A method and system for measuring displacement of micro-movement targets based on OFDM signals

Abstract

The present invention discloses a method and system for measuring the displacement of a micro-motion target based on an OFDM signal. A signal transceiver transmits and receives OFDM signals according to a standard communication protocol. During normal communication, a receiver obtains multiple frames of OFDM signals and obtains channel state information. Then, after eliminating synchronization errors, a stationary target is screened out in a generated range Doppler map, and a reference point is determined in combination with a super-resolution estimation result. The index numbers of the micro-motion target and the stationary reference target in the channel impulse response are determined according to a known rough distance, and the carrier phase is extracted respectively. Secondly, the micro-motion target carrier phase difference is compensated and the integer ambiguity is eliminated to obtain the final carrier phase difference of the micro-motion target. The micro-motion target carrier phase difference simultaneous equations obtained in other different coverage directions are solved to obtain the overall micro-motion displacement of the micro-motion target. Finally, the continuous time micro-motion displacement is summed to obtain the total displacement. Therefore, the present invention can realize the monitoring of the micro-displacement of the micro-motion target by using communication signals.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology and relates to a displacement measurement method, and specifically to a method and system for measuring the displacement of a micro-motion target based on an orthogonal frequency division multiplexing (OFDM) signal. The method and system can be applied indoors and outdoors, for example, to measure the deformation of a building, the displacement of a mountain, and the micro-displacement slowly generated by a mine landslide. Background Art

[0002] As the degree of informatization continues to deepen, wireless communication technology is developing rapidly. In the evolution of the next generation of wireless communication technology, the integration of multi-dimensional capabilities is a key point, among which the integrated communication perception capability, that is, in the process of communication, the communication signal is used to simultaneously perceive the environment and targets within the coverage of the communication signal in multiple dimensions. As an important fusion technology, this technology will promote the traditional wireless network with only a single information transmission function to develop in the direction of being able to perceive everything at the same time, greatly enhancing the value of the network and realizing the multi-polarization of wireless network capabilities. The integrated synaesthesia technology will play a significant role in the creation of various smart scenarios in the future, and bring objective economic value and social benefits.

[0003] OFDM technology is widely used in current wireless communication networks, including WIFI networks, fourth-generation and fifth-generation wireless communication networks. The transceiver signals of the above communication systems all use OFDM technology at the physical layer to generate signals with bandwidths ranging from several megahertz to hundreds of megahertz and composed of multiple orthogonal subcarriers. During the communication process, by measuring the OFDM signal, the RSSI and channel state information (CSI) of the OFDM signal can be obtained, which can be used for various sensing applications of the target to achieve interawareness integration. The existing sensing scenarios usually include ranging, positioning, life feature monitoring, behavior recognition, target reconstruction and other applications. Among them, ranging applications can determine the static / moving state of the target and measure the moving distance. It is also a pre-step for target positioning and target reconstruction. The early methods of using wireless signals for ranging are usually based on the measured RSSI, combined with the empirical propagation model, to derive the relationship between RSSI and distance, and realize the distance measurement between the receiver and the transmitter. The ranging accuracy is usually at the level of several meters. Also based on RSSI measurement, some existing methods have further improved the ranging accuracy by establishing an RSSI fingerprint library and integrating machine learning, AI and other technologies, but the accuracy has not yet exceeded the meter level. The CSI obtained based on OFDM signals characterizes the frequency response of different sub-frequency points to the wireless channel, which can reflect the time domain and frequency domain characteristics of the signal during the propagation of the multipath channel. It is a fine-grained measurement value, so it can also be used to achieve better ranging accuracy. At present, mainstream ranging algorithms are mostly based on CSI measurement values ​​and use super-resolution algorithms for parameter estimation. Commonly used algorithms include subspace-based methods, maximum likelihood-based methods, compressed sensing-based methods, and matrix bundle-based methods. Existing literature or methods show that sub-meter ranging accuracy can be achieved. Although sub-meter ranging accuracy can meet some perception applications, such as positioning and tracking of personnel in indoor and outdoor scenes, coarse-grained target reconstruction, intrusion detection and other applications, for small moving targets, that is, when the displacement generated by the target movement is only a few centimeters or millimeters, it is difficult for existing methods or systems to achieve high displacement measurement accuracy.The reasons are as follows: first, when the super-resolution parameter estimation method is used, the accuracy of ranging or displacement measurement is limited by the configured wireless hardware resources. For example, for an OFDM signal with a bandwidth of 200MHz, the corresponding signal time of flight (ToF) resolution is 5ns, that is, the resolution for target ranging is 1.5 meters; second, during the transmission and reception process, wireless signals usually experience attenuation of multipath channels, electromagnetic wave interference, noise introduced by transceiver devices, phase shift, etc., which affect the signal-to-noise ratio of the received signal; third, during the solution process of the algorithm itself, due to approximation, equivalence and other operations, the solution will be ambiguous, and the parameter search space and granularity set considering the complexity will not match the actual parameters. All of the above reasons lead to the fact that when only the super-resolution algorithm is used to estimate the displacement of micro-motion targets, it is impossible to achieve centimeter-level or millimeter-level displacement estimation accuracy, and it is impossible to solve the problem of displacement monitoring of micro-motion targets such as landslide detection and bridge deformation.

[0004] In response to the above-mentioned difficulties in micro-displacement measurement, existing technologies that achieve centimeter-level or even millimeter-level ranging accuracy based on wireless signals include UWB ranging technology, radio frequency identification (RFID) ranging technology, radar ranging technology, etc. UWB is a wireless carrier communication technology that uses nanosecond non-sinusoidal narrow pulses to transmit data. The operating frequency band is 3.25GHz to 6.75GHz, and the typical bandwidth is 500MHz or 1GHz. It can obtain sub-nanosecond ToF estimation of accurate time; RFID, as one of the key technologies of the Internet of Things, is a radio frequency identification technology that does not require direct contact. It uses wireless radio frequency signals to read and transmit the information stored in electronic tags. It was originally used to improve the efficiency of warehousing and logistics industries. Since RFID can backscatter electromagnetic waves, existing methods use the signals scattered by RFID to measure the distance of RFID, and the ranging accuracy has reached the centimeter level, but the ranging range is small, usually only a few meters; radar ranging usually uses linear frequency modulated wave (FMCW) signals for ranging, and measures the distance to the target by measuring the difference frequency of the echo signal. The ranging accuracy can reach sub-centimeter level and is widely used for ranging of long-distance targets. The above three methods based on wireless signals require the use of dedicated equipment and unique signal modulation methods, and cannot yet achieve the integration of communication and perception.

[0005] In the field of deformation monitoring of mine landslides, structures, buildings, etc., the interferometric synthetic aperture radar (InSAR) method is usually used. Depending on the different platforms, it includes satellite-borne SAR, airborne SAR and ground-based SAR. Ground-based SAR has been used as a mature displacement monitoring product with great commercial value due to its flexible deployment and high resolution. In the implementation of the InSAR method, first, the transmitter transmits a frequency modulated continuous wave. After being reflected by the monitored target, the receiver processes the received signal and can image the reflected target. It is characterized in that a linear slide or a circular slide is installed on the transceiver side, which increases the aperture of the radar antenna and expands the resolution of the radar in the azimuth direction; secondly, the images generated at different times are denoised, permanent scattering points are selected, pixel phase unwrapping is performed, and atmospheric errors are compensated; thirdly, the differential phase of the corresponding pixel is calculated based on the selected permanent scattering points; finally, the deformation displacement of the monitored target is calculated based on the differential phase. Mature commercial products based on ground-based InSAR methods can achieve sub-millimeter displacement measurement accuracy. However, this type of product is relatively expensive, usually ranging from hundreds of thousands to millions of dollars, and can only be deployed as dedicated displacement measurement equipment. It is unable to utilize the deployed wireless communication networks and facilities to achieve integrated communication perception.

[0006] In the method of using OFDM signals to simultaneously realize communication and ranging, for example, the patent application with publication number CN 116232831 A and titled "A communication method and system integrating communication and ranging based on OFDM technology" discloses a method of ranging based on OFDM signals. The main feature of this method is that a ranging frame symbol for ranging is designed in the OFDM signal frame at the transmitting end, and the receiving end performs sliding sampling on the received ranging frame, and calculates the maximum value of the correlation value through correlation operation, thereby finding the sampling point corresponding to the path signal, thereby realizing ranging of the target. This method requires additional design of ranging frames, increases the signaling overhead, and because the ToF of the signal is calculated based on the sampling points, its ranging accuracy is also constrained by the bandwidth of the OFDM signal, and cannot achieve millimeter-level displacement measurement accuracy.

[0007] For example, the patent application with publication number CN 110749859 A and titled "Indoor ranging method based on OFDM signal and three-frequency carrier phase ranging" discloses a method for indoor ranging using the carrier phase information of three subcarriers in OFDM signals. The main feature of this method is that three subcarriers of the carrier frequency are selected for ranging according to the Fisher information, and the ranging of the signal path is achieved based on the constraints of different carrier wavelengths by calculating the number of cycles experienced by the three subcarriers during channel transmission and the measured carrier phase difference. This method makes requirements on the selected ranging subcarriers, which need to be selected within a larger signal bandwidth, otherwise it will affect the ranging accuracy. The signal bandwidth in the example is 480MHz. In the current mainstream communication system, there are few systems that use the signal bandwidth in this method. In terms of ranging accuracy, this method can only achieve decimeter-level ranging accuracy at an optimal signal-to-noise ratio of 20dB, and it still cannot be applied in the micro-motion target displacement monitoring scenario. Summary of the invention

[0008] The purpose of the present invention is to address the deficiencies described in the above background technology in the prior art of micro-motion target displacement measurement, and to provide a method and system for micro-motion target displacement measurement based on OFDM signals, which can be integrated with the existing mainstream communication system to achieve communication perception integration.

[0009] To achieve the above object, the present invention provides a method and system for measuring the displacement of a micro-motion target based on OFDM signals, comprising the following steps:

[0010] Step 1: During the micro-motion target displacement monitoring activity, the transmitter and receiver continue to work, communicate normally, and obtain OFDM signals at equal intervals. At time t, P frames of OFDM signals are obtained. The OFDM signals obtained at continuous time intervals during the monitoring activity are expressed as [F 1 ,…,F t ,], where F t =[y 1 ,…,y P ];

[0011] Step 2: The OFDM signal transmitted by the transmitter is scattered by the monitored micro-motion target and propagates through the multipath channel to reach the receiver. The receiver receives a frame of OFDM signal transmitted as y p .

[0012] Step 3: The receiver performs RF and digital processing on the received OFDM signal and obtains CSI through channel estimation, which is expressed as H p ;

[0013] Step 4: Eliminate the frame detection delay difference of the CSI of the OFDM signal, perform IFFT transformation, and obtain the channel impulse response (CIR);

[0014] Step 5: Perform a two-dimensional FFT operation on the multi-frame CSI with the frame detection delay difference eliminated, and take the modulus value according to the operation result. The modulus value corresponds to the brightness value of each pixel in the imaging image, and perform imaging processing in the range and Doppler (Range-Doppler, RD) dimension;

[0015] Step 6: On the RD graph, select the Doppler f D =0Hz, that is, the point where the moving speed of the scattering target is zero, and the pixel point map representing the stationary target is retained accordingly;

[0016] Step 7: Using the multi-frame CSI that eliminates the frame detection delay difference, a super-resolution algorithm is used, including: an algorithm based on subspace matching, an algorithm based on matrix bundles, an algorithm based on maximum likelihood, or an algorithm based on compressed sensing, to estimate the channel wireless parameters and obtain the amplitude of the path signal of each scattering point after transmission through the multipath channel, ToF, (a l ,τ l ) and other parameters;

[0017] Step 8: Use the obtained scattering point signal path parameters (a l ,τ l ), using a clustering algorithm, after sorting by ToF size, the relative time difference Δτ between multiple scattering paths is calculated;

[0018] Step nine: According to the amplitude of the estimated scattering target signal path and the time difference between the paths, the pixel points on the pixel map of the stationary target are matched with the scattering path. Combined with the known coarse distance between the micro-motion target and the deployed transceiver, the pixel point index value corresponding to the micro-motion target scattering path can be obtained. The pixel point index value corresponds to the sampling point index value of the acquired CIR, and the two index values ​​are one-to-one corresponding.

[0019] Step 10: Extract the complex signal value at the index value corresponding to the micro-motion target scattering path in the CIR, perform FFT transformation, and extract the carrier phase value

[0020] Step 11: Extract the carrier phase value at multiple equally spaced time points Find the phase difference between adjacent time points

[0021] Step 12: In the above steps 9 to 11, the micro-motion target selected in step 9 is changed to a stationary scattering target, and steps 9 to 11 are repeated. Specifically, a stationary scattering target is selected as a reference point, and the pixel index value corresponding to the stationary target scattering path is obtained by combining the known coarse distance between the selected stationary reference point and the deployed transceiver. The pixel index value is corresponded to the sampling point index value of the acquired channel impulse response, and the phase jitter coefficient of the adjacent time point is obtained.

[0022] Step 13: Use the phase jitter coefficient of the stationary target to compensate the phase difference of the slightly moving target, and the compensated phase is:

[0023]

[0024] Step 14: Based on the small displacement of the micro-motion target, the phase after compensation is eliminated to obtain the whole cycle blur Expressed as

[0025] Step 15: Calculate the phase difference before and after the micro-motion target moves in multiple directions K Establish the following set of equations:

[0026]

[0027] In the formula, Δd t is the displacement of the micro-motion target at time t, λ is the wavelength of the carrier, η k is the residual phase error in the kth direction, and the overall displacement of the micro-motion target can be obtained by using but not limited to the least square method.

[0028] Step 16: During the monitoring activity, the relative displacements calculated in time series are summed up to obtain the total displacement of the micro-motion target during the monitoring activity.

[0029] Preferably, the OFDM signal transmitted in step 1 can be generated according to the 3GPP protocol TS 38.211 protocol. For example, an OFDM frame includes 5 time slots, including 3 downlink time slots, 1 uplink time slot, and 1 special time slot. The OFDM symbol used to measure the micro-motion target can be configured to be transmitted and received in the special time slot, and the transmission period is fixed, such as 2.5ms period transmission and reception. The transmitter sends the preamble code in the fourth and fifth OFDM symbols in the special time slot, and the receiver receives a complete OFDM symbol at the same symbol position. At time t, the OFDM symbol used to measure the micro-motion target in a frame is obtained, which is recorded as y p ; In order to reduce various errors in the measurement, the P frame OFDM symbol can be obtained at t, denoted as [y 1 ,…,y p ,,yP ];

[0030] Preferably, the OFDM symbol used to measure the displacement of the micro-motion target in step 2 may adopt a pseudo-random sequence (Pseudorandom Noise, PN), or may adopt a sounding reference signal (Sounding Reference Signal, SRS) defined in 3GPP TS 38.211;

[0031] Preferably, in step three, the baseband part of the transmitter will encode the source information, data packaging, symbol mapping, subcarrier allocation, IFFT transformation, attach CP, enter the RF part, undergo digital to analog conversion, IQ two-way modulation, up-conversion, filtering, amplification and then transmit through the antenna; the RF part of the receiver will perform low-noise amplification on the signal received by the antenna, down-convert the signal in I / Q two-way, automatic gain amplification, analog signal to digital signal conversion, obtain a time-domain discrete digital signal, enter the digital baseband processing, and then perform synchronization, frequency offset estimation and elimination, FFT operation, channel estimation and equalization module, inverse subcarrier allocation module, demodulation, CRC check and output. In channel estimation, the channel frequency response (CFR), that is, the channel state information CSI, can be obtained, which can be calculated based on the least squares method. The calculation without considering the channel noise is as follows:

[0032]

[0033] Where H(n) is the CFR at the nth subcarrier frequency, Y(n) is the sequence of the received y(t) signal sequence after FFT transformation, and X(n) is the known PN code or SRS code sequence;

[0034] Preferably, in step 4, based on the characteristic that the amplitude of the strong scattered signal path is stable during the channel coherence time, the frame detection deviation between different OFDM frames caused by the receiver synchronization process can be eliminated, and the processing includes:

[0035] (a) Perform FFT transformation on the measured H(n), and the result is χ(υ). The transformation process is expressed as:

[0036]

[0037] Where h(t) is the CIR corresponding to H(n), and the obtained CIR is the flipped form of the original signal in the time domain;

[0038] (b) In the power delay spectrum represented by the flipped CIR, find the sampling point index I corresponding to the spectrum peak max ;

[0039] (c) Based on the maximum index I obtained max , according to the subcarrier index number, the phase vector is generated as:

[0040]

[0041] Where N is the total number of subcarriers. Multiplying them, we get:

[0042]

[0043] In the formula, the phase of CSI is added by subcarrier, and the multiplication result for the nth subcarrier is:

[0044]

[0045] In the formula, Δf is the subcarrier spacing. After the transformation, it is equivalent to setting the index corresponding to the stable spectrum peak maximum path to zero in the time domain, eliminating the detection deviation between different OFDM frames.

[0046] Preferably, in step 5, after performing a two-dimensional FFT operation, an RD map is imaged, and the brightness of the pixels in the map is calculated by the following formula:

[0047]

[0048] Each pixel represents three-dimensional information, i.e., the amplitude intensity of the signal path with a distance of s and a Doppler-derived velocity of r; the horizontal axis of the RD graph is velocity, the vertical axis is distance, and the color scale represents the amplitude intensity;

[0049] Preferably, in step 7, a channel parameter estimation model is established based on compressed sensing theory as follows:

[0050]

[0051] In the formula, the operator ||.|| 0 represents the zero norm, α represents the amplitude of the multipath signal, and A represents the steering matrix formed by the subcarrier frequency and the flight time of the scattering path. The above formula can be solved by a super-resolution algorithm, such as the Orthogonal Matching Pursuit (OMP) algorithm, to obtain the channel parameters (a) of the scattering path. l ,τ l );

[0052] Preferably, in step eight, a density-based spatial clustering algorithm (DBSCAN) can be used to identify each path cluster, obtain parameters of different scattering paths according to the cluster, and calculate the relative time difference Δτ between multiple scattering paths. l ;

[0053] Preferably, in step nine, the pixels in the RD map are normalized according to the principle that the maximum amplitude is 0, and the amplitude of the scattering path parameter obtained by the super-resolution algorithm is also normalized according to the principle that the maximum value is 0, so that the super-resolution estimation result corresponds to the RD map in the amplitude dimension; according to the estimated channel parameter value, it can be determined that the path with a larger amplitude and a minimum ToF is the direct path signal between the transmitter and the receiver; the known transceiver deployment position and the coarse distance D of the monitored micro-motion target are converted into a ToF difference, and the coarse distance D can be obtained by a laser rangefinder, an infrared ranging telescope, a navigation satellite system, etc.:

[0054]

[0055] In the formula, c is the speed of light. The path with a spacing of Δτ from the direct path can be identified in the super-resolution estimation result as the path parameter of the micro-motion target scattering. This time interval corresponds to the number of sampling point intervals between the direct path pixel points and the micro-motion target pixel points determined in the RD map. The corresponding relationship is:

[0056]

[0057] In the formula, Δτ≈Δτ l,l+α , indicating that the lth path of the estimated signal path is the direct path, the l+αth path is the micro-motion target scattering path, Δn m.m+β is the number of sampling points between the mth and m+βth pixel points, N is the number of effective subcarriers of OFDM symbols, Δf is the bandwidth of OFDM subcarriers, and L is the number of estimated signal paths; from the amplitude matching result, it can be determined that the sampling point m corresponds to the direct path, and β is found to satisfy the above formula to complete the matching of the scattering path. The m+β value is the CIR sampling point index corresponding to the scattering target path;

[0058] Preferably, in step ten, the number of points for performing FFT is the number of valid subcarriers, and the phase of the center subcarrier, that is, subcarrier No. 0, is taken as the carrier phase; for the carrier phase values ​​derived from multiple frames, the mean method, probability density distribution method, and majority method can be used for numerical processing, but not limited to, to obtain the carrier phase value representing time t;

[0059] Preferably, in step twelve, the selection of the stationary reference point can be combined with the scene in which the monitoring target is located, and a target that is closer to the monitoring target is selected, and a rough distance between the stationary reference point and the transceiver is measured by a laser rangefinder, an infrared ranging telescope, a navigation satellite system, etc.;

[0060] Preferably, in step fourteen, obtaining the phase difference value without integer ambiguity error comprises the following steps:

[0061] First, the rough displacement of the micro-motion target is estimated, which is determined by the following formula:

[0062]

[0063] In the formula, Re(.) represents the real part, and λ is the wavelength of the carrier;

[0064] Secondly, based on the characteristics of the micro-motion target displacement, the displacement should be less than or equal to half the wavelength of the carrier. The following formula is used to determine whether there is an integer ambiguity error in the carrier phase difference:

[0065]

[0066] If the above equation is true, the calculated carrier phase does not have an integer ambiguity error. If it is not true, the calculation is as follows:

[0067] When If true, P1=1, otherwise P1=0,

[0068] When If true, P2=1, otherwise P2=0,

[0069] If P1>P2:

[0070]

[0071] If P2>P1:

[0072]

[0073] Therefore, the present invention adopts the above-mentioned method and system for measuring the displacement of a micro-motion target based on OFDM signals, which has

[0074] Beneficial effects:

[0075] 1. The present invention adopts the OFDM modulation signal widely used in the existing communication system as the measurement signal. While measuring the displacement of the micro-motion target, it can still maintain information transmission, enhance the value of the existing communication network, and realize communication integration.

[0076] 2. The present invention can measure the displacement of micro-motion targets indoors and outdoors, and the measurement accuracy can reach one tenth of the carrier wavelength or better. Compared with the existing measurement method based on OFDM signals, the measurement accuracy is significantly improved. 4G / 5G communication base stations deployed around mines, bridges, mountains, and buildings can be used to measure the deformation and micro-displacement sent by the above targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 is a flow chart of the present invention;

[0078] Figure 2 is the OFDM signal frame structure;

[0079] Figure 3 It is the time slot structure of OFDM signal;

[0080] Figure 4 It is a flow chart of OFDM signal transmission and reception;

[0081] Figure 5 It is a schematic diagram of an example test of the present invention;

[0082] Figure 6 The result image before eliminating the frame detection difference;

[0083] Figure 7 This is the result image after eliminating the frame detection difference;

[0084] Figure 8 RD diagram for imaging

[0085] Fig. 9 The channel parameter estimation result is shown in Figure 2.

[0086] Fig.10 Match the scattering path map to the joint RD map and the channel parameter estimation result;

[0087] Fig.11 is the extracted carrier phase image;

[0088] Specific implementation party

[0089] The technical solution of the present invention is further described below through the accompanying drawings and embodiments. Figure 1 , the specific steps for implementing the present invention are further described.

[0090] Step 1: In the micro-motion target displacement monitoring activity, the transmitter and the receiver work and communicate normally, and obtain multiple OFDM frames at equal intervals. The OFDM signal obtained at continuous time intervals in the monitoring activity is expressed as [F 1 ,…,F t ,…], proceed to step 2 to step 16;

[0091] In this embodiment, 100 OFDM frames are obtained at each equally spaced time point. The above 100 OFDM frames are transmitted and received at a fixed period of 2.5 ms. Each frame contains 5 time slots, and each time slot has 14 OFDM symbols. Figure 2 As shown in the figure, D represents the downlink time slot, which means that the base station sends information to the terminal user in normal communication, and U represents the uplink time slot, which means that the terminal user sends information to the base station side; S is a special time slot, including OFDM symbols for downlink and uplink services, and is used to avoid interference in the uplink and downlink conversion process. The special time slot S of the receiver and the transmitter can be configured as follows Figure 3 As shown. In the special time slot, the transmitter is configured to repeatedly send the preamble code for measuring the channel response twice on the 4th and 5th OFDM symbols, and the receiver is configured to receive the preamble code sent by the transmitter on the 4th and 5th OFDM symbols; in this embodiment, since the transceiver cannot achieve time synchronization without deviation, and considering the propagation delay of the signal, the transmitter repeats the preamble code sent twice to ensure that the receiver can receive a preamble code completely;

[0092] At each time point, the receiver obtains the preamble signal of the 100 frames used for measurement as F t =[y 1 ,…,y P ], the OFDM signal obtained in the monitoring activity is expressed as [F 1 ,…,F t ,…];

[0093] Step 2: The OFDM signal transmitted by the transmitter is scattered by the monitored micro-motion target and propagates through the multipath channel to reach the receiver. The receiver receives the transmitted OFDM signal. The received OFDM signal y(t) sequence can be expressed in the time domain as:

[0094]

[0095] When the signal arrives at the receiver, the filtered signal can be modeled as the convolution of a sinc pulse with the same bandwidth as the signal in the time domain and the arriving signal, where x(t) represents the transmitted preamble sequence and τ l is the propagation delay of the signal through the lth path, the symbol * represents the convolution operation, and B is the bandwidth of the signal;

[0096] Step 3: The receiver performs RF and digital processing on the received OFDM signal and obtains CSI through channel estimation; the baseband part of the transmitter will encode the source information, data packaging, symbol mapping, subcarrier allocation, IFFT transformation, attach CP, enter the RF part, undergo digital to analog conversion, IQ two-way modulation, up-conversion, filtering, amplification and then transmit; the RF part of the receiver will perform low-noise amplification on the received signal, down-convert the signal in two ways I / Q, automatic gain amplification, analog signal to digital signal conversion, obtain time-domain discrete digital signal, enter the digital baseband processing, and then perform synchronization, frequency offset estimation and elimination, FFT operation, channel estimation and equalization module, inverse subcarrier allocation module, demodulation, CRC check and output. The processing flow of the transmitter and receiver is as follows: Figure 4 In channel estimation, the OFDM symbol sequence y(t) received by the receiver in the time domain is transformed by FFT and expressed as:

[0097] Y=HX+N (17)

[0098] H represents the CSI of multiple subcarriers, which is composed of the channel frequency response H(n) of each subcarrier. X is the transmitted preamble sequence, which is known to the transceiver. N is additive white Gaussian noise. In this embodiment, the least square method is used to obtain the CSI with a length of 1584.

[0099] H=(X Η X) -1 X Η Y (18)

[0100] Step 4: Eliminate the frame detection delay difference of the CSI of the acquired OFDM signal, and perform IFFT on the CSI to obtain CIR; During the synchronization process, when using existing technologies such as the Schmidl algorithm, due to the influence of noise, the sampling starting point of each OFDM frame will be different. This example uses a frame detection delay difference elimination method based on power delay spectrum. Based on the results estimated from different packets, the power of the direct path is generally stronger and most stable, and the following transformation is performed:

[0101] (a) The result of FFT transformation of the measured H(n) is χ(υ), and the transformation process is expressed as:

[0102]

[0103] Where h(t) is the CIR of H(n). The CSI is transformed into the time domain, and the obtained CIR is the flipped form of the original CIR in the time domain.

[0104] (b) In the power delay spectrum represented by the flipped domain CIR, find the sampling point index I corresponding to the spectrum peak max ;

[0105] (c) Based on the maximum index I obtained max , according to the subcarrier index number, the phase vector is generated as:

[0106]

[0107] Where N is the total number of subcarriers. Multiplying them, we get:

[0108]

[0109] In the formula, the phase of CSI is added by subcarrier, and the multiplication result for the nth subcarrier is:

[0110]

[0111] In the formula, Δf is the subcarrier spacing. After the transformation, it is equivalent to setting the index corresponding to the stable spectrum peak maximum path to zero in the time domain, eliminating the detection deviation between different OFDM frames, and performing IFFT on the H′ sequence to obtain the channel impulse response h′ sequence;

[0112] Step 5: Perform two-dimensional FFT operation on multiple H′ with the frame detection delay difference eliminated, and take the modulus value according to the operation result, and perform imaging processing in the distance and Doppler dimensions according to the size of the modulus value corresponding to the brightness value of each pixel in the imaging; for time t, Indicates that each frame has 1584 subcarriers, for a total of 100 frames. ′t Perform two-dimensional FFT operation, perform RD imaging, perform IFFT change according to the column dimension, and perform FFT change according to the row dimension. The brightness of each pixel in the imaging image is calculated by the following formula:

[0113]

[0114] Each pixel represents a distance s, Doppler derived velocity r signal path amplitude intensity, the color scale plot shows the RD diagram of the scattered signal, the abscissa represents the velocity dimension, the ordinate represents the distance dimension;

[0115] Step 6: Since the monitored micro-motion target has a displacement of several millimeters to several centimeters over a long period of time, such as one day to several days, one month or one year, this displacement can be considered static relative to the measurement interval, such as 5 minutes or 10 minutes. Therefore, on the RD graph, select the Doppler f D =0Hz, that is, the point where the scattering target speed is zero, and the pixel point map representing the stationary target is retained accordingly. In this example, the pixel point corresponding to the horizontal axis equal to 51 can be retained, representing the pixel point of the stationary target scattering signal at different distances;

[0116] Step 7: Using H′ of multiple frames with different packet detection delays eliminated, a channel parameter estimation model based on compressed sensing theory is established as follows:

[0117]

[0118] In the formula, the operator ||.|| 0 Represents the zero norm, α represents the amplitude of the multipath signal, and A represents the steering matrix formed by the phase difference of the scattering path flight time at different subcarrier frequencies. The above formula can be solved by a super-resolution algorithm, such as the orthogonal matching pursuit OMP algorithm. By establishing an overcomplete matrix A′, the steering vectors of all possible paths, i.e. atoms, are generated according to the ToF range and step size of the estimated signal path, and a steering matrix is ​​formed. By finding the maximum value of the correlation between the complete matrix and the signal residual, the atoms representing the scattering path are found, and the signal is restored using the found atoms through orthogonal projection, and the difference is made with the measured signal to obtain the signal residual. After multiple iterations, the direct signal residual is close to zero. Based on the found atoms, the amplitude of the path signal of each scattering point after transmission through the multipath channel and the ToF parameters can be obtained;

[0119] Step 8: Use the obtained scattering point signal path parameters (a l ,τ l ), a clustering algorithm is used. In this example, the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to identify the path clusters. Each cluster C l =[p 1 ,…,p X The number of sample points p in ] x represents the estimated parameters of the scattering path belonging to this cluster (a x ,τ x ). In the cluster diagram, the cluster representing the stable scattering path will show the characteristics of concentrated sample points and small variance of ToF parameters. Therefore, clusters representing different scattering paths can be screened out, and the parameter mean of the sample points in the cluster can be calculated by cluster.

[0120]

[0121] The mean value of the ToF parameters of the scattering paths is used as the clustering, and then the ToF is sorted to calculate the relative time difference Δτ between multiple scattering paths;

[0122] Step 9: Normalize the pixels in the range Doppler image according to the principle that the maximum amplitude is 0; normalize the amplitude of the scattering path parameter obtained by the super-resolution algorithm according to the principle that the maximum value is 0, and then sort the pixels in the range Doppler image by brightness, sort the amplitude intensity of the scattering path obtained by super-resolution, and then perform path matching, so that the first correspondence between the super-resolution estimation result and the range Doppler image can be achieved in the amplitude dimension. In this example, the brightest pixel in the RD image corresponds to the path with the largest amplitude in the parameter estimation result, and so on.

[0123] According to the estimated channel parameter values, the direct path signal between the transmitter and the receiver can be determined. Since the direct path is the signal path from the transmitter to the receiver directly without being scattered by the scattering target, in this example, the path with larger amplitude and smallest ToF is determined as the direct path. The direct path can also be determined by a scoring function, as shown in the following formula:

[0124]

[0125] In the formula, the scoring function is the ToF mean of the cluster The signal strength Amp, variance σ, number of sample points P and other factors are weighted and scored;

[0126] In this example, the transceivers are deployed at similar locations, and the direct path can be used as a reference point. The difference between the location where the transceiver is deployed and the rough distance of the monitored micro-motion target can be obtained by a laser rangefinder, infrared optical ranging, or navigation satellite system, and converted into a flight time difference:

[0127]

[0128] Where c is the speed of light. The path with a spacing of Δτ from the reference point can be identified in the super-resolution algorithm estimation result and determined as the path scattered by the micro-motion target. In this example, for example, the direct path ToF estimation value is 42ns, and the detection target is about 36 meters away from the transceiver deployment, then Δτ = 240ns. Therefore, the scattering path near 282ns in the parameter estimation result is determined as the scattering path of the micro-motion target.

[0129] The time difference Δτ corresponds to the number of sampling points between the pixels of the stationary target in the Doppler image, and the corresponding relationship is:

[0130]

[0131] In the formula, Δτ≈Δτ l,l+α , represents the difference in ToF estimation between the lth direct path and the l+αth micro-movement target scattering path, Δn m.m+βis the number of sampling points between the mth and m+βth pixel points, N is the number of effective subcarriers of OFDM symbols, Δf is the bandwidth of OFDM subcarriers, and L is the total number of estimated signal paths; based on the amplitude matching result, in this example, the brighter point and the front m are determined as the direct path reference point, and β is found to satisfy the above formula, and the monitoring target β near 36 meters is calculated to be 37, completing the matching of the scattering path, and the m+β value is the sampling point index of the CIR corresponding to the scattering target path;

[0132] Step 10: Extract the complex signal value corresponding to the index value corresponding to the CIR micro-motion target scattering path, perform FFT transformation, and extract the carrier phase value In this example, 100 frames of estimation results are obtained, and 100 groups of results are transformed by FFT. The number of points of FFT operation is kept consistent with the number of effective subcarriers, which is 1584. 100 groups of frequency CFR can be obtained and the phase value can be calculated. Then the phase value of the middle frequency point, that is, the phase value of sub-frequency point 0, is taken as the carrier phase value. However, due to the random interference of the wireless environment, thermal noise of the transmitting and receiving equipment, interference and other influences, the carrier phase of 100 groups of CFR fluctuates within a range. When the phase fluctuation range is small, such as within the range of [-0.5rad, 0.5rad], the mean method can be used to average the carrier phase; if it fluctuates within the range of [-π, π], the probability density distribution function of the carrier phase can be calculated, and the carrier phase corresponding to the maximum probability density can be taken as

[0133] Step 11: Extract the carrier phase value at multiple equally spaced time points The carrier phase difference between adjacent time intervals is:

[0134]

[0135] The phase difference between adjacent time points in the detection activity is expressed as

[0136] Step 12: In the above steps 9 to 11, the micro-motion target selected in step 9 is changed to a stationary scattering target, and steps 9 to 11 are repeated. A stationary scattering point is selected as a reference point. The selection of the stationary reference point can be combined with the scene in which the monitoring target is located, and a target that is closer to the monitoring target is selected, such as the bottom of the slope in hillside landslide monitoring; the bridge foundation close to the shore in bridge monitoring; the nearby structure in building deformation monitoring, etc.; the rough distance between the stationary reference point and the transceiver can also be measured by the above-mentioned other measurement methods; combined with the known rough distance between the selected stationary reference point and the deployed transceiver, the pixel point index value corresponding to the stationary target scattering path is obtained according to step 9, and the phase jitter coefficient of adjacent time points can be obtained by corresponding the pixel point index value to the sampling point index value of the acquired CIR.

[0137] Step 13: Use the phase jitter coefficient of the stationary target to compensate the phase difference of the slightly moving target, and the compensated phase is:

[0138]

[0139] Step 14: Based on the small displacement of the micro-motion target, the phase after compensation is cleared of the whole cycle blur and the result is First, the rough displacement of the micro-motion target is estimated, which is determined by the following formula:

[0140]

[0141] In the formula, Re(.) represents the real part, λ is the wavelength of the carrier. For the sub-6GHz frequency band commonly used in communication systems, the wavelength is usually 5cm to 30cm, and for the 24GHz to 30GHz millimeter wave frequency band, the wavelength is usually 1cm to 1.5cm. Within a wavelength range, set Δd to a step size of one thousandth of a wavelength, search for the maximum value of the above formula, and take the Δd corresponding to the maximum value as the rough estimated displacement;

[0142] Secondly, based on the characteristics of the micro-motion target displacement, the displacement is usually less than or equal to half the wavelength of the carrier. The following formula is used to determine whether there is an integer ambiguity error in the carrier phase difference:

[0143]

[0144] If the above equation is true, the calculated carrier phase does not have an integer ambiguity error. If it is not true, the calculation is as follows:

[0145] When If true, P1=1, otherwise P1=0,

[0146] When If true, P2=1, otherwise P2=0,

[0147] If P1>P2:

[0148]

[0149] If P2>P1:

[0150]

[0151] This allows a carrier phase difference without integer ambiguity to be obtained.

[0152] Step 15: Send and receive signals in multiple directions K. K directions can be achieved in two ways. One is to mechanically rotate the transceiver antenna to send signals in K directions. The signals are scattered by scatterers in different areas or parts of the monitored target. The scattered signals are received by the receiver. Then, according to steps 1 to 14, the phase difference before and after the micro-motion target displacement in K directions is calculated.

[0153]

[0154] At time t, the following set of equations is established:

[0155]

[0156] Where Δd is the displacement of the micro-motion target, λ is the wavelength of the carrier, and η k is the residual phase error in the kth direction, and the overall displacement of the micro-motion target can be obtained by using but not limited to the least square method.

[0157] Step 16: During the monitoring activity, the relative displacement calculated from the data measured in time series is summed up to obtain the total displacement of the micro-motion target during the monitoring activity.

[0158] Δd all =Δd 1 +Δd 2 +…+Δd t +… (38)

[0159] Combined with experimental data, the technical effects of the present invention are further explained:

[0160] 1. Experimental conditions:

[0161] The transmitter and the receiver use a universal software radio device, specifically the N321 device of NI Company, with one radio frequency port for transmitting signals and one radio frequency port for receiving signals, which are synchronized through the clock inside the device, and the local oscillator signal of the transmitting end is set to be output to the local oscillator signal receiving end of the receiving end; the transmitting antenna and the receiving antenna use directional antennas to connect to the SMA port of the software radio device TX / RX through radio frequency cables; a PC is used for baseband processing of OFDM signals and the algorithm code implementation of the method of the present invention, the PC is equipped with Ubuntu system, and the modulation and demodulation of the interaesthesia integrated OFDM signal is realized by using the GNU RADIO framework. The transmitting power of the transmitter is 15dBm, the gain of the receiving end is 20dB, the antenna gain is 10dBi, the number of subcarriers of the OFDM signal is 2048, the effective subcarriers are 1584, and the bandwidth is 200MHz. In this example, a PN sequence is used as the preamble, with a length of 1584, and 232 zeros are padded before and after to generate a sequence of length 2048. As an OFDM symbol, the length of the cyclic prefix CP is 144.

[0162] 2. Experimental Environment

[0163] The transmitter and receiver are placed at the same location. Affected by the transmission power of the test equipment, the test target is placed at a distance of about 36 meters from the transceiver, measured with a tape measure. The test target is a flat plate covered with aluminum foil, which is placed on the displacement device to simulate the displacement of the micro-motion target. The adjustment step is mm. The entire test scene is the rooftop. Figure 5 shown.

[0164] 3. Experimental Procedure

[0165] (a) During the monitoring activity, a total of 6 measurements were performed at equal intervals of 5 minutes. After each measurement, the shifter was adjusted to slightly move the target by 2mm-4cm. In each measurement, 100 frames of OFDM signals were transmitted and received, and channel estimation was performed according to the above step 3 to obtain CSI.

[0166] (b) When receiving OFDM signals, the Schmidl synchronization algorithm used will cause deviations in the starting sampling points between frames. After processing according to step 4 above, the synchronization deviation between different frames can be eliminated. After elimination, the estimation result before elimination is as follows: Figure 6 , the estimated result after elimination is Figure 7 shown.

[0167] (c) Perform two-dimensional FFT calculation and make RD graph, such as Figure 8As shown, the horizontal axis represents the target speed, the column with index x=51 represents the scattering path of the stationary target, and the vertical axis represents the distance. Combined with the brightness of the pixels, the pixels at coordinates (51, 7), (51, 8), (51, 44) have high brightness, indicating the signal paths of multiple scattering targets;

[0168] (d) Use the OMP super-resolution algorithm to estimate parameters, and according to step 8, use the DBSCAN algorithm for clustering. In the clustering results, Fig. 9 The clusters shown represent different scattering paths, and the ToF mean of each cluster can be obtained. In the experiment, it is estimated that there are 5 paths, path_1 = 42ns, path_2 = 51ns, path_3 = 276ns, path_4 = 280ns, path_5 = 383ns. According to step nine, path_1 can be determined as a direct path. Combined with the rough distance of the micro-moving target from the transceiver of 36 meters, that is, 240ns, path_4 can be determined as the scattering path of the micro-moving target.

[0169] (e) Match the range Doppler map with the parameter estimation results: The amplitude of the path signal from path_1 to path_5 of the parameter estimation results is matched to the pixel points on the range Doppler map as (51, 7), (51, 8), (51, 43), (51, 44), (51, 51); combined with the time difference Δt = path_4-path_1 = 238ns, in a 200MHz bandwidth and 1584 effective subcarrier configuration, a pixel point on the vertical axis of the RD map represents 6.464ns. According to step nine, β = 238÷6.464≈37 can be calculated. Therefore, it can be determined that the pixel point (51, 7) represents the direct path, and the pixel point (51, 44) represents the scattering path of the micro-motion target, as shown in FIG. Fig.10 shown.

[0170] (f) Extract the sampling point value in the channel impulse response corresponding to the micro-motion target scattering path index 44, perform FFT transformation, the number of FFT transformation points is 1584, take the unwrapped phase of the transformation result, and take the phase corresponding to the frequency point 792. The results of 100 frames are as follows Fig.11 As shown in the figure, since the initial phase fluctuation is about 0.2 rad, the phase can be directly averaged to obtain the carrier phase representing the scattering path of the micro-motion target, and then the carrier phase difference of the 6 measurements in the monitoring activity can be obtained:

[0171] (g) In the experiment, since the distance between the transceiver and the detection target is within 100 meters, the environmental difference is small, and the external conditions of the transceiver remain the same, the phase jitter coefficient is not corrected. The selection of the stationary reference point and the carrier phase extraction method are consistent with the method of processing micro-motion targets;

[0172] (h) Set the search step size to 0.1mm, and eliminate the integer ambiguity of the calculated carrier phase difference according to step 14. In the experiment, the signal was transmitted and received only in one direction, and the micro-displacement of the scattering target in that direction was measured. Measurements in other directions can be achieved by mechanically adjusting the azimuth and inclination of the transmitting and receiving antennas to change the coverage direction or by using beamforming. The micro-displacement in one direction is calculated as follows:

[0173]

[0174] The displacement measurement of the target as a whole in multiple directions is calculated according to step 15.

[0175] 4. Experimental Results

[0176]

[0177] The target moved a total of 86 mm during the monitoring activity, and the displacement measured by the method of the present invention was 88.51 mm, with an overall error of 2.51 mm and a mean error of 0.53 mm. Compared with the prior art, the super-resolution algorithm based on OMP cannot correctly measure the target displacement.

[0178] Therefore, the present invention adopts the above-mentioned method for measuring the displacement of micro-motion targets based on OFDM signals. Through existing communication equipment, such as WIFI, 4G / 5G communication equipment, accurate displacement measurement of micro-motion targets can be performed by extracting the carrier phase of the micro-motion target scattering path signal during communication, thereby realizing the application of synaesthesia integration.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for measuring the displacement of a micro-motion target based on OFDM signals, comprising the following steps: Receiving, at a receiver, an OFDM signal that is scattered by a micro-motion target and transmitted from a transmitter through a multipath channel; estimating one or more channel frequency responses and one or more channel impulse responses based on an OFDM signal received at the receiver; Determine the sampling point sequence index number in the channel impulse response corresponding to the micro-motion target scattered signal in the time dimension based on the channel frequency response; Extracting a corresponding signal complex value in the channel impulse response based on the sampling point sequence index number; Based on the complex value, performing a discrete Fourier transform on the complex value to obtain a response value of the micro-motion target in the frequency domain; Extracting a carrier phase based on the response value in the frequency domain; Then, after the micro-motion target is displaced, the carrier phase is extracted again, and the carrier phase difference before and after the target is displaced is calculated; Calculating the displacement of the micro-motion target based on the calculated carrier phase difference before and after the target is displaced; and receiving, at a receiver in different directions, an OFDM signal scattered by a micro-motion target and transmitted from a transmitter through a multipath channel, and calculating displacements of the micro-motion target in different directions; Among them, the adopted OFDM signal can simultaneously communicate information; The estimated channel frequency response and channel impulse response are derived from the OFDM signal transmitted from the transmitting device, each of which is associated with the response of the propagation distance of the micro-motion target and the multiple scattering targets in the multipath channel in the frequency domain and the time domain; The sampling sequence index number of the channel impulse response corresponding to the micro-motion target scattered signal in the time dimension is determined by the multipath flight time estimated by the channel parameter estimation algorithm and the range Doppler two-dimensional image generated by the channel frequency response; The carrier phase difference before and after the extracted target micro-motion is compensated for the phase jitter coefficient determined by the static reference point; The carrier phase difference before and after the extracted target micro-motion is a phase difference value without integral ambiguity error determined by the micro-motion characteristics of the micro-motion target; The calculation of the displacement of the micro-motion target in different directions is achieved by adjusting the direction of the beam emitted by the transmitter and the direction of the beam received by the receiver.

2. According to the method described in claim 1, determining the sampling sequence index number of the channel impulse response corresponding to the micro-motion target scattered signal in the time dimension based on the channel impulse response includes determining it by using a known rough distance between the transceiver and the micro-motion target.

3. According to the method of any one of claims 1 to 2, determining the sampling sequence index number of the channel impulse response corresponding to the micro-motion target scattering path in the time dimension based on the channel impulse response comprises the following steps: First, a super-resolution algorithm is used to estimate the wireless channel parameters. The wireless parameters of the signal path from the transmitter to the receiver through one or more scattering points in the multipath channel are derived. The signal path wireless parameters include the amplitude and flight time of the signal. After clustering the paths, the paths are sorted by flight time and the time difference between adjacent paths is calculated. Secondly, a two-dimensional Fourier transform is performed on multiple channel frequency responses estimated from the OFDM signal, including an inverse Fourier transform in the subcarrier dimension or direction, and then a discrete Fourier transform is performed in the frame dimension or direction, and the obtained results are graphically displayed according to the size of the module value, and a range Doppler map derived based on the received multiple OFDM frames is obtained, the brightness of the pixel points in the map corresponds to the size of the module value, and one or more pixels with larger brightness in the map represent one or more superimposed scattering paths in the multipath channel; Thirdly, based on the characteristics that the micro-motion target has a small displacement and maintains a slow displacement for a period of time, the pixels with non-zero Doppler values ​​are removed from the range Doppler map to obtain the pixel map of the stationary target; Finally, according to the estimated amplitude of the signal path and the time difference between the two paths, the pixel points and the scattering paths are matched on the pixel map of the stationary target with the result of the channel parameter estimation to obtain the pixel index value corresponding to the scattering path of the micro-motion target, and the pixel index value corresponds to the sampling point sequence index value of the channel impulse response one by one; in, Multiple OFDM packets are transmitted and received at a fixed period; The brightness of the pixel of the stationary target in the range Doppler image corresponds to the estimated amplitude of the signal path; The estimated time difference between adjacent paths of the signal path corresponds to the number of pixels between the pixels of the stationary target in the range Doppler map, and the corresponding relationship is: In the formula, Δτ l,l+α is the time difference between the estimated lth and l+αth adjacent paths of the signal path, Δn m.m+β is the number of sampling points between the mth and m+βth sampling points, N is the number of effective subcarriers of OFDM symbols, Δf is the bandwidth of OFDM subcarriers, and L is the estimated number of signal paths. We can find l, m, α, β that make the above formula valid and complete the matching of the scattering path.

4. According to the method of claim 1, the number of points for performing discrete Fourier transform on the complex value is the number of effective subcarriers.

5. According to the method described in claim 1, the carrier phase extracted from the response value in the frequency domain is the phase value corresponding to the zero subcarrier, and the carrier phase difference before and after the target displacement is calculated 6. The method according to claim 5, wherein the phase jitter coefficient determined by the stationary reference point is compensated The following steps are involved: Based on the matched scattering paths in the range Doppler map, the pixel points representing the stationary target near the pixel points representing the slightly moving target are determined. Based on the pixel points of the stationary target, the carrier phase before and after the micro-moving target is displaced is extracted, and the phase jitter coefficient is calculated by difference. The phase difference before and after the micro-motion target is displaced after compensation is expressed as formula (2):

7. According to the method of claim 1, obtaining the phase difference value without integer fuzzy variable error comprises the following steps: First, the rough displacement of the micro-motion target is estimated, which is determined by equation (3): In the formula, Re(.) represents the real part, λ is the wavelength of the carrier, in, To compensate for the phase difference before and after the micro-motion target is displaced; Secondly, based on the characteristics of the micro-motion target displacement, the displacement is less than or equal to half the wavelength of the carrier. According to formula (4), it is judged whether there is an integral ambiguity error in the carrier phase difference: If equation (4) holds true, the calculated carrier phase does not have integer ambiguity error. If equation (4) does not hold true, the calculation is as follows: When If true, P1=1, otherwise P1=0, When If true, P2=1, otherwise P2=0, If P1>P2: If P2>P1:

8. According to the method described in claim 1, the phase difference before and after the micro-movement target moves is calculated in multiple directions K. Establish equation group (7): In the formula, Δd t is the displacement of the micro-motion target at time t, λ is the wavelength of the carrier, η k is the residual phase error in the kth direction, and the least square method is used to obtain the overall displacement of the micro-motion target.

9. According to the method described in claim 1, the calculation of the displacement of the micro-motion target in different directions can also be achieved by rotating the receiver and the transmitter to cover the direction.

10. The method according to claim 1, wherein the OFDM signal is transmitted after being modulated by an electromagnetic wave carrier.

11. The method according to claim 10, wherein the electromagnetic wave carrier frequency includes 500 MHz to 90 GHz.

12. The method of claim 1, wherein determining the amount of fine motion target displacement comprises determining the amount of fine motion target displacement with an accuracy within one tenth of a wavelength of the carrier wave.

13. The method according to any one of claims 1 or 10, wherein the OFDM signal comprises one or more OFDM frames consisting of one or more OFDM symbols.

14. The method according to any one of claims 1 or 10, wherein the OFDM signal includes one or more pseudo-random codes.

15. The method according to any one of claims 1 or 10, wherein the OFDM signal includes one or more sounding reference signals (SRS).

16. A system for measuring the displacement of micro-movement targets based on OFDM signals. include: A receiver, the receiver comprising one or more radio frequency signal sensing units and one or more signal processing units, wherein the radio frequency signal sensing unit is configured to detect OFDM signals, filter and amplify, frequency convert, and perform analog-to-digital conversion, wherein the signal processing unit is configured to demodulate the OFDM signal, and perform channel estimation based on training sequence codes and detection codes known to both the transmitting and receiving sides, and derive the channel frequency response and channel impulse response of the received OFDM signal; A transmitter, the transmitter comprising one or more radio frequency signal sensing units and one or more signal processing units, wherein the radio frequency signal sensing unit is configured to transmit OFDM signals, filter and amplify, frequency convert, and perform digital to analog conversion, wherein the signal processing unit is configured to modulate the OFDM signal; A synchronization unit, which provides a reference clock source and a pulse per second signal for the receiver and the transmitter; A micro-motion target displacement measurement unit, wherein the displacement measurement unit is configured as follows: Receiving, at a receiver, an OFDM signal that is scattered by a micro-motion target and transmitted from a transmitter through a multipath channel; estimating one or more channel frequency responses and one or more channel impulse responses based on an OFDM signal received at the receiver; Determine the sampling point sequence index number in the channel impulse response corresponding to the micro-motion target scattered signal in the time dimension based on the channel frequency response; Extracting a corresponding signal complex value in the channel impulse response based on the sampling point sequence index number; Based on the complex value, performing a discrete Fourier transform on the complex value to obtain a response value of the micro-motion target in the frequency domain; Extracting a carrier phase based on the response value in the frequency domain; Then, after the micro-motion target is displaced, the carrier phase is extracted again, and the carrier phase difference before and after the target is displaced is calculated; The displacement of the micro-motion target is calculated based on the calculated carrier phase difference before and after the displacement of the target; and in different directions, the receiver receives the OFDM signal scattered by the micro-motion target and transmitted from the transmitter through the multipath channel, and calculates the displacement of the micro-motion target in different directions; Among them, the adopted OFDM signal can simultaneously communicate information; The estimated channel frequency response and channel impulse response are derived from the OFDM signal transmitted from the transmitting device, each of which is associated with the response of the propagation distance of the micro-motion target and the multiple scattering targets in the multipath channel in the frequency domain and the time domain; The sampling sequence index number of the channel impulse response corresponding to the micro-motion target scattered signal in the time dimension is determined by the multipath flight time estimated by the channel parameter estimation algorithm and the range Doppler two-dimensional image generated by the channel frequency response; The carrier phase difference before and after the extracted target micro-motion is compensated for the phase jitter coefficient determined by the static reference point; The carrier phase difference before and after the extracted target micro-motion is a phase difference value without integral ambiguity error determined by the micro-motion characteristics of the micro-motion target; The calculation of the displacement of the micro-motion target in different directions is achieved by adjusting the beam direction emitted by the transmitter and the beam direction received by the receiver; A micro-motion target displacement solution equation group is generated based on the phase difference value and the micro-motion target displacement value, wherein the expression of each displacement carrier phase difference corresponds to the relationship between the displacement of the micro-motion target in one direction covered and the generated carrier phase difference; The equation group is solved to determine the relative displacement value of the micro-motion target, wherein the relative displacement value corresponds to the displacement amount of the micro-motion target relative to the static state.

17. The system according to claim 16, wherein both the receiver and the transmitter can be used in a communication system based on OFDM signal modulation.

18. According to the system of any one of claims 16 to 17, both the receiver and the transmitter can be configured as a transceiver with an integrated transceiver function.

19. According to the system of any one of claims 16 to 17, the receiver and the transmitter are arranged in an integrated manner to transmit and receive OFDM signals, or can be separated and arranged at a distance to transmit and receive OFDM signals in a collaborative manner.

20. According to the system of any one of claims 16 to 17, the synchronization unit can use a common clock source module or a synchronization clock source obtained in an independent manner.

21. The system according to any one of claims 16 to 17, wherein the radio frequency signal sensing units of the receiver and the transmitter are configured to have a beamforming function.

22. The system according to any one of claims 16 to 17, wherein the receiver and the transmitter can share a local oscillator.

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