STCA-MIMO radar fuzzy-parameter-free estimation method based on dimensionality reduction MUSIC

Abstract

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

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and in particular to an unambiguous parameter estimation method of STCA-MIMO radar based on dimension reduction MUSIC. Background Art

[0002] As a new waveform diversity radar system, STCA (Space-time Coding Array) can obtain additional degrees of freedom at the transmitting end by introducing a small time shift between transmitting array elements, thereby achieving full airspace coverage. MIMO (Multiple-Input Mutiple-Output) antenna system radar can separate the transmitting end signals by transmitting orthogonal signals between transmitting array elements and performing matched filtering at the receiving end. Compared with traditional phased array radars, MIMO radars have higher degrees of freedom, higher parameter estimation accuracy, stronger interference suppression capabilities and lower interception probability. In short, MIMO radars have stronger environmental adaptability. It can be seen that the combination of STCA and MIMO radar will have a very large application prospect. At present, STCA-MIMO radar introduces a linearly increasing time shift between array elements, and uses the Multiple Signal Classification (MUSIC) algorithm to search for spectrum peaks to estimate the angle and distance of the target. The MUSIC algorithm is a high-resolution Direction Of Arrival (DOA) estimation algorithm that overcomes the disadvantage of low resolution of ordinary beamforming. The MUSIC algorithm is based on the second-order statistics of the signal (the covariance matrix of the signal) and uses the orthogonality of the signal subspace and the noise subspace to obtain the DOA estimation of the signal.

[0003] However, the high amount of computation generated by the traditional MUSIC algorithm results in a long time consumption for two-dimensional search, and the transmission pattern of the STCA-MIMO radar is periodic in the distance dimension, which causes grating lobes to appear in the transmission pattern in the distance dimension. This causes distance ambiguity in the STCA-MIMO radar when measuring distance, thus affecting the parameter estimation performance of the STCA-MIMO radar target. Summary of the invention

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

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

[0006] Acquiring an echo signal at a target using an STCA-MIMO radar signal transmission model; wherein the STCA-MIMO radar signal transmission model is pre-constructed;

[0007] Obtaining a corresponding noise subspace according to the echo signal;

[0008] Obtaining an angle estimation matrix Q1 according to a receiving steering vector of the radar signal transmission model at any point in space;

[0009] Constructing a spatial spectrum function according to the angle estimation matrix Q1, performing a one-dimensional angle search on the spatial spectrum function, and obtaining a target angle estimation value of the angle at which the target is located;

[0010] Construct a spatial spectrum estimation matrix Qv2 according to the target angle estimation value and the noise subspace;

[0011] Obtaining a transmitting end distance steering vector according to the spatial spectrum estimation matrix Qv2;

[0012] The distance estimation values ​​obtained after taking the phase of the transmitting end distance steering vector are averaged to obtain the distance estimation value of the target.

[0013] Optionally, obtaining a corresponding noise subspace according to the echo signal includes:

[0014] Performing digital mixing on the echo signal and then performing matched filtering to obtain a preprocessed signal;

[0015] Obtaining a covariance matrix of the preprocessed signal;

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

[0017] Optionally, constructing a spatial spectrum function according to the angle estimation matrix Q1, performing a one-dimensional angle search on the spatial spectrum function, and obtaining a target angle estimation value at the angle where the target is located includes:

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

[0019] Inverting the spatial spectrum estimation matrix Qv1 to obtain an inverted spatial spectrum estimation matrix Qvt1;

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

[0021] Optionally, constructing a spatial spectrum estimation matrix Qv2 according to the target angle estimation value and the noise subspace includes:

[0022] Using the target angle estimation value, a transmitting end angle steering vector and a receiving end steering vector of the radar are obtained;

[0023] Obtaining the angle estimation matrix Q2 according to the Crocker inner product of the receiving end steering vector and the unit matrix;

[0024] The spatial spectrum estimation matrix Qv2 is constructed according to 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 array elements, and the receiving array includes N receiving array elements, the transmitting array and the receiving array are both uniform equidistant linear arrays, the array element spacings of the transmitting array and the receiving array are both half-wavelength width, and the transmission signal of the transmitting array element is a linear frequency modulation signal.

[0026] Optionally, the transmission signal of the transmission array is expressed as follows:

[0027] s m (t) = l m g(t-(m-1)Δt m );

[0028] Among them, s m (t) represents the transmission signal of the mth transmission element of the transmission array, m∈[1,2,...,m,...,M], M is the number of the transmission elements, t is the time, l m represents the coding coefficient of the mth transmitting array element, Δt m is the exponential time shift of the mth transmitting array element, g(·) represents the linear frequency modulation signal, and μ represents the frequency modulation coefficient.

[0029] Optionally, the exponential time shift of the mth transmitting array element is expressed as follows:

[0030] Δt m =(b^(m-1)-1)*Δt;

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

[0032] Optionally, the transmitting end distance steering vector is expressed as follows:

[0033]

[0034] Wherein, a(r0) represents the distance steering vector of the transmitting end, Q vt2 represents the inverse of the spatial spectrum estimation matrix Qv2, e1 is a unit vector, H represents the conjugate transpose, is the angle steering vector of the transmitter.

[0035] Optionally, the estimated distance value of the target is expressed as follows:

[0036]

[0037] in, is the estimated distance to the target, g0(m,1) represents the estimated distance to the mth transmitting array element, m∈[1,2,...,m,...,M], M is the number of transmitting array elements, b m Represents the time shift coefficient of the mth transmitting array element.

[0038] Optionally, the distance estimation value of the mth transmitting array element is expressed as follows:

[0039]

[0040] Wherein, angle(·) is the phase function, a(r0) is the distance steering vector of the transmitting end, μ is the frequency modulation coefficient, c is the speed of light, Δt m is the exponential time shift of the mth transmitting array element.

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

[0042] Through the above technical scheme, the STCA-MIMO radar signal transmission model is pre-constructed, and the transmitter distance steering vector is obtained according to the spatial spectrum estimation matrix Qv2; then the distance estimation value obtained after taking the phase of the transmitter distance steering vector is averaged to obtain the distance estimation value of the target, which effectively solves the problem that when the STCA-MIMO radar uses the traditional MUSIC algorithm for parameter estimation, the calculation amount is high and the time consumption is long, and when the time shift changes linearly, the transmission radiation pattern will have grating lobes in the distance dimension, resulting in distance ambiguity during ranging. Compared with the current STCA-MIMO radar target parameter estimation method, the high calculation amount of the traditional MUSIC algorithm is reduced, the distance ambiguity problem caused by linear time shift is solved, and the target parameter estimation performance is significantly improved.

[0043] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present invention but do not constitute a limitation of the present invention. In the accompanying drawings:

[0045] Figure 1 The present invention is a flowchart of a method for unambiguous parameter estimation of STCA-MIMO radar based on dimension reduction MUSIC according to an exemplary embodiment.

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

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

[0048] Figure 3a FIG. 4 is a schematic diagram of STCA-MIMO radar spatial spectrum under another linear time shift according to an exemplary embodiment.

[0049] Figure 3b FIG. 4 is a schematic diagram of STCA-MIMO radar spatial spectrum under another exponential time shift according to an exemplary embodiment.

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

[0051] Figure 5 It is a comparison diagram of distance estimation errors of a traditional MUSIC algorithm and the present invention under different signal-to-noise ratios according to an exemplary embodiment. DETAILED DESCRIPTION

[0052] Figure 1 is a flow chart of a method for unambiguous parameter estimation of STCA-MIMO radar based on dimension reduction MUSIC according to an exemplary embodiment, such as Figure 1 As shown, the method includes the following steps.

[0053] S101. Acquire an echo signal at a target using a 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 the present invention includes a transmitting array and a receiving array, wherein the transmitting array includes M transmitting array elements, the receiving array includes N receiving array elements, the transmitting array and the receiving array are both uniform equidistant linear arrays, the array element spacing of the transmitting array and the receiving array are both half-wavelength width, and the transmission signal of the transmitting array element is a linear frequency modulation signal.

[0055] Optionally, the transmit signal of the transmit 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 transmission signal of the mth transmitting element of the transmitting array, m∈[1,2,...,m,...,M], M is the number of transmitting elements, t is the time, l m represents the coding coefficient of the mth transmitting array element, Δt m is the exponential time shift of the mth transmitting array element, g(·) represents the linear frequency modulation signal, and μ represents the frequency modulation coefficient.

[0058] The exponential time shift of the mth transmitting array element is expressed as follows:

[0059] Δt m =(b^(m-1)-1)*Δt;

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

[0061] Furthermore, by selecting a suitable 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 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 array elements and the receiving array elements, and y n (t,θ0) is the target echo signal received by the nth receiving array element at time t under the STCA-MIMO radar system, n = 1, 2, …, n, …, N, and the transmission signals of each transmitting array element are orthogonal to each other.

[0064] S102: Obtain a corresponding noise subspace according to the echo signal.

[0065] Optionally, S102 may include:

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

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

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

[0069] It can be understood that the pre-processed signal obtained by digitally mixing the echo signal and then performing matched filtering can be expressed as follows:

[0070]

[0071] Among them, y represents the preprocessed signal, y n (t,θ0) represents the pre-processed signal obtained after the echo signal received by the nth (n=1,2,...,N) receiving array element is processed by digital mixing and matched filtering, β represents the complex scattering coefficient, represents the Kronecker product operation, b(θ0) represents the receiving steering vector of the STCA-MIMO radar at the target under the selected exponential time shift, a(R0,θ0) represents the transmitting 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 expressed 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 eigenvalue decomposition of the covariance matrix is ​​expressed as follows:

[0076]

[0077] Among them, V s Represents the matrix composed of eigenvectors corresponding to large eigenvalues, that is, the signal subspace of the preprocessed signal, Λ s represents a diagonal matrix composed of large eigenvalues, V n represents the matrix composed of eigenvectors corresponding to small eigenvalues, that is, the noise subspace of the echo preprocessing signal, Λ n represents a diagonal matrix of small eigenvalues.

[0078] S103, obtaining an angle estimation matrix Q1 according to a receiving steering vector of a radar signal transmission model at any point in space.

[0079] It can be understood that the angle estimation matrix Q1 is obtained by calculating the Crocker 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 refer to the following formula:

[0080]

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

[0082] S104 , constructing a spatial spectrum function according to the angle estimation matrix Q1 , performing a one-dimensional angle search on the spatial spectrum function, and obtaining a target angle estimation value of the angle where the target is located.

[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 according to the inverted spatial spectrum estimation matrix Qvt1 and the unit vector, and a one-dimensional angle search is performed on the spatial spectrum function to obtain a target angle estimation value.

[0087] It can be understood that the spatial spectrum estimation matrix Qv1 can be expressed as follows:

[0088]

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

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

[0091] Q vt1 =Q v1 -1 ;

[0092] The spatial spectrum function can be expressed as follows:

[0093] P(θ)=e1 T *Q vt1 *e1;

[0094] Among them, P(θ) is the spatial spectrum function, e1 is the unit vector, and the target angle estimation value can be obtained by performing a one-dimensional angle search according to the above formula:

[0095] S105 . Construct a spatial spectrum estimation matrix Qv2 according to the target angle estimation value and the noise subspace.

[0096] Optionally, S105 may include:

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

[0098] According to the Crocker inner product of the receiving end steering vector and the unit matrix, the angle estimation matrix Q2 is obtained;

[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 end angle steering vector and receiving end steering vector can be expressed as follows:

[0101]

[0102] in, represents the transmitting end angle steering vector, Represents the receive steering vector.

[0103] Then the angle estimation matrix Q2 can be expressed as follows:

[0104]

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

[0106]

[0107] S106. Obtain a transmitting end distance steering vector according to the spatial spectrum estimation matrix Qv2.

[0108] It can be understood that before calculating the transmitting end distance steering vector, the spatial spectrum estimation matrix Qv2 needs to be inverted, which can be expressed as follows:

[0109] Q vt2 =Q v2 -1 ;

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

[0111] The transmitter distance steering vector is expressed as follows:

[0112]

[0113] Where a(r0) represents the distance steering vector of the transmitter, Q vt2 represents the inverse spatial spectrum estimation matrix Qv2, e1 is the unit vector, H represents the conjugate transpose, is the angle steering vector of the transmitter.

[0114] S107, averaging the distance estimation values ​​obtained after taking the phase of the distance steering vector at the transmitting end to obtain the distance estimation value of the target.

[0115] Optionally, the distance estimate of the target is expressed as follows:

[0116]

[0117] in, is the estimated distance to the target, g0(m,1) represents the estimated distance to the mth transmitting array element, m∈[1,2,...,m,...,M], M is the number of transmitting array elements, b m Represents the time shift coefficient of the mth transmitting array element.

[0118] The distance estimate of the mth transmitting array element is expressed as follows:

[0119]

[0120] Where angle(·) is the phase function, a(r0) is the distance steering vector at the transmitter, μ is the frequency modulation coefficient, c is the speed of light, Δt m is the exponential time shift of the mth transmitting array 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 target scenario:

[0123] Given 1 target, its distance is 10km and angle is 10°. Figure 2a is a schematic diagram of STCA-MIMO radar spatial spectrum under linear time shift according to an exemplary embodiment. Figure 2b is a schematic diagram of STCA-MIMO radar spatial spectrum under exponential time shift according to an exemplary embodiment. Figure 2a It can be seen that the reduced-dimensional MUSIC spatial spectrum of the STCA-MIMO radar corresponding to the linear time shift has two peaks, and blurring occurs in the distance dimension. Figure 2b The reduced-dimensional MUSIC spatial spectrum of the STCA-MIMO radar corresponding to the exponential time shift has only one peak at the target, which effectively solves the distance ambiguity problem of STCA-MIMO radar parameter estimation under traditional linear time shift.

[0124] Simulation experiment 2: Parameter estimation in multi-target scenario:

[0125] Given 3 targets, their distance is 10km and their angles are 10°, 11° and 12° respectively. Figure 3a is a schematic diagram of STCA-MIMO radar spatial spectrum under another linear time shift according to an exemplary embodiment. Figure 3b is a schematic diagram of STCA-MIMO radar spatial spectrum under another exponential time shift according to an exemplary embodiment, Figure 3a It can be seen that the reduced-dimensional MUSIC spatial spectrum of the STCA-MIMO radar corresponding to the linear time shift has two sets of peaks, and blurring occurs in the distance dimension. Figure 3b The reduced-dimensional MUSIC spatial spectrum of the STCA-MIMO radar corresponding to the exponential time shift has only one set of peaks at the target, which effectively solves the distance ambiguity problem of STCA-MIMO radar parameter estimation under traditional linear time shift.

[0126] Simulation experiment three: This simulation uses a uniform linear array with 16 elements, the target position parameter is (0°, 10km), the element spacing is 0.015m, the time increment is i, the element number, the reference carrier frequency is 10GHz, the number of snapshots is 200, and 100 Monte Carlo experiments are performed. Figure 4 is a comparison diagram of angle estimation errors between the conventional MUSIC algorithm and the present invention under different signal-to-noise ratios according to an exemplary embodiment, Figure 5 It is a comparison diagram of distance estimation errors of a traditional 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 of the traditional MUSIC algorithm and the proposed method for parameter estimation under different signal-to-noise ratios is compared. The signal-to-noise ratio ranges from -20dB to 10dB, with a step size of 3dB. It can be seen that with the increase of the signal-to-noise ratio, the angle RMSE of the two algorithms both tends to decrease, and they have good target azimuth estimation performance. Figure 4 It can be seen that the azimuth estimation performance of the two methods is similar; Figure 5 It can be seen that in the estimation error of the target distance of the two algorithms, the distance RMSE of the traditional MUSIC algorithm decreases with the increase of SNR, while the estimation performance of the reduced-dimensional MUSIC algorithm is slightly worse than that of the traditional MUSIC algorithm, but it also decreases with the increase of SNR, but it is not as accurate as the traditional MUSIC algorithm. But in general, both have good target azimuth and spatial distance estimation performance, and the computational complexity of the reduced-dimensional MUSIC algorithm is much smaller than that of the traditional MUSIC algorithm.

[0127] The present invention effectively solves the problem that when the STCA-MIMO radar uses the traditional MUSIC algorithm for parameter estimation, the amount of calculation is high and the time consumption is long, and when the time shift changes linearly, its transmission pattern will have grating lobes in the distance dimension, resulting in distance ambiguity during ranging. Compared with the current parameter estimation method, the present invention reduces the high amount of calculation, solves the distance ambiguity problem caused by linear time shift, and significantly improves the target parameter estimation performance.

[0128] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

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

[0130] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A method for unambiguous parameter estimation of STCA-MIMO radar based on dimension reduction MUSIC, characterized in that: The method comprises: Acquiring an echo signal at a target using an STCA-MIMO radar signal transmission model; wherein the STCA-MIMO radar signal transmission model is pre-constructed; Obtaining a corresponding noise subspace according to the echo signal; Obtaining an angle estimation matrix Q1 according to a receiving steering vector of the radar signal transmission model at any point in space; Constructing a spatial spectrum function according to the angle estimation matrix Q1, performing a one-dimensional angle search on the spatial spectrum function, and obtaining a target angle estimation value of the angle at which the target is located; Construct a spatial spectrum estimation matrix Qv2 according to the target angle estimation value and the noise subspace; Obtaining a transmitting end distance steering vector according to the spatial spectrum estimation matrix Qv2; The distance estimation values ​​obtained after taking the phase of the transmitting end distance steering vector are averaged to obtain the distance estimation value of the target.

2. The unambiguous parameter estimation method of STCA-MIMO radar based on dimension reduction MUSIC according to claim 1, characterized in that: The obtaining a corresponding noise subspace according to the echo signal comprises: Performing digital mixing on the echo signal and then performing matched filtering to obtain a preprocessed signal; Obtaining a 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 of STCA-MIMO radar based on dimension reduction MUSIC according to claim 1, characterized in that: The step of constructing a spatial spectrum function according to the angle estimation matrix Q1, performing a one-dimensional angle search on the spatial spectrum function, and obtaining a target angle estimation value at the angle where the target is located includes: Construct a spatial spectrum estimation matrix Qv1 according to the noise subspace and the angle estimation matrix Q1; Inverting the spatial spectrum estimation matrix Qv1 to obtain an inverted spatial spectrum estimation matrix Qvt1; The spatial spectrum function is constructed according to the inverted spatial spectrum estimation matrix Qvt1 and the unit vector, and a one-dimensional angle search is performed on the spatial spectrum function to obtain the target angle estimation value.

4. The unambiguous parameter estimation method of STCA-MIMO radar based on dimension reduction MUSIC according to claim 1, characterized in that: The step of constructing a spatial spectrum estimation matrix Qv2 according to the target angle estimation value and the noise subspace includes: Using the target angle estimation value, a transmitting end angle steering vector and a receiving end steering vector of the radar are obtained; Obtaining the angle estimation matrix Q2 according to the Crocker inner product of the receiving end steering vector and the unit matrix; The spatial spectrum estimation matrix Qv2 is constructed according to the noise subspace and the angle estimation matrix Q2.

5. The unambiguous parameter estimation method of STCA-MIMO radar based on dimension reduction MUSIC according to claim 1, characterized in that: The STCA-MIMO radar signal transmission model includes a transmitting array and a receiving array, wherein the transmitting array includes M transmitting array elements, and the receiving array includes N receiving array elements. Both the transmitting array and the receiving array are uniform equidistant linear arrays, and the array element spacings of the transmitting array and the receiving array are both half-wavelength widths, and the transmission signal of the transmitting array element is a linear frequency modulation signal.

6. The unambiguous parameter estimation method of STCA-MIMO radar based on dimension reduction MUSIC according to claim 5, characterized in that: The transmit signal of the transmit array is expressed as follows: s m (t)=l m g(t-(m-1)Δt m ); Among them, s m (t) represents the transmission signal of the mth transmission element of the transmission array, m∈[1,2,...,m,...,M], M is the number of the transmission elements, t is the time, l m represents the coding coefficient of the mth transmitting array element, Δt m is the exponential time shift of the mth transmitting array element, g(·) represents the linear frequency modulation signal, and μ represents the frequency modulation coefficient.

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

8. The unambiguous parameter estimation method of STCA-MIMO radar based on dimension reduction MUSIC according to claim 1, characterized in that: The transmitter distance steering vector is expressed as follows: Wherein, a(r0) represents the distance steering vector of the transmitting end, Q vt2 represents the inverse of the spatial spectrum estimation matrix Qv2, e1 is a unit vector, H represents the conjugate transpose, is the angle steering vector of the transmitter.

9. The unambiguous parameter estimation method of STCA-MIMO radar based on dimension reduction MUSIC according to claim 8, characterized in that: The estimated distance to the target is expressed as follows: in, is the estimated distance to the target, g0(m,1) represents the estimated distance to the mth transmitting array element, m∈[1,2,...,m,...,M], M is the number of transmitting array elements, b m Represents the time shift coefficient of the mth transmitting array element.

10. The unambiguous parameter estimation method of STCA-MIMO radar based on dimension reduction MUSIC according to claim 9, characterized in that: The distance estimation value of the mth transmitting array element is expressed as follows: Wherein, angle(·) is the phase function, a(r0) is the distance steering vector of the transmitting end, μ is the frequency modulation coefficient, c is the speed of light, Δt m is the exponential time shift of the mth transmitting array element.

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