Perception performance optimization method based on MIMO-OFDM signal

Abstract

The present invention discloses a method for optimizing perceptual performance based on MIMO-OFDM signals, comprising the following steps: at a transmitting end, using beamforming technology to point the MIMO-OFDM signal as a transmitting signal to the direction of a communication target; at a communication receiving end and a radar receiving end, respectively calculating the communication rate and the Cramer-Rao lower bound of the target parameter to be estimated; based on the communication rate and the Cramer-Rao lower bound, optimizing the vector composed of the subcarrier power of the transmitting signal to achieve the best compromise value between the communication performance and the perceptual performance, and applying the optimized subcarrier power vector to the transmitting signal of the transmitting end; at the radar receiving end, constructing the echo signal into a third-order tensor form according to a multidimensional signal processing model, decoupling the target parameter to be estimated based on CPD, and finally estimating the target parameter using the phase information of the factor matrix. The present invention ensures that the estimation accuracy can be further improved by using the optimized subcarrier power vector.

Description

Technical Field

[0001] The present invention belongs to the field of radar communication, and in particular relates to a method for optimizing perception performance based on MIMO-OFDM (Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing) signals. Background Art

[0002] Radar sensing and wireless communication are two important applications of radio frequency signals. In the past, they were designed and developed on different frequency bands; in recent years, the overcrowding of spectrum in wireless communication networks and the ubiquitous sensing needs have promoted the development of communication radar integration technology. At the same time, with the continuous increase in communication frequency bands and the emergence of new radar systems such as OFDM radars, the similarity of working frequency bands and signal designs makes communication radar integration possible.

[0003] In the field of wireless communications, OFDM is a key technology because it is resistant to multipath fading, high-frequency band utilization, and easy to equalize. Similarly, the use of MIMO technology using a multi-antenna structure can provide diversity gain at the receiving end, improve the received signal-to-noise ratio, and combined with digital signal processing technology, it can achieve beamforming and improve the communication quality of directional users. Therefore, most of the current communication systems use MIMO-OFDM technology. If the perception function can be realized on the basis of the existing MIMO-OFDM communication system, it can effectively reduce costs and save resources.

[0004] If you want to use MIMO-OFDM signals to achieve both communication and perception functions, you must consider the performance tradeoff between the two. When deriving communication performance indicators and perception performance indicators, since the subcarrier power allocation scheme will affect both communication performance and perception performance, it is necessary to jointly optimize the communication and perception performance to achieve the best performance compromise between them.

[0005] In the MIMO-OFDM communication radar system, how to make full use of the acquired data to estimate the parameters of the target is also a problem worth studying. Moreover, the performance of the estimation algorithm can be further improved by using an optimized power scheme. Summary of the invention

[0006] Purpose of the invention: By utilizing the existing MIMO-OFDM communication system and combining it with digital precoding, a method for optimizing perceptual performance is proposed to ensure that a high parameter estimation accuracy is obtained at a certain communication rate.

[0007] Technical solution: A method for optimizing the perception performance of MIMO-OFDM signals, comprising the following steps:

[0008] (1) Generate a MIMO-OFDM radar communication integrated signal with beamforming vectors at the transmitter;

[0009] (2) The communication target receives the radar communication integrated signal through the receiving antenna and calculates the corresponding communication information;

[0010] (3) The radar receiver receives the echo signal irradiated to the communication target and extracts the corresponding radar information;

[0011] (4) Based on the communication information and radar information, an optimization problem is constructed with the subcarrier power vector as the optimization target, the communication rate as the objective function, and the radar performance index as the constraint condition, and the optimal subcarrier power vector is solved;

[0012] (5) A multidimensional signal processing model is constructed based on CPD (Canonical Polyadic Decomposition) to estimate the angle, speed, and distance of the target, and the estimation accuracy is further improved by using the optimized subcarrier power vector.

[0013] Furthermore, the step (1) includes the following steps:

[0014] The transmitter and receiver of the MIMO-OFDM signal are separated from each other; the MIMO-OFDM baseband signal x with beamforming vector on the qth data block at the transmitting end q (t) is expressed as Where t represents time, n represents the nth subcarrier, N represents the number of subcarriers, and w n The dimension corresponding to the nth subcarrier is M T ×1 transmit beamforming vector, M T is the number of transmitting antennas, s q (n) is the communication symbol on the nth subcarrier in the qth data block, △f = 1 / T is the frequency interval between adjacent subcarriers, T s =T+T cp The duration of each data block, T cp is the length of the cyclic prefix, ξ(t-(q-1)T s ) is a period of 1 with an amplitude of T s Rectangular pulse signal, when (q-1)-T cp ≤t≤(q-1)T s +T,ξ(t-(q-1)T s) is 1, otherwise it is 0; the transmission signal on Q data blocks is expressed as -T cp ≤t≤(Q-1)T s +T; then the baseband signal is up-converted, and the radar communication integrated signal is expressed as in represents the real part, f c Indicates the carrier frequency.

[0015] Furthermore, the step (2) includes the following steps:

[0016] Transmitting signal Through an additive white Gaussian noise channel, the signal at the kth communication target is down-converted, analog-to-digital converted and demodulated, then the communication data on the nth carrier in the qth data block is expressed as in (·) T represents the transpose of (·), α k , A' k , They represent the transmission angle, path loss, and phase offset caused by time delay and Doppler frequency shift of the kth communication target, respectively. and They represent the carrier set assigned to the kth communication target and the corresponding transmission steering vector, d represents the distance between adjacent antennas, and λ = c / f c represents the wavelength of the carrier, c represents the speed of light, v'(n) represents Gaussian white noise with a mean of 0 and a variance of n'0 / T, and n'0 represents the noise power spectrum density at the communication end; then the communication rate between the transmitter and the kth communication target is in is the average transmit power of the nth subcarrier.

[0017] Furthermore, the step (3) includes the following steps:

[0018] 1. At the radar receiving end, the echo signal irradiated to the communication target is down-converted to obtain the signal in is the array gain factor, M R is the number of receiving antennas, is the attenuation coefficient including secondary propagation loss and reflection coefficient, f k =v k / λ and β k denote the Doppler frequency and arrival angle of the kth target, v k represents the speed of the kth target, λ represents the carrier frequency f c The corresponding wavelength, τ k It is a dual-base delay. represents the receive steering vector, is a complex Gaussian white noise process with power spectral density n0;

[0019] ii. Matched filtering Then the signal in the nth subcarrier in the qth data block is in (·) * represents the conjugate of (·), and defines v q (n) has a mean of zero and a covariance matrix of p n n0TI complex Gaussian vector, I is M R ×M R The identity matrix of

[0020] (iii) The vector in step (ii) is converted into (q) =[y q (1),y q (2),…,y q (N)]=A R D q (C)B T +V (q) Arrangement, where A R =[a r (β1),a r (β2),…,a r (β K )], C=[c(f1),c(f2),…,c(f K )], B=[b(τ1),b(τ2),…,b(τ K )], V (q) =[v q (1),v q (2),…,v q (N)],D q (·) means that a diagonal matrix is ​​constructed using the elements of the qth row of the matrix. Combination have in represents the Khatri-Rao product, V = [V (1)T ,…,V (Q)T ] T ; By stacking the columns of matrices Y, S, and V vertically, we get a matrix of dimension M R The vector form of QN×1 is y=s+v;

[0021] (iv) The closed-form solution of the Cramer-Rao lower bound of the speed, distance and arrival angle of the kth target is calculated by using the vector radar receiving signal obtained in step (iii) using the Jacobian matrix, which are respectively expressed as in, p=[p1,…,p N ] T , [·] n Represents the nth element of a vector.

[0022] Furthermore, the step (4) includes the following steps:

[0023] i first uses the sum rate of the communication target As the objective function, the total power and the Cramer-Rao lower bound As a constraint, it constitutes a non-convex optimization problem;

[0024] ii Using matrix transformation equivalence, the non-convex optimization problem is transformed into a convex optimization problem: and Transformed to and in and Deformations of the Cramer-Rao lower bound matrices representing velocity and distance, respectively, The matrix is ​​semi-positive definite; thus, the non-convex optimization problem is transformed into a semi-positive definite (SDP) optimization problem, and the convex optimization toolbox is used to solve the optimal power allocation method.

[0025] Furthermore, the step (5) includes the following steps:

[0026] According to the multidimensional signal processing model, the received signal is represented as a third-order tensor. After the received signal is subjected to canonical multivariate decomposition (CPD), the estimated value of the factor matrix is ​​obtained.

[0027] II The same columns of the three factor matrices correspond to the estimated parameters β of the same target k , τ k 、f k ; Since the factor matrix has a Vandermonde structure, Perform an inverse discrete Fourier transform (IDFT) to estimate the angle of arrival. Perform discrete Fourier transform (DFT) to estimate the target speed. The corresponding subcarrier data in the image is subjected to an inverse discrete Fourier transform (IDFT) to estimate the target distance, and when the optimized subcarrier power vector is used at the transmitting end, the estimation accuracy can be further improved.

[0028] Beneficial effects: The present invention is applicable to application scenarios that use MIMO-OFDM signals to communicate and detect targets. By using existing communication base stations to transmit communication signals, the use cost is effectively reduced while saving spectrum resources. The present invention proposes a method for optimizing perception performance based on MIMO-OFDM signals, which uses the communication rate and the Cramer-Rao lower bound to achieve the best compromise between communication performance and perception performance and obtain the best subcarrier power vector; the optimized subcarrier power vector is used at the transmitting end, and the CPD is used to process the echo signal at the receiving end to decouple the target's estimated parameters. Compared with the case before the subcarrier power vector is optimized, the optimized subcarrier power vector can be used to further improve the estimation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a system model diagram of the present invention;

[0030] Figure 2 It is the decomposition diagram of the third-order tensor in different slicing methods;

[0031] Figure 3 It is a trade-off performance diagram between the communication rate and the Cramer-Rao lower bound under different antennas;

[0032] Figure 4 It is the relationship between the total transmitted power and the Cramer-Rao lower bound under different antennas;

[0033] Figure 5 It is the estimated performance diagram under different signal-to-noise ratios based on CPD;

[0034] Figure 6 It is a performance improvement diagram of the CPD estimation method using the optimized subcarrier power vector. DETAILED DESCRIPTION

[0035] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present invention.

[0036] Application scenarios of the present invention are as follows Figure 1As shown: The transmitter and receiver based on MIMO-OFDM signal are separated from each other. The transmitter sends the MIMO-OFDM signal to multiple communication receivers. At the same time, the radar receiver estimates the position and speed of the communication receiver using the received echo signal. The main features of this system are that the angle of the communication receiver relative to the transmitter is known, and the communication information is known at the radar receiving end. The communication and radar functions share an integrated transmission signal, and the radar and communication receivers are processed separately.

[0037] In the integrated communication radar system of the present invention, the transmitter communicates with K targets simultaneously and estimates the parameters of the targets using MIMO-OFDM signals. The transmitter and radar receiver are respectively equipped with M T and M R antennas, and each communication target has only a single antenna. In order to transmit information bits to K targets, different subcarrier sets are assigned to different targets, and the radar receiver uses the echo signal to estimate the target parameters. The transmitted baseband OFDM signal is divided into Q data blocks, each of which has N orthogonal subcarriers, and the frequency interval between adjacent subcarriers is △f = 1 / T. The duration of each block is T s =T+T cp , where T cp is the length of the cyclic prefix, so the baseband signal transmitted in the qth data block can be expressed as:

[0038]

[0039] in is the transmit beamforming vector of the nth subcarrier, s q (n) is the transmission symbol on the nth subcarrier in the qth data block, ξ(t-(q-1)T s ) is a period of 1 with an amplitude of T s Rectangular pulse signal, when (q-1)-T cp ≤t≤(q-1)T s +T,ξ(t-(q-1)T s ) is 1, and 0 in other cases. Therefore, the transmission signal on Q data blocks can be expressed as -T cp ≤t≤(Q-1)T s +T. Then the baseband signal is up-converted, that is where f c Indicates the carrier frequency.

[0040] This system considers the narrowband signal model, so the delay caused by speed and antenna spacing on the baseband signal is ignored. Assume that there are K targets within the detection range, use α k , A' k , Respectively represent the transmission angle (DoD), path loss, and phase offset caused by delay and Doppler frequency shift of the kth communication target, wherein therefore at the kth target, the communication data on the nth carrier in the qth data block can be expressed as:

[0041]

[0042] in The transmission steering vector of the kth communication target and the allocated carrier set are and d represents the antenna spacing, λ=c / f c represents the wavelength of the carrier and c represents the speed of light. express The transpose of , v'(n) is a complex Gaussian white noise term with a mean of 0 and a variance of n'0 / T, and n'0 represents the noise power spectrum density of the communication end. Then the communication rate between the transmitter and the kth communication end is:

[0043]

[0044] in is the average transmit power of the nth subcarrier.

[0045] At the radar receiving end, the echo signal is down-converted to obtain:

[0046]

[0047] in is the array gain factor, is the attenuation coefficient, including secondary propagation loss and reflection coefficient. τ k is the bistatic delay and the receiving steering vector f k =v k / λ and β k denote the Doppler frequency and arrival angle of the kth target, respectively. is a complex Gaussian white noise process with a power spectral density of n0. The approximation in (4) is because when f k T s When <<1, the phase rotation within an OFDM block caused by the Doppler frequency can be approximated to be a constant.

[0048] After matched filtering, the signal in the nth subcarrier in the qth data block can be expressed as:

[0049]

[0050] in For simplicity, define vq (n) has a mean of zero and a covariance matrix of p n n0TI complex Gaussian vector, I is M R ×M R The vectors in (6) are transformed into a matrix in the following form:

[0051]

[0052] Among them A R =[a r (β1),a r (β2),…,a r (β K )], C=[c(f1),c(f2),…,c(f K )], B=[b(τ1),b(τ2),…,b(τ K )], V (q) =[v q (1),v q (2),…,v q (N)],D q (·) means that a diagonal matrix is ​​formed using the elements of the qth row of the matrix. Constructing a large matrix, we can get:

[0053]

[0054] in represents the Khatri-Rao product, V = [V (1)T ,…,V (Q)T ] T By stacking the columns of Y, S, and V vertically, we can get a matrix of dimension M. R The vector form of QN×1 is y=s+v.

[0055] Since the power vector p=[p1,p2,...p n ] T and the beamforming vector It will affect the estimation performance and communication performance. Obviously, maximizing the received signal-to-noise ratio of the target is beneficial to the communication and estimation performance. In order to maximize the received signal-to-noise ratio (SNR) of the kth target, let It is assumed that the transmitter can estimate the DoA using the uplink pilot signal from the target, so that optimal transmit beamforming can be achieved.

[0056] When using a massive MIMO antenna array at the transmitter, the transmit beam will be narrow enough because the transmit steering vector satisfies:

[0057]

[0058] Therefore, when When , the communication transmission rate between the transmitter and the kth target is:

[0059]

[0060] And (6) can be simplified to

[0061]

[0062] In this case, using different subcarriers to illuminate different targets can eliminate the interference between the echoes of different targets and obtain a more concise expression for the Cramer-Rao lower bound. Define φ k = sinβ k , then the parameters to be estimated are grouped into and in ε=[A1,…,A K ] and u k =[v k ,r k ,β k ] T Since y is The function first calculates the Fisher information matrix J(γ) of γ based on y, and then uses the chain rule to calculate the Fisher information matrix of the estimated parameter u:

[0063] J(u)=QJ(γ)Q T , (11)

[0064] Where Q is the Jacobian matrix. The specific form is as follows:

[0065]

[0066] in represents the partial derivative of γ with respect to u, and

[0067]

[0068] I1 is a K×K identity matrix. Since y has a mean μ=s and the covariance matrix is

[0069] Gaussian vector of I2 is M R Q×M R Q is the identity matrix. J(γ) can be calculated as follows:

[0070]

[0071] According to the above analysis, the Fisher information matrix J(u) of the estimated parameter u can be expressed as:

[0072]

[0073] where O is a matrix of dimension K × K and has no effect on the Cramer-Rao lower bound of the estimated parameters. is a 3×3 matrix, and The i-th row and j-th column element of can be expressed as:

[0074]

[0075] Therefore, the Cramer-Rao lower bound matrix for the kth objective is:

[0076]

[0077] Among them When , the nth element in each vector can be expressed as

[0078] when When , the other items are 0. The inverse matrix of is complex for further analysis, so the following approximation is used to obtain a lower bound on the Cramer-Rao bound. The approximate Cramer-Rao bound matrix of the unknown parameters of the k-th objective is:

[0079]

[0080] and

[0081]

[0082] Based on (19) and (20), the lower bound of the Cramer-Rao bound on the estimated parameters of the kth objective is:

[0083]

[0084]

[0085]

[0086] in

[0087] Since the power allocation between subcarriers affects both the target parameter estimation and the communication performance, the balance between the transmission rate and the Cramer-Rao bound should be considered to optimize the subcarrier power vector. Therefore, the optimization problem can be expressed as:

[0088]

[0089] where p T is the total power constraint, are the Cramer-Rao bounds for speed, distance, and DoA estimation, respectively. The Cramer-Rao bounds for speed and distance in (24) are not convex constraints on p. By transforming these constraints into convex constraints through matrix transformation, the optimization problem can be rewritten as:

[0090]

[0091] in represents a matrix that satisfies the semi-positive definite condition, and

[0092]

[0093]

[0094] The optimization problem in (24) is a semidefinite programming (SDP) problem, which can be solved by the convex optimization toolbox.

[0095] In the traditional serial-parallel estimation, which estimates the angle first and then jointly estimates the speed and distance, the angle estimation error will affect the estimation accuracy of the speed and distance. In order to decouple the estimation of angle, speed and distance, an estimation method based on tensor decomposition is proposed. According to the multidimensional signal processing model, the received signal in equation (7) is expressed as the following third-order tensor:

[0096]

[0097] in represents the outer product of vectors. Comparing (28) and (5), we can get The element at (m,n,q) is It is known that the outer product of three vectors is a third-order tensor of rank 1. From (28), we can see that is the sum of K rank-1 tensors. In tensor decomposition, decomposing a tensor into several rank-1 tensors is called canonical multivariate decomposition (CPD). Therefore, our goal is to use CPD to extract Obtained Make and The error between is minimized, that is

[0098]

[0099] To solve problem (29), define A R , B, C as The factor matrix of Figure 2 As shown, first consider The forward slice of

[0100] S q =A R D q (C)B T . (30)

[0101] By vertically stacking S q , and the following expansion matrix is ​​obtained:

[0102]

[0103] Similarly, The longitudinal and transverse slices of the complex are expressed as:

[0104]

[0105]

[0106] By expanding The forward, longitudinal and transverse slices of can be obtained:

[0107]

[0108] in The noise terms Y1, Y2, and Y3 are The expanded matrix in different slice forms. Assume that the number of targets is known. The problem in (29) can be reformulated as:

[0109]

[0110] The above problem can be solved by the alternating least squares algorithm, which uses the following equation:

[0111]

[0112] Alternating Optimization Update A R , B, C until convergence.

[0113] After performing canonical multivariate decomposition (CPD) on the received wavelet signal, the estimated value of the factor matrix is ​​obtained It can be seen that the same columns of the three factor matrices correspond to the estimated parameters β of the same target. k , τ k 、f k In order to Estimated β k ,right Perform an inverse discrete Fourier transform (IDFT):

[0114]

[0115] where a k (m r )yes The mth r elements. At the same time, Perform a discrete Fourier transform (DFT):

[0116]

[0117] where c k (q) Yes The qth element of . and hour, and The maximum value will appear. Therefore, the DoA and speed estimation of the kth target can be obtained by the following formula:

[0118]

[0119] in and for and The corresponding maximum values ​​are obtained respectively and Since different subcarrier sets are used to detect different targets, The corresponding subcarrier data in the k :

[0120]

[0121] where b k (n) Yes The nth element of is the number of carriers in the subcarrier set allocated to the kth target. Therefore, the distance estimate can be obtained by the following formula:

[0122]

[0123] in for The corresponding maximum value value.

[0124] like Figure 3As shown in the figure, the simulation demonstrates the best compromise curve that can be achieved between spectrum efficiency and detection performance by optimizing the subcarrier power vector: Obviously, when the number of transmitting and receiving antennas is small, as the constraints are relaxed, the maximum value of spectrum utilization is smaller, and as the number of transmitting and receiving antennas increases, the maximum value of spectrum utilization is larger; and the communication performance can be significantly improved by sacrificing relatively small detection performance, which is consistent with theoretical expectations.

[0125] like Figure 4 As shown in the figure, the simulation shows the lowest Cramer-Rao lower bound that can be achieved under different total transmit powers after average power allocation and optimized power allocation. It can be clearly seen that after subcarrier power optimization, under the same total transmit power, the detection performance is significantly improved, and the fewer the number of antennas and the lower the power, the more obvious the improvement effect.

[0126] like Figure 5 and Figure 6 As shown in the figure, under the same signal-to-noise ratio, the proposed CPD-based estimation method is closer to the Cramer-Rao lower bound than the traditional estimation algorithm; and the performance of the estimation method can be further improved by using the optimized subcarrier power vector.

[0127] Simulations have proved that the perception performance optimization method based on MIMO-OFDM signals proposed in the present invention can achieve a balance between communication rate and detection performance by jointly optimizing the two; and by using the optimized subcarrier power vector, the estimation performance is further improved after the CPD-based estimation method.

Claims

1. A method for optimizing the perception performance of a MIMO-OFDM signal, comprising the following steps: (1) Generate a MIMO-OFDM radar communication integrated signal with beamforming vectors at the transmitter; (2) The communication target receives the radar communication integrated signal through the receiving antenna and calculates the corresponding communication information; (3) The radar receiver receives the echo signal irradiated to the communication target and extracts the corresponding radar information; (4) Based on the communication information and radar information, an optimization problem is constructed with the subcarrier power vector as the optimization target, the communication rate as the objective function, and the radar performance index as the constraint condition, and the optimal subcarrier power vector is solved; (5) Building a multi-dimensional signal processing model based on CPD to estimate the arrival angle, speed and distance of the target, and using the optimized subcarrier power vector to further improve the estimation accuracy; the step (1) includes the following steps: The transmitter and receiver of the MIMO-OFDM signal are separated from each other; the MIMO-OFDM baseband signal x with beamforming vector on the qth data block at the transmitting end q (t) is expressed as Where t represents time, n represents the nth subcarrier, N represents the total number of subcarriers, and w n The dimension corresponding to the nth subcarrier is M T ×1 transmit beamforming vector, M T is the number of transmitting antennas, s q (n) is the communication symbol on the nth subcarrier in the qth data block, △f = 1 / T is the frequency interval between adjacent subcarriers, T s =T+T cp The duration of each data block, T cp is the length of the cyclic prefix, ξ(t-(q-1)T s ) is a period of 1 with an amplitude of T s Rectangular pulse signal, when (q-1)-T cp ≤t≤(q-1)T s +T,ξ(t-(q-1)T s ) is 1, otherwise it is 0; the transmission signal on Q data blocks is expressed as -T cp ≤t≤(Q-1)T s +T; then the baseband signal is up-converted, and the radar communication integrated signal is expressed as in represents the real part, f c represents the carrier frequency; the step (2) comprises the following steps: Transmitting signal Through an additive white Gaussian noise channel, the signal at the kth communication target is down-converted, analog-to-digital converted and demodulated, then the communication data on the nth carrier in the qth data block is expressed as in (·) T represents the transpose of (·), α k , A' k , They represent the transmission angle, path loss, and phase offset caused by time delay and Doppler frequency shift of the kth communication target, respectively. and They represent the carrier set assigned to the kth communication target and the corresponding transmission steering vector, d represents the distance between adjacent antennas, and λ = c / f c represents the wavelength of the carrier, c represents the speed of light, v'(n) represents Gaussian white noise with a mean of 0 and a variance of n'0 / T, and n'0 represents the noise power spectrum density at the communication end; then the communication rate between the transmitter and the kth communication target is in is the average transmission power of the nth subcarrier; the step (3) comprises the following steps:

1. At the radar receiving end, the echo signal irradiated to the communication target is down-converted to obtain the signal in is the array gain factor, M R is the number of receiving antennas, is the attenuation coefficient including secondary propagation loss and reflection coefficient, f k =v k / λ and β k denote the Doppler frequency and arrival angle of the kth target, v k represents the speed of the kth target, λ represents the carrier frequency f c The corresponding wavelength, τ k It is a dual-base delay. represents the receive steering vector, is a complex Gaussian white noise process with power spectral density n0; ii. Matched filtering Then the signal in the nth subcarrier in the qth data block is in (·) * represents the conjugate of (·), and defines v q (n) has a mean of zero and a covariance matrix of p n n0TI complex Gaussian vector, I is M R ×M R The identity matrix of (iii) The vector in step (ii) is converted into (q) =[y q (1),y q (2),…,y q (N)]=A R D q (C)B T +V (q) Arrangement, where A R =[a r (β1),a r (β2),...,a r (β K )], C=[c(f1),c(f2),…,c(f K )], B=[b(τ1),b(τ2),…,b(τ K )], V (q) =[v q (1),v q (2),…,v q (N)],D q (·) means that a diagonal matrix is ​​constructed using the elements of the qth row of the matrix. Combination have in represents the Khatri-Rao product, V = [V (1)T ,…,V (Q)T ] T ; By stacking the columns of matrices Y, S, and V vertically, we get a matrix of dimension M R The vector form of QN×1 is y=s+v; (iv) The closed-form solution of the Cramer-Rao lower bound of the speed, distance and arrival angle of the kth target is calculated by using the vector radar receiving signal obtained in step (iii) using the Jacobian matrix, which are respectively expressed as in, p=[p1,…,p N ] T , [·] n Represents the nth element of a vector.

2. The method for optimizing the perceptual performance based on MIMO-OFDM signals according to claim 1, characterized in that: The step (4) comprises the following steps: i first uses the sum rate of the communication target As the objective function, the total power and the Cramer-Rao lower bound As a constraint, it constitutes a non-convex optimization problem; ii Using matrix transformation equivalence, the non-convex optimization problem is transformed into a convex optimization problem: and Transformed to and in and The deformation of the Cramer-Rao lower bound matrix representing speed and distance respectively, "≥0" means that the matrix is ​​semi-positive definite; thus, the non-convex optimization problem is transformed into a semi-positive definite optimization problem, and the optimal subcarrier power vector is solved using the convex optimization toolbox.

3. The method for optimizing the perceptual performance based on MIMO-OFDM signals according to claim 2, characterized in that: The step (5) comprises the following steps: According to the multidimensional signal processing model, the received signal is represented as a third-order tensor. After the received signal is decomposed into a standard multivariate decomposition, the estimated value of the factor matrix is ​​obtained. II The same columns of the three factor matrices correspond to the estimated parameters β of the same target k , τ k 、f k ; Since the factor matrix has a Vandermonde structure, Perform inverse discrete Fourier transform to estimate the angle of arrival. Perform discrete Fourier transform to estimate the target speed. The corresponding subcarrier data in the inverse discrete Fourier transform is used to estimate the target distance, and when the optimized subcarrier power vector is used at the transmitting end, the estimation accuracy can be further improved.

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