A vehicle positioning method and device based on spatial spectrum estimation

Abstract

The application discloses a vehicle positioning method and device based on a spatial spectrum estimation method, and the method comprises the following steps: receiving positioning signals of vehicles to be positioned by using multiple coprime antenna arrays and obtaining first received signals by considering noise interference; calculating fourth-order cumulant matrices of the first received signals, constructing a virtual matrix, eliminating Gaussian noise, vectorizing the fourth-order cumulant matrices to obtain second received signals, and calculating a position matrix of a virtual array according to positions of antennas in the coprime antenna array; performing spatial smoothing processing on the second received signals corresponding to a uniform linear part of the virtual array to obtain a smoothing matrix; when the number of vehicles to be positioned is unknown, constructing a noise subspace according to the smoothing matrix and a hyperparameter; constructing a spatial spectrum function according to the noise subspace and a direction vector and estimating a wave arrival direction; and determining vehicle positions in combination with position information of the array. The application can simultaneously position multiple vehicles in real time and with high precision under the condition that the number of vehicles is unknown, and can meet actual application requirements.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Vehicles, and in particular to a vehicle positioning method and device based on spatial spectrum estimation. BACKGROUND

[0002] With the increasing number of vehicles in modern society, urban traffic is facing severe challenges. In order to solve the increasingly complex traffic problems, people are committed to developing related technologies of intelligent transportation or intelligent vehicles. Intelligent vehicles are a comprehensive ecological system based on automatic driving technology and Internet of Vehicles technology, in which real-time and accurate vehicle positioning information is a key factor to ensure safe traffic.

[0003] In modern vehicle positioning technology, the Global Positioning System (GPS) is the most common solution. However, the receiving device of the GPS positioning technology is relatively expensive, and it is difficult to complete accurate positioning in environments with more shielding or more closed environments, which cannot meet the practical requirements of vehicle positioning. The application of other positioning technologies also has some defects, for example, the RSSI-based positioning needs to know the spatial fading characteristics of the signal, and the TDOA-based positioning is sensitive to time difference measurement, which cannot achieve stable high-precision positioning in real scenarios.

[0004] The Direction Of Arrival (DOA) estimation is a branch of array signal processing. The vehicle positioning method based on DOA estimation algorithm only relies on the accuracy of the DOA estimation algorithm, is simple to implement, has low cost, and can obtain relatively accurate vehicle position information. However, the existing vehicle positioning technology based on DOA estimation algorithm basically uses a uniform linear array to receive signals emitted by vehicles. A uniform linear array with N antennas can at most locate N-1 number of vehicles, which cannot meet the actual application requirements. Moreover, these methods need to know the number of vehicles to be positioned in advance, otherwise they cannot be implemented, which is also difficult to achieve in real application scenarios. SUMMARY

[0005] The purpose of the present application is to provide a vehicle positioning method and device based on spatial spectrum estimation, to solve the technical problems in the prior art that the number of vehicles to be positioned is limited and the number of vehicles to be positioned must be known in advance, which cannot meet the actual application requirements.

[0006] The purpose of the present application can be achieved by the following technical solutions:

[0007] A vehicle positioning method based on spatial spectrum estimation, comprising:

[0008] The positioning signal emitted by the vehicle to be positioned is received by multiple coprime antenna arrays, and a first received signal is obtained by considering the noise interference in the environment. The first received signal is the signal received by the coprime antenna array containing Gaussian noise.

[0009] A fourth-order cumulant matrix of the first received signal is calculated, and a virtual array is constructed to eliminate Gaussian noise by using the fourth-order cumulant. The fourth-order cumulant matrix is vectorized to obtain a second received signal, which is the received signal of the virtual array. A position matrix of the virtual array is calculated according to the positions of the antennas in the coprime antenna array.

[0010] A uniform linear part of the virtual array is obtained according to the position matrix and used as a sub-virtual array. The second received signal corresponding to the sub-virtual array is processed by forward and backward spatial smoothing to obtain a smoothing matrix.

[0011] When the number of vehicles to be positioned is unknown, a noise subspace is constructed according to the smoothing matrix and a hyperparameter. The hyperparameter is a positive number that ensures that the noise subspace satisfies the inverse condition.

[0012] A spatial spectrum function is constructed according to the noise subspace and a direction vector. The spatial spectrum function is searched for a spectral peak to estimate the direction of arrival of the positioning signal. The current position of the vehicle to be positioned is determined according to the position information of the coprime antenna array and the direction of arrival.

[0013] Optionally, receiving the positioning signal emitted by the vehicle to be positioned by multiple coprime antenna arrays and obtaining a first received signal by considering the noise interference in the environment includes:

[0014] The positioning signal emitted by the vehicle to be positioned is received by three coprime antenna arrays, and a first received signal is obtained by considering the noise interference in the environment. The three coprime antenna arrays form a right-angled triangle.

[0015] Optionally, the positioning signal is a far-field narrow-band uncorrelated signal.

[0016] Optionally, vectorizing the fourth-order cumulant matrix to obtain a second received signal includes:

[0017] According to x v =vec(C x )=A s (θ)C v The fourth-order cumulant matrix is vectorized to obtain a second received signal.

[0018] Where x v is the second received signal, C x is the fourth-order cumulant matrix of the first received signal, C v is the fourth-order cumulant matrix of the positioning signal, and Av (θ) is the direction matrix of the virtual array, where θ is the direction of arrival and vec represents the vectorization operation.

[0019] Optionally, performing forward and backward spatial smoothing on the second received signal corresponding to the sub-virtual array to obtain a smoothing matrix includes:

[0020] The second received signal corresponding to the sub-virtual array is subjected to forward spatial smoothing to obtain a first smoothing result;

[0021] The second received signal corresponding to the sub-virtual array is subjected to backward spatial smoothing to obtain a second smoothing result;

[0022] The smoothing matrix is ​​obtained by averaging the first smoothing result and the second smoothing result.

[0023] Optionally, before constructing the noise subspace based on the smoothing matrix and hyperparameters, the method further includes:

[0024] The regression model is trained using machine learning methods, and the signal-to-noise ratio in the actual application scenario is input into the regression model to obtain the value of the hyperparameter.

[0025] The present invention also provides a vehicle positioning device based on spatial spectrum estimation, comprising:

[0026] The first received signal acquisition module is used to receive the positioning signal emitted by the vehicle to be located using multiple coprime antenna arrays, and to obtain the first received signal by taking into account noise interference in the environment. The first received signal is a signal containing Gaussian noise received by the coprime antenna arrays.

[0027] The virtual array construction and calculation module is used to calculate the fourth-order cumulant matrix of the first received signal and construct a virtual array, and use the fourth-order cumulant to eliminate Gaussian noise; vectorize the fourth-order cumulant matrix to obtain the second received signal, which is the received signal of the virtual array; and calculate the position matrix of the virtual array based on the position of the antennas in the coprime antenna array.

[0028] The spatial smoothing module is used to obtain the uniform linear part of the virtual array according to the position matrix and use it as a sub-virtual array, and to perform forward and backward spatial smoothing processing on the second received signal corresponding to the sub-virtual array to obtain a smoothing matrix.

[0029] A noise subspace construction module is used to construct a noise subspace based on the smoothing matrix and hyperparameters when the number of vehicles to be located is unknown. The hyperparameters are positive numbers that ensure the noise subspace satisfies the inversion condition.

[0030] The vehicle location information determination module is used to construct a spatial spectrum function based on the noise subspace and the direction vector, perform spectral peak search on the spatial spectrum function to estimate the direction of arrival corresponding to the positioning signal, and determine the current location of the vehicle to be located based on the location information of the coprime antenna array and the direction of arrival.

[0031] Optionally, the first received signal is obtained by using multiple coprime antenna arrays to receive the positioning signal emitted by the vehicle to be located, and taking into account noise interference in the environment:

[0032] The first receiving signal acquisition module uses three coprime antenna arrays to receive the positioning signal emitted by the vehicle to be located, and takes into account noise interference in the environment to obtain the first received signal. The three coprime antenna arrays form a right triangle.

[0033] Optionally, the positioning signal is a far-field narrowband uncorrelated signal.

[0034] Optionally, vectorizing the fourth-order cumulant matrix to obtain the second received signal includes:

[0035] According to x v =vec(C x ) = A v (θ)C s The second received signal is obtained by vectorizing the fourth-order cumulant matrix;

[0036] Where, x v For the second received signal, C x C is the fourth-order cumulant matrix of the first received signal. s Let A be the fourth-order cumulant matrix of the positioning signal. v (θ) is the direction matrix of the virtual array, where θ is the direction of arrival and vec represents the vectorization operation.

[0037] This invention provides a vehicle positioning method and apparatus based on spatial spectrum estimation. The method includes: receiving positioning signals emitted by a vehicle to be located using multiple coprime antenna arrays, and obtaining a first received signal considering environmental noise interference, wherein the first received signal is a signal containing Gaussian noise received by the coprime antenna arrays; calculating the fourth-order cumulant matrix of the first received signal and constructing a virtual array, using the fourth-order cumulant to eliminate Gaussian noise; vectorizing the fourth-order cumulant matrix to obtain a second received signal, wherein the second received signal is the received signal of the virtual array; and calculating the virtual array based on the positions of the antennas in the coprime antenna arrays. A position matrix is ​​used to obtain a uniform linear portion of the virtual array as a sub-virtual array. The second received signal corresponding to the sub-virtual array is then subjected to forward and backward spatial smoothing to obtain a smoothing matrix. When the number of vehicles to be located is unknown, a noise subspace is constructed based on the smoothing matrix and hyperparameters, where the hyperparameters are positive numbers that ensure the noise subspace satisfies the inverse condition. A spatial spectrum function is constructed based on the noise subspace and direction vector. A peak search is performed on the spatial spectrum function to estimate the direction of arrival (DOA) corresponding to the positioning signal. The current position of the vehicle to be located is determined based on the position information of the coprime antenna array and the DOA.

[0038] Based on the above technical solution, the beneficial effects of this invention are:

[0039] This invention utilizes multiple coprime antenna arrays to receive positioning signals transmitted by vehicles to be located. Compared to traditional uniform linear arrays, coprime antenna arrays have larger element apertures, which can improve the degrees of freedom and resolution of DOA estimation and improve vehicle positioning performance. By employing fourth-order cumulants, a virtual array with a larger aperture is constructed while resisting Gaussian noise, greatly improving the degrees of freedom and spatial resolution, and increasing the accuracy of DOA estimation, thereby improving the accuracy of vehicle positioning. Moreover, compared to using a uniform linear array with the same number of antennas for vehicle positioning, this invention can simultaneously estimate a larger number of vehicles.

[0040] Furthermore, this invention introduces hyperparameters to construct a noise subspace. This method replaces the traditional method of finding the noise subspace, which avoids feature decomposition, reduces computation, lowers the computational complexity of vehicle positioning algorithms, and meets the real-time requirements of vehicle network for vehicle location information.

[0041] The noise subspace constructed by this invention does not require the number of vehicles to be located. By using this noise subspace to construct a spatial spectrum function and perform DOA estimation, DOA estimation can be achieved even when the number of sources is unknown. That is, high-precision vehicle positioning can be performed in real time without knowing the number of vehicles to be located in advance, which is more in line with real-world application scenarios. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating a first embodiment of the method of the present invention;

[0043] Figure 2 This is a schematic diagram of the vehicle positioning model in this invention;

[0044] Figure 3 This is a simplified diagram of the vehicle positioning model in this invention;

[0045] Figure 4 This is a schematic diagram of the geometric structure of the two sub-arrays in the coprime antenna array of the present invention;

[0046] Figure 5 This is a schematic diagram of the geometric structure of the coprime antenna array of the present invention;

[0047] Figure 6 This is a flowchart illustrating Embodiment 2 of the method of the present invention;

[0048] Figure 7 This is a schematic diagram of the support vector regression method used in determining hyperparameters in this invention;

[0049] Figure 8 This is a schematic diagram of the structure of an embodiment of the device of the present invention. Detailed Implementation

[0050] This invention provides a vehicle positioning method and apparatus based on spatial spectrum estimation to solve the technical problem that existing technologies have limited capacity for vehicle positioning and require prior knowledge of the number of vehicles to be located, thus failing to meet practical application needs.

[0051] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0053] Localization methods based on DOA estimation algorithms rely solely on the accuracy of the DOA estimation algorithm. This invention improves upon existing DOA estimation algorithms to obtain more accurate vehicle location information; furthermore, it is simpler to implement and has a lower cost.

[0054] Most existing vehicle localization technologies based on DOA estimation use uniform linear arrays to receive signals emitted by vehicles. However, in uniform linear array-based DOA estimation methods, the Nyquist sampling theorem restricts the distance between two array elements to no more than half the wavelength of the incident signal; otherwise, angular ambiguity will occur. Furthermore, the limited inter-element distance of a uniform linear array restricts spatial resolution and degrees of freedom. A uniform linear array with N antennas can only locate a maximum of N-1 vehicles, which is insufficient for practical applications. To improve this situation, this invention considers using other array structures, such as coprime antenna arrays, which consist of two coprime uniform subarrays intersecting. Coprime arrays, with the same number of physical elements as uniform linear arrays, have larger element apertures, which can improve the degrees of freedom and resolution of DOA estimation, thereby improving vehicle localization performance.

[0055] A commonly used DOA estimation algorithm is the MUSIC (Multiple Signal Classification) algorithm, which decomposes the observation space into a signal subspace and a noise subspace. The eigenvectors corresponding to the signal in the covariance matrix of the array's received signal form the signal subspace, while the eigenvectors corresponding to all the smallest eigenvalues ​​(noise variances) in this covariance matrix form the noise subspace. The MUSIC algorithm utilizes the orthogonality of these two complementary spaces to estimate the spatial signal's orientation. A typical MUSIC algorithm utilizes the second-order statistical properties of the received signal, i.e., the covariance matrix of the array's received data. To improve vehicle localization technology, this invention modifies the DOA algorithm by using a fourth-order cumulant instead of the covariance matrix, which can suppress the effect of Gaussian white noise. Furthermore, the fourth-order cumulant can expand the effective aperture of the array, increase the number of effective array elements, and achieve virtual array expansion, thereby improving the performance of DOA estimation and vehicle localization.

[0056] Furthermore, all current vehicle localization technologies based on DOA estimation algorithms require prior knowledge of the number of signal sources, i.e., the number of vehicles to be located; otherwise, they cannot be implemented, which is difficult to achieve in real-world applications. To address this issue, this invention constructs a propagator that does not require the number of signal sources K. Using this propagator to construct a spatial spectrum function for DOA estimation, DOA estimation can be achieved even when the number of signal sources is unknown. This means that high-precision real-time vehicle localization can be performed without prior knowledge of the number of vehicles to be located, making it more suitable for real-world applications.

[0057] Please see Figure 1 In one aspect, the present invention provides an embodiment of a vehicle localization method based on spatial spectrum estimation, comprising:

[0058] S100: Receive the positioning signal emitted by the vehicle to be located using multiple coprime antenna arrays, and obtain a first received signal considering noise interference in the environment. The first received signal is a signal containing Gaussian noise received by the coprime antenna arrays.

[0059] S200: Calculate the fourth-order cumulant matrix of the first received signal and construct a virtual array, using the fourth-order cumulant to eliminate Gaussian noise; vectorize the fourth-order cumulant matrix to obtain the second received signal, which is the received signal of the virtual array; calculate the position matrix of the virtual array based on the positions of the antennas in the coprime antenna array;

[0060] S300: Obtain the uniform linear portion of the virtual array according to the position matrix and use it as a sub-virtual array; perform forward and backward spatial smoothing processing on the second received signal corresponding to the sub-virtual array to obtain a smoothing matrix.

[0061] S400: When the number of vehicles to be located is unknown, a noise subspace is constructed based on the smoothing matrix and hyperparameters, wherein the hyperparameters are positive numbers that ensure the noise subspace satisfies the inversion condition;

[0062] S500: Construct a spatial spectrum function based on the noise subspace and the direction vector, perform a spectral peak search on the spatial spectrum function to estimate the direction of arrival corresponding to the positioning signal, and determine the current position of the vehicle to be located based on the position information of the coprime antenna array and the direction of arrival.

[0063] Please see Figure 2 and Figure 3 , Figure 2 For the vehicle localization model based on the spatial spectrum estimation (also known as DOA estimation) algorithm, three coprime antenna arrays form a triangle (such as a right triangle), with each coprime antenna array located at a vertex of the triangle. Figure 3 This is a simplified schematic diagram of the vehicle positioning model. For ease of calculation, the three coprime antenna arrays are represented by a thickened straight line, and the center antennas of the three arrays are designated as their respective reference points, denoted as Q1(0,a), Q2(0,0), and Q3(b,0). The position coordinates of the vehicle to be located are denoted as S(x,y). The angles between the vehicle and the coprime antenna arrays are defined as the angles between the incident signal and the normal to the antenna arrays, denoted as θ1, θ2, and θ3.

[0064] In step S100, a positioning signal emitted by the vehicle to be located is received using a plurality of coprime antenna arrays, and a first received signal is obtained by taking into account noise interference in the environment. The first received signal is a signal containing Gaussian noise received by the coprime antenna arrays.

[0065] Please seeFigure 4 and Figure 5 ,like Figure 4 The diagram shows two uniform linear subarrays. The first subarray has M antennas (elements) with an antenna spacing of Nd, and the second subarray has N antennas with an antenna spacing of Md, where M and N are coprime integers, and d is half the wavelength of the received signal, i.e., d = λ / 2. Antennas at positions that are even multiples of Nd are removed from the first subarray, and antennas at positions that are even multiples of Md are removed from the second subarray. The remaining antennas from the two subarrays are combined and supplemented with source antennas to construct the following structure: Figure 5 The coprime antenna arrays shown have L antennas in each array.

[0066] In one embodiment of the invention, a coprime antenna array is used to receive positioning signals emitted by the vehicle. This array has a larger element aperture, which can improve the degrees of freedom and resolution of DOA estimation and improve the performance of vehicle positioning.

[0067] In one embodiment of the present invention, the three coprime antenna arrays are multiple-input multiple-output (MIMO) antenna arrays. The vehicle's position information can be obtained by jointly calculating the data obtained from two coprime antenna arrays. However, since there is usually a certain error in actual application scenarios, combining the three coprime antenna arrays in pairs to perform three vehicle positioning calculations, and then averaging the three results, can reduce the positioning error.

[0068] It is understood that this invention can use two coprime antenna arrays for vehicle positioning, or it can use three coprime antenna arrays. Using three coprime antenna arrays is to reduce the positioning error. The vehicle position can be calculated using information from any two of the coprime antenna arrays (including the positions of the antenna arrays themselves and the signal angle obtained from DOA estimation). Using information from all three coprime antenna arrays, the vehicle position can be calculated three times, and the average value is taken, which reduces the positioning error and makes the positioning more accurate. Of course, an even larger number of coprime antenna arrays can be used to construct the antenna model.

[0069] In a preferred embodiment, three identical coprime antenna arrays can form a right triangle, with each coprime antenna array located at a vertex of the right triangle. Of course, other relative positions of the three coprime antenna arrays are also possible.

[0070] In one embodiment of the present invention, the signal source is the vehicle to be located. There can be one signal source or multiple signal sources simultaneously. The signal source signal is the positioning signal, which is a far-field narrowband uncorrelated signal. A mathematical model is established using the first received signal received by the coprime antenna array when the positioning signal is incident.

[0071] Assume that the coprime antenna array constructed in step S100 receives narrowband uncorrelated signals {s1(t), s2(t), ..., s...} from K far-field signal sources (the vehicle to be located) at time t. k (t),…,s K (t)}, with directions of arrival {θ1,θ2,…,θ}. k ,…,θ K}, θ k Let k represent the k-th direction of arrival, k = 1, 2, ..., K. Then, the signal received by the i-th antenna in each coprime antenna array from the k-th signal source at time t can be modeled as: Among them, s k (t) represents the far-field narrowband uncorrelated signal (positioning signal) emitted by the k-th vehicle to be located at time t; u i Let λ represent the position of the i-th antenna, λ represent the carrier wavelength, T represent the snapshot number, and j be the imaginary unit. 2 =-1.

[0072] The signal from the signal source is subject to interference from other signals during transmission in the environment. Therefore, the first received signal of the coprime antenna array contains Gaussian noise. The received signal of the i-th antenna in each coprime antenna array at time t can be expressed by a linear equation: Where n i (t) represents the Gaussian noise of the i-th antenna in each coprime antenna array at time t.

[0073] The first received signal received by each coprime antenna array is written in vector form:

[0074] x(t)=A(θ)s(t)+n(t);

[0075] Where x(t)=[x1(t),x2(t),…,x L (t)] T Let A(θ) be the first received signal vector of the coprime antenna array at time t; A(θ) = [a(θ1), a(θ2), ..., a(θ)] K )],A(θ)∈V L×K This is the array direction matrix; a(θ k )∈V L, k∈{1,2,…,K} is the array direction vector; s(t)=[s1(t),s2(t),…,s K (t)] T ,s(t)∈V K The signal vector of the signal source is the location signal vector; n(t) = [n1(t), n2(t), ..., n i (t),…,n L (t)] T , n(t)∈V L Let V be a noise vector, where V represents the complex space, the superscript T indicates the transpose operation, and n i (t) represents the Gaussian noise experienced by the i-th antenna in each coprime antenna array at time t.

[0076] In step S200, the fourth-order cumulant matrix of the first received signal is calculated and a virtual array is constructed. Gaussian noise is eliminated using the fourth-order cumulant. The fourth-order cumulant matrix is ​​vectorized to obtain the second received signal, which is the received signal of the virtual array. The position matrix of the virtual array is calculated based on the position of the antennas in the coprime antenna array.

[0077] In one embodiment of the invention, a fourth-order cumulant is used to eliminate Gaussian noise and construct a large-aperture virtual array. The fourth-order cumulant C is calculated for both the Gaussian noise and the signal source signal (positioning signal). s And solve for the fourth-order cumulant matrix C of the array received signal (first received signal). x .

[0078] Calculate the fourth-order cumulant of Gaussian noise: Where cum{·} represents the cumulative operation, (·) * This represents the conjugate operation, where g, h, p, q = 1, ..., L. It can be seen that the fourth-order cumulant of Gaussian noise is 0.

[0079] Then, calculate the fourth-order cumulant of the signal source:

[0080]

[0081] Among them, C sk This represents the fourth-order cumulant of the signal emitted by the k-th signal source.

[0082] Calculate the fourth-order cumulant matrix C of the received signal from the array. x :

[0083]

[0084] Where R{·} represents the statistical expectation, (·) HThis indicates the conjugate transpose. It represents the Kronecker product.

[0085] The fourth-order cumulant matrix C of the signal source signal is calculated in the same way. s ,

[0086] The fourth-order cumulant matrix C of the array received signal x The fourth-order cumulant matrix C of the signal source signal s It has the following relationship:

[0087] Among them, A v (θ)=[a v (θ1),a v (θ2),…,a v (θ K [)] is the direction matrix of the virtual array, where the direction vector of the virtual array is

[0088] Next, the fourth-order cumulant matrix C of the vectorized array received signal. x Establish the received signal x of the virtual array v The mathematical model is used to calculate the position u of the virtual antenna in the virtual array. v .

[0089] In one embodiment of the present invention, C is vectorized. x The second received signal of the virtual array is modeled as follows:

[0090] x v =vec(C x ) = A v (θ)C s ;

[0091] Where, x v For the second received signal, C x C is the fourth-order cumulant matrix of the first received signal. s Let A be the fourth-order cumulant matrix of the positioning signal. v (θ) is the direction matrix of the virtual array, where θ is the direction of arrival and vec represents the vectorization operation.

[0092] x v The elements are: i = (g-1)L 3 +(h-1)L 2 +(p-1)L+q.

[0093] Using matrix u vThe location of the virtual antenna is represented by a vector of all ones: 1 = [1, 1, ..., 1] T ∈R L Where R represents real space. The positions of the physical antennas in a coprime antenna array are represented as u = [u1, u2, ..., u...]. L ] T The position indication matrix u of the virtual antenna in the virtual array. v for:

[0094]

[0095] In one embodiment of the present invention, a fourth-order cumulant is used to suppress the effect of Gaussian white noise. Furthermore, the fourth-order cumulant can expand the effective aperture of the array, increase the number of effective array elements, and achieve virtual expansion of the array, thereby improving the performance of DOA estimation and vehicle localization.

[0096] In step S300, the uniform linear portion of the virtual array is obtained according to the position matrix and used as a sub-virtual array. The second received signal corresponding to the sub-virtual array is subjected to forward and backward spatial smoothing processing to obtain a smoothing matrix.

[0097] In one embodiment of the present invention, a uniform linear portion of the virtual array is obtained based on the position matrix and used as a sub-virtual array. Then, the second received signal corresponding to the sub-virtual array is subjected to forward and backward spatial smoothing to obtain a smoothing matrix R. vs .

[0098] It should be noted that the sub-virtual array is essentially a virtual array, therefore, the sub-virtual array has a corresponding second received signal.

[0099] Virtual array location set VP v for:

[0100] VP v ={-u g +u h +u p -u q |u g ,u h ,u p ,u q Let P ∈ P, g, h, p, q = 1, ..., L, where P represents the set of physical antenna positions. The virtual array is a sparse array containing a uniform linear portion around the origin, with a length L′ = 2(MN + M + N) - 1. The position matrix and the corresponding received signal matrix of the virtual uniform linear array portion are represented as follows: and in, Select matrix Defined as:

[0101]

[0102] Where i and m represent the number of rows and columns of the selection matrix G, respectively, and the range of i is 1, 2, 3…L. 4 The range of m is 1, 2, 3…L′.

[0103] The smoothing matrix is ​​obtained by spatially smoothing the uniform linear portion of the virtual array based on the second received signal and the position matrix, including:

[0104] The uniform linear portion of the virtual array is subjected to forward spatial smoothing based on the second received signal and the position matrix to obtain a first smoothing result; the uniform linear portion of the virtual array is subjected to backward spatial smoothing based on the second received signal and the position matrix to obtain a second smoothing result; the first smoothing result and the second smoothing result are averaged to obtain a smoothing matrix.

[0105] Specifically, for The process of performing forward and backward spatial smoothing mainly includes:

[0106] (1) Perform forward spatial smoothing. First define... A 2D matrix of all zeros R f ,Pick In Replace R with elements f We can take a column from the given elements to form a submatrix. Let the starting element position be 1. Take continuously from front to back One element, which replaces R. f The i-th column forms a submatrix R fi Calculate the covariance matrix of each submatrix and then calculate the average value.

[0107] (2) Perform backward spatial smoothing. First define... A 2D matrix of all zeros R b Then select In There are elements, let the starting element position be . Take continuously from back to front For each element, take the complex conjugate of the selected element and replace R with it. b The i-th column forms a subarray R bi Calculate the covariance matrix of each submatrix and then calculate the average value.

[0108] (3) After obtaining the results of forward space smoothing and backward space smoothing, calculate the average of the two to obtain the result of forward and backward space smoothing, that is, obtain the smoothing matrix.

[0109] It should be noted that the virtual uniform linear array is the virtual Uniform Linear Array (ULA) part.

[0110] In step S400, when the number of vehicles to be located is unknown, a noise subspace is constructed based on the smoothing matrix and hyperparameters, wherein the hyperparameters are positive numbers that ensure the noise subspace satisfies the inverse condition.

[0111] In one embodiment of the invention, in conjunction with R vs We construct a propagation subspace (also called noise subspace) that does not require the number of signal sources (referred to as the number of sources) K, in order to construct the spatial spectrum function.

[0112] When the number of information sources K is known, eigenvalue decomposition can be used to select R. vs A specific column forms a full column rank subspace, which is used to construct a propagation subspace. The orthogonality between the subspace and the signal direction vector is used to construct a spatial spectral function for DOA estimation.

[0113] When the number of information sources K is unknown, there are two cases when estimating the number of information sources:

[0114] The first scenario: When the estimated number of sources is less than the actual number of sources K, in R... vs If the rank of the subspace constructed by the selected specific columns is less than K, the constructed propagation subspace and direction vector do not satisfy orthogonality. Therefore, it is impossible to correctly construct the spatial spectrum function for DOA estimation.

[0115] The second scenario: When the estimated number of sources is greater than the actual number of sources K, the constructed subspace is not full rank, and the inversion operation in the propagation subspace cannot be performed because the condition is not met. This is because the smoothing matrix R... vs The rank of the source is always greater than or equal to the number of sources, using R. vs The entire matrix constructs a propagation subspace, which ensures that the propagation subspace is orthogonal to the direction vector.

[0116] And in order to solve To address the issue that the inversion operation in the propagation subspace may not be possible due to the subspace not being full rank, a hyperparameter is introduced to satisfy the inversion condition.

[0117] The constructed propagation subspace takes the following form:

[0118]

[0119] Here, τ is a positive hyperparameter whose value is determined using machine learning methods; U im It is the propagation subspace, which is orthogonal to the direction vector of the system. This property can be used to construct a spatial spectrum function to obtain the angle of the signal. The smoothing matrix R obtained earlier vs The conjugate transpose of , here (·) H This indicates the conjugate transpose operation.

[0120] By using the hyperparameter τ, the conditions for the inversion operation can be ensured. At this point, it is not necessary to know the number of sources K to correctly construct the spatial spectral function and perform DOA estimation. Furthermore, due to the smoothing matrix R... vs yes Dimensional, at most can estimate simultaneously Each wave direction corresponds to the ability to simultaneously [do something]. The system locates a number of vehicles. Given coprime integers M and N, it can identify at most MN+M+N-1 directions of arrival (DOAs), which is more than the number of DOA that a uniform linear array with the same number of antennas can estimate.

[0121] This embodiment uses mathematical methods to introduce hyperparameters multiplied by the identity matrix to construct the propagation sub-U. im It is also a noise subspace, which can ensure U im The inverse operation in the expression can be performed, while ensuring that U im Orthogonal to the direction vector. This method replaces the traditional method of finding the noise subspace, avoiding eigenvalue decomposition and reducing computational cost. Furthermore, traditional methods require the number of signal sources K to select the eigenvectors corresponding to the noise to form the noise subspace. This innovative method eliminates the need for the number of signal sources K, meaning that in vehicle localization, real-time, high-precision vehicle localization can be achieved without prior knowledge of the number of sources.

[0122] In one embodiment of the present invention, before constructing the noise subspace based on the smoothing matrix and hyperparameters, the method further includes: training a regression model using a machine learning method, and inputting the signal-to-noise ratio in the actual application scenario into the regression model to obtain the values ​​of the hyperparameters.

[0123] In one embodiment of the present invention, machine learning methods are used to obtain the values ​​of hyperparameters. The values ​​of hyperparameters can be determined through the following steps:

[0124] (1) Data collection. For each experiment, record the signal-to-noise ratio, hyperparameters, number of snapshots, and the obtained signal angle, etc. Repeat the experiment multiple times to obtain the original dataset.

[0125] (2) Perform data preprocessing. Remove duplicate, missing, and outlier values ​​from the original dataset, split the dataset into training and test sets, and use normalization methods to scale the features of the dataset.

[0126] (3) Determine the model function. Obtaining hyperparameter values ​​is a regression problem. Through training, the correlation between variables is discovered, the relationship between variables is determined, and finally, given known variables, the required hyperparameter values ​​are predicted. Here, the model function is used: y = wx + b, where y is the hyperparameter to be solved, x is the variable affecting the hyperparameter values ​​(e.g., signal-to-noise ratio), w is the weight, and b is the bias. The purpose of training is to solve for the unknowns w and b to obtain the specific functional expression of y. When the signal-to-noise ratio is known, the hyperparameter values ​​can be obtained.

[0127] (4) Train and optimize the model using Support Vector Regression (SVR). For linear regression problems, the goal is to fit a regression line to the data to minimize the error caused by bias. Here, Support Vector Regression (SVR) is used. The SVR model sets a threshold error tolerance ε around the regression line, such as... Figure 7 As shown. For samples outside the tolerance range, the loss value is calculated, which is the difference between the predicted value and the true value. After obtaining the loss value, the model updates each parameter through backpropagation to reduce the loss between the true value and the predicted value, so that the predicted value generated by the model moves closer to the true value, thereby achieving the purpose of learning.

[0128] Introducing slack variables ξ i This allows some samples to be outside the interval band, such as Figure 7 As shown, ξ represents the distance between the projection of the upper edge sample point onto the upper edge line and the sample point itself. i This represents the distance between the projection of the lower edge sample point onto the lower edge line and the sample point itself. SVR optimizes the model by maximizing the tolerance width and minimizing the total loss. The SVR objective function can be expressed as:

[0129]

[0130] in,

[0131] and ξ i The value can be:

[0132]

[0133]

[0134] To minimize the objective function, a Lagrangian function is constructed based on the constraints, taking into account w, b, and ξ respectively. i , Calculate the partial derivatives and set them to zero. Finally, use the Sequential Minimal Optimization (SMO) algorithm to solve for the coefficients w and b of the regression model.

[0135] (5) Based on the specific form of the regression model to be solved, input the signal-to-noise ratio in the actual application scenario to obtain the value of the hyperparameter.

[0136] In one embodiment of the present invention, a machine learning method is used, in which support vector regression is applied to train and optimize the model to obtain the optimal hyperparameter values ​​for the actual application scenario, thereby making the DOA estimation angle more accurate and thus making the vehicle positioning more accurate.

[0137] In step S500, a spatial spectrum function is constructed based on the noise subspace and direction vector. The direction of arrival corresponding to the positioning signal is estimated by searching the spectrum peaks of the spatial spectrum function. The current position of the vehicle to be located is determined based on the position information of the antenna in the coprime antenna array and the direction of arrival.

[0138] In one embodiment of the present invention, a spatial spectrum function is constructed based on the noise subspace and the corresponding direction vector. The angles are traversed to find the spectral peaks, and the directions of arrival θ1, θ2, and θ3 are obtained for the three coprime antenna arrays, respectively.

[0139] Using the propagation subspace U im Construct the following spatial spectral function:

[0140]

[0141] By traversing all angles, the spectral peaks are found, and the corresponding angles are the directions of arrival. The directions of arrival for the three coprime antenna arrays are θ1, θ2, and θ3, respectively.

[0142] It should be noted that a v (θ) is the direction vector corresponding to the propagation subspace, a v (θ) and the direction vector a of the virtual array v (θ k They are essentially the same, where θ refers to any angle.

[0143] Based on the known position information of the three coprime antenna arrays and the directions of arrival obtained from the three coprime antenna arrays, the current position information of the vehicle to be located is solved.

[0144] In one embodiment of the present invention, the center antennas of three arrays are set as reference points in the vehicle positioning model, with their positions denoted as Q1(0,a), Q2(0,0), and Q3(b,0), respectively. The DOA estimation results of the three coprime antenna arrays are obtained according to the preceding steps, and the obtained directions of arrival are θ1, θ2, and θ3, respectively. The position S(x,y) of the vehicle to be located is then solved as follows, based on the known information:

[0145]

[0146]

[0147]

[0148] Based on the three formulas above, the target vehicle can achieve cross-positioning.

[0149] Based on the expressions for tanθ1 and tanθ2, the vehicle coordinates are calculated as follows:

[0150]

[0151]

[0152] Based on the expressions for tanθ2 and tanθ3, the vehicle coordinates are calculated as follows:

[0153]

[0154]

[0155] Based on the expressions for tanθ1 and tanθ3, the vehicle coordinates are calculated as follows:

[0156]

[0157]

[0158] Based on the results of the three cross-locations above, the average value is taken to obtain the current position of the vehicle to be located:

[0159]

[0160]

[0161] This completes the task of determining the current location of the vehicle to be located.

[0162] The vehicle localization method based on spatial spectrum estimation provided in this embodiment utilizes multiple coprime antenna arrays to receive localization signals transmitted by the vehicle to be located. Compared with traditional uniform linear arrays, coprime antenna arrays have larger element apertures, which can improve the degrees of freedom and resolution of DOA estimation and improve the performance of vehicle localization. By using fourth-order cumulants, a virtual array with a larger aperture is constructed while resisting Gaussian noise, which greatly improves the degrees of freedom and spatial resolution, improves the accuracy of DOA estimation, and thus improves the accuracy of vehicle localization. Moreover, the number of vehicles to be located is not limited by the number of antennas in the coprime antenna array, and a larger number of vehicles can be located simultaneously.

[0163] Furthermore, this embodiment introduces hyperparameters to construct a noise subspace. This method replaces the traditional method of finding the noise subspace, which avoids feature decomposition, reduces the amount of computation, lowers the computational complexity of the vehicle positioning algorithm, and can meet the real-time requirements of vehicle network for vehicle location information.

[0164] The noise subspace constructed in this embodiment does not require the number of vehicles to be located. By using the noise subspace to construct the spatial spectrum function and perform DOA estimation, DOA estimation can be achieved even when the number of sources is unknown. That is, high-precision vehicle positioning can be performed in real time without knowing the number of vehicles to be located in advance, which is more in line with real-world usage scenarios.

[0165] Please see Figure 6 In another aspect, the present invention also provides another embodiment of a vehicle localization method based on spatial spectrum estimation, comprising:

[0166] Step 1: Construct three identical coprime antenna arrays to receive the positioning signal emitted by the vehicle to be located;

[0167] Step 2: Establish mathematical models for far-field narrowband uncorrelated incident signals and coprime antenna array received signals;

[0168] Step 3: Use fourth-order cumulants to eliminate Gaussian noise and construct a large-aperture virtual array; calculate the fourth-order cumulants of the Gaussian noise and the signal source signal, and solve for the fourth-order cumulant matrix C of the received signal from the coprime antenna array. x ;

[0169] Step 4: Vectorize the fourth-order cumulant matrix C of the received signal from the array. x Establish a virtual array to receive signal x v The mathematical model is used to calculate the position u of the virtual antenna. v ;

[0170] Step 5: Perform forward and backward spatial smoothing on the uniform linear portion of the virtual array to obtain the smoothing matrix R. vs ;

[0171] Step 6, combine with R vs We construct a propagator that does not require the number of sources K to construct the spatial spectrum function;

[0172] Step 7: Use machine learning methods to obtain the values ​​of hyperparameters;

[0173] Step 8: Construct the spatial spectrum function, traverse the angles, find the spectral peaks, and obtain the directions of arrival θ1, θ2, and θ3 for the three coprime antenna arrays respectively.

[0174] Step 9: Based on the known positions of the three coprime antenna arrays and the obtained directions of arrival, determine the current location of the vehicle.

[0175] Existing vehicle localization methods based on DOA estimation typically employ uniform linear arrays to receive vehicle localization signals and use second-order covariance to amplify the array's aperture. This invention constructs a coprime antenna array using fourth-order cumulants, which, while resisting Gaussian noise, creates a virtual array with a larger aperture, significantly improving degrees of freedom and spatial resolution, thus enhancing the accuracy of DOA estimation and consequently improving vehicle localization accuracy. Furthermore, this invention can simultaneously locate a larger number of vehicles. Moreover, this invention constructs a propagation subspace that does not require the number of signal sources K. Using this propagation subspace to construct a spatial spectrum function for DOA estimation allows for DOA estimation even when the number of signal sources is unknown. This means that high-precision real-time vehicle localization can be achieved without prior knowledge of the number of vehicles to be located, which is more in line with real-world application scenarios.

[0176] Please see Figure 8 In another aspect, the present invention also provides an embodiment of a vehicle positioning device based on spatial spectrum estimation, comprising:

[0177] The first received signal acquisition module 11 is used to receive the positioning signal emitted by the vehicle to be located using multiple coprime antenna arrays, and to obtain the first received signal by taking into account noise interference in the environment. The first received signal is a signal containing Gaussian noise received by the coprime antenna arrays.

[0178] The virtual array construction and calculation module 22 is used to calculate the fourth-order cumulant matrix of the first received signal and construct a virtual array, and use the fourth-order cumulant to eliminate Gaussian noise; vectorize the fourth-order cumulant matrix to obtain the second received signal, which is the received signal of the virtual array; and calculate the position matrix of the virtual array based on the position of the antenna in the coprime antenna array.

[0179] The spatial smoothing module 33 is used to obtain the uniform linear part of the virtual array according to the position matrix and use it as a sub-virtual array, and to perform forward and backward spatial smoothing processing on the second received signal corresponding to the sub-virtual array to obtain a smoothing matrix.

[0180] The noise subspace construction module 44 is used to construct a noise subspace based on the smoothing matrix and hyperparameters when the number of vehicles to be located is unknown. The hyperparameters are positive numbers that ensure the noise subspace satisfies the inversion condition.

[0181] The vehicle location information determination module 55 is used to construct a spatial spectrum function based on the noise subspace and the direction vector, perform spectral peak search on the spatial spectrum function to estimate the direction of arrival corresponding to the positioning signal, and determine the current location of the vehicle to be located based on the location information of the coprime antenna array and the direction of arrival.

[0182] In a preferred embodiment, receiving the positioning signal emitted by the vehicle to be located using a multi-coprime antenna array, and obtaining the first received signal considering noise interference in the environment, includes:

[0183] The first receiving signal acquisition module uses three coprime antenna arrays to receive the positioning signal emitted by the vehicle to be located, and takes into account noise interference in the environment to obtain the first received signal. The three coprime antenna arrays form a right triangle.

[0184] In a preferred embodiment, the positioning signal is a far-field narrowband uncorrelated signal.

[0185] In a preferred implementation, the virtual array construction and calculation module vectorizes the fourth-order cumulant matrix to obtain the second received signal, including:

[0186] According to x v =vec(C x ) = A v (θ)C s The second received signal is obtained by vectorizing the fourth-order cumulant matrix;

[0187] Where, x v For the second received signal, C x C is the fourth-order cumulant matrix of the first received signal. s Let A be the fourth-order cumulant matrix of the positioning signal. v (θ) is the direction matrix of the virtual array, where θ is the direction of arrival and vec represents the vectorization operation.

[0188] This embodiment provides a vehicle positioning device based on spatial spectrum estimation. It utilizes multiple coprime antenna arrays to receive positioning signals transmitted by the vehicle to be located. Compared to traditional uniform linear arrays, coprime antenna arrays have larger element apertures, which can improve the degrees of freedom and resolution of DOA estimation and improve vehicle positioning performance. By employing fourth-order cumulants, a virtual array with a larger aperture is constructed while resisting Gaussian noise, which greatly improves the degrees of freedom and spatial resolution, and improves the accuracy of DOA estimation, thereby enhancing the accuracy of vehicle positioning. Moreover, compared to using a uniform linear array with the same number of antennas for vehicle positioning, this embodiment can simultaneously estimate a larger number of vehicles.

[0189] Furthermore, this embodiment introduces hyperparameters to construct a noise subspace. This method replaces the traditional method of finding the noise subspace, which avoids feature decomposition, reduces the amount of computation, lowers the computational complexity of the vehicle positioning algorithm, and can meet the real-time requirements of vehicle network for vehicle location information.

[0190] The noise subspace constructed in this embodiment does not require the number of vehicles to be located. By using the noise subspace to construct the spatial spectrum function and perform DOA estimation, DOA estimation can be achieved even when the number of sources is unknown. That is, high-precision vehicle positioning can be performed in real time without knowing the number of vehicles to be located in advance, which is more in line with real-world usage scenarios.

[0191] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0192] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or electrical connection shown or discussed between each other can be through some interfaces; the indirect coupling or electrical connection between devices or units can be electrical, mechanical, or other forms.

[0193] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0194] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0195] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0196] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle localization method based on spatial spectrum estimation, characterized in that, include: Multiple coprime antenna arrays are used to receive positioning signals emitted by the vehicle to be located, and noise interference in the environment is taken into account to obtain a first received signal, which is a signal containing Gaussian noise received by the coprime antenna arrays. Calculate the fourth-order cumulant matrix of the first received signal and construct a virtual array to eliminate Gaussian noise using the fourth-order cumulant. The second received signal is obtained by vectorizing the fourth-order cumulant matrix, and the second received signal is the received signal of the virtual array; the position matrix of the virtual array is calculated based on the positions of the antennas in the coprime antenna array; The uniform linear portion of the virtual array is obtained based on the position matrix and used as a sub-virtual array. The second received signal corresponding to the sub-virtual array is then subjected to forward and backward spatial smoothing to obtain a smoothing matrix. When the number of vehicles to be located is unknown, a noise subspace is constructed based on the smoothing matrix and hyperparameters, where the hyperparameters are positive numbers that ensure the noise subspace satisfies the inversion condition. A spatial spectrum function is constructed based on the noise subspace and direction vector. The spatial spectrum function is then used to perform a spectrum peak search to estimate the direction of arrival corresponding to the positioning signal. The current position of the vehicle to be located is determined based on the position information of the coprime antenna array and the direction of arrival. The vectorization of the fourth-order cumulant matrix to obtain the second received signal includes: according to The second received signal is obtained by vectorizing the fourth-order cumulant matrix; in, For the second received signal, This is the fourth-order cumulant matrix of the first received signal. This is the fourth-order cumulant matrix of the positioning signal. The orientation matrix of the virtual array, For the Poda square, vec represents the vectorization operation.

2. The vehicle localization method based on spatial spectrum estimation according to claim 1, characterized in that, The method utilizes multiple coprime antenna arrays to receive positioning signals emitted by the vehicle to be located, and considers environmental noise interference to obtain the first received signal, which includes: The positioning signal transmitted by the vehicle to be located is received by three coprime antenna arrays, and the first received signal is obtained by taking into account noise interference in the environment. The three coprime antenna arrays form a right triangle.

3. The vehicle localization method based on spatial spectrum estimation according to claim 1, characterized in that, The positioning signal is a far-field narrowband uncorrelated signal.

4. The vehicle localization method based on spatial spectrum estimation according to claim 1, characterized in that, The smoothing matrix is ​​obtained by performing forward and backward spatial smoothing on the second received signal corresponding to the sub-virtual array, including: The second received signal corresponding to the sub-virtual array is subjected to forward spatial smoothing to obtain a first smoothing result; The second received signal corresponding to the sub-virtual array is subjected to backward spatial smoothing to obtain a second smoothing result; The smoothing matrix is ​​obtained by averaging the first smoothing result and the second smoothing result.

5. The vehicle localization method based on spatial spectrum estimation according to claim 1, characterized in that, Before constructing the noise subspace based on the smoothing matrix and hyperparameters, the following steps are also included: The regression model is trained using machine learning methods, and the signal-to-noise ratio in the actual application scenario is input into the regression model to obtain the value of the hyperparameter.

6. A vehicle positioning device based on spatial spectrum estimation, characterized in that, include: The first received signal acquisition module is used to receive the positioning signal emitted by the vehicle to be located using multiple coprime antenna arrays, and to obtain the first received signal by taking into account noise interference in the environment. The first received signal is a signal containing Gaussian noise received by the coprime antenna arrays. The virtual array construction and calculation module is used to calculate the fourth-order cumulant matrix of the first received signal and construct a virtual array, and use the fourth-order cumulant to eliminate Gaussian noise. The second received signal is obtained by vectorizing the fourth-order cumulant matrix, and the second received signal is the received signal of the virtual array; the position matrix of the virtual array is calculated based on the positions of the antennas in the coprime antenna array; The spatial smoothing module is used to obtain the uniform linear part of the virtual array according to the position matrix and use it as a sub-virtual array, and to perform forward and backward spatial smoothing processing on the second received signal corresponding to the sub-virtual array to obtain a smoothing matrix. A noise subspace construction module is used to construct a noise subspace based on the smoothing matrix and hyperparameters when the number of vehicles to be located is unknown. The hyperparameters are positive numbers that ensure the noise subspace satisfies the inversion condition. The vehicle location information determination module is used to construct a spatial spectrum function based on the noise subspace and the direction vector, perform a spectrum peak search on the spatial spectrum function to estimate the direction of arrival corresponding to the positioning signal, and determine the current location of the vehicle to be located based on the location information of the coprime antenna array and the direction of arrival. The virtual array construction and calculation module vectorizes the fourth-order cumulant matrix to obtain the second received signal, including: according to The second received signal is obtained by vectorizing the fourth-order cumulant matrix; in, For the second received signal, This is the fourth-order cumulant matrix of the first received signal. This is the fourth-order cumulant matrix of the positioning signal. The orientation matrix of the virtual array, The direction of arrival is vec, which represents the vectorization operation.

7. The vehicle positioning device based on spatial spectrum estimation method according to claim 6, characterized in that, The method utilizes multiple coprime antenna arrays to receive positioning signals emitted by the vehicle to be located, and considers environmental noise interference to obtain the first received signal, which includes: The first receiving signal acquisition module uses three coprime antenna arrays to receive the positioning signal emitted by the vehicle to be located, and takes into account noise interference in the environment to obtain the first received signal. The three coprime antenna arrays form a right triangle.

8. The vehicle positioning device based on spatial spectrum estimation method according to claim 6, characterized in that, The positioning signal is a far-field narrowband uncorrelated signal.

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