High-resolution near-field distributed optical fiber acoustic detection method based on covariance fitting

Abstract

The invention discloses a high-resolution near-field distributed optical fiber acoustic detection method based on covariance fitting, and belongs to the field of underwater acoustic detection and signal processing. The invention solves the problems of low real-time azimuth estimation precision and poor detection stability in the aspect of dynamic scene adaptation in the existing azimuth estimation method under long-time-varying, multi-path, strong-noise and multi-source coherent interference in the actual underwater environment. Through combination of covariance fitting and sparse reconstruction, adaptive separation and high-precision azimuth estimation of signal and noise power under the conditions of multi-source coherence and low signal-to-noise ratio are realized, and the method has the advantages of high robustness, high resolution, no need of prior modeling, easiness in engineering implementation and the like. The method is mainly applied to azimuth estimation under coherent interference in an underwater environment.

Description

Technical Field

[0001] This invention belongs to the field of underwater acoustic detection and signal processing. Background Technology

[0002] Distributed Acoustic Sensing (DAS) utilizes phase-sensitive optical time-domain reflectometry (Φ-OTDR) technology to achieve continuous sound field monitoring along the entire fiber by detecting phase changes in the Rayleigh scattering signal. This technology offers advantages such as no need for local power supply, strong resistance to electromagnetic interference, low cost, and remote monitoring capabilities, and has been widely applied in fields such as oil and gas pipeline leak detection, earthquake monitoring, and marine environmental exploration.

[0003] However, existing DAS systems primarily rely on far-field assumptions for target location estimation. For nearby sound sources, sound waves propagate spherically, rendering the assumptions of traditional plane wave models invalid and leading to decreased direction estimation accuracy. Especially in complex underwater acoustic environments with low signal-to-noise ratios, limited sampling samples, and strong sound source coherence, existing algorithms (such as MUSIC, CBF, and LASSO) suffer from insufficient resolution and poor robustness.

[0004] Therefore, there is an urgent need for a robust algorithm framework that can perform high-precision orientation estimation of coherent multi-sound sources under near-field conditions, so as to improve the positioning accuracy and anti-interference capability of DAS system in underwater target detection.

[0005] A literature search revealed the following papers, all of which studied joint estimation of underwater acoustic communication and time-varying channel modeling:

[0006] Literature review revealed the following documents, all of which investigated key algorithms and signal processing methods for DAS systems in underwater near-field target detection and localization:

[0007] Muñoz F, Soto M A. Enhancing fiber-optic distributed acoustic sensingcapabilities with blind near-field array signal processing[J]. NatureCommunications, 2022, 13(1): 4019. (hereinafter referred to as document 1);

[0008] Cao W, Cheng G, Xing G, et al. Near-field target localization based on the distributed acoustic sensing optical fiber in shallow water[J]. Optical Fiber Technology, 2023, 75: 103198. (hereinafter referred to as document 2);

[0009] Drylerakis KT, Belal M, Mestre R, et al. Source detection and tracking for underwater distributed acoustic sensing[C] / / 2024 32nd EuropeanSignal Processing Conference (EUSIPCO). IEEE, 2024: 1292-1296. (hereinafter referred to as document 3);

[0010] Reference 1 addresses the issue of decreased positioning accuracy of DAS under near-field conditions by proposing an algorithmic framework based on blind near-field array signal processing. This framework improves azimuth estimation performance by optimizing the array structure and signal reconstruction model. However, this research mainly focuses on the algorithmic modeling level and does not fully consider the actual underwater channel characteristics and robustness under low signal-to-noise ratio conditions. It also falls short in real-time covariance estimation under complex multi-source scenarios.

[0011] Reference 2 proposes a spherical wave propagation model and near-field localization method based on a Φ-OTDR system for shallow water environments, achieving target localization and signal enhancement through a multi-layer fiber optic receiving structure. However, this method relies on strong source priors and stable sound field assumptions, and does not provide in-depth analysis of azimuth resolution under multi-source coherence and high-noise conditions.

[0012] Reference 3 constructs an underwater DAS source detection and tracking framework that integrates feature extraction, principal component analysis (PCA), and Gaussian mixture density filtering (GM-PHD), enabling a certain level of real-time monitoring capability. However, this scheme is mainly geared towards post-processing analysis, has high algorithm complexity, and does not consider adaptive estimation of signal and noise power, thus limiting its accuracy and robustness in long-term time-varying channel environments.

[0013] Furthermore, while the methods in the three aforementioned papers focus on improving algorithm performance and array signal reconstruction, none systematically analyze the signal robustness and system adaptability under complex underwater acoustic channel conditions. These methods are mostly based on static or ideal acoustic field assumptions, making them ill-suited to the long-term time-varying, multipath, strong noise, and multi-source coherent interference encountered in real-world underwater environments. This results in low real-time azimuth estimation accuracy and poor detection stability when adapting to dynamic scenarios. Therefore, achieving high-precision azimuth estimation and robust detection of near-field multi-source signals in complex, dynamic, and broadband underwater acoustic channels, and constructing a DAS detection framework that combines real-time performance and robustness, has become a crucial technological direction requiring breakthroughs in this field. Summary of the Invention

[0014] The purpose of this invention is to address the problems of low real-time azimuth estimation accuracy and poor detection stability in adapting to dynamic scenes in existing underwater azimuth estimation methods under conditions of long time variation, multipath, strong noise, and multi-source coherent interference. This invention provides a high-resolution near-field distributed fiber optic acoustic detection method based on covariance fitting.

[0015] A high-resolution near-field distributed fiber optic acoustic detection method based on covariance fitting, comprising:

[0016] Step 1: Using the continuous linear sensor array in the Φ-OTDR distributed fiber optic acoustic sensing system, sound field signals are acquired and preprocessed in the near-field region to obtain the sound field observation matrix of the sensor array in the near-field region. ;

[0017] Step 2: Based on the sound field observation matrix Construct the observation covariance matrix ;

[0018] At the same time, the incoming wave directions of all target sound sources in the near field region are defined as belonging to the set. , For the first Candidate azimuth angles , This represents the total number of candidate azimuth angles.

[0019] Based on each candidate azimuth angle Constructing near-field sound source steering vectors ;

[0020] A near-field sound source steering matrix is ​​constructed using the steering vectors of all candidate azimuth angles within the near-field region. ;

[0021] Step 3: Utilize Construct the extended model covariance matrix ;

[0022] Step 4: Based on the covariance matrix of the extended model Observation covariance matrix Harmony sound field observation matrix The optimal power vector corresponding to the set of incoming wave directions is obtained by fitting the near-field sparse iterative covariance estimation method. ;

[0023] Step 5: Optimal power vector The candidate azimuth angles corresponding to one or more of the maximum power are used as the estimated arrival azimuth angles of each target sound source.

[0024] Preferably, ;

[0025] in, For transpose, The first in the sensor array The preprocessed sound field signal corresponding to each array element For the time, and ; , The total number of array elements. Indicates time The multi-source mixed signal below, For the first The receiving channel corresponding to each array element at time The received noise, For signal carrier frequency, For the first The time delay difference in the arrival of the sound waves from each array element relative to the first array element. It is a natural constant. It is the imaginary unit.

[0026] Preferably, in step two, ;in, It is the conjugate transpose. This represents the number of sampling points within the sampling time.

[0027] Preferably, in step two, ;

[0028] in, Candidate azimuth angle Next The time delay difference in the arrival of the sound waves from each array element relative to the first array element. , For the first The spacing between each array element and the first array element. , The total number of array elements. For signal carrier frequency, The speed of sound in water, For transpose, It is a natural constant. It is the imaginary unit.

[0029] Preferably, in step three, ;in, for The identity matrix, It is an integer.

[0030] Preferably, in step four, based on the covariance matrix of the extended model... Observation covariance matrix Harmony sound field observation matrix The optimal power vector corresponding to the set of incoming wave directions is obtained by fitting the near-field sparse iterative covariance estimation method. The implementation methods include:

[0031] Step 41: Based on the covariance matrix of the extended model and observation covariance matrix Solve for the power vector corresponding to the set of incoming wave directions under the initial conditions. , It is the conjugate transpose;

[0032] Step 42, the Round iteration, The initial value is 1, using the power vector and extended model covariance matrix Update the observation covariance matrix ;

[0033] Step 4.3: Utilize the updated observation covariance matrix Extended model covariance matrix Harmony sound field observation matrix Solve for the linear estimation coefficient matrix ;

[0034] Step 4: Use linear estimation of the coefficient matrix Update the power vector ;

[0035] Steps four and five: Determine the first... In rounds of iteration, the power vector before and after the update is updated. If the convergence constraint is satisfied, and the result is yes, then update the power vector. As the optimal power vector The result was no. Return to step four two.

[0036] Preferably, in step four-two, the observation covariance matrix is ​​updated. The implementation method is as follows: .

[0037] Preferably, in step four, the power vector is updated. The implementation method is as follows: ;

[0038] in, This represents the number of sampling points within the sampling time. For linear estimation coefficient matrix The Middle line, number Column elements, Candidate azimuth angle The power of the sound field signal emitted by the corresponding target sound source. .

[0039] Preferably, the convergence constraint is: ;

[0040] in, For the first Updated power vector after round of iterations The value, For the first Power vector before update in round iteration The value, The threshold value is used.

[0041] The beneficial effects of this invention are:

[0042] This invention achieves high-precision signal separation and azimuth estimation under complex interference environments by introducing adaptive covariance modeling and sparse reconstruction mechanisms, balancing algorithm stability and engineering feasibility, and providing a new robust solution for underwater near-field multi-source detection. Specific advantages are as follows:

[0043] 1) This invention combines sparse reconstruction and covariance fitting to achieve high-precision orientation separation and estimation of multiple targets under near-field conditions, which can significantly improve resolution and detection sensitivity under the same array spacing.

[0044] 2) It can achieve adaptive separation of signal and noise power under multi-source coherence conditions. See step four for details on the extended model covariance matrix B and the observation covariance matrix. By fitting the signal and noise and utilizing their uncorrelated characteristics, adaptive separation of the two is achieved during the iterative process, which significantly improves the robustness and reliability of the orientation estimation.

[0045] 3) This invention provides a novel fitting method (NF-SPICE method) that utilizes the observation covariance matrix. With the extended model covariance matrix The least squares fitting process between them can achieve adaptive solution of power distribution without the need to preset regularization parameters, and has the ability to automatically estimate noise and adapt to dynamic channels.

[0046] 4) This invention does not rely on specific signal prior models and array calibration conditions, but only involves matrix operations and simple iterations. It has a simple structure and strong engineering feasibility.

[0047] Furthermore, the method of this invention has a simple structure, low computational complexity, and is easy to implement in parallel. It balances real-time performance with engineering deployability and is applicable to various application scenarios such as marine monitoring, shipborne acoustic early warning, and underwater unmanned vehicle array detection. Attached Figure Description

[0048] Figure 1 This is a flowchart of the high-resolution near-field distributed fiber optic acoustic detection method based on covariance fitting described in this invention.

[0049] Figure 2 This is a performance comparison chart of the method of this invention and the classical method in the simulation;

[0050] Figure 3 This is a diagram showing the equipment status in an outdoor testing scenario; among them, Figure 3 Image (a) shows the actual product of the new fiber optic hydrophone used. Figure 3 (b) is a schematic diagram of the water tank test topology;

[0051] Figure 4 These are orientation estimation diagrams for the method of this invention and the classical method in a water tank experiment; wherein,

[0052] Figure 4 (a) is the orientation history map estimated by the CBF method;

[0053] Figure 4 (b) is the orientation history map estimated by the MUSIC method;

[0054] Figure 4 (c) is the azimuth history map estimated by the smoothing MUSIC method;

[0055] Figure 4 (d) is the azimuth history map estimated by the FBSS-MUSIC method;

[0056] Figure 4 (e) is the orientation history map estimated by the adaptive LASSO method;

[0057] Figure 4 (f) is the azimuth history diagram estimated by the method of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0060] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0061] This invention aims to achieve high-precision orientation estimation and robust target detection in complex underwater acoustic environments through adaptive power estimation and covariance modeling reconstruction. To address the problems of near-field spherical propagation and multi-source coherent interference, a joint signal and noise observation covariance matrix is ​​constructed. Furthermore, this method achieves sparse fitting of the power spectrum through iterative solution using the NF-SPICE method, accurately separating the target signal and noise components under conditions of low signal-to-noise ratio, strong multipath propagation, and finite snapshots. Simultaneously, this method utilizes an adaptive sparse reconstruction mechanism driven by covariance fitting to complete the acoustic field observation matrix without requiring preset regularization parameters. Power adaptive estimation enables super-resolution extraction of near-field multi-target azimuth information; combined with the continuous spatial sampling characteristics of the Φ-OTDR fiber array, it can effectively improve the spatial resolution and anti-interference capability of the distributed acoustic system.

[0062] Specific Implementation Method 1: Combination Figure 1 This embodiment describes a high-resolution near-field distributed fiber optic acoustic detection method based on covariance fitting, which includes:

[0063] Step 1: Using the continuous linear sensor array in the Φ-OTDR distributed fiber optic acoustic sensing system, sound field signals are acquired and preprocessed in the near-field region to obtain the sound field observation matrix of the sensor array in the near-field region. ;

[0064] Specifically, ;

[0065] in, For transpose, The first in the sensor array The preprocessed sound field signal corresponding to each array element For the time, and ; , The total number of array elements. Indicates time The multi-source mixed signal below, For the first The receiving channel corresponding to each array element at time The received noise, For signal carrier frequency, For the first The time delay difference in the arrival of the sound waves from each array element relative to the first array element. It is a natural constant. The imaginary unit, The first in the sensor array The carrier component of the preprocessed sound field signal corresponding to each array element;

[0066] Step 2: Based on the sound field observation matrix Construct the observation covariance matrix ;

[0067] Meanwhile, since the incoming directions of target sound sources are sparse in space, the incoming directions of all target sound sources in the near-field region are defined as belonging to a set. , For the first Candidate azimuth angles , This represents the total number of candidate azimuth angles.

[0068] Based on each candidate azimuth angle Constructing near-field sound source steering vectors A near-field sound source steering matrix is ​​constructed using the steering vectors of all candidate azimuth angles within the near-field region. ;

[0069] Step 3: Utilize Construct the extended model covariance matrix ;

[0070] Specifically, ;in, for The identity matrix, It is an integer;

[0071] Step 4: Based on the covariance matrix of the extended model Observation covariance matrix Harmony sound field observation matrix The optimal power vector corresponding to the set of incoming wave directions is obtained by fitting the near-field sparse iterative covariance estimation method (NF-SPICE). ;

[0072] Step 5: Optimal power vector The candidate azimuth angles corresponding to one or more of the maximum power are used as the estimated arrival azimuth angles of each target sound source.

[0073] The core of the proposed NF-SPICE method lies in adaptively matching the covariance matrix of the extended model through weighted least squares fitting. With the observed covariance matrix This process achieves adaptive separation and estimation of signal and noise power without the need for preset regularization parameters, thus successfully solving the problem of resolving coherent signals and significantly improving the robustness of orientation estimation. Simultaneously, this method can make the target power spectrum peaks sharp, achieving high-resolution positioning of near-range targets and effectively improving angular and range resolution and detection sensitivity under the same physical aperture.

[0074] This invention utilizes a distributed fiber optic acoustic sensing system, employing optical fibers as a continuous linear sensing array to acquire and preprocess sound field signals within a monitoring area, thereby obtaining a sound field observation matrix. Then, based on Construct the observation covariance matrix Next, the optimal power vector is fitted and solved using the near-field sparse iterative covariance estimation method (NF-SPICE) of this invention; based on the optimal power vector... The candidate azimuth angles corresponding to one or more of the maximum power in the signal are used as the estimated azimuth angles of arrival for each target sound source. This invention achieves adaptive separation and high-precision azimuth estimation of signal and noise power under multi-source coherence and low signal-to-noise ratio conditions by combining covariance fitting and sparse reconstruction. It has the advantages of strong robustness, high resolution, no need for prior modeling, and easy engineering implementation.

[0075] Furthermore, in step two, , ;

[0076] in, It is the conjugate transpose. This represents the number of sampling points within the sampling time. Indicates candidate azimuth angle Next The time delay difference in the arrival of the sound waves from each array element relative to the first array element. , For the first The spacing between each array element and the first array element. , The total number of array elements. For signal carrier frequency, The speed of sound in water, For transpose, It is a natural constant. It is the imaginary unit.

[0077] The observation covariance matrix constructed in this preferred embodiment This method effectively utilizes the second-order statistical properties of the signal in the spatial domain, which not only contains key information about the target's orientation but also has a certain noise suppression capability. This lays a solid data foundation for accurate orientation estimation under the near-field model and provides an accurate near-field physical model foundation for the entire method. This allows the subsequent near-field sparse iterative covariance estimation method (NF-SPICE) to play its full role, ultimately achieving joint high-resolution localization of the target's orientation and distance in the near-field region.

[0078] Constructing near-field sound source steering vector The core advantage lies in the use of an accurate near-field spherical wave model, which allows the subsequent near-field sparse iterative covariance estimation method (NF-SPICE) to play a full role, ultimately achieving high-resolution estimation of the target's orientation in the near-field region.

[0079] Furthermore, this invention provides a method using the near-field sparse iterative covariance estimation method NF-SPICE to solve for the optimal power vector corresponding to the set of incoming wave directions. Therefore, in step four, based on the covariance matrix of the extended model... Observation covariance matrix Harmony sound field observation matrix The optimal power vector corresponding to the set of incoming wave directions is obtained by fitting the near-field sparse iterative covariance estimation method. The implementation methods include:

[0080] Step 41: Based on the covariance matrix of the extended model and observation covariance matrix Solve for the power vector corresponding to the set of incoming wave directions under the initial conditions. , It is the conjugate transpose;

[0081] Step 42, the Round iteration, The initial value is 1, using the power vector and extended model covariance matrix Update the observation covariance matrix ;

[0082] Specifically, update the observation covariance matrix. The implementation method is as follows: ;

[0083] Step 4.3: Utilize the updated observation covariance matrix Extended model covariance matrix Harmony sound field observation matrix Solve for the linear estimation coefficient matrix ;

[0084] Step 4: Use linear estimation of the coefficient matrix Update the power vector ;

[0085] Specifically, update the power vector. The implementation method is as follows: ;

[0086] in, This represents the number of sampling points within the sampling time. For linear estimation coefficient matrix The Middle line, number Column elements, Candidate azimuth angle The power of the sound field signal emitted by the corresponding target sound source. .

[0087] Steps four and five: Determine the first... In rounds of iteration, the power vector before and after the update is updated. If the convergence constraint is satisfied, and the result is yes, then update the power vector. As the optimal power vector The result was no. Return to step four two.

[0088] The convergence constraint is: ;

[0089] in, For the first Updated power vector after round of iterations The value, For the first Power vector before update in round iteration The value, The threshold value is used.

[0090] The preferred embodiment proposes a near-field sparse iterative covariance estimation method, NF-SPICE, which achieves high-resolution robust estimation and noise separation of near-field coherent signals without prior parameters through adaptive covariance fitting.

[0091] Verification experiment:

[0092] The high-resolution near-field distributed fiber acoustic detection method based on covariance fitting proposed in this invention was simulated and verified. The basic parameters of the simulation signal are shown in Table 1.

[0093] Table 1. Basic parameters of the simulated signal

[0094]

[0095] Under the aforementioned signal and array element parameters, the performance of the method of this invention and classical methods (CBF, MUSIC, SmoothingMUSIC, FBSS-MUSIC, Adaptive LASSO) were simulated, and the processing results are as follows. Figure 2 As shown. By Figure 2 As shown, the NF-SPICE method of this invention can accurately estimate the azimuth of two sound sources 1 and 2. Classical methods cannot distinguish coherent signals and have an error of more than 1° from the accurate target azimuth.

[0096] Finally, the high-resolution near-field distributed fiber optic acoustic detection method based on covariance fitting proposed in this invention was verified through field data processing. The field test signal parameters are shown in Table 2 below. The test site was a channel pool, and the equipment deployment layout is shown in the diagram below. Figure 3 As shown.

[0097] Table 2 Test signal parameters

[0098]

[0099] The results of the water tank test data processing are as follows: Figure 4 As shown, Figure 4 (a), (b), (c), and (d) in the figure represent the target azimuth estimates obtained by the classical CBF, MUSIC, smoothed MUSIC, and FBSS-MUSIC methods, respectively. The two targets are merged into one, each having only one azimuth at 0°, making it impossible to separate the two sound sources. While other existing high-resolution methods (such as adaptive LASSO) can distinguish between -20° and 0° azimuths to some extent, see... Figure 4 (e) is an example, but its estimation results are discontinuous, unstable, and contain obvious spurious spectral peaks. Figure 4 (f) shows the method of the present invention. It can be seen that the method of the present invention (NF-SPICE) can clearly, continuously and stably distinguish the location of two sound sources.

[0100] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.

Claims

1. A high-resolution near-field distributed fiber optic acoustic detection method based on covariance fitting, characterized in that, The method includes: Step 1: Using the continuous linear sensor array in the Φ-OTDR distributed fiber optic acoustic sensing system, sound field signals are acquired and preprocessed in the near-field region to obtain the sound field observation matrix of the sensor array in the near-field region. ; Step 2: Based on the sound field observation matrix Construct the observation covariance matrix ; At the same time, the incoming wave directions of all target sound sources in the near field region are defined as belonging to the set. , For the first Candidate azimuth angles , This represents the total number of candidate azimuth angles. Based on each candidate azimuth angle Constructing near-field sound source steering vectors ; A near-field sound source steering matrix is ​​constructed using the steering vectors of all candidate azimuth angles within the near-field region. ; Step 3: Utilize Construct the extended model covariance matrix ; Step 4: Based on the covariance matrix of the extended model Observation covariance matrix Harmony sound field observation matrix The optimal power vector corresponding to the set of incoming wave directions is obtained by fitting the near-field sparse iterative covariance estimation method. ; Step 5: Optimal power vector The candidate azimuth angles corresponding to one or more of the maximum power are used as the estimated azimuth angles of arrival of each target sound source.

2. The high-resolution near-field distributed fiber optic acoustic detection method based on covariance fitting according to claim 1, characterized in that, ； in, For transpose, The first in the sensor array The preprocessed sound field signal corresponding to each array element For the time, and ; , The total number of array elements. Indicates time The multi-source mixed signal below, For the first The receiving channel corresponding to each array element at time The received noise, For signal carrier frequency, For the first The time delay difference in the arrival of the sound waves from each array element relative to the first array element. It is a natural constant. It is the imaginary unit.

3. The high-resolution near-field distributed fiber optic acoustic detection method based on covariance fitting according to claim 1, characterized in that, In step two, ;in, It is the conjugate transpose. This represents the number of sampling points within the sampling time.

4. The high-resolution near-field distributed fiber optic acoustic detection method based on covariance fitting according to claim 1, characterized in that, In step two, ； in, Candidate azimuth angle Next The time delay difference in the arrival of the sound waves from each array element relative to the first array element. , For the first The spacing between each array element and the first array element. , The total number of array elements. For signal carrier frequency, The speed of sound in water, For transpose, It is a natural constant. It is the imaginary unit.

5. The high-resolution near-field distributed fiber optic acoustic detection method based on covariance fitting according to claim 1, characterized in that, In step three, ;in, for The identity matrix, It is an integer.

6. The high-resolution near-field distributed fiber optic acoustic detection method based on covariance fitting according to claim 1, characterized in that, In step four, based on the covariance matrix of the extended model... Observation covariance matrix Harmony sound field observation matrix The optimal power vector corresponding to the set of incoming wave directions is obtained by fitting the near-field sparse iterative covariance estimation method. The implementation methods include: Step 41: Based on the covariance matrix of the extended model and observation covariance matrix Solve for the power vector corresponding to the set of incoming wave directions under the initial conditions. , It is the conjugate transpose; Step 42, the Round iteration, The initial value is 1, using the power vector and extended model covariance matrix Update the observation covariance matrix ; Step 4.3: Utilize the updated observation covariance matrix Extended model covariance matrix Harmony sound field observation matrix Solve for the linear estimation coefficient matrix ; Step 4: Use linear estimation of the coefficient matrix Update the power vector ; Steps four and five: Determine the first... In rounds of iteration, the power vector before and after the update is updated. If the convergence constraint is satisfied, and the result is yes, then update the power vector. As the optimal power vector The result was no. Return to step four two.

7. The high-resolution near-field distributed fiber optic acoustic detection method based on covariance fitting according to claim 6, characterized in that, In step 42, the observation covariance matrix is ​​updated. The implementation method is as follows: .

8. The high-resolution near-field distributed fiber optic acoustic detection method based on covariance fitting according to claim 6, characterized in that, In step 44, the power vector is updated. The implementation method is as follows: ; in, This represents the number of sampling points within the sampling time. For linear estimation coefficient matrix The Middle line, number Column elements, Candidate azimuth angle The power of the sound field signal emitted by the corresponding target sound source. .

9. The high-resolution near-field distributed fiber optic acoustic detection method based on covariance fitting according to claim 6, characterized in that, The convergence constraint is: ; in, For the first The updated power vector after round of iterations The value, For the first Power vector before update in round iteration The value, The threshold value is used.

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