Multi-target fast direction estimation method based on blind sparseness

Abstract

The invention provides a multi-target fast direction estimation method based on blind sparseness. The method comprises the following steps: receiving a multi-source noise-containing signal through a planar array antenna; converting the multi-source noise-containing signal into a direction angle multi-source noise-containing signal model based on the relationship among the direction angle, the pitch angle and the azimuth angle; performing equal grid division on the direction angle, and based on a division result, screening out high-probability direction angle grid points from all grid points obtained by division; generating a sub-dictionary according to the steering vector of each high-probability direction angle grid point; based on an orthogonal matching pursuit fast algorithm, obtaining an estimation signal according to the sub-dictionary, the multi-source noise-containing signal and a preset threshold value; obtaining a target signal according to the estimation signal and a preset threshold value, and obtaining direction angle information corresponding to the target signal; and converting the direction angle information corresponding to the target signal into a pitch angle and an azimuth angle to obtain a direction estimation result of the multi-source noisy signal, so that the direction estimation of the multi-source noisy signal is efficiently completed under the condition that the number of targets is unknown.

Description

Technical Field

[0001] The invention relates to the technical field of array signal spatial spectrum estimation, and in particular to a multi-target fast direction estimation method based on blind sparsity. Background Art

[0002] With the development of modern electronic information technology, spatial spectrum estimation has been widely used in radar and communication systems. Traditional spatial spectrum estimation methods usually require a large number of sensors and high sampling rates to accurately reconstruct signals, while compressed sensing allows signal reconstruction at a lower sampling rate, which can reduce hardware cost and complexity. In many practical applications, the direction of arrival of the signal is usually sparse, that is, there are only a few directions in the high-dimensional space where the signal exists. Compressed sensing uses this sparsity to restore the complete information of the signal through optimization algorithms. The orthogonal matching pursuit algorithm is one of the classic algorithms in the field of compressed sensing. However, when the algorithm is applied to a two-dimensional scene, the two-dimensional signal needs to be rearranged into a one-dimensional long signal, and the length and number of atoms in the corresponding dictionary will also increase greatly. The resulting orthogonal matching pursuit algorithm will have a very high computational complexity and cannot meet the real-time requirements of practical applications. At the same time, the algorithm requires a priori conditions on the number of targets. Therefore, in order to meet the requirements of practical applications, it is necessary to study the blind sparsity fast orthogonal matching pursuit fast algorithm (BS-FOMP) for multi-source targets.

[0003] At present, the research on DOA estimation in the field of compressed sensing mainly focuses on signal sparsity, signal observation and signal reconstruction. Among the signal reconstruction algorithms, the Basis Pursuit (BP) algorithm and the Orthogonal Matching Pursuit (OMP) algorithm are the most popular. Pursuit, OMP) are two classic algorithms. The basis pursuit algorithm is also called the minimization norm algorithm. It is a convex optimization problem based on linear programming and pursues the global optimum. Therefore, it has a very high computational complexity. The orthogonal matching pursuit algorithm will obtain a slightly worse reconstructed signal, but it has higher efficiency. Therefore, it is more suitable for the long signal obtained by rearranging the two-dimensional signal into one dimension. The rearranged one-dimensional long signal will also lead to a large number of operations in the orthogonal matching pursuit algorithm. Therefore, many scholars have studied the fast algorithm of the orthogonal matching pursuit algorithm. Y.Liu et al. studied the fast orthogonal matching pursuit algorithm for two-dimensional angle estimation in MIMO (multiple input multiple output) radar. Each atom in the dictionary is represented as the Kronecker product of two vectors, and the dictionary is decomposed into two sub-dictionaries. This fast orthogonal matching pursuit algorithm has good reconstruction quality similar to that of the orthogonal matching pursuit algorithm, which greatly improves the computational efficiency; Nagaraju L et al. extended the orthogonal matching pursuit algorithm for one-dimensional signals in the article Sparse and non-aware high-probability spatial spectrum estimation based on compressed sensing extended orthogonal matching pursuit algorithm. In this article, the spatial spectrum of the impulse signal on the antenna array was estimated using the compressed sensing framework without knowing the total input signal and achieved good estimation results; in 2016, Li Shaodong et al. studied the fast orthogonal matching pursuit algorithm based on Bayesian testing under the multi-measurement vector model. This algorithm proposed a fast orthogonal matching pursuit (Fast Orthogonal Matching Pursuit based on Bayesian Testing, FOMP-BT) algorithm based on Bayesian testing under the multiple observation vector (MMV) model. First, the total number of iterations of the algorithm and the amount of computation of each iteration were reduced by the idea of ​​new atomic group selection and inversion to improve the reconstruction efficiency of the algorithm; in 2021, Dou Huijing et al. proposed a spatial spectrum estimation algorithm for two-dimensional uniform L-type array signals based on compressed sensing theory. This method has higher estimation accuracy and resolution than traditional algorithms under high signal-to-noise ratio and multi-shot conditions, and reduces the amount of computation through compressed sampling; in 2023, C Wang et al. proposed a fast orthogonal matching pursuit algorithm (FOMP) based on rectangular arrays. The main idea is to reduce the number of atoms in the complete dictionary of the orthogonal matching pursuit algorithm, thereby greatly reducing the required amount of calculation. This method improves the traditional algorithm and improves the efficiency of the algorithm.

[0004] The traditional classic signal reconstruction algorithm, namely the orthogonal matching pursuit algorithm, is only applicable to simple one-dimensional signals with a known number of targets when used for array signal spatial spectrum estimation (Direction of Arrival, DOA). When facing two-dimensional signals with multiple targets, the high-dimensional sparse signal needs to be rearranged into a one-dimensional long signal when using this algorithm. At the same time, when estimating the two-dimensional direction of arrival, it is necessary to discretize the two-dimensional grid and use different angle values ​​to combine the steering vector to form a compressed sensing dictionary matrix. Due to the existence of two-dimensional angle combinations in the steering vector, the orthogonal matching pursuit algorithm for two-dimensional direction of arrival estimation has a very high computational complexity and low estimation efficiency. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a multi-target fast direction estimation method based on blind sparsity, which specifically includes:

[0006] In a first aspect, the present invention provides a method for fast direction estimation of multiple targets based on blind sparsity, comprising:

[0007] Receiving a multi-source noisy signal through a planar array antenna, wherein the multi-source noisy signal includes signals originating from multiple targets;

[0008] Based on the relationship between the first direction angle, the second direction angle, the pitch angle and the azimuth angle, the multi-source noisy signal is converted into a direction angle multi-source noisy signal model, where the first direction angle is the angle between the target and the x-axis, and the second direction angle is the angle between the target and the y-axis;

[0009] Performing equal grid division on the first direction angle and the second direction angle respectively, determining the steering vectors of all grid points obtained by the division, and generating an overcomplete redundant dictionary according to the steering vectors of the grid points;

[0010] Based on the grid division results, the angular power of each grid point is determined according to the overcomplete redundant dictionary and the angular multi-source noise signal model;

[0011] According to a preset first threshold value, and according to the directional angle power of each grid point, high-probability directional angle grid points are screened out from all the grid points obtained by division;

[0012] Generate a sub-dictionary according to the steering vector of each high-probability direction angle grid point;

[0013] Based on the orthogonal matching pursuit fast algorithm, an estimated signal is obtained according to the sub-dictionary, the multi-source noisy signal and the preset second threshold;

[0014] Obtaining a target signal according to the estimated signal and a preset third threshold, and acquiring directional angle information corresponding to the target signal;

[0015] Based on the relationship between the first direction angle, the second direction angle, the pitch angle and the azimuth angle, the direction angle information corresponding to the target signal is converted into the pitch angle and the azimuth angle to obtain the direction estimation result of the multi-source noisy signal.

[0016] In a second aspect, the present invention further provides a multi-target fast direction estimation device based on blind sparsity, comprising:

[0017] A receiving module, used for receiving a multi-source noisy signal through a planar array antenna, where the multi-source noisy signal includes signals originating from multiple targets;

[0018] A processing module, for converting the multi-source noisy signal into a directional angle multi-source noisy signal model based on the relationship between the first directional angle, the second directional angle, the pitch angle and the azimuth angle, wherein the first directional angle is the angle between the target and the x-axis, and the second directional angle is the angle between the target and the y-axis;

[0019] The processing module is further used to perform equal grid division on the first direction angle and the second direction angle respectively, determine the steering vectors of all grid points obtained by the division, and generate an over-complete redundant dictionary according to the steering vectors of each grid point;

[0020] The processing module is also used to determine the angular power of each grid point based on the grid division result, according to the overcomplete redundant dictionary and the angular multi-source noise signal model;

[0021] The processing module is further used to screen out high-probability directional angle grid points from all the grid points obtained by division according to a preset first threshold and the directional angle power of each grid point;

[0022] The processing module is further used to generate a sub-dictionary according to the steering vector of each high-probability direction angle grid point;

[0023] The processing module is further used to obtain an estimated signal based on an orthogonal matching pursuit fast algorithm according to the sub-dictionary, the multi-source noise-containing signal and a preset second threshold;

[0024] The processing module is further used to obtain a target signal according to the estimated signal and a preset third threshold value, and obtain direction angle information corresponding to the target signal;

[0025] The processing module is also used to convert the directional angle information corresponding to the target signal into the pitch angle and azimuth angle based on the relationship between the first directional angle, the second directional angle, the pitch angle and the azimuth angle, so as to obtain the direction estimation result of the multi-source noisy signal.

[0026] In a third aspect, the present invention further provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0027] Memory, used to store computer programs;

[0028] The processor is used to implement any method provided in the first aspect when executing a program stored in the memory.

[0029] In a fourth aspect, the present invention provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, any one of the methods provided in the first aspect is implemented.

[0030] Beneficial effects of the present invention:

[0031] The multi-target fast direction estimation method based on blind sparsity provided by the present invention receives a multi-source noisy signal through a planar array antenna, and the multi-source noisy signal includes signals from multiple targets; based on the relationship between a first direction angle, a second direction angle, a pitch angle and an azimuth angle, the multi-source noisy signal is converted into a direction angle multi-source noisy signal model, wherein the first direction angle is the angle between the target and the axis, and the second direction angle is the angle between the target and the axis; the first direction angle and the second direction angle are respectively subjected to equal grid division, the steering vectors of all grid points obtained by the division are determined, and an overcomplete redundant dictionary is generated according to the steering vectors of each grid point; based on the grid division result, the directional angle power of each grid point is determined according to the overcomplete redundant dictionary and the directional angle multi-source noisy signal model; according to a preset first threshold value, the directional angle power of each grid point is obtained from the division. High-probability directional grid points are screened out from all the grid points obtained; a sub-dictionary is generated according to the steering vector of each high-probability directional grid point; based on the orthogonal matching pursuit fast algorithm, an estimated signal is obtained according to the sub-dictionary, the multi-source noisy signal and a preset second threshold; according to the estimated signal and the preset third threshold, a target signal is obtained, and the directional angle information corresponding to the target signal is obtained; based on the relationship between the first directional angle, the second directional angle, the pitch angle and the azimuth angle, the directional angle information corresponding to the target signal is converted into the pitch angle and the azimuth angle, and the direction estimation result of the multi-source noisy signal is obtained, which realizes the situation of unknown number of targets and complex multi-source noise scenes, without losing the estimation accuracy and speed, and without determining the sparsity in advance, the direction estimation of multiple targets can be successfully realized, which not only meets the needs of actual scenes, but also has strong practicality and wide application potential.

[0032] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A schematic diagram of a flow chart of a multi-target fast direction estimation method based on blind sparsity provided by the present invention;

[0034] Figure 2A planar array antenna model provided by the present invention;

[0035] Figure 3 A relationship model between a direction angle, a pitch angle and an azimuth angle provided by the present invention;

[0036] Figure 4 A schematic diagram of the structure of a multi-target fast direction estimation device based on blind sparsity provided by the present invention;

[0037] Figure 5 A schematic diagram of a simulation result provided by the present invention;

[0038] Figure 6 Another schematic diagram of simulation results provided by the present invention;

[0039] Figure 7 The present invention provides another schematic diagram of simulation results. DETAILED DESCRIPTION

[0040] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0041] When estimating the direction of multi-source targets, the pitch angle and azimuth of the signal can be restored with high precision through low snapshot signals by using the compressed sensing method, which can effectively reduce the amount of calculation. For the orthogonal matching pursuit algorithm, the two-dimensional signal needs to be rearranged into one dimension. However, when the method of constructing the dictionary of one-dimensional spatial spectrum estimation is extended to the field of two-dimensional spatial spectrum estimation, the number of dictionary atoms also increases greatly due to the expansion of the signal dimension, making the algorithm very complex. At the same time, the classic orthogonal matching pursuit algorithm requires the condition of signal sparsity to obtain the final estimation result, so this type of algorithm is not suitable for practical scenarios. Existing studies have proposed a fast two-dimensional direction estimation algorithm to improve the high complexity of the two-dimensional wave arrival direction estimation of multiple targets. However, the number of iterations in the orthogonal matching pursuit algorithm strictly depends on the sparsity of the signal to obtain an accurate estimate. In the case of unknown sparsity, it is difficult to achieve fast two-dimensional DOA estimation of multiple targets, which does not meet the actual application scenarios.

[0042] In order to solve the problems existing in the prior art, the present invention provides a multi-target fast direction estimation method based on blind sparsity, which is specifically aimed at fast direction estimation of multiple targets in complex scenes with unknown target numbers. Firstly, a multi-source noisy signal is received through a planar array and converted into a directional angle multi-source noisy signal model; secondly, the directional angle is divided into equal grids, and after the directional angle power calculation is normalized in the case of an unknown target number, a threshold is set to filter the steering vectors at the high-probability directional angle grid points as a sub-dictionary and the indexes at these grid points are stored; then, for complex multi-source scenes, a new iterative method is proposed for the orthogonal matching pursuit fast algorithm to eliminate the dependence on sparsity; next, the estimated signal is normalized, and a threshold judgment is set to set the grid with a smaller estimated probability to zero to ensure the accuracy of the estimation; finally, the filtered directional angle is converted into a pitch angle and an azimuth angle to achieve fast estimation of multi-source targets. Aiming at multi-target complex scenes, the present invention proposes a new iterative method based on the orthogonal matching pursuit fast algorithm, and performs threshold processing after the iteration is completed considering the influence of noise, thereby realizing multi-target fast direction estimation under blind sparsity conditions, meeting the needs of real scenes, and having high practical value and broad application prospects.

[0043] Figure 1 A flowchart of a multi-target fast direction estimation method based on blind sparsity provided by the present invention is shown in FIG. Figure 1 As shown, the method includes:

[0044] S101. Receive multi-source noisy signals through a planar array antenna.

[0045] Among them, the multi-source noisy signals include signals originating from multiple targets.

[0046] An antenna system composed of many identical single antennas arranged in a certain pattern is also called an antenna array. The independent units that make up the antenna array are called array elements or antenna units. Figure 2 As shown, the planar array antenna is an antenna system in which array elements are arranged on the same plane.

[0047] In a possible implementation, when the planar array antenna is an M×N array, the signal received by any (m,n)th array element is expressed as:

[0048]

[0049] Among them, Y mn represents the signal received by the (m,n)th array element, k represents the index of the target, k=1,2,3,…,K, K represents the total number of targets, σ k represents the complex reflection coefficient of the kth target, N mnrepresents the noise signal received by the (m,n)th array element, θ k represents the pitch angle of the kth target, represents the azimuth of the kth target, represents the steering vector of the mth array element in the x-axis direction, represents the steering vector of the nth array element in the y-axis direction,

[0050]

[0051] exp() represents an exponential function with the natural constant e as the base, j represents the imaginary part of the complex number, λ represents the carrier wavelength, d represents the array element spacing, m represents the index of the array element in the x-axis direction, and n represents the index of the array element in the y-axis direction.

[0052] S102: based on the relationship between the first direction angle, the second direction angle, the pitch angle and the azimuth angle, convert the multi-source noisy signal into a direction angle multi-source noisy signal model.

[0053] See also Figure 3 , the first direction angle is the angle between the target and the x-axis, and the second direction angle is the angle between the target and the y-axis.

[0054] Optionally, the relationship between the first direction angle, the second direction angle, the elevation angle and the azimuth angle is expressed as:

[0055]

[0056] Among them, β represents the first direction angle, γ represents the second direction angle, and θ represents the pitch angle. represents the azimuth, cos represents the cosine function, and sin represents the sine function.

[0057] Furthermore, in a possible implementation, when the planar array antenna is an M×N array, the directional angle multi-source noise signal model corresponding to the signal received by any (m,n)th array element is expressed as:

[0058]

[0059] Among them, β k represents the first direction angle corresponding to the kth target, γ k represents the second direction angle corresponding to the kth target, a x (β k ) represents the steering vector of the mth array element in the x-axis direction, a y (γ k ) represents the steering vector of the nth array element in the y-axis direction,

[0060]

[0061] Since directly extending the classic method of constructing dictionaries for one-dimensional spatial spectrum estimation to the field of two-dimensional spatial spectrum estimation will cause the number of dictionary atoms to increase exponentially, greatly increasing the complexity of the calculation, based on the above method, the direction angle is used to replace the pitch angle and azimuth angle to construct the dictionary, and the problem of estimating the pitch angle and azimuth angle is converted into the estimation of the direction angle, which can reduce the complexity and improve the construction efficiency.

[0062] S103, performing equal grid division on the first direction angle and the second direction angle respectively, determining the steering vectors of all grid points obtained by the division, and generating an over-complete redundant dictionary according to the steering vector of each grid point.

[0063] Exemplarily, the first direction angle β is divided into P grid points, and the second direction angle γ is divided into Q grid points.

[0064] S104, based on the grid division result, according to the overcomplete redundant dictionary and the azimuth multi-source noise signal model, determine the azimuth power of each grid point.

[0065] In a possible implementation, based on the grid division result, according to the overcomplete redundant dictionary and the azimuth multi-source noise signal model, the azimuth power of each grid point is determined, which is expressed as:

[0066]

[0067] Among them, P row1 (β p ) represents the row angular power corresponding to the pth grid point in the divided grid, P col1 (γ q ) represents the column angular power corresponding to the qth grid point in the divided grid, β p represents the first direction angle of the pth grid point, γ q It represents the second direction angle of the qth grid point, and the superscript H represents the conjugate transpose A row1 and A col1 They represent the steering vector of the first row and the steering vector of the first column of the overcomplete redundant dictionary, respectively, and can be expressed as:

[0068] A row1 =[a x (β 1 ) x (β 2 ) x (β 3 )...a x (β P )] M×P (m=1,2,3,...,M),

[0069] Acol1 =[a y (γ 1 ) y (γ 2 ) y (γ 3 )...a y (γ Q )] N×Q (n=1,2,3,...,N),

[0070] Y row1 and Y col1 They represent the signals received by the first row of array elements and the first column of array elements in the planar array antenna respectively:

[0071] Y row1 =[Y 11 Y 21 Y 31 ... Y M1 ] H M×1 ,

[0072] Y col1 =[Y 11 Y 12 Y 13 ... Y 1N ] H N×1 .

[0073] This method can be used to determine the directional angle power when the number of targets is unknown.

[0074] S105 , according to a preset first threshold and the angular power of each grid point, selecting high-probability angular grid points from all the grid points obtained by division.

[0075] In a possible implementation, according to a preset first threshold value, according to the angular power of each grid point, high-probability angular grid points are screened out from all the grid points obtained by division, including: normalizing the angular power of each grid point, comparing the angular power of each normalized grid point with the first threshold value, and determining the grid point whose angular power is greater than the first threshold value as the high-probability angular grid point, expressed as:

[0076] P row1 (β p )>ε 1 ,

[0077] P col1 (γ q )>ε 1 ,

[0078] Among them, ε1 represents the first threshold, P row1 (β p ) represents the row angular power corresponding to the pth grid point in the divided grid, P col1 (γ q ) represents the column angular power corresponding to the qth grid point in the divided grid.

[0079] S106. Generate a sub-dictionary according to the steering vector of each high-probability directional angle grid point.

[0080] S107 . Obtain an estimated signal based on an orthogonal matching pursuit fast algorithm according to the sub-dictionary, the multi-source noisy signal and a preset second threshold.

[0081] In a possible implementation, based on an orthogonal matching pursuit fast algorithm, an estimated signal is obtained according to a sub-dictionary, a multi-source noisy signal and a preset second threshold, including the following steps A1-A6:

[0082] A1. Filter out the high-probability direction angle grid points from the grid division results corresponding to the first direction angle and store them in the first data set X. a The high-probability direction angle grid points selected from the grid division results corresponding to the second direction angle are stored in the second data set X b .

[0083] Accordingly, the first data set X a and the second dataset X b The steering vectors of the high-probability direction angle grid points stored in are determined as a sub-dictionary.

[0084] A2. Merge and store the first data set X in a preset order a and the second dataset X b The high probability direction angle grid points in the , and the merged storage result is obtained, which is expressed as:

[0085] Ω={x=(x b -1)×Q+x a |x a ∈X a ,x b ∈X b},

[0086] Among them, Ω represents the merged storage result, x represents the storage location where the high probability grid point may appear, and X a represents the first data set, X b represents the second data set, x a represents the high probability grid points in the first data set,

[0087] x brepresents the high probability grid points in the second data set, and Q represents the total number of grids corresponding to the second direction angle.

[0088] A3. According to the multi-source noisy signal, construct the initial residual vector, which is expressed as:

[0089] r = vec(Y),

[0090] Among them, r represents the initial residual vector, Y represents the multi-source noisy signal, and vec() represents vectorization.

[0091] A4. Based on the initial residual vector, sub-dictionary and multi-source noisy signal, a fast algorithm based on orthogonal matching pursuit is executed to screen the guidance vector in the sub-dictionary, and after each iteration, it is determined whether the current residual is less than the second threshold. If so, the iteration is stopped to obtain the target grid point and the target sparse estimation.

[0092] The target grid point is a high-probability direction angle grid point corresponding to the currently selected steering vector, and the target sparse estimation includes the signal amplitude corresponding to the target grid point.

[0093] This method uses the residual to determine the number of iterations, does not rely on the assumption of sparsity, simplifies the fast algorithm based on orthogonal matching pursuit, and makes the algorithm easier to implement and debug in practical applications. At the same time, when facing complex and uncertain signal environments, this method shows stronger robustness and can effectively deal with noise and interference. Therefore, it shows stronger adaptability and practicality in multi-target direction estimation, and is suitable for practical applications in various complex signal environments.

[0094] Exemplarily, based on the initial residual vector, the sub-dictionary and the multi-source noisy signal, a fast algorithm based on orthogonal matching pursuit is executed to screen the grid points in the sub-dictionary, and after each iteration, it is determined whether the current residual is less than the second threshold. If so, the iteration is stopped to obtain the target grid point index and the target sparse estimation, including the following steps B1-B6:

[0095] B1. Project the sub-dictionary onto the initial residual vector and query the sub-dictionary based on the projection result, expressed as:

[0096]

[0097] Among them, λ k Represents the index of the queried grid point, Indicates finding the index corresponding to the maximum value of the projection result, a j Represents a sub-dictionary, r k-1 Represents the residual vector of the previous iteration.

[0098] B2. The steering vector of the queried grid point is stored in a preset atom set, and the index corresponding to the steering vector of the queried grid point is stored in a preset index set, which is expressed as:

[0099]

[0100] Λ k =Λ k-1 ∪{λ k},

[0101] in, represents the set of atoms, Λ k Represents an index set, Indicates the index corresponding to the queried index, λ k Indicates the index of the queried grid point.

[0102] B3. Solving the least squares problem The new estimate of the sparse signal is obtained, expressed as:

[0103]

[0104] Among them, x k represents the sparse signal estimate obtained at the kth iteration, represents an atomic set, y represents a vectorized multi-source noisy signal, and the superscript T represents the transpose of the matrix.

[0105] B4. Update the residual in each iteration according to the new estimate of the sparse signal, expressed as:

[0106]

[0107] B5. Determine whether the updated residual is less than the second threshold. If so, stop the iteration, which is expressed as:

[0108] r k <ε 2 ,

[0109] Among them, ε 2 Indicates the second threshold.

[0110] B6. After the iteration, the corresponding storage order is matched in Ω according to the output index set, and the estimated signal is reconstructed according to the matched storage order, which is expressed as:

[0111] ind=Ω(Λ k ),

[0112] y_ bs (ind) = x k ,

[0113] Among them, ind represents the storage order of the grid points corresponding to the index, Ω(Λk ) indicates that the storage order corresponding to the target grid points is matched in the merged storage result, y_ bs () represents the estimated one-dimensional long signal, x k represents the sparse signal estimate obtained at the kth iteration.

[0114] A5. Based on the storage order obtained by matching, an estimated signal is obtained according to the target sparse estimation.

[0115] A6. Convert the estimated signal into a two-dimensional form to obtain a two-dimensional direction angle estimation signal, expressed as [y_bs] P×Q .

[0116] S108. Obtain a target signal according to the estimated signal and a preset third threshold, and obtain direction angle information corresponding to the target signal.

[0117] In one possible implementation, a target signal is obtained based on an estimated signal and a preset third threshold, including: normalizing the signal amplitudes corresponding to all grid points included in the estimated signal; comparing the signal amplitudes at each normalized grid point with the third threshold, and setting the signal amplitudes at the grid points where the signal amplitudes are less than the third threshold to 0, to obtain the target signal.

[0118] It is expressed as:

[0119] y _bs (p,q)<ε 3 ,

[0120] Among them, y _bs (p,q) represents the estimated signal at the grid point (p,q), ε 3 Indicates the third threshold.

[0121] By this method, the situation that the estimated number does not match the actual number of information sources in the estimation process can be effectively avoided, thereby significantly improving the accuracy and reliability of direction estimation. At the same time, in complex environments, signal interference and background noise may lead to erroneous estimates. By excluding low-probability grids, the algorithm's resistance to interference can be enhanced, and its stability in practical applications can be improved, so that the final direction estimation is more accurate. It improves the problem that the orthogonal matching pursuit fast algorithm does not consider the influence of noise, introduces independent and identically distributed Gaussian white noise, and performs threshold screening again after the iteration to exclude low-probability grid points, so that it can still maintain high estimation accuracy and reliability when facing the inevitable noise interference in practical applications. This processing can effectively avoid the situation that the estimated number does not match the actual number of information sources in the estimation process, thereby significantly improving the accuracy and reliability of direction estimation. At the same time, in complex environments, signal interference and background noise may lead to erroneous estimates. By excluding low-probability grids, the algorithm's resistance to interference can be enhanced, and its stability in practical applications can be improved, so that the final direction estimation is more accurate.

[0122] S109: based on the relationship between the first direction angle, the second direction angle, the pitch angle and the azimuth angle, convert the direction angle information corresponding to the target signal into the pitch angle and the azimuth angle to obtain the direction estimation result of the multi-source noisy signal.

[0123] It is expressed as:

[0124]

[0125] The multi-target fast direction estimation method based on blind sparsity provided by the present invention receives a multi-source noisy signal through a planar array antenna, and the multi-source noisy signal includes signals from multiple targets; based on the relationship between a first direction angle, a second direction angle, a pitch angle and an azimuth angle, the multi-source noisy signal is converted into a direction angle multi-source noisy signal model, wherein the first direction angle is the angle between the target and the x-axis, and the second direction angle is the angle between the target and the y-axis; the first direction angle and the second direction angle are respectively subjected to equal grid division, the steering vectors of all grid points obtained by the division are determined, and an overcomplete redundant dictionary is generated according to the steering vectors of each grid point; based on the grid division result, the directional angle power of each grid point is determined according to the overcomplete redundant dictionary and the directional angle multi-source noisy signal model; according to a preset first threshold value, the directional angle power of each grid point is determined from the division. High-probability directional angle grid points are screened out from all the obtained grid points; a sub-dictionary is generated according to the guidance vector of each high-probability directional angle grid point; based on the orthogonal matching pursuit fast algorithm, an estimated signal is obtained according to the sub-dictionary, a multi-source noisy signal and a preset second threshold; a target signal is obtained according to the estimated signal and a preset third threshold, and the directional angle information corresponding to the target signal is obtained; based on the relationship between the first directional angle, the second directional angle, the pitch angle and the azimuth angle, the directional angle information corresponding to the target signal is converted into the pitch angle and the azimuth angle, and the direction estimation result of the multi-source noisy signal is obtained, which realizes the situation of unknown number of targets and complex multi-source noise scenes, without losing the estimation accuracy and speed, and without determining the sparsity in advance, the direction estimation of multiple targets can be successfully achieved, which not only meets the needs of actual scenes, but also has strong practicality and broad application potential.

[0126] Figure 4 A schematic diagram of the structure of a multi-target fast direction estimation device based on blind sparsity provided by the present invention is shown in FIG. Figure 4 As shown, the device comprises:

[0127] The receiving module 41 is used to receive multi-source noisy signals through a planar array antenna, where the multi-source noisy signals include signals from multiple targets.

[0128] The processing module 42 is used to convert the multi-source noisy signal into a directional angle multi-source noisy signal model based on the relationship between the first directional angle, the second directional angle, the pitch angle and the azimuth angle, where the first directional angle is the angle between the target and the x-axis, and the second directional angle is the angle between the target and the y-axis.

[0129] The processing module 42 is further used to perform equal grid division on the first direction angle and the second direction angle respectively, determine the steering vectors of all grid points obtained by the division, and generate an over-complete redundant dictionary according to the steering vector of each grid point.

[0130] The processing module 42 is further used to determine the angular power of each grid point based on the grid division result, according to the overcomplete redundant dictionary and the angular multi-source noise signal model.

[0131] The processing module 42 is further configured to screen out high-probability directional grid points from all the grid points obtained by division according to a preset first threshold and the directional power of each grid point.

[0132] The processing module 42 is further configured to generate a sub-dictionary according to the steering vector of each high-probability direction angle grid point.

[0133] The processing module 42 is further configured to obtain an estimated signal based on an orthogonal matching pursuit fast algorithm according to the sub-dictionary, the multi-source noisy signal and a preset second threshold.

[0134] The processing module 42 is further used to obtain a target signal according to the estimated signal and a preset third threshold, and acquire direction angle information corresponding to the target signal.

[0135] The processing module 42 is further used to convert the directional angle information corresponding to the target signal into the pitch angle and the azimuth angle based on the relationship between the first directional angle, the second directional angle, the pitch angle and the azimuth angle, so as to obtain the direction estimation result of the multi-source noisy signal.

[0136] In order to further prove the beneficial effects of the present invention, the present invention also provides a set of experimental data. Figure 5 The present invention provides a two-dimensional signal directional angle estimation result obtained by a classical orthogonal matching pursuit algorithm. The planar array antenna is an M×N array, M=10 and N=8 are set, the directional angle of the received signal and the steering vector are converted, an over-complete redundant dictionary matrix is ​​constructed for the directional angle, and the dictionary matrix is ​​directly used for orthogonal matching pursuit. The iteration termination condition is the number of targets, and the signal-to-noise ratio is set to 30dB. In the result diagram, the x-axis represents the first directional angle α, the y-axis represents the second directional angle β, and the z-axis represents the target spectrum peak amplitude.

[0137] Figure 6 The present invention provides a two-dimensional signal direction angle estimation result obtained by performing a classic orthogonal matching pursuit fast algorithm after screening a sub-dictionary, that is, a two-dimensional signal direction angle estimation result obtained by an orthogonal matching pursuit fast algorithm, wherein the first threshold ε 1 =0.9, the result is Figure 5 The corresponding method estimates the angle after filtering the dictionary matrix.

[0138] Figure 7 The present invention provides a two-dimensional signal direction angle direction estimation result obtained by a multi-target fast direction estimation method based on blind sparsity, the second threshold ε 2 =0.001, the third threshold ε3 =0.7, the result is Figure 6 On the basis of the corresponding method, the iteration termination condition is optimized from the number of iterations to the target sparsity to the signal residual judgment, and the estimated angle is obtained after excluding the low-probability grid points.

[0139] Table 1 is Figure 5 , Figure 6 , Figure 7 The running complexity comparison results of the corresponding three methods are shown. The average running time of the three methods is 0.015s under the multi-source noisy model. The average running time of the classic orthogonal matching pursuit method is 0.0013s after the sub-dictionary is selected. The average running time of the new iterative method is 0.0015s. At the same time, it can be seen that the direction estimation in the three images is accurate, which shows that the iterative method can ensure the accuracy and speed of direction estimation. It can be seen from the above result graph that without losing the estimation accuracy and speed, the multi-target fast direction estimation method based on blind sparsity proposed in the present invention can match the actual application of complex scenes and realize efficient and accurate two-dimensional direction estimation when the number of targets is unknown.

[0140] Table 1. Comparison results of operation complexity

[0141]

[0142] The present invention also provides a structure of an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus.

[0143] Memory, used to store computer programs;

[0144] The processor is used to implement the steps provided in the above method embodiment when executing the program stored in the memory.

[0145] The communication interface is used for communication between the above electronic device and other devices.

[0146] The method provided in the embodiment of the present invention can be applied to electronic devices. Specifically, the electronic device can be: a desktop computer, a portable computer, an intelligent mobile terminal, a server, etc. This is not limited here, and any electronic device that can implement the present invention belongs to the protection scope of the present invention.

[0147] The present invention also provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps provided in the above method embodiment are implemented.

[0148] As for the device / electronic device / storage medium embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the specific content and beneficial effects and other related matters can be referred to the partial description of the method embodiment.

[0149] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0150] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A multi-target fast direction estimation method based on blind sparsity, characterized in that: include: Receiving a multi-source noisy signal by means of a planar array antenna, wherein the multi-source noisy signal includes signals originating from a plurality of targets; Based on the relationship between the first direction angle, the second direction angle, the pitch angle and the azimuth angle, the multi-source noisy signal is converted into a direction angle multi-source noisy signal model, wherein the first direction angle is the angle between the target and the x-axis, and the second direction angle is the angle between the target and the y-axis; Performing equal grid division on the first direction angle and the second direction angle respectively, determining the steering vectors of all grid points obtained by the division, and generating an overcomplete redundant dictionary according to the steering vectors of the grid points; Based on the grid division result, the angular power of each grid point is determined according to the overcomplete redundant dictionary and the angular multi-source noise signal model; According to a preset first threshold value, and according to the directional angle power of each of the grid points, high-probability directional angle grid points are screened out from all the grid points obtained by division; generating a sub-dictionary according to the steering vectors of the high-probability directional angle grid points; Based on an orthogonal matching pursuit fast algorithm, an estimated signal is obtained according to the sub-dictionary, the multi-source noisy signal and a preset second threshold; Obtaining a target signal according to the estimated signal and a preset third threshold, and acquiring directional angle information corresponding to the target signal; Based on the relationship between the first direction angle, the second direction angle, the pitch angle and the azimuth angle, the direction angle information corresponding to the target signal is converted into the pitch angle and the azimuth angle to obtain the direction estimation result of the multi-source noisy signal.

2. The method according to claim 1, characterized in that When the planar array antenna is an M×N array, the signal received by any (m,n)th array element is expressed as: Among them, Y mn represents the signal received by the (m,n)th array element, k represents the index of the target, k=1,2,3,…,K, K represents the total number of targets, σ k represents the complex reflection coefficient of the kth target, N mn represents the noise signal received by the (m,n)th array element, θ k represents the pitch angle of the kth target, represents the azimuth of the kth target, represents the steering vector of the mth array element in the x-axis direction, represents the steering vector of the nth array element in the y-axis direction, exp() represents an exponential function with the natural constant e as the base, j represents the imaginary part of the complex number, λ represents the carrier wavelength, d represents the array element spacing, m represents the index of the array element in the x-axis direction, and n represents the index of the array element in the y-axis direction.

3. The method according to claim 2, characterized in that When the planar array antenna is an M×N array, the directional angle multi-source noise signal model corresponding to the signal received by any (m,n)th array element is expressed as: Among them, β k represents the first direction angle corresponding to the kth target, γ k represents the second direction angle corresponding to the kth target, a x (β k ) represents the steering vector of the mth array element in the x-axis direction, a y (γ k ) represents the steering vector of the nth array element in the y-axis direction, 4. The method according to claim 3, characterized in that Based on the grid division result, the angular power of each grid point is determined according to the overcomplete redundant dictionary and the angular multi-source noise signal model, which is expressed as: Among them, P row1 (β p ) represents the row angular power corresponding to the pth grid point in the divided grid, P col1 (γ q ) represents the column angular power corresponding to the qth grid point in the divided grid, β p represents the first direction angle of the pth grid point, γ q represents the second direction angle of the qth grid point, the superscript represents the conjugate transpose, A row1 and A col1 They represent the steering vector of the first row and the steering vector of the first column of the overcomplete redundant dictionary respectively, and the expressions are: A row1 =[a x (b1) a x (b2) a x (b3) ... a x (b P )] M×P (m=1,2,3,...,M), A col1 =[a y (c1) a y (c2) a y (c3) ... a y (c Q )] N×Q (n=1,2,3,...,N), Y row1 and Y col1 They represent the signals received by the first row of array elements and the first column of array elements in the planar array antenna respectively, and the expressions are: AND row1 =[And 11 AND 21 AND 31 ... AND M1 ] H M×1 , AND col1 =[And 11 AND 12 AND 13 ... AND 1N ] H N×1 。 5. The method according to claim 3, characterized in that: The step of selecting high-probability directional grid points from all the grid points obtained by division according to the preset first threshold and the directional power of each grid point includes: Normalizing the directional angular power of each grid point, comparing the normalized directional angular power of each grid point with the first threshold, and determining the grid point whose directional angular power is greater than the first threshold as the high-probability directional angular grid point, expressed as: P row1 (b p )>ε1, P col1 (c q )>ε1, Among them, ε1 represents the first threshold, P row1 (β p ) represents the row angular power corresponding to the grid points in the divided grid, P col1 (γ q ) represents the column angular power corresponding to the grid points in the divided grid.

6. The method according to claim 5, characterized in that The method based on the orthogonal matching pursuit fast algorithm, obtaining the estimated signal according to the sub-dictionary, the multi-source noisy signal and a preset second threshold, includes: The high-probability direction angle grid points selected from the grid division results corresponding to the first direction angle are stored in the first data set X a The high-probability direction angle grid points selected from the grid division results corresponding to the second direction angle are stored in the second data set X b ; Merge and store the first data set X in a preset order a and the second data set X b The high probability direction angle grid points in the , and the merged storage result is obtained, which is expressed as: Ω={x=(x b -1)×Q+x a |x a ∈X a ,x b ∈X b }, Among them, Ω represents the combined storage result, x represents the storage location where the high probability grid point may appear, Q represents the total number of grids corresponding to the second direction angle, and X a represents the first data set, X b represents the second data set, x a represents the high probability grid points in the first data set, x b represents high probability grid points in the second data set; According to the multi-source noisy signal, an initial residual vector is constructed, which is expressed as: r = vec(Y), Among them, r represents the initial residual vector, Y represents the multi-source noisy signal, and vec() represents vectorization; Based on the initial residual vector, the sub-dictionary and the multi-source noisy signal, the orthogonal matching pursuit based fast algorithm is executed to screen the steering vector in the sub-dictionary, and after each iteration, it is determined whether the current residual is less than the second threshold value, if so, the iteration is stopped to obtain a target grid point and a target sparse estimate, wherein the target grid point is a high probability directional angle grid point corresponding to the currently screened steering vector, and the target sparse estimate includes a signal amplitude corresponding to the target grid point; Matching the storage order corresponding to the target grid points in the combined storage result; Based on the storage order obtained by matching, the estimated signal is obtained according to the target sparse estimation.

7. The method according to claim 6, characterized in that The step of obtaining a target signal according to the estimated signal and a preset third threshold comprises: Normalizing the signal amplitudes corresponding to all grid points included in the estimated signal; The normalized signal amplitude at each grid point is compared with the third threshold value, and the signal amplitude at the grid point where the signal amplitude is less than the third threshold value is set to 0 to obtain the target signal.

8. A multi-target fast direction estimation device based on blind sparsity, characterized in that: include: A receiving module, used for receiving a multi-source noisy signal through a planar array antenna, wherein the multi-source noisy signal includes signals originating from multiple targets; a processing module, configured to convert the multi-source noisy signal into a directional angle multi-source noisy signal model based on the relationship between the first directional angle, the second directional angle, the pitch angle and the azimuth angle, wherein the first directional angle is the angle between the target and the x-axis, and the second directional angle is the angle between the target and the y-axis; The processing module is further used to perform equal grid division on the first direction angle and the second direction angle respectively, determine the steering vectors of all grid points obtained by the division, and generate an over-complete redundant dictionary according to the steering vectors of the grid points; The processing module is further used to determine the angular power of each grid point based on the grid division result, the overcomplete redundant dictionary, and the angular multi-source noise signal model; The processing module is further used to screen out high-probability directional grid points from all the grid points obtained by division according to a preset first threshold and the directional power of each grid point; The processing module is further used to generate a sub-dictionary according to the steering vector of each high-probability direction angle grid point; The processing module is further used to obtain an estimated signal based on the sub-dictionary, the multi-source noisy signal and a preset second threshold value based on an orthogonal matching pursuit fast algorithm; The processing module is further used to obtain a target signal according to the estimated signal and a preset third threshold, and obtain direction angle information corresponding to the target signal; The processing module is also used to convert the directional angle information corresponding to the target signal into the pitch angle and azimuth angle based on the relationship between the first directional angle, the second directional angle, the pitch angle and the azimuth angle, so as to obtain the direction estimation result of the multi-source noisy signal.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, for implementing any of the methods described in claims 1-7 when executing a program stored in a memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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