A STCA-MIMO radar unambiguous parameter estimation method based on dimension reduction MUSIC

Abstract

The application relates to a STCA-MIMO radar unambiguous parameter estimation method based on dimension reduction MUSIC, which comprises the following steps: obtaining echo signals at a target by using a STCA-MIMO radar signal emission model; obtaining a corresponding noise subspace according to the echo signals; obtaining an angle estimation matrix Q1 according to a receiving direction vector of the radar signal emission model at any point in space; constructing a spatial spectrum function according to the angle estimation matrix Q1, performing one-dimensional angle searching on the spatial spectrum function, and obtaining a target angle estimation value of the target angle; constructing a spatial spectrum estimation matrix Qv2 according to the target angle estimation value and the noise subspace; obtaining a transmitting end distance direction vector according to the spatial spectrum estimation matrix Qv2; averaging distance estimation values obtained after phase taking of the transmitting end distance direction vector, and obtaining a distance estimation value of the target. Through the technical scheme, the operation amount and time consumption are reduced, and the parameter estimation performance is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar, in particular to a STCA-MIMO radar unambiguous parameter estimation method based on dimension reduction MUSIC. BACKGROUND

[0002] STCA (Space-time Coding Array) is a new waveform diversity radar system, which can obtain additional degrees of freedom at the transmitting end by introducing a small time shift between the transmitting elements, thereby realizing full-space coverage. MIMO (Multiple-Input Mutiple-Output) antenna system radar can separate the signals at the transmitting end by transmitting mutually orthogonal signals between the transmitting elements and performing matched filtering at the receiving end. Compared with the traditional phased array radar, the MIMO radar has higher degrees of freedom, higher parameter estimation accuracy, stronger interference suppression capability and lower interception probability. In general, the MIMO radar has stronger environmental adaptability. Therefore, the combination of STCA and MIMO radar will have great application prospects. At present, the STCA-MIMO radar introduces a linearly increasing time shift between the elements, and uses the multiple signal classification algorithm (MUSIC) to perform spectral peak search to estimate the angle and distance of the target.

[0003] However, the high computational complexity of the traditional MUSIC algorithm leads to a long time for two-dimensional search, and the transmitting pattern of the STCA-MIMO radar presents periodicity in the distance dimension, which causes the transmitting pattern to have grating lobes in the distance dimension, resulting in a distance ambiguity problem when the STCA-MIMO radar measures the distance, thereby affecting the parameter estimation performance of the STCA-MIMO radar target. SUMMARY

[0004] In order to solve the above problems existing in the prior art, the present application provides a STCA-MIMO radar unambiguous parameter estimation method based on dimension reduction MUSIC.

[0005] According to a first aspect of an embodiment of the present application, a STCA-MIMO radar unambiguous parameter estimation method based on dimension reduction MUSIC is provided, and the method comprises:

[0006] The echo signal at the target is obtained using the STCA-MIMO radar signal transmission model; wherein the STCA-MIMO radar signal transmission model is pre-constructed.

[0007] The corresponding noise subspace is obtained based on the echo signal;

[0008] The angle estimation matrix Q1 is obtained based on the receiving steering vector at any point in space according to the radar signal transmission model.

[0009] A spatial spectrum function is constructed based on the angle estimation matrix Q1. A one-dimensional angle search is performed on the spatial spectrum function to obtain the target angle estimate of the target's location.

[0010] Construct a spatial spectrum estimation matrix Qv2 based on the target angle estimate and the noise subspace;

[0011] The transmitter range steering vector is obtained based on the spatial spectrum estimation matrix Qv2.

[0012] The distance estimate obtained by taking the phase of the range steering vector of the transmitter is averaged to obtain the distance estimate of the target.

[0013] Optionally, obtaining the corresponding noise subspace based on the echo signal includes:

[0014] The echo signal is digitally mixed and then matched filtered to obtain a preprocessed signal.

[0015] Obtain the covariance matrix of the preprocessed signal;

[0016] The noise subspace is obtained by performing eigenvalue decomposition on the covariance matrix.

[0017] Optionally, the step of constructing a spatial spectrum function based on the angle estimation matrix Q1, and performing a one-dimensional angle search on the spatial spectrum function to obtain the target angle estimate of the target's location includes:

[0018] Construct a spatial spectrum estimation matrix Qv1 based on the noise subspace and the angle estimation matrix Q1;

[0019] Inverting the spatial spectrum estimation matrix Qv1 yields the inverted spatial spectrum estimation matrix Qvt1;

[0020] The spatial spectrum function is constructed based on the inverted spatial spectrum estimation matrix Qvt1 and the unit vector. A one-dimensional angle search is performed on the spatial spectrum function to obtain the target angle estimate.

[0021] Optionally, constructing the spatial spectrum estimation matrix Qv2 based on the target angle estimate and the noise subspace includes:

[0022] The target angle estimation value is used to obtain the radar's transmitting angle steering vector and receiving angle steering vector;

[0023] The angle estimation matrix Q2 is obtained by taking the Krock inner product of the receiving end steering vector and the identity matrix.

[0024] The spatial spectrum estimation matrix Qv2 is constructed based on the noise subspace and the angle estimation matrix Q2.

[0025] Optionally, the STCA-MIMO radar signal transmission model includes a transmitting array and a receiving array, wherein the transmitting array includes M transmitting elements and the receiving array includes N receiving elements. Both the transmitting array and the receiving array are uniformly spaced linear arrays, and the element spacing of the transmitting array and the receiving array is half a wavelength. The transmitted signal of the transmitting element is a linear frequency modulated signal.

[0026] Optionally, the transmitted signal of the transmitting array is represented as follows:

[0027] s m (t)=l m g(t-(m-1)Δt m );

[0028] Among them, s m (t) represents the transmitted signal of the m-th transmitting element of the transmitting array, m∈[1,2,...,m,...,M], M is the number of transmitting elements, and t is the time. m Δt represents the coding coefficient of the m-th transmitting element. m Let g(·) be the exponential time shift of the m-th transmitting element, g(·) represent the linear frequency modulated signal, and μ represent the frequency modulation coefficient.

[0029] Optionally, the exponential time shift of the m-th transmitting element is represented as follows:

[0030] Δt m = (b^(m-1)-1)*Δt;

[0031] Wherein, Δt is a preset reference time shift, and b^(m-1) represents the time shift coefficient of the (m-1)th transmitting element.

[0032] Optionally, the transmitter distance steering vector is represented as follows:

[0033]

[0034] Where a(r0) represents the range steering vector of the transmitter, Q vt2 The spatial spectrum estimation matrix Qv2 represents the inverse, e1 is a unit vector, and H represents the conjugate transpose. This is the angular guidance vector for the transmitter.

[0035] Optionally, the distance estimate of the target location is expressed as follows:

[0036]

[0037] in, Let g0(m,1) represent the distance estimate of the target, where m ∈ [1,2,...,m,...,M], and M is the number of the transmitting elements. m This represents the time shift coefficient of the m-th transmitting element.

[0038] Optionally, the distance estimate of the m-th transmitting element is expressed as follows:

[0039]

[0040] Where angle(·) is the phase function, a(r0) is the distance steering vector of the transmitter, μ is the frequency modulation coefficient, c is the speed of light, and Δt m It represents the exponential time shift of the m-th transmitting element.

[0041] The technical solution provided by this invention may include the following beneficial effects:

[0042] The above technical solution pre-constructs an STCA-MIMO radar signal transmission model and obtains the transmitter range steering vector based on the spatial spectrum estimation matrix Qv2. Then, by averaging the range estimates obtained after taking the phase of the transmitter range steering vector, the range estimate of the target is obtained. This effectively solves the problems of high computational load and long time consumption when using the traditional MUSIC algorithm for parameter estimation in STCA-MIMO radar. Furthermore, when the time shift is linear, grating lobes appear in the range dimension of the transmission pattern, leading to range ambiguity during ranging. Compared to current STCA-MIMO radar target parameter estimation methods, this solution reduces the high computational load of the traditional MUSIC algorithm, solves the range ambiguity problem caused by linear time shift, and significantly improves the target parameter estimation performance.

[0043] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0044] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the following detailed description to explain the invention, but do not constitute a limitation thereof. In the drawings:

[0045] Figure 1 This is a flowchart illustrating an STCA-MIMO radar unambiguous parameter estimation method based on a dimension-reduced MUSIC, according to an exemplary embodiment.

[0046] Figure 2a This is a schematic diagram of the spatial spectrum of an STCA-MIMO radar under linear time shift, according to an exemplary embodiment.

[0047] Figure 2b This is a schematic diagram of the spatial spectrum of an STCA-MIMO radar under exponential time shift, according to an exemplary embodiment.

[0048] Figure 3a This is a schematic diagram of the STCA-MIMO radar spatial spectrum under a linear time shift, according to an exemplary embodiment.

[0049] Figure 3b This is a schematic diagram of the spatial spectrum of STCA-MIMO radar under an exponential time shift, according to an exemplary embodiment.

[0050] Figure 4 This is a comparison chart of the angle estimation errors of the conventional MUSIC algorithm and the present invention under different signal-to-noise ratios, according to an exemplary embodiment.

[0051] Figure 5 This is a comparison chart of the distance estimation errors of the conventional MUSIC algorithm and the present invention under different signal-to-noise ratios, according to an exemplary embodiment. Detailed Implementation

[0052] Figure 1 This is a flowchart illustrating an unambiguous parameter estimation method for STCA-MIMO radar based on dimensionality-reduced MUSIC, according to an exemplary embodiment. Figure 1 As shown, the method includes the following steps.

[0053] S101. Obtain the echo signal at the target using the STCA-MIMO radar signal transmission model; wherein, the STCA-MIMO radar signal transmission model is pre-constructed.

[0054] Optionally, the STCA-MIMO radar signal transmission model used in this invention includes a transmitting array and a receiving array, wherein the transmitting array includes M transmitting elements and the receiving array includes N receiving elements. Both the transmitting and receiving arrays are uniformly spaced linear arrays, and the element spacing of both the transmitting and receiving arrays is half a wavelength. The transmitted signal of the transmitting elements is a linear frequency modulated signal.

[0055] Optionally, the transmitted signal of the transmitting array is represented as follows:

[0056] s m (t)=l m g(t-(m-1)Δt m );

[0057] Among them, s m (t) represents the transmitted signal of the m-th transmitting element in the transmitting array, where m ∈ [1, 2, ..., m, ..., M], M is the number of transmitting elements, and t is the time interval. m Δt represents the coding coefficient of the m-th transmitting element. m Let g(·) be the exponential time shift of the m-th transmitting element, g(·) represent the linear frequency modulated signal, and μ represent the frequency modulation coefficient.

[0058] The exponential time shift of the m-th transmitting element is represented as follows:

[0059] Δt m = (b^(m-1)-1)*Δt;

[0060] Where Δt is the preset reference time shift, and b^(m-1) represents the time shift coefficient of the (m-1)th transmitting element.

[0061] Furthermore, by selecting an appropriate exponential time shift for the STCA-MIMO radar, the echo signal at the target can be represented as follows:

[0062]

[0063] Where e represents the natural exponent, j represents the imaginary unit, θ0 represents the angle between the target's position and the radar signal transmission model, f0 represents the signal carrier frequency, R0 represents the distance between the target and the signal transmission model, λ represents the wavelength of the transmitted signal, c represents the speed of light in vacuum, d represents the spacing between the transmitting and receiving array elements, and y n (t,θ0) represents the target echo signal received by the nth receiving element at time t under the STCA-MIMO radar system, where n = 1, 2, ..., n, ..., N, and the transmitted signals of each transmitting element are mutually orthogonal.

[0064] S102. Obtain the corresponding noise subspace based on the echo signal.

[0065] Optionally, S102 may include:

[0066] The echo signal is digitally mixed and then matched filtered to obtain the preprocessed signal.

[0067] Obtain the covariance matrix of the preprocessed signal;

[0068] The noise subspace is obtained by eigenvalue decomposition of the covariance matrix.

[0069] It is understandable that the preprocessed signal obtained by digitally mixing the echo signal and then performing matched filtering can be represented as follows:

[0070]

[0071] Where y represents the preprocessed signal, y n (t,θ0) represents the preprocessed signal obtained after digital mixing and matched filtering of the echo signal received by the nth (n=1,2,...,N) receiving array element, and β represents the complex scattering coefficients. The Kronecker product operation is represented, b(θ0) represents the receive steering vector of the STCA-MIMO radar at the target under the selected exponential time shift, a(R0,θ0) represents the transmit steering vector of the STCA-MIMO radar at the target under the selected exponential time shift, T represents the transpose operation, and n represents the noise vector.

[0072] Furthermore, the covariance matrix obtained from the preprocessed signal can be represented as follows:

[0073] R = E{yy H};

[0074] Where R represents the covariance matrix, E{·} represents the expectation operation, and H represents the conjugate transpose;

[0075] The covariance matrix is ​​decomposed into eigenvalues ​​as follows:

[0076]

[0077] Among them, V s Λ represents the matrix composed of eigenvectors corresponding to the large eigenvalues, i.e., the signal subspace of the preprocessed signal. s V represents a diagonal matrix composed of large eigenvalues. n The matrix representing the eigenvectors corresponding to the small eigenvalues, i.e., the noise subspace of the echo preprocessed signal, Λ n This represents a diagonal matrix composed of small eigenvalues.

[0078] S103. Obtain the angle estimation matrix Q1 based on the receiving steering vector at any point in space according to the radar signal transmission model.

[0079] Understandably, the angle estimation matrix Q1 is obtained by calculating the Krock inner product of the receiving steering vector of the STCA-MIMO radar at any point in space and the M×M dimensional unit diagonal matrix. The calculation process can be referenced in the following formula:

[0080]

[0081] Where Q1 is the angle estimation matrix Q1, b(θ) represents the receiving guidance vector of the selected STCA-MIMO radar at any point in space under exponential time shift, I M Describe an M×M unit diagonal matrix. This represents the Kronecker product operation.

[0082] S104. Construct a spatial spectrum function based on the angle estimation matrix Q1, and perform a one-dimensional angle search on the spatial spectrum function to obtain the target angle estimate.

[0083] Optionally, S104 may include:

[0084] Construct the spatial spectrum estimation matrix Qv1 based on the noise subspace and the angle estimation matrix Q1;

[0085] Invert the spatial spectrum estimation matrix Qv1 to obtain the inverted spatial spectrum estimation matrix Qvt1;

[0086] A spatial spectrum function is constructed based on the inverted spatial spectrum estimation matrix Qvt1 and the unit vector. A one-dimensional angle search is then performed on the spatial spectrum function to obtain the target angle estimate.

[0087] Understandably, the spatial spectrum estimation matrix Qv1 can be represented as follows:

[0088]

[0089] Among them, Q v1 Let Qv1 be the spatial spectrum estimation matrix.

[0090] The process of estimating the spatial spectrum matrix Qv1 can be represented as follows:

[0091] Q vt1 =Q v1 -1 ;

[0092] The spatial spectral function can be represented as follows:

[0093] P(θ=e1) T *Q vt1 *e1;

[0094] Where P(θ) is the spatial spectral function and e1 is the unit vector, the target angle estimate can be obtained by performing a one-dimensional angle search according to the above formula.

[0095] S105. Construct the spatial spectrum estimation matrix Qv2 based on the target angle estimate and the noise subspace.

[0096] Optionally, S105 may include:

[0097] The target angle estimation value is used to obtain the radar's transmitting angle steering vector and receiving angle steering vector;

[0098] The angle estimation matrix Q2 is obtained by using the Krock inner product of the receiver steering vector and the identity matrix.

[0099] The spatial spectrum estimation matrix Qv2 is constructed based on the noise subspace and the angle estimation matrix Q2.

[0100] For example, the radar's transmitting angle steering vector and receiving steering vector can be represented as follows:

[0101]

[0102] in, Indicates the transmitter angle steering vector. This indicates the receiving guide vector.

[0103] The angle estimation matrix Q2 can then be represented as follows:

[0104]

[0105] The spatial spectrum estimation matrix Qv2 can be represented as follows:

[0106]

[0107] S106. Obtain the transmitter range steering vector based on the spatial spectrum estimation matrix Qv2.

[0108] Understandably, the spatial spectrum estimation matrix Qv2 needs to be inverted before calculating the transmitter range steering vector, which can be represented as follows:

[0109] Q vt2 =Q v2 -1 ;

[0110] Among them, Q vt2 Find the inverse spatial spectrum estimation matrix Qv2.

[0111] The transmitter range steering vector is represented as follows:

[0112]

[0113] Where a(r0) represents the transmitter range steering vector, Q vt2 Qv2 represents the inverse spatial spectral estimation matrix, e1 is a unit vector, and H denotes the conjugate transpose. This is the angular guidance vector for the transmitter.

[0114] S107. Average the range estimates obtained after taking the phase of the range steering vector at the transmitter to obtain the range estimate of the target location.

[0115] Optionally, the estimated distance to the target is expressed as follows:

[0116]

[0117] in, Let g0(m,1) represent the distance estimate of the target location, where m∈[1,2,...,m,...,M] and M is the number of transmission elements. m This represents the time shift coefficient of the m-th transmitting element.

[0118] The distance estimate for the m-th transmitting element is expressed as follows:

[0119]

[0120] Where angle(·) is the phase function, a(r0) is the transmitter distance steering vector, μ is the frequency modulation coefficient, c is the speed of light, and Δt m It represents the exponential time shift of the m-th transmitting element.

[0121] In one embodiment, the following simulation experiment is performed according to the present invention:

[0122] Simulation Experiment 1: Parameter Estimation in a Single-Objective Scene

[0123] Given a target at a distance of 10km and an angle of 10°. Figure 2a This is a schematic diagram of the spatial spectrum of an STCA-MIMO radar under linear time shift, according to an exemplary embodiment. Figure 2b This is a schematic diagram of the spatial spectrum of an STCA-MIMO radar under exponential time shift, according to an exemplary embodiment. Figure 2a It can be seen that the reduced-dimensional music space spectrum of the STCA-MIMO radar corresponding to linear time shift has two peaks, and ambiguity appears in the range dimension. Figure 2b The reduced-dimensional MUSIC spatial spectrum of the STCA-MIMO radar corresponding to exponential time shift has only one peak at the target, effectively solving the range ambiguity problem of parameter estimation of STCA-MIMO radar under traditional linear time shift.

[0124] Simulation Experiment 2: Parameter Estimation in Multi-Objective Scenarios

[0125] Given three targets, each 10 km away, with angles of 10°, 11°, and 12° respectively. Figure 3a This is a schematic diagram of the spatial spectrum of STCA-MIMO radar under linear time shift, according to an exemplary embodiment. Figure 3b This is a schematic diagram of the spatial spectrum of STCA-MIMO radar under exponential time shift, illustrated according to an exemplary embodiment. Figure 3a It can be seen that the reduced-dimensional music space spectrum of the STCA-MIMO radar corresponding to linear time shift has two sets of peaks, and ambiguity appears in the range dimension. Figure 3b The reduced-dimensional MUSIC spatial spectrum of STCA-MIMO radar corresponding to exponential time shift has only one set of peaks at the target, effectively solving the range ambiguity problem of parameter estimation of STCA-MIMO radar under traditional linear time shift.

[0126] Simulation Experiment 3: This simulation uses a uniform linear array with 16 array elements, target position parameters (0°, 10km), array element spacing of 0.015m, time increment, i is the array element number, reference carrier frequency of 10GHz, number of snapshots of 200, and 100 Monte Carlo experiments are conducted. Figure 4 This is a comparison chart of the angle estimation errors of the traditional MUSIC algorithm and the present invention under different signal-to-noise ratios, according to an exemplary embodiment. Figure 5 This is a comparison chart of the distance estimation errors of the conventional MUSIC algorithm and the present invention under different signal-to-noise ratios, according to an exemplary embodiment. Figure 4 , Figure 5 The root mean square error (RMSE) of the traditional MUSIC algorithm and the proposed method for parameter estimation was compared under different signal-to-noise ratios (SNRs), ranging from -20 dB to 10 dB with a step size of 3 dB. It can be seen that as the SNR increases, the RMSE of both algorithms decreases, demonstrating excellent target azimuth estimation performance. Figure 4 It can be seen that the azimuth estimation performance of the two methods is approximately the same; from Figure 5 It can be seen that, regarding the target distance estimation errors of the two algorithms, the traditional MUSIC algorithm shows a decreasing trend in distance RMSE as the SNR increases, while the dimensionality reduction MUSIC algorithm's estimation performance is slightly worse than that of the traditional MUSIC algorithm, but it also shows a decreasing trend as the SNR increases, although it is not as accurate as the traditional MUSIC algorithm. Overall, however, both algorithms have good target azimuth and spatial distance estimation performance, and the computational complexity of the dimensionality reduction MUSIC algorithm is much lower than that of the traditional MUSIC algorithm.

[0127] This invention effectively solves the problems of high computational load and long time consumption when STCA-MIMO radar uses the traditional MUSIC algorithm for parameter estimation, and the appearance of grating lobes in the range dimension of its transmission pattern when the time shift changes linearly, which leads to range ambiguity during ranging. Compared with the current parameter estimation method, this invention reduces the high computational load, solves the range ambiguity problem caused by linear time shift, and significantly improves the target parameter estimation performance.

[0128] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0129] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction.

[0130] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

Claims

1. A method for unambiguous parameter estimation of STCA-MIMO radar based on dimensionality-reduced MUSIC, characterized in that, The method includes: The echo signal at the target is obtained using the STCA-MIMO radar signal transmission model; wherein the STCA-MIMO radar signal transmission model is pre-constructed. The corresponding noise subspace is obtained based on the echo signal; The angle estimation matrix Q1 is obtained based on the receiving steering vector at any point in space according to the radar signal transmission model. A spatial spectrum function is constructed based on the angle estimation matrix Q1. A one-dimensional angle search is performed on the spatial spectrum function to obtain the target angle estimate of the target's location. Construct a spatial spectrum estimation matrix Qv2 based on the target angle estimate and the noise subspace; The transmitter range steering vector is obtained based on the spatial spectrum estimation matrix Qv2. The distance estimate obtained by taking the phase of the range steering vector of the transmitter is averaged to obtain the distance estimate of the target.

2. The unambiguous parameter estimation method for STCA-MIMO radar based on dimensionality reduction MUSIC as described in claim 1, characterized in that, The step of obtaining the corresponding noise subspace based on the echo signal includes: The echo signal is digitally mixed and then matched filtered to obtain a preprocessed signal. Obtain the covariance matrix of the preprocessed signal; The noise subspace is obtained by performing eigenvalue decomposition on the covariance matrix.

3. The unambiguous parameter estimation method for STCA-MIMO radar based on dimensionality reduction MUSIC as described in claim 1, characterized in that, The step of constructing a spatial spectrum function based on the angle estimation matrix Q1, and performing a one-dimensional angle search on the spatial spectrum function to obtain the target angle estimate of the target's location includes: Construct a spatial spectrum estimation matrix Qv1 based on the noise subspace and the angle estimation matrix Q1; Inverting the spatial spectrum estimation matrix Qv1 yields the inverted spatial spectrum estimation matrix Qvt1; The spatial spectrum function is constructed based on the inverted spatial spectrum estimation matrix Qvt1 and the unit vector. A one-dimensional angle search is performed on the spatial spectrum function to obtain the target angle estimate.

4. The unambiguous parameter estimation method for STCA-MIMO radar based on dimensionality reduction MUSIC as described in claim 1, characterized in that, The step of constructing the spatial spectrum estimation matrix Qv2 based on the target angle estimate and the noise subspace includes: The target angle estimation value is used to obtain the radar's transmitting angle steering vector and receiving angle steering vector; The angle estimation matrix Q2 is obtained by taking the Krock inner product of the receiving end steering vector and the identity matrix. The spatial spectrum estimation matrix Qv2 is constructed based on the noise subspace and the angle estimation matrix Q2.

5. The unambiguous parameter estimation method for STCA-MIMO radar based on dimensionality reduction MUSIC as described in claim 1, characterized in that, The STCA-MIMO radar signal transmission model includes a transmitting array and a receiving array. The transmitting array includes M transmitting elements, and the receiving array includes N receiving elements. Both the transmitting array and the receiving array are uniformly spaced linear arrays, and the element spacing of the transmitting array and the receiving array is half a wavelength. The transmitted signal of the transmitting element is a linear frequency modulated signal.

6. The method for unambiguous parameter estimation of STCA-MIMO radar based on dimensionality reduction MUSIC according to claim 5, characterized in that, The transmitted signal of the transmitting array is represented as follows: s m (t)=l m g(t-(m-1)Δt m ); Among them, s m (t) represents the transmitted signal of the m-th transmitting element of the transmitting array, m∈[1,2,...,M], M is the number of transmitting elements, and t is the time. m Δt represents the coding coefficient of the m-th transmitting element. m Let g(·) be the exponential time shift of the m-th transmitting element, and g(·) represent the linear frequency modulated signal.

7. The method for unambiguous parameter estimation of STCA-MIMO radar based on dimensionality reduction MUSIC as described in claim 6, characterized in that, The exponential time shift of the m-th transmitting element is represented as follows: Δt m =(b^(m-1)-1)*Δt; Wherein, Δt is a preset reference time shift, and b^(m-1) represents the time shift coefficient of the (m-1)th transmitting element.

8. The unambiguous parameter estimation method for STCA-MIMO radar based on dimensionality reduction MUSIC as described in claim 1, characterized in that, The transmitter distance steering vector is represented as follows: Where a(r0) represents the range steering vector of the transmitter, Q vt2 The spatial spectrum estimation matrix Qv2 represents the inverse, e1 is a unit vector, and H represents the conjugate transpose. This is the angular guidance vector for the transmitter.

9. The unambiguous parameter estimation method for STCA-MIMO radar based on dimensionality reduction MUSIC as described in claim 8, characterized in that, The estimated distance to the target is expressed as follows: in, Let g0(m,1) represent the distance estimate of the target, where m ∈ [1,2,...,M], and M is the number of the transmitting elements. m This represents the time shift coefficient of the m-th transmitting element.

10. The method for unambiguous parameter estimation of STCA-MIMO radar based on dimensionality reduction MUSIC according to claim 9, characterized in that, The distance estimate for the m-th transmitting element is expressed as follows: Where angle(·) is the phase function, a(r0) is the distance steering vector of the transmitter, μ is the frequency modulation coefficient, c is the speed of light, and Δt m It represents the exponential time shift of the m-th transmitting element.

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