A near-field MIMO wideband OFDM sensing and communication integrated system and signal processing method

Abstract

This invention relates to a near-field MIMO broadband OFDM sensing integrated system and signal processing method. The near-field MIMO broadband OFDM sensing integrated system includes: a base station and K single-antenna users in the near field serving the base station. The signal processing method includes the following steps: Step A, user angle and distance sensing signal processing; Step B, user velocity sensing signal processing; Step C, initial sensing, beamforming design, data transmission and velocity sensing, and beam prediction and tracking signal processing steps with setting i variables. The superior technical effect of this invention is that the proposed FD-PI and FD-BC algorithms can significantly improve the accuracy of user position sensing. Under low SNR, compared with the PI and BC algorithms without FD sampling, the FD-PI and FD-BC algorithms have a more obvious advantage.

Description

Technical Field

[0001] This invention belongs to the field of mobile communication technology, specifically relating to a MIMO wideband OFDM integrated sensing system and signal processing method. Technical Background

[0002] The future 6G Integrated Sensing and Communication (ISAC) technology will deeply integrate communication and sensing functions, achieving synergy between communication and sensing through shared hardware and wireless resources. With the rapid development of mobile communication technology, the demand for higher data rates and more efficient communication is increasing. To meet these demands, communication systems are gradually developing towards higher frequency bands, larger bandwidths, and larger-scale antenna arrays. However, in high-frequency, wideband, and large-scale array communication scenarios, near-field effects and wideband beam-squinting effects are prevalent. In these situations, the traditional far-field MIMO model is no longer applicable, posing new challenges to communication and sensing.

[0003] As a crucial function of ISAC (Inter-Spatial Target Awareness), the perception of spatial target position and velocity is essential for optimizing communication beamforming strategies and improving communication anti-interference performance. Traditional spatial target perception algorithms mostly consider far-field scenarios. In this case, signal modeling is based on a plane wave model. Target position perception is essentially target azimuth estimation, and target velocity perception only includes the projection component of the target's true velocity onto the line connecting the target and the BS (Browser Base Station). Furthermore, in far-field scenarios, beamforming signal energy is concentrated in a specific direction. However, with future mobile communication demands for higher data rates, higher frequency bands and larger antenna arrays will be used, increasing the Rayleigh distance and resulting in a higher proportion of near-field signals within the BS coverage area. Therefore, a spherical wave signal model needs to be considered. At this point, a new range domain degree of freedom is introduced. Thus, target position perception includes both target angle and range estimation, while target velocity perception yields the target's true velocity, including its radial and lateral velocities. During beamforming, signal energy is focused on a specific point in space. Based on the differences in signal models, traditional far-field-based sensing algorithms are no longer applicable in near-field scenarios. Furthermore, existing target location sensing algorithms suitable for near-field scenarios, such as 2D-MUSIC and 2D-Capon, suffer from excessive complexity due to the need for multi-dimensional spatial searches. In addition, in the high-frequency broadband scenarios prevalent in future mobile communications, relying solely on phase shifters (PS) to control beamforming results in signal energy focusing as a function of frequency, leading to beam squint effects. Since most traditional sensing algorithms are based on narrowband assumptions, broadband beam squint effects also pose a challenge to spatial target sensing. To address this issue, a True Time Delay (TTD) module can be used to implement programmable phase shift control. Some existing research combines TTD with PS and connects it in series with each element in the antenna array to control the beam squint focusing points of different carriers in the broadband OFDM signal. This allows for the sensing of the angle and distance of spatial targets based on the frequency of the maximum gain signal fed back by the user in space. While this method solves the problem of spatial target sensing in broadband environments, its frequency domain carrier resource utilization and sensing accuracy still need improvement.

[0004] Based on the above-mentioned technical problems, this invention addresses the spatial target perception problem under near-field broadband effects. Considering a large-scale MIMO OFDM integrated sensing system, it proposes low-complexity calibration algorithms FD-PI (Forced Degressive Parabolic Interpolation) and FD-BC (Forced Degressive Barycenter Calibration) to improve the accuracy of base station perception of user angle and distance. It also proposes a signal processing method based on the Expectation Maximization (EM) algorithm to enable base station perception of user speed. Summary of the Invention

[0005] To address the technical problems existing in the prior art, this invention proposes a near-field MIMO broadband OFDM integrated sensing system and signal processing method.

[0006] The near-field MIMO broadband OFDM sensing integrated system of the present invention includes:

[0007] A base station (BS) and K single-antenna users in the near field serving the BS.

[0008] Furthermore, a uniform linear array (ULA) with N = 2L + 1 antennas is deployed on the BS side. The carrier frequency and signal bandwidth of the OFDM signal transmitted by the BS are f c Given W and M+1 subcarriers, the subcarrier spacing is Δf = W / M, and the frequency of the m-th subcarrier is f. m =f c -W / 2+mW / M The baseband frequency is Where m = 0, 1, ..., M.

[0009] Furthermore, each antenna of the BS is connected in series with a true time delay unit (TTD) and a phase shifter (PS) to control the beamforming point, i.e., the beam angle of the signal energy of different frequency subcarriers. Therefore, the beamforming vector of the BS array... The expression for is as follows (1):

[0010]

[0011] In equation (1), The baseband frequency φ of the signal n Let t represent the phase shift of the nth (n = -L, ..., 0, ..., L) PS. n The delay of the nth TTD is expressed by the following formula (2):

[0012]

[0013] In equation (2), r start，n and r end，n Indicates the beam slant start point (the beam slant point of the 0th subcarrier (r)). start θ start) ) and endpoint (beam slant point (r) of the Mth subcarrier end θ end The distance to antenna n, and the beam slant point of subcarrier m, are expressed as: Then we get the following equations (3) and (4):

[0014]

[0015] Let the location of user k be (x k y k ), the corresponding polar coordinates are (r k θ k ), User k moves at a speed of v k , use v r，k and v θ，k Let r represent the radial and lateral velocities of the user relative to the center of the ULA of the BS, respectively. n，k v represents the distance between the BS antenna n and the user k. n，k Indicates the user's speed v k The projection component on the line connecting the BS antenna n and the user.

[0016] The present invention also provides a signal processing method based on a near-field MIMO broadband OFDM integrated sensing system, comprising the following steps:

[0017] Step A, User Angle and Distance Sensing Signal Processing: In the user position, i.e. angle and distance sensing stage, the BS first sends an OFDM modulated all-1 pilot signal controlled by the beam look-out path to the user. The BS then obtains the final angle and distance sensing result through the uplink signal frequency fed back by the user.

[0018] Step B, User Speed ​​Sensing Signal Processing: Assume the BS uses a circulator and full-duplex technology to enable the transmitter and receiver to share the antenna array. For user k, the BS knows the current position of user k and transmits an OFDM signal with signal energy focused on the user's position. That is, the starting and ending points of the beam squint are set to be... The EM algorithm is used to perceive the user's speed through the user's echo signal;

[0019] Step C includes initial sensing, beamformer design, data transmission and velocity sensing, and beam prediction and tracking signal processing steps for setting i variables.

[0020] Further, in step A, during the user's position, i.e., angle and distance perception stage, the BS first sends an OFDM-modulated all-1 pilot signal controlled by a beam-look-out path to the user. The user uses the received OFDM signal to generate a corrected spectrum and uses the calibration algorithm FD-PI or FD-BC proposed in this invention to obtain the signal frequency with the maximum amplitude, which is then fed back to the BS. The BS then obtains the final angle and distance perception result through the uplink signal frequency fed back by the user, specifically including:

[0021] Step A1, taking user k as an example, in the user angle perception stage, the starting point and ending point of beam squint are set as (r... mid θ min ) and (r mid θ max ), where r min ≤r mid ≤r max The corrected spectrum of the received signal from user k is obtained, denoted as:

[0022] Step A2, calibrate the corrected spectrum using the following calibration algorithm:

[0023] Step A2.1, perform spectral peak search on the discrete corrected spectrum, as shown in the following equation (5):

[0024]

[0025] Let the number of observation points used for calibration be... The amplitude of the observation points used for calibration is represented as a vector. The corresponding index is represented by vector m′, and the amplitudes of the observation points to the left and right of the peak point are represented by vectors m′ and m′, respectively. and The corresponding index is represented as vector m′. l and m′ r The number of observation points may be less than the peak point because the peak point is closer to the edge. Therefore, the vector used to store the amplitude and index of the observation points is initialized to empty;

[0026] Step A2.2, if the FD-PI calibration algorithm is selected, then assume the number of observation points used for calibration is... Odd number:

[0027] Step A2.2.1, using m′ l This represents the last valid point to the left of the peak point and is initialized to m′. l =m′, set variable i = m′-1;

[0028] Step A2.2.2, if the conditions are met simultaneously If i≥1, then proceed to steps A2.2.3 to A2.2.4; otherwise, skip steps A2.2.3 to A2.2.4 and proceed directly to step A2.2.5.

[0029] Step A2.2.3, if Then update the vector m′ l And the last valid point m′ to the left of the peak point l It is expressed as the following formula (6):

[0030]

[0031] If the above conditions are not met, proceed to the next step;

[0032] Step A2.2.4: Update variable i to i = i-1, and return to execute step A2.2.2;

[0033] Step A2.2.5, using m′ r This represents the last valid point to the right of the observed peak point and is initialized to m′. r =m′, set variable i = m′+1;

[0034] Step A2.2.6, if the conditions are met simultaneously and Then proceed to steps A2.2.7 to A2.2.8; otherwise, skip steps A2.2.7 to A2.2.8 and proceed directly to step A2.2.9.

[0035] Step A2.2.7, if Then update the vector m′ r and the last valid point m′ to the right of the peak point. r It is expressed as the following formula (7):

[0036]

[0037] If the above conditions are not met, proceed to the next step;

[0038] Step A2.2.8: Update variable i to i = i + 1, and return to execute step A2.2.6;

[0039] Step A2.2.9 yields the observation point amplitudes and corresponding indices used in the FD-PI algorithm, expressed as equation (8):

[0040]

[0041] Will m and m′ are used as the ordinate and abscissa, respectively, for the quadratic function y = p²x. 2 The regression fit of +p1x+p0 yields the fractional index of the calibrated peak value. The expression is as follows (9):

[0042]

[0043] Step A2.3: If the FD-BC calibration algorithm is selected, then set the number of observation points used for calibration. Even numbers:

[0044] Step A2.3.1, if If not, proceed to steps A2.3.2 to A2.3.9; otherwise, skip to step A2.3.10.

[0045] Step A2.3.2: Initialize the index of the last valid point to the left of the peak as m′. l =m′, initialize variable i = m′-1;

[0046] Step A2.3.3, if the conditions are met simultaneously If i ≥ 1, then proceed to steps A2.3.4 to A2.3.5; otherwise, skip steps A2.3.4 to A2.3.5 and proceed directly to step A2.3.6.

[0047] Step A2.3.4, if Then, using the same formula (6), update the vector. m′ l And the last valid point m′ to the left of the peak point l If the above conditions are not met, proceed to the next step.

[0048] Step A2.3.5: Update variable i to i = i - 1, and return to execute step A2.3.3;

[0049] Step A2.3.6: Initialize the index of the last valid point to the right of the peak as m′. r =m′, initialize variable i = m′+1;

[0050] Step A2.3.7, if the conditions are met simultaneously and Then proceed to steps A2.3.8 through A2.3.9; otherwise, skip to step A2.3.18.

[0051] Step A2.3.8, if Then, using the same formula (7), update the vector. m′ r And the last valid point m′ to the left of the peak point rIf the above conditions are not met, proceed to the next step.

[0052] Step A2.3.9: Update variable i to i = i + 1, and return to execute step A2.3.7;

[0053] Step A2.3.10: Initialize the index of the last valid point to the left of the peak as m′. l =m′, initialize variable i = m′-1;

[0054] Step A2.3.11, if the conditions are met simultaneously If i≥1, then proceed to steps A2.3.12 to A2.3.13; otherwise, skip steps A2.3.12 to A2.3.13 and proceed directly to step A2.3.14.

[0055] Step A2.3.12, if Then, using the same formula (6), update the vector. m′ l And the last valid point m′ to the left of the peak point l If the above conditions are not met, proceed to the next step.

[0056] Step A2.3.13: Update variable i to i = i - 1, and return to execute step A2.3.11;

[0057] Step A2.3.14: Initialize the index of the last valid point to the right of the peak as m′. r =m′, initialize variable i = m′+1;

[0058] Step A2.3.15, if the conditions are met simultaneously and Then proceed to steps A2.3.16 to A2.3.17; otherwise, skip steps A2.3.16 to A2.3.17 and proceed directly to step A2.3.18.

[0059] Step A2.3.16, if Then, using the same formula (7), update the vector. m′ r And the last valid point m′ to the left of the peak point r If the above conditions are not met, proceed to the next step.

[0060] Step A2.3.17: Update variable i to i = i + 1, and return to execute step A2.3.15;

[0061] Step A2.3.18, obtain the observation point amplitudes used in the FD-BC algorithm. And the corresponding index m′, the expression is the same as in equation (8), where, This represents the actual number of observation points, satisfying... Will Using m and m′ as the ordinate and abscissa respectively, the fractional index expression of the calibrated peak value is obtained as follows (10):

[0062]

[0063] Step A3, index the fractional multiple of the calibrated peak value. Obtain the signal frequency corresponding to the maximum value of the corrected spectrum. The expression is: Next, the user feeds back the frequency information to the BS, and the BS will... θ start =θ min θ end =θ max Substitute into (3) to calculate the angle information of user k, which is expressed as

[0064] Step A4: Obtain the angle estimation result for user k. Next, the distance to user k is sensed, at which point the starting and ending points of the beam squint are set to... and The corrected spectrum of the received signal from user k is represented as follows:

[0065] Step A5, referring to step A2, correct the spectrum. Using a calibration algorithm, the fractional index of the peak value is obtained.

[0066] Step A6, referring to step A3, index the obtained corrected spectrum peak fraction multiple. Mapping to frequency The user reports this frequency information back to the BS, and the BS will... r start =r min r end =r max , Substituting into equation (4), the user's distance information is calculated and expressed as follows:

[0067] Further, in step B, the user speed sensing signal processing flow, assuming the BS achieves a shared antenna array between the transmitter and receiver through a circulator and full-duplex technology, still taking user k as an example, the BS knows the current position of user k and transmits an OFDM signal with signal energy focused on the user's position, that is, setting the start and end points of beam squint to be... And the user's speed is perceived through the user's echo signal, specifically including:

[0068] Step B1: Construct a model of the echo signal received by BS from user k. Let the total echo signal received by BS from user k at sampling time t0 be represented as... Its expression is as follows (11):

[0069]

[0070] Step B2, optimize the problem formulation:

[0071] Unknown parameter v k =[v r，k v θ，k ] T The estimation of β can be transformed into the following optimization problem:

[0072]

[0073] Step B3, set Solve the optimization problem in equation (12) using the Expectation Maximization (EM) algorithm:

[0074] Step B3.1: Randomly initialize the initial velocity of user k. The initialization constant h0 is used for the first iteration of the EM algorithm and is used to determine the condition for the end of the loop.

[0075] Step B3.2, assume the speed of user k is constant and known to be... The channel fading coefficient is then estimated using the least squares method, as shown in equation (13):

[0076]

[0077] In equation (13), Indicates a false reversal.

[0078] Step B3.3, obtain the estimated results of the channel fading coefficient. Substituting into equation (12), for a fixed channel fading coefficient... The speed estimation of user k is transformed into the following optimization problem, as shown in equation (14):

[0079]

[0080] To solve this problem, a gradient-based approach is used, with the objective function G(v) k For user speed v k Taking the derivative, the user velocity estimation result is finally obtained through the following gradient descent process, as shown in equation (15):

[0081]

[0082] In equation (15), γ (i) Let represent the learning rate in the i-th iteration, and the resulting user speed estimate is expressed as:

[0083] Step B3.4, if the user speed estimation result obtained in the previous step... satisfy Then As the final estimate of user speed, user speed perception is complete; otherwise, settings are set. Then proceed to step B3.2.

[0084] Further, step C: Beam prediction and tracking signal processing flow, for user k, the specific steps of beam prediction and tracking are as follows:

[0085] Step C1, Initial Sensing: Given that the user's position and velocity are considered constant within a coherent processing interval (CPI), let the duration of one CPI be T0. In the first CPI, set i = 1. At this time, the BS does not perform data transmission but only focuses on the sensing of the user's position and velocity. Specifically, in the first CPI, the BS first obtains the user's position sensing result according to the process in step A, represented as... Next, set the starting and ending points of the beam squint to be... Substituting equations (1) and (2), we obtain the beamforming vector of BS at this time. Then, an OFDM signal is transmitted to the user. Based on the user's echo signal, the initial velocity of user k is estimated according to the process in step B, and expressed as follows:

[0086] Step C2, beamformer design, given the estimated user position and velocity within the i-th CPI. and The estimated location of the user in the next CPI is then represented as: A beamformer is designed based on the user's position within the (i+1)th CPI, i.e., the starting and ending points of the beam angle are set to be... Substituting equations (1) and (2), we obtain the beamforming vector, expressed as:

[0087] Step C3, Data Transmission and Speed ​​Awareness: Within the (i+1)th CPI, utilize the beamformer obtained in the previous CPI. Effective data transmission is performed, and based on the process in step B, the radial and lateral velocities of the user at the (i+1)th CPI are obtained from the echo signal reflected by the user, denoted as...

[0088] Step C4: Set i = i + 1 and jump to step C2.

[0089] Compared with the prior art in this field, the present invention has the following superior technical effects:

[0090] 1. The near-field MIMO broadband OFDM integrated sensing system and signal processing method described in this invention, and the proposed FD-PI and FD-BC algorithms can significantly improve the accuracy of user position perception. At low SNR, compared with the PI and BC algorithms without FD sampling, the FD-PI and FD-BC algorithms have a clear advantage, and even at low SNR... The larger the number of sampling points, the higher the sensing accuracy of FD-PI. The law governing the change of the FD-BC algorithm with the number of sampling points is as follows: The larger the size, the higher the perception accuracy.

[0091] 2. The near-field MIMO broadband OFDM integrated sensing system and signal processing method described in this invention, the proposed FD-BC algorithm can significantly improve the accuracy of user position perception. At low SNR, compared with the BC algorithm without FD sampling, the FD-BC algorithm has a significant advantage. The variation of the FD-BC algorithm with the number of sampling points is as follows: The larger the size, the higher the perception accuracy.

[0092] 3. The near-field MIMO broadband OFDM integrated sensing system and signal processing method described in this invention, the proposed EM algorithm for user speed perception, can make full use of frequency domain resources and effectively save time domain resources, laying the foundation for subsequent beam prediction and tracking signal processing. Attached Figure Description

[0093] Figure 1 is a schematic diagram of the system model in an embodiment of the present invention;

[0094] Figure 2 This is a schematic diagram of user position and speed perception in an embodiment of the present invention;

[0095] Figure 3 is a schematic diagram of the RMSE-SNR simulation of angle perception in an embodiment of the present invention. Detailed Implementation Plan

[0096] To better understand the principles and features of this invention, the specific implementation methods of the near-field MIMO broadband OFDM sensing integrated system and signal processing method described in this invention are now described in detail with reference to Figures 1 to 3 in the specification.

[0097] Example

[0098] The near-field MIMO broadband OFDM sensing integrated system model described in this invention, as shown in Figure 1, includes a base station (BS) and K single-antenna users in the near field served by the BS. Let the carrier frequency and signal bandwidth of the OFDM signal transmitted by the BS be f. c Given W and M+1 subcarriers, the subcarrier spacing is Δf = W / M, and the frequency of the m-th subcarrier is f. m =f c -W / 2+mW / M, baseband frequency is Each antenna in the BS array is connected in series with a true time delay unit (TTD) and a phase shifter (PS) to control the beamforming point, i.e., the beam angle of different subcarriers. Therefore, the beamforming vector of the BS array... The expression is:

[0099]

[0100] in, φ represents the baseband frequency of the signal. n Let t represent the phase shift of the nth (n = -1, ..., 0, ..., L) PS. n The delay of the nth TTD is expressed as follows:

[0101]

[0102] Where, r start，n and r end，n Indicates the beam slant start point (the beam slant point of the 0th subcarrier (r)). start θ start )) and the endpoint (the beam slant point (r) of the Mth subcarrier end θ end The distance from the antenna n.

[0103] A schematic diagram of user position and speed sensing in this invention, as shown below. Figure 2 As shown, let the location of user k be (x k y k ), the corresponding polar coordinates are (r k θ k ), User k moves at a speed of v k , use v r，k and v θ，k Let r represent the radial and lateral velocities of the user relative to the center of the ULA of the BS, respectively. n，k v represents the distance between the BS antenna n and the user k. n，k Indicates the user's speed v kThe projection component on the line connecting the BS antenna n and the user, and the signal gain of the m-th subcarrier received by user k are expressed as follows: Frequency deviation caused by mobility Relative to subcarrier frequency f m It can be ignored; α m =c / 4πf m r represents the channel gain on the m-th subcarrier. For ease of analysis, we multiply both sides of the equation by r. To eliminate the effects of fading in different subcarrier channels, the corrected received signal gain is obtained as follows (16):

[0104]

[0105] in,

[0106]

[0107] Based on the above description of the near-field MIMO broadband OFDM integrated sensing system, the steps of the signal processing method of the present invention are as follows:

[0108] Step A, User Angle and Distance Sensing Signal Processing: In the user position, i.e., angle and distance sensing stage, the BS first sends an OFDM-modulated all-1 pilot signal controlled by the beam-look-out path to the user. The BS then obtains the final angle and distance sensing result through the uplink signal frequency fed back by the user. Let the range of the user's position be {(r, θ)|r min ≤r≤r max θ min ≤θ≤θ max};

[0109] Step A1, taking user k as an example, in the user angle perception stage, the starting point and ending point of beam squint are set as (r... mid θ min ) and (r min θ max ), where r min ≤r mid ≤r max Substituting this into equation (16) yields the corrected spectrum of the user k received signal. in:

[0110]

[0111] In the above formula,

[0112] At this time, the beam slant path is similar to Figure 1a T3 or T4 in the "beam slant path diagram";

[0113] Step A2, apply the calibration algorithm proposed in this work to the corrected spectrum in equation (17):

[0114] Step A2.1 involves performing a peak search on the discrete corrected spectrum, as shown below:

[0115]

[0116] Let the number of observation points used for calibration be... The amplitude of the observation points used for calibration is represented as a vector. The corresponding index is represented by vector m′, and the amplitudes of the observation points to the left and right of the peak point are represented by vectors m′ and m′, respectively. and The corresponding index is represented as vector m′. l and m′ r The number of observation points may be less than the peak point because the peak point is closer to the edge. Therefore, the vector used to store the amplitude and index of the observation points is initialized to empty;

[0117] Step A2.2, if the FD-PI calibration algorithm is selected, then assume the number of observation points used for calibration is... It is an odd number;

[0118] Step A2.2.1, using m′ l This represents the last valid point to the left of the peak point and is initialized to m′. l =m′, set variable i = m′-1;

[0119] Step A2.2.2, if the conditions are met simultaneously If i ≥ 1, then proceed to steps A2.2.3 to A2.2.4; otherwise, skip steps A2.2.3 to A2.2.4 and proceed directly to step A2.2.5.

[0120] Step A2.2.3, if Then update the vector m′ l And the last valid point m′ to the left of the peak point l , is represented as:

[0121] m′ l =[m′ l T i ] T ,m′ l =i,

[0122] If the above conditions are not met, proceed to the next step;

[0123] Step A2.2.4: Update variable i to i = i-1, and return to execute step A2.2.2;

[0124] Step A2.2.5, using m′ r This represents the last valid point to the right of the observed peak point and is initialized to m′. r =m′, set variable i = m′+1;

[0125] Step A2.2.6: If the conditions are met simultaneously and Then proceed to steps A2.2.7 to A2.2.8; otherwise, skip steps A2.2.7 to A2.2.8 and proceed directly to step A2.2.9.

[0126] Step A2.2.7, if Then update the vector m′ r and the last valid point m′ to the right of the peak point. r , is represented as:

[0127] m′ r =[m′ r T i] T ,m′ r =i,

[0128] If the above conditions are not met, proceed to the next step;

[0129] Step A2.2.8: Update variable i to i = i + 1, and return to execute step A2.2.6;

[0130] Step A2.2.9 yields the observation point magnitudes and corresponding indices used in the FD-PI algorithm, represented as follows:

[0131]

[0132] Will m and m′ are used as the ordinate and abscissa, respectively, for the quadratic function y = p²x. 2 The regression fit of +p1x+p0 yields the fractional index of the calibrated peak value. The expression is:

[0133]

[0134] Step A2.3: If the FD-BC calibration algorithm is selected, then set the number of observation points used for calibration. Even numbers:

[0135] Step A2.3.1, if If so, proceed to steps A2.3.2 to A2.3.9; otherwise, skip directly to step A2.3.10.

[0136] Step A2.3. Initialize the index of the last valid point to the left of the peak as m′. l =m′, initialize variable i = m′-1;

[0137] Step A2.3.3, if the conditions are met simultaneously If i ≥ 1, then proceed to steps A2.3.4 to A2.3.5; otherwise, skip steps A2.3.4 to A2.3.5 and proceed directly to step A2.3.6.

[0138] Step A2.3.4, if Then in step A2.2.3, update the vector. m′ l And the last valid point m′ to the left of the peak point l If the above conditions are not met, proceed to the next step.

[0139] Step A2.3.5: Update variable i to i = i - 1, and return to execute step A2.3.3;

[0140] Step A2.3.6: Initialize the index of the last valid point to the right of the peak as m′. r =m′, initialize variable i = m′+1;

[0141] Step A2.3.7, if the conditions are met simultaneously and Then proceed to steps A2.3.8 through A2.3.9; otherwise, skip to step A2.3.18.

[0142] Step A2.3.8, if Then, following step A2.2.7, update the vector. m′ r And the last valid point m′ to the left of the peak point r If the above conditions are not met, proceed to the next step.

[0143] Step A2.3.9: Update variable i to i = i + 1, and return to execute step A2.3.7;

[0144] Step A2.3.10: Initialize the index of the last valid point to the left of the peak as m′. l =m′, initialize variable i = m′-1;

[0145] Step A2.3.11, if the conditions are met simultaneously If i≥1, then proceed to steps A2.3.12 to A2.3.13; otherwise, skip steps A2.3.12 to A2.3.13 and proceed directly to step A2.3.14.

[0146] Step A2.3.12, if Then, following step A2.2.3, update the vector. m′ l And the last valid point m′ to the left of the peak point l If the above conditions are not met, proceed to the next step.

[0147] Step A2.3.13: Update variable i to i = i - 1, and return to execute step A2.3.11;

[0148] Step A2.3.14: Initialize the index of the last valid point to the right of the peak as m′. r =m′, initialize variable i = m′+1;

[0149] Step A2.3.15, if the conditions are met simultaneously and Then proceed to steps A2.3.16 to A2.3.17; otherwise, skip steps A2.3.16 to A2.3.17 and proceed directly to step A2.3.18.

[0150] Step A2.3.16, if Then, following step A2.2.7, update the vector. m′ r And the last valid point m′ to the left of the peak point r If the above conditions are not met, proceed to the next step.

[0151] Step A2.3.17: Update variable i to i = i + 1, and return to execute step A2.3.15;

[0152] Step A2.3.18 is the same as step A2.2.9, to obtain the observation point amplitudes used in the FD-BC algorithm. And the corresponding index m′, where, This represents the actual number of observation points, satisfying... Will Using m and m′ as the ordinate and abscissa respectively, the expression for the fractional index of the calibrated peak value is obtained as follows:

[0153]

[0154] Step A3, index the fractional multiple of the calibrated peak value. Obtain the signal frequency corresponding to the maximum value of the corrected spectrum. The expression is: Next, the user feeds back the frequency information to the BS, and the BS will... θ start =θ min θ end =θ max Substitute into the following formula:

[0155]

[0156] Calculate the angle information of user k, represented as

[0157] Step A4, after obtaining the angle estimation result for user k. Next, the distance to user k is sensed, at which point the starting and ending points of the beam squint are set to... and Substituting this into equation (16), we obtain the corrected spectrum of the user k received signal. in:

[0158]

[0159] Ignoring errors in angle perception, we get:

[0160]

[0161] Then the beam slant path is similar to Figure 1a T1 or T2 in the "beam slant path diagram";

[0162] Step A5, referring to step A2, applies the calibration algorithm proposed in this work to the corrected spectrum in equation (18) to obtain the fractional index of the peak value of the corrected power spectrum after calibration.

[0163] Step A6, referring to step A3, index the obtained corrected spectrum peak fraction multiple. Mapping to frequency Next, user k feeds back the frequency information to BS, and BS will... r start =r min r end =r max , Substitute into the following formula:

[0164] The calculated user distance information is represented as

[0165] Step B, User Speed ​​Sensing Signal Processing Flow: Assuming the BS achieves a shared antenna array between the transmitter and receiver through a circulator and full-duplex technology, and still taking user k as an example, the BS knows the current position of user k and transmits an OFDM signal with signal energy focused on the user's position. That is, the starting and ending points of the beam squint are set to... Beam slant path is similar to Figure 1a T7 in the "beam slant path map" is used to obtain the user's speed perception through the user's echo signal:

[0166] Step B1, construct the model of the echo signal received by BS from user k: Let the total echo signal received by BS from user k at sampling time t0 be represented as:

[0167] in Its expression is:

[0168]

[0169] In equation (19), G t and G r σ is the gain of the transmitting and receiving antennas. RCS Represents the radar cross section, ⊙ represents the Hadamard product, and the matrix... Vector a m (r, θ) and d m (v r v θ ) are the array response vector and the Doppler frequency shift vector, respectively, and are expressed as follows (20):

[0170]

[0171] in, Let the OFDM baseband signal of the m-th subcarrier transmitted by antenna l of BS be expressed as the following equation (21):

[0172]

[0173] Let be the noise vector. The total user k echo signal matrix received by BS at sampling time t0 is expressed as:

[0174]

[0175] in, Vector β = [β 0 , ..., β M ] T;

[0176] Step B2, Optimization problem construction: unknown parameter v k =[v r，k v θ，k ] T The estimation of β can be transformed into the following optimization problem:

[0177]

[0178] Step B3, set Solve the optimization problem in step B2 using the Expectation-Maximization (EM) algorithm:

[0179] Step B3.1: Randomly initialize the initial velocity of user k. The initialization constant h0 is used for the first iteration of the EM algorithm and is used to determine the condition for the end of the loop.

[0180] Step B3.2: Assume the speed of user k is constant and known to be... The channel fading coefficient was estimated using the least squares method as follows:

[0181]

[0182] in, Indicates a false reversal.

[0183] Step B3.3, obtain the estimated results of the channel fading coefficient. Substituting this into the optimization problem in step B2, for a fixed channel fading coefficient... Estimating the speed of user k is transformed into the following optimization problem:

[0184]

[0185] To solve this problem, a gradient-based approach is used, with the objective function G(v) k For user speed v k Taking the derivative, the final user velocity estimate is obtained through the following gradient descent process:

[0186]

[0187] Where, γ (i) Let represent the learning rate in the i-th iteration. The resulting user speed estimate is expressed as:

[0188] Step B3.4, if the user speed estimation result obtained in the previous step... satisfy Then As the final estimate of user speed, user speed perception is complete; otherwise, settings are set. Then proceed to step B3.2;

[0189] Step C, Beam Prediction and Tracking Signal Processing Flow: Taking user k as an example, the following are the steps of the beam prediction and tracking framework:

[0190] Step C1, Initial Perception: Given that the user's position and velocity can be considered constant within a coherent processing interval (CPI), let the duration of one CPI be T0. In the first CPI, set i = 1. At this time, the BS does not transmit data but only focuses on perceiving the user's position and velocity. Specifically, in the first CPI, the BS first obtains the user's position perception result according to the process in step A, represented as... Next, set the starting and ending points of the beam squint to be... Substituting equations (1) and (2) above, we obtain the beamforming vector of BS at this time. Then, an OFDM signal is transmitted to the user. Based on the user's echo signal, the initial velocity of user k is estimated according to the process in step B, and expressed as:

[0191]

[0192] Step C2, Beamformer Design: Given the estimated user position and velocity within the i-th CPI, and Then the user's location in the next CPI can be estimated, expressed as:

[0193]

[0194] A beamformer is designed based on the user's position within the (i+1)th CPI, i.e., the starting and ending points of the beam angle are set to be... Substituting equations (1) and (2), we obtain the beamforming vector, expressed as:

[0195] Step C3, Data Transmission and Speed ​​Awareness: Within the (i+1)th CPI, utilize the beamformer obtained in the previous CPI. Effective data transmission is possible, and based on the process in step B, the radial and lateral velocities of the user at the (i+1)th CPI can be obtained from the echo signal reflected by the user, expressed as follows:

[0196] Step C4: Set i = i + 1 and jump to step C2.

[0197] In summary, to verify the superiority of the low-complexity calibration algorithm proposed in this embodiment, Figure 3 shows the angle sensing mean square error (RMSE) of the FD-PI algorithm and the FD-BC algorithm as a function of signal-to-noise ratio (SNR). In the simulation, the number of antennas was set to N = 128, the number of subcarriers to M = 2048, the user's angle to be uniformly distributed within the sensing range, the element spacing to be half a wavelength, the signal bandwidth to be W = 3 GHz, f0 = 30 GHz, and the number of Monte Carlo experiments to be 1000. Figure 3a The RMSE of the FD-PI algorithm as a function of SNR was simulated, and the uncalibrated case ("Original") and the PI algorithm without forced landing (FD) sampling were compared. It can be seen that, compared to the uncalibrated case, the FD-PI algorithm significantly improves the user's angle perception accuracy; compared to the PI algorithm without FD sampling, especially at low SNR, the PD-PI algorithm proposed in this invention has a more significant advantage. Furthermore, Figure 3a The simulation also included different numbers of sampling points. The performance of the algorithm can be obtained under the simulation conditions of this invention when the SNR is low. The larger the SNR, the higher the sensing accuracy of FD-PI; however, there exists an optimal number of sampling points at high SNR. Figure 3b The RMSE of the FD-BC algorithm as a function of SNR was simulated. The trend is similar to that of the FD-PI algorithm, except that under the simulation conditions of this invention, the trend of the FD-BC algorithm as a function of the number of sampling points is different. The larger the size, the higher the perception accuracy.

[0198] This invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection of this invention is defined by the appended claims.

Claims

1. A near-field MIMO-based broadband OFDM sensing integrated system, characterized in that, include: A base station (BS) and K near-field single-antenna users serving the BS. The BS is equipped with a uniform linear array of N = 2L + 1 antennas. The carrier frequency and signal bandwidth of the OFDM signal transmitted by the BS are f and f, respectively. c Given W and M+1 subcarriers, the subcarrier spacing is Δf = W / M, and the frequency of the m-th subcarrier is f. m =f c -W / 2+mW / M, baseband frequency is Where m = 0, 1, ..., M; Each antenna of the BS array is connected in series with a true time delay unit and a phase shifter to control the beamforming point (i.e., the beam angle) of the signal energy focusing point of different frequency subcarriers. Therefore, the beamforming vector of the BS array... The expression for is as follows (1): In equation (1), The baseband frequency φ of the signal n Let t represent the phase shift of the nth (n = -L, ..., 0, ..., L) PS. n The delay of the nth TTD is expressed by the following formula (2): In equation (2), r start，n and r end，n Indicates the beam slant start point (the beam slant point of the 0th subcarrier (r)). start θ start And the distance from the endpoint to antenna n, let the beam slant point of subcarrier m be represented as... Then we get the following equations (3) and (4): Let the location of user k be (x k y k ), the corresponding polar coordinates are (r k θ k ), User k moves at a speed of v k , use v r，k and v θ，k Let r represent the radial and lateral velocities of the user relative to the center of the ULA of the BS, respectively. n，k v represents the distance between the BS antenna n and the user k. n，k Indicates the user's speed v k The projection component on the line connecting the BS antenna n and the user.

2. The signal processing method based on the near-field MIMO broadband OFDM integrated sensing system as described in claim 1, comprising the following steps: Step A, User Angle and Distance Sensing Signal Processing: In the user position, i.e. angle and distance sensing stage, the BS first sends an OFDM modulated all-1 pilot signal controlled by the beam look-out path to the user. The BS then obtains the final angle and distance sensing result through the uplink signal frequency fed back by the user. Step B, User Speed ​​Sensing Signal Processing: Assume the BS uses a circulator and full-duplex technology to enable the transmitter and receiver to share the antenna array. For user k, the BS knows the current position of user k and transmits an OFDM signal with signal energy focused on the user's position. That is, the starting and ending points of the beam squint are set to be... The EM algorithm is used to perceive the user's speed through the user's echo signal; Step C includes initial sensing, beamformer design, data transmission and velocity sensing, and beam prediction and tracking signal processing steps for setting i variables.

3. The signal processing method based on the near-field MIMO broadband OFDM sensing integrated system as described in claim 1, in step A, during the user position, i.e., angle and distance sensing stage, the BS first sends an OFDM-modulated all-1 pilot signal controlled by the beam-look-off path to the user. The user uses the received OFDM signal to generate a corrected spectrum and uses the calibration algorithm FD-PI or FD-BC proposed in this invention to obtain the signal frequency with the maximum amplitude and feeds it back to the BS. The BS then obtains the final angle and distance sensing result through the uplink signal frequency fed back by the user, specifically including: Step A1, taking user k as an example, in the user angle perception stage, the starting point and ending point of beam squint are set as (r... mid θ min ) and (r mid θ max ), where r min ≤r mid ≤r max The corrected spectrum of the received signal from user k is obtained, denoted as: Step A2, calibrate the corrected spectrum using the following calibration algorithm: Step A2.1, perform spectral peak search on the discrete corrected spectrum, as shown in the following equation (5): Let the number of observation points used for calibration be... The amplitude of the observation points used for calibration is represented as a vector. The corresponding index is represented by vector m′, and the amplitudes of the observation points to the left and right of the peak point are represented by vectors m′ and m′, respectively. and The corresponding index is represented as vector m′. l and m′ r The number of observation points may be less than the peak point because the peak point is closer to the edge. Therefore, the vector used to store the amplitude and index of the observation points is initialized to empty; Step A2.2, if the FD-PI calibration algorithm is selected, then assume the number of observation points used for calibration is... Odd number: Step A2.2.1, using m′ l This represents the last valid point to the left of the peak point and is initialized to m′. l =m′, set variable i = m′-l; Step A2.2.2, if the conditions are met simultaneously If i≥1, then proceed to steps A2.2.3 to A2.2.4; otherwise, skip steps A2.2.3 to A2.2.4 and proceed directly to step A2.2.

5. Step A2.2.3, if Then update the vector m′ l And the last valid point m′ to the left of the peak point l It is expressed as the following formula (6): If the above conditions are not met, proceed to the next step; Step A2.2.4: Update variable i to i = i-1, and return to execute step A2.2.2; Step A2.2.5, using m′ r This represents the last valid point to the right of the observed peak point and is initialized to m′. r =m′, set variable i = m′+1; Step A2.2.6, if the conditions are met simultaneously and Then proceed to steps A2.2.7 to A2.2.8; otherwise, skip steps A2.2.7 to A2.2.8 and proceed directly to step A2.2.

9. Step A2.2.7, if Then update the vector m′ r and the last valid point m′ to the right of the peak point. r It is expressed as the following formula (7): If the above conditions are not met, proceed to the next step; Step A2.2.8: Update variable i to i = i + 1, and return to execute step A2.2.6; Step A2.2.9 yields the observation point amplitudes and corresponding indices used in the FD-PI algorithm, expressed as equation (8): Will m and m′ are used as the ordinate and abscissa, respectively, for the quadratic function y = p²x. 2 The regression fit of +p1x+p0 yields the fractional index of the calibrated peak value. The expression is as follows (9): Step A2.3: If the FD-BC calibration algorithm is selected, then set the number of observation points used for calibration. Even numbers: Step A2.3.1, if If not, proceed to steps A2.3.2 to A2.3.9; otherwise, skip to step A2.3.

10. Step A2.3.2: Initialize the index of the last valid point to the left of the peak as m′. l =m′, initialize variable i = m′-1; Step A2.3.3, if the conditions are met simultaneously If i ≥ 1, then proceed to steps A2.3.4 to A2.3.5; otherwise, skip steps A2.3.4 to A2.3.5 and proceed directly to step A2.3.

6. Step A2.3.4, if Then, using the same formula (6), update the vector. m′ l And the last valid point m′ to the left of the peak point l If the above conditions are not met, proceed to the next step. Step A2.3.5: Update variable i to i = i - 1, and return to execute step A2.3.3; Step A2.3.6: Initialize the index of the last valid point to the right of the peak as m′. r =m′, initialize variable i = m′+1; Step A2.3.7, if the conditions are met simultaneously and Then proceed to steps A2.3.8 through A2.3.9; otherwise, skip to step A2.3.

18. Step A2.3.8, if Then, using the same formula (7), update the vector. m′ r And the last valid point m′ to the left of the peak point r If the above conditions are not met, proceed to the next step. Step A2.3.9: Update variable i to i = i + 1, and return to execute step A2.3.7; Step A2.3.10: Initialize the index of the last valid point to the left of the peak as m′. l =m′, initialize variable i = m′-l; Step A2.3.11, if the conditions are met simultaneously If i≥1, then proceed to steps A2.3.12 to A2.3.13; otherwise, skip steps A2.3.12 to A2.3.13 and proceed directly to step A2.3.

14. Step A2.3.12, if Then, using the same formula (6), update the vector. m′ l And the last valid point m′ to the left of the peak point l If the above conditions are not met, proceed to the next step. Step A2.3.13: Update variable i to i = i - 1, and return to execute step A2.3.11; Step A2.3.14: Initialize the index of the last valid point to the right of the peak as m′. r =m′, initialize variable i = m′+1; Step A2.3.15, if the conditions are met simultaneously and Then proceed to steps A2.3.16 to A2.3.17; otherwise, skip steps A2.3.16 to A2.3.17 and proceed directly to step A2.3.

18. Step A2.3.16, if Then, using the same formula (7), update the vector. m′ r And the last valid point m′ to the left of the peak point r If the above conditions are not met, proceed to the next step. Step A2.3.17: Update variable i to i = i + 1, and return to execute step A2.3.15; Step A2.3.18, obtain the observation point amplitudes used in the FD-BC algorithm. And the corresponding index m′, the expression is the same as in equation (8), where, This represents the actual number of observation points, satisfying... Will Using m and m′ as the ordinate and abscissa respectively, the fractional index expression of the calibrated peak value is obtained as follows (10): Step A3, index the fractional multiple of the calibrated peak value. Obtain the signal frequency corresponding to the maximum value of the corrected spectrum. The expression is: Next, the user feeds back the frequency information to the BS, and the BS will... θ start =θ min θ end =θ max Substitute into (3) to calculate the angle information of user k, which is expressed as Step A4: Obtain the angle estimation result for user k. Next, the distance to user k is sensed, at which point the starting and ending points of the beam squint are set to... and The corrected spectrum of the received signal from user k is represented as follows: Step A5, referring to step A2, correct the spectrum. Using a calibration algorithm, the fractional index of the peak value is obtained. Step A6, referring to step A3, index the obtained corrected spectrum peak fraction multiple. Mapping to frequency The user reports this frequency information back to the BS, and the BS will... r start =r min r end =r max , Substituting into equation (4), the user's distance information is calculated and expressed as follows:

4. Based on the signal processing method of the near-field MIMO broadband OFDM integrated sensing system as described in claim 1, step B, user speed sensing signal processing flow, assumes that BS realizes the transmitter and receiver sharing the antenna array through a circulator and full-duplex technology. Taking user k as an example, BS knows the current position of user k and transmits an OFDM signal with signal energy focused on the user's position, that is, the starting point and ending point of beam slant are set to be... And the user's speed is perceived through the user's echo signal, specifically including: Step B1: Construct a model of the echo signal received by BS from user k. Let the total echo signal received by BS from user k at sampling time t0 be represented as... Its expression is as follows (11): Step B2, optimize the problem formulation: Unknown parameter v k =[v r，k v θ，k ] T The estimation of β can be transformed into the following optimization problem: Step B3, set Solve the optimization problem in equation (12) using the Expectation Maximization (EM) algorithm: Step B3.1: Randomly initialize the initial velocity of user k. The initialization constant h0 is used for the first iteration of the EM algorithm and is used to determine the condition for the end of the loop. Step B3.2, assume the speed of user k is constant and known to be... The channel fading coefficient is then estimated using the least squares method, as shown in equation (13): In equation (13), Indicates a false reversal. Step B3.3, obtain the estimated results of the channel fading coefficient. Substituting into equation (12), for a fixed channel fading coefficient... The speed estimation of user k is transformed into the following optimization problem, as shown in equation (14): To solve this problem, a gradient-based approach is used, with the objective function G(v) k For user speed v k Taking the derivative, the user velocity estimation result is finally obtained through the following gradient descent process, as shown in equation (15): In equation (15), γ (i) Let represent the learning rate in the i-th iteration, and the resulting user speed estimate is expressed as: Step B3.4, if the user speed estimation result obtained in the previous step... satisfy Then As the final estimate of user speed, user speed perception is complete; otherwise, settings are set. Then proceed to step B3.

2.

5. The signal processing method based on the near-field MIMO broadband OFDM inductive integrated system as described in claim 1, step C: beam prediction and tracking signal processing flow, for user k, the specific steps of beam prediction and tracking are as follows: Step C1, Initial Sensing: Given that the user's position and velocity are considered constant within a coherent processing interval (CPI), let the duration of one CPI be T0. In the first CPI, set i = 1. At this time, the BS does not perform data transmission but only focuses on the sensing of the user's position and velocity. Specifically, in the first CPI, the BS first obtains the user's position sensing result according to the process in step A, represented as... Next, set the starting and ending points of the beam squint to be... Substituting equations (1) and (2), we obtain the beamforming vector of BS at this time. Then, an OFDM signal is transmitted to the user. Based on the user's echo signal, the initial velocity of user k is estimated according to the process in step B, and expressed as follows: Step C2, beamformer design, given the estimated user position and velocity within the i-th CPI. and The estimated location of the user in the next CPI is then represented as: A beamformer is designed based on the user's position within the (i+1)th CPI, i.e., the starting and ending points of the beam angle are set to be... Substituting equations (1) and (2), we obtain the beamforming vector, expressed as: Step C3, Data Transmission and Speed ​​Awareness: Within the (i+1)th CPI, utilize the beamformer obtained in the previous CPI. Effective data transmission is performed, and based on the process in step B, the radial and lateral velocities of the user at the (i+1)th CPI are obtained from the echo signal reflected by the user, denoted as... Step C4: Set i = i + 1 and jump to step C2.

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