Covariance matrix based angle estimation method and apparatus

Abstract

The application discloses a covariance matrix-based angle estimation method and device. The method comprises the following steps: receiving at least two echo signals through a target array composed of at least two signal receivers, wherein the echo signal is a signal returned after a radio signal sent by a vehicle-mounted device interacts with a target object; calculating a signal covariance matrix corresponding to the at least two echo signals; vectorizing the signal covariance matrix to obtain a target vector, wherein the target vector is used to represent autocorrelation information and cross-correlation information; constructing an angle space spectrum based on the target vector, wherein the angle space spectrum is a spectrum describing the distribution and intensity of the at least two signals in different directions or angles; and determining the angle value of the maximum peak position on the angle space spectrum as a target angle, wherein the target angle is an estimated angle between the vehicle-mounted device and the target object. The application solves the technical problem that it is difficult to improve the main sidelobe ratio without reducing the angle measurement accuracy.

Description

Technical Field

[0001] This application relates to the field of computer science, and more specifically, to a method and apparatus for estimating angles based on the covariance matrix. Background Technology

[0002] With the widespread adoption and development of intelligent driving technology in vehicles, the functions implemented by ADAS (Advanced Driver Assistance Systems) are becoming increasingly diverse, and similarly, the demands on the performance of automotive millimeter-wave radar are also increasing. Accurately estimating the target angle is a crucial function of radar perception. Existing MIMO arrays form virtual array elements based on different transmit and receive positions. This M*N virtual array formed by an M-transmit N-receive MIMO array requires a large aperture to improve angle measurement performance, and thus remains a sparse array. Sparse arrays suffer from low main and side lobes. Traditional methods directly use the raw amplitude and phase values ​​of the array's receiving antennas for angle estimation, which can easily lead to incorrect target estimation when multipath effects or excessive noise are present.

[0003] A low main-sidelobe ratio reduces the robustness of the radar's angle measurement function. Therefore, existing technologies present a challenge in improving the main-sidelobe ratio without compromising angle measurement accuracy.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This application provides an angle estimation method and apparatus based on the covariance matrix, which at least solves the technical problem of difficulty in improving the main lobe-to-side lobe ratio without reducing the angle measurement accuracy.

[0006] According to one aspect of the embodiments of this application, an angle estimation method based on a covariance matrix is ​​provided, comprising: receiving at least two echo signals through a target array composed of at least two signal receivers, wherein the echo signals are signals returned after a radio signal transmitted by an on-board device interacts with a target object; calculating the signal covariance matrix corresponding to the at least two echo signals, wherein the signal covariance matrix includes autocorrelation information and cross-correlation information, wherein the autocorrelation information is used to represent the correlation between different dimensions within the same echo signal in the at least two echo signals, and the cross-correlation information is used to represent the correlation between different echo signals in the at least two echo signals, and the cross-correlation information includes amplitude and phase information of missing array element positions in the target array; vectorizing the signal covariance matrix to obtain a target vector, wherein the target vector is used to characterize the autocorrelation information and the cross-correlation information; constructing an angle spatial spectrum based on the target vector, wherein the angle spatial spectrum is a spectrum describing the distribution and intensity of the at least two signals in different directions or angles; determining the angle value of the maximum peak position on the angle spatial spectrum as the target angle, wherein the target angle is an estimated angle between the on-board device and the target object.

[0007] According to another aspect of the embodiments of this application, an angle estimation device based on a covariance matrix is ​​also provided, comprising: a receiving unit, configured to receive at least two echo signals through a target array composed of at least two signal receivers, wherein the echo signals are signals returned after a radio signal transmitted by a vehicle-mounted device interacts with a target object; and a calculation unit, configured to calculate a signal covariance matrix corresponding to the at least two echo signals, wherein the signal covariance matrix includes autocorrelation information and cross-correlation information, wherein the autocorrelation information is used to represent the correlation between different dimensions within the same echo signal in the at least two echo signals, and the cross-correlation information is used to represent the correlation between the at least two echo signals. The correlation between different echo signals in the target array includes amplitude and phase information of the missing array element positions. A vector unit is used to vectorize the signal covariance matrix to obtain a target vector, where the target vector characterizes the autocorrelation and cross-correlation. A construction unit is used to construct an angle space spectrum based on the target vector, where the angle space spectrum describes the distribution and intensity of the at least two signals in different directions or angles. A determination unit is used to determine the angle value of the maximum peak position on the angle space spectrum as the target angle, where the target angle is an estimated angle between the vehicle-mounted device and the target object.

[0008] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, wherein the computer program is configured to execute the above-described angle estimation method based on the covariance matrix at runtime.

[0009] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-described angle estimation method based on the covariance matrix through the computer program.

[0010] In this embodiment, an angle estimation method using a covariance matrix with higher angle measurement performance is adopted to improve angle measurement accuracy. Furthermore, by using a signal covariance matrix containing autocorrelation and cross-correlation, the missing information due to the sparse array is supplemented. As the missing information is supplemented, the main lobe ratio of the array spatial spectrum can be improved, thereby achieving the technical effect of improving the main lobe ratio without reducing angle measurement accuracy. This solves the technical problem of difficulty in improving the main lobe ratio without reducing angle measurement accuracy. Attached Figure Description

[0011] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0012] Figure 1 This is a schematic diagram of the hardware environment for an angle estimation method based on the covariance matrix provided in an embodiment of this application;

[0013] Figure 2 This is a schematic diagram of the flow of an optional angle estimation method based on the covariance matrix according to an embodiment of this application;

[0014] Figure 3 This is a schematic diagram of an optional angle estimation method based on the covariance matrix according to an embodiment of this application;

[0015] Figure 4 This is a schematic diagram of the flow of another optional angle estimation method based on the covariance matrix according to an embodiment of this application;

[0016] Figure 5 This is a schematic diagram of an optional angle estimation device based on the covariance matrix according to an embodiment of this application;

[0017] Figure 6 This is a schematic diagram of the structure of an optional electronic device according to an embodiment of this application. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0019] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] According to one aspect of the embodiments of this application, an angle estimation method based on the covariance matrix is ​​provided. Optionally, as an optional implementation, the above-described angle estimation method based on the covariance matrix can be applied to, but is not limited to, [examples of other methods]. Figure 1 The hardware environment shown consists of a terminal device 102, a server 112, and a network 110. The terminal device 102 may include, but is not limited to, a display 104, a processor 106, and a memory 108, while the server 112 includes a database 114 and a processing engine 116.

[0021] In this embodiment, the terminal device can be a terminal device configured with a target client, which may include, but is not limited to, at least one of the following: mobile phone (such as Android phone, iOS phone, etc.), laptop computer, tablet computer, PDA, KID (Kobile Internet Device), PAD, desktop computer, smart TV, etc. The target client may be a video client, instant messaging client, browser client, educational client, etc. The network may include, but is not limited to, wired network and wireless network. The wired network includes: local area network (LAN), metropolitan area network (MAN), and wide area network (WAN). The wireless network includes: Bluetooth, Wi-Fi, and other networks that enable wireless communication. The server may be a single server, a server cluster consisting of multiple servers, or a cloud server. The above is merely an example, and no limitation is made in this embodiment.

[0022] As used herein, the term "system" refers to mechanical and electrical hardware, software, firmware, electronic control components, processing logic, and / or processor devices that, individually or in combination, provide the described functionality. This may include, but is not limited to, application-specific integrated circuits (ASICs), electronic circuits, processors (shared, dedicated, or grouped) that execute one or more software or firmware programs, memory containing software or firmware instructions, combinational logic circuits, and / or other components.

[0023] Alternatively, as an optional implementation, such as Figure 2 As shown, the angle estimation method based on the covariance matrix can be performed by electronic devices, such as... Figure 1 The specific steps for the terminal device 102 or server 112 shown include:

[0024] S202, receiving at least two echo signals through a target array consisting of at least two signal receivers, wherein the echo signals are signals returned after the radio signals sent by the vehicle-mounted equipment interact with the target object;

[0025] S204, calculate the signal covariance matrix corresponding to at least two echo signals, wherein the signal covariance matrix contains autocorrelation and cross-correlation. The autocorrelation is used to represent the correlation between different dimensions within the same echo signal in at least two echo signals, and the cross-correlation is used to represent the correlation between different echo signals in at least two echo signals. The cross-correlation contains amplitude and phase information of the missing array element positions in the target array.

[0026] S206, the signal covariance matrix is ​​vectorized to obtain the target vector, where the target vector is used to characterize the autocorrelation and cross-correlation.

[0027] S208, construct an angle space spectrum based on the target vector, where the angle space spectrum is the spectrum describing the distribution and intensity of at least two signals in different directions or angles;

[0028] S210, the angle value of the position of the maximum peak on the angle space spectrum is determined as the target angle, where the target angle is the estimated angle between the vehicle-mounted equipment and the target object.

[0029] Optionally, in this embodiment, the aforementioned angle estimation method based on the covariance matrix can be applied, but is not limited to, intelligent driving scenarios. For example, MIMO arrays can be used to form virtual array elements based on different transmit and receive positions. However, the M*N virtual array formed by such an M-transmit N-receive MIMO array requires a large aperture to improve angle measurement performance. This virtual array is still a sparse array, which suffers from a low main lobe-to-side lobe ratio. Therefore, there is a conflict between improving angle measurement performance and increasing the main lobe-to-side lobe ratio. To resolve this conflict, this embodiment proposes the aforementioned angle estimation method based on the covariance matrix, which, with a fixed number of array elements, increases the main lobe-to-side lobe ratio of the spatial spectrum, thereby improving the robustness of array angle measurement.

[0030] Specifically, in this embodiment, the signal receiver can be, but is not limited to, an antenna arranged on the vehicle-mounted equipment. For example, the transmitting end divides the data to be transmitted into multiple independent data streams and transmits them simultaneously through multiple transmitting antennas. The signal on each transmitting antenna is considered an independent input; the receiving end is equipped with multiple receiving antennas and simultaneously receives signals from multiple transmitting antennas. The signal on each receiving antenna is considered an independent output.

[0031] Optionally, in this embodiment, the target array can be, but is not limited to, a target array consisting of at least two signal receivers. By employing multiple receivers in the target array and utilizing the spatial differences between the receivers to capture the phase differences or time differences of the arriving signals, the direction of the signal source can be estimated and located. For example, a linear target array consists of a row of equally spaced signal receivers, typically in a straight or curved shape. By measuring the phase difference or time difference of the signals arriving at different receivers, the angle or position of the signal source relative to the array can be estimated. Similarly, a planar target array consists of equally spaced signal receivers arranged on a two-dimensional plane, typically in a rectangular, circular, or other geometric shape. By measuring the phase difference or time difference of the signals arriving at different receivers, the azimuth and elevation angles of the signal source relative to the array can be estimated, achieving three-dimensional positioning.

[0032] Optionally, when a signal sent by a signal transmitter encounters a target object, some of the energy is absorbed by the target object, while the rest is reflected back in different directions. This reflected signal is called an echo signal. In this embodiment, the echo signal is the signal returned after the radio signal sent by the vehicle-mounted device interacts with the target object.

[0033] Optionally, in this embodiment, for at least two echo signals, the corresponding signal covariance matrix can be calculated. The signal covariance matrix is ​​a symmetric matrix, where the diagonal elements represent the variance of each signal, and the off-diagonal elements represent the covariance between different signals. By analyzing the signal covariance matrix, the correlation between signals and the statistical characteristics of the signals can be understood.

[0034] To further illustrate, optionally assume there are two echo signals x1 and x2, which are measured by a receiver. Assume the sample data of the echo signals is X = [x1 x2], where xi represents the sample vector of the i-th echo signal. The signal covariance matrix C can be calculated using the following formula:

[0035] C = (1 / N) * X^T * X

[0036] Where N represents the number of samples, and X^T represents the transpose of matrix X.

[0037] Optionally, in this embodiment, the autocorrelation information is used to represent the correlation between different dimensions within the same echo signal in at least two echo signals. The correlation between different dimensions within the same echo signal can be represented using an autocorrelation function or an autocorrelation matrix. In other words, the autocorrelation information may include, but is not limited to, the aforementioned autocorrelation function or autocorrelation matrix.

[0038] To further illustrate, optionally, for an echo signal x(t), the autocorrelation function R_xx(tau) of x(t) is defined as:

[0039] R_xx(tau)=E[x(t)*x(t+tau)]

[0040] Here, tau represents the time or space lag (or offset), and E represents the expected value operator.

[0041] Furthermore, when multiple dimensions are involved, an autocorrelation matrix can be used to represent the correlation between different dimensions within the same echo signal. The autocorrelation matrix R_xx is a symmetric matrix, and its elements are defined as follows:

[0042] R_xx(i,j)=E[x_i(t)*x_j(t)]

[0043] Here, x_i(t) and x_j(t) represent different dimensions of the echo signal x(t). The diagonal elements of the autocorrelation matrix represent the variance of each dimension itself, and the off-diagonal elements represent the covariance between different dimensions.

[0044] Optionally, in this embodiment, the cross-correlation information is used to represent the correlation between different echo signals in at least two echo signals. The cross-correlation function or cross-correlation matrix can be used to represent the correlation between different echo signals. In other words, the cross-correlation information may include, but is not limited to, a cross-correlation function or a cross-correlation matrix.

[0045] To further illustrate, optionally for two echo signals x(t) and y(t), the cross-correlation function R_xy(tau) of x(t) and y(t) is defined as:

[0046] R_xy(tau)=E[x(t)*y(t+tau)]

[0047] Here, tau represents the time or space lag (or offset), and E represents the expected value operator.

[0048] Furthermore, when multiple dimensions are involved, a cross-correlation matrix can be used to represent the correlation between different echo signals. The cross-correlation matrix R_xy is a matrix whose elements are defined as:

[0049] R_xy(i,j)=E[x_i(t)*y_j(t+tau)]

[0050] Where x_i(t) and y_j(t+tau) represent different dimensions of the i-th echo signal x(t) and the j-th echo signal y(t+tau), respectively.

[0051] Optionally, in this embodiment, the mutual information includes amplitude and phase information of the missing element positions in the target array. This amplitude and phase information can refer to, but is not limited to, the phase difference between the element position and the reference position, reflecting the phase delay or phase difference of the signal propagating in the array. For example, in a linear array, the distance between each element position and the reference position can result in different propagation delays or phase differences.

[0052] Optionally, in this embodiment, the process of vectorizing the signal covariance matrix can be understood, but is not limited to, as converting the signal covariance matrix into a column vector or row vector to simplify calculation or storage.

[0053] To further illustrate, optionally assume the signal covariance matrix is ​​C, with a size of N×N. Extract the diagonal elements to obtain a column vector d of length N, representing the diagonal elements of matrix C. Extract the upper triangular (or lower triangular) portion of matrix C (excluding the diagonal) and concatenate it column-wise into a vector u (length N×(N-1) / 2). Vertically concatenate vector u and column vector d to obtain the vectorized signal covariance matrix v. The length of the vectorized signal covariance matrix v is N+N×(N-1) / 2, where the first N elements are diagonal elements, and the remaining elements are the upper triangular (or lower triangular) portion.

[0054] Optionally, in this embodiment, the angular spatial spectrum is a spectrum describing the distribution and intensity of at least two signals in different directions or angles. For example, in the angular spatial spectrum, the horizontal axis represents different angles or directions, and the vertical axis represents the power, energy, or amplitude of the signal. The value at each angle reflects the relative intensity or distribution of the signal in that direction.

[0055] It should be noted that the target array, consisting of at least two signal receivers, receives at least two echo signals and performs angle estimation based on these signals. To improve the angle measurement performance, the target array needs a larger aperture, but this results in a sparse array, which suffers from a low main lobe-to-side lobe ratio. Therefore, there is a conflict between improving angle measurement performance and increasing the main lobe-to-side lobe ratio. To resolve this conflict, this embodiment uses a signal covariance matrix containing both autocorrelation and cross-correlation information to fill in the missing information caused by the sparse array. With the addition of this missing information, the main lobe-to-side lobe ratio of the array's spatial spectrum can be increased, thus achieving the technical effect of improving the main lobe-to-side lobe ratio without reducing angle measurement accuracy, thereby resolving the aforementioned conflict.

[0056] Further examples, such as Figure 3 As shown in (a), at least two echo signals are received by a target array consisting of at least two signal receivers, wherein the echo signals are signals returned after the radio signals transmitted by the vehicle-mounted device 302 interact with the target object 304; the signal covariance matrix corresponding to the at least two echo signals is calculated; the signal covariance matrix is ​​vectorized to obtain the target vector; further as... Figure 3 As shown in (b), an angle space spectrum 306 is constructed based on the target vector, and the angle value 308 of the maximum peak position on the angle space spectrum 306 is determined as the target angle. The horizontal axis of the angle space spectrum 306 represents different angles or directions, and the vertical axis represents the power, energy or amplitude of the signal.

[0057] The embodiments provided in this application employ a covariance matrix with higher angle measurement performance for angle estimation, thereby improving angle measurement accuracy. Furthermore, by using a signal covariance matrix that includes autocorrelation and cross-correlation, the missing information caused by the sparse array is supplemented. As the missing information is supplemented, the main lobe ratio of the array spatial spectrum can be improved, thus achieving the technical effect of improving the main lobe ratio without reducing the angle measurement accuracy.

[0058] As an optional approach, the signal covariance matrix corresponding to at least two echo signals is calculated, including:

[0059] The signal covariance matrix is ​​calculated using autocorrelation and cross-correlation, where the signal covariance matrix contains N-dimensional autocorrelation and K-dimensional cross-correlation, where K is the product of P and N, N is greater than P and the difference between N and P is 1, and N is the number of signal receivers in at least two signal receivers.

[0060] Optionally, in this embodiment, N is greater than P and the difference between N and P is 1, which can be understood, but is not limited to, as P being (N-1) and the product of P and N being (N-1)×N. Thus, the signal covariance matrix contains N-dimensional autocorrelation and (N-1)×N-dimensional cross-correlation.

[0061] To further illustrate, assuming the target array is a multi-transmitter, multi-receiver array with M transmitters and N receivers, then the echo signal x is an N×1 dimensional vector, forming R... xx Let be an N×N dimensional signal covariance matrix, which contains N-dimensional autocorrelation and (N-1)×N-dimensional cross-correlation.

[0062] As an alternative approach, the signal covariance matrix is ​​calculated using autocorrelation and cross-correlation, including:

[0063] S1-1, Obtain the noise vector and signal vector, wherein the signal vector is the product of the angle steering vectors corresponding to at least two echo signals and the signal vectors corresponding to at least two transmitted signals, the transmitted signal is the radio signal sent by the vehicle-mounted device to the target object through an array composed of at least two signal receivers, and the noise vector is used to represent the environmental noise associated with the vehicle-mounted device;

[0064] S1-2, calculate the sum of the signal steering vector and the signal noise vector to obtain the first mathematical model corresponding to at least two echo signals;

[0065] S1-3, calculate the product of the first mathematical model and the second mathematical model to obtain the signal covariance matrix, where the second mathematical model is the mathematical model obtained by performing the conjugate transpose of the first mathematical model.

[0066] Optionally, in this embodiment, the vehicle-mounted device may be, but is not limited to, a vehicle-mounted radar, and the first mathematical model of the echo signal x of the vehicle-mounted radar is as shown in the following formula (1):

[0067] x(t)=A(θ)s(t)+N(t) (1)

[0068] Where t is time, A(θ) is the guide vector in the direction of angle θ, s(t) is the transmitted signal vector, and N(t) is the signal noise.

[0069] To further illustrate, the possible signal covariance matrix is ​​shown in the following formula:

[0070] R xx =E[x(t)x H (t)] (2)

[0071] Among them, R xx Let x(t) be the covariance matrix of the received signal, and x(t) be the first mathematical model. H (t) represents the second mathematical model obtained by performing the conjugate transpose of the first mathematical model, and E represents the expectation.

[0072] As an alternative approach, the signal covariance matrix is ​​vectorized to obtain the target vector, including:

[0073] Reorganize the signal covariance matrix to obtain N 2 A vector of dimension N, where the target vector includes N 2 A vector of dimension.

[0074] To further illustrate, the optional covariance matrix R xx Vectorization will result in a vector z, as shown in the following formula (3):

[0075] z = vec(R) xx (3)

[0076] As an alternative approach, an angular space spectrum can be constructed based on the target vector, including:

[0077] S2-1, Obtain L×N 2 An angle guidance matrix of dimension, wherein the angle guidance matrix contains the positions of missing array elements and the positions of each array element of the target array, and L is the maximum number of spatial angles;

[0078] S2-2, Calculate the angle guidance matrix and N 2 The product of vectors of different dimensions yields the angular space spectrum.

[0079] To further illustrate, the angular space spectrum P can be formed using a vector containing all autocorrelation and cross-correlation information, as shown in the following formula (4):

[0080] P=B(θ)·z (4)

[0081] Where z is N 2 A vector of dimension L×N, where B(θ) is an L×N vector with angle θ. 2 The guiding matrix is ​​dimensional, and the expression for B(θ) is shown in the following formula (5):

[0082] B(θ)=e -j2πdsin(θ) (5)

[0083] Where d is 1×N 2 The distance vector, θ, is an L×1 dimensional spatial angle vector containing all angles, and its expression is shown in the following formulas (6) and (7):

[0084] d={d m -d n |d m ,d n ∈D} (6)

[0085] θ={-90°,…,(c-1)*l,…,90°} (7)

[0086] Where, d m and d n Let θ be the distance between the m-th and n-th array elements, D be the set of array element distances, c be the number of spatial angles to be searched, l be the step size, and the resulting L×1 dimensional vector B(θ) is the spatial spectrum of the angle θ. The position of the maximum peak is the position of the target angle, and the maximum value of c is L.

[0087] As an optional approach, the angle value at the location of the maximum peak in the angle space spectrum is determined as the target angle, including:

[0088] S3-1, obtain the first angle value of the position of the maximum peak and the second angle value of the position of the second largest peak in the angle space spectrum;

[0089] S3-2, obtain the ratio between the first angle value and the second angle value, and determine the ratio as the main lobe-side lobe ratio. The main lobe-side lobe ratio is used to measure the energy ratio between the main lobe and the side lobes in the vehicle-mounted device. The main lobe is the main energy concentration area in the vehicle-mounted device, and the side lobes are other energy distribution areas in the vehicle-mounted device besides the main lobe.

[0090] S3-3, when the main lobe-to-side lobe ratio is greater than or equal to a preset threshold, the first angle value is determined as the target angle;

[0091] S3-4, when the main lobe-to-side lobe ratio is less than a preset threshold, a new echo signal is received through the target array, and the target angle is re-estimated using the new echo signal.

[0092] Optionally, in this embodiment, the main lobe-side lobe ratio is used to measure the energy ratio between the main lobe and the side lobes in the vehicle-mounted device. The main lobe is the main energy concentration area in the vehicle-mounted device, and the side lobes are other energy distribution areas in the vehicle-mounted device besides the main lobe. In this embodiment, the main lobe-side lobe ratio is measured by the ratio between the first angle value of the maximum peak position on the angular space spectrum and the second angle value of the second maximum peak position on the angular space spectrum.

[0093] It should be noted that, in order to further improve the main-sidelobe ratio, when the main-sidelobe ratio is less than a preset threshold, new echo signals are received through the target array, and the target angle is re-estimated using the new echo signals until a main-sidelobe ratio greater than or equal to the preset threshold is obtained, so as to ensure that the main-sidelobe ratio is within an acceptable range.

[0094] The embodiments provided in this application obtain a first angle value at the position of the maximum peak and a second angle value at the position of the second largest peak in the angle space spectrum; obtain the ratio between the first angle value and the second angle value, and determine the ratio as the main lobe-side lobe ratio, wherein the main lobe-side lobe ratio is used to measure the energy ratio between the main lobe and the side lobes in the vehicle-mounted device, the main lobe being the main energy concentration area in the vehicle-mounted device, and the side lobes being other energy distribution areas in the vehicle-mounted device besides the main lobe; when the main lobe-side lobe ratio is greater than or equal to a preset threshold, the first angle value is determined as the target angle; when the main lobe-side lobe ratio is less than the preset threshold, a new echo signal is received through the target array, and the target angle is re-estimated using the new echo signal, thereby achieving the purpose of ensuring that the main lobe-side lobe ratio is within an acceptable range, thus realizing the technical effect of improving the main lobe-side lobe ratio.

[0095] As an alternative, for ease of understanding, the above-mentioned angle estimation method based on the covariance matrix is ​​applied to the intelligent driving scenario. Without reducing the angle measurement accuracy, the main lobe-side lobe ratio is improved, thus avoiding a decrease in the robustness of the radar angle measurement function.

[0096] Optionally, in this embodiment, since there are array holes in the sparse array, the holes will reduce the main lobe-to-side lobe ratio, and supplementing the holes will introduce other errors. However, in actual needs, the main lobe-to-side lobe ratio can be improved at the cost of sacrificing accuracy.

[0097] It should be noted that this embodiment uses the received signals of the array to form the covariance matrix. The covariance matrix contains all the autocorrelation and cross-correlation information of the array. The cross-correlation information contains the amplitude and phase information of the missing array element positions in the coefficient array. Supplementing this missing information can improve the main-sidelobe ratio of the array spatial spectrum. In contrast, ordinary beamforming spatial spectrum only utilizes the array's autocorrelation information and some cross-correlation information, resulting in greater sparsity between arrays and a lower main-sidelobe ratio in the spatial spectrum.

[0098] To further illustrate, optional examples include... Figure 4 As shown, the specific steps are as follows:

[0099] S402, acquire the target echo signal x, where x should contain the received data of all virtual array elements of the MIMO array;

[0100] S404, according to R xx =E[x(t)x H (t)] Obtain the covariance matrix R xx ;

[0101] S406, for R xx Recombining them to form a vector z = {R} xx (1,1),…,R xx (i,j),…,R xx (M,M)}, where i,j=1,2,…,M;

[0102] S408, calculate the angular spatial spectrum, P = B(θ)·z;

[0103] S410, peak value of the spatial spectrum of the search angle;

[0104] S412, when a single target is incident, the location of the maximum peak value in the spatial spectrum is the target's location. The ratio of the maximum peak value to the second largest peak value is the main lobe-side lobe ratio.

[0105] The embodiments provided in this application improve the main lobe-to-side lobe ratio, thereby enhancing the robustness of angle measurement; by utilizing all autocorrelation and cross-correlation information, no additional errors are introduced, and the angle measurement accuracy is not reduced even in multi-target scenarios.

[0106] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0107] According to another aspect of the embodiments of this application, a covariance matrix-based angle estimation apparatus for implementing the above-described angle estimation method based on the covariance matrix is ​​also provided. For example... Figure 5 As shown, the device includes:

[0108] The receiving unit 502 is configured to receive at least two echo signals through a target array consisting of at least two signal receivers, wherein the echo signals are signals returned after the radio signals transmitted by the vehicle-mounted equipment interact with the target object;

[0109] The calculation unit 504 is used to calculate the signal covariance matrix corresponding to at least two echo signals. The signal covariance matrix contains autocorrelation information and cross-correlation information. The autocorrelation information is used to represent the correlation between different dimensions within the same echo signal in at least two echo signals. The cross-correlation information is used to represent the correlation between different echo signals in at least two echo signals. The cross-correlation information contains the amplitude and phase information of the missing array element positions in the target array.

[0110] Vector unit 506 is used to vectorize the signal covariance matrix to obtain the target vector, wherein the target vector is used to characterize the autocorrelation and cross-correlation.

[0111] The construction unit 508 is used to construct an angular space spectrum based on the target vector, wherein the angular space spectrum is a spectrum describing the distribution and intensity of at least two signals in different directions or angles;

[0112] The determining unit 510 is used to determine the angle value of the maximum peak position on the angle space spectrum as the target angle, wherein the target angle is the estimated angle between the vehicle-mounted equipment and the target object.

[0113] For specific implementation examples, please refer to the example shown in the lighting rendering device of the virtual model above, which will not be repeated here.

[0114] As an optional solution, computing unit 504 includes:

[0115] The first calculation module is used to calculate the signal covariance matrix using autocorrelation and cross-correlation. The signal covariance matrix contains N-dimensional autocorrelation and K-dimensional cross-correlation, where K is the product of P and N, N is greater than P and the difference between N and P is 1, and N is the number of signal receivers in at least two signal receivers.

[0116] For specific implementation examples, please refer to the example shown in the above virtual model lighting rendering method, which will not be repeated here.

[0117] As an optional solution, the first computing module includes:

[0118] The acquisition submodule is used to acquire noise vectors and signal vectors, wherein the signal vector is the product of the angle steering vectors corresponding to at least two echo signals and the signal vectors corresponding to at least two transmitted signals, the transmitted signal is the radio signal sent by the vehicle-mounted device to the target object through an array composed of at least two signal receivers, and the noise vector is used to represent the environmental noise associated with the vehicle-mounted device;

[0119] The first calculation submodule is used to calculate the sum of the signal steering vector and the signal noise vector to obtain the first mathematical model corresponding to at least two echo signals;

[0120] The second calculation submodule is used to calculate the product of the first mathematical model and the second mathematical model to obtain the signal covariance matrix. The second mathematical model is the mathematical model obtained by performing the conjugate transpose of the first mathematical model.

[0121] For specific implementation examples, please refer to the example shown in the above virtual model lighting rendering method, which will not be repeated here.

[0122] As an alternative, vector unit 506 includes:

[0123] The reconstruction module is used to reconstruct the signal covariance matrix to obtain N. 2 A vector of dimension N, where the target vector includes N 2 A vector of dimension.

[0124] For specific implementation examples, please refer to the example shown in the above virtual model lighting rendering method, which will not be repeated here.

[0125] As an optional solution, building unit 508 includes:

[0126] The first acquisition module is used to acquire L×N 2 An angle guidance matrix of dimension, wherein the angle guidance matrix contains the positions of missing array elements and the positions of each array element of the target array, and L is the maximum number of spatial angles;

[0127] The second calculation module is used to calculate the angle guidance matrix and N. 2 The product of vectors of different dimensions yields the angular space spectrum.

[0128] For specific implementation examples, please refer to the example shown in the above virtual model lighting rendering method, which will not be repeated here.

[0129] As an optional solution, unit 510 includes:

[0130] The second acquisition module is used to acquire the first angle value of the maximum peak position and the second angle value of the second maximum peak position on the angle space spectrum;

[0131] The third acquisition module is used to acquire the ratio between the first angle value and the second angle value, and to determine the ratio as the main lobe-side lobe ratio. The main lobe-side lobe ratio is used to measure the energy ratio between the main lobe and the side lobes in the vehicle-mounted device. The main lobe is the main energy concentration area in the vehicle-mounted device, and the side lobes are other energy distribution areas in the vehicle-mounted device besides the main lobe.

[0132] The determination module is used to determine the first angle value as the target angle when the main-side lobe ratio is greater than or equal to a preset threshold.

[0133] The estimation module is used to receive new echo signals through the target array and re-estimate the target angle using the new echo signals when the main-sidelobe ratio is less than a preset threshold.

[0134] For specific implementation examples, please refer to the example shown in the above virtual model lighting rendering method, which will not be repeated here.

[0135] According to another aspect of the embodiments of this application, an electronic device for implementing the above-described angle estimation method based on the covariance matrix is ​​also provided. This electronic device may be... Figure 6 The terminal device or server shown is illustrated in this embodiment. This example uses this electronic device for illustration. Figure 6 As shown, the electronic device includes a memory 602 and a processor 604. The memory 602 stores a computer program, and the processor 604 is configured to execute the steps in any of the above method embodiments via the computer program.

[0136] Optionally, in this embodiment, the aforementioned electronic device may be located in at least one of a plurality of network devices in a computer network.

[0137] Alternatively, as those skilled in the art will understand, Figure 6 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones (such as Android phones, iOS phones, etc.), tablets, PDAs, mobile internet devices (KIDs), PADs, and other terminal devices. Figure 6 This does not limit the structure of the aforementioned electronic devices. For example, the electronic device may also include components that are more... Figure 6 The more or fewer components shown (such as network interfaces, etc.), or having the same Figure 6 The different configurations shown.

[0138] The memory 602 can be used to store software programs and modules, such as the program instructions / modules corresponding to the angle estimation method and device based on the covariance matrix in this embodiment. The processor 604 executes various functional applications and data processing by running the software programs and modules stored in the memory 602, thereby realizing the aforementioned angle estimation method based on the covariance matrix. The memory 602 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 602 may further include memory remotely located relative to the processor 604, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. Specifically, the memory 602 may be used, but is not limited to, to store the aforementioned target angle and other information.

[0139] Optionally, the transmission device 606 described above is used to receive or send data via a network. Specific examples of the network described above may include wired networks and wireless networks. In one example, the transmission device 606 includes a Network Interface Controller (NIC), which can be connected to other network devices and routers via a network cable to communicate with the Internet or a local area network. In another example, the transmission device 606 is a radio frequency (RF) module, used for wireless communication with the Internet.

[0140] In addition, the aforementioned electronic device also includes: a display 608 for displaying information such as the target angle; and a connection bus 610 for connecting various module components in the aforementioned electronic device.

[0141] In other embodiments, the aforementioned terminal device or server can be a node in a distributed system, wherein the distributed system can be a blockchain system, which is a distributed system formed by connecting multiple nodes through network communication. The nodes can form a peer-to-peer (P2P) network, and any form of computing device, such as a server, terminal, or other electronic device, can become a node in the blockchain system by joining this peer-to-peer network.

[0142] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0143] According to one aspect of this application, a computer-readable storage medium is provided, from which a processor of a computer device reads computer instructions, and the processor executes the computer instructions, causing the computer device to perform an angle estimation method based on a covariance matrix provided in various alternative implementations.

[0144] Optionally, in this embodiment, the computer-readable storage medium described above may be configured to store information for performing the following steps:

[0145] S1, receiving at least two echo signals through a target array consisting of at least two signal receivers, wherein the echo signals are signals returned after the radio signals sent by the vehicle-mounted equipment interact with the target object;

[0146] S2, calculate the signal covariance matrix corresponding to at least two echo signals, wherein the signal covariance matrix contains autocorrelation and cross-correlation. The autocorrelation is used to represent the correlation between different dimensions within the same echo signal in at least two echo signals, and the cross-correlation is used to represent the correlation between different echo signals in at least two echo signals. The cross-correlation contains amplitude and phase information of the missing array element positions in the target array.

[0147] S3, vectorize the signal covariance matrix to obtain the target vector, where the target vector is used to represent the autocorrelation and cross-correlation.

[0148] S4, construct the angle space spectrum based on the target vector, where the angle space spectrum is the spectrum describing the distribution and intensity of at least two signals in different directions or angles;

[0149] S5, determine the angle value of the position of the maximum peak on the angle space spectrum as the target angle, where the target angle is the estimated angle between the vehicle-mounted equipment and the target object.

[0150] Optionally, in this embodiment, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROK), random access memory (RAK), disk or optical disk, etc.

[0151] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, 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 one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0152] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0153] In the several embodiments provided in this application, it should be understood that the disclosed client can be implemented in other ways. The device embodiments described above are merely illustrative; for example, 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 communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between units or modules, and may be electrical or other forms.

[0154] 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.

[0155] Furthermore, the functional units in the various embodiments of this application 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.

[0156] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. An angle estimation method based on the covariance matrix, characterized in that, include: At least two echo signals are received by a target array consisting of at least two signal receivers, wherein the echo signals are signals returned after the radio signals sent by the vehicle-mounted equipment interact with the target object; Calculate the signal covariance matrix corresponding to the at least two echo signals, wherein the signal covariance matrix contains autocorrelation information and cross-correlation information, the autocorrelation information is used to represent the correlation between different dimensions within the same echo signal in the at least two echo signals, the cross-correlation information is used to represent the correlation between different echo signals in the at least two echo signals, and the cross-correlation information contains the amplitude and phase information of the missing array element positions in the target array; The signal covariance matrix is ​​vectorized to obtain a target vector, wherein the target vector is used to characterize the autocorrelation information and the cross-correlation information. An angular space spectrum is constructed based on the target vector, wherein the angular space spectrum is a spectrum describing the distribution and intensity of the at least two signals in different directions or angles; The angle value at the position of the maximum peak on the angle space spectrum is determined as the target angle, wherein the target angle is the estimated angle between the vehicle-mounted device and the target object.

2. The method according to claim 1, characterized in that, The calculation of the signal covariance matrix corresponding to the at least two echo signals includes: The signal covariance matrix is ​​calculated using the autocorrelation information and the cross-correlation information, wherein the signal covariance matrix contains N-dimensional autocorrelation information and K-dimensional cross-correlation information, where K is the product of P and N, N is greater than P and the difference between N and P is 1, and N is the number of signal receivers among the at least two signal receivers.

3. The method according to claim 2, characterized in that, The step of calculating the signal covariance matrix using the autocorrelation and the cross-correlation includes: Obtain a noise vector and a signal vector, wherein the signal vector is the product of the angle steering vectors corresponding to the at least two echo signals and the signal vectors corresponding to the at least two transmitted signals, the transmitted signals are radio signals transmitted by the vehicle-mounted device to the target object through an array composed of at least two signal receivers, and the noise vector is used to represent the environmental noise associated with the vehicle-mounted device; The sum of the signal steering vector and the signal noise vector is calculated to obtain the first mathematical model corresponding to the at least two echo signals; The product of the first mathematical model and the second mathematical model is calculated to obtain the signal covariance matrix, wherein the second mathematical model is a mathematical model obtained by performing a conjugate transpose on the first mathematical model.

4. The method according to claim 2, characterized in that, The step of vectorizing the signal covariance matrix to obtain the target vector includes: The signal covariance matrix is ​​reorganized to obtain N. 2 A vector of dimension N, wherein the target vector includes the N 2 A vector of dimension.

5. The method according to claim 4, characterized in that, The construction of the angular space spectrum based on the target vector includes: Get L×N 2 An angle guidance matrix of dimension, wherein the angle guidance matrix contains the position of the missing array element and the position of each array element of the target array, and L is the maximum number of spatial angles; Calculate the angle guidance matrix and the N 2 The angular space spectrum is obtained by multiplying the vectors of the same dimension.

6. The method according to any one of claims 1 to 5, characterized in that, Determining the angle value of the maximum peak position on the angle space spectrum as the target angle includes: Obtain the first angle value of the maximum peak position and the second angle value of the second maximum peak position on the angle space spectrum; The ratio between the first angle value and the second angle value is obtained, and the ratio is determined as the main lobe-side lobe ratio. The main lobe-side lobe ratio is used to measure the energy ratio between the main lobe and the side lobes in the vehicle-mounted device. The main lobe is the main energy concentration area in the vehicle-mounted device, and the side lobes are other energy distribution areas in the vehicle-mounted device other than the main lobe. If the main-side lobe ratio is greater than or equal to a preset threshold, the first angle value is determined as the target angle. If the main-sidelobe ratio is less than the preset threshold, a new echo signal is received through the target array, and the target angle is re-estimated using the new echo signal.

7. An angle estimation device based on the covariance matrix, characterized in that, include: A receiving unit is configured to receive at least two echo signals via a target array consisting of at least two signal receivers, wherein the echo signals are signals returned after a radio signal transmitted by an on-board device interacts with a target object; The calculation unit is used to calculate the signal covariance matrix corresponding to the at least two echo signals, wherein the signal covariance matrix includes autocorrelation information and cross-correlation information, the autocorrelation information is used to represent the correlation between different dimensions within the same echo signal in the at least two echo signals, the cross-correlation information is used to represent the correlation between different echo signals in the at least two echo signals, and the cross-correlation information includes the amplitude and phase information of the missing array element positions in the target array; A vector unit is used to vectorize the signal covariance matrix to obtain a target vector, wherein the target vector is used to characterize the autocorrelation information and the cross-correlation information. A construction unit is used to construct an angular space spectrum based on the target vector, wherein the angular space spectrum is a spectrum describing the distribution and intensity of the at least two signals in different directions or angles; The determining unit is used to determine the angle value of the maximum peak position on the angle space spectrum as the target angle, wherein the target angle is the estimated angle between the vehicle-mounted device and the target object.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program is executed by a processor to perform the method described in any one of claims 1 to 6.

9. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method described in any one of claims 1 to 6 through the computer program.

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