Perception-assisted high-precision channel parameter estimation and target positioning method based on IRS (Inter-Reference Signal) and OFDM (Orthogonal Frequency Division Multiplexing)

Abstract

The invention relates to a sensing integration, a semi-passive intelligent reflecting surface and a broadband orthogonal frequency division multiplexing signal, in particular to a high-precision channel parameter estimation and target positioning method based on the semi-passive intelligent reflecting surface (hereinafter referred to as a reflecting surface) and the broadband orthogonal frequency division multiplexing signal. Comprising the following steps: step 1, establishing an OFDM signal model, a channel model and a perception model; step 2, extracting channel parameters based on FFT and a Newton-like method; and step 3, estimating the kinematics parameters of the target: calculating the position and speed of the target based on the nine channel parameters estimated in the step 2 and the known position coordinates of the base station and the IRS sensor array to obtain the kinematics parameters of the target. According to the invention, the computing resources of the user side are saved, and the energy overhead of the user side is reduced; pilot frequency overhead can be reduced, and spectrum efficiency can be improved; according to the invention, high-precision positioning and speed measurement can be realized.

Description

Technical Field

[0001] The invention relates to synaesthesia integration, intelligent reflection surface and broadband orthogonal frequency division multiplexing signal, and is a perception-assisted high-precision channel parameter estimation and target positioning method based on intelligent reflection surface and orthogonal frequency division multiplexing signal. Background Art

[0002] With the rapid development of intelligent Internet of Things and mobile communication networks, more and more devices are connected to wireless networks, which puts forward new requirements for future wireless networks: large capacity, low latency and high efficiency. In the past, by increasing the number of antennas, reducing the cell range and developing high-frequency band resources, hundreds of billions of devices have been connected to the Internet. However, simply increasing the number of antennas, increasing the power of the transmitter or using high-frequency resources will increase the power consumption of the system. In recent years, the introduction of intelligent reflecting surfaces (IRS) and integrated sensing and communication (ISAC) systems is believed to improve the performance of communication systems and reduce the energy consumption of the system, and is expected to become a key enabling technology for future mobile communications. The intelligent reflecting surface is a plane composed of a large number of passive reflecting elements, each of which can independently control the amplitude and phase of the incident signal, thereby realizing active intelligent reconfiguration of the wireless environment. However, the performance of the intelligent reflecting surface is highly dependent on the accuracy of the channel state information. Although a large number of channel estimation algorithms have been proposed, most of them complete the estimation on the user side through the downlink pilot signal and then transmit it back to the base station, or complete the estimation on the base station side through the user transmitting the uplink pilot signal. Both solutions require users to consume a lot of energy and spectrum resources, which is not conducive to the battery life and user experience of mobile devices. The integrated system integrates communication and perception functions, and can share a set of systems and spectrum resources to achieve perception and communication of targets within the service range. In addition, the semi-passive reflector can also realize the signal perception function by integrating a sensor array in the reflector. Combining these two technologies is expected to further improve the efficiency and performance of mobile communication systems.

[0003] Orthogonal Frequency-Division Multiplexing (OFDM) signal is a widely used communication signal with the characteristics of high spectrum efficiency, strong resistance to multipath effects and interference. Compared with narrowband signals, OFDM signals have dozens or even hundreds of orthogonal subcarriers. Combining OFDM with IRS system will introduce a large number of channel parameters, resulting in large amount of channel estimation calculation, long calculation time and poor real-time performance. The sparsity and parameterized channels of millimeter wave channels can reduce the number of channel parameters, while achieving high-precision positioning and speed measurement of communication targets. The channel parameter extraction method based on fast Fourier transform and the channel parameter optimization method based on Newton-like method can achieve fast and accurate parameter estimation. Channel parameters can be used to locate targets, predict, track and reconstruct channels, and then optimize base stations and reflector beams, thereby improving system communication performance and spectrum efficiency. Summary of the invention

[0004] The present invention focuses on the OFDM communication perception integrated system assisted by an intelligent reflecting surface, studies the positioning problem of communication targets in three-dimensional space, and improves the system's target positioning accuracy, energy efficiency, and communication spectrum efficiency. In the above-mentioned MIMO system, the sensing elements of the base station and the intelligent reflecting surface sense the communication signal reflected by the target, and the reflecting surface performs analog-to-digital conversion on the data and transmits it to the base station through a reliable link. Based on the communication signal and the perception model, the base station performs an FFT transform on the three-dimensional signal matrix, extracts channel information from the signal matrix, and further improves the estimation accuracy of the channel information by introducing auxiliary variables representing the channel information and using a Newton-like optimization method. Based on the corrected channel information, a three-dimensional target positioning and speed measurement method is proposed, thereby achieving high-precision and high-energy-efficiency positioning of the target, and further improving the spectrum efficiency of communication. The present invention can be used in deployment, monitoring, and control scenarios for low-altitude aircraft and autonomous driving vehicles.

[0005] Technical solution of the present invention:

[0006] A perception-assisted high-precision channel parameter estimation and target positioning method based on IRS and OFDM, comprising the following steps:

[0007] Step 1. Establish OFDM signal model, channel model and perception model;

[0008] First, an OFDM signal model is established; then, a reflection signal channel model is constructed, which is sent from the base station, reflected by the target, and sensed by the base station, IRS transverse and longitudinal sensor arrays, including the base station-target-base station link channel response, the base station-target-reflection surface transverse sensor array link channel response, and the base station-target-reflection surface longitudinal sensor array link channel response; then, a base station, IRS transverse and longitudinal sensor array receiving signal model is constructed, and the received signals are filled into the 3D symbol matrix Y 0 , Y 1 and Y 2 .

[0009] Step 2. Extract channel parameters based on FFT and Newton-like method;

[0010] Based on the fast Fourier algorithm, the received signal model obtained in step 1 roughly estimates the Doppler frequency shift in the horizontal and vertical directions of the base station and IRS sensor array, the signal arrival angle and the signal propagation delay, a total of 9 channel parameters;

[0011] Auxiliary variables are introduced to compensate for Doppler, cosine of arrival angle and delay, and an optimization problem is constructed and solved using a Newton-like method to improve the estimation accuracy of nine channel parameters.

[0012] Step 3. Estimate target kinematic parameters:

[0013] Based on the 9 channel parameters estimated in step 2 and the known position coordinates of the base station and IRS sensor array, the position and velocity of the target are calculated to obtain the kinematic parameters of the target.

[0014] Beneficial Effects

[0015] The present invention combines a synaesthesia integrated base station, a semi-passive intelligent transmitting surface and a broadband OFDM signal, and uses the downlink communication signal reflected by the target to complete high-precision estimation of the channel and high-precision positioning and speed measurement of the target at the base station end based on a 3D FFT algorithm and a Newton-like method. In terms of communication, the present invention uses an intelligent reflecting surface and an OFDM system to improve communication performance in practical applications. In terms of perception, the traditional method of channel estimation and positioning based on uplink on the base station side and downlink signal on the user side requires continuous transmission of a large number of pilots and consumes a large amount of energy and resources on the user side. The method proposed in the present invention receives the target reflected signal at the base station and the semi-passive reflecting surface, and completes the calculation on the base station side, which reduces the energy cost on the user side and saves the computing resources on the user side. In addition, because the transmitted signal is known to the base station, the pilot overhead can be reduced and the spectrum efficiency can be improved. Thanks to the linear sensor array of the base station linear array antenna and the semi-passive reflecting surface and the broadband OFDM signal, the present invention can obtain multi-dimensional information such as the distance, speed, angle, etc. of the target in 3D space, and realize high-precision positioning and speed measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The method of the present invention is an IRS-assisted broadband communication perception integrated system to place an order for target positioning scenarios;

[0017] Figure 2 A schematic diagram of the geometric relationship between the positions of various components in the method system of the present invention;

[0018] Figure 3 Flowchart for channel parameter and target kinematic parameter estimation based on FFT and Newton-like method

[0019] Figure 4 The relationship diagram between the mean square error and signal-to-noise ratio of the time delay of the method of the present invention is shown;

[0020] Figure 5 The relationship diagram between the mean square error and signal-to-noise ratio of the Doppler frequency shift of the method of the present invention;

[0021] Figure 6 A relationship diagram between the mean square error and signal-to-noise ratio of the cosine value of the angle of arrival of the method of the present invention;

[0022] Figure 7 This is a relationship diagram between the mean square error and signal-to-noise ratio of the target position and speed of the method of the present invention. DETAILED DESCRIPTION

[0023] The present invention selects a broadband OFDM communication system based on a semi-passive intelligent reflector to perform channel parameter estimation and positioning of three-dimensional space targets. OFDM signal model, channel model and perception model are established by utilizing the orthogonality of each subcarrier of the broadband OFDM signal and the flat fading of the subcarrier, the channel sparsity of the millimeter wave and the characteristics of system communication and perception integration. A channel parameter extraction algorithm based on 3D FFT and Newton-like method is proposed to achieve high-precision estimation of channel parameters. The relationship between the kinematic parameters of the target and the channel parameters is used to achieve target positioning and speed measurement. The technical method of the present invention achieves high-energy efficiency, high-precision and high-spectral efficiency channel estimation and target positioning through a communication and perception integrated system based on a semi-passive intelligent reflector and broadband OFDM signal.

[0024] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] Example

[0026] Figure 1 This paper describes the single target positioning scenario in the IRS-assisted broadband communication perception integrated system using OFDM signals, and describes the single target positioning and speed measurement scenarios in the IRS-assisted interawareness integrated system.

[0027] Figure 2It is a schematic diagram of the geometric relationship between the positions of various components in the system, describing the geometric relationship between the base station, IRS and target positions in the system, as well as the quantitative relationship between the target ground velocity and the radial velocity relative to the base station and IRS.

[0028] A perception-assisted high-precision channel parameter estimation and target positioning method based on IRS and OFDM, comprising the following steps:

[0029] Step 1. Establish OFDM signal model, channel model and perception model

[0030] First, an OFDM signal model is established; then, a reflection signal channel model is constructed, which is sent from the base station, reflected by the target, and sensed by the base station, IRS transverse and longitudinal sensor arrays, including the base station-target-base station link channel response, the base station-target-reflection surface transverse sensor array link channel response, and the base station-target-reflection surface longitudinal sensor array link channel response; then, a base station, IRS transverse and longitudinal sensor array receiving signal model is constructed, and the received signals are filled into the 3D symbol matrix Y 0 , Y 1 and Y 2 The details are as follows:

[0031] Specific steps for building an OFDM signal model :

[0032] Step (1.1), assume that the OFDM signal consists of Q consecutive OFDM symbols, each OFDM symbol consists of M orthogonal subcarriers, and the carrier center frequency is f c , the subcarrier spacing is Δf, and the symbol transmitted on the mth subcarrier of the qth OFDM symbol is s q，m , q=0,1,...,Q-1,m=0,1,...,M-1, then the equivalent low-pass signal of the qth OFDM can be expressed as

[0033]

[0034] c(t) satisfies

[0035]

[0036] Where T′=T+T cp , T is the duration of an OFDM symbol, T cp It is a cyclic prefix to avoid interference between OFDM symbols, usually T / 4.

[0037] Step (1.2), the time domain representation of the continuous signal formed by Q consecutive OFDM symbols is

[0038]

[0039] Specific steps for channel model construction :

[0040] In step (1.3), the transmitted broadband signal x(t) propagates in the wireless channel and is affected by path fading, delay and Doppler, causing changes in phase and amplitude.

[0041] Assume that the path attenuation is α, the propagation delay is τ, and the Doppler frequency shift caused by the target motion is f d , considering each subcarrier of the OFDM signal as a flat fading signal, the signal received by the nth element of the uniform linear array antenna with N receiving elements is

[0042]

[0043] Where cosθ is the cosine of the angle between the wave source direction and the antenna array direction, λ is the carrier wavelength, d is the spacing between adjacent elements of the linear array antenna, and w n (t) is Gaussian white noise.

[0044] Step (1.4), perform Fourier transform on the qth OFDM symbol signal at the receiving end to obtain the frequency domain representation of the signal

[0045]

[0046] Where W q，n，m is the frequency domain value of Gaussian noise.

[0047] Step (1.5), in the case of a single-input multiple-output channel, the frequency domain response of the wireless channel corresponding to the subcarrier m of the qth OFDM symbol on the nth antenna element is

[0048]

[0049] Step (1.6), consider the case of multiple input and multiple output. Assume that the base station has L transmitting antennas and the antenna steering vector is Then the frequency response vector of the wireless channel is

[0050]

[0051] Wherein, n∈{0,1,...,N-1} represents the serial number of the receiving antenna element or the sensor element, and q and m are the same as in step (1.5), representing the OFDM symbol number and the subcarrier number, respectively.

[0052] Step (1.7), such as Figure 3As shown in the figure, the system consists of three parts: ISAC base station, IRS semi-passive reflector and communication target. The base station and the target complete uplink and downlink communication and target perception and positioning with the assistance of the reflector. The horizontal and vertical uniform linear sensor arrays of the multi-antenna ISAC base station and the semi-passive IRS are recorded as 0, 1, and 2 respectively. The number of sensing elements of the horizontal and vertical uniform linear sensor arrays of the base station and the semi-passive IRS are N0, N1, and N2 respectively.

[0053] Step (1.8), let the Doppler shift of the base station-target-base station link be f d，0 , the signal arrival angle is θ0, the propagation delay is τ0, and the spacing between adjacent elements of the base station receiving antenna is d0, then the channel response of the link is

[0054]

[0055] Among them, n0∈{0, 1, ..., N0-1} represents the serial number of the base station receiving antenna element, and q and m are the same as step (1.5), representing the OFDM symbol number and subcarrier number respectively.

[0056] Step (1.9), let the Doppler frequency shift of the base station-target-reflector lateral sensor array link be f d，1 , the signal arrival angle is θ1, the propagation delay is τ1, and the spacing between adjacent elements of the transverse sensor array of the reflective surface is d1, then the channel response of the link is

[0057]

[0058] Among them, n1∈{0, 1, ..., N1-1} represents the serial number of the element of the lateral sensor array of the reflecting surface, and q and m are the same as step (1.5), representing the OFDM symbol number and the subcarrier number respectively.

[0059] Step (1.10), let the Doppler frequency shift of the base station-target-reflector longitudinal sensor array link be f d，2 , the signal arrival angle is θ2, the propagation delay is τ2, and the spacing between adjacent elements of the longitudinal sensor array of the reflector is d2, then the channel response of the link is

[0060]

[0061] Among them, n2∈{0, 1, ..., N2-1} represents the serial number of the longitudinal sensor array element of the reflecting surface, and q and m are the same as step (1.5), representing the OFDM symbol number and subcarrier number respectively.

[0062] Specific steps for building a model of receiving signals from base stations and semi-passive reflective surface sensor arrays

[0063] In step (1.11), consider that the signal is transmitted from the base station, passes through the line-of-sight path and the reflection path formed by the reflection surface to reach the target, and the communication is completed. Part of the signal reflected by the target returns to the base station, and the other part is reflected to the sensor array on the reflection surface. Because the signal after multiple reflections is weak, it is reasonable to assume that the reflected signal mainly comes from the line-of-sight path, and the reflection path signal passing through the reflection surface can be ignored. In addition, since the base station and the reflection surface are usually stationary and there is a reliable communication link between them, the signal from the base station to the reflection surface array sensor can be easily compensated.

[0064] Step (1.12), assuming that the beamforming vector of the base station is b , after passing through the base station-target-base station link, the base station receives the reflected OFDM signal, which is filtered, converted to digital, and demodulated. The frequency domain representation of the signal is:

[0065]

[0066] in is Gaussian white noise.

[0067] Step (1.13), similarly, the frequency domain representation of the reflected signal sent by the base station, reflected by the target, and sensed by the IRS lateral and longitudinal sensors is:

[0068]

[0069] in and is Gaussian white noise.

[0070] Step (1.14), in the calculation unit at the base station side, with q, n and m as subscripts, the received signal y q，n，m Fill in the three-dimensional matrix separately and Right now Used for subsequent estimation and calculation of channel parameters and kinematic parameters.

[0071] Step 2. Extract channel parameters based on FFT and Newton-like method;

[0072] This step is based on the 3D symbol matrix Y obtained from (1.14) using the Fast Fourier Transform Algorithm 0 , Y 1 and Y 2 , roughly estimate the Doppler frequency shift, signal arrival angle and signal propagation delay of the target in the horizontal and vertical directions relative to the base station and IRS sensor array, a total of 9 channel parameters;

[0073] Auxiliary variables are introduced to compensate for Doppler, cosine of arrival angle and time delay. An optimization problem is constructed and solved using a Newton-like method to improve the estimation accuracy of nine channel parameters.

[0074] The specific steps are as follows:

[0075] Step (2.1), at the base station, the transmitted downlink communication OFDM symbol s q，m is known and is used to compensate the perception signal for subsequent channel parameter estimation, i.e.

[0076]

[0077] Where * is the conjugate operator. Apply this operation to Y 0 , Y 1 and Y 2 , the signal matrices after compensation are and

[0078] Step (2.2), Perform FFT transformation of the first dimension at point F1, perform FFT transformation of the second dimension at point F2, and perform IFFT transformation of the third dimension at point F3 to obtain in

[0079]

[0080] In the formula q′=0,1,...,F1-1,n′=0,1,...,F2-1,m′=0,1,...,F3-1,F1≥Q,F2≥N,F3≥M. Apply this operation to and get and

[0081] Step (2.3), when the following conditions are met

[0082]

[0083] The amplitude of is maximized. Therefore, through the matrix and The index of the element with the largest amplitude in can be used to obtain the estimated value (rough estimate) of Doppler frequency shift, signal arrival angle and signal propagation delay, that is,

[0084]

[0085] Step (2.4), assuming the matrix and The indices of the elements with the largest magnitude are { q ′0,n′0,m′ n}, {q′1, n′1, m′1}, {q′2, n′2, m′2} , then the estimated values ​​of the channel parameters corresponding to the base station-target-base station link, the base station-target-reflector transverse sensor array link, and the base station-target-reflector longitudinal sensor array link are

[0086]

[0087] Step (2.5), because the number of FFT points is limited and discrete, the probability that the equation in step (2.3) is true is 0. In order to further improve the accuracy of channel parameter estimation and make the relationship in step (2.3) truly satisfied, an auxiliary variable δ is introduced fd , δcosθ and δt, for Specifically, by Introducing δ fd , the phase change produced by δcosθ and δτ makes the Doppler, the cosine of the angle of arrival and the time delay become fd +δf d , cosθ+δcosθ and τ+δτ, that is

[0088]

[0089] get Apply this action to and Get and

[0090] Step (2.6), Perform FFT transformation at point F1 on the first dimension, FFT transformation at point F2 on the second dimension, and IFFT transformation at point F3 on the third dimension to obtain in

[0091]

[0092] Apply this operation to and get and

[0093] Step (2.7), from Select the element at the position of the element with the maximum amplitude determined in step (2.3), that is, And construct the optimization problem

[0094]

[0095] Step (2.8), use the Newton-like method to solve the optimization problem and get the optimal solution as well as

[0096] Correct the estimated value in step (2.3) to obtain the accurate estimated value of the channel parameter

[0097]

[0098] Step (2.9), Take separately and Repeat steps (2.7)-(2.8) to construct and solve the optimization problem, and complete the channel parameter estimation for the base station-target-base station link, the base station-target-reflector lateral sensor link, and the base station-target-reflector longitudinal sensor link. The channel parameter estimation values ​​are

[0099] Step 3. Estimate target kinematic parameters:

[0100] Based on the 9 channel parameters estimated in step (2.9) and the known base station and IRS sensor array position coordinates, the position and velocity of the target are calculated to obtain the kinematic parameters of the target. The specific steps are as follows:

[0101] Step (3.1), such as Figure 2 As shown, the base station is placed along the y-axis direction, and the reflection surface is located in the xOZ plane. Assume that the position coordinates of the base station and the reflection surface are p0 = [x0, y0, z0] T and p IRS =[x IRS ,y IRS , z IRS ] T , the coordinates of the transverse and longitudinal sensor array centers of the reflective surface are p1 = [x1, y1, z1] T and p2 = [x2, y2, z2] T . Let the target position be p = [x, y, z] T , the distance between the target and the base station is r0, and the distances between the target and the horizontal and vertical sensor arrays of the reflective surface are r1 and r2 respectively. According to the geometric relationship, the positions of the base station, the reflective surface and the target in the system satisfy

[0102]

[0103] In the formula

[0104]

[0105] r1=cτ1-r0

[0106] r2=cτ2-r0

[0107] The estimated target position is

[0108]

[0109] Step (3.2), let the unit direction vectors from the target to the base station and the reflector array be e0, e1 and e2 respectively. From step (3.1), we can get

[0110]

[0111] Step (3.3), as shown in the figure, assume that the target ground speed is υ = [υ x , y , z ] T , whose radial velocities relative to the base station and the transverse and longitudinal sensor arrays of the reflector are υ0, υ1 and υ2 respectively. d The corresponding relationship between the radial velocity and the radial velocity can be obtained by estimating the radial velocity as

[0112]

[0113] Step (3.4), the radial velocity and ground velocity of the target satisfy

[0114]

[0115] Let E = [e0, e1, e2], v r =[υ0,υ1,υ2] T , then

[0116] υ r =E T υ

[0117] Therefore, the least squares estimate of the target velocity is

[0118]

[0119] in

[0120] Figure 3 The figure is a flow chart for estimating channel parameters and target kinematic parameters based on FFT and Newton-like method.

[0121] Using the method of the present invention, the channel parameters, positioning and speed measurement results are shown in the attached figure:

[0122] Figure 4 The relationship between the mean square error of the delay estimation and the received signal-to-noise ratio is shown. The results show that as the signal-to-noise ratio increases, the estimation value becomes more accurate and the estimation effect becomes better, and the accuracy is close to the Cramer-Rao theoretical lower bound.

[0123] Figure 5The relationship between the mean square error of Doppler frequency shift and the signal-to-noise ratio is shown in Figure 2. The results show that the accuracy of the algorithm of the present invention is close to the lower bound of the Cramer-Rao theory, and as the signal-to-noise ratio increases, the more accurate the estimated value is, the better the estimation effect is;

[0124] Figure 6 The relationship between the mean square error of the cosine of the angle of arrival and the signal-to-noise ratio is shown in Figure 2. The results show that the accuracy of the algorithm of the present invention is close to the lower bound of the Cramer-Rao theory, and as the signal-to-noise ratio increases, the more accurate the estimated value is, the better the estimation effect is;

[0125] Figure 7 The relationship between the mean square error and signal-to-noise ratio of the target position and speed is shown in Figure 2. The accuracy of the algorithm of the present invention is close to the lower bound of the Cramer-Rao theory, and as the signal-to-noise ratio increases, the more accurate the estimated value is, the better the estimation effect is.

[0126] The above description is only a description of the preferred embodiments of the present application, and is not intended to limit the scope of the present application. Any changes or modifications made by any person skilled in the art based on the above disclosed technical contents shall be deemed as equivalent effective embodiments and shall fall within the scope of protection of the technical solution of the present application.

Claims

1. A perception-assisted high-precision channel parameter estimation and target positioning method based on IRS and OFDM, characterized in that: Step 1. Establish OFDM signal model, channel model and perception model; First, an OFDM signal model is established; then, a reflection signal channel model is constructed, which is sent from the base station, reflected by the target, and sensed by the base station, IRS transverse and longitudinal sensor arrays, including the base station-target-base station link channel response, the base station-target-reflection surface transverse sensor array link channel response, and the base station-target-reflection surface longitudinal sensor array link channel response; then, a base station, IRS transverse and longitudinal sensor array receiving signal model is constructed, and the received signals are filled into the 3D symbol matrix Y 0 , Y 1 and Y 2 ; Step 2. Extract channel parameters based on FFT and Newton-like method; Based on the fast Fourier algorithm, the received signal model obtained in step 1 roughly estimates the Doppler frequency shift in the horizontal and vertical directions of the base station and IRS sensor array, the signal arrival angle and the signal propagation delay, a total of 9 channel parameters; Auxiliary variables are introduced to compensate for Doppler, cosine of arrival angle and delay, and an optimization problem is constructed and solved using a quasi-Newton method to improve the estimation accuracy of nine channel parameters. Step 3. Estimate target kinematic parameters: Based on the 9 channel parameters estimated in step 2 and the known position coordinates of the base station and IRS sensor array, the position and velocity of the target are calculated to obtain the kinematic parameters of the target.

2. The perception-assisted high-precision channel parameter estimation and target positioning method based on IRS and OFDM as claimed in claim 1, characterized in that: Step 1 is as follows: Specific steps for building an OFDM signal model ： Step (1.1), assume that the OFDM signal consists of Q consecutive OFDM symbols, each OFDM symbol consists of M orthogonal subcarriers, and the carrier center frequency is f c , the subcarrier spacing is Δf, and the symbol transmitted on the mth subcarrier of the qth OFDM symbol is s q,m , q=0,1,...,Q-1,m=0,1,...,M-1, then the equivalent low-pass signal of the qth OFDM is expressed as Among them, c(t) satisfies Where T′=T+T cp , T is the duration of an OFDM symbol, T cp It is a cyclic prefix to avoid interference between OFDM symbols; Step (1.2), the time domain representation of the continuous signal formed by Q consecutive OFDM symbols is Specific steps for channel model construction ： Step (1.3), the transmitted broadband signal x(t) propagates in the wireless channel, and is affected by path fading, delay, and Doppler, causing phase and amplitude changes; Assume that the path attenuation is α, the propagation delay is τ, and the Doppler frequency shift caused by the target motion is f d , considering each subcarrier of the OFDM signal as a flat fading signal, the signal received by the nth element of the uniform linear array antenna with N receiving elements is Where cosθ is the cosine of the angle between the wave source direction and the antenna array direction, λ is the carrier wavelength, d is the spacing between adjacent elements of the linear array antenna, and w n (t) is Gaussian white noise; Step (1.4), perform Fourier transform on the qth OFDM symbol signal at the receiving end to obtain the frequency domain representation of the signal Where Wq,n,m is the frequency domain value of Gaussian noise; Step (1.5), in the case of a single-input multiple-output channel, the frequency domain response of the wireless channel corresponding to the subcarrier m of the qth OFDM symbol on the nth antenna element is Step (1.6), consider the case of multiple input and multiple output. Assume that the base station has L transmitting antennas and the antenna steering vector is Then the frequency response vector of the wireless channel is Wherein, n∈{0, 1, ..., N-1} represents the serial number of the receiving antenna element or sensor element, q and m are the same as in step (1.5), representing the OFDM symbol number and the subcarrier number, respectively; Step (1.7), the system consists of three parts: ISAC base station, IRS semi-passive reflector and communication target. The base station and the target complete uplink and downlink communication and target perception and positioning with the assistance of the reflector. The horizontal and vertical uniform linear sensor arrays of the multi-antenna ISAC base station and the semi-passive IRS are recorded as 0, 1, and 2 respectively. The number of sensing elements of the horizontal and vertical uniform linear sensor arrays of the base station and the semi-passive IRS are N0, N1, and N2 respectively; Step (1.8), let the Doppler shift of the base station-target-base station link be f d，0 , the signal arrival angle is θ0, the propagation delay is τ0, and the spacing between adjacent elements of the base station receiving antenna is d0, then the channel response of the link is Where n0∈{0, 1, ..., N0-1} represents the serial number of the base station receiving antenna element, q and m are the same as in step (1.5), representing the OFDM symbol number and subcarrier number respectively; Step (1.9), let the Doppler frequency shift of the base station-target-reflector lateral sensor array link be f d，1 , the signal arrival angle is θ1, the propagation delay is τ1, and the spacing between adjacent elements of the transverse sensor array of the reflector is d1, then the channel response of the link is Wherein, n1∈{0, 1, ..., N1-1} represents the serial number of the transverse sensor array element of the reflective surface, q and m are the same as step (1.5), representing the OFDM symbol number and subcarrier number respectively; Step (1.10), let the Doppler frequency shift of the base station-target-reflector longitudinal sensor array link be f d，2 , the signal arrival angle is θ2, the propagation delay is τ2, and the spacing between adjacent elements of the longitudinal sensor array of the reflector is d2, then the channel response of the link is Wherein, n2∈{0, 1, ..., N2-1} represents the serial number of the longitudinal sensor array element of the reflective surface, q and m are the same as step (1.5), representing the OFDM symbol number and subcarrier number respectively; Specific steps for building a model of receiving signals from base stations and semi-passive reflective surface sensor arrays Step (1.11), consider that the signal is transmitted from the base station, passes through the direct line of sight path and the reflection path formed by the reflection surface to reach the target, and the communication is completed; part of the signal reflected by the target returns to the base station, and the other part is reflected to the sensor array on the reflection surface; In step (1.12), assuming that the beamforming vector of the base station is b, after passing through the base station-target-base station link, the base station receives the reflected OFDM signal after filtering, analog-to-digital conversion and demodulation, and the frequency domain representation of the perceived reflected OFDM signal is: in is Gaussian white noise; Step (1.13), the frequency domain representation of the reflected signal sent by the base station, reflected by the target, and sensed by the IRS lateral and longitudinal sensors is: in and is Gaussian white noise; Step (1.14), in the calculation unit at the base station side, with q, n and m as subscripts, the received signal y q，n，m Fill in the three-dimensional matrix separately and Right now Used for subsequent estimation and calculation of channel parameters and kinematic parameters.

3. The perception-assisted high-precision channel parameter estimation and target positioning method based on IRS and OFDM as claimed in claim 1, characterized in that: Step 2 is as follows: Step (2.1), at the base station, the transmitted downlink communication OFDM symbol sq,m is known and used to compensate the sensing signal for subsequent channel parameter estimation, that is, Where * is the conjugate operator; apply this operation to Y 0 , Y 1 and Y 2 , the signal matrices after compensation are Hehe Step (2.2), Perform FFT transformation of the first dimension at point F1, perform FFT transformation of the second dimension at point F2, and perform IFFT transformation of the third dimension at point F3 to obtain in In the formula q′=0,1,...,F1-1,n′=0,1,...,F2-1,m′=0,1,...,F3-1,F1≥Q,F2≥N,F3≥M;apply this operation to and get and Step (2.3), when the following conditions are met The amplitude of is maximized; therefore, through the matrix and The index of the element with the largest amplitude in can be used to obtain a rough estimate of the Doppler frequency shift, signal arrival angle, and signal propagation delay, that is, Step (2.4), assuming the matrix and The indices of the elements with the largest amplitude are {q′0, n′0, m′0}, {q′1, n′1, m′1}, {q′2, n′2, m′2}, respectively. The estimated values ​​of the channel parameters corresponding to the base station-target-base station link, the base station target-reflector transverse sensor array link, and the base station-target-reflector longitudinal sensor array link are Step (2.5), in order to further improve the accuracy of channel parameter estimation, an auxiliary variable δ is introduced fd , δcosθ and δτ, for The signal is compensated by Introducing δ fd , the phase change produced by δcosθ and δτ changes the Doppler, the cosine of the angle of arrival and the time delay to f respectively. d +δf d , cosθ+δcosθ and τ+δτ, that is get Apply this action to and Get and Step (2.6), Perform FFT transformation at point F1 on the first dimension, FFT transformation at point F2 on the second dimension, and IFFT transformation at point F3 on the third dimension to obtain in Apply this operation to and get and Step (2.7), from Select the element at the position of the element with the maximum amplitude determined in step (2.3), that is, And construct the optimization problem Step (2.8), use the Newton-like method to solve the optimization problem and get the optimal solution as well as Correct the estimated value in step (2.3) to obtain the accurate estimated value of the channel parameter Step (2.9), Take separately and Repeat steps (2.7)-(2.8) to construct and solve the optimization problem, and complete the channel parameter estimation for the base station-target-base station link, the base station-target-reflector lateral sensor link, and the base station-target-reflector longitudinal sensor link. The channel parameter estimation values ​​are 4. The perception-assisted high-precision channel parameter estimation and target positioning method based on IRS and OFDM as claimed in claim 1, characterized in that: Step 3 is as follows: Step (3.1), the base station is placed along the y-axis direction, and the reflection surface is located in the xOz plane. Assume that the position coordinates of the base station and the reflection surface are p0 = [x0, y0, z0] T and p IRS =[x IRS ,y IRS , z IRS ] T , the coordinates of the transverse and longitudinal sensor array centers of the reflective surface are p1 = [x1, y1, z1] T and p2 = [x2, y2, z2] T , let the target position be p = [x, y, z] T , the distance between the target and the base station is r0, and the distances between the target and the horizontal and vertical sensor arrays of the reflective surface are r1 and r2 respectively. According to the geometric relationship, the positions of the base station, the reflective surface and the target in the system satisfy In the formula r1=cτ1-r0 r2=cτ2-r0 The estimated target position is Step (3.2), let the unit direction vectors from the target to the base station and the horizontal and vertical sensor arrays of the reflection surface be e0, e1 and e2 respectively; From step (3.1), we can get Step (3.3), as shown in the figure, assume that the target ground speed is v = [v x , v y , v z ] T , whose radial velocities relative to the base station and the transverse and longitudinal sensor arrays of the reflective surface are v0, v1 and v2 respectively; d The corresponding relationship between the radial velocity and the radial velocity gives an estimate of the radial velocity: Step (3.4), the radial velocity and ground velocity of the target satisfy Let E = [e0, e1, e2], v r =[v0, v1, v2] T , then v r =E T v Therefore, the least squares estimate of the target velocity is in

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