Underwater target two-dimensional direction finding method and device and computing equipment

Abstract

The invention relates to the technical field of signal processing, and provides an underwater target two-dimensional direction finding method, which comprises the following steps that: a two-dimensional area array model is adopted to receive an incident signal in a sonar detection area, and the two-dimensional area array model comprises a uniform surface composed of a plurality of receiving array elements; the incident signal comprises an echo signal and a noise component of the underwater target; dividing the two-dimensional area array model into a plurality of sub-arrays; each sub-array in the L sub-arrays comprises a receiving array element; respectively eliminating non-correlation noise components of incident signals of the L sub-arrays to obtain a wave beam of each sub-array in the L sub-arrays, wherein the wave beam of each sub-array is a non-uniform diagonal load shedding minimum variance distortionless response result; and carrying out overlapped sub-array space smoothing on the wave beam of each sub-array in the L sub-arrays, solving and estimating the azimuth angle and the pitch angle of the underwater target, and determining the spatial orientation of the underwater target. According to the method, incoherent noise components are effectively eliminated, the anti-noise performance of the algorithm is remarkably improved, and the target two-dimensional angle estimation precision and resolution are improved.

Description

Technical Field

[0001] This application relates to the field of underwater detection technology, and in particular to a two-dimensional orientation finding method, apparatus and computing device for underwater targets. Background Technology

[0002] Acoustic target localization, as a core technology in underwater detection, continues to attract significant attention from the underwater acoustics community. The ability of unmanned observation platforms, such as autonomous underwater vehicles (AUVs), to estimate the azimuth, radial distance, and other positional parameters of non-cooperative targets is crucial. This is not only the foundation for target location awareness and continuous surveillance but also provides indispensable intelligence support for subsequent effective strikes or reasonable evasion. A commonly used direction-finding method involves processing the array-received signal using beamforming technology and determining the target's direction of arrival based on the spectral peak search results. On this basis, pulse compression processing is performed on the beam using a matched signal to estimate the target distance. Therefore, a high-resolution target direction-finding method that can retain phase information to achieve matched filtering is essential for accurate target localization.

[0003] Superbeamforming technology was initially used in radar beam pattern azimuth search systems to effectively reduce main lobe width and suppress side lobes, thereby achieving precise orientation. It is now widely used in underwater acoustic detection. Conventional superbeamforming methods have three main limitations: 1) Existing superbeamforming methods are mostly based on uniform linear array models, which means they lack elevation angle estimation capabilities and cannot determine the spatial orientation of the target; 2) The introduction of nonlinear summation operations during algorithm processing distorts the phase and frequency characteristics of the beam signal. Therefore, its output is only suitable for energy detection scenarios and cannot support refined processing such as matched filtering and spectrum analysis; 3) This method has poor noise interference resistance, and the matrix inversion operation in the processing results in poor robustness. Summary of the Invention

[0004] To achieve high-precision two-dimensional direction finding while improving the algorithm's noise resistance, this patent addresses the shortcomings of conventional superbeamforming methods in the two-dimensional direction finding process of underwater targets and proposes a two-dimensional direction finding method for underwater targets based on improved superbeamforming.

[0005] In a first aspect, embodiments of this application provide a two-dimensional orientation finding method for underwater targets, comprising: receiving incident signals within a sonar detection area using a two-dimensional array model, the two-dimensional array model comprising... A uniform surface composed of receiving array elements; the incident signal includes the echo signal from the underwater target and noise components; the two-dimensional surface array model is divided into L subarrays; each of the L subarrays includes There are L receiving array elements; the value of L is at least 4; the uncorrelated noise components of the incident signals of the L subarrays are eliminated to obtain the beam of each subarray in the L subarrays. The beam of each subarray is the result of non-uniform diagonal load reduction minimum variance distortion-free response (IDU-MVDR); the beam of each subarray in the L subarrays is solved by overlapping subarray spatial smoothing to estimate the azimuth and elevation angles of the underwater target and determine the spatial orientation of the underwater target.

[0006] In some possible implementations, a two-dimensional array model is used to receive incident signals within the sonar detection area, including: determining the steering vector a based on the path difference of the incident signals received by two adjacent receiving array elements. , ), guide vector a( , )for:

[0007]

[0008] in, , Indicates the azimuth and elevation angles of the incident signal. This indicates the path difference of the received signals between two adjacent array elements. The incident signal wavelength is given. The incident signal covariance matrix is ​​obtained by performing time-frequency conversion, expectation, and conjugate transpose operations based on the incident signal steering vector. The incident signal covariance matrix is:

[0009]

[0010] in, , These represent the expectation and conjugate transpose operations, respectively. For the received signal matrix:

[0011] in Indicates the first The frequency domain signal of each array element.

[0012] In some possible implementations, the azimuth and pitch angles of the underwater target are estimated based on the spatial smoothing solution of the overlapping subarrays of multiple subarrays, including: using the Non-Uniform Diagonal Load Reduction Minimum Variance Distortion-Free Response (IDU-MVDR) method to solve for the load reduction of each element in each of the L subarrays;

[0013] According to the The covariance matrix of the received signals of each subarray and the The load reduction amount for each element in the subarray is determined after the load reduction is completed. The covariance matrix of each subarray for:

[0014]

[0015] in, For the first The first in the team Individual element load reduction, ; No. The element load reduction includes the first Uncorrelated noise components on each array element; Indicated by It is a diagonal matrix with diagonal elements.

[0016] In some possible implementations, the non-uniform diagonal unloading minimum variance distortion-free response (IDU-MVDR) method is used to solve for the unloading amount of each element in each of the L subarrays, including: ensuring that the minimum eigenvalue of the covariance matrix after unloading is positive based on constraints, where the constraints are:

[0017]

[0018]

[0019] in, It is the identity matrix. It is the smallest eigenvalue of the covariance matrix; Represents the covariance matrix The Middle One diagonal element, The load reduction factor is used to maximize the total load reduction.

[0020] .

[0021] The load reduction of each array element is obtained by solving for the constraints and maximizing the total load reduction.

[0022] In some possible implementations, the beam of each of the L subarrays is obtained by eliminating the uncorrelated noise components of the incident signals of the L subarrays, including: based on the covariance matrix after diagonal offloading. Determine the Lth subarray Beams of individual arrays for:

[0023] .

[0024] in, For the azimuth and elevation angles in a two-dimensional array; For the first Frequency domain signal input for each subarray; Beam scanning vector:

[0025] .

[0026] In some possible implementations, the beams of the L subarrays include a first subarray beam, a second subarray beam, a third subarray beam, and a fourth subarray beam. The azimuth and elevation angles of the underwater target are estimated based on the spatial smoothing solution of the overlapping subarrays. The method further includes: determining the planar array superbeams based on the beams of the L subarrays; the planar array superbeams include a first- and third-superbeam and a second- and fourth-superbeam; the first- and third-superbeams are determined based on the "sum beam" and "difference beam" of the first and third subarray beams; the second- and fourth-superbeams are determined based on the "sum beam" and "difference beam" of the first and third subarray beams; and the main lobe width and side lobe height of the planar array superbeams are adjusted based on the superbeam index and the beam splitting weight coefficients.

[0027] In some possible implementations, estimating the azimuth and elevation angles of an underwater target based on the spatially smoothed solution of the planar array of multiple subarrays further includes: determining spatial weighting coefficients based on the super-beams of the planar array; multiplying the spatial weighting coefficients by the beams of the L subarrays respectively to obtain weighted beams with phase information; and estimating the azimuth and elevation angles of the underwater target based on the weighted beams with phase information.

[0028] Secondly, embodiments of this application provide a two-dimensional orientation-finding device for underwater targets. The device includes: a scene modeling module, used to receive incident signals within a sonar detection area using a two-dimensional array model, the two-dimensional array model comprising: A uniform surface composed of receiving array elements; the incident signal includes the echo signal from the underwater target and noise components; a subarray partitioning module is used to divide the two-dimensional surface array model into L subarrays; each of the L subarrays includes The receiver array has L elements, with L being at least 4; a denoising module is used to eliminate the uncorrelated noise components of the incident signals of the L subarrays to obtain the beam of each subarray, and the beam of each subarray is the result of non-uniform diagonal unloaded minimum variance distortion-free response (IDU-MVDR); a positioning module is used to perform overlapping subarray spatial smoothing solution on the beams of each subarray in the L subarrays to estimate the azimuth and elevation angles of the underwater target and determine the spatial orientation of the underwater target.

[0029] Thirdly, this application provides a computing device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute a method as described in any of the first aspects.

[0030] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to perform the method as described in any of the first aspects. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the various embodiments disclosed in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only a few embodiments disclosed in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] The accompanying drawings used in the description of the embodiments or prior art are briefly introduced below.

[0033] Figure 1 A flowchart illustrating the two-dimensional orientation finding method for underwater targets provided in this application embodiment;

[0034] Figure 2 This is a schematic diagram of the coordinates of a two-dimensional surface array provided in an embodiment of this application;

[0035] Figure 3 A schematic diagram illustrating the underwater target position estimation results obtained through simulation experiments using the method provided in the embodiments of this application;

[0036] Figure 4 A schematic diagram of the angle estimation RMSE curve obtained by simulation experiment using the method provided in the embodiments of this application;

[0037] Figure 5 A two-dimensional orientation finding device for underwater targets is provided in the embodiments of this application;

[0038] Figure 6 A computing device provided in an embodiment of this application. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be described below with reference to the accompanying drawings.

[0040] In the description of the embodiments of this application, the words "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0041] In the description of the embodiments in this application, the term "and / or" is merely a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, B existing alone, and A and B existing simultaneously. Furthermore, unless otherwise stated, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple terminals refer to two or more terminals.

[0042] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0043] In the description of the embodiments in this application, "some embodiments" are mentioned, which describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0044] In the description of the embodiments of this application, the terms "first, second, third, etc." or module A, module B, module C, etc. are used only to distinguish similar objects and do not represent a specific ordering of objects. It is understood that, where permitted, a specific order or sequence can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0045] In the description of the embodiments of this application, the reference numerals for the steps, such as S110, S120, etc., do not necessarily indicate that the steps will be executed in this manner. Where permissible, the order of the steps can be interchanged or executed simultaneously.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0047] In the high-precision target orientation problem, the first scheme, which is similar to the embodiment of this application, adopts the non-uniform diagonal unloading minimum variance distortion-free response (IDU-MVDR) method. It solves for the unloading amount of each array element through semi-positive definite optimization, maximizing the total unloading amount while constraining the minimum eigenvalue of the covariance matrix after unloading to a small positive value. This effectively suppresses noise interference, reduces the spatial spectrum background level, and maintains robustness. Although the algorithm proposed in the first scheme can effectively suppress noise interference and has high robustness, it can only distinguish targets with large angular differences, resulting in insufficient accuracy in target orientation estimation.

[0048] The second scheme, similar to the embodiment of this application, employs a uniform linear array model. By using the superbeam output as a weighting coefficient in conventional beamforming, it inherits the advantages of the superbeam's narrow main lobe and low side lobes, while avoiding the phase and spectral distortion problems caused by its nonlinear output. This supports subsequent matching filtering and spectral analysis. Although the second scheme, using a uniform linear array model, has high accuracy in azimuth estimation, its algorithm lacks noise immunity, and the limitations of the linear array model prevent it from estimating elevation angles, thus failing to determine the target's spatial orientation.

[0049] The underwater target two-dimensional direction finding method proposed in this application improves the uniform linear array model used in traditional superbeamforming methods to a two-dimensional area array model, enabling the method to simultaneously estimate the azimuth and elevation angles of underwater targets. It utilizes the minimum eigenvalue constraint of the covariance matrix after load reduction to eliminate unrelated noise components, improving the algorithm's noise immunity. Furthermore, it employs the spatial smoothing solution of overlapping subarrays to overcome signal cancellation between coherent echoes, improving the accuracy of target two-dimensional angle estimation. The superbeam output is multiplied as a weight by the IDU-MVDR outputs of each subarray to obtain a beamforming result with phase information, making it valuable for engineering applications.

[0050] Figure 1 A flowchart of a two-dimensional orientation finding method for underwater targets provided in an embodiment. Figure 1 As shown, it includes: S11, using a two-dimensional array model to receive the incident signal within the sonar detection area, the two-dimensional array model comprising... A uniform surface composed of receiving array elements; the incident signal includes the echo signal from the underwater target and noise components; S12, the two-dimensional surface array model is divided into L subarrays; each of the L subarrays includes S13, eliminate the uncorrelated noise components of the incident signals of the L subarrays to obtain the beam of each subarray, and the beam of each subarray is the result of minimum variance distortion-free response (IDU-MVDR); S14, perform overlapping subarray spatial smoothing solution on the beam of each subarray in the L subarrays to estimate the azimuth and elevation angles of the underwater target and determine the spatial orientation of the underwater target.

[0051] The above steps will be further discussed below with reference to the accompanying drawings and specific embodiments.

[0052] This application uses S11 to perform scene statistical modeling and constructs an active sonar detection system.

[0053] Specifically, the echo within the sonar detection area can be set by... A uniform array composed of receiving elements will receive the signal. A uniform surface array composed of 100 receiving array elements is used as a two-dimensional surface array model.

[0054] Without considering the directivity of a single array element, the first element on the two-dimensional surface array model can be determined. The coordinates of each array element are represented as follows:

[0055]

[0056] in , They are the first The coordinates of each array element on the Y-axis and Z-axis, with the Y-axis perpendicular to the Z-axis.

[0057] Based on this, the path difference of the incident signal received by two adjacent array elements is calculated. for:

[0058]

[0059] in, , This represents the azimuth and elevation angles of the incident signal received by the array element. , These represent the absolute values ​​of the difference between the y and z coordinates of two adjacent array elements, respectively. The incident signal received by an array element can be simply referred to as the received signal.

[0060] The steering vector a is determined based on the azimuth and elevation angles of the signal and the path difference between the received signals of two adjacent receiver elements. , ) is represented as:

[0061]

[0062] in The wavelength is the signal wavelength.

[0063] After time-frequency conversion based on the steering vector, the received signal matrix can be written as:

[0064]

[0065] in Indicates the first The frequency domain signal of each array element corresponds to the received signal covariance matrix as follows:

[0066]

[0067] , These represent the expectation and conjugate transpose operations, respectively.

[0068] S11 improves the uniform linear array model used in traditional superbeamforming methods to a two-dimensional surface array model, enabling the method in this application to simultaneously estimate the azimuth and elevation angles of underwater targets.

[0069] Next, in step S12, the two-dimensional surface model is divided into L subarrays according to the coordinate quadrants. Each of the L subarrays includes... Each receiving array element

[0070] In some possible implementations, L takes the value of at least 4. The array elements in the first four quadrants of the two-dimensional array model can be divided into four subarrays, each of which includes... Each receiving array element.

[0071] For step S13, this application utilizes the Non-Uniform Diagonal Unloading Minimum Variance Distortion-Free Response (IDU-MVDR) algorithm to solve the problem. The first in the team Individual element load reduction It can be expressed as the following positive semidefinite optimization problem:

[0072]

[0073]

[0074]

[0075] in, Indicates the construction with A diagonal matrix with diagonal elements. Indicates the first The covariance matrix of the received signals of each subarray Represents the covariance matrix The Middle One diagonal element, It is the identity matrix. The minimum eigenvalue of the covariance matrix after load reduction is determined. The size, empirically, ranges from 1. .

[0076] To solve this convex optimization problem, the CVX Convex Optimization Toolbox was used, yielding the result after load reduction. The covariance matrix of each subarray is:

[0077]

[0078] Constructing in a two-dimensional array space along the Y-axis and Z-axis A finely discrete grid of dimensions, in which ,

[0079] Using the covariance matrix after diagonal unloading Constructing the beam scanning vector:

[0080]

[0081] Then the first The beam of each subarray is:

[0082]

[0083] in, For the first Frequency domain signal input for each subarray.

[0084] For step S14, the beams of each subarray in the L subarrays are first overlapped.

[0085] When L is 4, the beams of two adjacent subarrays in the four subarrays are overlapped by "sum beam" and "difference beam" calculations to obtain the super-beam after overlap. Then, the main lobe width is adjusted based on the super-beam index and the beam splitting weight coefficient, and the side lobe height is adjusted based on the beam splitting weight coefficient.

[0086] For example, a super-beam can be constructed based on the beams of the four subarrays (IDU-MVDR outputs) after the beams of the first and third subarrays overlap, and a super-beam can be constructed based on the beams of the second and fourth subarrays overlap, as shown in the following formula:

[0087]

[0088] in, The super-beam index is used to adjust the main lobe width of the beam. For the beam splitting weighting coefficients of the beam formation, satisfying The super-beam formed by the overlap of the beams of the first subarray and the third subarray can be denoted as the first-third super-beam; the super-beam formed by the overlap of the beams of the second subarray and the fourth subarray can be denoted as the second-fourth super-beam.

[0089] According to the product theorem of basis matrices, and The resulting planar array superbeam is:

[0090]

[0091] In some possible implementations, the planar array superbeams can be determined based on the beams of the L subarrays; the planar array superbeams include at least one triple superbeam. And two- and four-beam supersonic waves; one- and three-beam supersonic waves Determined based on the sum and difference beams of the first and third subarray beams; 2-4 super-beam Determined based on the sum beam and difference beam of the second and fourth subarray beams.

[0092] Next, the spatial weighting coefficients are determined based on the planar array superbeam.

[0093] In some possible implementations, a planar array super-beam can be used. The spatial weighting coefficients are obtained by summing in the frequency domain:

[0094]

[0095] Finally, the spatial weighting coefficients are multiplied by the beams of the L subarrays respectively to obtain the weighted beams with phase information.

[0096] In some possible implementations, the beam results of the L subarrays can be summed to obtain the conventional beam output. Using spatial weighting coefficients right Weighted superbeams with phase information are obtained :

[0097]

[0098] According to the weighted beam with phase information Estimate the azimuth and pitch angles of the underwater target.

[0099] The simulation analysis will be performed below.

[0100] Monte Carlo simulation was used to verify the accuracy of target azimuth and elevation angle estimation in the embodiments of this application. The active detection sonar is equipped with... A uniform planar array with a certain number of elements, the element coordinate information is as follows: Figure 2 As shown. Assume there are two targets in the scene simultaneously, with targets 1 at an azimuth of 10.2° and an elevation of 3.8°, and targets 2 at an azimuth of 16.4° and an elevation of 2.6°, and both having the same echo intensity. The beam scanning direction is selected with azimuth angles from 0° to 20°, with an angle interval of 0.1°; and elevation angles from 0° to 10°, with an angle interval of 0.1°.

[0101] To verify the accuracy of the two-dimensional angle estimation and target resolution capability of the method provided in the embodiments of this application, the received signal segment of the detected target echo is extracted, and the proposed algorithm is used to process the signal segment.

[0102] Figure 3 The signal-to-noise ratio is given. The results of 10 simulation tests in the scenario show the two-dimensional angle estimation of the target. Here, "x" represents the true azimuth and pitch angles of the two targets, and "o" represents the estimated two-dimensional angle value of the target. As can be seen from the figure, the method provided in this embodiment can accurately estimate the azimuth and pitch angles of the target, with a maximum error within 1°. Based on the angle estimation results, two targets with similar angles can be distinguished.

[0103] To further examine the trend of target two-dimensional angle estimation error with SNR, Figure 4 , 5 The root mean square error (RMSE) between the estimated and actual two-dimensional angles of the target is given as a function of SNR in 100 trials.

[0104] The expression for RMSE is: RMSE = .

[0105] in, , They represent the first The estimated and actual values ​​of the azimuth in this experiment; , They are the first The estimated and actual values ​​of the pitch angle in this experiment The total number of trials.

[0106] The conventional area array superbeam method was selected as a comparison method.

[0107] As can be seen, the RMSE curves of the two targets are basically the same, and the RMSE decreases as the SNR increases. When the SNR is low, the RMSE of the method provided in this embodiment is significantly higher than that of the comparative method, indicating that the method provided in this embodiment has stronger noise resistance. When SNR=10dB, the RMSE values ​​of the two are similar and both tend to be below 0.2.

[0108] The core advantage of the method provided in this application lies in its innovative high-precision two-dimensional orientation finding technique for underwater targets. This technique first divides the array elements into four subarrays according to coordinate quadrants. The received signals from each subarray are processed using the IDU-MVDR algorithm, effectively eliminating incoherent noise components and significantly improving the algorithm's noise immunity. Based on this, the "sum beam" and "difference beam" are constructed using the IDU-MVDR output beams of each subarray, effectively compressing the main lobe width and suppressing the sidelobe height, thereby improving the accuracy and resolution of the target's two-dimensional angle estimation. Finally, by multiplying the super-beam output as a spatial weighting coefficient with the conventional IDU-MVDR output, a beamforming result with both noise immunity and high estimation accuracy, along with precise phase information, is obtained, providing a reliable technical approach for achieving accurate spatial orientation estimation of underwater targets.

[0109] This application's embodiments are based on a planar array target detection scenario, abandoning the uniform linear array model of traditional superbeamforming methods and instead using a two-dimensional planar array model. This is more in line with the working scenario of active detection by aircraft, enabling the detection system to not only acquire the target's azimuth angle information, but also to accurately estimate the target's pitch angle at the same time.

[0110] This application incorporates the IDU-MVDR algorithm into the super beamforming framework, which enables the algorithm to not only be robust against strong noise interference, but also to have the narrow main lobe and low side lobe performance of super beamforming algorithms, and to have high-precision two-dimensional angle estimation capabilities.

[0111] This application addresses the problem of lost target phase information in traditional super-beamforming methods. By using the super-beamforming results as weighting coefficients to spatially weight the IDU-MVDR beamforming results, the processed results not only retain the high-resolution advantages of super-beamforming but also allow for subsequent operations such as matched filtering, thus possessing high engineering practicality.

[0112] The high-precision underwater target orientation method based on IDU-MVDR superbeamforming proposed in this application not only has high robustness, but also can accurately measure the horizontal and pitch two-dimensional orientation of the target, which is of great significance for improving the accuracy of target spatial positioning in complex interference scenarios.

[0113] The above is an introduction to the underwater target direction finding method provided by the embodiments of this application. It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. In addition, in some possible implementations, each step in the above embodiments may be selectively executed according to the actual situation, and may be partially or fully executed, without limitation here. Furthermore, all or part of any feature of any of the above embodiments may be freely and arbitrarily combined without contradiction; the combined technical solution is also within the scope of this application.

[0114] Next, based on the above, the porous piezoelectric dielectric acoustic wave propagation device provided in the embodiments of this application will be described. For details regarding the concepts, formulas, etc., involved in the following content, please refer to the above text.

[0115] Figure 5 This application provides an underwater target direction finding device as an embodiment. For example... Figure 5 As shown, the underwater target direction finding device 50 includes a scene modeling module 51, a subarray division module 52, a noise reduction module 53, and a positioning module 54.

[0116] In the device, the scene modeling module 51 uses a two-dimensional array model to receive incident signals within the sonar detection area. The two-dimensional array model includes components from... A uniform surface composed of receiving array elements; the incident signal includes the echo signal from the underwater target and noise components; the subarray partitioning module 52 divides the two-dimensional surface array model into L subarrays; each of the L subarrays includes There are L receiving array elements; the value of L is at least 4; the denoising module 53 eliminates the uncorrelated noise components of the incident signals of the L subarrays to obtain the beam of each subarray in the L subarrays. The beam of each subarray is the result of non-uniform diagonal load reduction minimum variance distortion-free response (IDU-MVDR); the positioning module 54 performs overlapping subarray spatial smoothing solution on the beam of each subarray in the L subarrays to estimate the azimuth and elevation angles of the underwater target and determine the spatial orientation of the underwater target.

[0117] Specifically, the scene modeling module 51 uses a two-dimensional array model to receive incident signals within the sonar detection area, and determines the steering vector a based on the path difference of the incident signals received by two adjacent receiving array elements. , ), guide vector a( , )for:

[0118]

[0119] in, , Indicates the azimuth and elevation angles of the incident signal. This indicates the path difference of the received signals between two adjacent array elements. The incident signal wavelength is given. The incident signal covariance matrix is ​​obtained by performing time-frequency conversion, expectation, and conjugate transpose operations based on the incident signal steering vector. The incident signal covariance matrix is:

[0120]

[0121] in, , These represent the expectation and conjugate transpose operations, respectively. For the received signal matrix:

[0122] in Indicates the first The frequency domain signal of each array element.

[0123] Subarray partitioning module 52 divides the two-dimensional array model into L subarrays; each of the L subarrays includes There are 1 receiving array element; the value of L is at least 4.

[0124] The denoising module 53 uses the Non-Uniform Diagonal Load Reduction Minimum Variance Distortion-Free Response (IDU-MVDR) method to solve for the load reduction of each element in each of the L subarrays; according to the... The covariance matrix of the received signals of each subarray and the The load reduction amount for each element in the subarray is determined after the load reduction is completed. The covariance matrix of each subarray for:

[0125]

[0126] in, For the first The first in the team Individual element load reduction, ; No. The element load reduction includes the first Uncorrelated noise components on each array element; Indicated by It is a diagonal matrix with diagonal elements.

[0127] In some possible implementations, the denoising module 53 makes the minimum eigenvalue of the covariance matrix after load reduction positive based on the constraint condition:

[0128]

[0129]

[0130] in, It is the identity matrix. It is the smallest eigenvalue of the covariance matrix; Represents the covariance matrix The Middle One diagonal element, The load reduction factor is used to maximize the total load reduction.

[0131] .

[0132] The load reduction of each array element is obtained by solving for the constraints and maximizing the total load reduction.

[0133] In some possible implementations, the denoising module 53 determines the noise reduction based on the diagonally de-loaded covariance matrix. Determine the Lth subarray Beams of individual arrays for:

[0134] .

[0135] in, For the azimuth and elevation angles in a two-dimensional array; For the first Frequency domain signal input for each subarray; Beam scanning vector:

[0136] .

[0137] In some possible implementations, the beams of the L subarrays include a first subarray beam, a second subarray beam, a third subarray beam, and a fourth subarray beam. The positioning module 54 determines the planar array superbeams based on the beams of the L subarrays. The planar array superbeams include a first-third superbeam and a second-fourth superbeam. The first-third superbeam is determined based on the sum and difference of the first and third subarray beams. The second-fourth superbeam is determined based on the sum and difference of the first and third subarray beams. The main lobe width and side lobe height of the planar array superbeams are adjusted based on the superbeam index and the beam splitting weight coefficient.

[0138] In some possible implementations, the positioning module 54 determines the spatial weighting coefficients based on the planar array superbeams; multiplies the spatial weighting coefficients by the beams of the L subarrays respectively to obtain weighted beams with phase information; and estimates the azimuth and elevation angles of the underwater target based on the weighted beams with phase information.

[0139] As an example of a software functional unit, the scenario modeling module 51 may include code running on a computing instance. The computing instance may include at least one of a physical host (computing device), a virtual machine, or a container. Furthermore, the aforementioned computing instance may be one or more. For example, the scenario modeling module 51 may include code running on multiple hosts / virtual machines / containers. It should be noted that the multiple hosts / virtual machines / containers used to run the code may be distributed within the same region or in different regions. Further, the multiple hosts / virtual machines / containers used to run the code may be distributed within the same availability zone (AZ) or in different AZs, each AZ including one or more geographically proximate data centers. Typically, a region may include multiple AZs.

[0140] Similarly, multiple hosts / virtual machines / containers used to run this code can be distributed within the same Virtual Private Cloud (VPC) or across multiple VPCs. Typically, a VPC is set up within a region. Communication between two VPCs within the same region, as well as between VPCs in different regions, requires a communication gateway to be set up within each VPC to enable interconnection between VPCs.

[0141] This application also provides a computing device 60. For example... Figure 6 As shown, the computing device 60 includes a bus 62, a processor 64, a memory 66, and a communication interface 68. The processor 64, the memory 66, and the communication interface 68 communicate with each other via the bus 62. The computing device 60 can be a computing device or a terminal device. It should be understood that this application does not limit the number of processors and memories in the computing device 60.

[0142] Bus 62 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 6 The bus 64 is represented by only one line, but this does not mean that there is only one bus or one type of bus. The bus 64 may include a path for transmitting information between various components of the computing device 60 (e.g., memory 66, processor 64, communication interface 68).

[0143] Processor 64 may include any one or more processors such as a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0144] The memory 66 may include volatile memory, such as random access memory (RAM). The processor 104 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0145] The memory 66 stores executable program code, and the processor 64 executes the executable program code to implement the aforementioned functions. Figure 5 The underwater target direction finding device shown in the diagram performs the functions of all or part of the steps in the methods described above. That is, memory 66 stores information for executing the above-described methods. Figure 1 Instructions for all or part of the steps in the method of the illustrated embodiment.

[0146] The communication interface 68 uses transceiver modules, such as, but not limited to, network interface cards and transceivers, to enable communication between the computing device 60 and other devices or communication networks.

[0147] This application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to perform the method as described in any one of the first aspects.

[0148] It is understood that the processor in the embodiments of this application may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor may be a microprocessor or any conventional processor.

[0149] The method steps in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0150] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0151] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application.

Claims

1. A two-dimensional orientation finding method for underwater targets, characterized in that, The method includes: A two-dimensional array model is used to receive incident signals within the sonar detection area. The two-dimensional array model comprises... A uniform surface composed of receiving array elements; the incident signal includes the echo signal from the underwater target and noise components; The two-dimensional array model is divided into L subarrays; each of the L subarrays includes There are 1 receiving array element; the value of L is at least 4; The noise components of the incident signals of the L subarrays are eliminated respectively to obtain the beam of each of the L subarrays. The beam of each subarray is the result of non-uniform diagonal unloaded minimum variance distortion-free response (IDU-MVDR). The azimuth and elevation angles of the underwater target are estimated by performing overlapping subarray spatial smoothing on the beams of each of the L subarrays, thereby determining the spatial orientation of the underwater target.

2. The method according to claim 1, characterized in that, The method of receiving incident signals within the sonar detection area using a two-dimensional array model includes: The steering vector a is determined based on the path difference of the incident signals received by two adjacent receiving array elements. , The guiding vector a( , )for: in, , This indicates the azimuth and elevation angles of the incident signal. This indicates the path difference of the received signals between two adjacent array elements. The wavelength of the signal; The incident signal covariance matrix is ​​obtained by performing time-frequency conversion, expectation, and conjugate transpose operations on the incident signal steering vector. The incident signal covariance matrix is ​​as follows: in, , These represent the expectation and conjugate transpose operations, respectively. For the received signal matrix: in Indicates the first The frequency domain signal of each array element.

3. The method according to claim 1 or 2, characterized in that, The step of estimating the azimuth and elevation angles of an underwater target based on the spatially smoothed solution of the overlapping subarrays of the plurality of subarrays includes: The load reduction of each element in each of the L subarrays is solved using the Non-Uniform Diagonal Load Reduction Minimum Variance Distortion-Free Response (IDU-MVDR) method. According to the The covariance matrix of the received signals of each subarray and the The load reduction amount for each element in the subarray is determined after the load reduction is completed. The covariance matrix of each subarray for: in, For the first The first in the team Individual element load reduction, The first The element load reduction includes the first Uncorrelated noise components on each array element; Indicated by It is a diagonal matrix with diagonal elements.

4. The method according to claim 3, characterized in that, The method of using non-uniform diagonal load reduction minimum variance distortion-free response (IDU-MVDR) to solve for the load reduction of each element in each of the L subarrays includes: The constraint condition is to make the minimum eigenvalue of the covariance matrix after load reduction positive. The constraint condition is: in, It is the identity matrix. It is the smallest eigenvalue of the covariance matrix; Represents the covariance matrix The Middle One diagonal element, This is the load reduction factor; And maximize the total load reduction: 。 The load reduction of each array element is obtained by solving the constraints and maximizing the total load reduction.

5. The method according to claim 3, characterized in that, The step of eliminating the uncorrelated noise components of the incident signals of the L subarrays to obtain the beam of each of the L subarrays includes: Based on the covariance matrix after diagonal load reduction Determine the Lth subarray. Beams of individual arrays for: 。 in, The azimuth and elevation angles in the two-dimensional array; For the first Frequency domain signal input for each subarray; Beam scanning vector: 。 6. The method according to claim 5, characterized in that, The beams of the L subarrays include a first subarray beam, a second subarray beam, a third subarray beam, and a fourth subarray beam. The estimation of the azimuth and elevation angles of the underwater target based on the spatial smoothing solution of the overlapping subarrays further includes: The planar array super-beams are determined based on the beams of the L subarrays; the planar array super-beams include a first-three super-beam and a second-four super-beam; the first-three super-beams are determined based on the "sum beam" and "difference beam" of the first and third subarray beams; the second-four super-beams are determined based on the "sum beam" and "difference beam" of the first and third subarray beams. The main lobe width and side lobe height of the planar array superbeam are adjusted based on the superbeam index and the beam splitting weight coefficient.

7. The method according to claim 6, characterized in that, The step of estimating the azimuth and elevation angles of the underwater target based on the planar array spatial smoothing solution of the plurality of subarrays further includes: The spatial weighting coefficients are determined based on the planar array superbeam; The spatial weighting coefficients are multiplied by the beams of the L subarrays respectively to obtain weighted beams with phase information; The azimuth and elevation angles of the underwater target are estimated based on the weighted beam with phase information.

8. A two-dimensional orientation finding device for underwater targets, characterized in that, The device includes: The scene modeling module is used to receive incident signals within a sonar detection area using a two-dimensional array model, wherein the two-dimensional array model comprises... A uniform surface composed of receiving array elements; the incident signal includes the echo signal from the underwater target and noise components; The subarray partitioning module is used to divide the two-dimensional planar array model into L subarrays; each of the L subarrays includes There are 1 receiving array element; the value of L is at least 4; The noise reduction module is used to eliminate the uncorrelated noise components of the incident signals of the L subarrays to obtain the beam of each subarray. The beam of each subarray is the result of non-uniform diagonal unloaded minimum variance distortion-free response (IDU-MVDR). The positioning module is used to perform overlapping subarray spatial smoothing solution on the beams of each of the L subarrays to estimate the azimuth and elevation angles of the underwater target and determine the spatial orientation of the underwater target.

9. A computing device, characterized in that, include: At least one memory for storing programs; At least one processor is configured to execute a program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to perform the method as described in any one of claims 1-8.

10. A computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to perform the method as described in any one of claims 1-8.

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