Near-field target localization methods, devices, electronic equipment and storage media

Abstract

This application provides a near-field target localization method, apparatus, electronic device, and storage medium, belonging to the field of communication technology. The method includes: acquiring an initial received signal of a preset symmetric coprime array; determining a first covariance matrix of the initial received signal based on the initial received signal; performing calculations based on the first covariance matrix to obtain an equivalent received signal of the initial received signal; determining a second covariance matrix of the equivalent received signal based on the equivalent received signal; performing spatial smoothing on the second covariance matrix to obtain a third covariance matrix of the equivalent received signal; performing a one-dimensional angle spectral peak search on the third covariance matrix to obtain a first angle between the near-field target and the central array element; and performing a one-dimensional distance spectral peak search on the first covariance matrix based on the first angle to obtain a first distance between the near-field target and the central array element. This application significantly reduces the computational overhead during near-field target localization.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a near-field target localization method, apparatus, electronic device and storage medium. Background Technology

[0002] Integrated sensing and communication (ISAC) is widely recognized as a key technology for future 6G wireless networks. It achieves sensing and communication functions by sharing the same spectrum and hardware resources, significantly improving system communication capacity and spectral efficiency. In particular, wireless / radar sensing has been widely applied in practice, such as high-precision positioning and tracking, high-resolution imaging, and real-time positioning and mapping. As technology trends move towards higher frequency bands and larger transmitter array apertures, the Rayleigh distance, which distinguishes the boundary between the near and far fields, is greatly extended, making environmental targets more likely to be located in the near-field region of large-aperture arrays. Furthermore, compared to traditional far-field target localization, near-field target localization using large-aperture arrays can achieve higher sensing resolution.

[0003] Unlike far-field target localization based on plane waves, near-field target localization requires a more accurate spherical channel model. This model is characterized by the target's angle and distance, making existing far-field angle estimation methods inefficient when applied to near-field sensing systems and difficult to directly extend to the near field. To address this issue, several near-field localization methods have recently been proposed, such as using dense arrays of numerous sensors for near-field target localization, or employing two-dimensional multi-signal classification algorithms for global spatial search to obtain near-field target localization parameters. However, these methods all involve high signal processing complexity or significant computational cost, resulting in substantial computational overhead. Summary of the Invention

[0004] The main objective of this application is to provide a near-field target localization method, apparatus, electronic device, and storage medium, which aims to reduce the computational overhead of near-field target localization.

[0005] To achieve the above objectives, a first aspect of this application proposes a near-field target localization method, the method comprising:

[0006] Acquire the initial received signal of a preset symmetric coprime array, which is a sensor array composed of two uniform linear arrays symmetric about the central array element;

[0007] Based on the initial received signal, determine the first covariance matrix of the initial received signal;

[0008] The equivalent received signal of the initial received signal is obtained by performing calculations based on the first covariance matrix.

[0009] Based on the equivalent received signal, determine the second covariance matrix of the equivalent received signal;

[0010] Spatial smoothing is performed on the second covariance matrix to obtain the third covariance matrix of the equivalent received signal;

[0011] A one-dimensional angle spectrum peak search is performed on the third covariance matrix to obtain the first angle between the near-field target and the central array element.

[0012] Based on the first angle, a one-dimensional distance spectrum peak search is performed on the first covariance matrix to obtain the first distance between the near-field target and the central array element. The position information of the near-field target includes the first angle and the first distance.

[0013] To achieve the above objectives, a second aspect of this application provides a near-field target localization device, the device comprising:

[0014] The signal acquisition module is used to acquire the initial received signal of the preset symmetric coprime array. The symmetric coprime array is a sensor array composed of two uniform linear arrays that are symmetric about the central array element.

[0015] The first covariance matrix determination module is used to determine the first covariance matrix of the initial received signal based on the initial received signal.

[0016] The equivalent received signal determination module is used to perform calculations based on the first covariance matrix to obtain the equivalent received signal of the initial received signal;

[0017] The second covariance matrix determination module is used to determine the second covariance matrix of the equivalent received signal based on the equivalent received signal.

[0018] The third covariance matrix determination module is used to perform spatial smoothing on the second covariance matrix to obtain the third covariance matrix of the equivalent received signal.

[0019] The first angle determination module is used to perform a one-dimensional angle spectrum peak search on the third covariance matrix to obtain the first angle between the near-field target and the central array element.

[0020] The first distance determination module is used to perform a one-dimensional distance spectrum peak search on the first covariance matrix based on the first angle to obtain the first distance between the near-field target and the central array element. The position information of the near-field target includes the first angle and the first distance.

[0021] To achieve the above objectives, a third aspect of the present application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method of the first aspect described above.

[0022] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of the first aspect described above.

[0023] The near-field target localization method, apparatus, electronic device, and storage medium proposed in this application first acquire the initial received signal of a preset symmetric coprime array. Then, based on the initial received signal, a first covariance matrix of the initial received signal is determined. Subsequently, calculations are performed based on the first covariance matrix to obtain the equivalent received signal of the initial received signal. Next, based on the equivalent received signal, a second covariance matrix of the equivalent received signal is determined. Then, spatial smoothing is performed on the second covariance matrix to obtain a third covariance matrix of the equivalent received signal. A one-dimensional angular peak search is then performed on the third covariance matrix to obtain a first angle between the near-field target and the central array element. Finally, based on the first angle, a one-dimensional distance peak search is performed on the first covariance matrix to obtain a first distance between the near-field target and the central array element. This application extends the far-field virtual array method to the near-field, fully utilizing the mutual information between subarrays in the symmetric coprime array, thereby significantly reducing the computational overhead during near-field target localization. Attached Figure Description

[0024] Figure 1 This is a flowchart of the near-field target localization method provided in the embodiments of this application;

[0025] Figure 2 This is a schematic diagram of the structure of a symmetric coprime matrix provided in an embodiment of this application;

[0026] Figure 3 This is a spectral peak search diagram in the angle domain provided in the embodiments of this application;

[0027] Figure 4 This is a spectral peak search diagram in the distance domain provided in the embodiments of this application;

[0028] Figure 5 This is a graph showing the relationship between the root mean square error of the angle and the signal-to-noise ratio provided in the embodiments of this application;

[0029] Figure 6 This is a graph showing the relationship between the root mean square error of distance and the signal-to-noise ratio provided in the embodiments of this application;

[0030] Figure 7 This is a schematic diagram of the near-field target positioning device provided in the embodiments of this application;

[0031] Figure 8 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0033] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0035] First, let's analyze some of the terms used in this application:

[0036] A symmetric coprime matrix is ​​an array consisting of two subarrays that are coprime. Both subarrays are uniform linear arrays and are symmetrically distributed in structure. In a symmetric coprime matrix, the number of elements and the element spacing of the two subarrays both satisfy the coprime relation.

[0037] Far-field target localization refers to determining the location of a signal source (such as electromagnetic waves, sound waves, etc.) by measuring information such as the time difference, phase difference, or amplitude difference of the signal arriving at each element of the array when the signal source (such as an antenna array, microphone array, etc.) is located in the far-field region of the receiving array.

[0038] Near-field target localization primarily addresses situations where the signal source and the receiving array are relatively close. In this case, the wavefront of the signal reaching each element of the array is no longer a plane wave, but rather a spherical wave. Therefore, near-field target localization requires not only knowledge of the signal's direction of arrival (DOA) but also the distance between the signal source and the array to accurately determine the signal source's location.

[0039] Currently, technological trends are moving towards higher frequency bands and larger transmitter array apertures, significantly expanding the Rayleigh distance that distinguishes the near-field and far-field boundaries. This makes environmental targets more likely to be located in the near-field region of large-aperture arrays. Compared to traditional far-field target localization, near-field target localization using large-aperture arrays can achieve higher sensing resolution, but it also introduces new challenges. For example, unlike plane-wave-based far-field localization, near-field localization requires consideration of a more accurate spherical wave model (characterized by the target's angle and distance). This makes existing far-field angle estimation methods inefficient when applied to near-field sensing systems and difficult to directly extend to the near field.

[0040] To address the aforementioned issues, existing near-field target localization methods typically fall into three categories. The first is based on dense arrays employing numerous sensors or array elements. However, this method relies heavily on a large number of sensors or array elements, leading to high power consumption, high hardware costs, and high signal processing complexity. The second method uses two-dimensional multi-signal classification algorithms for localization, but this requires global spatial searching in both angle and distance dimensions, resulting in significant computational overhead. The third method decomposes the entire array into two symmetrical, uniform, sparse arrays, decouples the two subarrays, estimates angles separately, and finally utilizes coprime properties to eliminate angle ambiguity. However, this method divides the coprime array into two independent subarrays, failing to fully utilize the mutual information between the subarrays and thus neglecting the high degrees of freedom provided by the coprime array. This method also suffers from high computational costs.

[0041] Based on this, embodiments of this application provide a near-field target localization method, apparatus, electronic device, and storage medium, aiming to reduce the computational overhead of near-field target localization methods.

[0042] The near-field target localization method, apparatus, electronic device, and storage medium provided in this application are specifically described through the following embodiments. First, the near-field target localization method in this application embodiment is described.

[0043] The near-field target localization method provided in this application relates to the field of communication technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the near-field target localization method, but is not limited to the above forms.

[0044] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0045] Figure 1 This is a flowchart of the near-field target localization method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S107.

[0046] Step S101: Obtain the initial received signal of the preset symmetric coprime array. The symmetric coprime array is a sensor array composed of two uniform linear arrays that are symmetric about the central array element.

[0047] Step S102: Determine the first covariance matrix of the initial received signal based on the initial received signal;

[0048] Step S103: Perform calculations based on the first covariance matrix to obtain the equivalent received signal of the initial received signal;

[0049] Step S104: Determine the second covariance matrix of the equivalent received signal based on the equivalent received signal;

[0050] Step S105: Perform spatial smoothing on the second covariance matrix to obtain the third covariance matrix of the equivalent received signal;

[0051] Step S106: Perform a one-dimensional angular spectrum peak search on the third covariance matrix to obtain the first angle between the near-field target and the central array element;

[0052] Step S107: Based on the first angle, perform a one-dimensional distance spectrum peak search on the first covariance matrix to obtain the first distance between the near-field target and the central array element. The position information of the near-field target includes the first angle and the first distance.

[0053] Figure 2 is a schematic structural diagram of the symmetric co-prime array provided by an embodiment of the present application. Please refer to Figure 2 . In the embodiment of the present application, a symmetric co-prime array can be pre-constructed. A symmetric co-prime array refers to a sensor array composed of two sparse uniform linear arrays (i.e., the first sub-array and the second sub-array) that are symmetric about the central array element. The first sub-array consists of M - 1 sensors with a sensor spacing of Nd, and the second sub-array consists of N - 1 sensors with a sensor spacing of Md. M and N are co-prime integers, and M < N. Among them, the minimum unit d of the sensor spacing is less than or equal to λ / 4. To reduce the mutual coupling between sensors, d = λ / 4 can be determined, where λ is the carrier wavelength. Then, it can be known that the symmetric co-prime array can be regarded as composed of U = 2V - 1 sensors, where V is the number of sensors in the basic co-prime array, and V = M + N - 1. Assuming that the central array element of the symmetric co-prime array is located at the origin, the set S′ of the position indices of the sensors in the symmetric co-prime array can be obtained as S′ = {Mnd|n = -N + 1, -N + 2, …, N - 1} ∪ {Nmd|m = -M + 1, -M + 2, …, M - 1},

[0054] where Mnd is the position index subset of the second sub-array, and Nmd is the position index subset of the first sub-array. Sensors with negative position indices refer to sensors located on the negative half-axis of the y-axis, and sensors with positive position indices refer to sensors located on the positive half-axis of the y-axis. After arranging the position index set in ascending order, the position vectors s = [s1, s2,..., s u of each sensor in the symmetric co-prime array can be obtained.

[0055] Assume that all targets are located in the Fresnel near-field region of the very large-scale array, and the range of the targets is greater than the Fresnel region and not greater than the Rayleigh distance Z R = 2D 2 / λ, where D is the aperture of the symmetric coprime array, D = 2M(N-1)d. In this case, electromagnetic propagation follows a uniform spherical wave model. This application's embodiments primarily consider near-field communication scenarios in high-frequency bands such as millimeter waves and terahertz. In these scenarios, due to severe path loss and shadowing, the power of non-line-of-sight channel paths can be neglected. Therefore, this application's embodiments only consider line-of-sight paths, and the channel steering vector can satisfy the following formula:

[0056]

[0057] Where, b(θ) k r k ) represents an angle θ k And the distance is r k Channel steering vector at time, θ k Let r be the angle between the k-th near-field target and the central array element. k Let d be the distance between the k-th near-field target and the central array element, e be the natural constant, π be pi, λ be the carrier wavelength, and d be the distance between the k-th near-field target and the central array element. -V，k Let d be the distance from the Vth sensor located on the negative half of the y-axis to the kth near-field target. -u，k Let d be the distance from the u-th sensor located on the negative half of the y-axis to the k-th near-field target. V，k Let r be the distance from the Vth sensor located on the positive y-axis to the kth near-field target. k Let T be the distance between the k-th near-field target and the central array element, and let T denote the transpose of the matrix.

[0058] Based on the Fresnel approximation, the phase delay of the u-th sensor satisfies the following formula:

[0059]

[0060] Where, d u,k Let be the distance from the u-th sensor to the k-th near-field target. r k Let s be the distance between the k-th near-field target and the central array element. u Let θ be the position vector of the u-th sensor. k Let p be the angle between the k-th near-field target and the central array element. u (θ k ( ) is an angle of θ k The first-order term of the phase delay of the u-th sensor, p u (θ k ) = -2s u πsinθ k / λ, q u (θ k r k ( ) is an angle of θ kAnd the distance is r k The second-order term of the phase delay of the u-th sensor,

[0061] Therefore, the initial received signal of the symmetric coprime array at time t satisfies the following formula:

[0062]

[0063] Where y(t) represents the initial received signal at time t, k is the label of the near-field target, K is the total number of near-field targets, and b(θ) k r k ( ) is an angle of θ k And the distance is r k Channel steering vector at time, x k z(t) is the signal vector of the k-th near-field target transmitted at time t, z(t) is the additive white Gaussian noise received at time t, x(t) is the signal vector of the near-field target transmitted at time t, and B(θ, r) represents the steering matrix of the K symmetric coprime targets.

[0064] The covariance matrix can be used to quantify the correlation of a signal across different dimensions. Therefore, after acquiring an initial received signal with a symmetric coprime matrix, the electronic device can determine the first covariance matrix of the initial received signal based on the initial received signal. Ignoring noise, the covariance matrix of the initial received signal can be expressed as R = B(θ, r)B H (θ, r), where R is the covariance matrix of the initial received signal, and B(θ, r) represents the steering matrix of K symmetric coprime near-field targets. H (θ, r) is the conjugate transpose of the steering matrix. Since the covariance matrix of the initial received signal cannot be obtained directly, an approximate version of the covariance matrix of the initial received signal, namely the first covariance matrix, can be determined, and subsequent operations can be performed based on the first covariance matrix.

[0065] Generally, the degrees of freedom are limited by the number of sensors. However, due to the non-Topperlitz structure of the covariance matrix of a sparse array, the degrees of freedom of a symmetric coprime matrix are increased. To fully utilize the degrees of freedom of the symmetric coprime matrix, the electronic device can virtualize the first covariance matrix, that is, perform further calculations based on the first covariance matrix to obtain the equivalent received signal of the initial received signal. Subsequently, based on the equivalent received signal, the second covariance matrix of the equivalent received signal is determined. Since the second covariance matrix obtained directly from the equivalent received signal is rank 1 (i.e., ... r d To receive the equivalent signal, The second covariance matrix is ​​the conjugate transpose of the equivalent received signal, and rank represents the rank of the matrix. Since it is a matrix that cannot be used to sense multiple near-field targets, spatial smoothing of the second covariance matrix is ​​required to obtain the third covariance matrix of the equivalent received signal, thus recovering the full-rank covariance matrix. Based on the full-rank third covariance matrix, the maximum number of spectral peaks that can be identified is MN+1, allowing the subsequent two-stage spectral peak search process to continue.

[0066] The position information of the near-field target includes a first angle and a first distance. During the two-stage spectral peak search, the electronic device can sequentially determine the first angle and the first distance to complete the localization of the near-field target. Specifically, the third covariance matrix is ​​a covariance matrix that depends only on the angle. Therefore, in the two-stage spectral peak search, the first stage involves performing a one-dimensional angle spectral peak search on the third covariance matrix to obtain the first angle between the near-field target and the central array element. The first covariance matrix is ​​a covariance matrix that depends on both the angle and the distance. After obtaining the first angle, since the angle is known, the second stage of the spectral peak search can be performed, i.e., based on the first angle, a one-dimensional distance spectral peak search is performed on the first covariance matrix to obtain the first distance between the near-field target and the central array element.

[0067] Steps S101 to S107 of this embodiment involve first acquiring the initial received signal of a preset symmetric coprime array, then determining the first covariance matrix of the initial received signal based on the initial received signal, subsequently performing calculations based on the first covariance matrix to obtain the equivalent received signal of the initial received signal, then determining the second covariance matrix of the equivalent received signal based on the equivalent received signal, then performing spatial smoothing on the second covariance matrix to obtain the third covariance matrix of the equivalent received signal, then performing a one-dimensional angular peak search on the third covariance matrix to obtain the first angle between the near-field target and the central array element, and finally performing a one-dimensional distance peak search on the first covariance matrix based on the first angle to obtain the first distance between the near-field target and the central array element. This application extends the virtual array method of the far field to the near field, making full use of the mutual information between the subarrays in the symmetric coprime array, thereby significantly reducing the computational overhead of near-field target localization.

[0068] In some embodiments, determining a first covariance matrix of the initial received signal based on the initial received signal includes:

[0069] Obtain the number of snapshots of near-field targets and the signal vector of signals transmitted by near-field targets;

[0070] Based on the least squares method, the steering matrix of the symmetric coprime matrix is ​​determined according to the initial received signal and the signal vector.

[0071] The first covariance matrix is ​​determined based on the guidance matrix and the number of snapshots.

[0072] As mentioned above, without considering noise, the covariance matrix of the initial received signal can be expressed as R = B(θ, r)B H (θ, r), where R is the covariance matrix of the initial received signal, and B(θ, r) represents the steering matrix of K symmetric coprime near-field targets. H (θ, r) is the conjugate transpose of the steering matrix of the K near-field targets in the symmetric coprime matrix. However, since the covariance matrix of the initial received signal cannot be directly obtained in actual engineering experiments, this embodiment determines the first covariance matrix based on the least squares method and replaces the covariance matrix of the initial received signal with the first covariance matrix for subsequent calculations. First, the electronic device can obtain the number of snapshots of the near-field targets and the signal vector of the signals transmitted by the near-field targets. The signal vector of the signals transmitted by the near-field targets refers to the signal vector of the pilot signals transmitted by the near-field targets, and the number of snapshots of the near-field targets is the number of pilot signals transmitted by the near-field targets; both are known data. Thus, based on the least squares method, the steering matrix of the symmetric coprime matrix can be determined according to the initial received signal and the signal vector, that is, it satisfies the following formula:

[0073]

[0074] in, For the guiding matrix, Let y(t) represent the steering matrix corresponding to the minimum F-norm, y(t) be the initial received signal at time t, and x(t) be the signal vector of the near-field target transmitted signal at time t.

[0075] Thus, the guiding matrix at time t can be obtained. in Let be the steering matrix at time t, y(t) be the initial received signal at time t, and x(t) be the signal vector of the near-field target transmitted signal at time t. H Let t be the conjugate transpose of the signal vector of the near-field target transmitting the signal at time t.

[0076] Therefore, based on the guidance matrix and the number of snapshots, the first covariance matrix can be determined. The first covariance matrix satisfies the following formula:

[0077]

[0078] in, Let T represent the first covariance matrix, and T be the number of snapshots. Let be the steering matrix at time t. Let be the conjugate transpose of the guiding matrix at time t.

[0079] It should be noted that as the number of snapshots increases, the difference between the first covariance matrix and the covariance matrix of the initial received signal gradually decreases. Thus, the first covariance matrix of the initial received signal can be determined.

[0080] In some embodiments, calculations are performed based on the first covariance matrix to obtain an equivalent received signal from the initial received signal, including:

[0081] Decouple the angles and distances in the first covariance matrix to obtain the fourth covariance matrix;

[0082] The fourth covariance matrix is ​​vectorized to obtain the equivalent received signal of the initial received signal.

[0083] To fully utilize the degrees of freedom of the symmetric coprime matrix, the electronic device can virtualize the first covariance matrix. Specifically, for each element in the first covariance matrix, there are multiple terms in the summation, and each term is related to angle and distance. This makes traditional one-dimensional multi-signal classification algorithms inapplicable due to the coupling of angle and distance. Therefore, to fully utilize all elements in the first covariance matrix for accurate target localization, rather than just using the anti-angle elements, the electronic device can first decouple the angle and distance in the first covariance matrix to obtain the fourth covariance matrix. Subsequently, the fourth covariance matrix can be vectorized to obtain the equivalent received signal of the initial received signal. The equivalent received signal satisfies the following formula:

[0084]

[0085] Where, r d For the equivalent received signal, R d Let S be the fourth covariance matrix, S be the autospectral matrix, C be the cross-spectral matrix, and c be the vectorized cross-spectral matrix, i.e., c = vec(C). V (θ) is the equivalent orientation matrix. Represents the Kronecker product, a(θ) k ( ) is an angle of θ k The far-field steering vector at time, a * (θ k ( ) is an angle of θ k The conjugate of the far-field steering vector at time, vec() represents a vectorization operation.

[0086] In some embodiments, the angles and distances in the first covariance matrix are decoupled to obtain a fourth covariance matrix, including:

[0087] Based on the first covariance matrix, determine the fifth covariance matrix after the first covariance matrix is ​​symmetric about the second diagonal;

[0088] The product of the first element in the first covariance matrix and the second element in the fifth covariance matrix is ​​determined as the third element, so as to obtain the fourth covariance matrix composed of the third element;

[0089] In this matrix, the first, second, and third elements are located in the same number of rows and columns.

[0090] During the decoupling of angles and distances in the first covariance matrix, the electronic device can determine the fifth covariance matrix R, which is symmetric about the second diagonal, based on the first covariance matrix. a The (i′, j′)th element in the first covariance matrix can be represented as:

[0091]

[0092] in, Let θ represent the (i′, j′)th element in the first covariance matrix, k represent the label of the near-field target, K represent the total number of near-field targets, and θ represent the total number of near-field targets. k Let r be the angle between the k-th near-field target and the central array element. k Let b(θ) be the distance between the k-th near-field target and the central array element. k r k ( ) is an angle of θ k And the distance is r k Channel steering vector at time, b H (θ k r k ( ) is an angle of θ k And the distance is r k The conjugate transpose of the channel steering vector at time p j′ (θ k ( ) is an angle of θ k The first-order term of the phase delay of the i′-th sensor, p j′ (θ k ( ) is an angle of θ k The first-order term of the phase delay of the j′-th sensor, q i′ (θ k r k ( ) is an angle of θ k And the distance is r k The second-order term of the phase delay of the i′-th sensor, q j′ (θ k r k ( ) is an angle of θ k And the distance is r k The second-order term of the phase delay of the j′-th sensor, where j is the imaginary unit, π is pi, λ is the carrier wavelength, and s i′Let s be the position vector of the i′-th sensor. j′ Let P be the position vector of the j′-th sensor. i′，j′ (θ k ( ) is an angle of θ k The first-order term of the (i′, j′)th element of the first covariance matrix. Q i′，j′ (θ k r k ( ) is an angle of θ k And the distance is r k The second-order term of the (i′, j′)th element of the first covariance matrix.

[0093] Based on the properties of symmetric coprime matrices and their first covariance matrix, it is convenient to design near-field target localization methods. For a symmetric coprime matrix, the known position vector of the sensor satisfies... For the first covariance matrix, two elements symmetric about the second diagonal, for example... and If the subscripts satisfy p = 2V - j′ and q = 2V - i′, then the two elements have the same first-order term and opposite second-order terms, i.e., P p，q (θ k ) = P i′，j′ (θ k ), Q p，q (θ k r k )=-Q i′，j′ (θ k r k ).

[0094] As can be seen from the aforementioned properties, for the fifth covariance matrix R a For the (i′, j′)th element in the equation, we can obtain Among them, R a This is the fifth covariance matrix. Let R be the first covariance matrix. a ] i′，j′ Let (i', j') be the (i', j')th element in the fifth covariance matrix. Let V be the (2V-j′, 2V-i′)th element in the first covariance matrix, where V is the number of sensors in the basic coprime matrix.

[0095] Therefore, based on the product of the first element in the first covariance matrix and the second element in the fifth covariance matrix, the third element can be determined, thus obtaining the fourth covariance matrix composed of the third element. Here, the first element can be any element in the first covariance matrix, the second element is the element in the fifth covariance matrix that shares the same position as the first element, and the third element is the element in the fourth covariance matrix that shares the same position as both the first and second elements. It can be understood that "same position" means that they occupy the same number of rows and columns in their respective matrices. At this point, the fourth covariance matrix satisfies... R d Let R be the fourth covariance matrix. a This is the fifth covariance matrix. Let be the first covariance matrix, and ⊙ denotes the Hadamard product operation.

[0096] Therefore, the (i′, j′)th element in the fourth covariance matrix satisfies the following formula:

[0097]

[0098] Among them, [R d ] i′，j′ P is the (i′, j′)th element in the fourth covariance matrix. i′，j′ (θ k ( ) is an angle of θ k Q is the first-order term of the (i′, j′)th element of the first covariance matrix. i′，j′ (θ k r k ( ) is an angle of θ k And the distance is r k The second-order term of the (i′, j′)th element of the first covariance matrix, w is the index of the first summation symbol, u′ is the index of the second summation symbol, and P i′，j′ (θ u′ ( ) is an angle of θ u′ The first-order term of the (i′, j′)th element in the first covariance matrix, P i′，j′ (θ w ( ) is an angle of θ w The first-order term of the (i′, j′)th element in the first covariance matrix, Q i′，j′ (θ u′ r u′ ( ) is an angle of θ u′ And the distance is r u′ Q is the second-order term of the (i′, j′)th element of the first covariance matrix. i′，j′ (θ w r w ( ) is an angle of θ w And the distance is rw The second-order term of the (i′, j′)th element of the first covariance matrix, [S] i′，j′ Let be the (i′, j′)th element of the autospectral matrix, [C] i′，j′ Let be the (i′, j′)th element of the cross-spectral matrix. The autospectral matrix can be considered as the covariance matrix of the far field, then... A(θ) is the far-field steering matrix. It is worth noting that, based on the expression of the self-spectral matrix, in order to avoid angular ambiguity, the minimum sensor spacing element d must be less than or equal to λ / 4.

[0099] Where the far-field steering vector a(θ) k It satisfies the following formula:

[0100]

[0101] a(θ k ( ) is an angle of θ k The far-field steering vector at time θ k Let s be the angle between the k-th near-field target and the central array element. u Let u be the position vector of the u-th sensor. λ is the carrier wavelength, e is the natural constant, and j is the imaginary unit.

[0102] In this way, the decoupled fourth covariance matrix can be obtained, so that the equivalent received signal can be determined based on the fourth covariance matrix.

[0103] In some embodiments, spatial smoothing is performed on the second covariance matrix to obtain a third covariance matrix of the equivalent received signal, including:

[0104] Divide the symmetric coprime array into multiple subarrays and determine the equivalent received signal of each subarray;

[0105] Based on the equivalent received signals of each subarray, the virtual covariance matrix of each subarray is determined;

[0106] The third covariance matrix is ​​obtained by averaging the virtual covariance matrices of multiple subarrays.

[0107] After obtaining the equivalent received signal, the steps for determining the first covariance matrix described earlier can be referenced to determine the second covariance matrix of the equivalent received signal. However, since the second covariance matrix obtained directly from the equivalent received signal is a rank-1 matrix and cannot be used to sense multiple near-field targets, spatial smoothing is required to obtain the third covariance matrix of the equivalent received signal, thus restoring a full-rank covariance matrix. Specifically, the electronic device can remove the equivalent steering matrix A. VThe repeated elements in (θ) are used to obtain the equivalent guiding matrix after removing the repeated elements. This is the equivalent guiding vector after removing duplicate elements. Subsequently, after processing the first covariance matrix, a virtual array formed by the difference sets of the original symmetric coprime matrix can be obtained (for subsequent processing, continuous difference sets are extracted to form the virtual array). For a symmetric coprime matrix, the number of continuous difference sets is at least 2MN+1. Therefore, based on the preset number of subarray sensors and the degree of subarray overlap, the virtual array based on the original symmetric coprime matrix can be divided into MN+1 overlapping subarrays, each with MN+1 sensors. Thus, the equivalent received signal of the i-th subarray is:

[0108]

[0109] in, Let be the equivalent received signal of the i-th subarray. The equivalent guiding matrix of the i-th submatrix after removing duplicate elements. Let be the cross-spectral matrix after the vectorization of the i-th subarray.

[0110] Subsequently, the electronic device can determine the virtual covariance matrix of each subarray based on the equivalent received signals of each subarray. Then, the virtual covariance matrices of multiple subarrays are averaged to obtain the third covariance matrix R. v The third covariance matrix R v Satisfy the following formula:

[0111]

[0112] Among them, R v Let M be the third covariance matrix, M be the parameters of the first submatrix of the symmetric coprime matrix, and N be the parameters of the second submatrix of the symmetric coprime matrix. Let be the virtual covariance matrix of the i-th subarray. Let be the equivalent received signal of the i-th subarray. It is the conjugate transpose of the equivalent received signal of the i-th subarray.

[0113] In this way, the third covariance matrix of the equivalent received signal can be obtained, which is then used for subsequent spectral peak search.

[0114] In some embodiments, a one-dimensional angular spectral peak search is performed on the third covariance matrix to obtain the first angle between the near-field target and the central array element, including:

[0115] Eigenvalue decomposition is performed on the third covariance matrix to obtain the signal space and noise space;

[0116] Based on the spectral peak search model in the angle domain, a one-dimensional spectral peak search is performed in the signal space and noise space to obtain the first angle.

[0117] Specifically, after obtaining the third covariance matrix, which is only related to the angle, the electronic device can perform a one-dimensional angle spectrum peak search on the third covariance matrix to obtain the first angle between the near-field target and the central array element. First, the electronic device can search the third covariance matrix R... v Eigenvalue decomposition is performed to obtain the signal space and noise space of the third covariance matrix. v Satisfy the following formula:

[0118]

[0119] Among them, R v It is the third covariance matrix. It is the signal space of the third covariance matrix. It is the conjugate transpose of the signal space of the third covariance matrix. These are the eigenvalues ​​of the third covariance matrix in the signal space. It is the noise space of the third covariance matrix. It is the conjugate transpose of the noise space of the third covariance matrix. It is the eigenvalue of the noise space of the third covariance matrix.

[0120] Therefore, based on the spectral peak search model in the angle domain, a one-dimensional spectral peak search can be performed in the signal space and noise space to obtain the first angle of the near-field target. The spectral peak search model in the angle domain satisfies the following formula:

[0121]

[0122] in, Let L be the first angle detected, and L be the number of first angles detected. It is the steering vector of a virtual array of continuous apertures. Equivalent to the signal space of the third covariance matrix, It is the conjugate transpose of the guide vector of a virtual array of continuous apertures. It is the noise space of the third covariance matrix. It is the conjugate transpose of the noise space of the third covariance matrix, and argmax represents the parameterization of the function.

[0123] In this way, the first angle of the near-field target can be determined based on the third covariance matrix, so as to further locate the near-field target.

[0124] In some embodiments, the near-field target includes multiple targets, and the first distance includes sub-distances between each target and the central array element; after obtaining the first distance between the near-field target and the central array element by performing a one-dimensional distance spectrum peak search on the first covariance matrix based on the first angle, the method further includes:

[0125] The sub-distance that is within a preset distance interval and whose spectral power in the distance domain is greater than or equal to the preset spectral power is determined as the second distance. The preset distance interval is determined based on the carrier wavelength and the aperture of the symmetric coprime array.

[0126] The target corresponding to the second distance is identified as the real target;

[0127] The angle between the real target and the central array element is defined as the second angle. The position information of the real target includes the second angle and the second distance.

[0128] Specifically, near-field targets include multiple targets, among which there are real targets and overlapping targets. Therefore, the first angle includes the angles of the real targets and the angles of the unwanted overlapping targets. To obtain an accurate distance estimate corresponding to the angles of the real targets, the characteristic that the angles of overlapping targets do not have obvious spectral peaks in the distance domain can be used to detect the real targets among the near-field targets. Therefore, firstly, the first covariance matrix can be decomposed into eigenvalues ​​to obtain the eigenvalue-decomposed first covariance matrix, i.e. Similarly, Let U represent the first covariance matrix. s Let U be the signal space of the first covariance matrix. n Let ∑ be the noise space of the first covariance matrix. s Let ∑ be the eigenvalues ​​of the first covariance matrix in the signal space. n U represents the eigenvalues ​​of the first covariance matrix in the noise space. s H U is the conjugate transpose of the signal space of the first covariance matrix. n H It is the conjugate transpose of the noise space of the first covariance matrix.

[0129] Based on the spectral peak search model in the range domain, the sub-distances between each target and the central array element can be determined according to the first angle and the first covariance matrix, thus obtaining the first distance. The spectral peak search model in the range domain satisfies the following formula:

[0130]

[0131] in, The first distance, For the first angle And the channel steering vector at a distance of r, Un is the noise subspace of the first covariance matrix, U n H is the conjugate transpose of the noise subspace of the first covariance matrix is the conjugate transpose of the channel steering vector when the first angle is and the distance is r, and argmax represents finding the argument of a function

[0132] After obtaining the first distance, the electronic device can further remove the position information of cross targets. Specifically, the electronic device can determine the sub - distances within a preset distance interval among multiple sub - distances and with spectral power greater than or equal to a preset spectral power in the distance domain as the second distance. The preset distance interval is a distance interval determined according to the carrier wavelength and the aperture of the symmetric co - prime array. The preset distance interval can be [1.2D, 2D 2 / λ], where D is the aperture of the symmetric co - prime array and λ is the carrier wavelength. The preset spectral power can be determined according to the actual situation. For example, the preset spectral power can be 20 dB. Subsequently, the electronic device can determine the target corresponding to the second distance as the real target and determine the angle between the real target in the first angle and the central array element as the second angle, so as to obtain the position information (i.e., the second distance and the second angle) of the real target in the near - field target, improve the accuracy of near - field target positioning, and reduce the influence of cross targets on the positioning result

[0133] In a specific embodiment of the present application, the near - field target positioning method may include the following steps

[0134] I. System modeling

[0135] Construct a sensing system of a symmetric co - prime array with a large aperture for detecting K incoherent targets in a narrowband. The symmetric co - prime array consists of two sparse uniform linear arrays with different sensor spacings. Specifically, the first sub - array consists of M - 1 sensors with a sensor - to - sensor spacing of Nd, and the second sub - array consists of N - 1 sensors with a sensor - to - sensor spacing of Md, where d = λ / 4, and λ represents the carrier wavelength. In addition, M and N are co - prime integers, and it is assumed that M < N. Therefore, the symmetric co - prime array can be regarded as consisting of U = 2V - 1 sensors, where V = M + N - 1 represents the number of sensors of the basic co - prime array

[0136] Assume that the central array element is located at the origin. Then the set of position indices of the sensors S′={Mnd|n = - N + 1, - N + 2, …, N - 1}∪{Nmd|m = - M + 1, - M + 2, …, M - 1}. Among them, Mnd is the subset of position indices of the second sub - array, and Nmd is the subset of position indices of the first sub - array. For the sake of easy expression, arrange this set in ascending order to obtain the position vector s = [s1, s2,... s U .

[0137] Furthermore, it is assumed that all targets are located in the Fresnel near-field region of the ultra-large-scale array, and the target range is larger than the Fresnel region. And not greater than the Rayleigh distance Z R =2D 2 / λ, where D is the aperture of the symmetric coprime array, D = 2M(N-1)d. Therefore, electromagnetic propagation follows a uniform spherical wave model. This application's embodiments primarily consider near-field communication scenarios in high-frequency bands such as millimeter waves and even terahertz. In these near-field communication scenarios, due to severe path loss and shadowing, the power of non-line-of-sight channel paths can be neglected. Therefore, considering only line-of-sight paths, the channel steering vector can be defined as:

[0138]

[0139] in Let represent the distance from the u-th sensor to the k-th target. Based on the Fresnel approximation, the phase delay of the u-th sensor can be approximated as:

[0140]

[0141] in Therefore, for coprime array radar sensing, the initial received signal at time t can be modeled as:

[0142]

[0143] in Let represent the guidance matrix for K coprime targets. This represents the signal vector of a near-field target transmitting a signal. It is the received additive white Gaussian noise, where α 2 Indicates noise power.

[0144] II. Near-field target localization based on symmetric coprime matrix

[0145] Near-field target localization based on symmetric coprime matrices can achieve higher resolution than traditional dense arrays. Specifically, firstly, the symmetry of the symmetric coprime matrix is ​​used to construct an effective covariance matrix (i.e., the third covariance matrix). Then, a two-stage near-field multi-signal classification (MUSIC) method is used to systematically estimate the position information of the near-field target.

[0146] 1. Construct an effective covariance matrix

[0147] Initialization of the covariance matrix: Define T as the number of snapshots of the signal transmitted by the near-field target. Ignoring noise, the initial covariance matrix of the received signal is expressed as R = B(θ, r)B.H (θ, r). Since the covariance matrix of the initial received signal cannot be directly obtained in practice, an approximate version of the covariance matrix of the initial received signal is considered. That is, the first covariance matrix. To obtain the first covariance matrix, the least squares estimation method can be used, and the calculation process is given by the following equation:

[0148]

[0149] Then the guidance matrix at time t can be expressed as: To facilitate the design of the localization algorithm, the first covariance matrix is ​​first... The properties of the first covariance matrix are analyzed. The (i′, j′)th element can be represented as:

[0150]

[0151] Among them, the definition For the first term of the first covariance matrix, is the second-order term of the first covariance matrix.

[0152] Property 1: For a symmetric coprime matrix, the position of the sensor satisfies the following expression:

[0153]

[0154] Property 2: For the first covariance matrix Consider two elements that are symmetric about the second diagonal, for example... and Then their subscripts satisfy p = 2V - j′ and q = 2V - i′. These two elements have the same first-order term and opposite second-order terms. This can be represented as:

[0155] P p,q (θ k ) = P i′，j′ (θ k ), Q p，q (θ k r k )=-Q i′,j′ (θ k r k (7)

[0156] Decoupling of the first covariance matrix: It is worth noting that for the first covariance matrix... Each element in the first covariance matrix has K terms in the summation, each term being determined by the target angle and distance. This renders the traditional one-dimensional MUSIC algorithm inapplicable due to the coupling of angle and distance parameters. To fully utilize all elements of the first covariance matrix for accurate target localization, rather than just using the anti-angle elements, an efficient method is proposed to construct a decoupled fourth covariance matrix, which will benefit subsequent near-field target localization. Specifically, by utilizing property 2, the fourth covariance matrix R is defined. d for

[0157] R d =R⊙R a (8)

[0158] Where R a It is the fifth covariance matrix, obtained by symmetric analysis of the first covariance matrix about the second diagonal, i.e. Here, ⊙ denotes the Hadamard product of the matrices. The (i′, j′)th element of the fourth covariance matrix can be represented as:

[0159]

[0160] Among them, [R d ] i′，j′ P is the (i′, j′)th element in the fourth covariance matrix. i′，j′ (θ k ( ) is an angle of θ k Q is the first-order term of the (i′, j′)th element of the first covariance matrix. i′，j′ (θ k r k ( ) is an angle of θ k And the distance is r k The second-order term of the (i′, j′)th element of the first covariance matrix, w is the index of the first summation symbol, u′ is the index of the second summation symbol, and P i′，j′ (θ u′ ( ) is an angle of θ u′ The first-order term of the (i′, j′)th element in the first covariance matrix, P i′，j′ (θ w ( ) is an angle of θ w The first-order term of the (i′, j′)th element in the first covariance matrix, Q i′，j′ (θ u′ r u′ ( ) is an angle of θ u′ And the distance is r u′ Q is the second-order term of the (i′, j′)th element of the first covariance matrix. i′，j′ (θ w r w ( ) is an angle of θw And the distance is r w The second-order term of the (i′, j′)th element of the first covariance matrix, S and C are defined as the autospectral matrix and cross-spectral matrix, respectively. [S] i′，j′ Let be the (i′, j′)th element of the autospectral matrix, [C] i′，j′ Let be the (i′, j′)th element of the cross-spectral matrix. The autospectral matrix can be viewed as the covariance matrix of the far field. Where A(θ) is the far-field steering matrix. Furthermore, a(θ) k ) is defined as:

[0161]

[0162] Through the above steps, a partially decoupled fourth covariance matrix is ​​obtained. This decoupled fourth covariance matrix contains the angles of both the real target and the intersecting targets. Therefore, distinguishing between the angles of the real target and the intersecting targets is a crucial issue in subsequent steps. Furthermore, according to the expression of the S matrix, to avoid angular ambiguity, the sensor spacing must satisfy d ≤ λ / 4.

[0163] Virtualization of the covariance matrix: Typically, the degrees of freedom in a sensing system are limited by the number of sensors. However, the non-Topperlitz structure of the covariance matrix in a sparse array provides an increase in degrees of freedom. To fully utilize the degrees of freedom offered by the sparse array, the fourth covariance matrix R is first vectorized. d To obtain the equivalent received signal:

[0164]

[0165] in, A V (θ) is the equivalent orientation matrix. c = vec(C), This represents the Kronecker product operation. It's important to note that the second covariance matrix obtained directly from the equivalent received signal is a rank-1 matrix, i.e., Therefore, it cannot be directly used to sense multiple near-field targets. To recover the full-rank covariance matrix (i.e., the third covariance matrix), spatial smoothing techniques can be used. First, the equivalent steering matrix A is removed. V The repeated elements in (θ) can be used to obtain the equivalent guiding matrix after removing the repeated elements. This is the equivalent guiding vector after removing duplicate elements. Then, the virtual array composed of continuous difference sets is extracted. It can be seen that for a symmetric coprime matrix, the number of continuous difference sets is at least 2MN+1, so it can be divided into MN+1 overlapping subarrays, where each subarray has MN+1 sensors.

[0166] Therefore, the equivalent received signal of the i-th subarray can be expressed as:

[0167]

[0168] in and Let represent the equivalent steering matrix of the i-th subarray after removing duplicate elements and the cross-spectral matrix of the i-th subarray after vectorization, respectively. The third covariance matrix is ​​given by the following equation:

[0169]

[0170] R v It is a full-rank matrix, so it can identify at most MN+1 spectral peaks, which will be used in subsequent steps.

[0171] 2. Two-stage MUSIC algorithm

[0172] Based on the constructed third covariance matrix, an efficient two-stage MUSIC localization method is proposed for continuously estimating the position information of near-field targets.

[0173] Phase 1: Angle Estimation: First, for the obtained third covariance matrix R... v It can be seen that the third covariance matrix R v The first angle includes decoupling. Therefore, in the first stage, the third covariance matrix R can be analyzed in the angle domain. v Perform a one-dimensional angular spectral peak search. The third covariance matrix R... v This includes both the perspective of the actual target and the perspective of the undesirable intersecting targets. First, consider the third covariance matrix R... v Eigenvalue decomposition yields:

[0174]

[0175] in and Let the signal space and noise space of the third covariance matrix be represented respectively. and Let represent the eigenvalues ​​corresponding to the signal space and noise space of the third covariance matrix, respectively. Then, the first angle is obtained from the following spectral peak search model.

[0176]

[0177] here This represents the steering vector of the virtual array of continuous apertures, where L is the detected first angle. The number of targets. The first angle includes both the angle of the actual target and the angle of the intersecting targets.

[0178] Phase 2: Distance Estimation: This phase identifies the angles of both the intersecting target and the true target, simultaneously obtaining an accurate distance estimate corresponding to the true target's angle. The key idea in this second phase is to utilize the fact that the angles of the intersecting target do not exhibit a significant spectral peak in the range domain, thereby detecting the true target. The basic principle is based on the theory that the signal space and noise space are orthogonal in the polar domain, i.e., B... H (θ,r)U n =0. After obtaining the first angle, since the first angle is known, the received signal can now be expressed as... Then, eigenvalue decomposition was performed on the first covariance matrix to obtain... Similarly, U s and U n Let ∑ represent the signal space and noise space of the first covariance matrix, respectively. s and ∑ n Let represent the eigenvalues ​​corresponding to the signal space and noise space of the first covariance matrix, respectively. Therefore, the spectral peak search model in the distance domain can be written as follows:

[0179]

[0180] For any first angle, if the target corresponding to it does not have a significant spectral peak in the range domain, then it can be determined that this angle is the angle of an intersecting target. If the target corresponding to any first angle has a spectral peak in the range domain, then it can be determined that this angle is the angle of an intersecting target. Therefore, we can determine that this is the perspective of the real target, i.e., the second perspective.

[0181] 3. Simulation Analysis

[0182] To verify the effectiveness and low-overhead characteristics of the proposed multi-beam training, a large-aperture coprime array is considered, where M=9, N=11, operating in the f=30GHz band. The number of snapshots is set to T=100, and there are K=4 near-field targets located at (-35°, 25m), (10°, 30m), (30°, 20m), and (30°, 40m). Two targets are located at the same angle but at different distances, thus this radar sensing configuration exhibits high complexity. Figure 3 This is a spectral peak search diagram in the angle domain provided in an embodiment of this application. Please refer to [link / reference]. Figure 3 By using the first-stage MUSIC algorithm, the angles of real targets can be detected very well, while the angles of intersecting targets will also appear. Figure 4This is a spectral peak search diagram in the distance domain provided in an embodiment of this application. Please refer to [link / reference]. Figure 4 . Figure 4 This includes the search results for spectral peaks in the range domain for the first angle detected in the first stage. The second stage, the MUSIC algorithm in the range domain, can effectively distinguish the angles of the real target and the angles of the intersecting target. Specifically, in the range domain, there are obvious spectral peaks only at the angles of the real target, while no obvious spectral peaks appear in the considered near-field region at the angles of the intersecting target. Furthermore, even if two targets are located at the same angle, they can still be distinguished in the range domain.

[0183] Figure 5 This is a graph showing the relationship between the root mean square error (RMSE) and the signal-to-noise ratio (SNR) provided in the embodiments of this application. Please refer to [link / reference]. Figure 5 .Depend on Figure 5 It can be observed that symmetric coprime arrays outperform dense uniform arrays in angle estimation. With the same number of antennas, the aperture formed by a symmetric coprime array is larger than that of a dense array, thus improving sensing resolution and accuracy. Furthermore, both symmetric coprime arrays and dense uniform arrays achieve lower RMSEs as the SNR increases. On the other hand, even at high SNRs, far-field methods are affected by significant RMSEs. This indicates that directly applying far-field methods (i.e., far-field virtual array methods) to near-field target localization is inappropriate. Moreover, as the number of near-field targets increases, the method proposed in this application's embodiments proves more effective than the subarray decomposition method. This is because, for the subarray decomposition method, each uniform sparse array exhibits a periodic spectrum during estimation. With a significant increase in the number of spectral peaks, the process of obtaining accurate common spectral peaks for the two subarrays becomes more complex, thus affecting the estimation accuracy.

[0184] Figure 6 This is a graph showing the relationship between the root mean square error of distance and the signal-to-noise ratio provided in the embodiments of this application. Please refer to it. Figure 6 As the SNR increases, the distance RMSE of the sparse array method provided in this application decreases more significantly than that of the dense uniform array. This is because, for arrays with the same number of sensors, sparse arrays are more likely to form near-field regions. The distance information contained in these near-field regions is more prominent, and the accuracy of sparse arrays is significantly higher compared to dense uniform arrays. Finally, it can be observed that as the number of near-field targets increases, the subarray decomposition method cannot achieve accurate distance estimation, while the method proposed in this application remains effective in providing accurate target localization.

[0185] Figure 7 This is a schematic diagram of the near-field target positioning device provided in the embodiments of this application. Please refer to it. Figure 7 This application also provides a near-field target localization device 700, which can implement the above-described near-field target localization method. The device 700 includes:

[0186] The signal acquisition module 710 is used to acquire the initial received signal of a preset symmetric coprime array. The symmetric coprime array is a sensor array composed of two uniform linear arrays that are symmetric about the central array element.

[0187] The first covariance matrix determination module 720 is used to determine the first covariance matrix of the initial received signal based on the initial received signal.

[0188] The equivalent received signal determination module 730 is used to perform calculations based on the first covariance matrix to obtain the equivalent received signal of the initial received signal;

[0189] The second covariance matrix determination module 740 is used to determine the second covariance matrix of the equivalent received signal based on the equivalent received signal.

[0190] The third covariance matrix determination module 750 is used to perform spatial smoothing on the second covariance matrix to obtain the third covariance matrix of the equivalent received signal.

[0191] The first angle determination module 760 is used to perform a one-dimensional angle spectrum peak search on the third covariance matrix to obtain the first angle between the near-field target and the central array element.

[0192] The first distance determination module 770 is used to perform a one-dimensional distance spectrum peak search on the first covariance matrix based on the first angle to obtain the first distance between the near-field target and the central array element. The position information of the near-field target includes the first angle and the first distance.

[0193] The specific implementation of the near-field target positioning device 700 is basically the same as the specific embodiment of the near-field target positioning method described above, and will not be repeated here.

[0194] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described near-field target localization method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0195] Figure 8 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Please refer to it. Figure 8 Electronic devices include:

[0196] The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0197] The memory 802 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 802 and is called and executed by the processor 801 using the near-field target localization method of the embodiments of this application.

[0198] The 803 input / output interface is used to implement information input and output.

[0199] The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0200] Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804);

[0201] The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0202] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described near-field target localization method.

[0203] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0204] The near-field target localization method, apparatus, electronic device, and storage medium provided in this application first acquire the initial received signal of a preset symmetric coprime array. Then, based on the initial received signal, a first covariance matrix of the initial received signal is determined. Subsequently, calculations are performed based on the first covariance matrix to obtain the equivalent received signal of the initial received signal. Next, based on the equivalent received signal, a second covariance matrix of the equivalent received signal is determined. Then, spatial smoothing is performed on the second covariance matrix to obtain a third covariance matrix of the equivalent received signal. A one-dimensional angular peak search is then performed on the third covariance matrix to obtain a first angle between the near-field target and the central array element. Finally, based on the first angle, a one-dimensional distance peak search is performed on the first covariance matrix to obtain a first distance between the near-field target and the central array element. This application extends the far-field virtual array method to the near-field, fully utilizing the mutual information between subarrays in the symmetric coprime array, thereby significantly reducing the computational overhead during near-field target localization.

[0205] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0206] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0207] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0208] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0209] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification 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.

[0210] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0211] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above 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; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0212] The units described above 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.

[0213] 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.

[0214] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of 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 multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0215] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A near-field target localization method, characterized in that, The method includes: Acquire the initial received signal of a preset symmetric coprime array, wherein the symmetric coprime array is a sensor array composed of two uniform linear arrays symmetric about the central array element; Based on the initial received signal, determine the first covariance matrix of the initial received signal; The equivalent received signal of the initial received signal is obtained by performing calculations based on the first covariance matrix. Based on the equivalent received signal, determine the second covariance matrix of the equivalent received signal; The second covariance matrix is ​​spatially smoothed to obtain the third covariance matrix of the equivalent received signal; A one-dimensional angle spectrum peak search is performed on the third covariance matrix to obtain the first angle between the near-field target and the central array element. Based on the first angle, a one-dimensional distance spectrum peak search is performed on the first covariance matrix to obtain the first distance between the near-field target and the central array element. The position information of the near-field target includes the first angle and the first distance. The step of performing calculations based on the first covariance matrix to obtain the equivalent received signal of the initial received signal includes: Based on the first covariance matrix, determine the fifth covariance matrix after the first covariance matrix is ​​symmetric about the second diagonal; The product of the first element in the first covariance matrix and the second element in the fifth covariance matrix is ​​determined as the third element, so as to obtain the fourth covariance matrix composed of the third element; wherein the first element, the second element and the third element are located in the same number of rows and columns in their respective matrices. The fourth covariance matrix is ​​vectorized to obtain the equivalent received signal of the initial received signal.

2. The method according to claim 1, characterized in that, The step of performing a one-dimensional angle spectral peak search on the third covariance matrix to obtain the first angle between the near-field target and the central array element includes: The third covariance matrix is ​​decomposed into eigenvalues ​​to obtain the signal space and noise space; Based on the spectral peak search model in the angle domain, a one-dimensional spectral peak search is performed on the signal space and the noise space to obtain the first angle.

3. The method according to claim 1, characterized in that, The near-field target includes multiple targets, and the first distance includes sub-distances between each target and the central array element; after performing a one-dimensional distance spectrum peak search on the first covariance matrix based on the first angle to obtain the first distance between the near-field target and the central array element, the method further includes: The sub-distance that is within a preset distance interval and has a spectral power greater than or equal to a preset spectral power in the distance domain is determined as the second distance. The preset distance interval is a distance interval determined based on the carrier wavelength and the aperture of the symmetric coprime array. The target corresponding to the second distance is identified as the real target; The angle between the real target and the central array element is determined as the second angle, and the position information of the real target includes the second angle and the second distance.

4. The method according to claim 1, characterized in that, The step of performing spatial smoothing on the second covariance matrix to obtain the third covariance matrix of the equivalent received signal includes: The symmetric coprime array is divided into multiple subarrays, and the equivalent received signal of each subarray is determined. Based on the equivalent received signals of each subarray, the virtual covariance matrix of each subarray is determined; The third covariance matrix is ​​obtained by averaging the virtual covariance matrices of the multiple subarrays.

5. The method according to claim 1, characterized in that, Determining the first covariance matrix of the initial received signal based on the initial received signal includes: Obtain the number of snapshots of the near-field target and the signal vector of the signal transmitted by the near-field target; Based on the least squares method, the steering matrix of the symmetric coprime matrix is ​​determined according to the initial received signal and the signal vector; The first covariance matrix is ​​determined based on the guidance matrix and the number of snapshots.

6. A near-field target positioning device, characterized in that, The device includes: The signal acquisition module is used to acquire the initial received signal of a preset symmetric coprime array, wherein the symmetric coprime array is a sensor array composed of two uniform linear arrays symmetric about the central array element. The first covariance matrix determination module is used to determine the first covariance matrix of the initial received signal based on the initial received signal. An equivalent received signal determination module is used to perform calculations based on the first covariance matrix to obtain the equivalent received signal of the initial received signal; The second covariance matrix determination module is used to determine the second covariance matrix of the equivalent received signal based on the equivalent received signal. The third covariance matrix determination module is used to perform spatial smoothing on the second covariance matrix to obtain the third covariance matrix of the equivalent received signal. The first angle determination module is used to perform a one-dimensional angle spectrum peak search on the third covariance matrix to obtain the first angle between the near-field target and the central array element. The first distance determination module is used to perform a one-dimensional distance spectrum peak search on the first covariance matrix based on the first angle to obtain the first distance between the near-field target and the central array element. The position information of the near-field target includes the first angle and the first distance. The equivalent received signal determination module is further configured to: Based on the first covariance matrix, determine the fifth covariance matrix after the first covariance matrix is ​​symmetric about the second diagonal; The product of the first element in the first covariance matrix and the second element in the fifth covariance matrix is ​​determined as the third element, so as to obtain the fourth covariance matrix composed of the third element; wherein the first element, the second element and the third element are located in the same number of rows and columns in their respective matrices. The fourth covariance matrix is ​​vectorized to obtain the equivalent received signal of the initial received signal.

7. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the near-field target localization method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the near-field target localization method according to any one of claims 1 to 5.

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