Parameter decoupling and estimation method and terminal in radar communication integrated system

Abstract

The application discloses a parameter decoupling and estimation method and a terminal of a radar communication integrated system, and the parameter decoupling and estimation method comprises the following steps: establishing a receiving end signal model based on frequency modulation continuous wave; introducing a reference matrix and a selection matrix, the reference matrix being a decoupled signal matrix, and the selection matrix being a 0-1 matrix determined according to activated antennas and frequencies; designing an optimization problem, decoupling each target parameter of the established receiving end signal model through the introduced reference matrix and selection matrix, and obtaining target parameter estimation and matching. Through decoupling, the application can separately estimate each parameter, and avoids high calculation complexity existing in joint estimation; when sparsity is constrained, a decomposition decoupled atom norm method based on a decoupled atom norm is proposed. The application has similar performance to joint parameter estimation using an orthogonal matching pursuit algorithm, and the calculation complexity is much lower than that of the OMP algorithm.

Description

Technical Field

[0001] This invention relates to a parameter decoupling and estimation method in an integrated radar and communication system, which belongs to the field of array signal processing technology. Background Technology

[0002] The scarcity of spectrum resources has spurred the development of integrated radar and communication systems. Dual-function radar communication systems utilize the same hardware platform to achieve both sensing and communication functions, enabling efficient information transmission between wireless devices simultaneously for target detection and identification. These functions share resources such as spectrum, power, and antennas, effectively alleviating the ever-increasing spectrum congestion crisis. Currently proposed integrated radar and communication systems for the transportation field are mainly divided into four types: radar waveform-centric systems, communication waveform-centric systems, coordinated waveform separation systems, and collaboratively designed waveform systems. In radar waveform-centric systems, index modulation techniques are frequently used to add additional communication information. Index modulation techniques increase the resources that can be modulated by using different antenna allocations and frequency allocations to transmit communication information. Frequency-modulated continuous wave (FM-Continuous Wave)-based integrated radar and communication systems achieve dual-function radar communication systems by applying spatial and frequency domain index modulation techniques to FM-Continuous Wave systems. However, when performing parameter estimation, the simultaneous use of virtual aperture technology and index modulation technology introduces coupling between target range, velocity, and angle. This forces parameter estimation to be performed jointly in three dimensions: range, velocity, and angle, resulting in significant computational complexity.

[0003] Considering the parameter estimation problem in a frequency-modulated continuous wave-based radar-communication integrated system, the main challenges are as follows:

[0004] 1. Since there is coupling between the target distance, velocity, and angle, and existing decoupling methods are not applicable, only three-dimensional joint parameter estimation can be performed. Such methods have extremely high computational complexity.

[0005] 2. When performing parameter estimation and decoupling, the use of virtual aperture technology should be considered to ensure that the virtual aperture is fully utilized. Summary of the Invention

[0006] Objective of the Invention: To overcome the shortcomings of existing technologies, this invention provides a parameter decoupling and estimation method and a signal receiving terminal for an integrated radar and communication system. The proposed parameter decoupling method, based on the decomposition of atomic norms, achieves performance similar to that of the joint estimation method based on orthogonal matching pursuit, but with significantly lower computational complexity. Furthermore, it fully utilizes the advantages of virtual apertures during decoupling, addressing the problems of high complexity and performance degradation of virtual apertures during decoupling in existing methods.

[0007] Technical solution: To achieve the above objectives, the technical solution adopted by this invention is as follows:

[0008] This invention first provides a parameter decoupling and estimation method in a radar-communication integrated system, comprising the following steps:

[0009] Establish an integrated system model based on frequency-modulated continuous wave;

[0010] A reference matrix and a selection matrix are introduced. The reference matrix is ​​the decoupled signal matrix, and the selection matrix is ​​a 0-1 matrix determined according to the activated antenna and frequency.

[0011] The design optimization problem is solved by decoupling the objective parameters of the established integrated system model through the introduction of reference and selection matrices, thereby obtaining the estimation and matching of each objective parameter.

[0012] In the steps of establishing a radar-communication integrated system model based on frequency-modulated continuous wave, the integrated system model established is as follows:

[0013]

[0014] in, The output signal is the signal in the ((n-1)K+k)th row of the output signal matrix, and the qth row is the signal in the output signal matrix. r The elements of the column represent the qth element. r The signal received by each antenna is the output signal obtained after matched filtering of the signal transmitted by the k-th active antenna in the n-th pulse; This indicates that the number of rows is NK and the number of columns is Q. r A complex matrix; N represents the number of transmitted pulses, K represents the number of active transmit antennas in each pulse, and Q... r Indicates the number of receiving antennas; L represents the number of targets, r l v represents the distance to the l-th target. l Let θ represent the radial velocity of the l-th target relative to the array. l β represents the angle between the l-th target and the array normal. l The coefficients represent the output signal after echo processing of the l-th target; This represents additive white Gaussian noise, a r (r l )∈C NK×1 a represents the steering vector related to the target distance. v (v l )∈C NK×1 a represents the steering vector related to the target velocity. t (θ l )∈C NK×1 This represents the launch array steering vector related to the target angle. This represents the receiver array steering vector related to the target angle, where the elements are as follows:

[0015]

[0016]

[0017]

[0018]

[0019] Where r, v, and θ represent the target's distance, velocity, and angle with the array normal, respectively, and a r,(n-1)K+k (r),a v,(n-1)K+k (v),a t,(n-1)K+k (θ) represent the guiding vector a r (r),a v (v),a t The (n-1)K+kth element in (θ), b r The qth in (θ) r There are elements; the frequency of the signal transmitted by the k-th activated antenna in the nth pulse is f. c +m n,k Δf, f c Let m represent the carrier frequency, Δf represent the frequency increment, and m n,k The frequency increment is represented by T0; the signal period is represented by d. T d represents the spacing between adjacent antennas in the transmitting array. R p represents the spacing between adjacent antennas of the receiving array. n,k This represents the index of the k-th active antenna in the n-th pulse, and c represents the speed of light.

[0020] Introducing a reference matrix and a selection matrix, where the reference matrix is ​​the decoupled signal matrix and the selection matrix is ​​a 0-1 matrix determined based on the activated antenna and frequency, the selection matrix is ​​introduced as follows: The reference matrix is Where M represents the total number of selectable frequency points, and P represents the number of transmitting antennas;

[0021] The elements of the ((n-1)K+k)th row and ((n-1)MP+(m-1)P+p)th column in matrix S are as follows:

[0022]

[0023] The reference matrix X is shown below:

[0024]

[0025] in, Indicates the Kronecker product. They are defined as follows:

[0026]

[0027]

[0028]

[0029]

[0030] Where m = 1, 2, ..., M.

[0031] The design optimization problem involves decoupling the target parameters of the established receiver signal model using an introduced reference matrix and a selection matrix, resulting in the estimation steps for each target parameter, including:

[0032] By designing an optimization problem to optimize and reconstruct the reference matrix, decoupling of the target parameters is achieved. The designed optimization problem is as follows:

[0033]

[0034]

[0035] in, In this context, τ represents the square of the result of the Frobenius norm operation; τ represents the regularization parameter; Tr(·) represents the trace operation; and T(u) represents the Tolliterz matrix constructed based on vector u. Let be the vectors to be optimized for constructing the Topletz matrix. The matrix represents the frequency f. c The matched filtering results of each antenna related to +mΔf, [·]≥0 indicates that the matrix is ​​a positive semi-definite matrix;

[0036] The design uses decomposed decoupled atomic norms as sparse constraints, transforming the three-parameter sparse constraints into multiple two-dimensional sparse constraints, each of which is implemented using decomposed atomic norms.

[0037] After optimizing the parameter estimation problem, the optimized Topelitz matrix is ​​decomposed to obtain the eigenvectors of the two objective parameters, and parameter estimation is performed on the two parameters separately; firstly, based on the optimized u... m With v m Calculate the covariance matrix R of the two parameters. u and R v :

[0038]

[0039]

[0040] For R u and R v Feature decomposition is performed, and a multi-signal classification algorithm is used to estimate the angle and velocity parameters of the target, resulting in an estimated set of angles. and estimated velocity set

[0041] Based on the estimated target angle and velocity parameters, the distance parameters are obtained through optimization, while simultaneously achieving parameter matching for each target; the optimization problem is as follows:

[0042]

[0043] Where, β l This represents the target echo coefficient to be estimated.

[0044] The covariance matrix is ​​processed using a multi-signal classification algorithm to estimate the target's angle and velocity parameters, resulting in an estimated set of angles. and estimated velocity set

[0045] The covariance matrix R is processed using a multi-signal classification algorithm. u and R v The specific process is as follows:

[0046] For the covariance matrix R v Perform eigenvalue decomposition to obtain eigenvalues ​​λ1 < λ2 < ... < λ N , and the corresponding eigenvectors u1, u2, ..., u N Based on the eigenvectors λ1, λ2, ..., λ corresponding to the smaller NL eigenvalues. N-L The velocity estimation spectrum Sp(v) of the target is formed as follows:

[0047]

[0048] By determining the abscissa corresponding to the spectral peak in Sp(v), the velocity estimation set of the target can be obtained.

[0049] For the covariance matrix R u Using the same method as described above for the covariance matrix R v Using the same processing method, we obtain the angle set.

[0050] The present invention also provides a signal receiving terminal, which uses the aforementioned parameter decoupling and estimation method to estimate the parameters of the received signal.

[0051] The parameter decoupling and estimation method and terminal for a frequency-modulated continuous wave-based radar-communication integrated system provided by this invention have the following advantages compared to existing technologies:

[0052] 1. Due to the coupling between target distance, velocity, and angle, most existing parameter estimation methods are not applicable. Joint parameter estimation, on the other hand, results in excessively high computational complexity. This method first decouples the parameters and then estimates each parameter separately, significantly reducing computational complexity.

[0053] 2. When performing parameter estimation decoupling, directly using the receiving array steering vector to estimate the target angle will cause the virtual aperture to fail, impairing the accuracy of parameter estimation and the number of detectable targets. This method preserves the complete virtual aperture steering vector during parameter decoupling, without affecting the estimation accuracy or the number of detectable targets. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of an integrated radar and communication scenario.

[0055] Figure 2 The flowchart shows the parameter decoupling and estimation method provided by this invention.

[0056] Figure 3 The graph shows a comparison of the distance estimation performance of the proposed method with other algorithms under different signal-to-noise ratios.

[0057] Figure 4 The graph shows a comparison of the estimation performance of the proposed method with other algorithms under different signal-to-noise ratios.

[0058] Figure 5 The graph shows a comparison of the speed estimation performance of the proposed method with other algorithms under different signal-to-noise ratios. Detailed Implementation

[0059] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0060] A parameter decoupling and estimation method in a radar-communication integrated system, such as Figure 1 As shown, it includes the following steps:

[0061] Step 1: Establish a receiver signal model for the integrated radar-communication system based on frequency-modulated continuous wave (FM-CVT). In the FM-CVT-based integrated radar-communication system, establish a receiver signal model based on FM-CVT:

[0062]

[0063] in, The output signal is the signal in the ((n-1)K+k)th row of the output signal matrix, and the qth row is the signal in the output signal matrix. r The elements of the column represent the qth element. r The signal received by each antenna is the output signal obtained after matched filtering of the signal transmitted by the k-th active antenna in the n-th pulse; This indicates that the number of rows is NK and the number of columns is Q. r A complex matrix; N represents the number of transmitted pulses, K represents the number of active transmit antennas in each pulse, and Q... r Indicates the number of receiving antennas, L represents the number of targets, and r l v represents the distance to the l-th target. l Let θ represent the radial velocity of the l-th target relative to the array. l β represents the angle between the l-th target and the array normal. l The coefficients represent the output signal after echo processing of the l-th target; This represents additive white Gaussian noise, a r (r l )∈C NK×1 a represents the steering vector related to the target distance. v (v l )∈C NK×1 a represents the steering vector related to the target velocity. t (θ l )∈C NK×1 This represents the launch array steering vector related to the target angle. This represents the receiver array steering vector related to the target angle, where the elements are as follows:

[0064]

[0065]

[0066]

[0067]

[0068] a r,(n-1)K+k (r),a v,(n-1)K+k (v),a t,(n-1)K+k (θ) represent the guiding vector a r (r),a v (v),a t The (n-1)K+kth element in (θ), b r The qth in (θ) r There are elements. The frequency of the signal transmitted by the k-th activated antenna in the nth pulse is f.c +m n,k Δf, f c Let m represent the carrier frequency, Δf represent the frequency increment, and m n,k This represents the number of frequency increments. T0 represents the signal period, d T d represents the spacing between adjacent antennas in the transmitting array. R p represents the spacing between adjacent antennas of the receiving array. n,k This represents the index of the k-th active antenna in the n-th pulse, and c represents the speed of light.

[0069] Step 2: Introduce a reference matrix and a selection matrix. The reference matrix is ​​the decoupled signal matrix, and the selection matrix is ​​a 0-1 matrix determined based on the activated antenna and frequency. With reference matrix Where M represents the total number of selectable frequency points, and P represents the number of transmit antennas. The elements of the ((n-1)K+k)th row and ((n-1)MP+(m-1)P+p)th column in matrix S are shown below:

[0070]

[0071] The reference matrix X is shown below:

[0072]

[0073] in Indicates the Kronecker product. They are defined as follows:

[0074]

[0075]

[0076]

[0077]

[0078] Step 3: By introducing the reference matrix and selection matrix, the target parameters of the established receiver signal model are decoupled to obtain the estimates and matching of each target parameter. Since the phase change between adjacent elements in the output signal matrix Y is caused by multiple target parameters, these parameters are considered coupled and cannot be estimated separately. In the introduced reference matrix X, the phase change between adjacent elements is caused by only one target parameter; therefore, the target parameters in the reference matrix X are decoupled. The decoupling of the target parameters is achieved by designing an optimization problem to optimize and reconstruct the reference matrix. This optimization problem is as follows:

[0079]

[0080]

[0081] Where τ represents the regularization parameter, Tr(·) represents the trace operation, and T(u) represents the Topelitz matrix constructed based on vector u. This optimization problem is a positive semidefinite programming problem.

[0082] First, a decomposed decoupled atomic norm was designed and used as a sparse constraint, transforming the three-parameter sparse constraint into a multi-dimensional sparse constraint, with each two-dimensional sparse constraint implemented using a decomposed atomic norm. After optimizing the parameter estimation problem using the proposed decoupling method, the optimized Topletz matrix was decomposed to obtain the eigenvectors of the two objective parameters, and parameter estimation was performed on each of the two parameters. First, based on the optimized u... m With v m Calculate the covariance matrix R of the two parameters. u and R v :

[0083]

[0084]

[0085] For R u and R v Feature decomposition is performed, and the angle and velocity parameters of the target are estimated using the Multiple Signal Classification (MUSIC) algorithm.

[0086] Based on the estimated target angle and velocity parameters, the distance parameters are obtained through optimization, while simultaneously achieving parameter matching for each target. The optimization problem is as follows:

[0087]

[0088] Among them ||·|| F The expression in the text represents the operation of calculating the Frobenius norm. Represents the estimated set of angles. This represents the estimated set of velocities. By solving this optimization problem, the distance estimate of the target can be obtained, and the pairing of parameters can be achieved.

[0089] The following is a verification example of the present invention, applied to a radar-communication integrated system based on frequency-modulated continuous wave, which verifies that the present invention can achieve performance similar to the joint estimation method based on orthogonal matching pursuit (OMP), and the computational complexity and computation time are much lower than those of the joint estimation method based on the OMP algorithm.

[0090] Table 1 Simulation Parameters

[0091]

[0092] The simulation parameters are shown in Table 1, and the root mean square error curves of the parameter estimation results are shown in Table 1. Figure 3 , Figure 4 , Figure 5 As shown, the curves represented by DDANM & Matching represent the method proposed in this invention, while the curves represented by DDANM & HOOL represent a variation of the proposed method, where the matching part is implemented using a tensor decomposition method based on the HOOI method. The curve represented by OMP represents the joint estimation method based on the OMP algorithm. It can be seen that the performance of the two methods is generally comparable as the signal-to-noise ratio (SNR) changes. At low SNR, the OMP algorithm has an advantage, while at high SNR, the method proposed in this invention has an advantage.

[0093] Table 2 Calculation Time

[0094]

[0095] Table 2 shows the time required for a single parameter estimation using the proposed method and the joint estimation method based on orthogonal matching pursuit. The proposed method has a significant advantage in computation time.

[0096] This invention, using a radar-communication integrated system as an example, verifies the target parameter estimation capability achievable by the proposed method. To address the parameter coupling problem during parameter estimation within the system, a parameter decoupling method is proposed, allowing for the individual estimation of each parameter. Furthermore, to constrain sparsity, a decomposition and decoupling atomic norm method is proposed. The proposed method avoids the high computational complexity inherent in joint estimation and exhibits performance similar to that of joint parameter estimation using the orthogonal matching pursuit algorithm.

[0097] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A parameter decoupling and estimation method in a radar-communication integrated system, characterized in that, Includes the following steps: Establish a receiver signal model based on frequency-modulated continuous wave; A reference matrix and a selection matrix are introduced. The reference matrix is ​​the decoupled signal matrix, and the selection matrix is ​​a 0-1 matrix determined according to the activated antenna and frequency. The design optimization problem involves decoupling the target parameters of the established receiver signal model by introducing a reference matrix and a selection matrix, thereby obtaining estimates and matching of the target parameters. The design optimization problem involves decoupling the target parameters of the established receiver signal model using an introduced reference matrix and selection matrix, resulting in the estimation and matching steps for each target parameter, including: By designing an optimization problem to optimize and reconstruct the reference matrix, decoupling of the target parameters is achieved. The designed optimization problem is as follows: in, S is the output signal; S is the selection matrix; X is the reference matrix; The expression in the table represents the square of the result of the Frobenius norm operation. Represents the regularization parameter. T(u) represents the trace operation. m ) represents based on vector u m The constructed Topulitz matrix, T(v m ) represents based on vector v m The constructed Toplitz matrix, Let be the vectors to be optimized for constructing the Topletz matrix. Represents the relationship between the transmission frequency and the matrix. The relevant matched filtering results for each antenna, This indicates that the matrix is ​​a positive semi-definite matrix; This indicates the total number of selectable frequency points; Indicates the number of transmitting antennas; Indicates the number of transmitted pulses. Indicates the number of receiving antennas; The design uses decomposed decoupled atomic norms as sparse constraints, transforming the three-parameter sparse constraints into multiple two-dimensional sparse constraints, each of which is implemented using decomposed atomic norms.

2. The parameter decoupling and estimation method according to claim 1, characterized in that, In the step of establishing the receiver signal model based on frequency-modulated continuous wave, the established receiver signal model is as follows: in, For the output signal, the first signal of the output signal matrix is... Okay, number The elements of the column represent the first element. The signal received by the antenna is processed by the first antenna. The first pulse The output signal obtained after matched filtering of the transmitted signal from each active antenna; Indicates the number of rows. The number of columns is Complex matrices; Indicates the number of transmitted pulses. This indicates the number of transmit antennas activated in each pulse. Indicates the number of receiving antennas; Indicates the target quantity. Indicates the first Distance to each target Indicates the first The radial velocity of each target relative to the array, Indicates the first The angle between each target and the array normal. Indicates the first The coefficients of the output signal after echo processing of each target; This represents additive white Gaussian noise. This represents the steering vector related to the target distance. This represents the steering vector related to the target velocity. This represents the launch array steering vector related to the target angle. This represents the receiver array steering vector related to the target angle, where the elements are as follows: in These represent the target's distance, velocity, and angle with the array normal, respectively. Representing the guide vectors The first in One element, express The first in The element; the first The first pulse The frequency of the active antenna transmission signal is: , Indicates the carrier frequency. Indicates frequency increment. Indicates the number of frequency increments; Indicates the signal period. Indicates the spacing between adjacent antennas in the transmitting array. Indicates the spacing between adjacent antennas of the receiving array. Indicates the first The first pulse The index of each active antenna. It represents the speed of light.

3. The parameter decoupling and estimation method according to claim 2, characterized in that, Introducing a reference matrix and a selection matrix, where the reference matrix is ​​the decoupled signal matrix and the selection matrix is ​​a 0-1 matrix determined based on the activated antenna and frequency, the selection matrix is ​​introduced as follows: The reference matrix is ,in This indicates the total number of selectable frequency points. Indicates the number of transmitting antennas; Select Matrix The first in Okay, number The elements of the column are shown below: Reference Matrix As shown below: in, Indicates the Kronecker product. They are defined as follows: in, .

4. The parameter decoupling and estimation method according to claim 3, characterized in that, After optimizing the parameter estimation problem, the optimized Topulitz matrix is ​​decomposed to obtain the eigenvectors of the two objective parameters, and parameter estimation is performed on the two parameters separately. First, based on the optimized... and Calculate the covariance matrix of the two parameters. and : right and Feature decomposition is performed, and a multi-signal classification algorithm is used to estimate the angle and velocity parameters of the target, resulting in an estimated set of angles. and estimated velocity set ; Based on the estimated target angle and velocity parameters, the distance parameters are obtained through optimization, while simultaneously achieving parameter matching for each target; the optimization problem is as follows: in, This represents the target echo coefficient to be estimated.

5. The parameter decoupling and estimation method according to claim 4, characterized in that, The covariance matrix is ​​processed using a multi-signal classification algorithm to estimate the target's angle and velocity parameters, resulting in an estimated set of angles. and estimated velocity set : The covariance matrix is ​​processed using a multi-signal classification algorithm. and The specific process is as follows: For covariance matrix Perform eigenvalue decomposition to obtain eigenvalues. With the corresponding feature vector Based on the smaller of them The eigenvectors corresponding to each eigenvalue Forming the velocity estimation spectrum of the target : By determining The x-axis corresponding to the spectral peaks in the spectrum can be used to obtain the target velocity estimation set. ; For covariance matrix Using the same covariance matrix as described above Using the same processing method, we obtain the angle set. .

6. A signal receiving terminal, characterized in that, The parameter decoupling and estimation method described in any one of claims 1-5 is used to estimate the parameters of the received signal.

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