Direct positioning and synchronization method for maneuvering asynchronous targets in MIMO-OFDM systems

Abstract

This invention discloses a method for direct positioning and synchronization of maneuvering asynchronous targets in a MIMO-OFDM system. This method, belonging to the field of navigation and positioning technology, separates the received signals from each anchor point in the time domain, constructs a frequency offset model and a dynamic offset compensation model for the maneuvering target, performs elimination based on the relative relationship between the received phase shifts of the first two pilot blocks, and formulates a nonlinear optimization problem. A linearized iterative solution is then used to jointly obtain estimates of the target's motion parameters and frequency offset, achieving high-precision time-varying position determination and frequency synchronization of the target. Compared to traditional direct positioning methods, this method can be extended to directly position and synchronize dynamic asynchronous targets and is applicable to MIMO-OFDM systems, offering improved robustness and a wider range of application scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of navigation and positioning, and in particular to a direct positioning and synchronization method for a maneuvering asynchronous target in a MIMO-OFDM system. Background Art

[0002] Location awareness is one of the indispensable basic functions in the Internet of Things, Internet of Vehicles, smart cities and wireless sensor networks, and has received widespread attention in academia and industry.

[0003] The paper "A Robust TSWLS Localization of Moving Target in Widely Separated MIMO Radars," published in the April 2023 issue of the IEEE Transactions on Aerospace and Electronic Systems journal, Volume 59, Issue 2, Pages 897-906, states that positioning techniques can be broadly categorized as two-step positioning and direct positioning. Two-step positioning techniques first acquire observations from the received signal and then determine the target position based on the geometric relationships contained in these observations. Common observations include time of arrival (TOA), angle of arrival (AOA), received signal strength (RSS), and Doppler shift (DS). Unlike the two-step method, direct position determination (DPD) utilizes preprocessed received signals to estimate the target position in a single step, eliminating the need for any intermediate observations. The direct positioning method can obtain the best estimation performance and can reach the Cramer-Rao lower bound. Especially in the case of low signal-to-noise ratio, the positioning accuracy is much better than the two-step positioning method.

[0004] Current research on direct positioning technology suffers from the following three shortcomings. First, related research primarily focuses on the positioning of static or quasi-static targets. When applied to maneuvering targets, these traditional methods fail to match the motion model, resulting in significant positioning errors. Second, most current research assumes complete frequency synchronization between the target and the anchor point, which requires a high-precision crystal oscillator or a wired connection between the two. When these conditions are not met, traditional methods fail in asynchronous networks. Third, there is currently limited research on multiple-input, multiple-output, orthogonal frequency division multiplexing (MIMO-OFDM) signal systems. MIMO-OFDM technology, due to its advantages such as spatial diversity and multipath mitigation, plays a vital role in today's wireless communication systems. Therefore, research on direct positioning technology based on MIMO-OFDM signals is necessary.

[0005] Therefore, it is of great significance to achieve high-precision direct positioning and synchronization of maneuvering asynchronous targets in MIMO-OFDM systems, which is an unresolved problem. Summary of the Invention

[0006] In view of this, the present invention provides a direct positioning and synchronization method for a maneuvering asynchronous target in a MIMO-OFDM system, which can achieve high-precision direct positioning and synchronization of a maneuvering asynchronous target in a MIMO-OFDM system.

[0007] To achieve the above-mentioned objectives, the technical solution of the present invention is: a direct positioning and synchronization method for mobile asynchronous targets in a MIMO-OFDM system. The method is based on a time-division broadcast network, has N stationary anchor points in the network, the position targets of the anchor points are known, and the frame format and pilot symbols of the MIMO-OFDM signals sent by each anchor point are exactly the same; the target is a mobile asynchronous target.

[0008] In step 1, each anchor point establishes time-division communication with the target: each anchor point broadcasts the MIMO-OFDM signal in chronological order, and the mobile asynchronous target passively receives the signal and separates the signal sent from each anchor point in the time domain. It then performs OFDM demodulation to recover the original transmitted data with noise.

[0009] Step 2: constructing a mathematical model for the maneuvering asynchronous target, wherein the mathematical model includes a frequency offset model and a dynamic offset compensation model.

[0010] The frequency deviation model includes two parts: Doppler frequency shift caused by relative motion and carrier frequency deviation caused by the instability of the crystal oscillator of the target.

[0011] The dynamic offset compensation model compensates for the dynamic position offset of the maneuvering target during the time-division broadcasting process.

[0012] In step 3, for the received signals from each anchor point, data from all receiving antennas is collected; based on the relative relationship between the received phase shifts of the first two pilot blocks, other unknowns except the frequency offset are eliminated; and in combination with the dynamic offset compensation model, a nonlinear joint optimization estimation problem is established directly based on the OFDM demodulated signal for the motion parameters and crystal oscillator frequency offset of the maneuvering asynchronous target.

[0013] Step 4: linearize the nonlinear joint optimization estimation problem and solve it based on an iterative algorithm, outputting the time-varying position estimation result and frequency synchronization result of the maneuvering asynchronous target.

[0014] Furthermore, in step 1, each anchor point establishes time-division communication with the target: that is, each anchor point broadcasts MIMO-OFDM signals in chronological order, and the mobile asynchronous target passively receives the signals and sequentially separates the signals sent from each anchor point in the time domain. It then performs OFDM demodulation to recover the original transmitted data with noise. The specific process includes the following steps:

[0015] In step 1.1, the anchor point performs OFDM modulation on the original data symbols. The implementation method is as follows:

[0016]

[0017] in, represents any original OFDM pilot symbol on the mth transmitting antenna, is a K-dimensional real number field, is the modulated OFDM pilot symbol, K is the number of OFDM subcarriers, M is the total number of transmit antennas, and F is the K-point DFT matrix.

[0018] The expressions of each element in F are:

[0019]

[0020] Among them, k1+1 is the row index, k2+1 is the column index, and e is the natural base.

[0021] vector The kth element in Denotes that k∈{1,2,…,K} is the pilot index in each OFDM pilot symbol.

[0022] After OFDM modulation is completed, each anchor point broadcasts the signal in sequence according to the time sequence.

[0023] In step 1.2, after the signals transmitted by each anchor point are transmitted through the channel, the maneuvering asynchronous target receives the signals and separates them in the time domain.

[0024] The time domain baseband equivalent signal of the i-th pilot block sent from the n-th anchor point and received from the l-th receiving antenna Expressed as:

[0025]

[0026] Where i∈{1,2} is the pilot block index; l∈{1,2,…,L} is the receiving antenna index, L is the number of receiving antennas; f n is the frequency deviation; T s is the OFDM symbol duration, IT s is the pilot block duration; is additive complex Gaussian white noise; finally is the time domain MIMO channel matrix, is an L×M dimensional complex field, and its expression is:

[0027]

[0028] Among them, β n Signal propagation attenuation; represents the multi-antenna steering vector, Ω is the arrival / departure angle, S is the number of antennas, is the angle of arrival, is the departure angle, L is the total number of receiving antennas, and M is the total number of transmitting antennas.

[0029] Step 1.3: Perform OFDM demodulation on the received signal. This is achieved by:

[0030]

[0031] in, To receive OFDM symbols, is the time domain baseband equivalent signal in equation (3) is a K-dimensional complex field.

[0032] Furthermore, step 2 is to construct a mathematical model for the maneuvering asynchronous target, wherein the mathematical model includes a frequency offset model and a dynamic offset compensation model, and specifically includes the following steps:

[0033] Step 2.1: Construct the frequency deviation model as follows:

[0034]

[0035] Among them, the first term b is the carrier frequency deviation caused by the instability of the crystal oscillator; the second term represents the Doppler frequency shift caused by relative motion, and λ is the signal wavelength. Represents time t n The position and velocity of the target at is the position of anchor point #n, is a D-dimensional real number field, where D is the dimension of the space.

[0036] Step 2.2: Use the uniform acceleration trajectory to construct a dynamic offset compensation model for the maneuvering target:

[0037]

[0038] Among them, p0, v0 and a are the initial position, initial velocity and acceleration of the target respectively, △t n =t n -t0 is the time interval; combined with the dynamic offset compensation model, the frequency offset model (6) is further expressed as:

[0039]

[0040] Furthermore, in step 3, for the received signals from each anchor point, data on all receiving antennas are collected; based on the relative relationship between the received phase shifts of the first two pilot blocks, other unknowns except the frequency offset are eliminated; and in combination with the dynamic offset compensation model, a nonlinear joint optimization estimation problem is established directly based on the OFDM demodulated signal for the motion parameters and crystal oscillator frequency offset of the maneuvering asynchronous target, specifically including the following steps.

[0041] Step 3.1: For the received signal from anchor point #n, the data from all receiving antennas are aggregated to obtain the expressions for the first two received pilot blocks:

[0042]

[0043] in, To send the pilot block matrix, is an M×K-dimensional real number field; represents the noise matrix after OFDM demodulation; is the phase-shift diagonal matrix, diag(·) represents the diagonal matrix operator; represents the phase shift difference between the first two received pilot blocks, and ξ is the unknown quantity vector:

[0044]

[0045] in, It is a 3D+1 dimensional real number field.

[0046] Step 3.2, according to the first two pilot block expressions (9), eliminate the unknown matrix H n and Γ n , and vectorize the matrices on both sides of the equation at the same time to get:

[0047] y n,2 =d n (ξ)(y n,1 -w n,2 )+w n,2 (11)

[0048] in, vec{·} represents the matrix vectorization operator, is an LK-dimensional real number field; further combined with the received signals from N anchor points, we get:

[0049]

[0050] in, d=[d0,d1,…,d N-1 ] T ; blkdiag{·} represents a block diagonal matrix operator; w=w2-W1d(ξ) represents a complex OFDM demodulation noise vector.

[0051] Step 3.3, based on the maximum likelihood (ML) criterion, directly according to the OFDM demodulation signal expression (12), a nonlinear joint optimization estimation problem is established:

[0052]

[0053] Here, ||·|| represents the Euclidean norm of the vector.

[0054] Furthermore, step 4 is to perform linearization processing on the nonlinear joint optimization estimation problem and solve it based on an iterative algorithm, and output the time-varying position estimation result and frequency synchronization result of the maneuvering asynchronous target, which specifically includes the following steps:

[0055] First, given the unknown quantity, estimate the initial value

[0056]

[0057] For d(ξ) Taylor expansion at:

[0058]

[0059] in, is the Jacobian matrix, is the estimated value correction.

[0060] Substituting formula (15) into formula (12), we get:

[0061]

[0062] Further define the residual vector r:

[0063]

[0064] And define the auxiliary matrix

[0065]

[0066] Equation (16) can be written as the following linear equations:

[0067]

[0068] According to the above formula, the maximum likelihood estimate of the estimated value correction △ξ is:

[0069]

[0070] Since the unknowns are all real numbers, Further update:

[0071]

[0072] in, is the real part operator.

[0073] use Estimate the initial value of the unknown quantity Make corrections, the correction method is:

[0074]

[0075] Then, the updated estimated value of the unknown quantity is As the new estimated initial value, re-submit (15) to perform model linearization processing and solve the estimated value correction This process continues iterating until the algorithm converges.

[0076] Preferably, the algorithm converges, and the judgment method is: set the maximum number of iterations and the convergence threshold, if is less than the convergence threshold, the algorithm converges; if If it is not less than the convergence threshold, but the maximum number of iterations has been reached, the algorithm converges.

[0077] Beneficial effects:

[0078] This invention proposes a direct positioning and synchronization method for maneuvering asynchronous targets in MIMO-OFDM systems, capable of accurately outputting the target's motion parameters and crystal oscillator frequency offset. This method separates the received signals from each anchor point in the time domain, constructs a frequency offset model and a dynamic offset compensation model for the maneuvering target, performs elimination based on the relative relationship between the received phase shifts of the first two pilot blocks, and formulates a nonlinear optimization problem. A linearized iterative solution is then used to jointly obtain estimates of the target's motion parameters and frequency offset, achieving high-precision time-varying position determination and frequency synchronization of the target. Compared to two-step positioning and synchronization algorithms, this method achieves higher estimation accuracy, achieving the Cramer-Rao lower bound, and is the theoretically optimal method. Compared to traditional direct positioning algorithms, this method can be extended to directly position and synchronize maneuvering asynchronous targets and is applicable to MIMO-OFDM systems, offering greater robustness and a wider range of application scenarios. Furthermore, compared to existing direct positioning methods based on grid-based exhaustive search, this iterative algorithm significantly reduces computational complexity while maintaining accuracy, making it more suitable for practical engineering implementation. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 This is a schematic diagram of a time-division broadcast network;

[0080] Figure 2 Schematic diagram of the block periodic pilot format for MIMO-OFDM transmission signals;

[0081] Figure 3 Flowchart of the direct positioning and synchronization method for mobile asynchronous targets in a MIMO-OFDM system provided by the present invention;

[0082] Figure 4 This is an alternative flow chart of step 4 in the direct positioning and synchronization method provided by the present invention. DETAILED DESCRIPTION

[0083] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0084] The present invention provides a direct positioning and synchronization method for mobile asynchronous targets in a MIMO-OFDM system, which is based on a time-division broadcast network. Figure 1 As shown, specifically: there are N stationary anchor points in the network, the position target of the anchor point is known, and each anchor point serially broadcasts the MIMO-OFDM signal in a predetermined time sequence, that is, anchor point #n at time t n The signal is sent at n=0, 1, ..., N-1; the maneuvering target in the network passively receives the signal, the target's time-varying position is unknown, and the target crystal oscillator is unstable, which produces an unknown frequency offset when the transmitting carrier frequency is reconstructed locally.

[0085] The signal format based on the technical method of the present invention is as follows: Without loss of generality, the present invention assumes that the frame format and pilot symbols of the MIMO-OFDM signal sent by each anchor point are exactly the same. The signal format of any anchor point is as follows: Figure 2 As shown in the figure, gray and black solid circles represent pilots, and white hollow circles represent data. Each pilot block contains M OFDM symbols, which are distributed across M transmit antennas. This invention utilizes a block-based periodic pilot structure, meaning that for a single transmit antenna, all OFDM pilot symbols transmitted successively are identical. This invention utilizes the first two pilot blocks sent by each anchor point, pilot block #1 and pilot block #2, to achieve direct positioning and synchronization. This invention targets mobile asynchronous targets.

[0086] A direct positioning and synchronization method for mobile asynchronous targets in a MIMO-OFDM system is based on a time-division broadcast network. The network has N stationary anchor points, the positions of the anchor points are known, and the frame format and pilot symbols of the MIMO-OFDM signal sent by each anchor point are exactly the same. The target is a mobile asynchronous target. The specific process of this method is as follows: Figure 3 As shown, the following steps are included:

[0087] In step 1, each anchor point establishes time-division communication with the target: each anchor point broadcasts the MIMO-OFDM signal in chronological order, and the mobile asynchronous target passively receives the signal and separates the signal sent from each anchor point in the time domain. It then performs OFDM demodulation to recover the original transmitted data with noise.

[0088] The specific process includes the following steps:

[0089] In step 1.1, the anchor point performs OFDM modulation on the original data symbols. The implementation method is as follows:

[0090]

[0091] in, represents any original OFDM pilot symbol on the mth transmitting antenna, is a K-dimensional real number field, is the modulated OFDM pilot symbol, K is the number of OFDM subcarriers, M is the total number of transmit antennas, and F is the K-point DFT matrix;

[0092] The expressions of each element in F are:

[0093]

[0094] Among them, k1+1 is the row index, k2+1 is the column index, and e is the natural base.

[0095] vector The kth element in Denotes that k∈{1,2,…,K} is the pilot index in each OFDM pilot symbol.

[0096] After OFDM modulation is completed, each anchor point broadcasts the signal in sequence according to the time sequence;

[0097] Step 1.2: After the signals transmitted by each anchor point are transmitted through the channel, the maneuvering asynchronous target receives the signals and separates them in the time domain.

[0098] The time domain baseband equivalent signal of the i-th pilot block sent from the n-th anchor point and received from the l-th receiving antenna Expressed as:

[0099]

[0100] Where i∈{1,2} is the pilot block index; l∈{1,2,…,L} is the receiving antenna index, L is the number of receiving antennas; f n is the frequency deviation; T s is the OFDM symbol duration, IT s is the pilot block duration; is additive complex Gaussian white noise; finally is the time domain MIMO channel matrix, is an L×M complex field, and its expression is:

[0101]

[0102] Among them, β n Signal propagation attenuation; represents the multi-antenna steering vector, Ω is the arrival / departure angle, S is the number of antennas, is the angle of arrival, is the departure angle, L is the total number of receiving antennas, and M is the total number of transmitting antennas;

[0103] Step 1.3: Perform OFDM demodulation on the received signal. This is achieved by:

[0104]

[0105] in, To receive OFDM symbols, is the time domain baseband equivalent signal in equation (3) is a K-dimensional complex field.

[0106] Step 2: construct a mathematical model for the maneuvering asynchronous target, which includes a frequency offset model and a dynamic offset compensation model;

[0107] The frequency deviation model includes two parts: the Doppler frequency shift caused by relative motion and the carrier frequency deviation caused by the instability of the target's crystal oscillator.

[0108] The dynamic offset compensation model compensates for the dynamic position offset of maneuvering targets during time-division broadcasting.

[0109] The specific steps include:

[0110] Step 2.1: Construct the frequency deviation model as follows:

[0111]

[0112] Among them, the first term b is the carrier frequency deviation caused by the instability of the crystal oscillator; the second term represents the Doppler frequency shift caused by relative motion, and λ is the signal wavelength. Represents time t n The position and velocity of the target at is the position of anchor point #n, is a D-dimensional real number field, where D is the dimension of the space;

[0113] Step 2.2: Use the uniform acceleration trajectory to construct a dynamic offset compensation model for the maneuvering target:

[0114]

[0115] Among them, p0, v0 and a are the initial position, initial velocity and acceleration of the target respectively, △t n =t n -t0 is the time interval; combined with the dynamic offset compensation model, the frequency offset model (6) is further expressed as:

[0116]

[0117] In step 3, for the received signals from each anchor point, data from all receiving antennas is collected. Based on the relative relationship between the received phase shifts of the first two pilot blocks, other unknowns except the frequency offset are eliminated. In combination with the dynamic offset compensation model, a nonlinear joint optimization estimation problem is established directly based on the OFDM demodulated signal for the motion parameters and crystal oscillator frequency offset of the maneuvering asynchronous target.

[0118] The specific steps include:

[0119] Step 3.1: For the received signal from anchor point #n, the data from all receiving antennas are aggregated to obtain the expressions for the first two received pilot blocks:

[0120]

[0121] in, To send the pilot block matrix, is an M×K-dimensional real number field; represents the noise matrix after OFDM demodulation; is the phase-shift diagonal matrix, diag(·) represents the diagonal matrix operator; represents the phase shift difference between the first two received pilot blocks, and ξ is the unknown quantity vector:

[0122]

[0123] in, is a 3D+1 dimensional real number field;

[0124] Step 3.2, according to the first two pilot block expressions (9), eliminate the unknown matrix H n and Γ n , and vectorize the matrices on both sides of the equation at the same time to get:

[0125] y n,2 =d n (ξ)(y n,1 -w n,2 )+w n,2 (11)

[0126] in, vec{·} represents the matrix vectorization operator, is an LK-dimensional real number field; further combined with the received signals from N anchor points, we get:

[0127]

[0128] in, d=[d0,d1,…,d N-1 ] T ; blkdiag{·} represents a block diagonal matrix operator; w = w2-W1d(ξ) represents the composite OFDM demodulation noise vector;

[0129] Step 3.3, based on the maximum likelihood (ML) criterion, directly according to the OFDM demodulation signal expression (12), a nonlinear joint optimization estimation problem is established:

[0130]

[0131] Here, ||·|| represents the Euclidean norm of the vector.

[0132] Step 4: Linearize the nonlinear joint optimization estimation problem and solve it based on an iterative algorithm, outputting the time-varying position estimation results and frequency synchronization results of the maneuvering asynchronous target.

[0133] The specific steps include:

[0134] First, given the unknown quantity, estimate the initial value

[0135]

[0136] For d(ξ) Taylor expansion at:

[0137]

[0138] in, is the Jacobian matrix, is the estimated value correction;

[0139] Substituting formula (15) into formula (12), we get:

[0140]

[0141] Further define the residual vector r:

[0142]

[0143] And define the auxiliary matrix

[0144]

[0145] Equation (16) can be written as the following linear equations:

[0146]

[0147] According to the above formula, the maximum likelihood estimate of the estimated value correction △ξ is:

[0148]

[0149] Since the unknowns are all real numbers, Further update:

[0150]

[0151] in, is the real part operator;

[0152] use Estimate the initial value of the unknown quantity Make corrections, the correction method is:

[0153]

[0154] Then, the updated estimated value of the unknown quantity is As the new estimated initial value, re-submit (15) to perform model linearization processing and solve the estimated value correction This process continues iterating until the algorithm converges.

[0155] In the embodiment of the present invention, the method for judging the convergence of the algorithm is as follows: setting the maximum number of iterations and the convergence threshold, if is less than the convergence threshold, the algorithm converges. If it is not less than the convergence threshold, but the maximum number of iterations has been reached, the algorithm converges. Figure 4 shown.

[0156] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for direct positioning and synchronization of maneuvering asynchronous targets in a MIMO-OFDM system, characterized in that: The method is based on a time-division broadcast network with N stationary anchor points. The anchor point locations are known, and the frame format and pilot symbols of the MIMO-OFDM signals sent by each anchor point are exactly the same. The target is a maneuverable asynchronous target. Step 1: Each anchor point establishes time-division communication with the target. Each anchor point broadcasts a MIMO-OFDM signal in chronological order. The mobile asynchronous target passively receives the signal and separates the signal from each anchor point in the time domain. This signal is then demodulated by OFDM to recover the original transmitted data with noise. The specific process includes the following steps: In step 1.1, the anchor point performs OFDM modulation on the original data symbols. The implementation method is as follows: in, represents any original OFDM pilot symbol on the mth transmitting antenna, is a K-dimensional real number field, is the modulated OFDM pilot symbol, K is the number of OFDM subcarriers, M is the total number of transmit antennas, and F is the K-point DFT matrix; The expressions of each element in F are: Among them, k1+1 is the row index, k2+1 is the column index, and e is the natural base; vector The kth element in Denotes that k∈{1,2,…,K} is the pilot index in each OFDM pilot symbol; After OFDM modulation is completed, each anchor point broadcasts the signal in sequence according to the time sequence; Step 1.2: After the signals transmitted by each anchor point are transmitted through the channel, the maneuvering asynchronous target receives the signals and separates them in the time domain. The time domain baseband equivalent signal of the i-th pilot block sent from the n-th anchor point and received from the l-th receiving antenna Expressed as: Where i∈{1,2} is the pilot block index; l∈{1,2,…,L} is the receiving antenna index, L is the number of receiving antennas; f n is the frequency deviation; T s is the OFDM symbol duration, IT s is the pilot block duration; is additive complex Gaussian white noise; finally is the time domain MIMO channel matrix, is an L×M complex field, and its expression is: Among them, β n Signal propagation attenuation; represents the multi-antenna steering vector, Ω is the arrival / departure angle value, S is the number of antennas, is the angle of arrival, is the departure angle, L is the total number of receiving antennas, and M is the total number of transmitting antennas; Step 1.3: Perform OFDM demodulation on the received signal. This is achieved by: in, To receive OFDM symbols, is the time domain baseband equivalent signal in equation (3) is a K-dimensional complex field; Step 2: constructing a mathematical model for the maneuvering asynchronous target, wherein the mathematical model includes a frequency offset model and a dynamic offset compensation model; The frequency deviation model includes two parts: the Doppler frequency shift caused by relative motion and the carrier frequency deviation caused by the instability of the crystal oscillator of the target; The dynamic offset compensation model compensates for the dynamic position offset of the maneuvering target during the time-division broadcasting process; Step 2 specifically includes the following steps: Step 2.1: Construct the frequency deviation model as follows: Among them, the first term b is the carrier frequency deviation caused by the instability of the crystal oscillator; the second term represents the Doppler frequency shift caused by relative motion, and λ is the signal wavelength. Represents time t n The position and velocity of the target at is the position of anchor point #n, is a D-dimensional real number field, where D is the dimension of the space; Step 2.2: Use the uniform acceleration trajectory to construct a dynamic offset compensation model for the maneuvering target: Among them, p0, v0 and a are the initial position, initial velocity and acceleration of the target respectively, Δt n =t n -t0 is the time interval; combined with the dynamic offset compensation model, the frequency offset model (6) is further expressed as: Step 3: For the received signals from each anchor point, data from all receiving antennas is collected; based on the relative relationship between the received phase shifts of the first two pilot blocks, other unknowns except the frequency offset are eliminated; and in combination with the dynamic offset compensation model, a nonlinear joint optimization estimation problem is established directly based on the OFDM demodulated signal for the motion parameters and crystal oscillator frequency offset of the maneuvering asynchronous target. Step 4: linearize the nonlinear joint optimization estimation problem and solve it based on an iterative algorithm, outputting the time-varying position estimation result and frequency synchronization result of the maneuvering asynchronous target.

2. The method for direct positioning and synchronization of a mobile asynchronous target in a MIMO-OFDM system according to claim 1, wherein: Step 3, for the received signals from each anchor point, collects data from all receiving antennas; eliminates other unknowns except the frequency offset based on the relative relationship between the received phase shifts of the first two pilot blocks; and, in combination with the dynamic offset compensation model, establishes a nonlinear joint optimization estimation problem based directly on the OFDM demodulated signal for the motion parameters and crystal oscillator frequency offset of the maneuvering asynchronous target. Specifically, the steps include: Step 3.1: For the received signal from anchor point #n, the data from all receiving antennas are aggregated to obtain the expressions for the first two received pilot blocks: in, To send the pilot block matrix, is an M×K-dimensional real number field; represents the noise matrix after OFDM demodulation; is the phase-shift diagonal matrix, diag(·) represents the diagonal matrix operator; represents the phase shift difference between the first two received pilot blocks, and ξ is the unknown quantity vector: in, is a 3D+1 dimensional real number field; Step 3.2, according to the first two pilot block expressions (9), eliminate the unknown matrix H n and Γ n , and vectorize the matrices on both sides of the equation at the same time to get: y n,2 =d n (ξ)(y n,1 -w n,2 )+w n,2 (11) in, vec{·} represents the matrix vectorization operator, is an LK-dimensional real number field; further combined with the received signals from N anchor points, we get: in, d=[d0,d1,…,d N-1 ] T ; blkdiag{·} represents a block diagonal matrix operator; w = w2-W1d(ξ) represents the composite OFDM demodulation noise vector; Step 3.3, based on the maximum likelihood (ML) criterion, directly according to the OFDM demodulation signal expression (12), a nonlinear joint optimization estimation problem is established: Here, ||·|| represents the Euclidean norm of the vector.

3. The method for direct positioning and synchronization of a mobile asynchronous target in a MIMO-OFDM system according to claim 2, wherein: Step 4, linearizing the nonlinear joint optimization estimation problem and solving it based on an iterative algorithm, outputting a time-varying position estimation result and a frequency synchronization result of the maneuvering asynchronous target, specifically includes the following steps: First, given the unknown quantity, estimate the initial value For d(ξ) Taylor expansion at: in, is the Jacobian matrix, is the estimated value correction; Substituting formula (15) into formula (12), we get: Further define the residual vector r: And define the auxiliary matrix Equation (16) can be written as the following linear equations: According to the above formula, the maximum likelihood estimate of the estimated value correction Δξ is: Since the unknowns are all real numbers, Further update: in, is the real part operator; use Estimate the initial value of the unknown quantity Make corrections, the correction method is: Then, the updated estimated value of the unknown quantity is As the new estimated initial value, re-submit (15) to perform model linearization processing and solve the estimated value correction This process continues iterating until the algorithm converges.

4. The method for direct positioning and synchronization of a mobile asynchronous target in a MIMO-OFDM system according to claim 3, wherein: The algorithm converges, and the judgment method is: set the maximum number of iterations and the convergence threshold. is less than the convergence threshold, the algorithm converges. If the value is not less than the convergence threshold, but the maximum number of iterations has been reached, the algorithm converges.

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