Method and device for estimating direction of arrival of radar, computer equipment, storage medium and program product

Abstract

The invention relates to a radar direction of arrival estimation method and device, computer equipment, a storage medium and a program product. The method comprises the steps of establishing a signal model of a source target and a multi-input multi-output radar, determining a signal subspace according to the signal model, determining a first selection matrix and a second selection matrix, carrying out submatrix division on the signal subspace through the first selection matrix, obtaining a reference direction cosine according to the first submatrix obtained through division, and obtaining a reference direction cosine according to the reference direction cosine; and performing sub-matrix division on the signal sub-space through a second selection matrix, obtaining a fuzzy direction cosine according to a second sub-matrix obtained through division, performing phase compensation on the fuzzy direction cosine through a reference direction cosine to obtain a target direction cosine, and determining a two-dimensional arrival direction of the echo signal according to the target direction cosine. By adopting the method, space response and polarization response information output by the array can be fully utilized, and high-precision two-dimensional arrival direction estimation is realized.

Description

Technical Field

[0001] The present application relates to the technical field of sensor arrays, and in particular to a radar direction of arrival estimation method, device, computer equipment, storage medium and program product. Background Art

[0002] Direction-of-Arrival (DOA) estimation is a key technology in radar systems, especially in the application of Multiple Input Multiple Output (MIMO) radar. MIMO radar systems use multiple transmitting and receiving array elements to achieve accurate measurement of the target direction.

[0003] In traditional methods, for co-located MIMO radars, their direction-finding algorithms usually rely on the spatial phase characteristics of the scalar sensor array. When performing angle measurements based on the spatial phase characteristics, the control of the element spacing in the scalar sensor array is crucial. If the element spacing is not appropriate, a measured phase difference may correspond to multiple actual target angles, making it impossible to accurately determine the direction of arrival of the target signal. Summary of the invention

[0004] Based on this, it is necessary to provide a radar direction of arrival estimation method, device, computer equipment, storage medium and program product that can improve the accuracy of DOA estimation in response to the above technical problems.

[0005] In a first aspect, the present application provides a radar direction of arrival estimation method, comprising:

[0006] Establish the signal model of source target and MIMO radar, and determine the signal subspace according to the signal model;

[0007] Determine a first selection matrix and a second selection matrix; the first selection matrix is ​​used to obtain elements in the signal subspace that meet the rotational invariance requirement of the polarization response; the second selection matrix is ​​used to obtain elements in the signal subspace that meet the spatial invariance requirement;

[0008] Performing submatrix division on the signal subspace using a first selection matrix, and obtaining reference direction cosines according to a first submatrix obtained by the division;

[0009] The signal subspace is divided into sub-matrices by using a second selection matrix, and fuzzy direction cosines are obtained according to a second sub-matrix obtained by the division;

[0010] By performing phase compensation on the fuzzy direction cosines through the reference direction cosines, the target direction cosines are obtained, and the two-dimensional arrival direction of the echo signal is determined according to the target direction cosines; the two-dimensional arrival direction includes the azimuth angle and the pitch angle;

[0011] The expression of the signal model is: ; Among them, the matrix The matrix B is used to characterize the spatial response vector of the transmitting array and the matrix A is used to characterize the polarization response vector of the sensor array. It is used to characterize the spatial response vector of the receiving array, the matrix C is the radar cross section matrix of the multiple-input multiple-output radar, and the matrix N is the noise matrix;

[0012] Among them, the matrix The expression is , is the spatial response vector of the transmitting array to the kth source target; the expression of the matrix B is: , is the polarization response vector associated with the kth source target; the matrix The expression is , is the spatial response vector of the receiving array to the kth source target.

[0013] In one embodiment, the step of establishing a signal model of a source target and a multi-input multi-output includes:

[0014] Based on the electric response vector and magnetic response vector generated when the MIMO radar scans the source target, the polarization response expression of the MIMO radar is obtained;

[0015] According to the polarization response expression and the position information of the electromagnetic vector sensors in the transmitting array and the receiving array, the spatial response expressions of the transmitting array and the receiving array are respectively obtained;

[0016] A signal model is obtained according to the polarization response expression and the spatial response expressions of the transmitting array and the receiving array.

[0017] In one embodiment, the step of obtaining a signal subspace according to a signal model includes: obtaining a signal covariance matrix of the signal model; performing eigendecomposition on the signal covariance matrix to obtain multiple eigenvalues ​​and eigenvectors corresponding to the eigenvalues; and obtaining a signal subspace according to a combination of the eigenvectors.

[0018] In one embodiment, the step of obtaining reference direction cosines according to the first sub-matrix obtained by dividing includes:

[0019] The first sub-matrices obtained by the division are combined, and the combined matrices are subjected to eigendecomposition to obtain a first factor matrix;

[0020] Obtaining an electric field response vector and a magnetic field response vector according to diagonal elements of the first factor matrix;

[0021] The electric field response vector and the magnetic field response vector are subjected to normalized vector cross product processing to obtain reference direction cosines.

[0022] In one embodiment, the step of obtaining fuzzy direction cosines according to the second sub-matrix obtained by dividing includes:

[0023] Get the second factor matrix corresponding to the second submatrix;

[0024] The diagonal elements of the second factor matrix are phased respectively to obtain the reference phase corresponding to the echo signal;

[0025] Get the deterministic matrix of the signal model;

[0026] The product of the reference phase and the pseudo-inverse of the deterministic matrix is ​​used as the fuzzy direction cosine.

[0027] In one embodiment, the step of performing phase compensation on the fuzzy direction cosines by using the reference direction cosines to obtain the target direction cosines includes:

[0028] According to the signal period of the echo signal and the reference direction cosine, phase compensation is performed on the fuzzy direction cosine to obtain a phase compensation value;

[0029] The product of the phase compensation value and the pseudo-inverse matrix of the deterministic matrix is ​​used as the target direction cosine.

[0030] In a second aspect, the present application also provides a radar direction of arrival estimation device, comprising:

[0031] A signal model building module is used to build a signal model of a source target and a multi-input multi-output radar, and determine a signal subspace according to the signal model;

[0032] A selection matrix determination module is used to determine a first selection matrix and a second selection matrix; the first selection matrix is ​​used to obtain elements in the signal subspace that meet the rotational invariance requirement of the polarization response; the second selection matrix is ​​used to obtain elements in the signal subspace that meet the spatial invariance requirement;

[0033] A reference direction acquisition module, used to perform sub-matrix division on the signal subspace through a first selection matrix, and obtain reference direction cosines according to a first sub-matrix obtained by the division;

[0034] A fuzzy direction acquisition module, used for performing sub-matrix division on the signal subspace through a second selection matrix, and obtaining fuzzy direction cosines according to a second sub-matrix obtained by the division;

[0035] The arrival direction acquisition module is used to perform phase compensation on the fuzzy direction cosines through the reference direction cosines to obtain the target direction cosines, and determine the two-dimensional arrival direction of the echo signal according to the target direction cosines; the two-dimensional arrival direction includes the azimuth angle and the pitch angle;

[0036] The expression of the signal model is: ; Among them, the matrix The matrix B is used to characterize the spatial response vector of the transmitting array and the matrix A is used to characterize the polarization response vector of the sensor array. It is used to characterize the spatial response vector of the receiving array, the matrix C is the radar cross section matrix of the multiple-input multiple-output radar, and the matrix N is the noise matrix;

[0037] Among them, the matrix The expression is , is the spatial response vector of the transmitting array to the kth source target; the expression of the matrix B is: , is the polarization response vector associated with the kth source target; the matrix The expression is , is the spatial response vector of the receiving array to the kth source target.

[0038] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method steps of any one of the first aspects are implemented.

[0039] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements any one of the method steps in the first aspect when the computer program is executed by a processor.

[0040] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which implements any one of the method steps in the first aspect when executed by a processor.

[0041] The above radar direction of arrival estimation method, device, computer equipment, storage medium and program product establish signal models of source target and multi-input multi-output radar, determine signal subspace according to the signal model, obtain elements in the signal subspace that meet the rotational invariance requirement of polarization response and the spatial invariance requirement by selecting matrices, obtain rough reference direction cosines and high-precision fuzzy direction cosines according to the submatrix obtained by dividing the signal subspace, perform phase compensation on the fuzzy direction cosines by reference direction cosines, obtain high-precision unambiguous target direction cosines, and finally obtain accurate two-dimensional direction of arrival. The method is applicable to scenarios with limited number of array elements and limited array space, reduces mutual coupling effects while the array geometry of the radar is not constrained, and can make full use of the spatial response and polarization response information of the array output to achieve high-precision two-dimensional direction of arrival estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 1 is a schematic flow chart of a method for estimating a direction of arrival of a radar in one embodiment;

[0044] Figure 2 is a schematic flow chart of a method for estimating a direction of arrival of a radar in another embodiment;

[0045] Figure 3 is a curve diagram showing a change in a scattering result in one embodiment;

[0046] Figure 4 is a curve diagram showing a change in direction of arrival estimation in one embodiment;

[0047] Figure 5 is a curve diagram of angle estimation in one embodiment;

[0048] Figure 6 is a structural block diagram of a radar direction of arrival estimation device in one embodiment;

[0049] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0051] In an exemplary embodiment, Figure 1 As shown, a radar direction of arrival estimation method is provided, and the method is applied to a multi-input multi-output radar using a sensor array; the sensor array includes a transmitting array and a receiving array composed of multiple electromagnetic vector sensors arranged at irregular intervals. The method includes the following steps 102 to 110. Among them:

[0052] S102: Establish a signal model of the source target and the MIMO radar, and determine a signal subspace according to the signal model.

[0053] Optionally, the operation of a multiple-input multiple-output (MIMO) radar involves two links: transmission and reception. At the transmitting end, the radar's transmitting array will actively transmit a certain number of orthogonal waveforms. These orthogonal waveforms can distinguish different transmission signals when they propagate in space, which is convenient for the subsequent processing of echo signals. The transmitted signal propagates within the detection range. When it encounters a far-field non-correlated target, it will be reflected back by the target to form an echo signal, and then the echo signal is received by the receiving array. The receiving array will perform matched filtering operations on the echo signal. Through matched filtering, it can enhance useful signals, suppress noise, and extract the signal features we care about, and finally output array data.

[0054] Optionally, since the echo signals arriving at the receiving array from different directions will have time delays or phase differences when arriving at each array element, these time delays or phase differences are closely related to the direction of arrival (DOA) of the signal. For example, under the common setting of the far-field non-correlated target assumption, the phase difference between array elements is related to the angle of arrival of the signal. The signal model can be used to express this angle-related time delay or phase difference information in the form of an accurate mathematical expression.

[0055] Optionally, the signal model of the MIMO radar includes the spatial response vectors and polarization response vectors of the transmitting and receiving arrays, wherein the spatial response vector reflects the spatial response of the array to incident signals from different directions. For example, when a signal reaches the array from a certain direction, the signal strength and phase received by different array elements will vary due to different spatial positions, and the spatial response vector can describe such differences; the polarization response vector reflects the response of the array according to the polarization characteristics of the signal.

[0056] The expression of the signal model is: ; Among them, the matrix The matrix B is used to characterize the spatial response vector of the transmitting array and the matrix A is used to characterize the polarization response vector of the sensor array. It is used to characterize the spatial response vector of the receiving array, the matrix C is the radar cross section matrix of the multiple-input multiple-output radar, and the matrix N is the noise matrix;

[0057] Among them, the matrix The expression is , is the spatial response vector of the transmitting array to the kth source target; the expression of the matrix B is: , is the polarization response vector associated with the kth source target; the matrix The expression is , is the spatial response vector of the receiving array to the kth source target.

[0058] Furthermore, after the signal model is constructed, a signal covariance matrix is ​​established based on the model and the collected valid signal data, wherein the signal covariance matrix is ​​used to describe the correlation between signals, including the relevant information of signals between different array elements or subarrays. Afterwards, the estimated value of the signal subspace can be obtained by performing eigendecomposition operation on the signal covariance matrix.

[0059] S104: Determine a first selection matrix and a second selection matrix; the first selection matrix is ​​used to obtain elements in the signal subspace that meet the rotational invariance requirement of the polarization response; the second selection matrix is ​​used to obtain elements in the signal subspace that meet the spatial invariance requirement.

[0060] Optionally, since the signal subspace contains many elements related to the polarization response, and the polarization response has the characteristic of rotational invariance, the elements that meet the requirements of the polarization response rotational invariance can be screened out from the complex signal subspace through the first selection matrix. Among them, rotational invariance means that when the polarization state of the source signal rotates within a certain range, certain characteristics of the polarization response remain unchanged, which makes it unnecessary for the algorithm to perform separate calculations and processing for each possible polarization rotation angle, but can use this invariance to extract key polarization information. Therefore, by screening the elements that meet the requirements of the polarization response rotational invariance, the amount of calculation and the difficulty of algorithm implementation can be reduced.

[0061] Similarly, in the signal subspace, the array's response to incident signals from different directions has the characteristic of spatial invariance. Through the second selection matrix, the elements that meet the spatial invariance requirement can be accurately obtained. Among them, spatial invariance means that when the signal is incident on the sensor array from different directions, the relative spatial relationship between different array elements in the array (such as array element spacing, array element geometric layout, etc.) causes the signal phase difference and other characteristics to be fixed, and only related to the direction of arrival of the signal.

[0062] S106: Divide the signal subspace into sub-matrices using a first selection matrix, and obtain reference direction cosines according to a first sub-matrix obtained by the division.

[0063] Optionally, the entire signal subspace is divided into submatrices through the first selection matrix, and the internal elements of the first submatrix obtained after the division all meet the requirements of the rotation invariance of the polarization response. Therefore, the divided submatrices can be transformed into each other by multiplying by a specific diagonal factor matrix. Based on this, by performing eigendecomposition on the results of specific different submatrix combinations, a specific factor matrix whose diagonal elements are the ratios of different elements in the polarization response can be obtained. Finally, the elements in the factor matrix are used to construct the specific vector required by the normalization (VCP) technology, and the vector is subjected to VCP operation to obtain rough direction cosines.

[0064] S108: performing sub-matrix division on the signal subspace using the second selection matrix, and obtaining fuzzy direction cosines according to a second sub-matrix obtained by the division.

[0065] Optionally, similar to the first sub-matrix, the second sub-matrices divided by the second selection matrix can also be transformed into each other through a specific diagonal matrix, and the diagonal elements in the second sub-matrix are composed of ratios of different spatial responses. By constructing a factor matrix, a specific vector required for the normalization (VCP) technology is constructed through the elements in the factor matrix, and the VCP operation is performed on the vector, thereby obtaining the fuzzy direction cosines.

[0066] S110: performing phase compensation on the fuzzy direction cosines by using reference direction cosines to obtain target direction cosines, and determining the two-dimensional arrival direction of the echo signal according to the target direction cosines; the two-dimensional arrival direction includes an azimuth angle and a pitch angle.

[0067] Optionally, when the array element spacing is greater than half a wavelength, since the exponential mapping form of the spatial response is periodic, the result of taking the phase of the matrix diagonal elements may be an erroneous phase value, that is, the ambiguous direction cosine is an erroneous phase value. Since the reference direction cosine is obtained independently of the spatial response, that is, it is unambiguous, the ambiguous direction cosine obtained by spatial rotation invariance is phase-compensated and restored, and an unambiguous high-precision direction cosine estimation, that is, the target direction cosine, can be obtained, and finally a high-precision two-dimensional direction of arrival (2D-DOA) estimation can be achieved.

[0068] In the above radar direction of arrival estimation method, by establishing signal models of source targets and multi-input multi-output radars, the signal subspace is determined according to the signal model, and the elements that meet the rotational invariance requirements of the polarization response and the spatial invariance requirements in the signal subspace are respectively obtained by selecting matrices, and rough reference direction cosines and high-precision fuzzy direction cosines are obtained according to the submatrices obtained by dividing the signal subspace. The fuzzy direction cosines are phase compensated by the reference direction cosines to obtain high-precision and unambiguous target direction cosines, and finally an accurate two-dimensional direction of arrival is obtained. The method can be applicable to scenarios with a limited number of array elements and limited array space. While the array geometry of the radar is not constrained, the mutual coupling effect is reduced, and the spatial response and polarization response information of the array output can be fully utilized to achieve high-precision two-dimensional direction of arrival estimation.

[0069] In an exemplary embodiment, the step of establishing a signal model of a source target and a multi-input multi-output includes: obtaining a polarization response expression of the multi-input multi-output radar based on an electric response vector and a magnetic response vector generated when the multi-input multi-output radar scans the source target; obtaining spatial response expressions of the transmitting array and the receiving array respectively according to the polarization response expression and position information of electromagnetic vector sensors in the transmitting array and the receiving array; and obtaining a signal model according to the polarization response expression and the spatial response expressions of the transmitting array and the receiving array.

[0070] Optionally, when the MIMO radar scans the source target, it will generate corresponding electric response vectors and magnetic response vectors. These vectors are quantitative descriptions of the electromagnetic field in different aspects during the interaction between the radar and the target. The electric response vector reflects the changes related to the electric field, such as the changes in the electric field strength and direction with factors such as target characteristics and radar scanning angle; the magnetic response vector reflects similar changes in the magnetic field. By comprehensively analyzing and processing these electric response vectors and magnetic response vectors, the polarization response expression of the multi-input multi-output radar can be extracted from them, so that the response characteristics of the radar to source target signals in different polarization states can be accurately described.

[0071] Furthermore, the spatial response expression of the transmitting array is derived by combining the obtained polarization response expression and the position information of the electromagnetic vector sensor (EMVS) in the transmitting array. Due to the different positions of the EMVS in the transmitting array, the signals transmitted by them will show different characteristics when propagating in space. The polarization information in the polarization response expression will also interact with these spatial characteristics and jointly determine the propagation state of the transmitted signal in space. By combining the polarization response expression with the position information of the transmitting array, the spatial response expression of the transmitting array can be obtained. The spatial response expression can clearly reflect the various changes caused by the layout and polarization characteristics of the transmitting array when the transmitted signal propagates in space, such as the intensity distribution and phase change of the signal in different directions.

[0072] Similarly, for the receiving array, the spatial response expression is obtained based on the polarization response expression and the position information of the electromagnetic vector sensor contained in it. When the echo signal reflected by the source target reaches the receiving array, the signals received by the EMVS at different positions will have different spatial performances due to the differences in the propagation paths. At the same time, the polarization information carried by the echo signal itself is also involved in this process, affecting the reception of the signal by the receiving array. Taking these factors into consideration, the spatial response expression of the receiving array can be obtained, which can accurately present the spatial response characteristics of the receiving array when receiving the echo signal based on its own spatial layout and signal polarization characteristics.

[0073] Furthermore, the polarization response expression, the spatial response expression of the transmitting array, and the spatial response expression of the receiving array are combined to construct a complete signal model, which comprehensively and meticulously describes the interaction between the source target and the multi-input multi-output radar. It not only includes the polarization characteristics of the signal, but also covers the spatial propagation characteristics of the signal in the transmitting and receiving stages.

[0074] As an example, a co-located EMVS consists of six components (three electric dipoles and three magnetic loops) located at a point in space, which can measure the electric and magnetic fields respectively. For a scenario where k perfectly polarized transverse electromagnetic (TEM) plane waves hit the EMVS, the noise-free output vector corresponding to the kth (k=1, 2, …, k) TEM wave is (that is, the kth polarization response vector) can be expressed as:

[0075]

[0076] in, , , and They represent elevation angle, azimuth angle, auxiliary polarization angle and polarization phase difference respectively.

[0077] Further, It can be broken down into:

[0078]

[0079] in:

[0080]

[0081]

[0082] in, is a matrix related only to direction, is a vector related only to polarization. From this we can see that can be divided into two parts, where the first three entities correspond to the electrical response vector , and the rest corresponds to the magnetic response vector ,Right now:

[0083]

[0084]

[0085] Where b(k) represents the kth element in vector b, Indicates transpose.

[0086] For the EMVS matrix, and The VCP between them satisfies:

[0087]

[0088] in, represents the vector cross product operation, represents conjugation, represents the Frobenius norm.

[0089] Further, for the irregularly spaced monostatic EMVS-MIMO radar system, the radar setup consists of two basic components: an M-element arbitrary geometry polarization transmit array and an N-element arbitrary geometry polarization receive array. In order to accurately locate the positions of the transmit and receive EMVS units in the reference Cartesian coordinate system, the to represent the position of the mth EMVS transmitter, represents the position of the nth receiving EMVS. And the distances between the elements of the array transmitting and receiving arrays are and Each antenna in the system is paired with a complete, co-located EMVS, and the transmit arrays collectively transmit M complete polarization narrowband pulse waveforms, represented as Since the echo is line-of-sight propagation, the far-field target can be interpreted as a point target. In this case, the baseband normalized electric field signal emitted by the mth transmitting EMVS to the kth far-field target can be expressed as:

[0090]

[0091] Where t represents the fast time index, As a weight vector, the polarization state of the transmitted waveform is controlled. Including the horizontal and vertical components of the waveform, represents the waveform transmitted by the mth EMVS transmitter. Assuming the waveform are mutually orthogonal and have unit power over the pulse duration, that is, for any and , then:

[0092]

[0093] Where T is the pulse duration. Therefore, the echo reflected by the kth coherent target is expressed as:

[0094]

[0095] Where l is the pulse index, represents the radar cross-section (RCS) coefficient of the kth coherent target during the lth pulse. The waveform vector is expressed as ,in, is the spatial response vector of the transmitting array to the kth target, .

[0096] Furthermore, the receiving EMVS collects the lth pulse data (echoes of far-field non-correlated targets) in a snapshot manner, and its data can be specifically described as

[0097]

[0098] in, represents the Kronecker product, Corresponding to a 2×2 scattering matrix, it comprehensively represents the polarization transformation characteristics of the kth target, represents the polarization response vector associated with the kth target. is the spatial response vector of the receiving array to the kth target, . is zero and has a variance of The Gaussian white noise vector is unrelated to the transmitted waveform. The echo generated by the reflection of the mth transmitted waveform by K coherent targets can be restored by matched filtering:

[0099]

[0100] The matched filtering outputs of the M transmit waveform echoes are stacked into a new vector, expressed as:

[0101]

[0102] From this we can get:

[0103]

[0104] in, represents the Khatri–Rao product, , , , , .

[0105] When L snapshots are collected, the stack of L matched filtered signal vectors of M transmit waveforms can be represented as:

[0106]

[0107] in, represents the RCS matrix, represents the noise matrix.

[0108] In this embodiment, by using the polarization response of the output sensed by the electromagnetic vector sensor array in the signal model, the polarization response information can be fully utilized to more finely characterize the source signals arriving from different directions, so that the signal model can more accurately reflect the propagation characteristics and response patterns of the signal in the sensor array.

[0109] In an exemplary embodiment, the step of obtaining a signal subspace according to a signal model includes: obtaining a signal covariance matrix of the signal model; performing eigendecomposition on the signal covariance matrix to obtain multiple eigenvalues ​​and eigenvectors corresponding to the eigenvalues; and obtaining a signal subspace according to a combination of eigenvectors.

[0110] Optionally, based on the signal model, the effective signal information actually received by the sensor array is obtained, and the signal covariance matrix of the signal model is obtained based on the effective signal, wherein the signal covariance matrix is ​​used to describe the correlation between signals, and it can reflect the degree of correlation between signals received at different times or different array elements. Afterwards, the signal covariance matrix is ​​subjected to eigendecomposition. Eigendecomposition is a method of decomposing a matrix into eigenvalues ​​and eigenvectors. For the signal covariance matrix, its inherent structure and characteristics can be revealed through eigendecomposition. Specifically, after eigendecomposition, a set of eigenvalues ​​and eigenvectors corresponding to each eigenvalue are obtained. The eigenvalues ​​reflect the importance or "weight" of the "component" represented by the corresponding eigenvector in the entire signal covariance matrix, and the eigenvectors constitute a special vector space. They have a specific linear relationship with each other, and these eigenvectors carry different modes and characteristic information of the signal.

[0111] Furthermore, the signal subspace is obtained based on the combination of eigenvectors. Usually, the eigenvectors corresponding to larger eigenvalues ​​are used to span the signal subspace, because the components represented by these eigenvectors account for a larger proportion in the signal and can better reflect the main characteristics and information of the signal. However, the eigenvectors corresponding to smaller eigenvalues ​​are often regarded as related to noise and are excluded from the construction of the signal subspace. By selecting appropriate eigenvectors for combination, the signal subspace can be constructed, which contains the core information of the signal.

[0112] Exemplarily, in combination with the above embodiment, if C is uncorrelated with N, the covariance matrix of X is:

[0113]

[0114] in, represents the conjugate transpose, represents the covariance matrix of the source, represents the power of the noise, Represents the 6M×6M identity matrix.

[0115] In the Eigenvalue Decomposition (EVD) form, It can be expressed as:

[0116]

[0117] in, and denote the qth eigenvalue and qth eigenvector respectively, where the eigenvalue satisfies:

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] in, represents the operation of constructing a diagonal matrix, and are the signal subspace and the noise subspace respectively. and span the same subspace, that is:

[0126]

[0127] in, is a non-singular matrix. When there are L (L > 1) snapshots available, It can be estimated by:

[0128]

[0129] Therefore, its EVD is expressed as:

[0130]

[0131] in, , , , , , , and Respectively express , , , , , , and The estimated value of .

[0132] In this embodiment, by combining the polarization response of the output sensed by the electromagnetic vector sensor array in the signal model, the polarization response information can be fully utilized to more finely characterize the source signals arriving from different directions, so that the signal model can more accurately reflect the propagation characteristics and response mode of the signal in the multi-input multi-output radar.

[0133] In an exemplary embodiment, the step of obtaining reference direction cosines based on the first sub-matrix obtained by division includes: combining the first sub-matrices obtained by division, and performing eigendecomposition on the combined matrix to obtain a first factor matrix; obtaining an electric field response vector and a magnetic field response vector based on the diagonal elements of the first factor matrix; and performing normalized vector cross multiplication processing on the electric field response vector and the magnetic field response vector to obtain reference direction cosines.

[0134] Optionally, in the signal processing process, based on the rotation invariance of the polarization response in the signal subspace, a specific selection matrix is ​​used to divide the signal subspace to obtain a first submatrix, each of which contains specific information related to the polarization response. These scattered submatrices are integrated through a specific combination method to comprehensively reflect the characteristic relationship of the signal in terms of polarization. After that, the combined matrix is ​​eigendecomposed, and the first factor matrix obtained after the eigendecomposition has elements that carry the key characteristics contained in the combined matrix.

[0135] Optionally, the diagonal elements of the first factor matrix contain key information closely related to the polarization response, from which the necessary information for constructing the electric field response vector and the magnetic field response vector can be extracted. The electric field response vector and the magnetic field response vector are important mathematical representations for describing the polarization characteristics of the signal, and can comprehensively characterize the key characteristics of the signal in the electromagnetic field, such as the polarization state and direction.

[0136] Optionally, the normalized vector cross product (VCP) technology is a processing method unique to EMVS. It normalizes the acquired electric field response vector and magnetic field response vector, which can eliminate the influence of factors such as vector amplitude difference on the result, so that the subsequent cross product operation can focus more on the directional characteristics of the vector. The final reference direction cosine is an important intermediate result, which characterizes the angle information of the signal in a certain dimension and is the data basis for 2D-DOA estimation.

[0137] For example, can be rewritten as:

[0138]

[0139] in, .

[0140] Further we can get:

[0141]

[0142] definition , .

[0143] because is a constant, then:

[0144]

[0145] It can be seen that the direction cosines can be estimated from the normalized polarization response vector, for example, using a signal processing algorithm (ESPRIT-Like) to determine the direction cosines from the signal subspace , that is:

[0146]

[0147] in, Indicates taking The qth row of . Define the first selection matrix as:

[0148]

[0149] in, Represents the identity matrix The vth row of , then:

[0150]

[0151] Will Substituting in, we get:

[0152]

[0153] in, and They represent the matrix inverse and pseudo-inverse operations respectively.

[0154] It can be seen that the left side of the formula is consistent with the EVD expression, that is, EVD can be obtained and Therefore, EVD can be obtained and The estimated values ​​of and . After that, the remaining factor matrix can be expressed as:

[0155]

[0156] make for The kth diagonal element of . Construct and , the reference direction cosines are obtained by the following formula:

[0157]

[0158] Furthermore, the calculation method of 2D-DOA is:

[0159]

[0160]

[0161] in, of The sign of is determined by the sign of . The displacement effect caused by The 2D-DOA is automatically matched. is independent of the spatial response moment A that includes the EMVS array geometry. Therefore, There is no ambiguity.

[0162] In this embodiment, by constructing a specific selection matrix, the signal subspace is divided into sub-matrices. Based on the rotational invariance between different sub-matrices, they can be transformed into each other through factor matrices in the form of specific diagonal matrices. By decomposing different combinations of sub-matrices to obtain the factor matrix, the reference direction cosines can be accurately obtained.

[0163] In an exemplary embodiment, the step of obtaining fuzzy direction cosines according to the second sub-matrix obtained by division includes: obtaining a second factor matrix corresponding to the second sub-matrix; taking the phases of the diagonal elements of the second factor matrix respectively to obtain a reference phase corresponding to the echo signal; obtaining a deterministic matrix of the signal model; and taking the product of the reference phase and the pseudo-inverse of the deterministic matrix as the fuzzy direction cosines.

[0164] Optionally, the signal subspace is divided into sub-matrices by constructing a specific selection matrix. In a single base, the principles based on the spatial rotation invariance of the transmitting and receiving arrays are the same, and only the spatial rotation invariance of the transmitting array is used as an example for explanation.

[0165] Specifically, in signal processing, according to the inherent spatial rotation invariance of the signal subspace, the signal subspace is divided using a selection matrix to obtain a second submatrix, and the second submatrix contains information related to the spatial rotation invariance. Similar to the processing method of the first submatrix, a second factor matrix corresponding to the second submatrix is ​​obtained, wherein the diagonal elements of the second factor matrix generally contain information related to the spatial response.

[0166] Furthermore, taking into account the spatial rotation invariance, the phases of the diagonal elements of the second factor matrix are taken, and this information is converted into more intuitive phase information. The phase information can reflect the relative phase relationship of the signal source on different array elements. Because in array signal processing, the arrival direction of the signal source is closely related to the phase difference between the array elements, the phases of these diagonal elements can be used as the reference phase corresponding to the source signal, providing a basis for the subsequent calculation of the fuzzy direction cosines.

[0167] Optionally, the reference phase is a key intermediate quantity for calculating the ambiguous direction cosines, which reflects the phase change caused by different array element positions and the direction of arrival of the signal source during the spatial propagation process. Although a preliminary and possibly ambiguous phase information is obtained at this time, it provides an important basis for subsequent compensation processing. By further processing these reference phases, we can get closer to the final precise direction cosines.

[0168] For example, for any array geometry, it satisfies:

[0169]

[0170] in, , .

[0171] Define the selection matrix as:

[0172]

[0173] Then, the rotation invariance can be expressed as:

[0174]

[0175] Similarly, using replace We can get:

[0176]

[0177] It can be seen that the EVD on the right side of the formula gives and Assumptions and have been estimated by the ESPRIT-Like algorithm, then It can be estimated by the following calculation:

[0178]

[0179] If the spacing between array elements satisfies , then:

[0180]

[0181] in, Represents the phase operation. It can be expressed in matrix form as:

[0182]

[0183] in:

[0184]

[0185] Since F is deterministic, the blurred direction cosines can be obtained through spatial rotation invariance:

[0186]

[0187] in, , express The kth diagonal element of .

[0188] In this embodiment, the signal subspace is divided into sub-matrices by selecting a matrix, and the combination results of specific sub-matrices are eigendecomposed through spatial rotation invariance to obtain a factor matrix whose diagonal elements are spatial response ratios. The diagonal elements of the factor matrix are phased and multiplied by the pseudo-inverse of the deterministic matrix to obtain high-precision fuzzy direction cosines.

[0189] In an exemplary embodiment, the step of performing phase compensation on the ambiguous direction cosines through reference direction cosines to obtain target direction cosines includes: performing phase compensation on the ambiguous direction cosines according to the signal period of the echo signal and the reference direction cosines to obtain phase compensation values; and taking the product of the phase compensation values ​​and the pseudo-inverse matrix of the deterministic matrix as the target direction cosines.

[0190] Optionally, the signal period of the transmitted signal and the received signal reflects the temporal repetitiveness of the signal. Due to the influence of factors such as spatial response, there is phase ambiguity in the fuzzy direction cosines, and the time periodic information contained in the signal period can be used to judge and correct the phase deviation according to the law of the signal itself.

[0191] Furthermore, the reference direction cosines are relatively accurate and unambiguous direction cosine information that has been obtained. Since the reference direction cosines have correctly reflected the angular relationship of the signal in a certain dimension, the phase difference between it and the fuzzy direction cosines, combined with the periodic constraints provided by the signal period, can be used to calculate the specific phase difference value that needs to be compensated for the fuzzy direction cosines, that is, the phase compensation value, so that the phase of the fuzzy direction cosines is close to the accurate phase of the reference direction cosines, eliminating the ambiguity in the fuzzy direction cosines. The target direction cosines are obtained by multiplying the phase compensation value with the pseudo-inverse matrix of the deterministic matrix. The target direction cosines are the result after phase compensation. It eliminates the ambiguity in the previous fuzzy direction cosines and accurately reflects the direction cosine information of the source signal. Finally, based on the established relationship between the target direction cosines and 2D-DOA, the 2D-DOA of the source signal can be accurately determined, and the high-precision estimation of the arrival direction of the source signal can be achieved.

[0192] For example, due to the exponential mapping is periodic and repeats at intervals of 2π, so once ,formula may fail. Therefore, it should be modified to:

[0193]

[0194] in, is an integer, then:

[0195]

[0196] in, is a real-valued integer vector.

[0197] In order to accurately estimate the direction cosines from the spatial response vector, it is necessary to first determine ,because The direction cosines are estimated by the normalized VCP technique, so the constructed Should be:

[0198]

[0199] therefore, It can be determined as:

[0200]

[0201] Afterwards, you can restore it by The true phase of:

[0202]

[0203] Afterwards, the target direction cosines are determined as follows:

[0204]

[0205] Finally, the 2D-DOA is estimated as:

[0206]

[0207]

[0208] In this embodiment, by performing phase compensation on the ambiguous direction cosines by using reference direction cosines, it is possible to achieve unambiguous high-precision direction cosine estimation, and further obtain high-precision two-dimensional direction of arrival estimation.

[0209] In an exemplary embodiment, Figure 2 As shown, a radar direction of arrival estimation method is provided, the method comprising the following steps:

[0210] Based on the electric response vector and magnetic response vector generated when the MIMO radar scans the source target, the polarization response expression of the MIMO radar is obtained; according to the polarization response expression and the position information of the electromagnetic vector sensors in the transmitting array and the receiving array, the spatial response expressions of the transmitting array and the receiving array are obtained respectively; according to the polarization response expression and the spatial response expressions of the transmitting array and the receiving array, the signal model is obtained.

[0211] Obtain a signal covariance matrix of the signal model; perform eigendecomposition on the signal covariance matrix to obtain a plurality of eigenvalues ​​and eigenvectors corresponding to the eigenvalues; and obtain a signal subspace according to a combination of the eigenvectors.

[0212] Determine a first selection matrix and a second selection matrix; the first selection matrix is ​​used to obtain elements in the signal subspace that meet the rotational invariance requirement of the polarization response; the second selection matrix is ​​used to obtain elements in the signal subspace that meet the spatial invariance requirement;

[0213] The signal subspace is divided into submatrices by a first selection matrix, the first submatrices obtained by the division are combined, and the combined matrix is ​​eigendecomposed to obtain a first factor matrix; the electric field response vector and the magnetic field response vector are obtained according to the diagonal elements of the first factor matrix; the electric field response vector and the magnetic field response vector are subjected to normalized vector cross multiplication processing to obtain reference direction cosines.

[0214] The signal subspace is divided into submatrices through a second selection matrix to obtain a second factor matrix corresponding to the second submatrix; the diagonal elements of the second factor matrix are phased to obtain a reference phase corresponding to the echo signal; the deterministic matrix of the signal model is obtained; and the product of the reference phase and the pseudo-inverse of the deterministic matrix is ​​used as the fuzzy direction cosine.

[0215] According to the signal period of the echo signal and the reference direction cosines, the fuzzy direction cosines are phase compensated to obtain a phase compensation value; the product of the phase compensation value and the pseudo-inverse matrix of the deterministic matrix is ​​used as the target direction cosines.

[0216] The two-dimensional arrival direction of the echo signal is determined according to the target direction cosine; the two-dimensional arrival direction includes the azimuth angle and the pitch angle.

[0217] The expression of the signal model is: ; Among them, the matrix The matrix B is used to characterize the spatial response vector of the transmitting array and the matrix A is used to characterize the polarization response vector of the sensor array. It is used to characterize the spatial response vector of the receiving array, the matrix C is the radar cross section matrix of the multiple-input multiple-output radar, and the matrix N is the noise matrix;

[0218] Among them, the matrix The expression is , is the spatial response vector of the transmitting array to the kth source target; the expression of the matrix B is: , is the polarization response vector associated with the kth source target; the matrix The expression is , is the spatial response vector of the receiving array to the kth source target.

[0219] Exemplarily, a large number of simulations are performed using Monte Carlo experiments to evaluate the performance of the method proposed in the embodiment of the present application for 2D-DOA estimation. Assuming that the signal-to-noise ratio (SNR) is SNR=10dB, the number of snapshots L=200, the number of transmitting array elements M=6, the number of receiving array elements N=8, and k=3 far-field, incoherent targets appearing within the common detection range of the transmitting and receiving arrays in the EMVS-MIMO radar, the corresponding source signal parameters are as follows: , , , , using the root mean square error (RMSE) as the evaluation indicator, the calculation formula is:

[0220]

[0221] in, represents the jth Monte Carlo experiment The estimate, Same reason.

[0222] Furthermore, the test results are as follows Figure 3-Figure 5 shown. Figure 3 The scattering results of the radar direction of arrival estimation method in the present application when the array elements of the transmitting and receiving arrays are randomly distributed in the three-dimensional spatial range of 3λ, compared with the respective coarse 2D-DOA estimates on the left, the right side shows a more tightly clustered scattering pattern, therefore, the direction of arrival estimation method provided by the embodiment of the present application has higher estimation accuracy.

[0223] Further, Figure 4 The curve of RMSE of DOA estimation versus SNR when the array elements of a non-regularly spaced single-base EMVS-MIMO radar are randomly distributed in a range of 3λ in three-dimensional space is shown. The method provided in the embodiment of the present application is compared with the ESPRIT-Like algorithm and the Cramer-Rao bound (CRB), the theoretical lower limit of 2D-DOA estimation accuracy. Figure 4 It can be seen that as the signal-to-noise ratio increases, the radar direction of arrival estimation method provided in the present application can improve the accuracy of DOA estimation.

[0224] Furthermore, when the transmitting and receiving array elements of the irregularly spaced single-base EMVS-MIMO radar are randomly distributed in a three-dimensional space, the angle estimation RMSE of the method provided by the embodiment of the present application and the comparison algorithm changes with P. Figure 5 It can be seen that the direction of arrival estimation method based on the sensor array provided in the present application can improve the accuracy of DOA estimation compared with the comparison algorithm.

[0225] In this embodiment, by establishing a signal model of a source target and a multi-input multi-output radar, a signal subspace is determined according to the signal model, and elements satisfying the rotational invariance requirement of the polarization response and the spatial invariance requirement in the signal subspace are respectively obtained by selecting a matrix, and according to the submatrix obtained by dividing the signal subspace, a rough reference direction cosine and a high-precision fuzzy direction cosine are obtained, and the fuzzy direction cosines are phase compensated for by the reference direction cosines to obtain high-precision and unambiguous target direction cosines, and finally an accurate two-dimensional direction of arrival is obtained. The invention can be applicable to scenarios with a limited number of array elements and a limited array space, and can reduce the mutual coupling effect while the array geometry of the radar is not constrained, and can make full use of the spatial response and polarization response information of the array output to achieve high-precision two-dimensional direction of arrival estimation.

[0226] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0227] Based on the same inventive concept, the embodiment of the present application also provides a radar direction of arrival estimation device for implementing the radar direction of arrival estimation method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of the radar direction of arrival estimation device provided below can refer to the limitations of the radar direction of arrival estimation method above, and will not be repeated here.

[0228] In an exemplary embodiment, Figure 6 As shown, a radar direction of arrival estimation device is provided, comprising: a signal model building module 10, a selection matrix determination module 20, a reference direction acquisition module 30, a fuzzy direction acquisition module 40 and an arrival direction acquisition module 50, wherein:

[0229] The signal model building module is used to build the signal model of the source target and the multi-input multi-output radar, and determine the signal subspace according to the signal model.

[0230] The selection matrix determination module is used to determine a first selection matrix and a second selection matrix; the first selection matrix is ​​used to obtain elements in the signal subspace that meet the rotational invariance requirement of the polarization response; the second selection matrix is ​​used to obtain elements in the signal subspace that meet the spatial invariance requirement.

[0231] The reference direction acquisition module is used to divide the signal subspace into sub-matrices through a first selection matrix, and obtain reference direction cosines according to a first sub-matrix obtained by the division.

[0232] The fuzzy direction acquisition module is used to divide the signal subspace into sub-matrices through a second selection matrix, and obtain the fuzzy direction cosines according to the second sub-matrix obtained by the division.

[0233] The arrival direction acquisition module is used to perform phase compensation on the fuzzy direction cosines through the reference direction cosines to obtain the target direction cosines, and determine the two-dimensional arrival direction of the echo signal according to the target direction cosines; the two-dimensional arrival direction includes the azimuth angle and the pitch angle.

[0234] The expression of the signal model is: ; Among them, the matrix The matrix B is used to characterize the spatial response vector of the transmitting array and the matrix A is used to characterize the polarization response vector of the sensor array. It is used to characterize the spatial response vector of the receiving array, the matrix C is the radar cross section matrix of the multiple-input multiple-output radar, and the matrix N is the noise matrix;

[0235] Among them, the matrix The expression is , is the spatial response vector of the transmitting array to the kth source target; the expression of the matrix B is: , is the polarization response vector associated with the kth source target; the matrix The expression is , is the spatial response vector of the receiving array to the kth source target.

[0236] In an exemplary embodiment, the signal model building module 10 is also used to obtain the spatial response expressions of the transmitting array and the receiving array respectively according to the polarization response expressions and the position information of the electromagnetic vector sensors in the transmitting array and the receiving array; and obtain the signal model according to the polarization response expressions and the spatial response expressions of the transmitting array and the receiving array.

[0237] In an exemplary embodiment, the signal model building module 10 is also used to obtain a signal covariance matrix of the signal model; perform eigendecomposition on the signal covariance matrix to obtain multiple eigenvalues ​​and eigenvectors corresponding to the eigenvalues; and obtain a signal subspace based on a combination of eigenvectors.

[0238] In an exemplary embodiment, the reference direction acquisition module 30 is also used to combine the first sub-matrices obtained by the division, perform eigendecomposition on the combined matrix, and obtain a first factor matrix; obtain the electric field response vector and the magnetic field response vector according to the diagonal elements of the first factor matrix; perform normalized vector cross multiplication processing on the electric field response vector and the magnetic field response vector to obtain the reference direction cosines.

[0239] In an exemplary embodiment, the fuzzy direction acquisition module 40 is also used to obtain a second factor matrix corresponding to the second sub-matrix; take the phase of the diagonal elements of the second factor matrix respectively to obtain a reference phase corresponding to the echo signal; obtain the deterministic matrix of the signal model; and use the product of the reference phase and the pseudo-inverse of the deterministic matrix as the fuzzy direction cosine.

[0240] In an exemplary embodiment, the arrival direction acquisition module 50 is also used to perform phase compensation on the ambiguous direction cosines according to the signal period of the echo signal and the reference direction cosines to obtain a phase compensation value; and the product of the phase compensation value and the pseudo-inverse matrix of the deterministic matrix is ​​used as the target direction cosines.

[0241] Each module in the above radar direction of arrival estimation device can be implemented in whole or in part by software, hardware and a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0242] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a radar direction of arrival estimation method is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0243] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0244] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0245] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0246] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0247] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0248] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0249] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A radar direction of arrival estimation method, characterized in that: The invention is applied to a multi-input multi-output radar using a sensor array; the sensor array includes a transmitting array and a receiving array composed of a plurality of electromagnetic vector sensors arranged at irregular intervals; the method includes: Establishing a signal model of a source target and the MIMO radar, and determining a signal subspace according to the signal model; Determine a first selection matrix and a second selection matrix; the first selection matrix is ​​used to obtain elements in the signal subspace that meet the rotational invariance requirement of the polarization response; the second selection matrix is ​​used to obtain elements in the signal subspace that meet the spatial invariance requirement; Performing submatrix division on the signal subspace using the first selection matrix, and obtaining reference direction cosines according to a first submatrix obtained by the division; Performing submatrix division on the signal subspace by using the second selection matrix, and obtaining fuzzy direction cosines according to the second submatrix obtained by the division; Phase compensation is performed on the fuzzy direction cosines by using the reference direction cosines to obtain target direction cosines, and a two-dimensional arrival direction of the echo signal is determined according to the target direction cosines; the two-dimensional arrival direction includes an azimuth angle and a pitch angle; Wherein, the expression of the signal model is: ; Among them, the matrix The matrix A is used to characterize the spatial response vector of the transmitting array, the matrix B is used to characterize the polarization response vector of the sensor array, and the matrix A spatial response vector for characterizing the receiving array, a matrix C is a radar cross section matrix of the MIMO radar, and a matrix N is a noise matrix; Among them, the matrix The expression is , is the spatial response vector of the transmitting array to the kth source target; the expression of the matrix B is: , is the polarization response vector associated with the kth source target; the matrix The expression is , is the spatial response vector of the receiving array to the kth source target.

2. The method according to claim 1, characterized in that: The establishing of the signal model of the source target and the multi-input multi-output includes: Based on the electric response vector and the magnetic response vector generated when the MIMO radar scans the source target, obtaining the polarization response expression of the MIMO radar; According to the polarization response expression and the position information of the electromagnetic vector sensors in the transmitting array and the receiving array, respectively obtaining the spatial response expressions of the transmitting array and the receiving array; A signal model is obtained according to the polarization response expression and the spatial response expressions of the transmitting array and the receiving array.

3. The method according to claim 1, characterized in that The acquiring a signal subspace according to the signal model comprises: Obtaining a signal covariance matrix of the signal model; Performing eigendecomposition on the signal covariance matrix to obtain a plurality of eigenvalues ​​and eigenvectors corresponding to the eigenvalues; A signal subspace is obtained according to the combination of the eigenvectors.

4. The method according to claim 1, characterized in that: According to the first sub-matrix obtained by division, the reference direction cosines are obtained, including: The first sub-matrices obtained by the division are combined, and the combined matrices are subjected to eigendecomposition to obtain a first factor matrix; Obtaining an electric field response vector and a magnetic field response vector according to the diagonal elements of the first factor matrix; A normalized vector cross multiplication process is performed on the electric field response vector and the magnetic field response vector to obtain reference direction cosines.

5. The method according to claim 1, characterized in that The step of obtaining fuzzy direction cosines according to the second sub-matrix obtained by dividing includes: Obtain a second factor matrix corresponding to the second submatrix; Taking phases of the diagonal elements of the second factor matrix respectively to obtain reference phases corresponding to the echo signal; Obtaining a deterministic matrix of the signal model; The product of the reference phase and the pseudo-inverse of the deterministic matrix is ​​used as the fuzzy direction cosine.

6. The method according to claim 5, characterized in that The phase compensation of the fuzzy direction cosines by using the reference direction cosines to obtain the target direction cosines includes: Performing phase compensation on the ambiguous direction cosines according to the signal period of the echo signal and the reference direction cosines to obtain a phase compensation value; The product of the phase compensation value and the pseudo-inverse matrix of the deterministic matrix is ​​used as the target direction cosine.

7. A radar direction of arrival estimation device, characterized in that: The device comprises: A signal model building module, used to build a signal model of a source target and the MIMO radar, and determine a signal subspace according to the signal model; A selection matrix determination module is used to determine a first selection matrix and a second selection matrix; the first selection matrix is ​​used to obtain elements in the signal subspace that meet the rotational invariance requirement of the polarization response; the second selection matrix is ​​used to obtain elements in the signal subspace that meet the spatial invariance requirement; A reference direction acquisition module, used for performing sub-matrix division on the signal subspace by using the first selection matrix, and obtaining reference direction cosines according to a first sub-matrix obtained by the division; A fuzzy direction acquisition module, used for performing sub-matrix division on the signal subspace by using the second selection matrix, and obtaining fuzzy direction cosines according to the second sub-matrix obtained by the division; An arrival direction acquisition module is used to perform phase compensation on the fuzzy direction cosines through the reference direction cosines to obtain target direction cosines, and determine the two-dimensional arrival direction of the echo signal according to the target direction cosines; the two-dimensional arrival direction includes an azimuth angle and a pitch angle; Wherein, the expression of the signal model is: ; Among them, the matrix The matrix A is used to characterize the spatial response vector of the transmitting array, the matrix B is used to characterize the polarization response vector of the sensor array, and the matrix A spatial response vector for characterizing the receiving array, a matrix C is a radar cross section matrix of the MIMO radar, and a matrix N is a noise matrix; Among them, the matrix The expression is , is the spatial response vector of the transmitting array to the kth source target; the expression of the matrix B is: , is the polarization response vector associated with the kth source target; the matrix The expression is , is the spatial response vector of the receiving array to the kth source target.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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