Super-resolution estimation method of target parameters based on OCDM ISAC system

Abstract

The application provides a target parameter super-resolution estimation method based on an OCDM ISAC system, and mainly solves the problems that the distance and speed resolution is limited by the time-frequency resources occupied by the system and the poor sensing accuracy in the prior art. The scheme adopted by the application is to perform modulation symbol elimination on echo signals, to perform joint super-resolution estimation on the target distance and speed by using a subspace projection method after rough estimation of the distance and speed, and to improve the sensing accuracy of the ISAC system without increasing the time-frequency overhead of the system.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology and mainly relates to a super-resolution estimation method for target distance and velocity. Background Technology

[0002] Compared to traditional wireless communication and sensing systems, the ISAC (Integrated Sensing and Communication) system can effectively improve spectrum and hardware equipment utilization and is considered one of the key technologies in 5G (5th Generation Mobile Communication Technology).

[0003] In ISAC systems, the design of integrated waveforms is a key issue. The most common approach is based on Orthogonal Frequency Division Multiplexing (OFDM). However, OFDM is highly sensitive to Doppler shift. In contrast, Orthogonal Chirp Division Multiplexing (OCDM) offers stronger anti-interference capabilities and greater robustness to Doppler shift, making it more suitable for the numerous high-dynamic scenarios in 5G. Therefore, in terms of communication performance, OCDM is superior to OFDM and is considered a more ideal alternative. For sensing functions, in OCDM-based ISAC systems, the distance and velocity parameters of the target can be obtained by processing the echo signal, thus enabling sensing capabilities.

[0004] Reference 1, “Wang Jingqi, Zeng Huan, Tao Zhan, et al. A novel radar-communication integrated system based on OCDM [J]. Journal of Microwave, 2022, 38(06):14-18,” proposes an OCDM radar signal processing method based on 2D Discrete Fourier Transform (DFT), which effectively removes random communication data while significantly reducing computational complexity.

[0005] Reference 2, “DE OLIVEIRA LG, ALABD MB, NUSS B, et al. An OCDM radar-communication system[C] / / 2020 14th European Conference on Antennas and Propagation(EuCAP), Copenhagen, Denmark.2020:1-5.” uses zero-padding DFT to achieve digital pulse compression and target range and velocity estimation after removing the influence of data symbols.

[0006] Reference 3, "BHATTACHARJEE S, MISHRA KV, ANNAVAJJALA R, et al. Evaluation of Orthogonal Chirp Division Multiplexing for Automotive Integrated Sensing and Communications [C] / / 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore. 2022: 8742-8746", describes how the received radar signal is processed by down-conversion and matched filtering to obtain an approximate expression for the radar received signal. Based on this, a likelihood function is constructed, and the parameters that maximize the function are found using a two-dimensional complex periodic graph, thereby obtaining the target range and velocity estimates.

[0007] References 1 and 2 employ a self-transmitting and self-receiving mode and estimate target range and velocity based on DFT, which are simple to operate and easy to implement. However, the accuracy of range and velocity resolution is limited by the time-frequency resources occupied by the system. Reference 3 considers bistatic ISAC and estimates target parameters based on matched filtering, but discretizes range and velocity, resulting in low sensing accuracy and complex computation. Therefore, to improve sensing accuracy, this invention proposes a super-resolution target parameter estimation method based on an OCDM ISAC system to achieve high-precision target parameter estimation. Summary of the Invention

[0008] To overcome the shortcomings of existing technologies and address the limitations of time-frequency resources on range and velocity resolution, thereby improving sensing accuracy, this invention provides a target parameter super-resolution estimation method based on an OCDM ISAC system. After performing modulation symbol cancellation and coarse estimation of range and velocity on the echo signal, a subspace projection method is used to achieve joint super-resolution estimation of target range and velocity, improving the sensing accuracy of the ISAC system without increasing system time-frequency overhead.

[0009] A super-resolution estimation method for target parameters based on the OCDM ISAC system includes the following steps:

[0010] Step 1: Use the 2D DFT method to process the sampling results of all symbols in the p-th baseband pulse and the sampling results of the received signals of all symbols in the p-th pulse to obtain a coarse estimate of the target distance and a coarse estimate of the target velocity.

[0011] Step 2: Utilize the target distance R i The rough estimate of the target velocity v i The coarse estimation result and the known modulation data symbols are used to compensate for the sampling result of the received signal of the s-th symbol of the p-th pulse to obtain the vector of the s-th symbol;

[0012] Step 3: For the vector of the s-th symbol, use the subspace projection method to perform super-resolution estimation of the target range and velocity, and obtain the super-resolution estimate of the target range and the super-resolution estimate of the velocity.

[0013] Step 4: Use the defuzzing method to process the super-resolution estimation of the target distance and the super-resolution estimation of the velocity to obtain the final estimation results of the target distance and the velocity.

[0014] Furthermore, a coarse estimate of the target distance is obtained. And the rough estimate of the target speed The specific process is as follows:

[0015] Step 1-1: To highlight the effects of time delay and Doppler shift, ignore the time delay caused by the pulse period and symbol period, and transmit the s-th symbol x in the p-th pulse. p,s (t) is represented as:

[0016]

[0017] Where t is time, j is the imaginary unit; where N p In a OCDM pulse signal, each pulse contains N s One OCDM symbol, signal parameters including the number of subcarriers N, carrier frequency f. cSymbol period T s and pulse period T p Where p = 0, 1, ..., N p -1,s=0,1,…,N s -1, X(k) is the data symbol modulated on the k-th subcarrier;

[0018] For the s-th transmitted symbol x in the p-th pulse p,s If (t) is sampled, then the sampling result x p,s (n) is represented as:

[0019]

[0020] Where n = 0, 1, ..., N-1;

[0021] Based on equation (1), the received signal y of the i-th target p,s,i (t) is:

[0022]

[0023] Among them, A i τ is the complex amplitude factor of the attenuation and phase shift of the i-th target during propagation and scattering; i f is the time delay corresponding to the i-th target. d,i For the Doppler frequency shift of the i-th target, c is the speed of light, f c For the carrier frequency, using a single-base ISAC, assume there are N t There are targets, and the distance between the i-th target and the transmitter is R. i The relative velocity is v i i = 1, 2, ..., N t ;

[0024] To simplify the derivation, the influence of noise is ignored in equation (3), and the received signal y is... p,s,i (t) Samples are used to obtain the sampled signal y of the i-th target. p,s,i (n):

[0025]

[0026] In equation (4), and phase shift In comparison, the phase shift within the symbol period Since it is relatively small, we ignore it and express equation (4) as:

[0027]

[0028] Performing a Fourier transform on equation (2) yields the Fourier transform F of the sampling result of the p-th pulse and s-th symbol in the baseband. tx,s(k), performing a Fourier transform on equation (5) yields the Fourier transform F of the sampled signal of the s-th symbol of the p-th pulse. rx,s (k):

[0029]

[0030] Where DFT(·) performs a DFT operation on the content within the parentheses, k = 0, 1, ..., N-1;

[0031] After performing a DFT on the N samples of the s-th symbol, the intermediate variable F is obtained. tx,s and intermediate variable F rx,s :

[0032]

[0033] in It is an N-dimensional complex space;

[0034] According to equation (7), the emission matrix F tx and the receiving matrix F rx Represented as:

[0035]

[0036] in For N×N s Complex space;

[0037] Step 1-2: Let This involves performing a dot product of the conjugate matrices of the receiving matrix and the transmitting matrix to remove the influence of communication information on radar detection, where ⊙ represents the Hadamard product, (·). H To take the conjugate transpose of the matrix, F is expressed as:

[0038]

[0039] Where F is an intermediate variable. Let be the time delay vector of the i-th target, expressed as:

[0040]

[0041] Its elements are and

[0042] Let be the Doppler vector of the i-th target. Represented as:

[0043]

[0044] Its elements are and For N s Complex space;

[0045] Steps 1-3: From equation (10), the target's distance information R i Linear phase shifts of different subcarriers contained within the same OCDM symbol are calculated. The distance estimate of the reflecting target is obtained by performing the discrete inverse Fourier transform (IDFT); from equation (11), the Doppler information f of the target is obtained. d,i Contained in the linear phase shift between the same subcarrier in the frequency domain of adjacent OCDM symbols, by calculation The DFT of the target is used to obtain the velocity estimate of the reflecting target; and according to equation (9), the range effect and Doppler effect of the target are completely orthogonal in the frequency domain; therefore, by performing IDFT and DFT on the columns and rows of matrix F respectively, and then performing spectral peak search, the range and velocity estimate of the target can be obtained.

[0046] Let the s-th column of the intermediate variable F be f. s Where s = 0, 1, ..., N s -1, perform IDFT on each column of F separately, and obtain Let F be the IDFT matrix, i.e.:

[0047]

[0048] in IDFT(·) performs the IDFT operation on the content within the parentheses, and then... Perform a DFT on each row to obtain for The DFT matrix; let The nth line Where n = 0, 1, ..., N-1, then we have:

[0049]

[0050] in

[0051] set up The element in the l-th row and m-th column is f. l,m ,make:

[0052]

[0053] but:

[0054]

[0055] in To round down;

[0056]

[0057] Thus, the distance R of the i-th target is obtained. i rough estimate results and the velocity v of the i-th target i rough estimate results

[0058] Furthermore, using distance R i Coarse estimation results of the target distance speed v i The rough estimate of the target speed The sampling result y of the received signal of the s-th symbol of the p-th pulse and the known modulated data symbols. p,s Compensation is performed to obtain the vector r corresponding to the s-th symbol. s The specific process is as follows:

[0059] Step 2-1: Based on the sampling results of the received signal of the s-th symbol of the p-th pulse and the received signal of the s-th symbol of the p-th pulse, obtain... y p,s,i For y p,s The i-th component, and Taking the i-th target as an example, y p,s,i Represented as:

[0060]

[0061] in, It is an intermediate variable, and It is an N×N dimensional complex space;

[0062] It is an intermediate variable, and

[0063] D c =diag[X(0),X(1),…,X(N-1)], D c It is an intermediate variable, and

[0064] It is the distance steering vector, and

[0065] w i =[w i (0),w i (1),…,w i (N-1)] T w iIt is a noise vector, and

[0066] Φ is the discrete Fresnel inverse transform matrix, and its element in the nth row and kth position is... diag(·) is an operation for constructing a diagonal matrix;

[0067] Step 2-2: Using distance R i rough estimate results and velocity v i rough estimate results Calculate the coarse estimation results of the time delay And Doppler rough estimation results

[0068]

[0069] The time delay compensation matrix is ​​obtained based on the coarse time delay estimation results and the Doppler coarse estimation results. and Doppler compensation matrix

[0070]

[0071] in Since Φ is a unitary matrix and D is known c Using Φ, D c The time delay compensation matrix and the Doppler compensation matrix compensate for equation (18), then multiplying equation (18) by the left multiplier. in Let it be an intermediate variable, and let:

[0072]

[0073] Where D p,s,i Define y as an intermediate variable. p,s,i The compensation vector is r p,s,i Then r p,s,i Represented as:

[0074]

[0075] And order:

[0076]

[0077] Where r p,s For r p,s.i The sum vector;

[0078] Steps 2-3: To simultaneously estimate the target's range and velocity, the s-th symbol in all pulses is arranged in a column, thus obtaining the vector r corresponding to the s-th symbol. s :

[0079]

[0080] In the formula, It is an intermediate variable, and in For N p N×N p N-dimensional complex space;

[0081] For distance and velocity guiding vectors, The Kronecker product of matrices;

[0082] As the velocity steering vector, and For N p Complex space;

[0083] It is the distance guide vector. and

[0084] Furthermore, for the vector r corresponding to the s-th symbol... s s = 0, 1, ..., N s -1, Using the subspace projection method, target range and velocity super-resolution estimation is performed to obtain the target range super-resolution estimate. Super-resolution estimation of speed The specific process is as follows:

[0085] Step 3-1: From N s The covariance matrix of the data with symbols is estimated.

[0086]

[0087] Step 3-2: For Perform eigenvalue decomposition, that is, let Where Σ is The diagonal matrix formed by the eigenvalues ​​of , i.e. The eigenvalues ​​are arranged in descending order, i.e. Each column of U Eigenvalues The corresponding feature vector;

[0088] Step 3-3: Using existing target source number estimation methods in the field of array signal processing, obtain the target number N. t Take the first N t The eigenvectors corresponding to each eigenvalue Constructing a matrix

[0089] Steps 3-4: Discretize R and v at fixed intervals and calculate the corresponding τ and f. d The distance-velocity steering vector is obtained. structure Search above the threshold γ R,v The peak value, and then the corresponding time delay. and Doppler shift Calculate the super-resolution estimate of the target distance. Super-resolution estimation of speed

[0090] in λ is the wavelength, λ = c / f c .

[0091] Furthermore, a deblurring method is used for super-resolution estimation of the target distance. Super-resolution estimation of speed The process is performed to obtain the final estimate of the target distance. The final estimate of the speed The specific process is as follows:

[0092] Step 4-1: Obtain the target distance estimate using the method described in Step 3. The maximum unambiguous distance of this method The distance is relatively small, so pulse compression is used to estimate the target distance to resolve the range ambiguity. And combining the two, to reduce the error generated during the combination, let:

[0093]

[0094] Where α is the distance ambiguity coefficient. For fuzzy distance estimation, and letting q = -1, 0, 1, calculate the distance for different values ​​of q. and The absolute value of the difference, and take the one with the smallest absolute value of the difference. As the final estimate of the target distance

[0095] Step 4-2: Obtain the target velocity estimate using the method described in Step 3. After that, the maximum unambiguous speed is right De-velocity ambiguity and obtain target velocity estimate using 2D DFT method. Combining the two, let:

[0096]

[0097] Where β is the velocity ambiguity coefficient. For fuzzy velocity estimation, calculations are performed for different values ​​of q. and The absolute value of the difference, and take the one with the smallest absolute value of the difference. As the final estimate of the target distance

[0098] The beneficial effects of this invention are as follows: This invention achieves super-resolution estimation of target distance and velocity by compensating for the echo signal and using the subspace projection method. It also eliminates distance and velocity ambiguity by combining pulse compression and 2D DFT methods. Simulation verification shows that the method proposed in this invention can accurately estimate target parameters and significantly improve the system's perception accuracy. Attached Figure Description

[0099] Figure 1 This is a diagram of the OCDM transmit signal structure;

[0100] Figure 2 This is a flowchart of the super-resolution estimation of target parameters;

[0101] Figure 3 These are simulation results for joint range-velocity estimation, where (a) is the 2D DFT method and (b) is the super-resolution method.

[0102] Figure 4 These are the simulation results for resolving distance ambiguity, where (a) is before deambiguity and (b) is after deambiguity.

[0103] Figure 5 These are the simulation results of defuzzification of velocity, where (a) is before defuzzification and (b) is after defuzzification;

[0104] Figure 6 is the relative error of parameter estimation, where (a) is the relative error of distance estimation and (b) is the relative error of velocity estimation. Detailed Implementation

[0105] Based on application requirements, determine the signal parameters and the transmitted signal format as follows: Figure 1 As shown, emit N p OCDM pulse signals, each pulse containing N s One OCDM symbol, signal parameters including the number of subcarriers N, carrier frequency f. c Symbol period T s and pulse period T p ;

[0106] Assume the continuous time-domain expression for the p-th pulse and s-th symbol in the baseband is:

[0107] pT p +sT s<t≤pT p +(s+1)T s

[0108] Where p = 0, 1, ..., N p -1,s=0,1,…,N s -1, X(k) is the data symbol modulated on the k-th subcarrier;

[0109] Using monobase ISAC, assume there exists N t There are targets, and the distance between the i-th target and the transmitter is R. i The relative velocity is v i i = 1, 2, ..., N t And disregarding multipath signals, the received signal of the s-th symbol of the p-th pulse is:

[0110]

[0111] Among them, A i Let τ be the complex amplitude factor of the attenuation and phase shift of the i-th target during propagation and scattering. i f is the time delay corresponding to the i-th target. d,i For the Doppler frequency shift of the i-th target, c is the speed of light, f c Let x be the carrier frequency, w(t) be Gaussian white noise; for x p,s (t) and y p,s (t) is sampled, with a sampling period of T. s / N, the sampling result of the p-th pulse and s-th symbol in the baseband is

[0112]

[0113] The sampling result of the received signal of the s-th symbol of the p-th pulse is

[0114]

[0115] in

[0116] Figure 2 A flowchart illustrating the super-resolution estimation process for target parameters is provided.

[0117] A super-resolution estimation method for target parameters based on the OCDM ISAC system includes the following steps:

[0118] Step 1: Use the 2D DFT method to analyze N in the p-th baseband pulse. s The sampling results of each symbol and N in the p-th pulse s The sampling results of the received signal of each symbol are processed to obtain a coarse estimate of the target distance. And the rough estimate of the target speed The specific process is as follows:

[0119] Step 1-1: To highlight the effects of time delay and Doppler shift, ignore the time delay caused by the pulse period and symbol period, and transmit the s-th symbol x in the p-th pulse. p,s (t) is represented as:

[0120]

[0121] Where t is time and j is the imaginary unit;

[0122] Where N p In a OCDM pulse signal, each pulse contains N s One OCDM symbol, signal parameters including the number of subcarriers N, carrier frequency f. c Symbol period T s and pulse period T p Where p = 0, 1, ..., N p -1,s=0,1,…,N s -1, X(k) is the data symbol modulated on the k-th subcarrier;

[0123] For the s-th transmitted symbol x in the p-th pulse p,s If (t) is sampled, then the sampling result x p,s (n) is represented as:

[0124]

[0125] Where n = 0, 1, ..., N-1;

[0126] Based on equation (1), the received signal y of the i-th target p,s,i (t) is:

[0127]

[0128] Among them, A i τ is the complex amplitude factor of the attenuation and phase shift of the i-th target during propagation and scattering; i f is the time delay corresponding to the i-th target. d,i For the Doppler frequency shift of the i-th target, c is the speed of light, f c For the carrier frequency, using a single-base ISAC, assume there are N t There are targets, and the distance between the i-th target and the transmitter is R. i The relative velocity is v i i = 1, 2, ..., N t ;

[0129] To simplify the derivation, the influence of noise is ignored in equation (3), and the received signal y is... p,s,i (t) Samples are used to obtain the sampled signal y of the i-th target. p,s,i (n):

[0130]

[0131] In equation (4), and phase shift In comparison, the phase shift within the symbol period Since it is relatively small, we ignore it and express equation (4) as:

[0132]

[0133] Performing a Fourier transform on equation (2) yields the Fourier transform F of the sampling result of the p-th pulse and s-th symbol in the baseband. tx,s (k), performing a Fourier transform on equation (5) yields the Fourier transform F of the sampled signal of the s-th symbol of the p-th pulse. rx,s (k):

[0134]

[0135] Where DFT(·) performs a DFT operation on the content within the parentheses, k = 0, 1, ..., N-1;

[0136] After performing a DFT on the N samples of the s-th symbol, the intermediate variable F is obtained. tx,s and intermediate variable F rx,s :

[0137]

[0138] in It is an N-dimensional complex space;

[0139] According to equation (7), the emission matrix F tx and the receiving matrix F rx Represented as:

[0140]

[0141] in For N×N s Complex space;

[0142] Step 1-2: Let This involves multiplying the conjugate matrices of the receive matrix and the transmit matrix by a dot product to remove the influence of communication information on radar detection, where ⊙ is the Hadamard product, (·). H To take the conjugate transpose of the matrix, F is expressed as:

[0143]

[0144] Where F is an intermediate variable. Let be the time delay vector of the i-th target, expressed as:

[0145]

[0146] Its elements are and

[0147] Let be the Doppler vector of the i-th target. Represented as:

[0148]

[0149] Its elements are and For N s Complex space;

[0150] Steps 1-3: From equation (10), we can see that the target's distance information R i Linear phase shifts of different subcarriers contained within the same OCDM symbol are calculated. The inverse discrete Fourier transform (IDFT) is used to obtain the range estimate of the reflecting target; from equation (11), it can be seen that the Doppler information f of the target is... d,i Contained in the linear phase shift between the same subcarrier in the frequency domain of adjacent OCDM symbols, by calculation The DFT of the target is used to obtain the velocity estimate of the reflecting target; and as can be seen from equation (9), the range effect and Doppler effect of the target are completely orthogonal in the frequency domain; therefore, by performing IDFT and DFT on the columns and rows of matrix F respectively, and then performing spectral peak search, the range and velocity estimate of the target can be obtained.

[0151] Let the s-th column of the intermediate variable F be f. s Where s = 0, 1, ..., N s -1, perform IDFT on each column of F separately, and obtain Let F be the IDFT matrix, i.e.:

[0152]

[0153] in IDFT(·) performs the IDFT operation on the content within the parentheses, and then... Perform a DFT on each row to obtain for The DFT matrix; let The nth line Where n = 0, 1, ..., N-1, then we have:

[0154]

[0155] in

[0156] set up The element in the l-th row and m-th column is f. l,m ,make:

[0157]

[0158] but:

[0159]

[0160] in To round down, we obtain the distance R of the i-th target. i rough estimate results and the velocity v of the i-th target i rough estimate results

[0161]

[0162] Step 2: To perform super-resolution estimation using the subspace projection method, the target distance R is utilized. i rough estimate results Target velocity v i rough estimate results The sampling result y of the received signal of the s-th symbol of the p-th pulse and the known modulated data symbols. p,s Compensation is performed to obtain the vector r corresponding to the s-th symbol. s The specific process is as follows:

[0163] Step 2-1: Based on the sampling results of the received signal of the s-th symbol of the p-th pulse and the received signal of the s-th symbol of the p-th pulse, obtain... y p,s,i For y p,s The i-th component, and Taking the i-th target as an example, y p,s,i Represented as:

[0164]

[0165] in,

[0166] It is an intermediate variable, and It is an N×N dimensional complex space;

[0167] It is an intermediate variable, and

[0168] D c =diag[X(0),X(1),…,X(N-1)] is an intermediate variable, and

[0169] It is the distance steering vector, and

[0170] w i =[w i (0),w i (1),…,w i (N-1)] T It is a noise vector, and

[0171] Φ is the discrete Fresnel inverse transform matrix, and its element in the nth row and kth position is... diag(·) is an operation for constructing a diagonal matrix;

[0172] Step 2-2: Using distance R i rough estimate results and velocity v i rough estimate results Calculate the coarse estimation results of the time delay And Doppler rough estimation results

[0173]

[0174] The time delay compensation matrix is ​​obtained based on the coarse time delay estimation results and the Doppler coarse estimation results. and Doppler compensation matrix

[0175]

[0176] in Since Φ is a unitary matrix and D is known c Using Φ, D c The time delay compensation matrix and the Doppler compensation matrix compensate for equation (18), then multiplying equation (18) by the left multiplier. in Let it be an intermediate variable, and let:

[0177]

[0178] Where D p,s,i Define y as an intermediate variable. p,s,i The compensation vector is rp,s,i Then r p,s,i Represented as:

[0179]

[0180] And order:

[0181]

[0182] Where r p,s For r p,s.i The sum vector;

[0183] Steps 2-3: To simultaneously estimate the target's range and velocity, the s-th symbol in all pulses is arranged in a column, thus obtaining the vector r corresponding to the s-th symbol. s :

[0184]

[0185] In the formula, It is an intermediate variable, and

[0186] For distance and velocity guiding vectors, The Kronecker product of matrices;

[0187] As the velocity steering vector, and

[0188] It is the distance guide vector. and

[0189] Step 3: For the vector r corresponding to the s-th symbol s s = 0, 1, ..., N s -1, Using the subspace projection method, target range and velocity super-resolution estimation is performed to obtain the target range super-resolution estimate. Super-resolution estimation of speed The specific process is as follows:

[0190] Step 3-1: From N s The covariance matrix of the data with symbols is estimated.

[0191]

[0192] Step 3-2: For Perform eigenvalue decomposition, that is, let Where Σ is The diagonal matrix formed by the eigenvalues ​​of , i.e. The eigenvalues ​​are arranged in descending order, i.e. Each column of U Eigenvalues The corresponding feature vector;

[0193] Step 3-3: Using existing target source number estimation methods in the field of array signal processing, obtain the target number N. t Take the first N t The eigenvectors corresponding to each eigenvalue Constructing a matrix

[0194] Steps 3-4: Discretize R and v at fixed intervals and calculate the corresponding τ and f. d The distance-velocity steering vector is obtained. structure Search above the threshold γ R,v The peak value, and then the corresponding time delay. and Doppler shift Calculate the super-resolution estimate of the target distance. Super-resolution estimation of speed

[0195] in λ is the wavelength, λ = c / f c ;

[0196] Step 4: Super-resolution estimation of target distance using defuzzing methods Super-resolution estimation of speed The process is performed to obtain the final estimate of the target distance. The final estimate of the speed The specific process is as follows:

[0197] Step 4-1: Obtain the target distance estimate using the method described in Step 3. The maximum unambiguous distance of this method The distance is relatively small, so pulse compression is used to estimate the target distance to resolve the range ambiguity. And combining the two, to reduce the error generated during the combination, let:

[0198]

[0199] Where α is the distance ambiguity coefficient. For fuzzy distance estimation, and letting q = -1, 0, 1, calculate the distance for different values ​​of q. and The absolute value of the difference, and take the one with the smallest absolute value of the difference. As the final estimate of the target distance

[0200] Step 4-2: Obtain the target velocity estimate using the method described in Step 3. After that, the maximum unambiguous speed is right De-velocity ambiguity and obtain target velocity estimate using 2D DFT method. Combining the two, making

[0201]

[0202] Where β is the velocity ambiguity coefficient. For fuzzy velocity estimation, calculations are performed for different values ​​of q. and The absolute value of the difference, and take the one with the smallest absolute value of the difference. As the final estimate of the target distance

[0203] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0204] The effects of this invention can be achieved through Figure 3-6 The simulation results further illustrate this. In the simulation experiment, N = 256, N... s =32, N p =8,T s =5.12us, T p = 4.1ms, f c =5GHz.

[0205] Figure 3 The result is a joint estimation of the range and velocity of two targets with distances of 93m and 20m / s and 95m and 25m / s, respectively. Figure 3 (a) shows the result of estimating the target parameters using the traditional DFT-based method. As can be seen, there is only one peak, and the distance and velocity corresponding to this peak are 93m and 21.46m / s, respectively. The target distance and velocity estimates were not obtained accurately. Figure 3 (b) The results of estimating the target parameters using the proposed super-resolution method are shown. The estimated target distances are 93.1 m and 95.2 m, and the estimated target velocities are 19.9 m / s and 24.8 m / s, respectively, with errors not exceeding 1 m and 1 m / s. Compared with traditional methods, the perception accuracy is improved. Similar to the distance estimation error, the magnitude of the error is related to the values ​​of R and v in Implementation 3. In this simulation, R and v were uniformly selected at intervals of 0.9 m and 0.6 m / s, respectively. To further reduce the estimation error, the selection interval can be decreased.

[0206] Figure 4 This is the result of resolving the distance ambiguity in the estimation results. When the target distance is 850m, it exceeds the maximum unambiguous distance. Appearance Figure 4 The fuzziness shown in (a) yields two estimation results, namely 81.9m and 849.9m; after processing with the proposed distance fuzziness resolution method, the result is 849.9m.

[0207] Figure 5 This is the result of de-ambiguing the estimated velocity. When the target distance and velocity are 80m and 90m / s respectively, the maximum unambiguous velocity is exceeded. Two sets of estimated results were obtained, namely 79.8m and 17.1m / s and 79.8m and 89.9m / s. After processing with the proposed velocity fuzzy resolution method, the results were 79.8m and 17.1m / s.

[0208] Simulation results show that the super-resolution method can accurately estimate the target parameters without causing ambiguity. Therefore, the method proposed in this invention effectively improves the system's perception accuracy.

[0209] Figure 6 It is the relative error of parameter estimation.

[0210] Figure 6 The relative error is used to estimate the parameters. Figure 6 (a) To represent the relative error of distance estimation, with target distances set to 30-90m, parameter estimation methods based on DFT and subspace projection (represented by "sub" in the legend) were used respectively. It can be seen that, except for a few points, the error of the subspace projection method is significantly lower than that of the DFT method. Calculations show that the average relative error of the DFT method is 0.025, and the average relative error of the subspace projection method is 0.0038. The fluctuation of error with target distance is because the DFT method discretizes distance and velocity into a grid. The closer the actual target distance is to the "grid lines," the smaller the error; conversely, the further away, the larger the error. For the subspace projection method, as described in Step 4 of Implementation 3, different selections of R also discretize the distance, thus causing the error to fluctuate with distance. Figure 6 (b) To assess the relative error of velocity estimation, with target velocities ranging from 5 to 25 m / s, parameter estimation methods based on DFT and subspace projection were employed. The results show that, except for a few points, the error of the subspace projection method is significantly lower than that of the DFT method. Calculations show that the average relative error of the DFT method in the figure is 0.12, while the average relative error of the subspace projection method is 0.017. The reason for the fluctuation of error with velocity is similar to the analysis of the relative error of distance estimation.

Claims

1. A super-resolution estimation method for target parameters based on an OCDM ISAC system, characterized in that, Includes the following steps: Step 1: Use the 2D DFT method to analyze the baseband... The sampling results of all symbols in the first pulse and the first pulse The sampling results of the received signals of all symbols in each pulse are processed to obtain a coarse estimate of the target distance and a coarse estimate of the target velocity. Step 2: Utilize target distance The rough estimate of the target speed The coarse estimation results and the known modulation data symbols for the first The first pulse The sampling results of the received signal of the nth symbol are compensated to obtain the nth symbol. A vector of symbols; Step 3: For the first A vector of symbols is used to perform super-resolution estimation of target range and velocity using the subspace projection method, resulting in super-resolution estimates of target range and velocity. Obtain super-resolution estimation of target distance Super-resolution estimation of speed The specific process is as follows: Step 3-1: From The covariance matrix of the data with symbols is estimated. : (24) Step 3-2: For Perform eigenvalue decomposition, that is, let ,in for The diagonal matrix formed by the eigenvalues ​​of , i.e. The eigenvalues ​​are arranged in descending order, i.e. , Each column Eigenvalues The corresponding feature vector; Step 3-3: Utilize existing target source quantity estimation methods in the field of array signal processing to obtain the target quantity. Take the front The eigenvectors corresponding to each eigenvalue Construct a matrix ; Steps 3-4: In the process of... and Discretize at fixed intervals and calculate the corresponding... and The distance-velocity steering vector is obtained. ,structure Searches exceeding the threshold The peak value, and then the corresponding time delay. and Doppler shift Calculate the super-resolution estimate of the target distance. Super-resolution estimation of speed : in , , For wavelength, ; At the speed of light, For carrier frequency; Step 4: Use the defuzzing method to process the super-resolution estimation of the target distance and the super-resolution estimation of the velocity to obtain the final estimation results of the target distance and the final estimation results of the velocity. Step 4-1: Obtain the target distance estimate using the method described in Step 3. The maximum unambiguous distance of this method The distance is relatively small, so pulse compression is used to estimate the target distance to resolve the range ambiguity. And combining the two, to reduce the error generated during the combination, let: (25) (26) in For distance ambiguity coefficients, For fuzzy distance estimation, and let ,for Calculate the different values ​​of respectively. and The absolute value of the difference, and take the one with the smallest absolute value of the difference. As the final estimate of the target distance ; Step 4-2: Obtain the target velocity estimate using the method described in Step 3. After that, the maximum unambiguous speed is , ,right De-velocity ambiguity and obtain target velocity estimate using 2D DFT method. And combining the two, let: (27) (28) in For speed ambiguity coefficients, For fuzzy velocity estimation, for Calculate the different values ​​of respectively. and The absolute value of the difference, and take the one with the smallest absolute value of the difference. As the final estimate of the target distance .

2. The target parameter super-resolution estimation method based on the OCDM ISAC system according to claim 1, characterized in that, A rough estimate of the target distance is obtained. And the rough estimate of the target speed The specific process is as follows: Step 1-1: To highlight the effects of time delay and Doppler shift, the time delay caused by the pulse period and symbol period is ignored, and the first... The first pulse One launch symbol Represented as: (1) in For a moment, The imaginary unit; where In each of the OCDM pulse signals, there are 10 pulses. One OCDM symbol, signal parameters including the number of subcarriers. carrier frequency Symbol period and pulse period ,in , For the first Data symbols modulated on each subcarrier; For the The first pulse One launch symbol Sampling is performed, and the sampling results are as follows. Represented as: (2) in ; Based on equation (1), the first Received signal of each target for: (3) in, For the first The complex amplitude factor of attenuation and phase shift of a target during propagation and scattering; For the first The time delay corresponding to each target For the first Doppler shift of the target , , At the speed of light, For the carrier frequency, using a single-base ISAC, it is assumed that there exists The first goal, the... The distance between the target and the transmitter is The relative speed is , ; To simplify the derivation, the influence of noise is ignored in equation (3), and the received signal is... Sampling was performed to obtain the first Sampling signal of each target : (4) In equation (4), and phase shift In comparison, the phase shift within the symbol period Since it is relatively small, we ignore it and express equation (4) as: (5) Performing a Fourier transform on equation (2) yields the baseband... The pulse number Fourier transform of the sampling results of each symbol Performing a Fourier transform on equation (5) yields the first... The first pulse Fourier transform of the sampled results of the received signal of each symbol : (6) in To perform a DFT operation on the content within the parentheses, ; For the a symbol After performing a DFT on each sample result, intermediate variables are obtained. and intermediate variables : ，(7) in , , for Complex space; According to equation (7), the emission matrix and receiving matrix Represented as: (8) in , for Complex space; Step 1-2: Let This involves performing a dot product of the conjugate matrices of the receive matrix and the transmit matrix to remove the influence of communication information on radar detection. For Hadama accumulation, To take the conjugate transpose of the matrix; then Represented as: (9) in As an intermediate variable, , For the first The time delay vector of each target is represented as: (10) Its elements are ,and ; For the first Doppler vectors of the targets Represented as: (11) Its elements are ,and , for Complex space; Steps 1-3: From equation (10), the target's distance information Linear phase shifts of different subcarriers contained within the same OCDM symbol are calculated. The distance estimate of the reflecting target is obtained by performing the discrete inverse Fourier transform (IDFT); from equation (11), the Doppler information of the target is obtained. Contained in the linear phase shift between the same subcarrier in the frequency domain of adjacent OCDM symbols, by calculation The velocity estimate of the reflecting target is obtained by performing the DFT; and according to equation (9), the range effect and Doppler effect of the target are completely orthogonal in the frequency domain; therefore, by performing the matrix The columns and rows are subjected to IDFT and DFT respectively, and then spectral peak search is performed to obtain the distance and velocity estimates of the target. Let intermediate variables be defined. The Listed as ,in ,right Perform IDFT on each column separately to obtain , for The IDFT matrix, i.e.: (12) in , To perform an IDFT operation on the content within the parentheses, and then... Perform a DFT on each row to obtain , for The DFT matrix; let The Behavior ,in Then we have: (13) in ; set up The Okay, number The elements of the column are ,make: (14) but: (15) in To round down; (16) (17) Thus, the first Distance to each target rough estimate results and the The speed of the target rough estimate results .

3. The target parameter super-resolution estimation method based on the OCDM ISAC system according to claim 1, characterized in that, Utilize distance Coarse estimation results of the target distance ,speed The rough estimate of the target speed and known modulation data symbols for the first The first pulse Sampling results of the received signal of each symbol Compensation will be provided to obtain the first The vector corresponding to each symbol The specific process is as follows: Step 2-1: Based on the first The first pulse The received signal of the first symbol and the first symbol The first pulse The sampling results of the received signal of each symbol are obtained. , for The Each component, and , with the first Taking one target as an example, Represented as: (18) in, , It is an intermediate variable, and , for Complex space; , It is an intermediate variable, and ; , It is an intermediate variable, and ; , It is the distance steering vector, and ; , Let be the noise vector, and ; Let f be the discrete Fresnel inverse transform matrix, and let f be the first of its 6th and 7th 6 ... Line 1 The elements are , Operations for constructing diagonal matrices; Step 2-2: Utilizing distance rough estimate results and speed rough estimate results Calculate the coarse estimate of the time delay. And Doppler rough estimation results : ； ； The time delay compensation matrix is ​​obtained based on the coarse time delay estimation results and the Doppler coarse estimation results. and Doppler compensation matrix : ；(19) in ,because It is a unitary matrix and is known ,use , The time delay compensation matrix and the Doppler compensation matrix compensate for equation (18), then multiplying equation (18) by the left multiplier. ,in Let it be an intermediate variable, and let: ；(20) in Define as an intermediate variable The compensation vector is ,but Represented as: ；(21) And order: ；(22) in for The sum vector; Steps 2-3: In order to simultaneously estimate the target's range and velocity, the first pulse in all pulses... If the symbols are arranged in a column, then the first symbol is obtained. The vector corresponding to each symbol : ；(23) In the formula, , It is an intermediate variable, and ,in for Complex space; For distance and velocity guiding vectors, , The Kronecker product of matrices; As the velocity steering vector, ,and , for Complex space; It is the distance guide vector. ,and .

4. A terminal device, comprising a processor, a memory, and a computer program stored in the memory, characterized in that, When the processor executes the computer program, it implements a target parameter super-resolution estimation method based on the OCDMISC system according to any one of claims 1-3.

5. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a target parameter super-resolution estimation method based on the OCDM ISAC system according to any one of claims 1-3.

Images