Target sensing method, device, equipment, medium and product

Abstract

The invention discloses a target sensing method, device and equipment, a medium and a product. The method comprises the following steps: acquiring the total number of antennas, a receiving signal from an array antenna and a preset angle grid length; determining a pattern grid dictionary of the array antennas according to the total number of the antennas and the preset angle grid length; determining an angle steering vector grid dictionary according to the array arrangement mode of the antenna array; determining a convex optimization problem according to the received signal, the pattern grid dictionary and the angle steering vector grid dictionary; and determining an estimated echo angle of the detection target according to the sparse solution of the convex optimization problem. Estimation of a target echo angle is converted into a convex optimization problem by constructing a directional diagram grid dictionary of an array antenna, and an estimated echo angle is obtained by solving a sparse convex optimization problem. Estimation of the echo angle under the condition that the array antenna directional diagrams are inconsistent is achieved, the high resolution of the echo angle is improved, and the method has a wider application scene.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a target perception method, device, equipment, medium and product. Background Art

[0002] With the development of radio technology, large-scale array antenna systems have been widely studied and applied in the fields of underwater acoustics, radar and wireless communications. Through beamforming technology, large-scale array antennas can provide additional diversity and multiplexing gain. In addition, through various signal echo angle estimation algorithms, the signal processing of array antennas can be used to obtain the angle information of the target, thereby perceiving the target echo. Estimation of target echo angle information has become an indispensable part of wireless communication and perception.

[0003] Methods for obtaining target echo angle information using array antennas include beamforming methods or some super-resolution angle estimation algorithms, such as CAOPN algorithm, MUSIC algorithm, ESPRIT algorithm and their extended algorithms, etc. For actual multi-channel antenna systems, the premise for the successful application of these algorithms is that the consistency of the array antenna is met. Under the premise that the consistency condition is met, the above-mentioned various echo angle estimation algorithms can achieve the expected effect for the angle estimation of single target echo or multi-target echo (incoherent echo).

[0004] However, in some scenarios, this consistency may not be met. For example, insufficient isolation between high-frequency antennas or complex environments around low-frequency array antennas may cause serious antenna mutual coupling, making it difficult to meet the consistency of different antenna patterns. Traditional beamforming algorithms have low angular resolution and cannot identify echoes with similar angles. Summary of the invention

[0005] The present invention provides a target perception method, device, equipment, medium and product to achieve echo angle determination when antenna directional patterns are inconsistent.

[0006] According to a first aspect of the present invention, there is provided a target perception method, comprising:

[0007] Obtain the total number of antennas, received signals from array antennas, and preset angle grid lengths;

[0008] Determining a directional pattern grid dictionary of the array antenna according to the total number of antennas and the preset angle grid length;

[0009] Determining an angle-steering vector grid dictionary according to an array arrangement of the antenna array;

[0010] Determining a convex optimization problem based on the received signal, the directional pattern grid dictionary, and the angle-steering vector grid dictionary;

[0011] According to the sparse solution of the convex optimization problem, an estimated echo angle of the detected target is determined.

[0012] According to a second aspect of the present invention, there is provided a target sensing device, comprising:

[0013] An information acquisition module is used to obtain the total number of antennas, the received signal from the array antenna and the preset angle grid length;

[0014] A first determination module, configured to determine a directional pattern grid dictionary of the array antenna according to the total number of antennas and the preset angle grid length;

[0015] A second determination module is used to determine an angle-steering vector grid dictionary according to an array arrangement of the antenna array;

[0016] A third determination module is used to determine a convex optimization problem according to the received signal, the directional pattern grid dictionary and the angle steering vector grid dictionary;

[0017] The angle determination module is used to determine the estimated echo angle of the detected target according to the sparse solution of the convex optimization problem.

[0018] According to a third aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0019] at least one processor; and

[0020] a memory communicatively connected to the at least one processor; wherein,

[0021] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the target perception method described in any embodiment of the present invention.

[0022] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the target perception method described in any embodiment of the present invention when executed.

[0023] According to a fifth aspect of the present invention, an embodiment of the present invention further provides a computer program product, the computer program product comprising a computer program, and the computer program implements the target perception method of any embodiment of the present invention when executed by a processor.

[0024] The technical solution of the embodiment of the present invention is to obtain the total number of antennas, the received signal from the array antenna and the preset angle grid length; determine the directional pattern grid dictionary of the array antenna according to the total number of antennas and the preset angle grid length; determine the angle steering vector grid dictionary according to the array arrangement of the antenna array; determine the convex optimization problem according to the received signal, the directional pattern grid dictionary and the angle steering vector grid dictionary; and determine the estimated echo angle of the detected target according to the sparse solution of the convex optimization problem. By constructing the directional pattern grid dictionary of the array antenna, the estimation of the target echo angle is converted into a convex optimization problem, and the estimated echo angle is obtained by solving the convex optimization problem with sparsity. The estimation of the echo angle under the condition of inconsistent array antenna directional patterns is realized, and the solution method based on sparsity belongs to the compressed sensing algorithm, thereby realizing the determination when multiple angle echoes are coherent, and has high-resolution characteristics, and has a wider range of application scenarios.

[0025] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 is a flow chart of a target perception method provided according to Embodiment 1 of the present invention;

[0028] Figure 2 is an example diagram of a coordinate system in a target perception method provided according to the first embodiment of the present invention;

[0029] Figure 3 is a flow chart of a target perception method provided according to Embodiment 2 of the present invention;

[0030] Figure 4 It is a structural diagram of a target sensing device provided according to Embodiment 3 of the present invention.

[0031] Figure 5 It is a schematic diagram of the structure of an electronic device implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention 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 interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] Embodiment 1

[0035] Figure 1 A flow chart of a target sensing method is provided for the first embodiment of the present invention. This embodiment is applicable to the echo angle determination when the antenna pattern is inconsistent. The method can be executed by a target sensing device, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0036] S110, obtaining the total number of antennas, received signals from array antennas, and a preset angle grid length.

[0037] In this embodiment, the total number of antennas can be understood as the total number of antennas included in the antenna array. The preset angle grid length can be understood as the number of divided grids. The array antenna can be understood as a special antenna composed of multiple antenna arrays. The received signal can be understood as the signal received by the array antenna.

[0038] S120, determining a directional pattern grid dictionary of the array antenna according to the total number of antennas and a preset angle grid length.

[0039] In this embodiment, the azimuth angle can be understood as the direction in which the signal reaches the array antenna, that is, the angle between the projection of the echo source on the xOy plane in the spatial rectangular coordinate system and the x-axis. The elevation angle can be understood as the angle between the echo source and the xOy plane in the spatial rectangular coordinate system. The pattern grid dictionary can be understood as a visual display of the radiation intensity of the antenna at different angles in space.

[0040] Specifically, according to the characteristics of electromagnetic wave transmission, the echo angles of the signals are all isolated points within the spatial range. Therefore, from the perspective of the spatial domain, the echo angles are sparse. The processor can first determine the azimuth grid vectors corresponding to all possible echo azimuth angles within the spatial range, and determine the elevation grid vectors corresponding to all possible echo elevation angles within the spatial range according to the preset angle grid length. Then, through the directions set by the azimuth grid and the elevation grid, the gain pattern of each antenna in the antenna array is measured in the far field, and then the pattern grid dictionary of the array antenna under far-field conditions is constructed.

[0041] For example, taking an array antenna space coordinate system as an example, Figure 2 This is an example diagram of a coordinate system in a target perception method provided in the first embodiment of the present invention, such as Figure 2 As shown in the figure, T is the position of a certain echo source, the angle between the source and the xOy plane is the pitch angle θ, the angle between the projection of the source on the xOy plane and the x-axis is the azimuth angle φ, r m is the position vector of the mth antenna relative to the first antenna, i.e., the reference origin, m∈{0,1,...,M-1}, M is the number of array antennas, and according to the definition r 0 = 0. Since the gain patterns of different antennas are inconsistent, the antenna array has different pattern gains for different elevation angles, azimuth angles and antenna numbers, which are specifically expressed as Φ(φ,θ,m).

[0042] S130. Determine an angle-steering vector grid dictionary according to an array arrangement of the antenna array.

[0043] In this embodiment, the array arrangement can be understood as the arrangement of antennas in space, for example, it can include rectangular grids, triangular grids, hexagonal and circular arrays, etc. The angle-steering vector grid dictionary can be understood as a vector used to describe the spatial phase difference, which is usually related to the geometric structure of the array and the frequency of the signal.

[0044] Specifically, the processor may determine the angle-steering vector grid dictionary according to the array arrangement of the antenna array and based on a general model of the received signal.

[0045] For example, a general model of a received signal is used as an example, and the above array antenna spatial coordinate system is used. Assume that the i-th echo is at an angle (φ i ,θ i ) reaches the antenna array and is received by the mth antenna. The time taken by the plane wavefront is (relative time with reference to the first channel):

[0046]

[0047] In the formula, e(φ i ,θ i ) represents the angle (φ i ,θ i ), i∈{0,1,…,P-1}, P is the total number of all echoes, c is the propagation speed of electromagnetic waves, and <·,·> represents the inner product of two vectors.

[0048] Assume that the signal strength of the i-th echo is γ i , the signal reaching array antenna m can be expressed as:

[0049]

[0050] Where: a m (φ i ,θ i )=exp(-j2πf 0 τ m (φ i ,θ i )),f 0 is the signal carrier frequency.

[0051] From equation (2), we can get the sum of multiple echoes received by the array antenna and the receiver noise, and the received signal y is:

[0052]

[0053] in:

[0054]

[0055]

[0056] Where ⊙ represents the Hadamard product, is the steering vector matrix of the array antenna.

[0057] In which, each column a(φ i ,θ i )=[a 0 (φ i ,θ i ),a 1 (φ i,θ i ),...,a M-1 (φ i ,θ i )] T is the array antenna steering vector; and are the echo angle vectors in multiple directions; is the signal strength vector of multiple echoes; is the receiver Gaussian white noise signal.

[0058] According to model (3), the least squares estimate of the echo angle (φ,θ) is:

[0059]

[0060] Where: ||·|| 2 L represents the vector 2 Norm.

[0061] S140, determining a convex optimization problem according to a received signal, a directional pattern grid dictionary, and an angle-steering vector grid dictionary.

[0062] In this embodiment, the convex optimization problem can be understood as a problem of solving a convex function minimization problem.

[0063] Specifically, the processor can construct the objective function in the convex optimization problem based on the received signal, the directional pattern grid dictionary and the angle-steering vector grid dictionary, and can determine the constraint conditions of the solution to the convex optimization problem by methods such as least squares constraints.

[0064] S150. Determine an estimated echo angle of the detected target according to a sparse solution to the convex optimization problem.

[0065] In this embodiment, the sparse solution can be understood as the solution of the unknown variable in the convex optimization problem, which can be the signal strength corresponding to the echo angle. Since the echo angle is sparse from the perspective of the spatial domain, the corresponding solution is a sparse solution. The detection target can be understood as the target to measure the estimated echo angle. The estimated echo angle can be understood as the angular direction of the received signal.

[0066] Specifically, the processor can solve the convex optimization problem to obtain a sparse solution, and by determining the sparse solutions with signal strength, that is, the echo signals corresponding to these angles, determine the azimuth vectors and pitch angle vectors corresponding to these sparse solutions, and then determine the estimated echo angle of the detected target through the azimuth vector and the pitch angle vector.

[0067] The technical solution of the embodiment of the present invention is to obtain the total number of antennas, the received signal from the array antenna and the preset angle grid length; determine the directional pattern grid dictionary of the array antenna according to the total number of antennas and the preset angle grid length; determine the angle steering vector grid dictionary according to the array arrangement of the antenna array; determine the convex optimization problem according to the received signal, the directional pattern grid dictionary and the angle steering vector grid dictionary; and determine the estimated echo angle of the detected target according to the sparse solution of the convex optimization problem. By constructing the directional pattern grid dictionary of the array antenna, the estimation of the target echo angle is converted into a convex optimization problem, and the estimated echo angle is obtained by solving the convex optimization problem with sparsity. The estimation of the echo angle under the condition of inconsistent array antenna directional patterns is realized, and the solution method based on sparsity belongs to the compressed sensing algorithm, thereby realizing the determination when multiple angle echoes are coherent, and has high-resolution characteristics, and has a wider range of application scenarios.

[0068] Embodiment 2

[0069] Figure 3 This is a flow chart of a target perception method provided by Embodiment 2 of the present invention. This embodiment is a further refinement of the above embodiments. Figure 3 As shown, the method includes:

[0070] S201, obtaining the total number of antennas, received signals from array antennas, and a preset angle grid length.

[0071] S202. Determine an azimuth grid vector corresponding to the azimuth within a first angle range according to a preset angle grid length.

[0072] In this embodiment, the first angle range may be understood as the maximum range of the azimuth angle in the echo angle, for example, it may be [-90°, 90°]. The azimuth grid vector may be understood as a vector used to describe the discretization representation of the azimuth angle in the spatial domain.

[0073] Specifically, due to the sparseness of the echo signal space, the processor can set a discrete azimuth grid vector α=[α 0 ,α 1 ,...,α J-1 ] T , where α j =180°·j / (J-1)-90°, j=0, 1, ..., J-1 represents all possible echo azimuths.

[0074] S203: Determine a pitch angle grid vector corresponding to the pitch angle within a second angle range according to a preset angle grid length.

[0075] In this embodiment, the second angle range may be understood as the maximum range of the elevation angle in the echo angle, for example, [0°, 180°]. The elevation angle grid vector may be understood as a vector used to describe the discretized representation of the elevation angle in the spatial domain.

[0076] Specifically, the processor may set a discrete pitch angle grid vector β=[β 0 ,β 1 ,...,β J-1 ] T , β j =180°·j / (J-1), j=0, 1, ..., J-1 represents all possible echo elevation angles; set J>>P, where P is the total number of echo signals, to ensure that the azimuth and elevation angle grids contain the actual echo angles.

[0077] S204: Determine a grid dictionary of the array antenna pattern according to the azimuth grid vector and the elevation grid vector.

[0078] Specifically, the processor may determine the pattern gain according to the elements in the azimuth grid vector and the elements in the elevation grid vector, and use the pattern gain as the element at the corresponding position in the pattern grid dictionary of the array antenna.

[0079] Further, based on the above embodiment, the steps of determining the directional pattern grid dictionary of the array antenna at different azimuth angles and elevation angles according to the azimuth angle grid vector and the elevation angle grid vector can be refined as follows:

[0080] The gain pattern of each antenna in the array antenna is determined according to the azimuth grid vector and the elevation grid vector; the total number of rows of the pattern grid dictionary is determined according to the total number of antennas; the total number of columns of the pattern grid dictionary is determined according to the square of the preset angle grid length; for each antenna, the spatial angle formed by the azimuth element in the azimuth grid vector and the elevation element in the elevation grid vector is determined; the elements in the pattern grid dictionary are determined according to the pattern gain corresponding to the spatial angle in the gain pattern, and the pattern grid dictionary of the array antenna at different azimuths and elevations is obtained.

[0081] In this embodiment, the gain pattern can be understood as a graphical representation of the gain of the antenna in different directions in space. The pattern grid dictionary can be composed of rows, columns and corresponding elements. The total number of rows corresponds to the total number of antennas, and the antennas are represented by rows. The total number of columns is the square of the preset angle grid length, and the columns represent the angles jointly formed by the azimuth angle and the elevation angle. Since the number of azimuth angles and elevation angles is the preset angle grid length, it is the square of the preset angle grid length. The azimuth element can be understood as an element in the azimuth grid vector. The elevation element can be understood as an element in the elevation grid vector. The spatial angle can be understood as an angle jointly formed by the azimuth angle and the elevation angle. The pattern gain can be understood as the ratio of the radiation capability of the antenna in a specific direction to the radiation capability of the reference antenna, usually expressed in logarithmic form. The element can be understood as a constituent element in the pattern grid dictionary.

[0082] Specifically, the processor can measure the gain pattern of each antenna in the array antenna in the far field according to the direction set by the azimuth grid vector and the elevation grid vector (the existing method can be used for measurement, and the specific measurement method is not discussed in the present invention). The rows of the azimuth grid vector represent the antennas, and the columns represent the angles. The processor can determine the total number of rows of the pattern grid dictionary according to the total number of antennas. The processor can determine the total number of columns of the pattern grid dictionary according to the square of the preset angle grid length. For each antenna, determine the spatial angle formed by the azimuth element in the azimuth grid vector and the elevation element in the elevation grid vector. According to the pattern gain corresponding to the spatial angle in the gain pattern, that is, the element under the corresponding row and column in the pattern grid dictionary, the pattern grid dictionary of the array antenna at different azimuths and elevations is obtained.

[0083] Exemplarily, the grid dictionary of the array antenna pattern is represented as Where K = J 2 ; The element in the m∈{0,1,...,M-1}th row and k∈{0,1,...,K-1}th column of the pattern grid dictionary Ψ is the mth antenna in the azimuth grid vector α. Elements and the k%Jth element β in the pitch angle grid vector β k％J The directional pattern gain at the determined angle, where Indicates rounding down, and % indicates integer remainder.

[0084] S205. Determine an angle-steering vector grid dictionary according to the array arrangement of the antenna array.

[0085] Exemplarily, following the above example, the element in the mth row and kth column of the steering vector grid dictionary Δ(α, β) is Where τ m(·,·) is defined by formula (1).

[0086] S206: Determine a received signal model according to the directional pattern grid dictionary and the angle-steering vector grid dictionary.

[0087] In this embodiment, the received signal model can be understood as representing the received signal in a model manner.

[0088] Specifically, for all possible echo angles, the processor may determine a received signal model based on an angle-steering vector grid dictionary and a directional pattern grid dictionary.

[0089] For example, using the description in the above example, let Represents the signal strength corresponding to all possible echo angles. The signal model based on the angle-steering vector grid dictionary Δ(α, β) and the directional pattern grid dictionary Ψ can be expressed as:

[0090] y=[Ψ⊙Δ(α,β)]·χ+n (5)

[0091] S207: Determine the objective function of the convex optimization problem according to the received signal and its noise power in combination with the received signal model.

[0092] In this embodiment, the noise power can be understood as the power of the noise in the received signal. The objective function can be understood as providing an optimization standard in the form of a function for comparing the advantages and disadvantages of different solutions.

[0093] Specifically, the processor can estimate the corresponding signal-to-noise ratio according to the received signal, and determine the noise power of the received signal through the signal-to-noise ratio, and determine the objective function of the convex optimization problem through the noise power, the received signal and the received signal model.

[0094] S208. Using the least squares constraint as a constraint condition for a sparse solution in a convex optimization problem, a convex optimization problem is obtained.

[0095] In this embodiment, the constraint condition can be understood as a constraint condition for selecting an optimal solution.

[0096] Specifically, the processor may use a least square constraint method as a constraint condition for a sparse solution in a convex optimization problem to obtain a convex optimization problem.

[0097] Exemplarily, the convex optimization problem can be:

[0098] min||χ|| 1

[0099]

[0100] In the formula, ε is a parameter, which represents a very small number, which is determined according to the noise power of the received signal. The formula represents the objective function; ||.|| 1 Indicates L 1 norm, through L 1 norm can improve sparsity, so by minimizing ||χ|| 1 Represent the constraints to find a sparse solution that satisfies the constraints.

[0101] S209. Determine non-zero elements in a sparse solution to the convex optimization problem.

[0102] In this embodiment, a non-zero element may be understood as an element whose value is not 0.

[0103] It can be understood that due to the spatial sparsity of the echo angle, the problem of estimating the echo angle can be expressed as follows: according to the received signal model, certain possible angles are searched from the angle-guided vector grid dictionary (that is, it corresponds to the process of determining the sparse solution of the convex optimization problem), and the signal strengths corresponding to these angles are estimated in χ so that the parameter estimation result satisfies the least squares constraint; and the signal strengths corresponding to the angles that have not been searched in χ are all 0, that is, there is no echo signal at this angle.

[0104] Specifically, the non-zero elements in the sparse solution are determined, that is, the sparse solution including the signal strength is obtained.

[0105] S210. Search for the index value of the non-zero element in the angle-oriented vector grid dictionary.

[0106] In this embodiment, the index value can be understood as the column value corresponding to the element in the angle-oriented vector grid dictionary.

[0107] Specifically, the processor may determine the column number corresponding to the non-zero element and use it as the index value.

[0108] S211. Determine the estimated echo angle of the detected target according to the index value, the azimuth grid vector and the elevation grid vector.

[0109] Specifically, the processor may determine the direction angle vector and the elevation angle vector corresponding to the index value in the azimuth angle grid vector and the elevation angle grid vector, respectively, and then obtain the estimated echo angle of the detected target.

[0110] Further, based on the above embodiment, the step of determining the estimated echo angle of the detected target according to the index value, the azimuth grid vector and the elevation grid vector can be refined as follows:

[0111] The azimuth vector corresponding to the index value is searched in the azimuth grid vector; the elevation vector corresponding to the index value is searched in the elevation grid vector; and the estimated echo angle of the detected target is determined according to the azimuth vector and the elevation vector.

[0112] Specifically, the processor can determine the corresponding azimuth vector in the azimuth grid vector according to the index value, and determine the corresponding pitch angle vector in the pitch angle grid vector according to the index value. The direction angle and the pitch angle are calculated according to the coordinates of the azimuth vector and the pitch angle vector in the spatial coordinate system, and then the estimated echo angle of the detected target is obtained.

[0113] For example, let the sparse solution The number of columns corresponding to non-zero elements in is That is, the index value is The azimuth vector of the estimated echo angle is The pitch angle vector is

[0114] The technical solution of the embodiment of the present invention determines the azimuth grid vector and the elevation grid vector according to the preset angle grid length, and then constructs the grid dictionary of the pattern of the array antenna at different azimuths and elevation angles. Since the antenna pattern at different azimuths and elevation angles is used, the two-dimensional angle information of the echo can be estimated. For a one-dimensional linear array antenna, the two-dimensional angle information of the echo can still be estimated, so it has a higher degree of freedom. The received signal model is determined by the pattern grid dictionary and the angle-steering vector grid dictionary, and then a convex optimization problem is constructed by the received signal and its noise power. For the sparse convex optimization problem, a compressed sensing algorithm is used for target estimation. This method has high resolution and can also be applied in scenarios where the echo is coherent. Considering the azimuth and elevation patterns of the array antenna makes the estimation result more accurate and the application scenarios wider. The angle estimation algorithm is a convex optimization problem, and there must be an optimal solution, which avoids the problem that the solution process may converge to a local optimum. The direction of the array antenna is realized. Figure 1 It supplements and expands the echo angle estimation under inconsistent conditions, meets the echo angle estimation under conditions of inconsistent array antenna patterns, and has a wider range of application scenarios.

[0115] Embodiment 3

[0116] Figure 4 This is a schematic diagram of the structure of a target sensing device provided in Embodiment 3 of the present invention. Figure 4 As shown, the device includes: an information acquisition module 41, a first determination module 42, a second determination module 43, a third determination module 44 and an angle determination module 45.

[0117] An information acquisition module 41 is used to obtain the total number of antennas, received signals from the array antennas, and a preset angle grid length;

[0118] A first determination module 42, configured to determine a directional pattern grid dictionary of the array antenna according to the total number of antennas and the preset angle grid length;

[0119] A second determination module 43 is used to determine an angle-steering vector grid dictionary according to the array arrangement of the antenna array;

[0120] A third determination module 44 is used to determine a convex optimization problem according to the received signal, the directional pattern grid dictionary and the angle steering vector grid dictionary;

[0121] The angle determination module 45 is used to determine the estimated echo angle of the detection target according to the sparse solution of the convex optimization problem.

[0122] The technical solution of the embodiment of the present invention is to obtain the total number of antennas, the received signal from the array antenna and the preset angle grid length; determine the directional pattern grid dictionary of the array antenna according to the total number of antennas and the preset angle grid length; determine the angle steering vector grid dictionary according to the array arrangement of the antenna array; determine the convex optimization problem according to the received signal, the directional pattern grid dictionary and the angle steering vector grid dictionary; and determine the estimated echo angle of the detected target according to the sparse solution of the convex optimization problem. By constructing the directional pattern grid dictionary of the array antenna, the estimation of the target echo angle is converted into a convex optimization problem, and the estimated echo angle is obtained by solving the convex optimization problem with sparsity. The estimation of the echo angle under the condition of inconsistent array antenna directional patterns is realized, and the solution method based on sparsity belongs to the compressed sensing algorithm, thereby realizing the determination when multiple angle echoes are coherent, and has high-resolution characteristics, and has a wider range of application scenarios.

[0123] Further, the first determining module 42 includes:

[0124] A first determining unit, configured to determine an azimuth grid vector corresponding to the azimuth within a first angle range according to a preset angle grid length;

[0125] A second determining unit, configured to determine a pitch angle grid vector corresponding to the pitch angle within a second angle range according to the preset angle grid length;

[0126] The third determining unit is used to determine the directional pattern grid dictionary of the array antenna according to the azimuth grid vector and the elevation grid vector.

[0127] The third determining unit is specifically used for:

[0128] Determining a gain pattern of each antenna in the array antenna according to the azimuth grid vector and the elevation grid vector;

[0129] Determining the total number of rows of the directional pattern grid dictionary according to the total number of antennas;

[0130] Determining the total number of columns of the directional pattern grid dictionary according to the square of the preset angle grid length;

[0131] For each of the antennas, determine a spatial angle formed by an azimuth element in the azimuth grid vector and an elevation element in the elevation grid vector;

[0132] According to the pattern gain corresponding to the spatial angle in the gain pattern, the elements in the pattern grid dictionary are determined to obtain the pattern grid dictionary of the array antenna at different azimuth angles and elevation angles.

[0133] Furthermore, the third determining module 44 is specifically configured to:

[0134] Determining a received signal model according to the directional pattern grid dictionary and the angle-steering vector grid dictionary;

[0135] Determining an objective function of a convex optimization problem according to the received signal and its noise power in combination with the received signal model;

[0136] The convex optimization problem is obtained by using the least square constraint as a constraint condition for the sparse solution in the convex optimization problem.

[0137] Furthermore, the angle determination module 45 includes:

[0138] An element determination unit, used to determine non-zero elements in a sparse solution to the convex optimization problem;

[0139] An index value determination unit, configured to search the angle-oriented vector grid dictionary for the index value of the non-zero element;

[0140] An angle determination unit is used to determine the estimated echo angle of the detected target according to the index value, the azimuth grid vector and the elevation grid vector.

[0141] Wherein, the angle determination unit is specifically used for:

[0142] Searching the azimuth vector corresponding to the index value in the azimuth grid vector;

[0143] Searching for the pitch angle vector corresponding to the index value in the pitch angle grid vector;

[0144] An estimated echo angle of the detected target is determined according to the azimuth angle vector and the pitch angle vector.

[0145] The target perception device provided in the embodiment of the present invention can execute the target perception method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0146] Embodiment 4

[0147] Figure 5 A schematic diagram of an electronic device 50 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0148] like Figure 5 As shown, the electronic device 50 includes at least one processor 51, and a memory connected to the at least one processor 51 in communication, such as a read-only memory (ROM) 52, a random access memory (RAM) 53, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 51 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 52 or the computer program loaded from the storage unit 58 to the random access memory (RAM) 53. In the RAM 53, various programs and data required for the operation of the electronic device 50 can also be stored. The processor 51, the ROM 52, and the RAM 53 are connected to each other via a bus 55. An input / output (I / O) interface 55 is also connected to the bus 55.

[0149] A number of components in the electronic device 50 are connected to the I / O interface 55, including: an input unit 56, such as a keyboard, a mouse, etc.; an output unit 57, such as various types of displays, speakers, etc.; a storage unit 58, such as a disk, an optical disk, etc.; and a communication unit 59, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 59 allows the electronic device 50 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0150] The processor 51 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 51 performs the various methods and processes described above, such as the target perception method.

[0151] In some embodiments, the target perception method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 58. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 50 via the ROM 52 and / or the communication unit 59. When the computer program is loaded into the RAM 53 and executed by the processor 51, one or more steps of the target perception method described above may be performed. Alternatively, in other embodiments, the processor 51 may be configured to perform the target perception method in any other appropriate manner (e.g., by means of firmware).

[0152] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0153] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0154] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0155] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0156] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0157] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0158] In one embodiment, the embodiment of the present invention further includes a computer program product, the computer program product includes a computer program, and the computer program implements the target perception method of any embodiment of the present invention when executed by a processor.

[0159] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0160] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0161] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A target perception method, characterized in that: include: Obtain the total number of antennas, received signals from array antennas, and preset angle grid lengths; Determining a directional pattern grid dictionary of the array antenna according to the total number of antennas and the preset angle grid length; Determining an angle-steering vector grid dictionary according to an array arrangement of the antenna array; Determining a convex optimization problem based on the received signal, the directional pattern grid dictionary, and the angle-steering vector grid dictionary; According to the sparse solution of the convex optimization problem, an estimated echo angle of the detected target is determined.

2. The method according to claim 1, characterized in that The determining, according to the total number of antennas and the preset angle grid length, a directional pattern grid dictionary of the array antenna comprises: Determine an azimuth grid vector corresponding to the azimuth within a first angle range according to a preset angle grid length; Determining a pitch angle grid vector corresponding to the pitch angle within a second angle range according to the preset angle grid length; A directional pattern grid dictionary of the array antenna is determined according to the azimuth grid vector and the elevation grid vector.

3. The method according to claim 2, characterized in that The step of determining a directional pattern grid dictionary of the array antenna according to the azimuth grid vector and the elevation grid vector comprises: Determining a gain pattern of each antenna in the array antenna according to the azimuth grid vector and the elevation grid vector; Determining the total number of rows of the directional pattern grid dictionary according to the total number of antennas; Determining the total number of columns of the directional pattern grid dictionary according to the square of the preset angle grid length; For each of the antennas, determine a spatial angle formed by an azimuth element in the azimuth grid vector and an elevation element in the elevation grid vector; According to the pattern gain corresponding to the spatial angle in the gain pattern, the elements in the pattern grid dictionary are determined to obtain the pattern grid dictionary of the array antenna at different azimuth angles and elevation angles.

4. The method according to claim 1, characterized in that Determining a convex optimization problem according to the received signal, the directional pattern grid dictionary, and the angle-steering vector grid dictionary includes: Determining a received signal model according to the directional pattern grid dictionary and the angle-steering vector grid dictionary; Determining an objective function of a convex optimization problem according to the received signal and its noise power in combination with the received signal model; The convex optimization problem is obtained by using the least square constraint as a constraint condition for the sparse solution in the convex optimization problem.

5. The method according to claim 2, characterized in that: Determining the estimated echo angle of the detected target according to the sparse solution of the convex optimization problem includes: Determining non-zero elements in a sparse solution to the convex optimization problem; Searching for the index value of the non-zero element in the angle-oriented vector grid dictionary; An estimated echo angle of the detected target is determined according to the index value, the azimuth grid vector, and the elevation grid vector.

6. The method according to claim 5, characterized in that The step of determining the estimated echo angle of the detected target according to the index value, the azimuth grid vector, and the elevation grid vector comprises: Searching the azimuth vector corresponding to the index value in the azimuth grid vector; Searching for the pitch angle vector corresponding to the index value in the pitch angle grid vector; An estimated echo angle of the detected target is determined according to the azimuth angle vector and the pitch angle vector.

7. A target sensing device, characterized in that: include: An information acquisition module is used to obtain the total number of antennas, the received signal from the array antenna and the preset angle grid length; A first determination module, configured to determine a directional pattern grid dictionary of the array antenna according to the total number of antennas and the preset angle grid length; A second determination module is used to determine an angle-steering vector grid dictionary according to an array arrangement of the antenna array; A third determination module is used to determine a convex optimization problem according to the received signal, the directional pattern grid dictionary and the angle steering vector grid dictionary; The angle determination module is used to determine the estimated echo angle of the detected target according to the sparse solution of the convex optimization problem.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the target perception method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the target perception method described in any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, which, when executed by a processor, implements the target perception method according to any one of claims 1 to 6.

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