An Adaptive Underwater Sound Source Passive Localization Method and System

Abstract

The present invention discloses an adaptive underwater sound source passive localization method and system. The method includes: acquiring the signals received by a hydrophone array; converting the signals received by the array to the frequency domain by using fast Fourier transform, obtaining the effective frequency band of the target signal source through spectrum analysis, and constructing a steering vector estimation; calculating the covariance matrix of the signals received by the array, and obtaining the eigenvalues and eigenvectors of the covariance matrix through eigenvalue decomposition; constructing a weight matrix by using the eigenvalues of the covariance matrix, weighting all the eigenvalues of the covariance matrix with the weight matrix to form a fully weighted spatial vector, and realizing adaptive fully weighted MUSIC spatial spectrum estimation through iterative operation and setting of iterative termination conditions; and obtaining the estimated value of the arrival azimuth. The present invention avoids the need for estimating the number of signal sources in the conventional MUSIC algorithm, and solves the situation of performance degradation of the beamformer caused by steering vector mismatch and noise subspace division error in practical applications.

Description

Technical Field

[0001] The present invention belongs to the field of adaptive underwater sound source passive localization, and particularly relates to an adaptive underwater sound source passive localization method and system. Background Technique

[0002] The signal processing technology of the hydrophone array determines the detection, localization and target extraction detection capabilities of the sonar. The passive localization method of underwater acoustic signals based on the array signal processing technology is of great significance for whether the hydrophone array can give full play to its advantages such as good platform concealment, high azimuth resolution and long detection range.

[0003] The Multiple Signal Classification (MUSIC) algorithm is a widely used high-resolution target azimuth estimation algorithm based on subspace. This method has high estimation accuracy and stability in a specific environment, but the accurate desired signal steering vector and the number of signal sources are the prerequisite conditions for the super-resolution direction-finding algorithm to exert its super-resolution performance.

[0004] The traditional beamforming method assumes that the steering vector of the desired signal is accurately known. However, in actual scenarios, the steering vector mismatch will seriously affect the directional performance of the beamformer. Usually, error analysis and correction of the measurement results are required to improve this phenomenon. The conventional method is to model the uncertain set of the steering vector or use the weighted vector to increase the inequality constraint to improve the robustness of the beamforming. However, the actual mismatch situation requires accurate modeling of the probability distribution and selection of appropriate parameters to maintain the robustness of the algorithm.

[0005] The MUSIC algorithm is a method based on matrix eigen-space decomposition, which performs super-resolution estimation of the arrival direction by using the orthogonality between the signal subspace of the signal source and the noise subspace. For the performance of the MUSIC algorithm, the accuracy of estimating the number of signal sources is very important. If there is an error in the estimated number of signal sources, the noise subspace determined according to the number of signal sources will not match the actual noise subspace. In the case of overestimating the number of signal sources, the estimated number of signal sources is more than the actual value, and the MUSIC spatial spectrum will produce a false alarm phenomenon; in the case of underestimating the number of signal sources, the estimated number of signal sources is less than the actual value, and the MUSIC spatial spectrum will produce a missed alarm phenomenon and a large deviation in the estimated arrival direction. Usually, the Akaike Information Criterion (AIC) and the Minimum Description Length (MDL) are used to estimate the number of signal sources, but the AIC and MDL methods will fail in a colored noise environment, and the computational complexity of using such signal source estimation algorithms is large, which will cause additional time consumption.

[0006] Therefore, it is a problem that must be solved in engineering applications to study an adaptive beamforming algorithm that can achieve accurate target estimation when the source frequency and number are unknown. Summary of the Invention

[0007] Embodiments of the present application provide an adaptive passive localization method, system, storage medium, and electronic device for underwater sound sources to solve the problem of degraded beamforming performance caused by steering vector mismatch and inaccurate estimation of the number of signal sources in existing adaptive passive localization methods for underwater sound sources in practical applications.

[0008] The present invention provides an adaptive passive localization method for underwater sound sources, which includes:

[0009] Acquisition step: Acquire the signals received by the hydrophone array;

[0010] Spectrum analysis step: Use the fast Fourier transform to convert the signal into the frequency domain, obtain the effective frequency band of the target signal source through spectrum analysis, and construct a steering vector estimate;

[0011] Calculation step: Calculate the covariance matrix of the array received signals, and obtain the eigenvalues and eigenvectors of the covariance matrix through eigenvalue decomposition;

[0012] Setting step: Set the iteration parameters and iteration termination conditions;

[0013] Obtaining step: Design a weight matrix according to the eigenvalues of the covariance matrix, weight all the eigenvalues of the received signal covariance matrix according to the weight matrix to form a fully weighted spatial vector; construct a MUSIC spatial spectrum function, obtain the current arrival azimuth estimate value through spectrum peak search, use the current arrival azimuth estimate value to calculate the current spatial spectrum cost function value; and obtain the weight matrix for the next iteration;

[0014] Judgment step: Estimate the spatial spectrum cost function value for the next iteration according to the current arrival azimuth estimate value and the weight matrix for the next iteration, and compare whether the absolute value of the difference between the current spatial spectrum cost function value and the estimated value of the spatial spectrum cost function for the next iteration satisfies the iteration termination condition. If it is satisfied, output the first judgment result, and the first judgment result is the target azimuth estimate value; otherwise, output the second judgment result. If the output result is the second judgment result, then add 1 to the iteration parameter value and jump to the obtaining step.

[0015] The above-mentioned adaptive passive localization method for underwater sound sources further includes:

[0016] Establish a signal model for a uniform linear array.

[0017] In the above-mentioned adaptive passive localization method for underwater sound sources, the obtaining step includes:

[0018] Weight all the eigenvectors of the covariance matrix with the weight matrix to form a fully weighted spatial vector.

[0019] The above adaptive passive localization method for underwater sound sources, wherein the obtaining step includes:

[0020] Estimation value obtaining step: Perform a spectral peak search on the spatial spectrum within a certain range to obtain an estimated value of the direction of arrival.

[0021] Current spatial spectrum cost function value obtaining step: Obtain the current spatial spectrum cost function value by using the current estimated direction of arrival and the definition of the spatial spectrum cost function.

[0022] Next iteration spatial spectrum cost function estimated value obtaining step: Calculate the estimated value of the spatial spectrum cost function for the next iteration by using the current estimated direction of arrival and the weight matrix for the next iteration through the definition of the spatial spectrum cost function.

[0023] The above adaptive passive localization method for underwater sound sources, wherein the judging step includes:

[0024] Set an iteration threshold; if the absolute value of the difference between the current spatial spectrum cost function value and the estimated value of the spatial spectrum cost function for the next iteration is less than the iteration threshold, the iteration termination condition is satisfied, and output the first judgment result; if the absolute value of the difference between the current spatial spectrum cost function value and the estimated value of the spatial spectrum cost function for the next iteration is not less than the iteration threshold, the iteration termination condition is not satisfied, and output the second judgment result.

[0025] The above adaptive passive localization method for underwater sound sources, wherein if the output result is the second judgment result, the iteration process is repeated.

[0026] The above adaptive passive localization method for underwater sound sources, wherein the calculating step includes:

[0027] Calculate the covariance matrix according to at least one signal vector, perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalues and eigenvectors of the covariance matrix.

[0028] The above adaptive passive localization method for underwater sound sources, wherein the obtaining step further includes:

[0029] Design the weight matrix by using the eigenvalues, consistently retain or amplify the noise eigenvectors, and reduce the signal eigenvectors to suppress the signal subspace components.

[0030] The above adaptive passive localization method for underwater sound sources, wherein the obtaining step includes:

[0031] Construct the MUSIC spatial spectrum function of the array according to the orthogonality between the steering vector estimation and the fully weighted spatial vector.

[0032] The present invention also provides an adaptive underwater sound source passive localization system, which includes:

[0033] An acquisition module, which acquires the signals received by the hydrophone array;

[0034] A spectrum analysis module, which uses the fast Fourier transform to convert the signals into the frequency domain, obtains the effective frequency band of the target signal source through spectrum analysis, and constructs the steering vector estimation;

[0035] A calculation module, which calculates the covariance matrix of the signals received by the array, and obtains the eigenvalues and eigenvectors of the covariance matrix through eigenvalue decomposition;

[0036] A setting module, which sets the iteration parameters and the iteration termination conditions;

[0037] An obtaining module, which designs a weight matrix according to the eigenvalues of the covariance matrix, weights all the eigenvalues of the received signal covariance matrix according to the weight matrix to form a fully weighted spatial vector; constructs the MUSIC spatial spectrum function, obtains the current arrival direction estimation value through spectrum peak search, uses the current arrival direction estimation value to calculate the current spatial spectrum cost function value; and obtains the weight matrix for the next iteration;

[0038] A judgment module, which estimates the spatial spectrum cost function value for the next iteration according to the current arrival direction estimation value and the weight matrix for the next iteration, compares whether the absolute value of the difference between the current spatial spectrum cost function value and the estimated value of the spatial spectrum cost function for the next iteration meets the iteration termination conditions. If it meets, it outputs the first judgment result, and the first judgment result is the target direction estimation value. Otherwise, it outputs the second judgment result. If the output result is the second judgment result, the iteration parameter value is incremented by one and jumps to the obtaining step.

[0039] The beneficial effects of the present invention are as follows:

[0040] The present invention obtains the frequency-domain spectrum by performing a fast Fourier transform on the received signals of the array elements, and estimates the steering vector through spectrum analysis, thereby solving the problem of degraded beamforming performance caused by the mismatch of the steering vector in practical applications, and improving the robustness of the algorithm. In the conventional MUSIC algorithm, the noise subspace is used for spatial spectrum estimation, and the noise subspace needs to be determined through the estimation of the number of signal sources. In the present invention, the eigenvalues of the covariance matrix are used to construct a weight matrix to weight all the eigenvectors, and the noise eigenvectors are consistently retained or amplified and the signal eigenvectors are reduced through iterative operations to adaptively determine the weight matrix and the fully weighted spatial vector, and obtain the MUSIC spatial spectrum function, eliminating the influence of the signal source estimation on the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation of the present application.

[0042] In the drawings:

[0043] Figure 1 is a flowchart of the method for passive localization of underwater sound sources adapted to the present invention;

[0044] Figure 2 is a flowchart of step S5 of the present invention;

[0045] Figure 3 is a general flowchart of the method for passive localization of underwater sound sources adapted to the present invention;

[0046] Figure 4 is a schematic diagram of spatial spectrum estimation of the present invention;

[0047] Figure 5 is a schematic structural diagram of the system for passive localization of underwater sound sources adapted to the present invention;

[0048] Figure 6 is a framework diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described and explained below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments provided in the present application without creative efforts shall fall within the scope of protection of the present application.

[0050] Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application. For those of ordinary skill in the art, without creative efforts, the present application can also be applied to other similar scenarios based on these drawings. In addition, it can also be understood that although the efforts made in such a development process may be complex and lengthy, for those of ordinary skill in the art related to the content disclosed in the present application, some design, manufacturing, or production changes based on the technical content disclosed in the present application are only conventional technical means and should not be understood as the content disclosed in the present application being insufficient.

[0051] When the term "embodiment" is mentioned in the present application, it means that the specific features, structures, or characteristics described in combination with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. Those of ordinary skill in the art explicitly and implicitly understand that the embodiments described in the present application can be combined with other embodiments without conflict.

[0052] Unless otherwise defined, the technical terms or scientific terms involved in the present application should be the ordinary meanings understood by those of ordinary skill in the technical field to which the present application belongs. The terms "a", "an", "one", "the", and similar words involved in the present application do not indicate a quantity limitation and can represent a singular or plural number. The terms "including", "comprising", "having", and any variations thereof involved in the present application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may further include other steps or units inherent to these processes, methods, products, or devices. The terms "connected", "coupled", and similar words involved in the present application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" involved in the present application refers to two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after. The terms "first", "second", "third", etc. involved in the present application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0053] The present invention will be described in detail below in conjunction with the embodiments shown in the accompanying drawings. It should be noted, however, that these embodiments are not intended to limit the present invention, and any equivalent transformation or substitution in terms of function, method, or structure made by those of ordinary skill in the art based on these embodiments shall fall within the protection scope of the present invention.

[0054] Before elaborating on each embodiment of the present invention in detail, an overview of the core inventive concept of the present invention is provided and will be elaborated in detail through the following several embodiments.

[0055] Embodiment 1:

[0056] Please refer to Figure 1 , Figure 1 which is a flowchart of the adaptive underwater sound source passive localization method. As Figure 1 shown, the adaptive underwater sound source passive localization method of the present invention includes:

[0057] Acquisition step S1: Acquire the signals received by the hydrophone array;

[0058] Spectrum analysis step S2: Use the fast Fourier transform to convert the signal into the frequency domain, obtain the effective frequency band of the target signal source through spectrum analysis, and construct a steering vector estimate;

[0059] Calculation step S3: Calculate the covariance matrix of the array received signals, and obtain the eigenvalues and eigenvectors of the covariance matrix through eigenvalue decomposition;

[0060] Setting step S4: Set the iteration parameters and iteration termination conditions;

[0061] Obtaining step S5: Design a weight matrix according to the eigenvalues of the covariance matrix, weight all the eigenvalues of the received signal covariance matrix according to the weight matrix to form a fully weighted space vector; construct a MUSIC space spectrum function, obtain the current arrival azimuth estimate value through spectrum peak search, use the current arrival azimuth estimate value to calculate the current space spectrum cost function value; and obtain the weight matrix for the next iteration;

[0062] Judgment step S6: Estimate the space spectrum cost function value for the next iteration according to the current arrival azimuth estimate value and the weight matrix for the next iteration, and compare whether the absolute value of the difference between the current space spectrum cost function value and the estimated value of the space spectrum cost function for the next iteration satisfies the iteration termination condition. If it is satisfied, output the first judgment result, and the first judgment result is the target azimuth estimate value; otherwise, output the second judgment result. If the output result is the second judgment result, then the iteration parameter value is incremented by 1 and jumps to the obtaining step.

[0063] Among them, it further includes:

[0064] Establish a signal model for a uniform linear array.

[0065] Among them, the obtaining step includes:

[0066] Weight all the eigenvectors of the covariance matrix with the weight matrix to form a fully weighted spatial vector.

[0067] Please refer to Figure 2 , Figure 2 is the flowchart of step S5. As Figure 2 shown, among them, the obtaining step S5 includes:

[0068] Estimated value obtaining step S51: Perform a spectral peak search on the spatial spectrum within a certain range to obtain an estimated value of the direction of arrival;

[0069] Current spatial spectrum cost function value obtaining step S52: Obtain the current spatial spectrum cost function value by using the current direction of arrival estimate value and the definition of the spatial spectrum cost function;

[0070] Next iteration spatial spectrum cost function estimated value obtaining step S53: Calculate the estimated value of the spatial spectrum cost function for the next iteration by using the current direction of arrival estimate value and the weight matrix for the next iteration through the definition of the spatial spectrum cost function.

[0071] Among them, the judgment step includes:

[0072] Set an iteration threshold; if the absolute value of the difference between the current spatial spectrum cost function value and the estimated value of the spatial spectrum cost function for the next iteration is less than the iteration threshold, the iteration termination condition is satisfied, and the first judgment result is output; if the absolute value of the difference between the current spatial spectrum cost function value and the estimated value of the spatial spectrum cost function for the next iteration is not less than the iteration threshold, the iteration termination condition is not satisfied, and the second judgment result is output.

[0073] Among them, if the output result is the second judgment result, the iteration process is repeated.

[0074] Among them, the calculation step includes:

[0075] Calculate the covariance matrix based on at least one signal vector, perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalues and eigenvectors of the covariance matrix.

[0076] Among them, the obtaining step further includes:

[0077] Design the weight matrix by using the eigenvalues, consistently retain or amplify the noise eigenvectors, and shrink the signal eigenvectors to suppress the signal subspace components.

[0078] Among them, the obtaining step includes:

[0079] Construct the MUSIC spatial spectrum function of the array according to the orthogonality between the estimated steering vector and the fully weighted spatial vector.

[0080] Specifically, as Figure 3 shown, in view of the steering vector mismatch and the number and position of signal sources that occur in practical applications, the present invention provides an adaptive passive underwater sound source localization algorithm based on steering vector estimation. The flowchart is as Figure 1 shown. The adaptive passive underwater sound source localization algorithm based on steering vector estimation according to the present invention includes the following steps:

[0081] The first step: Establish a signal model for a uniform linear array;

[0082] Furthermore, for N signals incident on M array elements, the signal data model received by the array is:

[0083] (1) X(t) = A(θ)s(t) + N(t); In formula (1), X(t) = [x1(t), x2(t), … x M (t)] T is the array data reception matrix, A(θ) = [a(θ1), a(θ2), …, a(θ N )]Λ is the M*N steering vector matrix of the array represents the steering vector in the direction of angle θ1, s(t) = [s1(n), s2(n), …, s N (n)] T is the N*1 dimensional arrival signal vector of the spatial signal, N(t) = [n1(t), n2(t), …, n M (t)] T is the M*1 dimensional noise data vector of the array.

[0084] The second step: Perform spectrum analysis on the received signal, obtain the effective frequency band of the signal through fast Fourier transform, and construct a steering vector estimation;

[0085] Specifically, perform a transform on the received signal through fast Fourier transform to obtain the frequency domain spectrum information of the signal, X(ω) = FFT(X(t)), and obtain the frequency estimation by solving the frequency corresponding to the maximum modulus of the spectrum in the frequency domain where f s is the signal sampling frequency. Construct a steering vector estimation matrix from the frequency estimation

[0086] The third step: Calculate the covariance matrix of the array received signal, perform eigenvalue decomposition on it, and the eigenvalues and eigenvectors of the covariance matrix can be obtained; ​

[0087] Specifically, a covariance matrix is obtained based on N received signal vectors Perform eigenvalue decomposition on the covariance matrix R Obtain eigenvalues λ and eigenvectors u

[0088] Fourth step: Initialize the iteration number parameter i = 1

[0089] Fifth step: Design a weight matrix using eigenvalues, consistently retain or amplify noise eigenvectors, and shrink signal eigenvectors to achieve the purpose of suppressing signal subspace components

[0090] Specifically, in the fifth step: Construct a weighting matrix

[0091] Sixth step: Weight all eigenvectors of the received signal covariance matrix with the weight matrix to form a fully weighted spatial vector

[0092] Specifically, use the weighting matrix to construct a fully weighted spatial vector U (i) w = R w (i) U, where U = [u1, u2, …, u M .

[0093] Seventh step: Use the orthogonality between the optimal steering vector estimate and the fully weighted spatial vector to construct the MUSIC spatial spectrum function of the array

[0094] Specifically, use the obtained steering vector estimate and the fully weighted spatial vector to construct the Music spatial spectrum function

[0095]

[0096] Eighth step: Through the spectral peak search of the spatial spectrum, solve for the angle corresponding to the modulus maximum value of the spectral peak to obtain the estimated value of the sound source target azimuth

[0097] Specifically, perform a spectral peak search of the spatial spectrum within to obtain the estimated value of the direction of arrival

[0098] Ninth step: Use the obtained estimated value of the target azimuth to calculate the current spatial spectrum cost function value and obtain the weight matrix for the next iteration

[0099] Specifically, obtain the current spatial spectrum cost function value at the estimated value of the direction of arrival

[0100] ​Step 10: Set the termination condition for the iterative process. If the condition is not met, the iteration jumps back to Step 5 for a loop; if the iteration termination condition is satisfied, the iteration ends and the final estimated value of the target azimuth is obtained.

[0101] Specifically, set the termination condition for the iterative process. If the termination condition is not met, then i = i + 1 and jump to Step 5 for the next iteration. If the termination condition is satisfied, the iteration terminates, and the estimated value of the target azimuth is where the iteration termination condition is to calculate the spatial spectrum cost function value for the (i + 1)-th iteration. If then the condition is satisfied and the iteration continues; otherwise, the iteration process ends. Here, ε is the set iteration threshold.

[0102] Furthermore, as Figure 4 shown, the specific implementation of the present invention is further verified through a simulation example. A hydrophone array model with 8 array elements and an element spacing of half a wavelength is used. Three uncorrelated signals with the same frequency are incident on the hydrophone array from [10°, 35°, 60°] respectively, and the signal-to-noise ratio is -5 dB. The spatial spectra obtained by using the conventional MUSIC and the adaptive MUSIC algorithm designed according to the present invention based on the steering vector estimation are used. The conventional MUSIC algorithm has three sharp spectral peaks at 10°, 39°, and 57° respectively, among which 39° and 57° are significantly deviated from the true source azimuths. However, the adaptive MUSIC algorithm adopted by the present invention has three sharp spectral peaks at 10°, 35°, and 60°. Therefore, it can be seen that the adaptive MUSIC algorithm designed by the present invention can correctly estimate the angles of multiple targets under low signal-to-noise ratio conditions, and the main lobe width of the spatial spectrum is narrow, having a higher spatial resolution compared with the conventional MUSIC algorithm.

[0103] Embodiment 2:

[0104] Please refer to Figure 5 , Figure 5 which is a schematic structural diagram of the adaptive underwater sound source passive localization system of the present invention.

[0105] As Figure 5 shown, an adaptive underwater sound source passive localization system of the present invention includes:

[0106] An acquisition module 11, which acquires the signals received by the hydrophone array.

[0107] A spectrum analysis module 12, which uses the fast Fourier transform to convert the signals into the frequency domain, obtains the effective frequency band of the target signal source through spectrum analysis, and constructs a steering vector estimation.

[0108] A calculation module 13, the calculation module 13 calculates the covariance matrix of the array received signal, and obtains the eigenvalues and eigenvectors of the covariance matrix through eigenvalue decomposition;

[0109] A setting module 14, the setting module 14 sets iteration parameters and iteration termination conditions;

[0110] An obtaining module 15, the obtaining module 15 designs a weight matrix according to the eigenvalues of the covariance matrix, weights all the eigenvalues of the received signal covariance matrix according to the weight matrix to form a fully weighted spatial vector; constructs a MUSIC spatial spectrum function, obtains the current arrival direction estimation value through spectrum peak search, uses the current arrival direction estimation value to calculate the current spatial spectrum cost function value; and obtains the weight matrix for the next iteration;

[0111] A judgment module 16, the judgment module 16 estimates the spatial spectrum cost function value for the next iteration according to the current arrival direction estimation value and the weight matrix for the next iteration, and compares whether the absolute value of the difference between the current spatial spectrum cost function value and the estimated value of the spatial spectrum cost function for the next iteration satisfies the iteration termination condition. If it is satisfied, the first judgment result is output, and the first judgment result is the target direction estimation value. Otherwise, the second judgment result is output. If the output result is the second judgment result, the iteration parameter value is incremented by one and jumps to the obtaining step.

[0112] Embodiment 3:

[0113] Combined with Figure 6 As shown, this embodiment discloses a specific implementation manner of an electronic device. The electronic device may include a processor 81 and a memory 82 storing computer program instructions.

[0114] Specifically, the above-mentioned processor 81 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured with one or more integrated circuits implementing the embodiments of the present application.

[0115] Among them, the memory 82 may include a mass memory for data or instructions. By way of example and not limitation, the memory 82 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 82 may include removable or non-removable (or fixed) media. Where appropriate, the memory 82 may be internal or external to the data processing device. In a particular embodiment, the memory 82 is a non-volatile memory. In a particular embodiment, the memory 82 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or a flash memory, or a combination of two or more of these. Where appropriate, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended date out dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0116] The memory 82 can be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 81.

[0117] The processor 81 reads and executes the computer program instructions stored in the memory 82 to implement any one of the adaptive underwater sound source passive localization methods in the above embodiments.

[0118] In some of the embodiments, the electronic device may further include a communication interface 83 and a bus 80. Among them, as Figure 6 shown, the processor 81, the memory 82, and the communication interface 83 are connected through the bus 80 and complete communication with each other.

[0119] The communication interface 83 is used to implement communication between the various modules, devices, units, and / or devices in the embodiments of the present application. The communication port 83 can also implement data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0120] Bus 80 includes hardware, software, or both, and couples components of an electronic device to each other. Bus 80 includes, but is not limited to, at least one of the following: Data Bus, Address Bus, Control Bus, Expansion Bus, Local Bus. By way of example and not limitation, Bus 80 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable bus or a combination of two or more of these. In a suitable case, Bus 80 may include one or more buses. Although embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0121] The electronic device can be based on adaptive underwater sound source passive positioning, so as to implement the combination Figure 1 - Figure 2 of the described method.

[0122] In addition, in combination with the adaptive underwater sound source passive positioning method in the above embodiments, embodiments of the present application can provide a computer-readable storage medium to implement. Computer program instructions are stored on the computer-readable storage medium; when the computer program instructions are executed by a processor, any one of the adaptive underwater sound source passive positioning methods in the above embodiments is implemented.

[0123] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0124] In summary, the beneficial effects of the present invention are as follows: the present invention obtains the frequency-domain spectrum by performing fast Fourier transform on the array element received signal, and estimates the steering vector through spectrum analysis, solving the problem of the degradation of beamforming performance caused by steering vector mismatch in practical applications, and improving the robustness of the adaptive algorithm; in the conventional MUSIC algorithm, the noise subspace is used for spatial spectrum estimation, and the number of signal sources needs to be estimated and the noise subspace needs to be determined. In the present invention, the eigenvalues of the received signal covariance matrix are used to construct a weight matrix to weight all the eigenvectors of the covariance matrix, and the noise eigenvectors are adaptively determined by iteratively retaining or amplifying the noise eigenvectors and reducing the signal eigenvectors in a consistent manner, and the weight matrix and the fully weighted spatial vector are obtained, and the MUSIC spatial spectrum function is obtained, eliminating the influence of signal source estimation on the algorithm.

[0125] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the protection scope of the invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present invention should be subject to the protection scope of the appended claims.

Claims

1. An adaptive underwater sound source passive localization method, characterized in that, Including: Obtaining step: Obtain the signals received by the hydrophone array; Spectrum analysis step: Use the fast Fourier transform to convert the signals into the frequency domain, obtain the effective frequency band of the target signal source through spectrum analysis, and construct a steering vector estimation; Calculation step: Calculate the covariance matrix of the array received signals, and obtain the eigenvalues and eigenvectors of the covariance matrix through eigenvalue decomposition; Setting step: Set the iteration parameters and iteration termination conditions; Obtaining step: Design a weight matrix according to the eigenvalues of the covariance matrix, weight all the eigenvalues of the received signal covariance matrix according to the weight matrix to form a fully weighted spatial vector; Construct a MUSIC spatial spectrum function, obtain the current arrival direction estimation value through spectrum peak search, use the current arrival direction estimation value to calculate the current spatial spectrum cost function value; And obtain the weight matrix for the next iteration; Judgment step: Estimate the spatial spectrum cost function value for the next iteration according to the current arrival direction estimation value and the weight matrix for the next iteration, and compare whether the absolute value of the difference between the current spatial spectrum cost function value and the estimated value of the spatial spectrum cost function for the next iteration satisfies the iteration termination condition. If it satisfies, output the first judgment result, and the first judgment result is the target direction estimation value. Otherwise, output the second judgment result. If the output result is the second judgment result, then increment the value of the iteration parameter by 1 and jump to the obtaining step; Wherein, the obtaining step includes: Estimation value obtaining step: Perform spectrum peak search on the spatial spectrum within a certain range to obtain the estimated value of the arrival direction; Current spatial spectrum cost function value obtaining step: Obtain the current spatial spectrum cost function value by using the current arrival direction estimation value and the definition of the spatial spectrum cost function; Next iteration spatial spectrum cost function estimated value obtaining step: Use the current arrival direction estimation value and the weight matrix for the next iteration to calculate the estimated value of the spatial spectrum cost function for the next iteration through the definition of the spatial spectrum cost function.

2. The adaptive underwater sound source passive localization method according to claim 1, wherein Also including: Establish a signal model for the uniform linear array.

3. The adaptive underwater sound source passive localization method according to claim 2, characterized in that, The obtaining step includes: Weight all the eigenvectors of the covariance matrix with the weight matrix to form a fully weighted spatial vector.

4. The adaptive underwater sound source passive localization method according to claim 1, characterized in that, The judgment step includes: Set an iteration threshold; If the absolute value of the difference between the current spatial spectrum cost function value and the estimated value of the spatial spectrum cost function for the next iteration is less than the iteration threshold, satisfying the iteration termination condition, output the first judgment result; If the absolute value of the difference between the current spatial spectrum cost function value and the estimated value of the spatial spectrum cost function for the next iteration is not less than the iteration threshold, not satisfying the iteration termination condition, output the second judgment result.

5. The adaptive underwater sound source passive localization method according to claim 4, characterized in that, If the output result is the second judgment result, then repeat the iteration process.

6. The adaptive underwater sound source passive localization method according to claim 1, wherein, The calculation step includes: Calculate the covariance matrix according to at least one signal vector, perform eigenvalue decomposition on the covariance matrix, and obtain the eigenvalues and eigenvectors of the covariance matrix.

7. The adaptive underwater sound source passive localization method according to claim 1, wherein The obtaining step further includes: Design the weight matrix using the eigenvalues, consistently retain or amplify the noise eigenvectors, and shrink the signal eigenvectors to suppress the signal subspace components.

8. The adaptive underwater sound source passive localization method according to claim 1, characterized in that, The obtaining step includes: Construct the MUSIC spatial spectrum function of the array according to the orthogonality between the steering vector estimation and the fully weighted spatial vector.

9. An adaptive underwater sound source passive localization system, characterized in that, The adaptive underwater sound source passive localization system is applied to the adaptive underwater sound source passive localization method according to any one of claims 1-8. The adaptive underwater sound source passive localization system includes: An acquisition module, which acquires the signals received by the hydrophone array. A spectrum analysis module, which uses fast Fourier transform to convert the signals into the frequency domain, obtains the effective frequency band of the target signal source through spectrum analysis, and constructs a steering vector estimation. A calculation module, which calculates the covariance matrix of the signals received by the array, and obtains the eigenvalues and eigenvectors of the covariance matrix through eigenvalue decomposition. A setting module, which sets the iteration parameters and the iteration termination conditions. An obtaining module, which designs a weight matrix according to the eigenvalues of the covariance matrix, weights all the eigenvalues of the received signal covariance matrix according to the weight matrix to form a fully weighted spatial vector; constructs a MUSIC spatial spectrum function, obtains the current arrival direction estimation value through spectrum peak search, uses the current arrival direction estimation value to calculate the current spatial spectrum cost function value; and obtains the weight matrix for the next iteration. A judgment module, which estimates the spatial spectrum cost function value for the next iteration according to the current arrival direction estimation value and the weight matrix for the next iteration, and compares whether the absolute value of the difference between the current spatial spectrum cost function value and the estimated value of the spatial spectrum cost function for the next iteration satisfies the iteration termination conditions. If it is satisfied, it outputs a first judgment result, and the first judgment result is the target direction estimation value; otherwise, it outputs a second judgment result. If the output result is the second judgment result, the value of the iteration parameter is incremented by one and jumps to the obtaining step. Wherein, the obtaining module first performs spectrum peak search on the spatial spectrum within a certain range to obtain the estimated value of the arrival direction; then obtains the current spatial spectrum cost function value by using the current arrival direction estimation value and the definition of the spatial spectrum cost function; finally, calculates the estimated value of the spatial spectrum cost function for the next iteration by using the current arrival direction estimation value and the weight matrix for the next iteration through the definition of the spatial spectrum cost function.

Images