Goniometric method, device and apparatus

Abstract

This application provides an angle measurement method, apparatus, and device, relating to the field of communication technology. The angle measurement method includes: receiving a first signal through an equidistant uniform linear array, the first signal including a signal reflected by a target to be measured, the equidistant uniform linear array including M array elements; iteratively obtaining M signal subspaces based on the first signal and a second signal, the second signal being a reference signal transmitted by the equidistant uniform linear array, the signal subspaces representing the angular directionality of the target to be measured; and obtaining the target angle corresponding to the target to be measured based on the M signal subspaces. This application can obtain the target angle corresponding to the target to be measured more accurately and can reduce complexity.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to an angle measurement method, apparatus and device. Background Technology

[0002] A sensor-integrated network refers to a network that uses the same network to achieve both communication and sensing functions. In a sensor-integrated network, it is necessary to acquire information such as the target's angle, distance, and speed, with acquiring the target's angle information being particularly crucial.

[0003] Currently, conventional beamforming (CBF) is commonly used to obtain the target's angle information. However, in some scenarios, the angle information obtained through this method is not accurate enough. Summary of the Invention

[0004] This application provides an angle measurement method, apparatus, and device to solve the technical problem that the angle information of a target obtained by current methods is not accurate enough.

[0005] In a first aspect, this application provides an angle measurement method, comprising:

[0006] The first signal is received by an equidistant uniform linear array. The first signal includes the signal reflected by the target to be measured. The equidistant uniform linear array includes M array elements, where M is a positive integer.

[0007] Based on the first signal and the second signal, M signal subspaces are obtained iteratively. The second signal is a reference signal transmitted by an equidistant uniform linear array. The signal subspaces are used to represent the angular directivity of the target to be measured.

[0008] Based on M signal subspaces, the target angle corresponding to the target to be measured is obtained.

[0009] In some optional implementations, based on the first signal and the second signal, M signal subspaces are iteratively obtained, including: based on the first signal and the second signal, performing the following operations until M signal subspaces are obtained: extracting frequency domain channel information from the first signal according to the first signal and the second signal; extracting the frequency domain channel information corresponding to the strongest path in the frequency domain channel information based on the frequency domain channel information, and performing time delay cancellation processing to obtain target frequency domain channel information; performing coherent information merging processing on the target frequency domain channel information to obtain merged frequency domain channel information; performing interference cancellation processing on the merged frequency domain channel information to obtain the i-th signal subspace, where i is an integer greater than 0 and less than or equal to M; obtaining the residual first signal and the reconstructed second signal according to the i-th signal subspace and the first signal, using the residual first signal as the new first signal, and using the reconstructed second signal as the new second signal.

[0010] In some optional implementations, extracting frequency domain channel information from the first signal based on the first signal and the second signal includes: multiplying each signal in the first signal by the conjugate value of each signal in the second signal to extract the frequency domain channel information from the first signal.

[0011] In some optional implementations, based on the frequency domain channel information, the frequency domain channel information corresponding to the strongest path in the frequency domain channel information is extracted, and delay cancellation processing is performed to obtain the target frequency domain channel information. This includes: performing inverse fast Fourier transform processing on the frequency domain channel information to obtain time domain channel information; obtaining the time domain channel information corresponding to the strongest path in the time domain channel information; performing delay cancellation processing based on the time domain channel information corresponding to the strongest path to obtain the target time domain channel information; and performing fast Fourier transform processing on the target time domain channel information to obtain the target frequency domain channel information.

[0012] In some optional implementations, interference cancellation processing is performed on the merged frequency domain channel information to obtain the i-th signal subspace, including: obtaining the covariance matrix corresponding to the merged frequency domain channel information based on the merged frequency domain channel information and the conjugate transpose of the merged frequency domain channel information; and obtaining the i-th signal subspace based on the merged frequency domain channel information and the covariance matrix.

[0013] In some optional implementations, obtaining the residual first signal and the reconstructed second signal based on the i-th signal subspace and the first signal includes: reconstructing the signal based on the conjugate transpose of the i-th signal subspace and the first signal to obtain the reconstructed second signal; obtaining the signal corresponding to the strongest path based on the i-th signal subspace and the reconstructed second signal; and obtaining the residual first signal based on the difference between the first signal and the signal corresponding to the strongest path.

[0014] In some optional implementations, the target angle corresponding to the target to be measured is obtained based on M signal subspaces, including: obtaining the target angle corresponding to the target to be measured by using a multi-signal classification method based on M signal subspaces.

[0015] In some optional implementations, based on M signal subspaces, a multi-signal classification method is used to obtain the target angle corresponding to the target to be measured, including: obtaining the feature value corresponding to each signal subspace in the M signal subspaces; determining N target signal subspaces from the M signal subspaces according to the feature value and threshold value, where N is the number of targets to be measured; obtaining the orthogonal complementary signal subspaces of the N target signal subspaces; obtaining the spatial spectrum corresponding to the multiple preset angles according to the orthogonal complementary signal subspaces and the steering vectors under multiple preset angles; and performing spectral peak search on the spatial spectrum to obtain the target angle corresponding to the target to be measured.

[0016] Secondly, this application provides an angle measuring device, comprising:

[0017] A receiving unit is used to receive a first signal through an equidistant uniform linear array. The first signal includes a signal reflected by the target to be measured. The equidistant uniform linear array includes M array elements, where M is a positive integer.

[0018] The first acquisition unit is used to iteratively acquire M signal subspaces based on the first signal and the second signal. The second signal is a reference signal transmitted by an equidistant uniform linear array. The signal subspaces are used to represent the angular pointing of the target to be measured.

[0019] The second acquisition unit is used to acquire the target angle corresponding to the target to be measured based on M signal subspaces.

[0020] Thirdly, this application provides an angle measuring device, comprising:

[0021] Memory, used to store computer programs;

[0022] A transceiver is used to send and receive data under the control of a processor.

[0023] A processor for reading computer programs from memory and executing angle measurement methods as provided in the first aspect.

[0024] Fourthly, this application provides a processor-readable storage medium storing a computer program for causing a processor to perform the angle measurement method provided in the first aspect.

[0025] Fifthly, this application provides a chip storing a computer program for causing the chip to perform the angle measurement method as provided in the first aspect.

[0026] In a sixth aspect, this application provides a computer program product, comprising: a computer program that, when executed by a processor, implements the angle measurement method as provided in the first aspect.

[0027] In the angle measurement method, apparatus, and device provided in this application, a first signal is received through an equidistant uniform linear array. The first signal includes a signal reflected by the target to be measured, and the equidistant uniform linear array includes M array elements. Based on the first signal and the second signal, M signal subspaces are iteratively obtained. The second signal is a reference signal transmitted by the equidistant uniform linear array, and the received signal is no longer used as prior information to obtain the signal subspace. Therefore, the signal subspaces can be obtained more accurately. Thus, based on the M signal subspaces, the target angle corresponding to the target to be measured can be obtained more accurately, effectively improving the angle measurement accuracy. Moreover, eigenvalue decomposition is not required, which can effectively reduce complexity.

[0028] It should be understood that the content described in the foregoing summary section is not intended to limit the key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application;

[0031] Figure 2 A flowchart of an angle measurement method provided in an embodiment of this application;

[0032] Figure 3 A flowchart of an angle measurement method provided in another embodiment of this application;

[0033] Figure 4 This is a schematic diagram of the structure of an angle measuring device provided in an embodiment of this application;

[0034] Figure 5 This is a schematic diagram of the structure of an angle measuring device provided in an embodiment of this application. Detailed Implementation

[0035] In this application, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. In the embodiments of this application, the term "multiple" refers to two or more, and other quantifiers are similar.

[0036] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0037] A sensor-communication integrated network refers to a network that uses a single network to achieve both communication and sensing functions. The entire communication network functions as a giant sensing system, acquiring information such as the distance, speed, and angle of a target from wireless signals to provide a wide range of services including high-precision positioning, gesture capture, motion recognition, passive object detection and tracking, imaging, and environmental reconstruction. In a sensor-communication integrated network, acquiring information such as the target's angle, distance, and speed is crucial, with obtaining the target's angle information being of paramount importance.

[0038] Currently, conventional beamforming (CBF) is commonly used to obtain the angular information of a target. Specifically, in the CBF method, the equidistant uniform linear array model describes a set of antennas arranged at equal intervals along a straight line. For example, if the equidistant uniform linear array model consists of M array elements, and assuming there are N narrowband far-field signal sources (i.e., targets, such as UAVs), satisfying M > N, then the received signal X(t) of the equidistant uniform linear array can be expressed as follows:

[0039] X(t)=A(θ)S(t)+N(t) Formula 1

[0040] Where X(t) is an M×K dimension matrix, and A(θ) represents an M×N dimension guiding vector matrix, A(θ)=[a(θ1)a(θ2)…a(θ) N )], where a(θ i S(t) represents the steering vector corresponding to the i-th target; S(t) represents the complex envelope of N narrowband far-field signal sources in N×K dimensions, S(t)=[s1(t)s2(t)…s N (t)] T s i (t) represents the i-th narrowband far-field signal source, (·) T N(t) represents the transpose; N(t) represents the noise received by the array element, N(t) = [n1(t)n2(t)…n N (t)] T n i (t) represents the noise corresponding to the i-th target; K represents the number of subcarriers in the frequency domain; t represents time t.

[0041] Based on Formula 1 above, assuming that the noise received by each array element is independent of each other, and that the noise and signal are also independent of each other, each steering vector in the steering vector array A(θ) satisfies the following Formula 2:

[0042]

[0043] Where n = 1, 2, 3, ..., N; θ n This represents the incident angle (i.e., beam pointing angle) of the nth narrowband far-field signal source; This represents the phase information of the nth target (narrowband far-field signal source). Where d represents the element spacing (i.e., the distance between two antennas); ω0 represents the carrier frequency; and c represents the speed of light propagation.

[0044] The covariance matrix R of the received signal X(t) X The following formula three must be satisfied:

[0045] R X =AR S A H +R N =AR S A H +σ 2 Formula 3

[0046] in,(·) H Indicates conjugate transpose; R S R represents the covariance matrix of S(t); N Let A represent the covariance matrix of N(t); A is A(θ); σ represents the noise power; and I represents the M×M identity matrix.

[0047] Based on the above formula, according to different angles θ k The guiding vector a(θ) k ) and covariance matrix R X Each angle θ is obtained using the following formula four. k The corresponding spatial spectrum P k :

[0048] P k =a(θ) k )R X a H (θ k Formula 4

[0049] Where, θ k For example, values ​​are taken within the range of [-90°, 90°] with an angle step size α, resulting in a series of values, where the angle step size α is, for example, 1°. According to Formula 4 above, the spatial spectrum value for each angle can be obtained. Furthermore, based on multiple spatial spectrum values, the spatial spectrum within the desired angle range can be obtained. Corresponding peaks can be searched within the spatial spectrum, and the angles corresponding to the N peaks can be selected as the final angle values.

[0050] The CBF method can accurately obtain the angle information of a target under certain scenarios. However, the CBF method is affected by Rayleigh limit and has a relatively wide beam. When the distance between two targets is close, the peak values ​​corresponding to the two targets in the spatial spectrum are relatively gradual, making it difficult to separate the two targets well. Therefore, the angle information of the target obtained by the CBF method is not accurate enough.

[0051] In addition, the angular information of the target can also be obtained through the Multiple Signal Classification (MUSIC) method, the Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) method, or the Multi-state Wiener filter method based on the covariance matrix.

[0052] Specifically, for the MUSIC method, referring to the CBF method mentioned above, the received signal X(t) of the equidistant uniform linear array satisfies: X(t)=A(θ)S(t)+N(t), and the covariance matrix R of the received signal X(t) is... X Satisfy: R X =AR S A H +R N =AR S A H +σ 2 I. The covariance matrix R can be calculated using the following formula (Equation 5). X Perform eigenvalue decomposition:

[0053]

[0054] Among them, U SN The eigenvectors are matrices formed by multiple eigenvectors; ∑ SN This represents the diagonal matrix formed by the eigenvalues ​​of the eigenvectors.

[0055] according to You can in U SN Select N columns as the signal space U S Then the remaining MN columns serve as the kernel space (null space) of the signal space, for example, using U N Represented by; where λ1, λ2, ..., λ M For ∑ SN The diagonal elements, arranged in descending order, λ1, λ2, ..., λ3. N This indicates that λ1, λ2, ..., λ M The first N values ​​are sorted from largest to smallest; Th represents the threshold value.

[0056] Different angles θ can be obtained using the following formula (Formula 6). k The corresponding spatial spectra P music (θ k ):

[0057]

[0058] For multiple spatial spectra P music (θ k Perform a peak search and select the angle values ​​corresponding to the N peaks as the final angle values.

[0059] The MUSIC method can accurately obtain the angle information of a target in certain scenarios. However, the MUSIC method involves eigenvalue decomposition. For large-scale antennas, the antenna dimension is relatively large, so the computational load is relatively large and the implementation is relatively complex.

[0060] The ESPRIT method divides the received signal into two structurally identical subarrays. Since the response coefficients of the two subarrays to the same signal source differ only by a phase difference, the rotation-invariant relationship (phase difference) between the two matrices can be used to obtain the angular information of the corresponding target. However, the ESPRIT method also involves eigenvalue decomposition, which has higher complexity and relatively lower accuracy, resulting in performance inferior to the MUSIC method.

[0061] The multi-stage Wiener filtering method based on the covariance matrix uses the received signal as prior information and extracts the signal subspace using multi-stage Wiener filtering. While this method reduces complexity, the prior information contains noise and interference, which is not further processed. Consequently, the covariance matrix of the received signal contains noise, and the extracted signal subspace also includes noise. Therefore, the extraction of the signal subspace is not accurate enough, resulting in low angle measurement accuracy. This is especially true in low signal-to-noise ratio (SNR) scenarios.

[0062] In view of this, embodiments of this application provide an angle measurement method. The equidistant uniform linear array includes M array elements. The original transmitted signal (reference signal) of the equidistant uniform linear array is used as prior information to avoid the problem of large measurement errors caused by inaccurate prior information. Referring to the structure of multi-level Wiener filtering, based on the signal received by the equidistant uniform linear array and the original transmitted signal, M signal subspaces are iteratively obtained, which can obtain the signal subspaces more accurately. Thus, based on the M signal subspaces, the target angle corresponding to the target to be measured is obtained more accurately, effectively improving the angle measurement accuracy. Moreover, it does not require eigenvalue decomposition, which can effectively reduce complexity.

[0063] The following section provides examples illustrating the application scenarios of the solution provided in this application.

[0064] Figure 1 This is a schematic diagram illustrating an application scenario provided by an embodiment of this application. For example... Figure 1As shown in the example, this application scenario uses a base station as the angle measuring device and three drones as the targets to be measured. The targets can also be vehicles or pedestrians, etc. This embodiment does not limit the targets to be measured. The base station 101 transmits and receives signals through an equidistant uniform linear array. The received signals include signals reflected by the drones 102. Based on the transmitted and received signals, the base station 101 uses the angle measuring method provided in this embodiment to obtain the target angle corresponding to the drones 102.

[0065] It should be noted that, Figure 1 This is merely a schematic diagram illustrating one application scenario provided by an embodiment of this application. This embodiment does not necessarily represent... Figure 1 The included equipment is not limited, nor is it restricted. Figure 1 The positional relationships between the devices are defined. The angle measuring device involved in the embodiments of this application may be a base station or a radar, etc.

[0066] It should be noted that the methods and apparatus provided in the embodiments of this application are based on the same application concept. Since the methods and apparatus solve problems in similar principles, the implementation of the apparatus and methods can refer to each other, and repeated parts will not be described again.

[0067] The technical solutions of the embodiments of this application and how the technical solutions of this application solve the above-mentioned technical problems are described in detail below with specific examples. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0068] Figure 2 This is a flowchart illustrating an angle measurement method provided in an embodiment of this application. Figure 2 As shown, the method in this application embodiment includes:

[0069] S201. Receive a first signal through an equidistant uniform linear array. The first signal includes the signal reflected by the target to be measured. The equidistant uniform linear array includes M array elements, where M is a positive integer.

[0070] In this embodiment, the equidistant uniform linear array comprises M array elements, which can be understood as the equidistant uniform linear array model being composed of M array elements; the target to be measured can be understood as a narrowband far-field signal source, specifically, for example, a drone. Signals are transmitted through the equidistant uniform linear array, and correspondingly, a first signal can be received through the equidistant uniform linear array. The first signal includes the signal reflected by the target to be measured, and also includes noise.

[0071] For example, referring to the current related technologies, assuming there are N narrowband far-field signal sources (targets to be measured) that satisfy M>N, the first signal (i.e. X(t)) received by the equidistant uniform linear array satisfies the above formula: X(t)=A(θ)S(t)+N(t), and the meaning of each parameter can be referred to the above related technologies.

[0072] Based on Formula 1 above, assuming that the noise received by each of the M array elements is independent of each other, and that the noise and signal are also independent of each other, each steering vector in the steering vector matrix A(θ) satisfies Formula 2 above: Where n = 1, ..., N, The meaning of each parameter can be found in the relevant technologies mentioned above.

[0073] S202. Based on the first signal and the second signal, M signal subspaces are obtained iteratively. The second signal is a reference signal transmitted by an equidistant uniform linear array. The signal subspaces are used to represent the angular directionality of the target to be measured.

[0074] In this step, the second signal is a reference signal (S(t)) transmitted by the equidistant uniform linear array. The reference signal can be understood as the original transmitted signal of the equidistant uniform linear array, corresponding to the original transmitted signal reflected by the target to be measured. The signal subspace contains the angular information of the target to be measured, which is obtained based on the phase of the array elements of the equidistant uniform linear array. Based on the first and second signals, M signal subspaces are obtained iteratively. For example, a multidimensional Wiener filter structure can be referenced, and multiple iterations based on the first and second signals can be performed to obtain the M signal subspaces.

[0075] It is understandable that, compared to the multi-level Wiener filtering method based on the covariance matrix, this method uses the received signal as prior information. Since the received signal contains noise, the prior information also includes noise, resulting in inaccurate signal subspace extraction and consequently, inaccurate angle measurement. However, this embodiment does not use the received signal as prior information. Instead, it uses the second signal as prior information, processing the first and second signals accordingly to iteratively obtain M signal subspaces, thus avoiding the problem of inaccurate prior information. For details on how to iteratively obtain M signal subspaces based on the first and second signals, please refer to subsequent embodiments; they will not be elaborated here.

[0076] S203. Based on M signal subspaces, obtain the target angle corresponding to the target to be measured.

[0077] For example, based on M signal subspaces, a multi-target angle measurement method (MUSIC) can be used to obtain the target angles corresponding to the targets to be measured. For instance, based on M signal subspaces, the MUSIC method can determine the number of targets to be measured, such as N targets. Then, based on the M signal subspaces, a signal space and a kernel space (null space) can be determined, and the target angles corresponding to the N targets to be measured can be obtained based on the kernel space. For details on how to use the MUSIC method to obtain the target angles corresponding to the targets to be measured, please refer to subsequent embodiments; they will not be elaborated here.

[0078] The angle measurement method provided in this application receives a first signal through an equidistant uniform linear array. The first signal includes a signal reflected by the target to be measured, and the equidistant uniform linear array includes M array elements. Based on the first signal and the second signal, M signal subspaces are iteratively obtained. The second signal is a reference signal transmitted by the equidistant uniform linear array, instead of using the received signal as prior information to obtain the signal subspace. Therefore, the signal subspaces can be obtained more accurately. Thus, based on the M signal subspaces, the target angle corresponding to the target to be measured can be obtained more accurately, effectively improving the angle measurement accuracy. Moreover, it does not require eigenvalue decomposition, which can effectively reduce complexity.

[0079] Figure 3 This is a flowchart illustrating an angle measurement method according to another embodiment of this application. Based on the above embodiments, the angle measurement method of this application is further described. Figure 3 As shown, the method in this application embodiment may include:

[0080] S301. Receive a first signal through an equidistant uniform linear array. The first signal includes the signal reflected by the target to be measured. The equidistant uniform linear array includes M array elements, where M is a positive integer.

[0081] For a detailed description of this step, please refer to [link / reference]. Figure 2 The relevant description of S201 in the illustrated embodiment will not be repeated here.

[0082] In this embodiment of the application, steps S302 to S307 can be iteratively executed based on the first signal and the second signal until M signal subspaces are obtained:

[0083] S302. Based on the first signal and the second signal, extract the frequency domain channel information from the first signal. The second signal is a reference signal transmitted by an equidistant uniform linear array.

[0084] In this embodiment, frequency domain channel information refers to relevant information describing the characteristics of a wireless communication channel in the frequency domain. Frequency domain channel information includes the channel frequency response, which fully describes the influence of the channel on signals of different frequencies, including changes in amplitude and phase. In this step, after receiving the first signal, the frequency domain channel information in the first signal can be extracted based on the first signal and the second signal.

[0085] Further, optionally, extracting frequency domain channel information from the first signal based on the first signal and the second signal may include: multiplying each signal in the first signal by the conjugate value of each signal in the second signal to extract the frequency domain channel information from the first signal.

[0086] For example, the frequency domain channel information in the first signal can be extracted by multiplying the conjugate value of each signal in the first signal X(t) with the conjugate value of each signal in the second signal S(t) using the following formula:

[0087]

[0088] in,(·) * Denotes conjugation, m = 1, ..., M, n = 1, ..., N; X m (t) represents each signal in X(t); H represents the conjugate value of each signal in S(t); m,n (t) represents any frequency domain channel information extracted from the first signal.

[0089] It is understood that this embodiment uses the original transmitted signal S(t) as prior information, and correlates the prior information with the measurement information (first signal) to avoid the problem of inaccurate prior information.

[0090] S303. Based on the frequency domain channel information, extract the frequency domain channel information corresponding to the strongest path in the frequency domain channel information, and perform time delay elimination processing to obtain the target frequency domain channel information.

[0091] In this step, the frequency domain channel information corresponding to the strongest path in the frequency domain channel information is the frequency domain characteristic of the path with the highest signal strength among all multipaths. After extracting the frequency domain channel information from the first signal based on Formula 7 above, the frequency domain channel information corresponding to the strongest path in the frequency domain channel information can be extracted based on the frequency domain channel information, and time delay cancellation processing can be performed to eliminate the influence of time delay on the merging of frequency domain channel information, thereby obtaining the target frequency domain channel information.

[0092] Further, optionally, based on the frequency domain channel information, extracting the frequency domain channel information corresponding to the strongest path in the frequency domain channel information and performing time delay cancellation processing to obtain the target frequency domain channel information may include: performing inverse fast Fourier transform processing on the frequency domain channel information to obtain time domain channel information; obtaining the time domain channel information corresponding to the strongest path in the time domain channel information; performing time delay cancellation processing based on the time domain channel information corresponding to the strongest path to obtain the target time domain channel information; and performing fast Fourier transform processing on the target time domain channel information to obtain the target frequency domain channel information.

[0093] For example, based on Formula 7 above, the frequency domain channel information can be processed by inverse fast Fourier transform using Formula 8 to obtain the time domain channel information h. m,n (t); the time-domain channel information [value, idx] corresponding to the strongest path in the time-domain channel information can be obtained through the following formula nine; delay cancellation processing can be performed based on the time-domain channel information corresponding to the strongest path through the following formula ten, i.e., K h m,n Only the value is retained in (t), and the other K-1 h values ​​are retained. m,n (t) are all set to 0 to obtain the target time-domain channel information h. tmp (t); The target time-domain channel information can be processed by Fast Fourier Transform (FFT) according to the following formula eleven to obtain the target frequency-domain channel information.

[0094] h m,n (t)=sqrt(K)*ifft(H m,n Formula 8 (t)

[0095] [value,idx]=max(abs(h m,n Formula Nine (t)

[0096]

[0097] Where m = 1, ..., M, n = 1, ..., N; sqrt(K) represents taking the square root of K; abs represents taking the absolute value; max represents taking the maximum value; ifft represents the inverse fast Fourier transform; fft represents the fast Fourier transform; K represents the number of subcarriers in the frequency domain; value represents K h m,n The largest value in (t) is idx, which represents the index of the value.

[0098] It is understandable that by using Fast Fourier Transform (FFT) for signal filtering, a single signal subspace can be obtained each time. After eliminating the effects of time delay, coherent merging can eliminate the adverse effects of multipath and time delay, thereby improving the accuracy of angle measurement, especially at low SNR.

[0099] S304. Perform coherent information merging processing on the target frequency domain channel information to obtain the merged frequency domain channel information.

[0100] For example, since the frequency domain eliminates the effect of time delay, the target frequency domain channel information can be directly processed by coherent information merging using the following formula (Equation 12) to form spatial channel information under multiple antennas. That is, the merged frequency domain channel information is obtained:

[0101]

[0102] S305. Perform interference cancellation processing on the merged frequency domain channel information to obtain the i-th signal subspace, where i is an integer greater than 0 and less than or equal to M.

[0103] In this step, for example, when there are multiple targets to be measured, there will be some interference and noise. Therefore, interference cancellation processing is required. After obtaining the merged frequency domain channel information, interference cancellation processing can be performed on the merged frequency domain channel information to obtain the i-th signal subspace.

[0104] Further, optionally, interference cancellation processing is performed on the merged frequency domain channel information to obtain the i-th signal subspace, which may include: obtaining the covariance matrix corresponding to the merged frequency domain channel information based on the merged frequency domain channel information and the conjugate transpose of the merged frequency domain channel information; and obtaining the i-th signal subspace based on the merged frequency domain channel information and the covariance matrix.

[0105] For example, the covariance matrix β corresponding to the merged frequency domain channel information can be obtained using Formula Thirteen, based on the merged frequency domain channel information and its conjugate transpose; the i-th signal subspace can be obtained using Formula Fourteen, based on the merged frequency domain channel information and the covariance matrix.

[0106] in, It consists of multiple The resulting M*N matrix.

[0107] S306. Determine if i is equal to M.

[0108] If i equals M, then continue with step S308; if i does not equal M, then continue with step S307.

[0109] S307. Based on the i-th signal subspace and the first signal, obtain the residual first signal and the reconstructed second signal, take the residual first signal as the new first signal, and take the reconstructed second signal as the new second signal, and execute step S302.

[0110] In this step, signal reconstruction is performed based on the i-th signal subspace and the first signal to obtain the reconstructed second signal. The residual first signal is then obtained based on the i-th signal subspace and the reconstructed second signal. The residual first signal is used as the new first signal, and the reconstructed second signal is used as the new second signal. Step S302 is then executed to extract the next signal subspace, thus obtaining M signal subspaces iteratively. The residual first signal represents the remaining portion of the first signal corresponding to the target to be measured after extracting a portion of the first signal from the current first signal.

[0111] Further, optionally, obtaining the residual first signal and the reconstructed second signal based on the i-th signal subspace and the first signal may include: reconstructing the signal based on the conjugate transpose of the i-th signal subspace and the first signal to obtain the reconstructed second signal; obtaining the signal corresponding to the strongest path based on the i-th signal subspace and the reconstructed second signal; and obtaining the residual first signal based on the difference between the first signal and the signal corresponding to the strongest path.

[0112] For example, the reconstructed second signal S can be obtained by reconstructing the signal based on the conjugate transpose of the i-th signal subspace and the first signal using the following formula (fifteen). n (t); The signal corresponding to the strongest path can be obtained using the following formula (16), based on the i-th signal subspace and the reconstructed second signal, i.e. Therefore, based on the difference between the first signal and the signal corresponding to the strongest path, the residual first signal X is obtained. m (t):

[0113]

[0114] It is understandable that by iteratively executing steps S302 to S307 above, M signal subspaces can be obtained:

[0115]

[0116] S308. Based on M signal subspaces, a multi-signal classification method is used to obtain the target angle corresponding to the target to be measured.

[0117] In this step, after obtaining M signal subspaces, the target angle corresponding to the target to be measured can be obtained based on the M signal subspaces using the Multiple Signal Classification (MUSIC) method.

[0118] Further, optionally, based on M signal subspaces, the target angle corresponding to the target to be measured is obtained by using a multi-signal classification method, which may include: obtaining the feature value corresponding to each signal subspace in the M signal subspaces; determining N target signal subspaces from the M signal subspaces according to the feature value and threshold value, where N is the number of targets to be measured; obtaining the orthogonal complementary signal subspaces of the N target signal subspaces; obtaining the spatial spectrum corresponding to the multiple preset angles according to the orthogonal complementary signal subspaces and the steering vectors under multiple preset angles; and performing spectral peak search on the spatial spectrum to obtain the target angle corresponding to the target to be measured.

[0119] For example, referring to the current MUSIC method, the obtained M signal subspaces H s (t) can be understood as U in the MUSIC method. SN It can be based on H s Each signal subspace in (t) and The product of the conjugate transposes of the eigenvalues ​​is used to obtain the eigenvalues ​​corresponding to each of the M signal subspaces, thus obtaining λ1 to λ2 in the MUSIC method. M For example, λ1 is The eigenvalues ​​(λ1 to λ) M Arrange them from largest to smallest, according to N target signal subspaces can be determined from M signal subspaces, where Th is the threshold value. Furthermore, from H... s Extract the first N from (t) As the signal space Us, N orthogonal complementary signal subspaces of target signal subspaces can be obtained from Us. The orthogonal complementary signal subspace is: I-Us H Us. Here, I represents the M×M identity matrix, and the orthogonal complement signal subspace can be understood as the kernel space (null space) U in the MUSIC method. N Using Formula 6 above, based on the orthogonal complementary signal subspace and the steering vectors at multiple preset angles, the spatial spectrum P corresponding to each preset angle can be obtained. music (θ k A spectral peak search is performed on the spatial spectrum, and N peaks are selected as the target angles corresponding to the N targets to be measured.

[0120] The angle measurement method provided in this application embodiment receives a first signal through an equidistant uniform linear array. The first signal includes a signal reflected by the target to be measured, and the equidistant uniform linear array includes M array elements. Based on the first signal and a second signal, frequency domain channel information is extracted from the first signal. The second signal is a reference signal transmitted by the equidistant uniform linear array, which uses the second signal as prior information to avoid the problem of large measurement errors caused by inaccurate prior information. Based on the frequency domain channel information, the frequency domain channel information corresponding to the strongest path in the frequency domain channel information is extracted and time delay cancellation processing is performed to obtain the target frequency domain channel information. This can eliminate the influence of multipath and time delay on the merging of frequency domain channel information, improving accuracy and efficiency. To improve the accuracy of angle measurement under low SNR, coherent information merging is directly performed on the target frequency domain channel information to obtain merged frequency domain channel information. Interference cancellation processing is then applied to the merged frequency domain channel information to obtain the i-th signal subspace, effectively eliminating interference signals. If i is not equal to M, based on the i-th signal subspace and the first signal, the residual first signal and the reconstructed second signal are obtained. The residual first signal is used as the new first signal, and the reconstructed second signal is used as the new second signal. The step of extracting frequency domain channel information from the first signal based on the first and second signals is performed iteratively to obtain M signal subspaces. Since the received signal is not used as prior information to obtain the signal subspace, the signal subspace can be obtained more accurately, and eigenvalue decomposition is not required, effectively reducing complexity. Based on the M signal subspaces, a multi-signal classification method is used to obtain the target angle corresponding to the target to be measured, which can more accurately obtain the target angle corresponding to the target to be measured, further improving the angle measurement accuracy.

[0121] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0122] Figure 4 This is a schematic diagram of the structure of an angle measuring device provided in an embodiment of this application, as shown below. Figure 4 As shown, the angle measuring device 400 of this application embodiment includes: a receiving unit 401, a first acquiring unit 402, and a second acquiring unit 403. Wherein:

[0123] The receiving unit 401 is used to receive a first signal through an equidistant uniform linear array. The first signal includes a signal reflected by the target to be measured. The equidistant uniform linear array includes M array elements, where M is a positive integer.

[0124] The first acquisition unit 402 is used to iteratively acquire M signal subspaces based on the first signal and the second signal. The second signal is a reference signal transmitted by an equidistant uniform linear array. The signal subspaces are used to represent the angular directionality of the target to be measured.

[0125] The second acquisition unit 403 is used to acquire the target angle corresponding to the target to be measured based on M signal subspaces.

[0126] In some optional implementations, the first acquisition unit 402 is specifically used to: perform the following operations based on the first signal and the second signal until M signal subspaces are acquired: extract frequency domain channel information from the first signal based on the first signal and the second signal; extract the frequency domain channel information corresponding to the strongest path in the frequency domain channel information based on the frequency domain channel information, and perform time delay elimination processing to obtain target frequency domain channel information; perform coherent information merging processing on the target frequency domain channel information to obtain merged frequency domain channel information; perform interference cancellation processing on the merged frequency domain channel information to obtain the i-th signal subspace, where i is an integer greater than 0 and less than or equal to M; and acquire the residual first signal and the reconstructed second signal based on the i-th signal subspace and the first signal, using the residual first signal as the new first signal and the reconstructed second signal as the new second signal.

[0127] In some optional embodiments, when the first acquisition unit 402 is used to extract frequency domain channel information from the first signal based on the first signal and the second signal, it is specifically used to: multiply each signal in the first signal by the conjugate value of each signal in the second signal to extract the frequency domain channel information from the first signal.

[0128] In some optional implementations, when the first acquisition unit 402 is used to extract the frequency domain channel information corresponding to the strongest path in the frequency domain channel information based on the frequency domain channel information, and perform time delay elimination processing to obtain the target frequency domain channel information, it is specifically used to: perform inverse fast Fourier transform processing on the frequency domain channel information to obtain time domain channel information; acquire the time domain channel information corresponding to the strongest path in the time domain channel information; perform time delay elimination processing based on the time domain channel information corresponding to the strongest path to obtain the target time domain channel information; and perform fast Fourier transform processing on the target time domain channel information to obtain the target frequency domain channel information.

[0129] In some optional implementations, when the first acquisition unit 402 performs interference cancellation processing on the merged frequency domain channel information to obtain the i-th signal subspace, it is specifically used to: obtain the covariance matrix corresponding to the merged frequency domain channel information based on the merged frequency domain channel information and the conjugate transpose of the merged frequency domain channel information; and obtain the i-th signal subspace based on the merged frequency domain channel information and the covariance matrix.

[0130] In some optional implementations, when the first acquisition unit 402 is used to acquire the residual first signal and the reconstructed second signal based on the i-th signal subspace and the first signal, it is specifically used to: reconstruct the signal based on the conjugate transpose of the i-th signal subspace and the first signal to obtain the reconstructed second signal; acquire the signal corresponding to the strongest path based on the i-th signal subspace and the reconstructed second signal; and acquire the residual first signal based on the difference between the first signal and the signal corresponding to the strongest path.

[0131] In some optional implementations, the second acquisition unit 403 is specifically used to: acquire the target angle corresponding to the target to be measured by using a multi-signal classification method based on M signal subspaces.

[0132] In some optional implementations, when the second acquisition unit 403 is used to acquire the target angle corresponding to the target to be measured based on M signal subspaces using a multiple signal classification method, it is specifically used to: acquire the feature value corresponding to each signal subspace in the M signal subspaces; determine N target signal subspaces from the M signal subspaces according to the feature value and threshold value, where N is the number of targets to be measured; acquire the orthogonal complementary signal subspaces of the N target signal subspaces; acquire the spatial spectrum corresponding to the multiple preset angles according to the orthogonal complementary signal subspaces and the steering vectors under multiple preset angles; and perform spectral peak search on the spatial spectrum to obtain the target angle corresponding to the target to be measured.

[0133] It should be noted that the angle measuring device provided in this application can implement the steps of all angle measuring methods in the above method embodiments and achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiments will not be described in detail here.

[0134] This application also provides an angle measuring device. Figure 5 This is a schematic diagram of the structure of an angle measuring device provided in one embodiment of this application. Figure 5 As shown, the angle measuring device includes:

[0135] Memory 501 is used to store computer programs;

[0136] Transceiver 502 is used to send and receive data under the control of the processor;

[0137] Processor 503 is used to read computer programs from memory and perform the following operations:

[0138] The first signal is received by an equidistant uniform linear array. The first signal includes the signal reflected by the target to be measured. The equidistant uniform linear array includes M array elements, where M is a positive integer.

[0139] Based on the first signal and the second signal, M signal subspaces are obtained iteratively. The second signal is a reference signal transmitted by an equidistant uniform linear array. The signal subspaces are used to represent the angular directivity of the target to be measured.

[0140] Based on M signal subspaces, the target angle corresponding to the target to be measured is obtained.

[0141] Among them, Figure 5 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (processor 503) and memory (memory 501). The bus architecture can also link various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. The transceiver 502 can be multiple elements, including transmitters and receivers, providing a unit for communicating with various other devices over transmission media, including wireless channels, wired channels, optical fibers, etc. The processor 503 is responsible for managing the bus architecture and general processing, and the memory 501 can store data used by the processor 503 during operation.

[0142] The processor 503 can be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a complex programmable logic device (CPLD). The processor can also adopt a multi-core architecture.

[0143] The processor 503 executes any method provided in an embodiment of this application according to the obtained executable instructions by calling a computer program stored in the memory 501. The processor and the memory may also be physically separated.

[0144] In some optional implementations, when the processor 503 iteratively acquires M signal subspaces based on the first signal and the second signal, it specifically performs the following operations based on the first signal and the second signal until M signal subspaces are acquired: extracting frequency domain channel information from the first signal based on the first signal and the second signal; extracting the frequency domain channel information corresponding to the strongest path in the frequency domain channel information based on the frequency domain channel information, and performing time delay cancellation processing to obtain target frequency domain channel information; performing coherent information merging processing on the target frequency domain channel information to obtain merged frequency domain channel information; performing interference cancellation processing on the merged frequency domain channel information to obtain the i-th signal subspace, where i is an integer greater than 0 and less than or equal to M; acquiring the residual first signal and the reconstructed second signal based on the i-th signal subspace and the first signal, using the residual first signal as the new first signal, and using the reconstructed second signal as the new second signal.

[0145] In some optional implementations, when the processor 503 extracts the frequency domain channel information from the first signal based on the first signal and the second signal, it is specifically used to: multiply each signal in the first signal by the conjugate value of each signal in the second signal to extract the frequency domain channel information from the first signal.

[0146] In some optional implementations, when the processor 503 extracts the frequency domain channel information corresponding to the strongest path in the frequency domain channel information based on the frequency domain channel information and performs time delay elimination processing to obtain the target frequency domain channel information, it specifically performs the following: performs inverse fast Fourier transform processing on the frequency domain channel information to obtain time domain channel information; obtains the time domain channel information corresponding to the strongest path in the time domain channel information; performs time delay elimination processing based on the time domain channel information corresponding to the strongest path to obtain the target time domain channel information; and performs fast Fourier transform processing on the target time domain channel information to obtain the target frequency domain channel information.

[0147] In some optional implementations, when the processor 503 performs interference cancellation processing on the merged frequency domain channel information to obtain the i-th signal subspace, it specifically performs the following: obtaining the covariance matrix corresponding to the merged frequency domain channel information based on the merged frequency domain channel information and the conjugate transpose of the merged frequency domain channel information; and obtaining the i-th signal subspace based on the merged frequency domain channel information and the covariance matrix.

[0148] In some optional implementations, when the processor 503 obtains the residual first signal and the reconstructed second signal based on the i-th signal subspace and the first signal, it specifically performs the following: reconstructs the signal based on the conjugate transpose of the i-th signal subspace and the first signal to obtain the reconstructed second signal; obtains the signal corresponding to the strongest path based on the i-th signal subspace and the reconstructed second signal; and obtains the residual first signal based on the difference between the first signal and the signal corresponding to the strongest path.

[0149] In some optional implementations, when the processor 503 obtains the target angle corresponding to the target to be measured based on M signal subspaces, it is specifically used to: obtain the target angle corresponding to the target to be measured by using a multi-signal classification method based on M signal subspaces.

[0150] In some optional implementations, when the processor 503 obtains the target angle corresponding to the target to be measured based on M signal subspaces using a multiple signal classification method, it specifically performs the following steps: obtaining the feature value corresponding to each signal subspace in the M signal subspaces; determining N target signal subspaces from the M signal subspaces based on the feature value and threshold value, where N is the number of targets to be measured; obtaining the orthogonal complementary signal subspaces of the N target signal subspaces; obtaining the spatial spectrum corresponding to the multiple preset angles based on the orthogonal complementary signal subspaces and the steering vectors under multiple preset angles; and performing spectral peak search on the spatial spectrum to obtain the target angle corresponding to the target to be measured.

[0151] It should be noted that the angle measuring device provided in this application can implement all the method steps in the above method embodiments and achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiments will not be described in detail here.

[0152] It should be noted that the division of units in the embodiments of this application is illustrative and only represents one logical functional division. In actual implementation, other division methods may be used. 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 units described above can be implemented in hardware or as software functional units.

[0153] If the aforementioned integrated units are implemented as software functional units and sold or used as independent products, they can be stored in a processor-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 a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. 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.

[0154] This application provides a processor-readable storage medium storing a computer program. The computer program is used to cause the processor to execute the angle measurement method provided in any embodiment of this application, so that the processor can implement all the method steps in the above method embodiments and achieve the same technical effect. Here, the parts that are the same as those in the method embodiments and the beneficial effects will not be described in detail.

[0155] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic storage (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical storage (e.g., CD, DVD, BD, HVD), semiconductor storage (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0156] This application also provides a chip that stores a computer program, which is used to cause the chip to execute the angle measurement method provided in any embodiment of this application.

[0157] An embodiment of this application also provides a computer program product containing instructions. The computer program is stored in a storage medium. At least one processor can read the computer program from the storage medium. When the at least one processor executes the computer program, it can implement all the method steps of the angle measurement method in any of the above method embodiments and achieve the same technical effect. Here, the parts that are the same as those in the method embodiments and the beneficial effects will not be described in detail.

[0158] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0159] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the steps in the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0160] These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means that are implemented in the steps of the flow. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0161] These processors can execute instructions that can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable apparatus provide for implementation of the process in the steps. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0162] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method of goniometry, characterized in that, include: A first signal is received by an equidistant uniform linear array, the first signal including the signal reflected by the target to be measured, the equidistant uniform linear array including M array elements, where M is a positive integer; Based on the first signal and the second signal, M signal subspaces are iteratively obtained, where the second signal is a reference signal transmitted by the equidistant uniform linear array, and the signal subspaces are used to represent the angular directivity of the target to be measured. Based on the M signal subspaces, the target angle corresponding to the target to be measured is obtained.

2. The goniometric method according to claim 1, characterized in that The step of iteratively obtaining M signal subspaces based on the first and second signals includes: Based on the first signal and the second signal, perform the following operations until the M signal subspaces are obtained: Based on the first signal and the second signal, extract the frequency domain channel information from the first signal; Based on the frequency domain channel information, extract the frequency domain channel information corresponding to the strongest path in the frequency domain channel information, and perform time delay elimination processing to obtain the target frequency domain channel information; The target frequency domain channel information is subjected to coherent information merging processing to obtain merged frequency domain channel information; The merged frequency domain channel information is subjected to interference cancellation processing to obtain the i-th signal subspace, where i is an integer greater than 0 and less than or equal to M; Based on the i-th signal subspace and the first signal, obtain the residual first signal and the reconstructed second signal, take the residual first signal as the new first signal, and take the reconstructed second signal as the new second signal.

3. The goniometric method according to claim 2, characterized in that The step of extracting frequency domain channel information from the first signal based on the first signal and the second signal includes: Multiply each signal in the first signal by the conjugate value of each signal in the second signal to extract the frequency domain channel information in the first signal.

4. The goniometric method according to claim 2, characterized in that The step of extracting the frequency domain channel information corresponding to the strongest path from the frequency domain channel information based on the frequency domain channel information, and performing delay cancellation processing to obtain the target frequency domain channel information includes: The frequency domain channel information is processed by inverse fast Fourier transform to obtain the time domain channel information; Obtain the time-domain channel information corresponding to the strongest path in the time-domain channel information; Delay cancellation processing is performed based on the time-domain channel information corresponding to the strongest path to obtain the target time-domain channel information; The target time-domain channel information is processed by Fast Fourier Transform to obtain the target frequency-domain channel information.

5. The goniometric method according to claim 2, characterized in that The step of performing interference cancellation processing on the merged frequency domain channel information to obtain the i-th signal subspace includes: Based on the merged frequency domain channel information and the conjugate transpose of the merged frequency domain channel information, obtain the covariance matrix corresponding to the merged frequency domain channel information; The i-th signal subspace is obtained based on the merged frequency domain channel information and the covariance matrix.

6. The goniometric method according to claim 2, characterized in that The step of obtaining the residual first signal and the reconstructed second signal based on the i-th signal subspace and the first signal includes: The signal is reconstructed by performing signal reconstruction based on the conjugate transpose of the i-th signal subspace and the first signal to obtain the reconstructed second signal; Based on the i-th signal subspace and the reconstructed second signal, obtain the signal corresponding to the strongest path; The residual first signal is obtained based on the difference between the first signal and the signal corresponding to the strongest path.

7. The goniometric method according to any one of claims 1 to 6, characterized in that, The step of obtaining the target angle corresponding to the target to be measured based on the M signal subspaces includes: Based on the M signal subspaces, the target angle corresponding to the target to be measured is obtained by using a multiple signal classification method.

8. The goniometric method according to claim 7, characterized in that The step of obtaining the target angle corresponding to the target to be measured based on the M signal subspaces using a multiple signal classification method includes: Obtain the feature value corresponding to each of the M signal subspaces; Based on the feature values ​​and threshold values, N target signal subspaces are determined from the M signal subspaces, where N is the number of the targets to be measured; Obtain the orthogonal complementary signal subspace of the N target signal subspaces; Based on the orthogonal complementary signal subspace and the guiding vectors at multiple preset angles, obtain the spatial spectra corresponding to the multiple preset angles respectively; A spectral peak search is performed on the spatial spectrum to obtain the target angle corresponding to the target to be measured.

9. A goniometer device, characterized by include: A receiving unit is configured to receive a first signal via an equidistant uniform linear array, the first signal including a signal reflected by the target to be measured, the equidistant uniform linear array including M array elements, where M is a positive integer; The first acquisition unit is used to iteratively acquire M signal subspaces based on the first signal and the second signal, wherein the second signal is a reference signal transmitted by the equidistant uniform linear array, and the signal subspaces are used to represent the angular directivity of the target to be measured. The second acquisition unit is used to acquire the target angle corresponding to the target to be measured based on the M signal subspaces.

10. A goniometric device, characterized by include: Memory, used to store computer programs; A transceiver is used to send and receive data under the control of a processor. A processor for reading a computer program from the memory and executing the angle measurement method according to any one of claims 1 to 8.

11. A processor-readable storage medium, comprising: The processor-readable storage medium stores a computer program that causes the processor to execute the angle measurement method according to any one of claims 1 to 8.

12. A chip, characterized by The chip stores a computer program that causes the chip to perform the angle measurement method according to any one of claims 1 to 8.

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