A sidelobe suppression method based on dynamic array element observation angle

Abstract

The present invention provides a sidelobe suppression method based on a dynamic array element observation angle, comprising: obtaining the position coordinates and echo signals of virtual array elements, obtaining a received signal matrix after matched filtering based on the echo signals; obtaining an array covariance matrix of the received signal matrix based on the received signal matrix after matched filtering; orthogonalizing the signal subspace and noise subspace obtained based on eigendecomposition of the array covariance matrix to obtain an orthogonalized array covariance matrix, reconstructing the orthogonalized array covariance matrix to obtain an array spectral density matrix; obtaining a spatial spectral density function of a two-dimensional multi-signal classification algorithm based on the array spectral density matrix and the received signal matrix after matched filtering; and then obtaining corresponding elevation and azimuth angles to reconstruct beam sidelobes. By improving the two-dimensional multi-signal classification algorithm and applying it to sidelobe suppression, the mainlobe and sidelobe positions are found, thereby reducing the sidelobes of the spectral density function and improving the radar imaging effect of multiple targets at close range.

Description

Technical Field

[0001] The present invention relates to the field of radar technology, and in particular to a sidelobe suppression method based on a dynamic array element observation angle. Background Art

[0002] Multiple Input Multiple Output (MIMO) radar is a new radar architecture that integrates the multiple input and multiple output technologies used in wireless communication systems with digital virtual array technology. MIMO radar uses multiple transmitting antennas to simultaneously transmit orthogonal signals to illuminate a target. The receiving antennas then receive and process the target's return signals to extract information such as the target's spatial position and motion state. This increases angular redundancy in target detection, achieving high-precision imaging and high angular resolution, overcoming the limitations of traditional single-transmitter, single-receiver radars in multi-target applications. In addition to safe construction and production safety monitoring, millimeter-wave radars also have broad application prospects in drones, security, intelligent transportation, industrial applications, radar detection, missile guidance, satellite remote sensing, and electronic countermeasures. Millimeter-wave radars are characterized by their small size, light weight, and high spatial resolution. Compared to optical seekers such as infrared, laser, and television, millimeter-wave seekers have superior penetration capabilities through fog, smoke, and dust, enabling all-weather operation. Furthermore, millimeter-wave seekers offer superior anti-interference and anti-stealth capabilities compared to other microwave seekers. Millimeter-wave radar can distinguish and identify very small targets, and can also identify multiple targets simultaneously. It boasts imaging capabilities, small size, good maneuverability, and concealment. Millimeter-wave MIMO radar offers the advantage of high data acquisition rates, making it more widely used in applications requiring high real-time performance. Furthermore, millimeter-wave MIMO radar can form data observation channels, or virtual elements, far exceeding the number of actual physical antenna array elements (the individual units that make up an antenna array are called elements), potentially enabling high-resolution imaging of multiple targets.

[0003] When millimeter-wave radar and MIMO technology are applied to near-field target detection, physical cost constraints and space utilization issues necessitate the use of sparse arrays in array design. However, sparse arrays, when used for target detection, can result in high sidelobes. These high sidelobes lead to two problems: First, when multiple targets are present, the close proximity of the primary target causes the sidelobes of the primary target to overwhelm the mainlobes of the secondary targets, especially when the primary target has strong scattering. Second, when multiple primary targets are present, the overlapping sidelobes can cause false targets to appear in the transmit and receive beams, affecting image observation. Both of these problems can be addressed simultaneously by reducing the high sidelobes during image illumination. Common sidelobe suppression methods include CF sidelobe suppression, increasing the number of transmitting and receiving elements, and windowing. In near-field 3D imaging, CF sidelobe suppression increases the contrast between strong and weak targets, suppressing weak targets. Increasing the number of transmitting and receiving elements increases cost and space utilization. However, the image weighting applied by the windowing function does not apply to all targets in the image, and the windowing method can also reduce imaging resolution. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a sidelobe suppression method based on dynamic array element observation angle to solve the problems in the prior art of radar detection of multiple targets at close range, such as poor imaging effect and low detection efficiency caused by mutual interference between the main lobes and side lobes of each target.

[0005] The embodiment of the present invention adopts the following technical solution: a sidelobe suppression method based on dynamic array element observation angle, comprising:

[0006] Obtaining the array data of the radar's transceiver antenna and initializing the array data to obtain the position coordinates of the virtual array elements;

[0007] Acquire the echo signal of the virtual array element, and perform matched filtering signal processing on the echo signal to obtain a received signal matrix after matched filtering;

[0008] Based on the received signal matrix after matched filtering, the array covariance matrix of the received signal matrix is ​​obtained, and the array covariance matrix is ​​eigen-decomposed to obtain the signal subspace and the noise subspace;

[0009] The signal subspace and the noise subspace are orthogonalized to obtain an orthogonalized array covariance matrix, and the orthogonalized array covariance matrix is ​​reconstructed to obtain an array spectral density matrix;

[0010] Based on the array spectral density matrix and the received signal matrix after matched filtering, the spatial spectral density function of the two-dimensional multi-signal classification algorithm is obtained;

[0011] A spectrum peak search is performed based on the spatial spectrum density function to obtain the corresponding elevation angle and azimuth angle to reconstruct the beam sidelobe.

[0012] In some embodiments, obtaining an array covariance matrix of the received signal matrix based on the matched filtered received signal matrix includes:

[0013] Decompose the received signal matrix after matched filtering to obtain the noise vector, the receiving matrix and the radar's transmitting and receiving joint manifold matrix;

[0014] The transmit array steering vector and the receive array steering vector in the proposed radar transmit and receive joint manifold matrix remain unchanged within a set number of spread spectrum signals. The expected value calculation of the received signal matrix after matched filtering is performed to obtain the array covariance matrix of the received signal matrix.

[0015] In some embodiments, the signal subspace and the noise subspace are orthogonalized to obtain an orthogonalized array covariance matrix, including:

[0016] The orthogonalization of the signal subspace and the noise subspace is performed based on the cross-correlation matrix of the receiving matrix, the noise variance and the radar's transmitting and receiving joint manifold matrix, and the orthogonalized array covariance matrix is ​​obtained.

[0017] In some embodiments, the radar's transmit and receive joint manifold matrix is ​​a matrix formed by transmit and receive steering vectors;

[0018] The transmit and receive steering vector is the tensor product of the transmit array steering vector and the receive array steering vector;

[0019] The transmitting array steering vector is the tensor product of the one-dimensional steering matrices of the transmitting array along the Y axis and the X axis of the plane coordinate system respectively; the receiving array steering vector is the tensor product of the one-dimensional steering matrices of the receiving array along the Y axis and the X axis of the plane coordinate system respectively.

[0020] In some embodiments, reconstructing the orthogonalized array covariance matrix to obtain an array spectral density matrix includes:

[0021] Reconstructing the received signal matrix after matched filtering to obtain a reconstructed matrix;

[0022] An improved array covariance matrix is ​​obtained based on the reconstruction matrix;

[0023] The orthogonal array covariance matrix is ​​conjugate reconstructed to obtain the array spectral density matrix.

[0024] In some embodiments, obtaining a spatial spectral density function of a two-dimensional multi-signal classification algorithm based on the array spectral density matrix and the received signal matrix after matched filtering includes:

[0025] Perform eigenvalue decomposition on the array spectral density matrix to obtain eigenvalues ​​and eigenvectors;

[0026] The eigenvectors are stretched into a new noise subspace. Based on the new noise subspace and the radar's transmit and receive joint manifold matrix obtained by decomposing the received signal matrix after matched filtering, the spatial spectral density function of the two-dimensional multi-signal classification algorithm is obtained.

[0027] In some embodiments, further comprising:

[0028] The spatial spectral density function is normalized based on the obtained elevation angle and azimuth angle to obtain the normalized wavenumber output function:

[0029] Continue the spectrum peak search to obtain the main lobe width of the elevation angle and the main lobe width of the azimuth angle;

[0030] The beam sidelobe values ​​are further reconstructed based on the main lobe width of the elevation angle and the main lobe width of the azimuth angle to obtain the wavenumber pattern data after sidelobe suppression.

[0031] In some embodiments, normalizing the spatial spectral density function based on the obtained elevation angle and azimuth angle to obtain a normalized wavenumber output function includes:

[0032] Perform a first spectrum peak search based on the spatial spectrum density function to obtain the corresponding elevation angle and azimuth angle. When the elevation angle and azimuth angle in the spatial spectrum density function are respectively equal to the elevation angle and azimuth angle obtained from the first spectrum peak search, the spatial spectrum density function obtains its maximum value.

[0033] The normalized wavenumber output function is the ratio of the spatial spectral density function to the maximum value of the spatial spectral density function.

[0034] In some embodiments, continuing the spectrum peak search to obtain the main lobe width of the elevation angle and the main lobe width of the azimuth angle includes:

[0035] The spectrum peak search is continued under the set angle condition at the maximum elevation spectrum peak to obtain the elevation main lobe width; the spectrum peak search is continued under the set angle condition at the maximum azimuth spectrum peak to obtain the azimuth main lobe width.

[0036] In some embodiments, beam sidelobe values ​​are further reconstructed based on the main lobe width of the elevation angle and the main lobe width of the azimuth angle to obtain wavenumber pattern data after sidelobe suppression, including:

[0037] Reconstruct the known beam sidelobe based on the main lobe width of the elevation angle and the main lobe width of the azimuth angle to obtain the reconstructed beam sidelobe spatial spectral density function;

[0038] The normalized wavenumber output function is subtracted from the reconstructed beam sidelobe spatial spectral density function to obtain the wavenumber pattern data after sidelobe suppression.

[0039] The beneficial effects of the embodiments of the present invention are:

[0040] By improving the two-dimensional multi-signal classification algorithm and applying it to sidelobe suppression, the main lobe and sidelobe positions are found, thereby reducing the sidelobes of the final spectral density function, improving the radar imaging effect of multiple targets at close range, and at the same time improving the radar's detection efficiency of targets, and the detection flexibility is strong. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0042] Figure 1 The figure is a flow chart of the sidelobe suppression method based on the dynamic array element observation angle of the present invention.

[0043] Figure 2 This is a changing trend diagram of RMSE and SNR of the present invention. DETAILED DESCRIPTION

[0044] Various aspects and features of the present invention are described herein with reference to the accompanying drawings.

[0045] It should be understood that various modifications may be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of an embodiment. Other modifications within the scope and spirit of the invention will occur to those skilled in the art.

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the general description of the invention given above and the detailed description of the embodiments given below, serve to explain the principles of the invention.

[0047] These and other characteristics of the invention will become apparent from the following description of a preferred form of embodiment given as a non-limiting example with reference to the accompanying drawings.

[0048] It should also be understood that although the invention has been described with reference to certain specific examples, those skilled in the art will be able to determine many other equivalent forms of the invention that have the characteristics of the above-mentioned "Invention Summary" and are therefore within the scope of protection defined thereby.

[0049] The above and other aspects, features and advantages of the present invention will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0050] Specific embodiments of the present invention will be described hereinafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of the present invention, which may be implemented in a variety of ways. Well-known and / or repetitive functions and structures are not described in detail to avoid obscuring the present invention with unnecessary or redundant detail. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but rather serve merely as a basis and representative basis for the above “Summary of the Invention” to teach those skilled in the art to variously employ the present invention in virtually any suitable detailed structure.

[0051] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," each of which may refer to one or more of the same or different embodiments according to the present invention.

[0052] In order to solve the problems in the background technology, the present invention provides a sidelobe suppression method based on dynamic array element observation angle.

[0053] like Figure 1 As shown, the sidelobe suppression method includes:

[0054] S1: Acquire the array data of the radar's transceiver antenna and initialize the array data to obtain the position coordinates of the virtual array element, that is, determine the specific position of the virtual array element.

[0055] S2: Acquire the echo signal of the virtual array element and perform matched filtering on the echo signal to obtain the received signal matrix after matched filtering. That is, after determining the position of the virtual array element, the echo signal received by the virtual array element is then matched filtered to maximize the signal-to-noise ratio of the output signal.

[0056] S3: Based on the received signal matrix after matched filtering, the array covariance matrix of the received signal matrix is ​​obtained, and the array covariance matrix is ​​eigendecomposed to obtain the signal subspace and noise subspace, so as to obtain a more accurate array spectral density matrix later.

[0057] S4: The signal subspace and the noise subspace are orthogonalized to obtain the orthogonalized array covariance matrix. The orthogonalized array covariance matrix is ​​reconstructed to obtain the array spectral density matrix. That is, the signal subspace and the noise subspace are orthogonalized, and the analytical expression of the orthogonalized array covariance matrix and the same response characteristics between the array elements are obtained. In order to decompose the information in the received signal matrix, the received signal data matrix is ​​reconstructed to obtain a reconstructed matrix. In order to further study the multidimensional random variables based on azimuth and elevation angles, the array covariance matrix of the reconstructed matrix is ​​calculated. The sum of the analytical expression and the new array covariance matrix is ​​the reconstructed array spectral density matrix.

[0058] S5: Based on the array spectral density matrix and the matched filtered received signal matrix, the spatial spectral density function of the two-dimensional multiple signal classification algorithm is obtained. Eigenvalue decomposition of the array spectral density matrix is ​​performed to obtain an improved two-dimensional MUSIC (Multiple Signal Classification) spatial spectral density function based on azimuth and elevation angles. The image spectrum peak width of this function is much smaller than the spectral density function of the traditional two-dimensional MUSIC algorithm.

[0059] S6: A spectral peak search is performed based on the spatial spectral density function to obtain the corresponding elevation and azimuth angles for beam sidelobe reconstruction. Subsequently, to obtain the maximum response value, i.e., the target's incoming direction, a two-dimensional spectral peak search is performed on the spatial spectral density function. The azimuth and elevation angles corresponding to the maximum value are obtained, representing the target's incoming direction. Based on the elevation and azimuth angles, the beam sidelobes are reconstructed, resulting in the final sidelobe-suppressed pattern data.

[0060] By improving the two-dimensional multi-signal classification algorithm and applying it to sidelobe suppression, the main lobe and sidelobe positions are found, thereby reducing the sidelobes of the final spectral density function. This improves the radar's imaging effect on multiple targets at close range, effectively avoiding the problem of high sidelobes in the echo of detected targets in the near field, improving target resolution, and increasing detection flexibility.

[0061] S1: Obtain the array data of the radar's transceiver antenna and initialize the array data to obtain the position coordinates of the virtual array elements, including:

[0062] Obtain the array data of the radar's transceiver antenna and initialize the array data. Assume that the transceiver antenna array is in the XOY rectangular coordinate system, the transmitting antenna Tx is evenly distributed along the X axis, the number is M, and the receiving antenna Rx is evenly distributed along the Y axis, the number of antennas is N. Then the virtual antenna array elements are located on the XOY plane, and the number is M×N. The antenna element spacing along the X axis is d x , the antenna element spacing along the Y axis is d y , then the virtual array element spacing formed by the antenna is half of the wavelength λ, that is, λ2, then the virtual array element coordinates of the antenna (xm 、y n )for:

[0063]

[0064] S2: Get the echo signal of the virtual array element and perform matched filtering signal processing on the echo signal to obtain the received signal matrix after matched filtering. Specifically, the radar transmitting array transmits K orthogonal waveforms, then the pitch angle and azimuth angle of the kth signal are respectively The radar receiving signal is subjected to matched filtering to obtain the receiving signal matrix X(t) after matched filtering.

[0065] S3: Based on the received signal matrix after matched filtering, the array covariance matrix of the received signal matrix is ​​obtained, including:

[0066] S31: Decompose the received signal matrix after matched filtering to obtain a noise vector, a receiving matrix, and a radar transmitting and receiving joint manifold matrix.

[0067] Specifically, the received signal matrix X(t) after matched filtering is decomposed as follows:

[0068]

[0069] Where, X(t)=[x1(t),x2(t),...,x MN (t)] is the 1×MN dimensional received signal matrix, s(t) is the received matrix, and N(t) is the noise vector, specifically the additive white Gaussian noise vector; is the joint manifold matrix of MIMO radar transmission and reception; the transmit and receive steering vector is the tensor product of the transmit array steering vector and the receive array steering vector. The transmit array steering vector is the tensor product of the one-dimensional steering matrices of the transmit array along the Y-axis and X-axis of the plane coordinate system, and the receive array steering vector is the tensor product of the one-dimensional steering matrices of the receive array along the Y-axis and X-axis of the plane coordinate system, that is: is the transmit and receive steering vector, where is the transmitting array steering vector, Steering vector for the receiving array; and are the one-dimensional steering matrices of the transmitting array along the Y-axis and X-axis respectively; and are the one-dimensional steering matrices of the receiving array along the Y-axis and X-axis respectively.

[0070] Since the array steering vector has the Vandermonde structure characteristic, its expression is:

[0071]

[0072] Here, i is an imaginary number.

[0073] Assuming that the transmitting antenna Tx and the receiving antenna Rx share a common antenna array, the following relationship can be obtained:

[0074]

[0075] S32: Under the condition that the transmitting array steering vector and the receiving array steering vector in the joint manifold matrix of the proposed radar transmission and reception remain unchanged within the set number of spread spectrum signals (chirp), the expected value calculation is performed on the received signal matrix after matched filtering to obtain the array covariance matrix R of the received signal matrix i :

[0076] R i =E{X(t)X H (t)} (5)

[0077] Where E is the calculation of X(t)X H Expected value operation of (t).

[0078] S33: Perform eigendecomposition on the array covariance matrix to obtain the signal subspace and noise subspace: that is:

[0079]

[0080] ∑ S R i The diagonal matrix consisting of K eigenvalues ​​taken from large to small in the eigendecomposition of s is the signal subspace spanned by the corresponding eigenvector. Similarly, ∑ E is a diagonal matrix consisting of NK eigenvalues, U E is the spanned noise subspace.

[0081] In some embodiments, S4: orthogonalizing the signal subspace and the noise subspace to obtain an orthogonalized array covariance matrix includes:

[0082] S41: Based on the cross-correlation matrix of the receiving matrix, the noise variance and the radar's transmit and receive joint manifold matrix, the signal subspace and the noise subspace are orthogonalized to obtain the orthogonalized array covariance matrix R i :

[0083]

[0084] Among them, R s represents the cross-correlation matrix of the receiving matrix s(t), σ 2 is the noise variance, and I is a unit vector.

[0085] S42: Reconstruct the orthogonalized array covariance matrix to obtain the array spectral density matrix, including:

[0086] Reconstruct the received signal matrix X(t) after matched filtering to obtain the reconstructed matrix T:

[0087] T=KX * (8)

[0088] Where * represents complex conjugate; K is the M-order anti-unit matrix.

[0089] S43: Improved array covariance matrix R based on reconstruction matrix T j :

[0090]

[0091] S44: Orthogonal array covariance matrix R i Perform conjugate reconstruction to obtain the final array spectral density matrix R:

[0092]

[0093] S5: Based on the array spectral density matrix and the received signal matrix after matched filtering, the spatial spectral density function of the two-dimensional multi-signal classification algorithm is obtained, including:

[0094] S51: Perform eigenvalue decomposition on the array spectral density matrix R and obtain the eigenvalues ​​[λ1,λ2,...,λ m ] and the eigenvectors [V1,V2,...,V m ];

[0095] S52: The feature vector [V1, V2, ..., V m ] to form a new noise subspace Based on the new noise subspace The spatial spectral density function of the two-dimensional multi-signal classification algorithm is obtained by decomposing the radar's transmit and receive joint manifold matrix obtained by the received signal matrix after matched filtering.

[0096]

[0097] In some embodiments, further comprising:

[0098] S61: normalizing the spatial spectral density function based on the obtained elevation angle and azimuth angle to obtain a normalized wavenumber output function, including:

[0099] The first spectrum peak search is performed based on the spatial spectrum density function to obtain the corresponding elevation angle and azimuth angle. When the elevation angle and azimuth angle in the spatial spectrum density function are equal to the elevation angle and azimuth angle obtained by the first spectrum peak search, the spatial spectrum density function obtains its maximum value. That is, for the improved two-dimensional MUSIC spatial spectrum density function Perform a two-dimensional peak search and obtain θ p as well as When θ=θ p , When the spatial spectral density function Get the maximum value P max ,and When , the spatial spectral density function reaches its maximum value, and the target distance r obtained after filtering is the direction of the target m confirmed after the first search.

[0100] Improved two-dimensional MUSIC spatial spectral density function Perform normalization processing to obtain the normalized wavenumber output function:

[0101]

[0102] Using angle information θ q Normalizing the improved two-dimensional MUSIC spatial spectral density function can eliminate data scale differences, make data easier to compare and process, and make the data more stable and reliable.

[0103] S62: Continuing the spectrum peak search to obtain the elevation main lobe width and the azimuth main lobe width. Specifically, the spectrum peak search is continued at the maximum elevation spectrum peak under the set angle condition to obtain the elevation main lobe width; and the spectrum peak search is continued at the maximum azimuth spectrum peak under the set angle condition to obtain the azimuth main lobe width.

[0104] Specifically, in order to maximize the measured azimuth and pitch angle θ q and the gap between the real target m position is further narrowed, in azimuth Continue to search for the spectrum peak of the small-angle beam near the maximum spectrum peak to find the azimuth main lobe width. The first zero points on the left and right of the azimuth are: Similarly, at the pitch angle θ q Perform a small-angle beam spectrum peak search again near the maximum spectrum peak to find the main lobe width of the pitch angle. The first zero points on the left and right are: θ - ,θ + , then the azimuth main lobe width is The main lobe width at the elevation angle is θ B :

[0105]

[0106] S63: Main lobe width θ based on the elevation angle B and the main lobe width in azimuth The beam sidelobe values ​​are further reconstructed to obtain the reconstructed beam sidelobe spatial spectral density function

[0107]

[0108] To obtain the normalized wave number output function Subtract the beam sidelobe value to obtain the wavenumber pattern data after sidelobe suppression

[0109]

[0110] By normalizing the beam sidelobe values, data processing becomes more convenient and faster. Then, the spatial spectral density function is searched for two peaks, and the beam sidelobe values ​​are reconstructed to accurately find the sidelobe position, so that the performance of the sidelobe suppression method is not affected by the target position.

[0111] By improving the two-dimensional MUSIC algorithm for sidelobe suppression, the correlation of signal sources between different array elements is reduced, and the angular resolution of the near-field target echo signal is greatly improved without significantly increasing the amount of calculation. When the signal source is unknown, this method has outstanding results in estimating the signal source.

[0112] Figure 2 The figure shows the variation trend of RMSE (root mean square error) with SNR (Signal to Interference plus Noise Ratio). The efficiency of target detection after sidelobe suppression can be judged by RMSE. Figure 2 It can be seen that the angle estimation performance of the improved 2D MUSIC algorithm is better than that of the basic 2D MUSIC algorithm, and lower RMSE can be obtained under low SNR and low snapshot number conditions.

[0113] The above describes in detail multiple embodiments of the present invention, but the present invention is not limited to these specific embodiments. Those skilled in the art can make various variations and modifications based on the concept of the present invention, and these variations and modifications should fall within the scope of protection required by the present invention.

Claims

1. A sidelobe suppression method based on dynamic array element observation angle, characterized in that: include: Obtaining the array data of the radar's transceiver antenna and initializing the array data to obtain the position coordinates of the virtual array elements; Acquire the echo signal of the virtual array element, and perform matched filtering signal processing on the echo signal to obtain a received signal matrix after matched filtering; Based on the received signal matrix after matched filtering, the array covariance matrix of the received signal matrix is ​​obtained, and the array covariance matrix is ​​eigen-decomposed to obtain the signal subspace and the noise subspace; The signal subspace and the noise subspace are orthogonalized to obtain an orthogonalized array covariance matrix, and the orthogonalized array covariance matrix is ​​reconstructed to obtain an array spectral density matrix; Based on the array spectral density matrix and the received signal matrix after matched filtering, the spatial spectral density function of the two-dimensional multi-signal classification algorithm is obtained; Perform spectrum peak search based on the spatial spectrum density function to obtain the corresponding elevation angle and azimuth angle to reconstruct the beam sidelobe; The method of obtaining an array covariance matrix of the received signal matrix based on the matched filtered received signal matrix includes: Decompose the received signal matrix after matched filtering to obtain the noise vector, the receiving matrix and the radar's transmitting and receiving joint manifold matrix; The transmit array steering vector and the receive array steering vector in the proposed radar transmit and receive joint manifold matrix remain unchanged within a set number of spread spectrum signals. The expected value calculation of the received signal matrix after matched filtering is performed to obtain the array covariance matrix of the received signal matrix.

2. The sidelobe suppression method based on dynamic array element observation angle according to claim 1, characterized in that: The orthogonal array covariance matrix obtained by orthogonalizing the signal subspace and the noise subspace includes: The orthogonalization of the signal subspace and the noise subspace is performed based on the cross-correlation matrix of the receiving matrix, the noise variance and the radar's transmitting and receiving joint manifold matrix, and the orthogonalized array covariance matrix is ​​obtained.

3. The sidelobe suppression method based on dynamic array element observation angle according to claim 1, characterized in that: The radar's transmit and receive joint manifold matrix is ​​a matrix formed by transmit and receive steering vectors; The transmit and receive steering vector is the tensor product of the transmit array steering vector and the receive array steering vector; The transmitting array steering vector is the tensor product of the one-dimensional steering matrices of the transmitting array along the Y axis and the X axis of the plane coordinate system respectively; the receiving array steering vector is the tensor product of the one-dimensional steering matrices of the receiving array along the Y axis and the X axis of the plane coordinate system respectively.

4. The sidelobe suppression method based on dynamic array element observation angle according to claim 2, characterized in that: Reconstruct the orthogonalized array covariance matrix to obtain the array spectral density matrix, including: Reconstructing the received signal matrix after matched filtering to obtain a reconstructed matrix; An improved array covariance matrix is ​​obtained based on the reconstruction matrix; The orthogonal array covariance matrix is ​​conjugate reconstructed to obtain the array spectral density matrix.

5. The sidelobe suppression method based on dynamic array element observation angle according to claim 1, characterized in that: The method of obtaining a spatial spectral density function of a two-dimensional multi-signal classification algorithm based on the array spectral density matrix and the received signal matrix after matched filtering includes: Perform eigenvalue decomposition on the array spectral density matrix to obtain eigenvalues ​​and eigenvectors; The eigenvectors are stretched into a new noise subspace. Based on the new noise subspace and the radar's transmit and receive joint manifold matrix obtained by decomposing the received signal matrix after matched filtering, the spatial spectral density function of the two-dimensional multi-signal classification algorithm is obtained.

6. The sidelobe suppression method based on dynamic array element observation angle according to any one of claims 1 to 5, characterized in that: Also includes: The spatial spectrum density function is normalized based on the obtained elevation angle and azimuth angle to obtain a normalized wavenumber output function; Continue the spectrum peak search to obtain the main lobe width of the elevation angle and the main lobe width of the azimuth angle; The beam sidelobe values ​​are further reconstructed based on the main lobe width of the elevation angle and the main lobe width of the azimuth angle to obtain the wavenumber pattern data after sidelobe suppression.

7. The sidelobe suppression method based on dynamic array element observation angle according to claim 6, characterized in that: The spatial spectral density function is normalized based on the obtained elevation angle and azimuth angle to obtain the normalized wavenumber output function, including: Perform a first spectrum peak search based on the spatial spectrum density function to obtain the corresponding elevation angle and azimuth angle. When the elevation angle and azimuth angle in the spatial spectrum density function are respectively equal to the elevation angle and azimuth angle obtained from the first spectrum peak search, the spatial spectrum density function obtains its maximum value. The normalized wavenumber output function is the ratio of the spatial spectral density function to the maximum value of the spatial spectral density function.

8. The sidelobe suppression method based on dynamic array element observation angle according to claim 7, characterized in that: Continue the spectrum peak search to obtain the main lobe width in elevation and azimuth, including: The spectrum peak search is continued under the set angle condition at the maximum elevation spectrum peak to obtain the elevation main lobe width; the spectrum peak search is continued under the set angle condition at the maximum azimuth spectrum peak to obtain the azimuth main lobe width.

9. The sidelobe suppression method based on dynamic array element observation angle according to claim 8, characterized in that: The beam sidelobe values ​​are further reconstructed based on the mainlobe widths of the elevation angle and the azimuth angle to obtain the wavenumber pattern data after sidelobe suppression, including: Reconstruct the known beam sidelobe based on the main lobe width of the elevation angle and the main lobe width of the azimuth angle to obtain the reconstructed beam sidelobe spatial spectral density function; The normalized wavenumber output function is subtracted from the reconstructed beam sidelobe spatial spectral density function to obtain the wavenumber pattern data after sidelobe suppression.

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