CN114089276A - Self-adaptive passive positioning method and system for underwater sound source - Google Patents
Self-adaptive passive positioning method and system for underwater sound source Download PDFInfo
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- G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
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Abstract
The invention discloses a self-adaptive passive positioning method and system for an underwater sound source. The method comprises the following steps: acquiring a hydrophone array receiving signal; converting the array receiving signals into a frequency domain by adopting fast Fourier transform, obtaining an effective frequency band of a target information source through frequency spectrum analysis, and constructing guide vector estimation; calculating a covariance matrix of array received signals, and obtaining an eigenvalue and an eigenvector of the covariance matrix through eigenvalue decomposition; adopting the eigenvalue of the covariance matrix to construct a weight matrix, weighting all eigenvalues of the covariance matrix by using the weight matrix to form a full-weighting space vector, and realizing self-adaptive full-weighting MUSIC space spectrum estimation through the setting of iteration operation and iteration termination conditions; and acquiring an estimated value of the direction of arrival. The invention avoids the estimation of the number of the information sources required by the conventional MUSIC algorithm, and solves the problem of performance reduction of the beam former caused by mismatching of the steering vector and error division of the noise subspace in practical application.
Description
Technical Field
The invention belongs to the field of passive positioning of a self-adaptive underwater sound source, and particularly relates to a passive positioning method and system of a self-adaptive underwater sound source.
Background
The hydrophone array signal processing technology determines the detection, positioning and target extraction detection capabilities of a sonar, and the underwater sound signal passive positioning method based on the array signal processing technology has important significance for whether the hydrophone array can exert the advantages of good platform concealment, high azimuth resolution, long detection distance and the like.
Multiple signal classification (MUSIC) is a widely used subspace-based high-resolution target orientation estimation algorithm. The method has high estimation precision and stability under a specific environment, but the accurate expected signal steering vector and the number of the information sources are the precondition that the super-resolution direction finding algorithm exerts the super-resolution performance.
Conventional beamforming methods assume that the steering vector of the desired signal is accurately known, but in practical scenarios, steering vector mismatch can severely affect the directional performance of the beamformer. Error analysis and correction of the measurement results are usually required to improve this phenomenon, and conventional methods are to model the uncertainty set of steering vectors or to improve the robustness of beamforming by adding inequality constraints to the weighting vectors. However, the actual situation of mismatch needs to be accurately modeled for probability distribution, and proper parameters are selected to be able to maintain the robustness of the algorithm.
The MUSIC algorithm is a method based on matrix characteristic space decomposition, and carries out super-resolution estimation on the direction of arrival through the orthogonality of an information source signal subspace and a noise subspace. For the performance of MUSIC calculation, the accuracy of source number estimation is very important. If the estimated number of sources is erroneous, this may result in the noise subspace determined from this number of sources not corresponding to the actual noise subspace. Under the condition that the number of signal sources is over-estimated, the estimated number of the signal sources is more than an actual value, and the MUSIC spatial spectrum generates a false alarm phenomenon; in the underestimation case, the number of estimated information sources is less than the actual value, and the MUSIC spatial spectrum generates a false alarm phenomenon and a larger estimation deviation of the direction of arrival. Usually, Akaike Information Criterion (AIC) and Minimum Description Length criterion (MDL) are adopted to estimate the number of the sources, but the AIC and MDL methods fail in a color noise environment, and the amount of calculation using such a source estimation algorithm is large, which results in extra time loss.
Therefore, the study of adaptive beamforming algorithms that can achieve accurate estimation of the target under the condition of unknown source frequency and number is a problem that must be solved in engineering application.
Disclosure of Invention
The embodiment of the application provides a method, a system, a storage medium and electronic equipment for passively positioning a self-adaptive underwater sound source, which are used for solving the problem of reduced beam forming performance caused by mismatching of a steering vector and inaccurate estimation of the number of information sources in practical application of the traditional method for passively positioning the self-adaptive underwater sound source.
The invention provides a self-adaptive passive positioning method for an underwater sound source, which comprises the following steps:
an acquisition step: acquiring a hydrophone array receiving signal;
a spectrum analysis step: converting the signal into a frequency domain by adopting fast Fourier transform, obtaining an effective frequency band of a target information source through frequency spectrum analysis, and constructing a guide vector estimation;
and (3) calculating: calculating a covariance matrix of array received signals, and obtaining an eigenvalue and an eigenvector of the covariance matrix through eigenvalue decomposition;
the setting step: setting iteration parameters and iteration termination conditions;
the obtaining step: designing a weight matrix according to the eigenvalue of the covariance matrix, and weighting all eigenvalues of the covariance matrix of the received signals according to the weight matrix to form a fully weighted space vector; constructing an MUSIC spatial spectrum function, obtaining a current arrival direction estimation value through spectrum peak search, and calculating a current spatial spectrum cost function value by using the current arrival direction estimation value; and obtaining a weight matrix of the next iteration;
a judging step: estimating a spatial spectrum cost function value of the next iteration according to the current direction-of-arrival estimation value and the weight matrix of the next iteration, comparing whether an absolute value of a difference between the current spatial spectrum cost function value and the spatial spectrum cost function estimation value of the next iteration meets an iteration termination condition, if so, outputting a first judgment result, wherein the first judgment result is a target direction estimation value, otherwise, outputting a second judgment result, and if the output result is the second judgment result, adding 1 to the iteration parameter value and jumping to the obtaining step.
The adaptive underwater sound source passive positioning method further comprises the following steps:
a signal model is built for the uniform linear array.
The adaptive underwater sound source passive positioning method comprises the following steps:
and weighting all the eigenvectors of the covariance matrix by using the weight matrix to form a fully weighted space vector.
The adaptive underwater sound source passive positioning method comprises the following steps:
an estimation value obtaining step: performing spectral peak search of a spatial spectrum within a certain range to obtain an estimated value of the direction of arrival;
obtaining a current spatial spectrum cost function value: obtaining a current spatial spectrum cost function value by using the current direction-of-arrival estimation value and the definition of a spatial spectrum cost function;
and obtaining the estimated value of the spatial spectrum cost function of the next iteration: and calculating the estimated value of the spatial spectrum cost function of the next iteration through the definition of the spatial spectrum cost function by utilizing the estimated value of the current direction of arrival and the weight matrix of the next iteration.
The adaptive passive underwater sound source positioning method comprises the following judging steps:
setting an iteration threshold; if the absolute value of the difference between the current spatial spectrum cost function value and the spatial spectrum cost function estimated value of the next iteration is smaller than the iteration threshold, the iteration termination condition is met, and the first judgment result is output; and if the absolute value of the difference between the current spatial spectrum cost function value and the spatial spectrum cost function estimated value of the next iteration is not smaller than the iteration threshold and does not meet the iteration termination condition, outputting a second judgment result.
In the above adaptive passive underwater sound source positioning method, if the output result is the second determination result, the iterative process is repeatedly executed.
The adaptive passive underwater sound source positioning method comprises the following calculation steps:
and calculating to obtain a covariance matrix according to at least one signal vector, and performing characteristic decomposition on the covariance matrix to obtain an eigenvalue and an eigenvector of the covariance matrix.
The adaptive passive underwater sound source positioning method comprises the following steps:
and designing the weight matrix by using the eigenvalue, keeping or amplifying the noise eigenvector in a consistent manner, and reducing the signal eigenvector so as to inhibit the signal subspace component.
The adaptive underwater sound source passive positioning method comprises the following steps:
and constructing the MUSIC spatial spectrum function of the array according to the orthogonality of the guide vector estimation and the full-weighted spatial vector.
The invention also provides a self-adaptive passive positioning system for the underwater sound source, which comprises the following components:
an acquisition module, which acquires a hydrophone array receiving signal;
the frequency spectrum analysis module converts the signal into a frequency domain by adopting fast Fourier transform, obtains an effective frequency band of a target information source through frequency spectrum analysis, and constructs guide vector estimation;
the calculation module calculates a covariance matrix of array received signals, and eigenvalues and eigenvectors of the covariance matrix are obtained through eigenvalue decomposition;
the setting module is used for setting iteration parameters and iteration termination conditions;
an obtaining module, wherein the obtaining module designs a weight matrix according to the eigenvalue of the covariance matrix, and weights all eigenvalues of the received signal covariance matrix according to the weight matrix to form a fully weighted space vector; constructing an MUSIC spatial spectrum function, obtaining a current arrival direction estimation value through spectrum peak search, and calculating a current spatial spectrum cost function value by using the current arrival direction estimation value; and obtaining a weight matrix of the next iteration;
and the judging module is used for estimating a spatial spectrum cost function value of the next iteration according to the current direction-of-arrival estimation value and the weight matrix of the next iteration, comparing whether the absolute value of the difference between the current spatial spectrum cost function value and the spatial spectrum cost function estimation value of the next iteration meets an iteration termination condition, outputting a first judging result if the absolute value meets the iteration termination condition, wherein the first judging result is a target direction estimation value, otherwise, outputting a second judging result, and if the output result is the second judging result, adding the iteration parameter values and jumping to obtain the step.
The invention has the beneficial effects that:
according to the method, the frequency domain spectrum is obtained by performing fast Fourier transform on the array element receiving signals, and the guide vector is estimated through spectrum analysis, so that the problem of reduced beam forming performance caused by real matching of the guide vector in practical application is solved, and the robustness of the algorithm is improved; in the invention, the eigenvalue of a covariance matrix is adopted to construct a weight matrix to weight all eigenvectors, the noise eigenvector is preserved or amplified through iterative operation consistency, the signal eigenvector is reduced, the weight matrix and the full weighting space vector are determined in a self-adaptive manner, the MUSIC spatial spectrum function is obtained, and the influence of information source estimation on the algorithm is eliminated.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application.
In the drawings:
FIG. 1 is a flow chart of a method of the present invention for accommodating passive localization of an underwater sound source;
FIG. 2 is a flow chart of substep S5 of the present invention;
FIG. 3 is a general flow chart of the present invention for accommodating passive localization of an underwater sound source;
FIG. 4 is a schematic diagram of the spatial spectrum estimation of the present invention;
FIG. 5 is a schematic diagram of the passive underwater sound source adaptive positioning system of the present invention;
fig. 6 is a frame diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The use of the terms "including," "comprising," "having," and any variations thereof herein, is meant to cover a non-exclusive inclusion; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as referred to herein means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The present invention is described in detail with reference to the embodiments shown in the drawings, but it should be understood that these embodiments are not intended to limit the present invention, and those skilled in the art should understand that functional, methodological, or structural equivalents or substitutions made by these embodiments are within the scope of the present invention.
Before describing in detail the various embodiments of the present invention, the core inventive concepts of the present invention are summarized and described in detail by the following several embodiments.
The first embodiment is as follows:
referring to fig. 1, fig. 1 is a flowchart of a passive adaptive underwater sound source positioning method. As shown in fig. 1, the adaptive underwater sound source passive positioning method of the present invention includes:
acquisition step S1: acquiring a hydrophone array receiving signal;
spectrum analysis step S2: converting the signal into a frequency domain by adopting fast Fourier transform, obtaining an effective frequency band of a target information source through frequency spectrum analysis, and constructing a guide vector estimation;
calculation step S3: calculating a covariance matrix of array received signals, and obtaining an eigenvalue and an eigenvector of the covariance matrix through eigenvalue decomposition;
setting step S4: setting iteration parameters and iteration termination conditions;
obtaining step S5: designing a weight matrix according to the eigenvalue of the covariance matrix, and weighting all eigenvalues of the covariance matrix of the received signals according to the weight matrix to form a fully weighted space vector; constructing an MUSIC spatial spectrum function, obtaining a current arrival direction estimation value through spectrum peak search, and calculating a current spatial spectrum cost function value by using the current arrival direction estimation value; and obtaining a weight matrix of the next iteration;
determination step S6: estimating a spatial spectrum cost function value of the next iteration according to the current direction-of-arrival estimation value and the weight matrix of the next iteration, comparing whether an absolute value of a difference between the current spatial spectrum cost function value and the spatial spectrum cost function estimation value of the next iteration meets an iteration termination condition, if so, outputting a first judgment result, wherein the first judgment result is a target direction estimation value, otherwise, outputting a second judgment result, and if the output result is the second judgment result, adding 1 to the iteration parameter value and jumping to the obtaining step.
Wherein, still include:
a signal model is built for the uniform linear array.
Wherein the obtaining step comprises:
and weighting all the eigenvectors of the covariance matrix by using the weight matrix to form a fully weighted space vector.
Referring to fig. 2, fig. 2 is a flowchart of substep S5. As shown in fig. 2, wherein the obtaining step S5 includes:
estimated value obtaining step S51: performing spectral peak search of a spatial spectrum within a certain range to obtain an estimated value of the direction of arrival;
current spatial spectrum cost function value obtaining step S52: obtaining a current spatial spectrum cost function value by using the current direction-of-arrival estimation value and the definition of a spatial spectrum cost function;
obtaining the estimated value of the spatial spectrum cost function of the next iteration at step S53: and calculating the estimated value of the spatial spectrum cost function of the next iteration through the definition of the spatial spectrum cost function by utilizing the estimated value of the current direction of arrival and the weight matrix of the next iteration.
Wherein the judging step comprises:
setting an iteration threshold; if the absolute value of the difference between the current spatial spectrum cost function value and the spatial spectrum cost function estimated value of the next iteration is smaller than the iteration threshold, the iteration termination condition is met, and the first judgment result is output; and if the absolute value of the difference between the current spatial spectrum cost function value and the spatial spectrum cost function estimated value of the next iteration is not smaller than the iteration threshold and does not meet the iteration termination condition, outputting a second judgment result.
And if the output result is the second judgment result, repeatedly executing the iterative process.
Wherein the calculating step comprises:
and calculating to obtain a covariance matrix according to at least one signal vector, and performing characteristic decomposition on the covariance matrix to obtain an eigenvalue and an eigenvector of the covariance matrix.
Wherein the obtaining step further comprises:
and designing the weight matrix by using the eigenvalue, keeping or amplifying the noise eigenvector in a consistent manner, and reducing the signal eigenvector so as to inhibit the signal subspace component.
Wherein the obtaining step comprises:
and constructing the MUSIC spatial spectrum function of the array according to the orthogonality of the guide vector estimation and the full-weighted spatial vector.
Specifically, as shown in fig. 3, the invention provides an adaptive underwater sound source passive localization algorithm based on steering vector estimation, aiming at the situations of steering vector mismatch and source number and position in practical application. The flow chart is shown in fig. 1, and the adaptive underwater sound source passive localization algorithm based on the steering vector estimation comprises the following steps:
the first step is as follows: establishing a signal model for the uniform linear array;
further, for N signals incident on M array elements, the data model of the signals received by the array is as follows:
(1) x (t) ═ a (θ) s (t) + n (t); in formula (1), x (t) ═ x1(t),x2(t),…xM(t)]TFor the array data reception matrix, a (θ) ═ a (θ)1),a(θ2),…,a(θN)]Λ is the matrix of M × N steering vectors of the array
Expressed at an angle theta1Directional vector in direction, s (t) ═ s1(n),s2(n),…,sN(n)]TIs an N x 1-dimensional arrival signal vector of the spatial signal, N (t) ═ N1(t),n2(t),…,nM(t)]TIs an M x 1 dimensional noisy data vector of the array.
The second step is that: carrying out spectrum analysis on the received signal, obtaining an effective frequency band of the signal through fast Fourier transform, and constructing a guide vector estimation;
specifically, the received signal is transformed by fast fourier transform to obtain frequency domain spectrum information of the signal, X (ω) ═ FFT (X (t)) is obtained, and a frequency estimation is obtained by solving a frequency corresponding to a modulo maximum of the frequency spectrum in the frequency domain
Wherein f issIs the signal sampling frequency. From frequency estimation
Constructing a steering vector estimation matrix
The third step: calculating a covariance matrix of array received signals, and decomposing eigenvalues of the covariance matrix to obtain eigenvalues and eigenvectors of the covariance matrix;
specifically, a covariance matrix is obtained from N received signal vectors
Performing feature decomposition on the covariance matrix R
And obtaining the eigenvalue lambda and the eigenvector u.
The fourth step: initializing iteration number parameter i as 1
The fifth step: designing a weight matrix by using the eigenvalue, keeping or amplifying the noise eigenvector in consistency, reducing the signal eigenvector and achieving the purpose of suppressing the signal subspace component;
specifically, the fifth step: constructing a weighting matrix
And a sixth step: weighting all eigenvectors of the covariance matrix of the received signals by using the weight matrix to form a full-weighted space vector;
specifically, a fully weighted space vector U is constructed using a weighting matrix(i) w＝Rw (i)U, wherein U ═ U1,u2,…,uM]。
The seventh step: constructing an MUSIC spatial spectrum function of the array by utilizing the orthogonality of the optimal guide vector estimation and the full-weighted spatial vector;
specifically, a Music spatial spectrum function is constructed by using the obtained guide vector estimation and the full-weighted spatial vector
Eighth step: solving an angle corresponding to a mode maximum value of a spectrum peak to obtain an estimated value of the sound source target azimuth through spectrum peak search of a spatial spectrum;
specifically, in
Performing spectral peak search of spatial spectrum within the range to obtain estimated value of direction of arrival
The ninth step: calculating a current spatial spectrum cost function value by using the obtained target azimuth estimation value, and obtaining a weight matrix of the next iteration;
specifically, a current direction of arrival estimate is obtained
Lower spatial spectral cost function value
The tenth step: setting an iteration process termination condition, and if the condition is not met, jumping into a fifth step of step circulation in an iteration mode; if the iteration termination condition is met, ending the iteration to obtain a final target azimuth estimation value;
specifically, an iteration process termination condition is set, if the termination condition is not satisfied, the process jumps to step 5 to perform the next iteration if i +1, if the termination condition is satisfied, the iteration is terminated, and the target azimuth estimation value is
Wherein the iteration termination condition is to calculate the spatial spectrum cost function value of the (i + 1) th iteration
If it is
And if the conditions are met, continuing the iteration, otherwise, ending the iteration process. Where epsilon is the set iteration threshold.
As shown in fig. 4, the specific implementation of the present invention is further verified by a simulation example, in which a hydrophone array model with an array element number of 8 and an array element spacing of one-half wavelength is adopted, three incoherent signals with the same frequency are respectively incident into the hydrophone array from [10 °,35 °,60 ° ] to a signal-to-noise ratio of-5 dB. The conventional MUSIC algorithm and the self-adaptive MUSIC algorithm based on guide vector estimation designed by the invention are adopted to obtain a spatial spectrum, the conventional MUSIC algorithm has three sharp spectral peaks at 10 degrees, 39 degrees and 57 degrees respectively, wherein the 39 degrees and 57 degrees obviously deviate from the true source direction, and the self-adaptive MUSIC algorithm adopted by the invention has three sharp spectral peaks at three 10 degrees, 35 degrees and 60 degrees, so that the self-adaptive MUSIC algorithm designed by the invention can correctly estimate the angles of multiple targets under the condition of low signal-to-noise ratio, and the main lobe width of the spatial spectrum is narrow, and the self-adaptive MUSIC algorithm has higher spatial resolution compared with the conventional MUSIC algorithm.
Example two:
referring to fig. 5, fig. 5 is a schematic structural diagram of an adaptive underwater sound source passive positioning system according to the present invention.
Fig. 5 shows an adaptive underwater sound source passive localization system according to the present invention, which includes:
the acquisition module 11, the acquisition module 11 acquires the hydrophone array received signal;
the frequency spectrum analysis module 12 is used for converting the signal into a frequency domain by adopting fast Fourier transform, obtaining an effective frequency band of a target information source through frequency spectrum analysis, and constructing a guide vector estimation;
the calculation module 13 is used for calculating a covariance matrix of array receiving signals, and obtaining an eigenvalue and an eigenvector of the covariance matrix through eigenvalue decomposition;
the setting module 14, the setting module 14 sets iteration parameters and iteration termination conditions;
an obtaining module 15, wherein the obtaining module 15 designs a weight matrix according to the eigenvalue of the covariance matrix, and weights all eigenvalues of the received signal covariance matrix according to the weight matrix to form a fully weighted space vector; constructing an MUSIC spatial spectrum function, obtaining a current arrival direction estimation value through spectrum peak search, and calculating a current spatial spectrum cost function value by using the current arrival direction estimation value; and obtaining a weight matrix of the next iteration;
a judging module 16, where the judging module 16 estimates a spatial spectrum cost function value of the next iteration according to the current direction of arrival estimate value and the weight matrix of the next iteration, compares whether an absolute value of a difference between the current spatial spectrum cost function value and the spatial spectrum cost function estimate value of the next iteration meets an iteration termination condition, outputs a first judgment result if the absolute value meets the iteration termination condition, where the first judgment result is a target direction estimate value, otherwise outputs a second judgment result, and if the output result is the second judgment result, adds the iteration parameter values and skips in the obtaining step.
Example three:
referring to fig. 6, this embodiment discloses a specific implementation of an electronic device. The electronic device may include a processor 81 and a memory 82 storing computer program instructions.
In particular, the processor 81 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory 82 may be used to store or cache various data files that need to be processed and/or used for communication, as well as possible computer program instructions executed by the processor 81.
The processor 81 reads and executes computer program instructions stored in the memory 82 to implement any one of the adaptive underwater sound source passive localization methods in the above-described embodiments.
In some of these embodiments, the electronic device may also include a communication interface 83 and a bus 80. As shown in fig. 6, the processor 81, the memory 82, and the communication interface 83 are connected via the bus 80 to complete communication therebetween.
The communication interface 83 is used for implementing communication between modules, devices, units and/or equipment in the embodiment of the present application. The communication port 83 may also be implemented with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
The bus 80 includes hardware, software, or both to couple the components of the electronic device to one another. Bus 80 includes, but is not limited to, at least one of the following: data Bus (Data Bus), Address Bus (Address Bus), Control Bus (Control Bus), Expansion Bus (Expansion Bus), and Local Bus (Local Bus). By way of example, and not limitation, Bus 80 may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (FSB), a Hyper Transport (HT) Interconnect, an ISA (ISA) Bus, an InfiniBand (InfiniBand) Interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a microchannel Architecture (MCA) Bus, a PCI (Peripheral Component Interconnect) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a Video Electronics Bus (audio Electronics Association), abbreviated VLB) bus or other suitable bus or a combination of two or more of these. Bus 80 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.
The electronic device may be based on adaptive underwater sound source passive localization, thereby implementing the methods described in connection with fig. 1-2.
In addition, in combination with the adaptive underwater sound source passive positioning method in the foregoing embodiments, embodiments of the present application may provide a computer-readable storage medium to implement the method. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any one of the adaptive passive underwater sound source localization methods in the above embodiments.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
In summary, the method has the advantages that the frequency domain spectrum is obtained by performing fast fourier transform on the array element received signals, and the steering vector is estimated through spectrum analysis, so that the problem of reduced beam forming performance caused by mismatching of the steering vector in practical application is solved, and the robustness of the adaptive algorithm is improved; in the invention, all eigenvectors of a weight matrix weighting covariance matrix are constructed by adopting eigenvalues of a received signal covariance matrix, the noise eigenvectors are kept or amplified through iterative operation consistency, the signal eigenvectors are reduced, the weight matrix and the full weighting space vector are determined in a self-adaptive manner, the MUSIC space spectrum function is obtained, and the influence of information source estimation on the algorithm is eliminated.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A passive self-adaptive underwater sound source positioning method is characterized by comprising the following steps:
an acquisition step: acquiring a hydrophone array receiving signal;
a spectrum analysis step: converting the signal into a frequency domain by adopting fast Fourier transform, obtaining an effective frequency band of a target information source through frequency spectrum analysis, and constructing a guide vector estimation;
and (3) calculating: calculating a covariance matrix of array received signals, and obtaining an eigenvalue and an eigenvector of the covariance matrix through eigenvalue decomposition;
the setting step: setting iteration parameters and iteration termination conditions;
the obtaining step: designing a weight matrix according to the eigenvalue of the covariance matrix, and weighting all eigenvalues of the covariance matrix of the received signals according to the weight matrix to form a fully weighted space vector; constructing an MUSIC spatial spectrum function, obtaining a current arrival direction estimation value through spectrum peak search, and calculating a current spatial spectrum cost function value by using the current arrival direction estimation value; and obtaining a weight matrix of the next iteration;
a judging step: estimating a spatial spectrum cost function value of the next iteration according to the current direction-of-arrival estimation value and the weight matrix of the next iteration, comparing whether an absolute value of a difference between the current spatial spectrum cost function value and the spatial spectrum cost function estimation value of the next iteration meets an iteration termination condition, if so, outputting a first judgment result, wherein the first judgment result is a target direction estimation value, otherwise, outputting a second judgment result, and if the output result is the second judgment result, adding 1 to the iteration parameter value and jumping to the obtaining step.
2. The adaptive passive underwater sound source localization method of claim 1, further comprising:
a signal model is built for the uniform linear array.
3. The adaptive passive underwater sound source localization method of claim 2, wherein said obtaining step comprises:
and weighting all the eigenvectors of the covariance matrix by using the weight matrix to form a fully weighted space vector.
4. The adaptive passive underwater sound source localization method of claim 1, wherein said obtaining step comprises:
an estimation value obtaining step: performing spectral peak search of a spatial spectrum within a certain range to obtain an estimated value of the direction of arrival;
obtaining a current spatial spectrum cost function value: obtaining a current spatial spectrum cost function value by using the current direction-of-arrival estimation value and the definition of a spatial spectrum cost function;
and obtaining the estimated value of the spatial spectrum cost function of the next iteration: and calculating the estimated value of the spatial spectrum cost function of the next iteration through the definition of the spatial spectrum cost function by utilizing the estimated value of the current direction of arrival and the weight matrix of the next iteration.
5. The adaptive passive underwater sound source localization method of claim 1, wherein the determining step comprises:
setting an iteration threshold; if the absolute value of the difference between the current spatial spectrum cost function value and the spatial spectrum cost function estimated value of the next iteration is smaller than the iteration threshold, the iteration termination condition is met, and the first judgment result is output; and if the absolute value of the difference between the current spatial spectrum cost function value and the spatial spectrum cost function estimated value of the next iteration is not smaller than the iteration threshold and does not meet the iteration termination condition, outputting a second judgment result.
6. The adaptive passive underwater sound source positioning method according to claim 5, wherein if the output result is the second determination result, the iterative process is repeatedly performed.
7. The method of adaptive passive underwater sound source localization according to claim 1, characterized in that said calculating step comprises:
and calculating to obtain a covariance matrix according to at least one signal vector, and performing characteristic decomposition on the covariance matrix to obtain an eigenvalue and an eigenvector of the covariance matrix.
8. The adaptive passive underwater sound source localization method of claim 1, wherein said obtaining step further comprises:
and designing the weight matrix by using the eigenvalue, keeping or amplifying the noise eigenvector in a consistent manner, and reducing the signal eigenvector so as to inhibit the signal subspace component.
9. The adaptive passive underwater sound source localization method of claim 1, wherein said obtaining step comprises:
and constructing the MUSIC spatial spectrum function of the array according to the orthogonality of the guide vector estimation and the full-weighted spatial vector.
10. An adaptive passive underwater sound source localization system, comprising:
an acquisition module, which acquires a hydrophone array receiving signal;
the frequency spectrum analysis module converts the signal into a frequency domain by adopting fast Fourier transform, obtains an effective frequency band of a target information source through frequency spectrum analysis, and constructs guide vector estimation;
the calculation module calculates a covariance matrix of array received signals, and eigenvalues and eigenvectors of the covariance matrix are obtained through eigenvalue decomposition;
the setting module is used for setting iteration parameters and iteration termination conditions;
an obtaining module, wherein the obtaining module designs a weight matrix according to the eigenvalue of the covariance matrix, and weights all eigenvalues of the received signal covariance matrix according to the weight matrix to form a fully weighted space vector; constructing an MUSIC spatial spectrum function, obtaining a current arrival direction estimation value through spectrum peak search, and calculating a current spatial spectrum cost function value by using the current arrival direction estimation value; and obtaining a weight matrix of the next iteration;
and the judging module is used for estimating a spatial spectrum cost function value of the next iteration according to the current direction-of-arrival estimation value and the weight matrix of the next iteration, comparing whether the absolute value of the difference between the current spatial spectrum cost function value and the spatial spectrum cost function estimation value of the next iteration meets an iteration termination condition, outputting a first judging result if the absolute value meets the iteration termination condition, wherein the first judging result is a target direction estimation value, otherwise, outputting a second judging result, and if the output result is the second judging result, adding the iteration parameter values and jumping to obtain the step.
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| CN114325579A (en) * | 2022-03-09 | 2022-04-12 | 网络通信与安全紫金山实验室 | Positioning parameter estimation method, apparatus, device, storage medium and program product |
| CN115113139A (en) * | 2022-05-12 | 2022-09-27 | 苏州清听声学科技有限公司 | Sound source identification method and device based on microphone array and electronic equipment |
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| CN114325579A (en) * | 2022-03-09 | 2022-04-12 | 网络通信与安全紫金山实验室 | Positioning parameter estimation method, apparatus, device, storage medium and program product |
| CN115113139A (en) * | 2022-05-12 | 2022-09-27 | 苏州清听声学科技有限公司 | Sound source identification method and device based on microphone array and electronic equipment |
| CN115113139B (en) * | 2022-05-12 | 2024-02-02 | 苏州清听声学科技有限公司 | Sound source identification method and device based on microphone array and electronic equipment |
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