolution positioning, thereby supporting new Internet of Things applications including smart cities, smart logistics, autonomous driving and robot navigation.

[0155] 3. Visualization of target positioning results

[0156] This experiment also visualizes the log-likelihood function generated by target positioning using a mobile antenna array and a traditional fixed array of the same size based on the maximum likelihood estimation algorithm. The image of the visualization result is shown in the figure below. Figure 4 and Figure 5 shown.

[0157] contrast Figure 4 and Figure 5 It can be found that: in the solution provided by the present invention, an obvious peak appears in the image, while the traditional fixed array of the control group does not have a peak that is significantly higher than other areas. This shows that the solution based on the mobile antenna array of the present invention can achieve accurate positioning of the target through the maximum likelihood estimation algorithm. However, the traditional fixed array cannot locate the target due to the blurred log-likelihood function caused by it. This further verifies the effectiveness of the mobile antenna array proposed by the present invention in constructing near-field channel characteristics and realizing near-field target positioning.

[0158] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for realizing near-field sensing based on a mobile antenna array, which is used to spatially locate a target in the near field, characterized in that: It includes the following steps: S1: N The antenna array is composed of a linear arrangement of co-located antenna units. The antenna array moves at a uniform speed along the extension direction of the antenna units to detect a plane detection area. The transmission signal of each antenna unit during the detection process is recorded as ; S2: The process of the antenna array moving from one side to the other is recorded as a sensing cycle; according to the preset sampling period T s Sampling the received signal of each antenna unit will give L Received signal Combination is the observation signal r; S3: Use the maximum likelihood estimation algorithm to estimate the observed signal r and its mean vector and covariance matrix Build a log-likelihood function : ， In the above formula, the superscript H represents the transpose conjugate operation; S4: Construct an optimization function based on the log-likelihood function to characterize the probability of the target appearing at any position in the detection area ; Search for the coordinates that satisfy the maximum optimization function value in the detection area ( x , y ) is the spatial position of the target: ， In the above formula, the movement direction of the antenna array is along the Y-axis; represents the estimated value of the complex target reflection coefficient; Represents the steering vectors for transmission and reception by the antenna array.

2. The method for implementing near-field sensing based on a mobile antenna array according to claim 1, wherein: In step S1, each antenna unit in the antenna array includes a transmitting antenna and a receiving antenna, and the distance between adjacent antenna units is not less than the carrier wavelength. half.

3. The method for implementing near-field sensing based on a mobile antenna array according to claim 2, wherein: In step S2, the antenna array obtains the received signal in each sampling period The expression is as follows: ， In the above formula, represents the noise at the array; b represents the complex target reflection coefficient; Represents the steering vectors for transmission and reception by the antenna array.

4. The method for implementing near-field sensing based on a mobile antenna array according to claim 3, wherein: Steering vector The vector expression of is as follows: ， Each element in According to the near-field spherical wavefront model, it is expressed as follows: ， In the above formula, Indicates the n The antenna unit is in the The distance to the target during a sampling period.

5. The method for implementing near-field sensing based on a mobile antenna array according to claim 4, wherein: The calculation method is as follows: Assuming the moving speed of the antenna array is v, then n The antenna unit is in the The vertical coordinate of the sampling period is : ， In the above formula, Indicates the spacing between adjacent antenna elements in the antenna array; Assume that the position coordinates of the target within the detection range are ( x , y ), then n The antenna unit is in the The distance to the target during the sampling period Satisfy the following formula: 。 6. The method for implementing near-field sensing based on a mobile antenna array according to claim 4, wherein: In step S3, the mean vector The expression is as follows: ， Covariance matrix The expression is as follows: ， In the above formula, Represents the power of noise; I NL Represents the identity matrix of dimension NL.

7. The method for implementing near-field sensing based on a mobile antenna array according to claim 6, wherein: In step S4, Satisfy the following formula: ， In the above formula, Represents the steering vector The conjugate matrix of .

8. The method for implementing near-field sensing based on a mobile antenna array according to claim 7, wherein: In step S4, the search process for the spatial position of the target within the detection area is as follows: (1) Divide the detection area into M independent grids according to the preset resolution; (2) The coordinates of the same relative position in each grid ( x , y ) as input, calculate the optimization function corresponding to each grid The value of (3) Traverse all the grids in the detection area and The center coordinates of the grid with the largest value ( x , y ) as the perceived spatial location of the target.

9. A near-field sensing device comprising a memory, a processor, and a computer program stored in the memory and executed in the processor, wherein: When the processor executes the computer program, the method for realizing near-field perception based on a mobile antenna array as described in any one of claims 1 to 8 is adopted to solve the position coordinates of the target within the detection range based on the transmitted signal, the observed signal, and the structural parameters and movement speed of the antenna array within a perception cycle.

10. A near-field sensing device for sensing a target in the near field and determining the target's spatial position, characterized in that: It includes: Antenna array, which consists of N The antenna units at the same location are arranged linearly at equal intervals. Each antenna unit includes a transmitting antenna and a receiving antenna. The distance between adjacent antenna units is not less than the carrier wavelength. half of A motion platform, used to drive the antenna array to move at a uniform speed along the extension direction of the antenna units; A data processing module is used to obtain the transmitted and received signals of the antenna array, and adopt the method for realizing near-field perception based on the mobile antenna array as described in any one of claims 1 to 8, and solve the position coordinates of the target within the detection range according to the transmitted signal, observation signal and structural parameters and movement speed of the antenna array within a perception cycle.

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