Near-field target positioning method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a near-field target positioning method and device, electronic equipment and a storage medium, and belongs to the technical field of communication. The method comprises the following steps: acquiring an initial receiving signal of a preset symmetric co-prime array; determining a first covariance matrix of the initial receiving signal according to the initial receiving signal; performing operation based on the first covariance matrix to obtain an equivalent receiving signal of the initial receiving signal; determining a second covariance matrix of the equivalent receiving signal according to the equivalent receiving signal; performing spatial smoothing processing on the second covariance matrix to obtain a third covariance matrix of the equivalent received signal; performing one-dimensional angle spectrum peak search on the third covariance matrix to obtain a first angle between the near-field target and the central array element; and based on the first angle, performing one-dimensional distance spectrum peak search on the first covariance matrix to obtain a first distance between the near-field target and the central array element. According to the embodiment of the invention, the calculation overhead during near-field target positioning can be remarkably reduced.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a near-field target positioning method, device, electronic device and storage medium. Background Art

[0002] Integrated sensing and communication (ISAC) has been recognized as a key technology for future 6G wireless networks. It can significantly improve the communication capacity and spectrum efficiency of the system by sharing the same spectrum and hardware resources to realize the sensing and communication functions. In particular, wireless / radar sensing has been widely used in practice, such as high-precision positioning and tracking, high-resolution imaging, and real-time positioning and mapping. As the technology trend moves towards higher frequency bands and larger transmitter array apertures, the Rayleigh distance that distinguishes the boundary between the near field and the far field is greatly expanded, making it more likely that environmental targets are located in the near field area of ​​the large aperture array. In addition, compared with traditional far-field target positioning, near-field target positioning of large aperture arrays can achieve higher sensing resolution.

[0003] Unlike far-field target positioning based on plane waves, near-field target positioning requires consideration of a more accurate spherical channel model, which is characterized by the angle and distance of the target. This makes the existing far-field angle estimation method inefficient when applied to near-field perception systems and difficult to directly extend to the near field. To solve this problem, a variety of near-field positioning methods have been proposed recently, such as near-field target positioning through a dense array with a large number of sensors, and global spatial search through a two-dimensional multi-signal classification algorithm to obtain the positioning parameters of near-field targets. However, the aforementioned methods all have high signal processing complexity or large amount of calculation, resulting in large computational overhead. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a near-field target positioning method, device, electronic device and storage medium, aiming to reduce the computational overhead of the near-field target positioning method.

[0005] To achieve the above object, a first aspect of an embodiment of the present application proposes a near-field target positioning method, the method comprising:

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

[0007] Determine a first covariance matrix of the initial received signal according to the initial received signal;

[0008] Performing operations based on the first covariance matrix to obtain an equivalent received signal of the initial received signal;

[0009] Determine a second covariance matrix of the equivalent received signal according to the equivalent received signal;

[0010] Performing spatial smoothing processing on the second covariance matrix to obtain a third covariance matrix of the equivalent received signal;

[0011] Performing a one-dimensional angle spectrum peak search on the third covariance matrix to obtain a 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 a first distance between the near-field target and the central array element, and the position information of the near-field target includes the first angle and the first distance.

[0013] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a near-field target positioning device, the device comprising:

[0014] A signal acquisition module is used to acquire an initial received signal of a preset symmetric coprime array, where the symmetric coprime array is a sensor array composed of two uniform linear arrays symmetrical about a central array element;

[0015] A first covariance matrix determination module, used to determine a first covariance matrix of the initial received signal according to the initial received signal;

[0016] An equivalent received signal determination module, configured to perform calculations based on the first covariance matrix to obtain an equivalent received signal of the initial received signal;

[0017] A second covariance matrix determination module, used to determine a second covariance matrix of the equivalent received signal according to the equivalent received signal;

[0018] A third covariance matrix determination module, used for performing spatial smoothing processing on the second covariance matrix to obtain a third covariance matrix of the equivalent received signal;

[0019] A first angle determination module is used to perform a one-dimensional angle spectrum peak search on the third covariance matrix to obtain a 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 a first distance between the near-field target and the central array element, wherein the position information of the near-field target includes the first angle and the first distance.

[0021] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the method of the above-mentioned first aspect when executing the computer program.

[0022] To achieve the above objectives, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method of the first aspect is implemented.

[0023] The near-field target positioning method, device, electronic device and storage medium proposed in the present application first obtain the initial received signal of the preset symmetric coprime array, and then determine the first covariance matrix of the initial received signal based on the initial received signal, then perform calculations based on the first covariance matrix to obtain the equivalent received signal of the initial received signal, and then determine the second covariance matrix of the equivalent received signal based on the equivalent received signal, and then perform spatial smoothing on the second covariance matrix to obtain the third covariance matrix of the equivalent received signal, and then 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, and finally 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 present application makes full use of the mutual information between subarrays in the symmetric coprime array by extending the virtual array method of the far field to the near field, thereby significantly reducing the computational overhead when positioning near-field targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart of a near-field target positioning method provided by an embodiment of the present application;

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

[0026] Figure 3 It is a spectrum peak search diagram in the angle domain provided by an embodiment of the present application;

[0027] Figure 4 It is a spectrum peak search diagram in the distance domain provided by an embodiment of the present application;

[0028] Figure 5 is a relationship diagram between the angle root mean square error and the signal-to-noise ratio provided in an embodiment of the present application;

[0029] Figure 6 is a relationship diagram between the distance root mean square error and the signal-to-noise ratio provided in an embodiment of the present application;

[0030] Figure 7 is a schematic diagram of the structure of a near-field target positioning device provided in an embodiment of the present application;

[0031] Figure 8 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0033] It should be noted that, although the functional modules are divided in the device schematic diagram and the 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 above 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 those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0035] First, some nouns involved in this application are analyzed:

[0036] Symmetric coprime array: An array consisting of two coprime subarrays. Both subarrays are uniform linear arrays and are symmetrically distributed in structure. In a symmetric coprime array, the number of array elements and the array element spacing of the two subarrays satisfy the coprime relationship.

[0037] Far-field target positioning: refers to when the signal source (such as electromagnetic waves, sound waves, etc.) is located in the far field area of ​​the receiving array (such as antenna array, microphone array, etc.), the position of the signal source is determined by measuring the time difference, phase difference or amplitude difference of the signal reaching each element of the array.

[0038] Near-field target positioning: It is mainly aimed at the situation where the distance between the signal source and the receiving array is relatively close. At this time, the wavefront of the signal reaching each element of the array is no longer a plane wave, but a spherical wave. Therefore, near-field target positioning requires not only knowing the direction of arrival (DOA) of the signal, but also the distance information between the signal source and the array to accurately determine the location of the signal source.

[0039] Currently, the technology trend is moving towards higher frequency bands and larger transmitter array apertures. The Rayleigh distance that distinguishes the boundary between the near field and the far field is greatly expanded, making it more likely that environmental targets are located in the near field area of ​​the large aperture array. Compared with traditional far-field target positioning, near-field target positioning of large aperture arrays can achieve higher sensing resolution, but it also brings new challenges. For example, unlike far-field positioning based on plane waves, near-field positioning needs to consider a more accurate spherical wave model (which is characterized by the angle and distance of the target), so this makes the existing far-field angle estimation method inefficient when applied to near-field perception systems and difficult to directly extend to the near field.

[0040] To solve the above problems, existing near-field target positioning methods generally include three types. The first is to locate the target based on a dense array using a large number of sensors or array elements. However, this method uses a large number of sensors or array elements, which leads to high power, high hardware cost and high signal processing complexity in the array. The second is to locate through a two-dimensional multi-signal classification algorithm, but this method has a large amount of calculation because it requires global spatial search in the two dimensions of angle and distance. The third is to decompose the entire array into two symmetrical uniform sparse arrays, then decouple the two sub-arrays and perform angle estimation separately, and finally use the coprime characteristics to eliminate angle ambiguity. However, this method divides the coprime array into two independent sub-arrays, fails to fully utilize the mutual information between the sub-arrays, and thus fails to utilize the high perception freedom provided by the coprime array. This method also has the problem of high computational overhead.

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

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

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

[0044] The present application can be used in many general or special computer system environments or configurations. For example: 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, distributed computing environments including any of the above systems or devices, etc. The present 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. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0045] Figure 1 is a flow chart of a near-field target positioning method provided in an embodiment of the present application, Figure 1 The method may include but is not limited to steps S101 to S107.

[0046] Step S101: acquiring an initial received signal of a preset symmetric coprime array, where the symmetric coprime array is a sensor array consisting of two uniform linear arrays symmetrical about a central array element;

[0047] Step S102: determining a first covariance matrix of the initial received signal according to the initial received signal;

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

[0049] Step S104: determining a second covariance matrix of the equivalent received signal according to 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 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 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 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 element of the symmetric co-prime array is located at the origin, then 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, D is the aperture of the symmetric coprime array, D = 2M(N-1)d. At this time, electromagnetic propagation follows the uniform spherical wave model. The embodiments of the present application mainly 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 the non-line-of-sight channel path can be ignored. Therefore, the embodiments of the present application only consider the line-of-sight path, then the channel steering vector can satisfy the following formula:

[0056]

[0057] Among them, b(θ k , r k ) represents the angle θ k And the distance is r k The channel steering vector at time θ k is the angle between the kth near-field target and the central array element, r k is the distance between the kth near-field target and the central array element, e is a natural constant, π is the circumference of a circle, λ is the carrier wavelength, and d -V，k is the distance from the Vth sensor located on the negative half of the y-axis to the kth near-field target, d -u，k is the distance from the uth sensor located on the negative half of the y-axis to the kth near-field target, d V，k is the distance from the Vth sensor located on the positive half of the y-axis to the kth near-field target, r k is the distance between the kth near-field target and the central array element, and T represents the transpose of the matrix.

[0058] Based on the Fresnel approximation, the phase delay of the u-th sensor can be obtained to satisfy the following formula:

[0059]

[0060] Among them, d u,k is the distance from the uth sensor to the kth near-field target, r k is the distance between the kth near-field target and the central array element, s u is the position vector of the u-th sensor, θ k is the angle between the kth near-field target and the central array element, p u (θ k ) is the angle 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 the angle θ kAnd the distance is r k The second-order term of the phase delay of the u-th sensor at time ,

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

[0062]

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

[0064] The covariance matrix can be used to quantify the correlation of signals in different dimensions. Therefore, after obtaining the initial received signal of the symmetric coprime matrix, the electronic device can determine the first covariance matrix of the initial received signal based on the initial received signal. Without considering the 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, B(θ, r) represents the steering matrix of the symmetric coprime array K near-field targets, and B H (θ, r) is the conjugate transpose of the steering matrix. Since the covariance matrix of the initial received signal cannot be directly obtained, an approximate version of the covariance matrix of the initial received signal, ie, the first covariance matrix, can be determined, and subsequent operations are performed based on the first covariance matrix.

[0065] Generally speaking, the degree of freedom is limited by the number of sensors, but due to the non-Toplitz structure of the sparse array covariance matrix, the degree of freedom of the symmetric coprime array is improved. In order to make full use of the degree of freedom of the symmetric coprime array, the electronic device can virtualize the first covariance matrix, that is, perform further operations based on the first covariance matrix to obtain an equivalent received signal of the initial received signal, and then determine the second covariance matrix of the equivalent received signal based on the equivalent received signal. Since the second covariance matrix directly obtained from the equivalent received signal is of rank 1 (i.e. r d is the equivalent received signal, The matrix (where rank represents the rank of the matrix) cannot be used to perceive multiple near-field targets at this time. Therefore, it is necessary to perform spatial smoothing on the second covariance matrix to obtain the third covariance matrix of the equivalent received signal to restore 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, so the subsequent two-stage spectral peak search process can be continued.

[0066] The position information of the near-field target includes a first angle and a first distance. In the two-stage spectrum peak search process, the electronic device can determine the first angle and the first distance in sequence to complete the positioning of the near-field target. Specifically, the third covariance matrix is ​​a covariance matrix that is only related to the angle. Therefore, in the two-stage spectrum peak search process, the spectrum peak search in the first stage is 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 covariance matrix is ​​a covariance matrix related to angle and distance. After obtaining the first angle, since the angle is known, the second stage spectrum peak search can be performed at this time, that is, 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.

[0067] Steps S101 to S107 shown in the embodiment of the present application are performed by first obtaining an initial received signal of a preset symmetric coprime array, and then determining a first covariance matrix of the initial received signal based on the initial received signal, and then performing operations based on the first covariance matrix to obtain an equivalent received signal of the initial received signal, and then determining a second covariance matrix of the equivalent received signal based on the equivalent received signal, and then performing spatial smoothing on the second covariance matrix to obtain a third covariance matrix of the equivalent received signal, and then performing a one-dimensional angle spectrum peak search on the third covariance matrix to obtain a first angle between the near-field target and the central array element, and finally performing a one-dimensional distance spectrum 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. The present application makes full use of the mutual information between subarrays in a symmetric coprime array by extending the far-field virtual array method to the near-field, thereby significantly reducing the computational overhead when locating near-field targets.

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

[0069] Obtain the number of snapshots of the near-field target and the signal vector of the signal sent by the near-field target;

[0070] Based on the least square method, the steering matrix of the symmetric coprime array is determined according to the initial received signal and the signal vector;

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

[0072] As can be seen from the previous text, without considering the 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, B(θ, r) represents the steering matrix of the symmetric coprime array K near-field targets, and B H (θ, r) is the conjugate transpose of the steering matrix of K near-field targets of the symmetric coprime matrix. However, since the covariance matrix of the initial received signal cannot be directly obtained in the actual engineering experiment, the embodiment of the present application 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 operations. First, the electronic device can obtain the number of snapshots of the near-field target and the signal vector of the signal sent by the near-field target. The signal vector of the signal sent by the near-field target refers to the signal vector of the pilot signal sent by the near-field target, and the number of snapshots of the near-field target is the number of pilot signals sent by the near-field target, both of which are known data. In this way, based on the least squares method, the steering matrix of the symmetric coprime array can be determined according to the initial received signal and the signal vector, that is, the following formula is satisfied:

[0073]

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

[0075] In this way, the steering matrix at time t can be obtained in is the steering matrix at time t, y(t) is the initial received signal at time t, x(t) is the signal vector of the near-field target sending signal at time t, and x(t) H is the conjugate transpose of the signal vector of the near-field target transmitting signal at time t.

[0076] Then, according to the steering matrix and the number of snapshots, the first covariance matrix can be determined. The first covariance matrix satisfies the following formula:

[0077]

[0078] in, represents the first covariance matrix, T is the number of snapshots, is the steering matrix at time t, is the conjugate transpose of the steering matrix at time t.

[0079] It should be noted that, when the number of snapshots increases, the difference between the first covariance matrix and the covariance matrix of the initial received signal will gradually decrease. In this way, the first covariance matrix of the initial received signal can be determined.

[0080] In some embodiments, performing operations based on the first covariance matrix to obtain an equivalent received signal of the initial received signal includes:

[0081] Decoupling the angle and distance in the first covariance matrix to obtain a fourth covariance matrix;

[0082] Vectorization processing is performed on the fourth covariance matrix to obtain an equivalent received signal of the initial received signal.

[0083] In order to make full use of 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 items in the sum, and each item is related to the angle and distance, which makes the traditional one-dimensional multi-signal classification algorithm inapplicable due to the coupling of angle and distance. Therefore, in order to make full use of all elements in the first covariance matrix for accurate target positioning, rather than just using anti-diagonal elements, first, the electronic device can decouple the angle and distance in the first covariance matrix to obtain a fourth covariance matrix. Subsequently, the fourth covariance matrix can be vectorized to obtain an equivalent received signal of the initial received signal. The equivalent received signal satisfies the following formula:

[0084]

[0085] Among them, r d is the equivalent received signal, R d is the fourth covariance matrix, S is the autospectral matrix, C is the cross-spectral matrix, c is the vectorized cross-spectral matrix, that is, c = vec (C), A V (θ) is the equivalent steering matrix, represents the Kronecker product, a(θ k ) is the angle θ k The far-field steering vector, a * (θ k ) is the angle θ k The conjugate of the far-field steering vector at , vec() represents a vectorized operation.

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

[0087] According to the first covariance matrix, determine a fifth covariance matrix after the first covariance matrix is ​​symmetric about the secondary diagonal line;

[0088] Determine the product of the first element in the first covariance matrix and the second element in the fifth covariance matrix as a third element to obtain a fourth covariance matrix composed of the third element;

[0089] The first element, the second element and the third element are located in the same number of rows and the same number of columns in their respective matrices.

[0090] In the process of decoupling the angle and distance in the first covariance matrix, the electronic device can determine, according to the first covariance matrix, a fifth covariance matrix R after the first covariance matrix is ​​symmetric about the secondary diagonal line. a The (i′, j′)th element in the first covariance matrix can be expressed as:

[0091]

[0092] in, represents the (i′, j′)th element in the first covariance matrix, k represents the number of the near-field target, K represents the total number of near-field targets, θ k is the angle between the kth near-field target and the central array element, r k is the distance between the kth near-field target and the central array element, b(θ k , r k ) is the angle θ k And the distance is r k The channel steering vector at time b H (θ k , r k ) is the angle θ k And the distance is r k The conjugate transpose of the channel steering vector at j′ (θ k ) is the angle θ k The first-order term of the phase delay of the i′th sensor, p j′ (θ k ) is the angle θ k The first-order term of the phase delay of the j′th sensor, q i′ (θ k , r k ) is the angle 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 the angle θ k And the distance is r k The second-order term of the phase delay of the j′th sensor at , j is the imaginary unit, π is the circumference of the circle, λ is the carrier wavelength, s i′is the position vector of the i′th sensor, s j′ is the position vector of the j′th sensor, P i′，j′ (θ k ) is the angle θ k The first-order term of the (i′, j′)th element of the first covariance matrix is Q i′，j′ (θ k , r k ) is the angle 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 arrays and the first covariance matrix, it is easy to design a near-field target positioning method. For a symmetric coprime array, the position vector of the known sensor satisfies For the first covariance matrix, the two elements are symmetric about the subdiagonal, e.g. and The subscripts satisfy p = 2V-j' and q = 2V-i', then the two elements have the same first-order terms and opposite second-order terms, that is, 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] From the above properties, we can see that for the fifth covariance matrix R a For the (i′, j′)th element in Among them, R a is the fifth covariance matrix, is the first covariance matrix, [R a ] i′，j′ is the (i′, j′)th element in the fifth covariance matrix, is the (2V-j′, 2V-i′)th element in the first covariance matrix, and V is the number of sensors in the basic coprime matrix.

[0095] Then, according to 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, and then the fourth covariance matrix composed of the third element can be obtained. Among them, the first element can be any element in the first covariance matrix, the second element is the element in the fifth covariance matrix with the same position as the first element, and the third element is the element in the fourth covariance matrix with the same position as the first element and the second element. It can be understood that the same position means that the number of rows and columns in the respective matrices are the same. At this time, the fourth covariance matrix satisfies R d is the fourth covariance matrix, R a is the fifth covariance matrix, is the first covariance matrix, and ⊙ represents the Hadamard product operation.

[0096] Then, we can get that the (i′, j′)th element in the fourth covariance matrix satisfies the following formula:

[0097]

[0098] Among them, [R d ] i′，j′ is the (i′, j′)th element in the fourth covariance matrix, P i′，j′ (θ k ) is the angle 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 the angle 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 first summation symbol subscript index, u′ is the second summation symbol subscript index, P i′，j′ (θ u′ ) is the angle θ u′ The first-order term of the (i′, j′)th element in the first covariance matrix, P i′，j′ (θ w ) is the angle 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 the angle θ u′ And the distance is r u′ The second-order term of the (i′, j′)th element of the first covariance matrix, Q i′，j′ (θ w , r w ) is the angle 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′ is the (i′, j′)th element of the autospectral matrix, [C] i′，j′ is the (i′, j′)th element of the cross-spectral matrix. The autospectral matrix can be regarded 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 autospectral matrix, in order to avoid angle ambiguity, the minimum unit d of the sensor spacing must be less than or equal to λ / 4.

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

[0100]

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

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

[0103] In some embodiments, performing spatial smoothing processing on the second covariance matrix to obtain a third covariance matrix of the equivalent received signal includes:

[0104] Divide the symmetric coprime array into multiple sub-arrays, and determine the equivalent received signal of each sub-array;

[0105] Determine a virtual covariance matrix of each sub-array based on an equivalent received signal of each sub-array;

[0106] The virtual covariance matrices of the multiple sub-matrices are averaged to obtain a third covariance matrix.

[0107] After obtaining the equivalent received signal, the steps of determining the first covariance matrix in the previous text can be referred to, and the second covariance matrix of the equivalent received signal can be determined according to the equivalent received signal. At this time, since the second covariance matrix obtained directly from the equivalent received signal is a matrix of rank 1, it cannot be used to perceive multiple near-field targets. Therefore, it is necessary to perform spatial smoothing on the second covariance matrix to obtain the third covariance matrix of the equivalent received signal to restore the 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 steering matrix after removing the repeated elements. is the equivalent steering vector after removing repeated 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 from it to form a virtual array). For a symmetric coprime array, it can be known that the number of continuous difference sets is at least 2MN+1. Then, according to the preset number of subarray sensors and the degree of subarray overlap, the virtual array based on the original symmetric coprime array can be divided into MN+1 overlapping subarrays, each of which has MN+1 sensors. In this way, the equivalent received signal of the i-th subarray is:

[0108]

[0109] in, is the equivalent received signal of the ith sub-array, is the equivalent steering matrix of the ith submatrix after removing repeated elements, is the cross-spectral matrix of the i-th sub-matrix after vectorization.

[0110] Subsequently, the electronic device can determine the virtual covariance matrix of each sub-array based on the equivalent received signal of each sub-array. Then, the virtual covariance matrices of multiple sub-matrices are averaged to obtain the third covariance matrix R v The third covariance matrix R v Satisfies the following formula:

[0111]

[0112] Among them, R v is the third covariance matrix, M is the first submatrix parameter of the symmetric coprime matrix, N is the second submatrix parameter of the symmetric coprime matrix, is the virtual covariance matrix of the ith submatrix, is the equivalent received signal of the ith sub-array, is the conjugate transpose of the equivalent received signal of the i-th sub-matrix.

[0113] In this way, the third covariance matrix of the equivalent received signal can be obtained to facilitate subsequent spectrum peak search.

[0114] In some embodiments, performing a one-dimensional angle spectrum peak search on the third covariance matrix to obtain a first angle between the near-field target and the central array element includes:

[0115] Perform eigenvalue decomposition on the third covariance matrix to obtain a signal space and a noise space;

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

[0117] Specifically, after obtaining the third covariance matrix related only 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 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. v Perform eigenvalue decomposition to obtain the signal space and noise space of the third covariance matrix. The third covariance matrix R after eigenvalue decomposition v Satisfies the following formula:

[0118]

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

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

[0121]

[0122] in, is the first angle detected, L is the number of the first angles detected, is the steering vector of the virtual array of continuous apertures, is equivalent to the signal space of the third covariance matrix, is the conjugate transpose of the steering vector of the virtual array of continuous apertures, 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 means to parameterize 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 later.

[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 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:

[0125] Determine a sub-distance among the multiple sub-distances 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 as a second distance, wherein the preset distance interval is a distance interval determined according to the carrier wavelength and the aperture of the symmetric coprime array;

[0126] determining the target corresponding to the second distance as the real target;

[0127] 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.

[0128] Specifically, the near-field target includes multiple targets, among which there are real targets and cross targets, so the first angle includes the angle of the real target and the angle of the undesired cross target. In order to obtain an accurate distance estimate corresponding to the angle of the real target, the real target in the near-field target can be detected by using the fact that the angle of the cross target has no obvious spectrum peak in the distance domain. Then, first, the first covariance matrix can be decomposed by eigenvalue to obtain the first covariance matrix after eigenvalue decomposition, that is, Likewise, represents the first covariance matrix, U s is the signal space of the first covariance matrix, U n is the noise space of the first covariance matrix, ∑ s is the eigenvalue of the signal space of the first covariance matrix, ∑ n is the eigenvalue of the noise space of the first covariance matrix, U s H is the conjugate transpose of the signal space of the first covariance matrix, U n H is the conjugate transpose of the noise space of the first covariance matrix.

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

[0130]

[0131] in, is the first distance, The first angle is And the channel steering vector when the distance is 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 for the first angle of and when the distance is r, it is the conjugate transpose of the channel steering vector, and argmax represents finding the parameter of the function.

[0132] After obtaining the first distance, the electronic device can further remove the position information of the cross target. Specifically, the electronic device can determine, as the second distance, the sub - distances among the multiple sub - distances that are within a preset distance interval and whose spectral power in the distance domain is greater than or equal to a preset spectral power. The preset distance interval is a distance interval determined according to the carrier wavelength and the aperture of the symmetric co - prime array, and 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, thereby improving the accuracy of near - field target positioning and reducing the influence of the cross target 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 with a large - aperture symmetric co - prime array 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 in 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 the position indices of the second sub - array, and Nmd is the subset of the 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] In addition, it is assumed that all targets are located in the Fresnel near-field region of the VLSA, and the target range is larger than the Fresnel region. and is no greater than the Rayleigh distance Z R =2D 2 / λ, D is the aperture of the symmetric coprime array, D = 2M(N-1)d. Therefore, electromagnetic propagation follows the uniform spherical wave model. The embodiments of the present application mainly 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 the non-line-of-sight channel path can be ignored. Therefore, considering only the line-of-sight path, the channel steering vector can be defined as:

[0138]

[0139] in represents 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 perception, the initial received signal at time t can be modeled as:

[0142]

[0143] in represents the steering matrix of K targets with symmetric and mutually prime matrix, The signal vector representing the signal sent by the near-field target, is the received additive Gaussian white noise, where α 2 Represents the noise power.

[0144] 2. Near-field target positioning based on symmetric coprime arrays

[0145] Near-field target positioning based on symmetric coprime arrays can achieve higher resolution than traditional dense arrays. Specifically, the symmetry of symmetric coprime arrays is first used to construct an effective covariance matrix (i.e., the third covariance matrix). Then, the position information of near-field targets is estimated in an orderly manner through a two-stage near-field multi-signal classification (MUSIC) method.

[0146] 1. Constructing a valid covariance matrix

[0147] Initialization of the covariance matrix: Define T to represent the number of snapshots of the near-field target transmission signal. Without considering noise, the covariance matrix of the initial received signal is expressed as R = B(θ, r)BH (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. In order to obtain the first covariance matrix, the least squares estimation method can be used, and the calculation process is given by the following formula:

[0148]

[0149] Then the steering matrix at time t can be expressed as In order to facilitate the design of the positioning algorithm, the first covariance matrix Specifically, the first covariance matrix The (i′, j′)th element of can be expressed as:

[0150]

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

[0152] Property 1: For symmetric coprime arrays, the position of the sensors satisfies the following expression:

[0153]

[0154] Property 2: For the first covariance matrix Consider two elements that are symmetric about the subdiagonal, such as and Then their subscripts satisfy p = 2V-j' and q = 2V-i'. These two elements have the same first-order terms and opposite second-order terms. They can be expressed 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 For each element in , there are K terms in the sum, each of which is determined by the target angle and distance. This makes the traditional one-dimensional MUSIC algorithm inapplicable due to the coupling of angle and distance parameters. In order to make full use of all elements in the first covariance matrix for accurate target positioning, rather than just using anti-diagonal elements, an effective method is proposed to construct a decoupled fourth covariance matrix, which will be beneficial for subsequent near-field target positioning. Specifically, by using Property 2, the fourth covariance matrix R is defined d for

[0157] R d =R⊙R a (8)

[0158] Where R a is the fifth covariance matrix, which is obtained by symmetry of the first covariance matrix about the secondary diagonal, that is, Here ⊙ represents the Hadamard product of the matrix. The (i′, j′)th element of the fourth covariance matrix can be expressed as:

[0159]

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

[0161]

[0162] Through the above steps, the partially decoupled fourth covariance matrix is ​​obtained. The decoupled fourth covariance matrix contains both the angle of the real target and the angle of the cross target. Therefore, in the subsequent steps, how to distinguish the angle of the real target and the angle of the cross target is an important issue. In addition, according to the expression of the S matrix, in order to avoid angle ambiguity, the sensor spacing is required to satisfy d≤λ / 4.

[0163] Virtualization of the covariance matrix: Generally speaking, the degrees of freedom of a perception system are limited by the number of sensors. However, due to the non-Toeplitz structure of the sparse array covariance matrix, the degrees of freedom are increased. In order to fully utilize the degrees of freedom brought by the sparse array, we first vectorize the fourth covariance matrix R d , and obtain the equivalent received signal:

[0164]

[0165] in, A V (θ) is the equivalent steering matrix, c=vec(C), It should be noted that the second covariance matrix obtained directly from the equivalent received signal is a matrix of rank 1, that is, Therefore, it cannot be used directly to sense multiple near-field targets. In order to restore the full-rank covariance matrix (i.e., the third covariance matrix), spatial smoothing technology can be used for processing. First, remove the equivalent steering matrix A V The repeated elements in (θ) can be obtained by removing the equivalent steering matrix after the repeated elements are removed. is the equivalent steering vector after removing repeated elements. Then the virtual array consisting of continuous difference sets is extracted. It can be seen that for symmetric coprime arrays, the number of continuous difference sets is at least 2MN+1, so it can be divided into MN+1 overlapping sub-arrays, each of which has MN+1 sensors.

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

[0167]

[0168] in and They represent the equivalent steering matrix of the ith sub-matrix after removing the repeated elements and the cross-spectral matrix of the ith sub-matrix after vectorization. The third covariance matrix is ​​given by:

[0169]

[0170] R v is a full-rank matrix, so at most MN+1 peaks can be identified, 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 to continuously estimate the position information of near-field targets.

[0173] Stage 1: Angle estimation: First, for the third covariance matrix R obtained v , we can know that the third covariance matrix R v Therefore, in the first stage, the third covariance matrix R can be calculated in the angle domain. v Perform a one-dimensional angle spectrum peak search. The third covariance matrix R v It also includes the angle of the real target and the angle of the undesired cross target. First, the third covariance matrix R v Perform eigenvalue decomposition and get:

[0174]

[0175] in and denote the signal space and noise space of the third covariance matrix respectively, and denote the eigenvalues ​​corresponding to the signal space and noise space of the third covariance matrix respectively. Then the first angle is obtained by the following peak search model

[0176]

[0177] here represents the steering vector of the virtual array of continuous apertures formed, L is the first angle detected The first angle includes both the real target angle and the cross target angle.

[0178] Phase 2: Distance estimation: The angle of the cross target and the angle of the real target can be identified, and an accurate distance estimate corresponding to the angle of the real target can be obtained. In the second phase, the key idea is to use the fact that the angle of the cross target has no obvious spectrum peak in the distance domain to detect the real target. The basic principle is to use the theory of orthogonality of signal space and noise space in the polar domain, that is, B H (θ,r)U n =0. After obtaining the first angle, since the first angle is known, the received signal can be re-expressed as Then the first covariance matrix is ​​decomposed into eigenvalues, and we get Similarly, U s and U n denote the signal space and noise space of the first covariance matrix, ∑ s and∑ n Respectively represent the eigenvalues ​​corresponding to the signal space and noise space of the first covariance matrix. Therefore, the spectrum peak search model in the distance domain can be expressed as follows:

[0179]

[0180] For any first angle, if the target corresponding to it has no obvious spectrum peak in the distance domain, then it can be determined that the angle is the angle of the cross target. Then it can be determined that it is the angle of the real target, that is, the second angle.

[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 frequency band of f = 30 GHz. 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), (30°, 40m). Two of the targets are located at the same angle but at different distances, so the radar sensing situation with such a setting has high complexity. Figure 3 This is a spectrum peak search diagram in the angle domain provided by the embodiment of the present application. Please refer to Figure 3 By using the first stage MUSIC algorithm, the angles of the real targets can be detected well, while the angles of the crossed targets will also appear. Figure 4This is a spectrum peak search diagram in the distance domain provided by the embodiment of the present application. Please refer to Figure 4 . Figure 4 The results of the spectrum peak search in the distance domain for the first angle detected in the first stage are included. The MUSIC algorithm in the distance domain in the second stage can well distinguish the angle of the real target from the angle of the cross target. Specifically, in the distance domain, there are obvious spectrum peaks only at the angle of the real target, while at the angle of the cross target, no obvious spectrum peaks appear in the considered near field region. In addition, even if two targets are located at the same angle, they can still be distinguished in the distance domain.

[0183] Figure 5 This is a graph showing the relationship between the angle root mean square error (RMSE) and the signal-to-noise ratio (SNR) provided in the embodiment of the present application. Please refer to Figure 5 .Depend on Figure 5 It can be obtained that the symmetric coprime array has better performance than the dense uniform array in angle estimation. When the number of antennas is the same, the aperture formed by the symmetric coprime array is larger than that of the dense array, thereby improving the sensing resolution and accuracy. As the SNR increases, both the symmetric coprime array and the dense uniform array will obtain a smaller RMSE. On the other hand, even at a high signal-to-noise ratio, the far-field method will be affected by a larger RMSE. This shows that it is inappropriate to directly apply the far-field method (i.e., the virtual array method of the far field) to near-field target positioning. In addition, as the number of near-field targets increases, the method proposed in the embodiment of the present application is proven to be more effective compared to the decomposition subarray method. This is because for the decomposition subarray method, each uniform sparse array exhibits a periodic spectrum during the estimation process. With the significant increase in the number of spectral peaks, the process of obtaining accurate common spectral peaks of two subarrays becomes more complicated, thereby affecting the accuracy of the estimation.

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

[0185] Figure 7 This is a schematic diagram of the structure of the near-field target positioning device provided in the embodiment of the present application. Figure 7 The embodiment of the present application further provides a near-field target positioning device 700, which can implement the above-mentioned near-field target positioning method. The device 700 includes:

[0186] The signal acquisition module 710 is used to acquire an initial received signal of a preset symmetric coprime array, where the symmetric coprime array is a sensor array composed of two uniform linear arrays symmetrical about a central array element;

[0187] A first covariance matrix determining module 720, configured to determine a first covariance matrix of an initial received signal according to the initial received signal;

[0188] An equivalent received signal determination module 730, configured to perform calculations based on the first covariance matrix to obtain an equivalent received signal of the initial received signal;

[0189] A second covariance matrix determining module 740 is used to determine a second covariance matrix of the equivalent received signal according to the equivalent received signal;

[0190] A third covariance matrix determination module 750 is used to perform spatial smoothing processing on the second covariance matrix to obtain a third covariance matrix of the equivalent received signal;

[0191] A first angle determination module 760 is used to perform a one-dimensional angle spectrum peak search on the third covariance matrix to obtain a 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 a first distance between the near-field target and the central array element, wherein 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 substantially the same as the specific implementation of the above-mentioned near-field target positioning method, and will not be described in detail herein.

[0194] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned near-field target positioning method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0195] Figure 8 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. Figure 8 . Electronic equipment includes:

[0196] The processor 801 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (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 the present application;

[0197] The memory 802 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store an operating system and other applications. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 802, and the processor 801 is called to execute the near-field target positioning method of the embodiment of the present application;

[0198] Input / output interface 803, used to implement information input and output;

[0199] The communication interface 804 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

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

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

[0202] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned near-field target positioning method is implemented.

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

[0204] The near-field target positioning method, device, electronic device and storage medium provided in the embodiment of the present application first obtain the initial received signal of the preset symmetric coprime array, and then determine the first covariance matrix of the initial received signal based on the initial received signal, and then perform calculations based on the first covariance matrix to obtain the equivalent received signal of the initial received signal, and then determine the second covariance matrix of the equivalent received signal based on the equivalent received signal, and then perform spatial smoothing on the second covariance matrix to obtain the third covariance matrix of the equivalent received signal, and then 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, and finally 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 present application makes full use of the mutual information between subarrays in the symmetric coprime array by extending the virtual array method of the far field to the near field, so as to significantly reduce the computational overhead when positioning near-field targets.

[0205] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

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

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

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

[0209] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0210] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0211] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0212] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0213] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0214] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, 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, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0215] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A near-field target positioning method, characterized in that: The method comprises: Acquire an initial received signal of a preset symmetric coprime array, wherein the symmetric coprime array is a sensor array composed of two uniform linear arrays symmetrical about a central array element; Determining a first covariance matrix of the initial received signal according to the initial received signal; Performing calculation 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 according to the equivalent received signal; Performing spatial smoothing processing on the second covariance matrix to obtain a third covariance matrix of the equivalent received signal; Performing a one-dimensional angle spectrum peak search on the third covariance matrix to obtain a first angle between a 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 a first distance between the near-field target and the central array element, wherein the position information of the near-field target includes the first angle and the first distance.

2. The method according to claim 1, characterized in that: The performing a one-dimensional angle spectrum peak search on the third covariance matrix to obtain a first angle between the near-field target and the central array element includes: Performing eigenvalue decomposition on the third covariance matrix to obtain a signal space and a noise space; Based on a spectrum peak search model in the angle domain, a one-dimensional angle spectrum 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 a plurality of targets, and the first distance includes a sub-distance between each of the targets 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: Determine a sub-distance among the plurality of sub-distances 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 as a second distance, wherein the preset distance interval is a distance interval determined according to the carrier wavelength and the aperture of the symmetric coprime array; determining the target corresponding to the second distance as the real target; The angle between the real target and the central array element is determined as a 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 performing spatial smoothing on the second covariance matrix to obtain a third covariance matrix of the equivalent received signal includes: Dividing the symmetric coprime matrix into a plurality of sub-arrays, and determining an equivalent received signal of each of the sub-arrays; Determining a virtual covariance matrix of each of the sub-arrays based on an equivalent received signal of each of the sub-arrays; An average operation is performed on the virtual covariance matrices of the plurality of sub-matrices to obtain the third covariance matrix.

5. The method according to claim 1, characterized in that The performing operation based on the first covariance matrix to obtain an equivalent received signal of the initial received signal includes: Decoupling the angle and the distance in the first covariance matrix to obtain a fourth covariance matrix; Vectorization processing is performed on the fourth covariance matrix to obtain an equivalent received signal of the initial received signal.

6. The method according to claim 5, characterized in that The decoupling of the angle and the distance in the first covariance matrix to obtain a fourth covariance matrix includes: Determine, according to the first covariance matrix, a fifth covariance matrix after the first covariance matrix is ​​symmetric about a secondary diagonal line; Determine the product of the first element in the first covariance matrix and the second element in the fifth covariance matrix as a third element, so as to obtain a fourth covariance matrix composed of the third element; The first element, the second element and the third element are located in the same number of rows and the same number of columns in their respective matrices.

7. The method according to claim 1, characterized in that The step of determining a first covariance matrix of the initial received signal according to the initial received signal comprises: Acquire the number of snapshots of the near-field target and the signal vector of the signal sent by the near-field target; Determine, based on the least square method, a steering matrix of the symmetric coprime matrix according to the initial received signal and the signal vector; The first covariance matrix is ​​determined according to the steering matrix and the number of snapshots.

8. A near-field target positioning device, characterized in that: The device comprises: A signal acquisition module, used to acquire an initial received signal of a preset symmetric coprime array, wherein the symmetric coprime array is a sensor array composed of two uniform linear arrays symmetrical about a central array element; A first covariance matrix determining module, used to determine a first covariance matrix of the initial received signal according to the initial received signal; An equivalent received signal determination module, configured to perform calculations based on the first covariance matrix to obtain an equivalent received signal of the initial received signal; A second covariance matrix determination module, used to determine a second covariance matrix of the equivalent received signal according to the equivalent received signal; A third covariance matrix determination module, configured to perform spatial smoothing processing on the second covariance matrix to obtain a third covariance matrix of the equivalent received signal; A first angle determination module is used to perform a one-dimensional angle spectrum peak search on the third covariance matrix to obtain a first angle between a 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 a first distance between the near-field target and the central array element, wherein the position information of the near-field target includes the first angle and the first distance.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the near-field target positioning method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the near-field target positioning method according to any one of claims 1 to 7 is implemented.

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