A perception-assisted high-precision channel parameter estimation and target localization method based on IRS and OFDM

Abstract

The present invention relates to an integrated synaesthesia system, a semi-passive intelligent reflector, and a broadband orthogonal frequency division multiplexing signal. The invention is a perception-assisted high-precision channel parameter estimation and target positioning method based on a semi-passive intelligent reflector (hereinafter referred to as the reflector) and an orthogonal frequency division multiplexing signal. The method comprises 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; Step 3. Estimating target kinematic parameters: Based on the nine channel parameters estimated in Step 2 and the known base station and IRS sensor array position coordinates, the target's position and velocity are calculated to obtain the target's kinematic parameters. The present invention saves user-side computing resources and reduces user-side energy consumption; it can reduce pilot frequency overhead and improve spectrum efficiency; and the present invention can achieve high-precision positioning and speed measurement.

Description

Technical Field

[0001] The present invention relates to synaesthesia integration, intelligent reflecting 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 reflecting surface and orthogonal frequency division multiplexing signal. Background Art

[0002] With the rapid development of the intelligent Internet of Things (IoT) and mobile communication networks, an increasing number of devices are connecting to wireless networks, placing new demands on future wireless networks: high capacity, low latency, and high efficiency. In the past, increasing the number of antennas, reducing cell ranges, and exploiting high-frequency bandwidth resources have enabled hundreds of billions of devices to connect to the internet. However, simply increasing the number of antennas, increasing transmitter power, or using high-frequency resources increases system power consumption. In recent years, the introduction of intelligent reflecting surfaces (IRS) and integrated sensing and communication (ISAC) systems has been proposed, believed to improve communication system performance and reduce system energy consumption, and is expected to become a key enabling technology for future mobile communications. An intelligent reflecting surface is a plane composed of a large number of passive reflective elements, each of which can independently control the amplitude and phase of the incident signal, thereby enabling active and intelligent reconfiguration of the wireless environment. However, the performance of an intelligent reflecting surface is highly dependent on the accuracy of channel state information. Although numerous channel estimation algorithms have been proposed, most perform estimation on the user side using downlink pilot signals and then transmit them back to the base station, or perform estimation on the base station side by having the user transmit uplink pilot signals. Both of these solutions require the user to consume significant amounts of energy and spectrum resources, negatively impacting the battery life and user experience of mobile devices. The integrated synaesthesia system integrates communication and perception functions, sharing a single system and spectrum resources to achieve both perception and communication with targets within the service range. Furthermore, semi-passive reflectors can also achieve signal perception by integrating sensor arrays within 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) signals are widely used communications signals, characterized by high spectral efficiency, robust multipath immunity, and interference resistance. Compared to narrowband signals, OFDM signals have dozens or even hundreds of orthogonal subcarriers. Combining OFDM with an IRS system introduces a large number of channel parameters, resulting in computationally intensive and time-consuming channel estimation, and poor real-time performance. Leveraging the sparsity and parameterized nature of millimeter-wave channels can reduce the number of channel parameters while enabling high-precision positioning and velocity measurement of communication targets. Channel parameter extraction methods based on fast Fourier transforms and channel parameter optimization methods based on Newton-like methods enable rapid and accurate parameter estimation. Channel parameters can be used for target positioning, channel prediction, tracking, and reconstruction, thereby optimizing base station and reflector beams, thereby improving system communication performance and spectral efficiency. Summary of the Invention

[0004] The present invention focuses on an integrated OFDM communication and perception 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 positioning and speed measurement method for three-dimensional targets 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 of 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 emitted by 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-reflecting surface transverse sensor array link channel response, and the base station-target-reflecting surface longitudinal sensor array link channel response; then, a receiving signal model of the base station, IRS transverse and longitudinal sensor arrays is constructed, and the received signals are respectively 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 Transform algorithm, the received signal model obtained in step 1 is used to roughly estimate the Doppler frequency shift, signal arrival angle, and signal propagation delay in the three directions of the base station and IRS sensor array, a total of 9 channel parameters;

[0011] 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 the nine channel parameters.

[0012] Step 3. Estimate target kinematic parameters:

[0013] Based on the nine 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 an integrated telepathic base station, a semi-passive intelligent transmitting surface, and broadband OFDM signals. Using downlink communication signals reflected by the target, the base station performs high-precision channel estimation and high-precision positioning and velocity measurement of the target based on a 3D FFT algorithm and a Newton-like method. In terms of communication, the present invention's use of intelligent reflecting surfaces and OFDM systems can improve communication performance in practical applications. In terms of perception, traditional methods for channel estimation and positioning based on uplink signals at the base station and downlink signals at the user side require the continuous transmission of a large number of pilot signals, consuming significant energy and resources on the user side. The proposed method receives target reflected signals at the base station and semi-passive reflecting surface and performs calculations at the base station, reducing energy consumption and conserving computing resources on the user side. Furthermore, because the transmitted signal is known to the base station, pilot signal overhead can be reduced, improving spectral efficiency. Thanks to the base station's linear array antenna, the linear sensor array of the semi-passive reflecting surface, and the broadband OFDM signal, the present invention can acquire multi-dimensional information such as the distance, velocity, and angle of a target in three-dimensional space, achieving high-precision positioning and velocity measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a target positioning scenario for an IRS-assisted broadband communication perception integrated system according to the method of the present invention;

[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 This is a relationship diagram between the mean square error of the time delay and the signal-to-noise ratio of the method of the present invention;

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

[0021] Figure 6 This is a relationship diagram between the mean square error of the cosine value of the angle of arrival and the signal-to-noise ratio 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 velocity 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. By utilizing the orthogonality of each subcarrier of the broadband OFDM signal and the flat fading of the subcarriers, the channel sparsity of the millimeter wave and the characteristics of the integrated communication and perception of the system, an OFDM signal model, a channel model and a perception model are established. A channel parameter extraction algorithm based on 3D FFT and a 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 signals.

[0024] The present invention will be 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 and perception integrated system using OFDM signals, and describes the single target positioning and speed measurement scenario in the IRS-assisted interawareness integrated system.

[0027] Figure 2This 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's ground velocity and its 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 emitted by 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-reflecting surface transverse sensor array link channel response, and the base station-target-reflecting surface longitudinal sensor array link channel response; then, a receiving signal model of the base station, IRS transverse and longitudinal sensor arrays is constructed, and the received signals are respectively filled into the 3D symbol matrix Y 0 , Y 1 and Y 2 The details are as follows:

[0031] Specific steps for building an orthogonal frequency division multiplexing signal model :

[0032] In 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] In 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, resulting in changes in phase and amplitude.

[0041] Assume that the path attenuation is α, the propagation delay is τ, and the Doppler 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] Where n∈{0,1,...,N-1} represents the serial number of the receiving antenna element or sensor element, and q and m are the same as in step (1.5), representing the OFDM symbol number and subcarrier number, respectively.

[0052] Step (1.7), such as Figure 3As shown in Figure 1, the system consists of an ISAC base station, an IRS semi-passive reflector, and a communication target. The base station and target, aided by the reflector, perform uplink and downlink communication and target perception and positioning. The horizontal and vertical uniform linear sensor arrays of the multi-antenna ISAC base station and semi-passive IRS are denoted as 0, 1, and 2, respectively. The number of sensing elements in the horizontal and vertical uniform linear sensor arrays of the base station and semi-passive IRS are N0, N1, and N2, respectively.

[0053] In 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] 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.

[0056] In step (1.9), let the Doppler shift of the base station-target-reflector transverse 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

[0057]

[0058] Wherein, 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 in step (1.5), representing the OFDM symbol number and subcarrier number, respectively.

[0059] In step (1.10), let the Doppler 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] Wherein, 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 in step (1.5), representing the OFDM symbol number and subcarrier number, respectively.

[0062] Specific steps for building a signal receiving model for base stations and semi-passive reflector sensor arrays

[0063] In step (1.11), consider a signal transmitted from the base station, passing through the line-of-sight path and the reflection path formed by the reflective surface to reach the target, completing communication. Part of the signal reflected by the target returns to the base station, while the remaining part reflects to the sensor array on the reflective surface. Because the signal after multiple reflections is weak, it is reasonable to assume that the reflected signal primarily originates from the line-of-sight path, while the signal from the reflection path through the reflective surface can be ignored. Furthermore, since the base station and the reflective surface are typically stationary and have a reliable communication link between them, the signal from the base station to the reflective surface array sensor can be easily compensated.

[0064] In step (1.12), assume 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 analog, and demodulated. The frequency domain representation of the signal is:

[0065]

[0066] in is Gaussian white noise.

[0067] Similarly, in 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:

[0068]

[0069] in and is Gaussian white noise.

[0070] Step (1.14), in the calculation unit on the base station side, with q, n and m as subscripts, the received signal y q，n，m Fill in the three-dimensional matrix respectively 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), for 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 the equation can be used to obtain the estimated values ​​(rough estimates) of Doppler frequency shift, signal arrival angle and signal propagation delay, that is,

[0084]

[0085] Step (2.4), assume that 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] In step (2.5), since 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 caused by δcosθ and δτ makes the Doppler, arrival angle cosine and 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), for Perform FFT transformation of the first dimension at F1, FFT transformation of the second dimension at F2, and IFFT transformation of the third dimension at F3 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) uses the Newton-like method to solve the optimization problem and obtain the optimal solution as well as

[0096] Correct the estimated value of 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 transverse 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 target position and velocity are calculated to obtain the target's kinematic parameters. 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 and the reflective surface is located in the xOZ plane. Assume that the position coordinates of the base station and the reflective 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, the distances between the target and the horizontal and vertical sensor arrays of the reflective surface are r1 and r2 respectively, then 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] In step (3.2), the unit direction vectors from the target to the base station and the reflector array are 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 ,υ y ,υ z ] T , whose radial velocities relative to the base station and the transverse and longitudinal sensor arrays of the reflecting surface are υ0, υ1 and υ2 respectively. d The corresponding relationship between the radial velocity and the radial velocity can be estimated as

[0112]

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

[0114]

[0115] Let E = [e0, e1, e2], 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 Flowchart for channel parameter and target kinematic parameter estimation 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 becomes more accurate and the estimation effect becomes better, and the accuracy approaches the Cramer-Rao 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 proposed algorithm is close to the lower bound of the Cramer-Rao theory, and as the signal-to-noise ratio increases, the estimated value becomes more accurate and the estimation effect becomes better.

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

[0125] Figure 7 The relationship between the mean square error and signal-to-noise ratio of the target position and velocity is shown in the figure. 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 estimate, the better the estimation effect.

[0126] The above description is only a description of the preferred embodiments of the present application and does not 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 content should be regarded as equivalent valid embodiments and 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 emitted by 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-reflecting surface transverse sensor array link channel response, and the base station-target-reflecting surface longitudinal sensor array link channel response; then, a receiving signal model of the base station, IRS transverse and longitudinal sensor arrays is constructed, and the received signals are respectively 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 Transform algorithm, the received signal model obtained in step 1 is used to roughly estimate the Doppler frequency shift, signal arrival angle, and signal propagation delay in the three directions of the base station and IRS sensor array, a total of nine channel parameters. 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 the nine channel parameters. Step 3. Estimate target kinematic parameters: Based on the nine 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 orthogonal frequency division multiplexing signal model ： In 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; In step (1.2), the time domain representation of the continuous signal formed by Q consecutive OFDM symbols is Specific steps for channel model construction ： 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 phase and amplitude changes. Assume that the path attenuation is α, the propagation delay is τ, and the Doppler 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 are the frequency domain values ​​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 Where 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 subcarrier number respectively; In step (1.7), the system consists of three parts: the ISAC base station, the IRS semi-passive reflector, and the 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 denoted as 0, 1, and 2, respectively. The number of sensing elements in the horizontal and vertical uniform linear sensor arrays of the base station and the semi-passive IRS are N0, N1, and N2, respectively. In 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; In step (1.9), let the Doppler shift of the base station-target-reflector transverse 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 Where 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 in step (1.5), representing the OFDM symbol number and subcarrier number, respectively; In step (1.10), let the Doppler 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 Where n2∈{0, 1, ..., N2-1} represents the serial number of the longitudinal sensor array element of the reflector, q and m are the same as in step (1.5), representing the OFDM symbol number and subcarrier number, respectively; Specific steps for building a signal receiving model for base stations and semi-passive reflector sensor arrays 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 reflective surface to reach the target, completing the communication. 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 reflective surface. In step (1.12), assuming that the beamforming vector of the base station is b, the frequency domain representation of the perceived reflected OFDM signal after filtering, analog-to-digital conversion, and demodulation is obtained by the base station through the base station-target-base station link. in is Gaussian white noise; In 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 on the base station side, with q, n and m as subscripts, the received signal y q，n，m Fill in the three-dimensional matrix respectively 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: In 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, i.e. 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), for 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 magnitude 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, the signal arrival angle and the signal propagation delay, that is, Step (2.4), assume that 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 In 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 caused by δcosθ and δτ makes the Doppler, arrival angle cosine and time delay become f respectively d +δf d , cosθ+δcosθ and τ+δτ, that is get Apply this action to and Get and Step (2.6), for Perform FFT transformation of the first dimension at F1, FFT transformation of the second dimension at F2, and IFFT transformation of the third dimension at F3 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) uses the Newton-like method to solve the optimization problem and obtain the optimal solution as well as Correct the estimated value of 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 transverse 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: In step (3.1), the base station is placed along the y-axis and the reflecting surface is located in the xOz plane. Assume that the position coordinates of the base station and the reflecting 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, the distances between the target and the horizontal and vertical sensor arrays of the reflective surface are r1 and r2 respectively, then 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 In step (3.2), the unit direction vectors from the target to the base station and the reflector array’s horizontal and vertical sensors are 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 reflecting surface are v0, v1 and v2 respectively; d The corresponding relationship between the radial velocity and the radial velocity can be estimated as 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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